imager glaas algorithm: Topics by Science.gov

Sample records for imager glaas algorithm

FluG affects secretion in colonies of Aspergillus niger.

PubMed

Wang, Fengfeng; Krijgsheld, Pauline; Hulsman, Marc; de Bekker, Charissa; Müller, Wally H; Reinders, Marcel; de Vries, Ronald P; Wösten, Han A B

2015-01-01

Colonies of Aspergillus niger are characterized by zonal heterogeneity in growth, sporulation, gene expression and secretion. For instance, the glucoamylase gene glaA is more highly expressed at the periphery of colonies when compared to the center. As a consequence, its encoded protein GlaA is mainly secreted at the outer part of the colony. Here, multiple copies of amyR were introduced in A. niger. Most transformants over-expressing this regulatory gene of amylolytic genes still displayed heterogeneous glaA expression and GlaA secretion. However, heterogeneity was abolished in transformant UU-A001.13 by expressing glaA and secreting GlaA throughout the mycelium. Sequencing the genome of UU-A001.13 revealed that transformation had been accompanied by deletion of part of the fluG gene and disrupting its 3' end by integration of a transformation vector. Inactivation of fluG in the wild-type background of A. niger also resulted in breakdown of starch under the whole colony. Asexual development of the ∆fluG strain was not affected, unlike what was previously shown in Aspergillus nidulans. Genes encoding proteins with a signal sequence for secretion, including part of the amylolytic genes, were more often downregulated in the central zone of maltose-grown ∆fluG colonies and upregulated in the intermediate part and periphery when compared to the wild-type. Together, these data indicate that FluG of A. niger is a repressor of secretion.
The transcriptomic fingerprint of glucoamylase over-expression in Aspergillus niger

PubMed Central

2012-01-01

Background Filamentous fungi such as Aspergillus niger are well known for their exceptionally high capacity for secretion of proteins, organic acids, and secondary metabolites and they are therefore used in biotechnology as versatile microbial production platforms. However, system-wide insights into their metabolic and secretory capacities are sparse and rational strain improvement approaches are therefore limited. In order to gain a genome-wide view on the transcriptional regulation of the protein secretory pathway of A. niger, we investigated the transcriptome of A. niger when it was forced to overexpression the glaA gene (encoding glucoamylase, GlaA) and secrete GlaA to high level. Results An A. niger wild-type strain and a GlaA over-expressing strain, containing multiple copies of the glaA gene, were cultivated under maltose-limited chemostat conditions (specific growth rate 0.1 h-1). Elevated glaA mRNA and extracellular GlaA levels in the over-expressing strain were accompanied by elevated transcript levels from 772 genes and lowered transcript levels from 815 genes when compared to the wild-type strain. Using GO term enrichment analysis, four higher-order categories were identified in the up-regulated gene set: i) endoplasmic reticulum (ER) membrane translocation, ii) protein glycosylation, iii) vesicle transport, and iv) ion homeostasis. Among these, about 130 genes had predicted functions for the passage of proteins through the ER and those genes included target genes of the HacA transcription factor that mediates the unfolded protein response (UPR), e.g. bipA, clxA, prpA, tigA and pdiA. In order to identify those genes that are important for high-level secretion of proteins by A. niger, we compared the transcriptome of the GlaA overexpression strain of A. niger with six other relevant transcriptomes of A. niger. Overall, 40 genes were found to have either elevated (from 36 genes) or lowered (from 4 genes) transcript levels under all conditions that were examined, thus defining the core set of genes important for ensuring high protein traffic through the secretory pathway. Conclusion We have defined the A. niger genes that respond to elevated secretion of GlaA and, furthermore, we have defined a core set of genes that appear to be involved more generally in the intensified traffic of proteins through the secretory pathway of A. niger. The consistent up-regulation of a gene encoding the acetyl-coenzyme A transporter suggests a possible role for transient acetylation to ensure correct folding of secreted proteins. PMID:23237452
Characterization of amylolytic enzyme overproducing mutant of Aspergillus luchuensis obtained by ion beam mutagenesis.

PubMed

Kojo, Toshihiro; Kadooka, Chihiro; Komohara, Marisa; Onitsuka, Shiori; Tanimura, Miya; Muroi, Yukiko; Kurazono, Shugo; Shiraishi, Yohei; Oda, Ken; Iwashita, Kazuhiro; Onoue, Masahira; Okutsu, Kayu; Yoshizaki, Yumiko; Takamine, Kazunori; Futagami, Taiki; Mori, Kazuki; Tamaki, Hisanori

2018-01-15

Aspergillus luchuensis is a kuro (black) koji fungus that has been used as a starch degrader for the awamori- and shochu-making industries in Japan. In this study, we investigated the effect of ion beam irradiation on A. luchuensis RIB2601 and obtained a high starch-degrading mutant strain U1. Strain U1 showed reduced growth rate, whereas it showed higher α-amylase, glucoamylase, and α-glucosidase activities on a mycelial mass basis than the wild type (wt) strain both on agar plates and in rice koji. In addition, strain U1 showed higher N-acetylglucosamine content in the cell wall and higher sensitivity to calcofluor white, suggesting a deficiency in cell wall composition. Interestingly, produced protein showed higher expression of acid-labile α-amylase (AmyA) and glucoamylase (GlaA) in strain U1, although real-time RT-PCR indicated no significant change in the transcription of the amyA or glaA gene. These results suggested that the high amylolytic activity of strain U1 is attributable to a high AmyA and GlaA production level, but the elevated production is not due to transcriptional regulation of the corresponding genes. Furthermore, RNA-seq analysis indicated that strain U1 shows transcriptional changes in at least 604 genes related to oxidation-reduction, transport, and glucosamine-containing compound metabolic processes, which may be involved in the deficient cell wall composition of strain U1.
Monitoring the inputs required to extend and sustain hygiene promotion: findings from the GLAAS 2013/2014 survey.

PubMed

Moreland, Leslie D; Gore, Fiona M; Andre, Nathalie; Cairncross, Sandy; Ensink, Jeroen H J

2016-08-01

There are significant gaps in information about the inputs required to effectively extend and sustain hygiene promotion activities to improve people's health outcomes through water, sanitation and hygiene (WASH) interventions. We sought to analyse current country and global trends in the use of key inputs required for effective and sustainable implementation of hygiene promotion to help guide hygiene promotion policy and decision-making after 2015. Data collected in response to the GLAAS 2013/2014 survey from 93 countries of 94 were included, and responses were analysed for 12 questions assessing the inputs and enabling environment for hygiene promotion under four thematic areas. Data were included and analysed from 20 External Support Agencies (ESA) of 23 collected through self-administered surveys. Firstly, the data showed a large variation in the way in which hygiene promotion is defined and what constitutes key activities in this area. Secondly, challenges to implement hygiene promotion are considerable: include poor implementation of policies and plans, weak coordination mechanisms, human resource limitations and a lack of available hygiene promotion budget data. Despite the proven benefits of hand washing with soap, a critical hygiene-related factor in minimising infection, GLAAS 2013/2014 survey data showed that hygiene promotion remains a neglected component of WASH. Additional research to identify the context-specific strategies and inputs required to enhance the effectiveness of hygiene promotion at scale are needed. Improved data collection methods are also necessary to advance the availability and reliability of hygiene-specific information. © 2016 John Wiley & Sons Ltd.
The intra- and extracellular proteome of Aspergillus niger growing on defined medium with xylose or maltose as carbon substrate.

PubMed

Lu, Xin; Sun, Jibin; Nimtz, Manfred; Wissing, Josef; Zeng, An-Ping; Rinas, Ursula

2010-04-20

The filamentous fungus Aspergillus niger is well-known as a producer of primary metabolites and extracellular proteins. For example, glucoamylase is the most efficiently secreted protein of Aspergillus niger, thus the homologous glucoamylase (glaA) promoter as well as the glaA signal sequence are widely used for heterologous protein production. Xylose is known to strongly repress glaA expression while maltose is a potent inducer of glaA promoter controlled genes. For a more profound understanding of A. niger physiology, a comprehensive analysis of the intra- and extracellular proteome of Aspergillus niger AB1.13 growing on defined medium with xylose or maltose as carbon substrate was carried out using 2-D gel electrophoresis/Maldi-ToF and nano-HPLC MS/MS. The intracellular proteome of A. niger growing either on xylose or maltose in well-aerated controlled bioreactor cultures revealed striking similarities. In both cultures the most abundant intracellular protein was the TCA cycle enzyme malate-dehydrogenase. Moreover, the glycolytic enzymes fructose-bis-phosphate aldolase and glyceraldehyde-3-phosphate-dehydrogenase and the flavohemoglobin FhbA were identified as major proteins in both cultures. On the other hand, enzymes involved in the removal of reactive oxygen species, such as superoxide dismutase and peroxiredoxin, were present at elevated levels in the culture growing on maltose but only in minor amounts in the xylose culture. The composition of the extracellular proteome differed considerably depending on the carbon substrate. In the secretome of the xylose-grown culture, a variety of plant cell wall degrading enzymes were identified, mostly under the control of the xylanolytic transcriptional activator XlnR, with xylanase B and ferulic acid esterase as the most abundant ones. The secretome of the maltose-grown culture did not contain xylanolytic enzymes, instead high levels of catalases were found and glucoamylase (multiple spots) was identified as the most abundant extracellular protein. Surprisingly, the intracellular proteome of A. niger growing on xylose in bioreactor cultures differed more from a culture growing in shake flasks using the same medium than from the bioreactor culture growing on maltose. For example, in shake flask cultures with xylose as carbon source the most abundant intracellular proteins were not the glycolytic and the TCA cycle enzymes and the flavohemoglobin, but CipC, a protein of yet unknown function, superoxide dismutase and an NADPH dependent aldehyde reductase. Moreover, vacuolar proteases accumulated to higher and ER-resident chaperones and foldases to lower levels in shake flask compared to the bioreactor cultures. The utilization of xylose or maltose was strongly affecting the composition of the secretome but of minor influence on the composition of the intracellular proteome. On the other hand, differences in culture conditions (pH control versus no pH control, aeration versus no aeration and stirring versus shaking) have a profound effect on the intracellular proteome. For example, lower levels of ER-resident chaperones and foldases and higher levels of vacuolar proteases render shake flask conditions less favorable for protein production compared to controlled bioreactor cultures.
The intra- and extracellular proteome of Aspergillus niger growing on defined medium with xylose or maltose as carbon substrate

PubMed Central

2010-01-01

Background The filamentous fungus Aspergillus niger is well-known as a producer of primary metabolites and extracellular proteins. For example, glucoamylase is the most efficiently secreted protein of Aspergillus niger, thus the homologous glucoamylase (glaA) promoter as well as the glaA signal sequence are widely used for heterologous protein production. Xylose is known to strongly repress glaA expression while maltose is a potent inducer of glaA promoter controlled genes. For a more profound understanding of A. niger physiology, a comprehensive analysis of the intra- and extracellular proteome of Aspergillus niger AB1.13 growing on defined medium with xylose or maltose as carbon substrate was carried out using 2-D gel electrophoresis/Maldi-ToF and nano-HPLC MS/MS. Results The intracellular proteome of A. niger growing either on xylose or maltose in well-aerated controlled bioreactor cultures revealed striking similarities. In both cultures the most abundant intracellular protein was the TCA cycle enzyme malate-dehydrogenase. Moreover, the glycolytic enzymes fructose-bis-phosphate aldolase and glyceraldehyde-3-phosphate-dehydrogenase and the flavohemoglobin FhbA were identified as major proteins in both cultures. On the other hand, enzymes involved in the removal of reactive oxygen species, such as superoxide dismutase and peroxiredoxin, were present at elevated levels in the culture growing on maltose but only in minor amounts in the xylose culture. The composition of the extracellular proteome differed considerably depending on the carbon substrate. In the secretome of the xylose-grown culture, a variety of plant cell wall degrading enzymes were identified, mostly under the control of the xylanolytic transcriptional activator XlnR, with xylanase B and ferulic acid esterase as the most abundant ones. The secretome of the maltose-grown culture did not contain xylanolytic enzymes, instead high levels of catalases were found and glucoamylase (multiple spots) was identified as the most abundant extracellular protein. Surprisingly, the intracellular proteome of A. niger growing on xylose in bioreactor cultures differed more from a culture growing in shake flasks using the same medium than from the bioreactor culture growing on maltose. For example, in shake flask cultures with xylose as carbon source the most abundant intracellular proteins were not the glycolytic and the TCA cycle enzymes and the flavohemoglobin, but CipC, a protein of yet unknown function, superoxide dismutase and an NADPH dependent aldehyde reductase. Moreover, vacuolar proteases accumulated to higher and ER-resident chaperones and foldases to lower levels in shake flask compared to the bioreactor cultures. Conclusions The utilization of xylose or maltose was strongly affecting the composition of the secretome but of minor influence on the composition of the intracellular proteome. On the other hand, differences in culture conditions (pH control versus no pH control, aeration versus no aeration and stirring versus shaking) have a profound effect on the intracellular proteome. For example, lower levels of ER-resident chaperones and foldases and higher levels of vacuolar proteases render shake flask conditions less favorable for protein production compared to controlled bioreactor cultures. PMID:20406453
Metabolic engineering of Aspergillus oryzae for efficient production of l-malate directly from corn starch.

PubMed

Liu, Jingjing; Li, Jianghua; Shin, Hyun-Dong; Du, Guocheng; Chen, Jian; Liu, Long

2017-11-20

l-Malate, an important chemical building block, has been widely applied in the food, pharmaceutical, and bio-based materials industries. In previous work, we engineered Aspergillus oryzae by rewiring the reductive tricarboxylic acid pathway to produce l-malate from glucose. To decrease the production cost, here, we further engineered A. oryzae to efficiently produce l-malate directly from corn starch with simultaneous liquefaction-saccharification and fermentation. First, a promoter PN5 was constructed by quintuple tandem of the 97-bp fragment containing the cis-element of the glucoamylase gene promoter (PglaA), and with the promoter PN5, the transcriptional level of glaA gene increased by 25-45%. Then, by co-overexpression of glaA, a-amylase (amyB) and a-glucosidase (agdA) genes with the promoter PN5, the l-malate titer increased from 55.5g/L to 72.0g/L with 100g/L corn starch in shake flask. In addition, to reduce the concentration of byproducts succinate and fumarate, a fumarase from Saccharomyces cerevisiae, which facilitated the transformation of fumarate to l-malate, was overexpressed. As a result, the concentration of succinate and fumarate decreased from 12.6 and 1.29g/L to 7.8 and 0.59g/L, and the l-malate titer increased from 72.0g/L to 78.5g/L. Finally, we found that the addition of glucose at the initial fermentation stage facilitated the cell growth and l-malate synthesis, and the l-malate titer further increased to 82.3g/L by co-fermentation of 30g/L glucose and 70g/L corn starch, with a productivity of 1.18g/L/h and a yield of 0.82g/g total carbon sources. Copyright © 2017 Elsevier B.V. All rights reserved.
Proteomic analysis of extracellular proteins from Aspergillus oryzae grown under submerged and solid-state culture conditions.

PubMed

Oda, Ken; Kakizono, Dararat; Yamada, Osamu; Iefuji, Haruyuki; Akita, Osamu; Iwashita, Kazuhiro

2006-05-01

Filamentous fungi are widely used for the production of homologous and heterologous proteins. Recently, there has been increasing interest in Aspergillus oryzae because of its ability to produce heterologous proteins in solid-state culture. To provide an overview of protein secretion by A. oryzae in solid-state culture, we carried out a comparative proteome analysis of extracellular proteins in solid-state and submerged (liquid) cultures. Extracellular proteins prepared from both cultures sequentially from 0 to 40 h were subjected to two-dimensional electrophoresis, and protein spots at 40 h were identified by peptide mass fingerprinting using matrix-assisted laser desorption ionization-time-of-flight mass spectrometry. We also attempted to identify cell wall-bound proteins of the submerged culture. We analyzed 85 spots from the solid-state culture and 110 spots from the submerged culture. We identified a total of 29 proteins, which were classified into 4 groups. Group 1 consisted of extracellular proteins specifically produced in the solid-state growth condition, such as glucoamylase B and alanyl dipeptidyl peptidase. Group 2 consisted of extracellular proteins specifically produced in the submerged condition, such as glucoamylase A (GlaA) and xylanase G2 (XynG2). Group 3 consisted of proteins produced in both conditions, such as xylanase G1. Group 4 consisted of proteins that were secreted to the medium in the solid-state growth condition but trapped in the cell wall in the submerged condition, such as alpha-amylase (TAA) and beta-glucosidase (Bgl). A Northern analysis of seven genes from the four groups suggested that the secretion of TAA and Bgl was regulated by trapping these proteins in the cell wall in submerged culture and that secretion of GlaA and XynG2 was regulated at the posttranscriptional level in the solid-state culture.
Proteomic Analysis of Extracellular Proteins from Aspergillus oryzae Grown under Submerged and Solid-State Culture Conditions

PubMed Central

Oda, Ken; Kakizono, Dararat; Yamada, Osamu; Iefuji, Haruyuki; Akita, Osamu; Iwashita, Kazuhiro

2006-01-01

Filamentous fungi are widely used for the production of homologous and heterologous proteins. Recently, there has been increasing interest in Aspergillus oryzae because of its ability to produce heterologous proteins in solid-state culture. To provide an overview of protein secretion by A. oryzae in solid-state culture, we carried out a comparative proteome analysis of extracellular proteins in solid-state and submerged (liquid) cultures. Extracellular proteins prepared from both cultures sequentially from 0 to 40 h were subjected to two-dimensional electrophoresis, and protein spots at 40 h were identified by peptide mass fingerprinting using matrix-assisted laser desorption ionization-time-of-flight mass spectrometry. We also attempted to identify cell wall-bound proteins of the submerged culture. We analyzed 85 spots from the solid-state culture and 110 spots from the submerged culture. We identified a total of 29 proteins, which were classified into 4 groups. Group 1 consisted of extracellular proteins specifically produced in the solid-state growth condition, such as glucoamylase B and alanyl dipeptidyl peptidase. Group 2 consisted of extracellular proteins specifically produced in the submerged condition, such as glucoamylase A (GlaA) and xylanase G2 (XynG2). Group 3 consisted of proteins produced in both conditions, such as xylanase G1. Group 4 consisted of proteins that were secreted to the medium in the solid-state growth condition but trapped in the cell wall in the submerged condition, such as α-amylase (TAA) and β-glucosidase (Bgl). A Northern analysis of seven genes from the four groups suggested that the secretion of TAA and Bgl was regulated by trapping these proteins in the cell wall in submerged culture and that secretion of GlaA and XynG2 was regulated at the posttranscriptional level in the solid-state culture. PMID:16672490
Raw starch conversion by Saccharomyces cerevisiae expressing Aspergillus tubingensis amylases

PubMed Central

2013-01-01

Background Starch is one of the most abundant organic polysaccharides available for the production of bio-ethanol as an alternative transport fuel. Cost-effective utilisation of starch requires consolidated bioprocessing (CBP) where a single microorganism can produce the enzymes required for hydrolysis of starch, and also convert the glucose monomers to ethanol. Results The Aspergillus tubingensis T8.4 α-amylase (amyA) and glucoamylase (glaA) genes were cloned and expressed in the laboratory strain Saccharomyces cerevisiae Y294 and the semi-industrial strain, S. cerevisiae Mnuα1. The recombinant AmyA and GlaA displayed protein sizes of 110–150 kDa and 90 kDa, respectively, suggesting significant glycosylation in S. cerevisiae. The Mnuα1[AmyA-GlaA] and Y294[AmyA-GlaA] strains were able to utilise 20 g l-1 raw corn starch as sole carbohydrate source, with ethanol titers of 9.03 and 6.67 g l-1 (0.038 and 0.028 g l-1 h-1), respectively, after 10 days. With a substrate load of 200 g l-1 raw corn starch, Mnuα1[AmyA-GlaA] yielded 70.07 g l-1 ethanol (0.58 g l-1 h-1) after 120 h of fermentation, whereas Y294[AmyA-GlaA] was less efficient at 43.33 g l-1 ethanol (0.36 g l-1 h-1). Conclusions In a semi-industrial amylolytic S. cerevisiae strain expressing the A. tubingensis α-amylase and glucoamylase genes, 200 g l-1 raw starch was completely hydrolysed (saccharified) in 120 hours with 74% converted to released sugars plus fermentation products and the remainder presumably to biomass. The single-step conversion of raw starch represents significant progress towards the realisation of CBP without the need for any heat pretreatment. Furthermore, the amylases were produced and secreted by the host strain, thus circumventing the need for exogenous amylases. PMID:24286270
Raw starch conversion by Saccharomyces cerevisiae expressing Aspergillus tubingensis amylases.

PubMed

Viktor, Marko J; Rose, Shaunita H; van Zyl, Willem H; Viljoen-Bloom, Marinda

2013-11-29

Starch is one of the most abundant organic polysaccharides available for the production of bio-ethanol as an alternative transport fuel. Cost-effective utilisation of starch requires consolidated bioprocessing (CBP) where a single microorganism can produce the enzymes required for hydrolysis of starch, and also convert the glucose monomers to ethanol. The Aspergillus tubingensis T8.4 α-amylase (amyA) and glucoamylase (glaA) genes were cloned and expressed in the laboratory strain Saccharomyces cerevisiae Y294 and the semi-industrial strain, S. cerevisiae Mnuα1. The recombinant AmyA and GlaA displayed protein sizes of 110-150 kDa and 90 kDa, respectively, suggesting significant glycosylation in S. cerevisiae. The Mnuα1[AmyA-GlaA] and Y294[AmyA-GlaA] strains were able to utilise 20 g l-1 raw corn starch as sole carbohydrate source, with ethanol titers of 9.03 and 6.67 g l-1 (0.038 and 0.028 g l-1 h-1), respectively, after 10 days. With a substrate load of 200 g l-1 raw corn starch, Mnuα1[AmyA-GlaA] yielded 70.07 g l-1 ethanol (0.58 g l-1 h-1) after 120 h of fermentation, whereas Y294[AmyA-GlaA] was less efficient at 43.33 g l-1 ethanol (0.36 g l-1 h-1). In a semi-industrial amylolytic S. cerevisiae strain expressing the A. tubingensis α-amylase and glucoamylase genes, 200 g l-1 raw starch was completely hydrolysed (saccharified) in 120 hours with 74% converted to released sugars plus fermentation products and the remainder presumably to biomass. The single-step conversion of raw starch represents significant progress towards the realisation of CBP without the need for any heat pretreatment. Furthermore, the amylases were produced and secreted by the host strain, thus circumventing the need for exogenous amylases.
New development of the image matching algorithm

NASA Astrophysics Data System (ADS)

Zhang, Xiaoqiang; Feng, Zhao

2018-04-01

To study the image matching algorithm, algorithm four elements are described, i.e., similarity measurement, feature space, search space and search strategy. Four common indexes for evaluating the image matching algorithm are described, i.e., matching accuracy, matching efficiency, robustness and universality. Meanwhile, this paper describes the principle of image matching algorithm based on the gray value, image matching algorithm based on the feature, image matching algorithm based on the frequency domain analysis, image matching algorithm based on the neural network and image matching algorithm based on the semantic recognition, and analyzes their characteristics and latest research achievements. Finally, the development trend of image matching algorithm is discussed. This study is significant for the algorithm improvement, new algorithm design and algorithm selection in practice.
Novel cooperative neural fusion algorithms for image restoration and image fusion.

PubMed

Xia, Youshen; Kamel, Mohamed S

2007-02-01

To deal with the problem of restoring degraded images with non-Gaussian noise, this paper proposes a novel cooperative neural fusion regularization (CNFR) algorithm for image restoration. Compared with conventional regularization algorithms for image restoration, the proposed CNFR algorithm can relax need of the optimal regularization parameter to be estimated. Furthermore, to enhance the quality of restored images, this paper presents a cooperative neural fusion (CNF) algorithm for image fusion. Compared with existing signal-level image fusion algorithms, the proposed CNF algorithm can greatly reduce the loss of contrast information under blind Gaussian noise environments. The performance analysis shows that the proposed two neural fusion algorithms can converge globally to the robust and optimal image estimate. Simulation results confirm that in different noise environments, the proposed two neural fusion algorithms can obtain a better image estimate than several well known image restoration and image fusion methods.
Cloning and overexpression of beta-N-acetylglucosaminidase encoding gene nagA from Aspergillus oryzae and enzyme-catalyzed synthesis of human milk oligosaccharide.

PubMed

Matsuo, Ichiro; Kim, Sunhwa; Yamamoto, Yuichi; Ajisaka, Katsumi; Maruyama, Jun-ich; Nakajima, Harushi; Kitamoto, Katsuhiko

2003-03-01

We isolated a beta-N-acetylglucosaminidase encoding gene from the filamentous fungus Aspergillus oryzae, and designated it nagA. The nagA gene encoded a polypeptide of 600 amino acids with significant similarity to glucosaminidases and hexosaminidases of various eukaryotes. A. oryzae strain carrying the nagA gene under the control of the improved glaA promoter produced large amounts of beta-N-acetylglucosaminidase in a wheat bran solid culture. The beta-N-acetylglucosaminidase was purified from crude extracts of the solid culture by column chromatographies on Q-Sepharose and Sephacryl S-200. This enzyme was used for synthesis of lacto-N-triose II, which is contained in human milk. By reverse hydrolysis reaction, lacto-N-triose II and its positional isomer were synthesized from lactose and D-N-acetylglucosamine in 0.21% and 0.15% yield, respectively.
Spaceborne SAR Imaging Algorithm for Coherence Optimized.

PubMed

Qiu, Zhiwei; Yue, Jianping; Wang, Xueqin; Yue, Shun

2016-01-01

This paper proposes SAR imaging algorithm with largest coherence based on the existing SAR imaging algorithm. The basic idea of SAR imaging algorithm in imaging processing is that output signal can have maximum signal-to-noise ratio (SNR) by using the optimal imaging parameters. Traditional imaging algorithm can acquire the best focusing effect, but would bring the decoherence phenomenon in subsequent interference process. Algorithm proposed in this paper is that SAR echo adopts consistent imaging parameters in focusing processing. Although the SNR of the output signal is reduced slightly, their coherence is ensured greatly, and finally the interferogram with high quality is obtained. In this paper, two scenes of Envisat ASAR data in Zhangbei are employed to conduct experiment for this algorithm. Compared with the interferogram from the traditional algorithm, the results show that this algorithm is more suitable for SAR interferometry (InSAR) research and application.
Spaceborne SAR Imaging Algorithm for Coherence Optimized

PubMed Central

Qiu, Zhiwei; Yue, Jianping; Wang, Xueqin; Yue, Shun

2016-01-01

This paper proposes SAR imaging algorithm with largest coherence based on the existing SAR imaging algorithm. The basic idea of SAR imaging algorithm in imaging processing is that output signal can have maximum signal-to-noise ratio (SNR) by using the optimal imaging parameters. Traditional imaging algorithm can acquire the best focusing effect, but would bring the decoherence phenomenon in subsequent interference process. Algorithm proposed in this paper is that SAR echo adopts consistent imaging parameters in focusing processing. Although the SNR of the output signal is reduced slightly, their coherence is ensured greatly, and finally the interferogram with high quality is obtained. In this paper, two scenes of Envisat ASAR data in Zhangbei are employed to conduct experiment for this algorithm. Compared with the interferogram from the traditional algorithm, the results show that this algorithm is more suitable for SAR interferometry (InSAR) research and application. PMID:26871446
Retinex enhancement of infrared images.

PubMed

Li, Ying; He, Renjie; Xu, Guizhi; Hou, Changzhi; Sun, Yunyan; Guo, Lei; Rao, Liyun; Yan, Weili

2008-01-01

With the ability of imaging the temperature distribution of body, infrared imaging is promising in diagnostication and prognostication of diseases. However the poor quality of the raw original infrared images prevented applications and one of the essential problems is the low contrast appearance of the imagined object. In this paper, the image enhancement technique based on the Retinex theory is studied, which is a process that automatically retrieve the visual realism to images. The algorithms, including Frackle-McCann algorithm, McCann99 algorithm, single-scale Retinex algorithm, multi-scale Retinex algorithm and multi-scale Retinex algorithm with color restoration, are experienced to the enhancement of infrared images. The entropy measurements along with the visual inspection were compared and results shown the algorithms based on Retinex theory have the ability in enhancing the infrared image. Out of the algorithms compared, MSRCR demonstrated the best performance.
Efficient image compression algorithm for computer-animated images

NASA Astrophysics Data System (ADS)

Yfantis, Evangelos A.; Au, Matthew Y.; Miel, G.

1992-10-01

An image compression algorithm is described. The algorithm is an extension of the run-length image compression algorithm and its implementation is relatively easy. This algorithm was implemented and compared with other existing popular compression algorithms and with the Lempel-Ziv (LZ) coding. The Lempel-Ziv algorithm is available as a utility in the UNIX operating system and is also referred to as the UNIX uncompress. Sometimes our algorithm is best in terms of saving memory space, and sometimes one of the competing algorithms is best. The algorithm is lossless, and the intent is for the algorithm to be used in computer graphics animated images. Comparisons made with the LZ algorithm indicate that the decompression time using our algorithm is faster than that using the LZ algorithm. Once the data are in memory, a relatively simple and fast transformation is applied to uncompress the file.
Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography

PubMed Central

Wang, Kun; Su, Richard; Oraevsky, Alexander A; Anastasio, Mark A

2012-01-01

Iterative image reconstruction algorithms for optoacoustic tomography (OAT), also known as photoacoustic tomography, have the ability to improve image quality over analytic algorithms due to their ability to incorporate accurate models of the imaging physics, instrument response, and measurement noise. However, to date, there have been few reported attempts to employ advanced iterative image reconstruction algorithms for improving image quality in three-dimensional (3D) OAT. In this work, we implement and investigate two iterative image reconstruction methods for use with a 3D OAT small animal imager: namely, a penalized least-squares (PLS) method employing a quadratic smoothness penalty and a PLS method employing a total variation norm penalty. The reconstruction algorithms employ accurate models of the ultrasonic transducer impulse responses. Experimental data sets are employed to compare the performances of the iterative reconstruction algorithms to that of a 3D filtered backprojection (FBP) algorithm. By use of quantitative measures of image quality, we demonstrate that the iterative reconstruction algorithms can mitigate image artifacts and preserve spatial resolution more effectively than FBP algorithms. These features suggest that the use of advanced image reconstruction algorithms can improve the effectiveness of 3D OAT while reducing the amount of data required for biomedical applications. PMID:22864062
Performance evaluation of image denoising developed using convolutional denoising autoencoders in chest radiography

NASA Astrophysics Data System (ADS)

Lee, Donghoon; Choi, Sunghoon; Kim, Hee-Joung

2018-03-01

When processing medical images, image denoising is an important pre-processing step. Various image denoising algorithms have been developed in the past few decades. Recently, image denoising using the deep learning method has shown excellent performance compared to conventional image denoising algorithms. In this study, we introduce an image denoising technique based on a convolutional denoising autoencoder (CDAE) and evaluate clinical applications by comparing existing image denoising algorithms. We train the proposed CDAE model using 3000 chest radiograms training data. To evaluate the performance of the developed CDAE model, we compare it with conventional denoising algorithms including median filter, total variation (TV) minimization, and non-local mean (NLM) algorithms. Furthermore, to verify the clinical effectiveness of the developed denoising model with CDAE, we investigate the performance of the developed denoising algorithm on chest radiograms acquired from real patients. The results demonstrate that the proposed denoising algorithm developed using CDAE achieves a superior noise-reduction effect in chest radiograms compared to TV minimization and NLM algorithms, which are state-of-the-art algorithms for image noise reduction. For example, the peak signal-to-noise ratio and structure similarity index measure of CDAE were at least 10% higher compared to conventional denoising algorithms. In conclusion, the image denoising algorithm developed using CDAE effectively eliminated noise without loss of information on anatomical structures in chest radiograms. It is expected that the proposed denoising algorithm developed using CDAE will be effective for medical images with microscopic anatomical structures, such as terminal bronchioles.

Comparison and analysis of nonlinear algorithms for compressed sensing in MRI.

PubMed

Yu, Yeyang; Hong, Mingjian; Liu, Feng; Wang, Hua; Crozier, Stuart

2010-01-01

Compressed sensing (CS) theory has been recently applied in Magnetic Resonance Imaging (MRI) to accelerate the overall imaging process. In the CS implementation, various algorithms have been used to solve the nonlinear equation system for better image quality and reconstruction speed. However, there are no explicit criteria for an optimal CS algorithm selection in the practical MRI application. A systematic and comparative study of those commonly used algorithms is therefore essential for the implementation of CS in MRI. In this work, three typical algorithms, namely, the Gradient Projection For Sparse Reconstruction (GPSR) algorithm, Interior-point algorithm (l(1)_ls), and the Stagewise Orthogonal Matching Pursuit (StOMP) algorithm are compared and investigated in three different imaging scenarios, brain, angiogram and phantom imaging. The algorithms' performances are characterized in terms of image quality and reconstruction speed. The theoretical results show that the performance of the CS algorithms is case sensitive; overall, the StOMP algorithm offers the best solution in imaging quality, while the GPSR algorithm is the most efficient one among the three methods. In the next step, the algorithm performances and characteristics will be experimentally explored. It is hoped that this research will further support the applications of CS in MRI.
Advancements to the planogram frequency–distance rebinning algorithm

PubMed Central

Champley, Kyle M; Raylman, Raymond R; Kinahan, Paul E

2010-01-01

In this paper we consider the task of image reconstruction in positron emission tomography (PET) with the planogram frequency–distance rebinning (PFDR) algorithm. The PFDR algorithm is a rebinning algorithm for PET systems with panel detectors. The algorithm is derived in the planogram coordinate system which is a native data format for PET systems with panel detectors. A rebinning algorithm averages over the redundant four-dimensional set of PET data to produce a three-dimensional set of data. Images can be reconstructed from this rebinned three-dimensional set of data. This process enables one to reconstruct PET images more quickly than reconstructing directly from the four-dimensional PET data. The PFDR algorithm is an approximate rebinning algorithm. We show that implementing the PFDR algorithm followed by the (ramp) filtered backprojection (FBP) algorithm in linogram coordinates from multiple views reconstructs a filtered version of our image. We develop an explicit formula for this filter which can be used to achieve exact reconstruction by means of a modified FBP algorithm applied to the stack of rebinned linograms and can also be used to quantify the errors introduced by the PFDR algorithm. This filter is similar to the filter in the planogram filtered backprojection algorithm derived by Brasse et al. The planogram filtered backprojection and exact reconstruction with the PFDR algorithm require complete projections which can be completed with a reprojection algorithm. The PFDR algorithm is similar to the rebinning algorithm developed by Kao et al. By expressing the PFDR algorithm in detector coordinates, we provide a comparative analysis between the two algorithms. Numerical experiments using both simulated data and measured data from a positron emission mammography/tomography (PEM/PET) system are performed. Images are reconstructed by PFDR+FBP (PFDR followed by 2D FBP reconstruction), PFDRX (PFDR followed by the modified FBP algorithm for exact reconstruction) and planogram filtered backprojection image reconstruction algorithms. We show that the PFDRX algorithm produces images that are nearly as accurate as images reconstructed with the planogram filtered backprojection algorithm and more accurate than images reconstructed with the PFDR+FBP algorithm. Both the PFDR+FBP and PFDRX algorithms provide a dramatic improvement in computation time over the planogram filtered backprojection algorithm. PMID:20436790
A real-time MTFC algorithm of space remote-sensing camera based on FPGA

NASA Astrophysics Data System (ADS)

Zhao, Liting; Huang, Gang; Lin, Zhe

2018-01-01

A real-time MTFC algorithm of space remote-sensing camera based on FPGA was designed. The algorithm can provide real-time image processing to enhance image clarity when the remote-sensing camera running on-orbit. The image restoration algorithm adopted modular design. The MTF measurement calculation module on-orbit had the function of calculating the edge extension function, line extension function, ESF difference operation, normalization MTF and MTFC parameters. The MTFC image filtering and noise suppression had the function of filtering algorithm and effectively suppressing the noise. The algorithm used System Generator to design the image processing algorithms to simplify the design structure of system and the process redesign. The image gray gradient dot sharpness edge contrast and median-high frequency were enhanced. The image SNR after recovery reduced less than 1 dB compared to the original image. The image restoration system can be widely used in various fields.
Quality Improvement of Liver Ultrasound Images Using Fuzzy Techniques.

PubMed

Bayani, Azadeh; Langarizadeh, Mostafa; Radmard, Amir Reza; Nejad, Ahmadreza Farzaneh

2016-12-01

Liver ultrasound images are so common and are applied so often to diagnose diffuse liver diseases like fatty liver. However, the low quality of such images makes it difficult to analyze them and diagnose diseases. The purpose of this study, therefore, is to improve the contrast and quality of liver ultrasound images. In this study, a number of image contrast enhancement algorithms which are based on fuzzy logic were applied to liver ultrasound images - in which the view of kidney is observable - using Matlab2013b to improve the image contrast and quality which has a fuzzy definition; just like image contrast improvement algorithms using a fuzzy intensification operator, contrast improvement algorithms applying fuzzy image histogram hyperbolization, and contrast improvement algorithms by fuzzy IF-THEN rules. With the measurement of Mean Squared Error and Peak Signal to Noise Ratio obtained from different images, fuzzy methods provided better results, and their implementation - compared with histogram equalization method - led both to the improvement of contrast and visual quality of images and to the improvement of liver segmentation algorithms results in images. Comparison of the four algorithms revealed the power of fuzzy logic in improving image contrast compared with traditional image processing algorithms. Moreover, contrast improvement algorithm based on a fuzzy intensification operator was selected as the strongest algorithm considering the measured indicators. This method can also be used in future studies on other ultrasound images for quality improvement and other image processing and analysis applications.
Quality Improvement of Liver Ultrasound Images Using Fuzzy Techniques

PubMed Central

Bayani, Azadeh; Langarizadeh, Mostafa; Radmard, Amir Reza; Nejad, Ahmadreza Farzaneh

2016-01-01

Background: Liver ultrasound images are so common and are applied so often to diagnose diffuse liver diseases like fatty liver. However, the low quality of such images makes it difficult to analyze them and diagnose diseases. The purpose of this study, therefore, is to improve the contrast and quality of liver ultrasound images. Methods: In this study, a number of image contrast enhancement algorithms which are based on fuzzy logic were applied to liver ultrasound images - in which the view of kidney is observable - using Matlab2013b to improve the image contrast and quality which has a fuzzy definition; just like image contrast improvement algorithms using a fuzzy intensification operator, contrast improvement algorithms applying fuzzy image histogram hyperbolization, and contrast improvement algorithms by fuzzy IF-THEN rules. Results: With the measurement of Mean Squared Error and Peak Signal to Noise Ratio obtained from different images, fuzzy methods provided better results, and their implementation - compared with histogram equalization method - led both to the improvement of contrast and visual quality of images and to the improvement of liver segmentation algorithms results in images. Conclusion: Comparison of the four algorithms revealed the power of fuzzy logic in improving image contrast compared with traditional image processing algorithms. Moreover, contrast improvement algorithm based on a fuzzy intensification operator was selected as the strongest algorithm considering the measured indicators. This method can also be used in future studies on other ultrasound images for quality improvement and other image processing and analysis applications. PMID:28077898
An improved non-uniformity correction algorithm and its hardware implementation on FPGA

NASA Astrophysics Data System (ADS)

Rong, Shenghui; Zhou, Huixin; Wen, Zhigang; Qin, Hanlin; Qian, Kun; Cheng, Kuanhong

2017-09-01

The Non-uniformity of Infrared Focal Plane Arrays (IRFPA) severely degrades the infrared image quality. An effective non-uniformity correction (NUC) algorithm is necessary for an IRFPA imaging and application system. However traditional scene-based NUC algorithm suffers the image blurring and artificial ghosting. In addition, few effective hardware platforms have been proposed to implement corresponding NUC algorithms. Thus, this paper proposed an improved neural-network based NUC algorithm by the guided image filter and the projection-based motion detection algorithm. First, the guided image filter is utilized to achieve the accurate desired image to decrease the artificial ghosting. Then a projection-based moving detection algorithm is utilized to determine whether the correction coefficients should be updated or not. In this way the problem of image blurring can be overcome. At last, an FPGA-based hardware design is introduced to realize the proposed NUC algorithm. A real and a simulated infrared image sequences are utilized to verify the performance of the proposed algorithm. Experimental results indicated that the proposed NUC algorithm can effectively eliminate the fix pattern noise with less image blurring and artificial ghosting. The proposed hardware design takes less logic elements in FPGA and spends less clock cycles to process one frame of image.
Synthetic aperture radar image formation for the moving-target and near-field bistatic cases

NASA Astrophysics Data System (ADS)

Ding, Yu

This dissertation addresses topics in two areas of synthetic aperture radar (SAR) image formation: time-frequency based SAR imaging of moving targets and a fast backprojection (BP) algorithm for near-field bistatic SAR imaging. SAR imaging of a moving target is a challenging task due to unknown motion of the target. We approach this problem in a theoretical way, by analyzing the Wigner-Ville distribution (WVD) based SAR imaging technique. We derive approximate closed-form expressions for the point-target response of the SAR imaging system, which quantify the image resolution, and show how the blurring in conventional SAR imaging can be eliminated, while the target shift still remains. Our analyses lead to accurate prediction of the target position in the reconstructed images. The derived expressions also enable us to further study additional aspects of WVD-based SAR imaging. Bistatic SAR imaging is more involved than the monostatic SAR case, because of the separation of the transmitter and the receiver, and possibly the changing bistatic geometry. For near-field bistatic SAR imaging, we develop a novel fast BP algorithm, motivated by a newly proposed fast BP algorithm in computer tomography. First we show that the BP algorithm is the spatial-domain counterpart of the benchmark o -- k algorithm in bistatic SAR imaging, yet it avoids the frequency-domain interpolation in the o -- k algorithm, which may cause artifacts in the reconstructed image. We then derive the band-limited property for BP methods in both monostatic and bistatic SAR imaging, which is the basis for developing the fast BP algorithm. We compare our algorithm with other frequency-domain based algorithms, and show that it achieves better reconstructed image quality, while having the same computational complexity as that of the frequency-domain based algorithms.
Quantum Image Steganography and Steganalysis Based On LSQu-Blocks Image Information Concealing Algorithm

NASA Astrophysics Data System (ADS)

A. AL-Salhi, Yahya E.; Lu, Songfeng

2016-08-01

Quantum steganography can solve some problems that are considered inefficient in image information concealing. It researches on Quantum image information concealing to have been widely exploited in recent years. Quantum image information concealing can be categorized into quantum image digital blocking, quantum image stereography, anonymity and other branches. Least significant bit (LSB) information concealing plays vital roles in the classical world because many image information concealing algorithms are designed based on it. Firstly, based on the novel enhanced quantum representation (NEQR), image uniform blocks clustering around the concrete the least significant Qu-block (LSQB) information concealing algorithm for quantum image steganography is presented. Secondly, a clustering algorithm is proposed to optimize the concealment of important data. Finally, we used Con-Steg algorithm to conceal the clustered image blocks. Information concealing located on the Fourier domain of an image can achieve the security of image information, thus we further discuss the Fourier domain LSQu-block information concealing algorithm for quantum image based on Quantum Fourier Transforms. In our algorithms, the corresponding unitary Transformations are designed to realize the aim of concealing the secret information to the least significant Qu-block representing color of the quantum cover image. Finally, the procedures of extracting the secret information are illustrated. Quantum image LSQu-block image information concealing algorithm can be applied in many fields according to different needs.
Analysis of image thresholding segmentation algorithms based on swarm intelligence

NASA Astrophysics Data System (ADS)

Zhang, Yi; Lu, Kai; Gao, Yinghui; Yang, Bo

2013-03-01

Swarm intelligence-based image thresholding segmentation algorithms are playing an important role in the research field of image segmentation. In this paper, we briefly introduce the theories of four existing image segmentation algorithms based on swarm intelligence including fish swarm algorithm, artificial bee colony, bacteria foraging algorithm and particle swarm optimization. Then some image benchmarks are tested in order to show the differences of the segmentation accuracy, time consumption, convergence and robustness for Salt & Pepper noise and Gaussian noise of these four algorithms. Through these comparisons, this paper gives qualitative analyses for the performance variance of the four algorithms. The conclusions in this paper would give a significant guide for the actual image segmentation.
Super-resolution algorithm based on sparse representation and wavelet preprocessing for remote sensing imagery

NASA Astrophysics Data System (ADS)

Ren, Ruizhi; Gu, Lingjia; Fu, Haoyang; Sun, Chenglin

2017-04-01

An effective super-resolution (SR) algorithm is proposed for actual spectral remote sensing images based on sparse representation and wavelet preprocessing. The proposed SR algorithm mainly consists of dictionary training and image reconstruction. Wavelet preprocessing is used to establish four subbands, i.e., low frequency, horizontal, vertical, and diagonal high frequency, for an input image. As compared to the traditional approaches involving the direct training of image patches, the proposed approach focuses on the training of features derived from these four subbands. The proposed algorithm is verified using different spectral remote sensing images, e.g., moderate-resolution imaging spectroradiometer (MODIS) images with different bands, and the latest Chinese Jilin-1 satellite images with high spatial resolution. According to the visual experimental results obtained from the MODIS remote sensing data, the SR images using the proposed SR algorithm are superior to those using a conventional bicubic interpolation algorithm or traditional SR algorithms without preprocessing. Fusion algorithms, e.g., standard intensity-hue-saturation, principal component analysis, wavelet transform, and the proposed SR algorithms are utilized to merge the multispectral and panchromatic images acquired by the Jilin-1 satellite. The effectiveness of the proposed SR algorithm is assessed by parameters such as peak signal-to-noise ratio, structural similarity index, correlation coefficient, root-mean-square error, relative dimensionless global error in synthesis, relative average spectral error, spectral angle mapper, and the quality index Q4, and its performance is better than that of the standard image fusion algorithms.
Novel medical image enhancement algorithms

NASA Astrophysics Data System (ADS)

Agaian, Sos; McClendon, Stephen A.

2010-01-01

In this paper, we present two novel medical image enhancement algorithms. The first, a global image enhancement algorithm, utilizes an alpha-trimmed mean filter as its backbone to sharpen images. The second algorithm uses a cascaded unsharp masking technique to separate the high frequency components of an image in order for them to be enhanced using a modified adaptive contrast enhancement algorithm. Experimental results from enhancing electron microscopy, radiological, CT scan and MRI scan images, using the MATLAB environment, are then compared to the original images as well as other enhancement methods, such as histogram equalization and two forms of adaptive contrast enhancement. An image processing scheme for electron microscopy images of Purkinje cells will also be implemented and utilized as a comparison tool to evaluate the performance of our algorithm.
Study on the algorithm of computational ghost imaging based on discrete fourier transform measurement matrix

NASA Astrophysics Data System (ADS)

Zhang, Leihong; Liang, Dong; Li, Bei; Kang, Yi; Pan, Zilan; Zhang, Dawei; Gao, Xiumin; Ma, Xiuhua

2016-07-01

On the basis of analyzing the cosine light field with determined analytic expression and the pseudo-inverse method, the object is illuminated by a presetting light field with a determined discrete Fourier transform measurement matrix, and the object image is reconstructed by the pseudo-inverse method. The analytic expression of the algorithm of computational ghost imaging based on discrete Fourier transform measurement matrix is deduced theoretically, and compared with the algorithm of compressive computational ghost imaging based on random measurement matrix. The reconstruction process and the reconstruction error are analyzed. On this basis, the simulation is done to verify the theoretical analysis. When the sampling measurement number is similar to the number of object pixel, the rank of discrete Fourier transform matrix is the same as the one of the random measurement matrix, the PSNR of the reconstruction image of FGI algorithm and PGI algorithm are similar, the reconstruction error of the traditional CGI algorithm is lower than that of reconstruction image based on FGI algorithm and PGI algorithm. As the decreasing of the number of sampling measurement, the PSNR of reconstruction image based on FGI algorithm decreases slowly, and the PSNR of reconstruction image based on PGI algorithm and CGI algorithm decreases sharply. The reconstruction time of FGI algorithm is lower than that of other algorithms and is not affected by the number of sampling measurement. The FGI algorithm can effectively filter out the random white noise through a low-pass filter and realize the reconstruction denoising which has a higher denoising capability than that of the CGI algorithm. The FGI algorithm can improve the reconstruction accuracy and the reconstruction speed of computational ghost imaging.
Digital watermarking algorithm research of color images based on quaternion Fourier transform

NASA Astrophysics Data System (ADS)

An, Mali; Wang, Weijiang; Zhao, Zhen

2013-10-01

A watermarking algorithm of color images based on the quaternion Fourier Transform (QFFT) and improved quantization index algorithm (QIM) is proposed in this paper. The original image is transformed by QFFT, the watermark image is processed by compression and quantization coding, and then the processed watermark image is embedded into the components of the transformed original image. It achieves embedding and blind extraction of the watermark image. The experimental results show that the watermarking algorithm based on the improved QIM algorithm with distortion compensation achieves a good tradeoff between invisibility and robustness, and better robustness for the attacks of Gaussian noises, salt and pepper noises, JPEG compression, cropping, filtering and image enhancement than the traditional QIM algorithm.
An improved NAS-RIF algorithm for image restoration

NASA Astrophysics Data System (ADS)

Gao, Weizhe; Zou, Jianhua; Xu, Rong; Liu, Changhai; Li, Hengnian

2016-10-01

Space optical images are inevitably degraded by atmospheric turbulence, error of the optical system and motion. In order to get the true image, a novel nonnegativity and support constants recursive inverse filtering (NAS-RIF) algorithm is proposed to restore the degraded image. Firstly the image noise is weaken by Contourlet denoising algorithm. Secondly, the reliable object support region estimation is used to accelerate the algorithm convergence. We introduce the optimal threshold segmentation technology to improve the object support region. Finally, an object construction limit and the logarithm function are added to enhance algorithm stability. Experimental results demonstrate that, the proposed algorithm can increase the PSNR, and improve the quality of the restored images. The convergence speed of the proposed algorithm is faster than that of the original NAS-RIF algorithm.
Fast template matching with polynomials.

PubMed

Omachi, Shinichiro; Omachi, Masako

2007-08-01

Template matching is widely used for many applications in image and signal processing. This paper proposes a novel template matching algorithm, called algebraic template matching. Given a template and an input image, algebraic template matching efficiently calculates similarities between the template and the partial images of the input image, for various widths and heights. The partial image most similar to the template image is detected from the input image for any location, width, and height. In the proposed algorithm, a polynomial that approximates the template image is used to match the input image instead of the template image. The proposed algorithm is effective especially when the width and height of the template image differ from the partial image to be matched. An algorithm using the Legendre polynomial is proposed for efficient approximation of the template image. This algorithm not only reduces computational costs, but also improves the quality of the approximated image. It is shown theoretically and experimentally that the computational cost of the proposed algorithm is much smaller than the existing methods.
Robust image modeling techniques with an image restoration application

NASA Astrophysics Data System (ADS)

Kashyap, Rangasami L.; Eom, Kie-Bum

1988-08-01

A robust parameter-estimation algorithm for a nonsymmetric half-plane (NSHP) autoregressive model, where the driving noise is a mixture of a Gaussian and an outlier process, is presented. The convergence of the estimation algorithm is proved. An algorithm to estimate parameters and original image intensity simultaneously from the impulse-noise-corrupted image, where the model governing the image is not available, is also presented. The robustness of the parameter estimates is demonstrated by simulation. Finally, an algorithm to restore realistic images is presented. The entire image generally does not obey a simple image model, but a small portion (e.g., 8 x 8) of the image is assumed to obey an NSHP model. The original image is divided into windows and the robust estimation algorithm is applied for each window. The restoration algorithm is tested by comparing it to traditional methods on several different images.
A novel image retrieval algorithm based on PHOG and LSH

NASA Astrophysics Data System (ADS)

Wu, Hongliang; Wu, Weimin; Peng, Jiajin; Zhang, Junyuan

2017-08-01

PHOG can describe the local shape of the image and its relationship between the spaces. The using of PHOG algorithm to extract image features in image recognition and retrieval and other aspects have achieved good results. In recent years, locality sensitive hashing (LSH) algorithm has been superior to large-scale data in solving near-nearest neighbor problems compared with traditional algorithms. This paper presents a novel image retrieval algorithm based on PHOG and LSH. First, we use PHOG to extract the feature vector of the image, then use L different LSH hash table to reduce the dimension of PHOG texture to index values and map to different bucket, and finally extract the corresponding value of the image in the bucket for second image retrieval using Manhattan distance. This algorithm can adapt to the massive image retrieval, which ensures the high accuracy of the image retrieval and reduces the time complexity of the retrieval. This algorithm is of great significance.
[Preliminary application of an improved Demons deformable registration algorithm in tumor radiotherapy].

PubMed

Zhou, Lu; Zhen, Xin; Lu, Wenting; Dou, Jianhong; Zhou, Linghong

2012-01-01

To validate the efficiency of an improved Demons deformable registration algorithm and evaluate its application in registration of the treatment image and the planning image in image-guided radiotherapy (IGRT). Based on Brox's gradient constancy assumption and Malis's efficient second-order minimization algorithm, a grey value gradient similarity term was added into the original energy function, and a formula was derived to calculate the update of transformation field. The limited Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm was used to optimize the energy function for automatic determination of the iteration number. The proposed algorithm was validated using mathematically deformed images, physically deformed phantom images and clinical tumor images. Compared with the original Additive Demons algorithm, the improved Demons algorithm achieved a higher precision and a faster convergence speed. Due to the influence of different scanning conditions in fractionated radiation, the density range of the treatment image and the planning image may be different. The improved Demons algorithm can achieve faster and more accurate radiotherapy.
Intermediate view reconstruction using adaptive disparity search algorithm for real-time 3D processing

NASA Astrophysics Data System (ADS)

Bae, Kyung-hoon; Park, Changhan; Kim, Eun-soo

2008-03-01

In this paper, intermediate view reconstruction (IVR) using adaptive disparity search algorithm (ASDA) is for realtime 3-dimensional (3D) processing proposed. The proposed algorithm can reduce processing time of disparity estimation by selecting adaptive disparity search range. Also, the proposed algorithm can increase the quality of the 3D imaging. That is, by adaptively predicting the mutual correlation between stereo images pair using the proposed algorithm, the bandwidth of stereo input images pair can be compressed to the level of a conventional 2D image and a predicted image also can be effectively reconstructed using a reference image and disparity vectors. From some experiments, stereo sequences of 'Pot Plant' and 'IVO', it is shown that the proposed algorithm improves the PSNRs of a reconstructed image to about 4.8 dB by comparing with that of conventional algorithms, and reduces the Synthesizing time of a reconstructed image to about 7.02 sec by comparing with that of conventional algorithms.
Improved Bat Algorithm Applied to Multilevel Image Thresholding

PubMed Central

2014-01-01

Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed. PMID:25165733

A Stereo Dual-Channel Dynamic Programming Algorithm for UAV Image Stitching

PubMed Central

Chen, Ruizhi; Zhang, Weilong; Li, Deren; Liao, Xuan; Zhang, Peng

2017-01-01

Dislocation is one of the major challenges in unmanned aerial vehicle (UAV) image stitching. In this paper, we propose a new algorithm for seamlessly stitching UAV images based on a dynamic programming approach. Our solution consists of two steps: Firstly, an image matching algorithm is used to correct the images so that they are in the same coordinate system. Secondly, a new dynamic programming algorithm is developed based on the concept of a stereo dual-channel energy accumulation. A new energy aggregation and traversal strategy is adopted in our solution, which can find a more optimal seam line for image stitching. Our algorithm overcomes the theoretical limitation of the classical Duplaquet algorithm. Experiments show that the algorithm can effectively solve the dislocation problem in UAV image stitching, especially for the cases in dense urban areas. Our solution is also direction-independent, which has better adaptability and robustness for stitching images. PMID:28885547
A Stereo Dual-Channel Dynamic Programming Algorithm for UAV Image Stitching.

PubMed

Li, Ming; Chen, Ruizhi; Zhang, Weilong; Li, Deren; Liao, Xuan; Wang, Lei; Pan, Yuanjin; Zhang, Peng

2017-09-08

Dislocation is one of the major challenges in unmanned aerial vehicle (UAV) image stitching. In this paper, we propose a new algorithm for seamlessly stitching UAV images based on a dynamic programming approach. Our solution consists of two steps: Firstly, an image matching algorithm is used to correct the images so that they are in the same coordinate system. Secondly, a new dynamic programming algorithm is developed based on the concept of a stereo dual-channel energy accumulation. A new energy aggregation and traversal strategy is adopted in our solution, which can find a more optimal seam line for image stitching. Our algorithm overcomes the theoretical limitation of the classical Duplaquet algorithm. Experiments show that the algorithm can effectively solve the dislocation problem in UAV image stitching, especially for the cases in dense urban areas. Our solution is also direction-independent, which has better adaptability and robustness for stitching images.
An enhanced fast scanning algorithm for image segmentation

NASA Astrophysics Data System (ADS)

Ismael, Ahmed Naser; Yusof, Yuhanis binti

2015-12-01

Segmentation is an essential and important process that separates an image into regions that have similar characteristics or features. This will transform the image for a better image analysis and evaluation. An important benefit of segmentation is the identification of region of interest in a particular image. Various algorithms have been proposed for image segmentation and this includes the Fast Scanning algorithm which has been employed on food, sport and medical images. It scans all pixels in the image and cluster each pixel according to the upper and left neighbor pixels. The clustering process in Fast Scanning algorithm is performed by merging pixels with similar neighbor based on an identified threshold. Such an approach will lead to a weak reliability and shape matching of the produced segments. This paper proposes an adaptive threshold function to be used in the clustering process of the Fast Scanning algorithm. This function used the gray'value in the image's pixels and variance Also, the level of the image that is more the threshold are converted into intensity values between 0 and 1, and other values are converted into intensity values zero. The proposed enhanced Fast Scanning algorithm is realized on images of the public and private transportation in Iraq. Evaluation is later made by comparing the produced images of proposed algorithm and the standard Fast Scanning algorithm. The results showed that proposed algorithm is faster in terms the time from standard fast scanning.
Dependence of Adaptive Cross-correlation Algorithm Performance on the Extended Scene Image Quality

NASA Technical Reports Server (NTRS)

Sidick, Erkin

2008-01-01

Recently, we reported an adaptive cross-correlation (ACC) algorithm to estimate with high accuracy the shift as large as several pixels between two extended-scene sub-images captured by a Shack-Hartmann wavefront sensor. It determines the positions of all extended-scene image cells relative to a reference cell in the same frame using an FFT-based iterative image-shifting algorithm. It works with both point-source spot images as well as extended scene images. We have demonstrated previously based on some measured images that the ACC algorithm can determine image shifts with as high an accuracy as 0.01 pixel for shifts as large 3 pixels, and yield similar results for both point source spot images and extended scene images. The shift estimate accuracy of the ACC algorithm depends on illumination level, background, and scene content in addition to the amount of the shift between two image cells. In this paper we investigate how the performance of the ACC algorithm depends on the quality and the frequency content of extended scene images captured by a Shack-Hatmann camera. We also compare the performance of the ACC algorithm with those of several other approaches, and introduce a failsafe criterion for the ACC algorithm-based extended scene Shack-Hatmann sensors.
Research and implementation of the algorithm for unwrapped and distortion correction basing on CORDIC for panoramic image

NASA Astrophysics Data System (ADS)

Zhang, Zhenhai; Li, Kejie; Wu, Xiaobing; Zhang, Shujiang

2008-03-01

The unwrapped and correcting algorithm based on Coordinate Rotation Digital Computer (CORDIC) and bilinear interpolation algorithm was presented in this paper, with the purpose of processing dynamic panoramic annular image. An original annular panoramic image captured by panoramic annular lens (PAL) can be unwrapped and corrected to conventional rectangular image without distortion, which is much more coincident with people's vision. The algorithm for panoramic image processing is modeled by VHDL and implemented in FPGA. The experimental results show that the proposed panoramic image algorithm for unwrapped and distortion correction has the lower computation complexity and the architecture for dynamic panoramic image processing has lower hardware cost and power consumption. And the proposed algorithm is valid.
An Image Encryption Algorithm Based on Information Hiding

NASA Astrophysics Data System (ADS)

Ge, Xin; Lu, Bin; Liu, Fenlin; Gong, Daofu

Aiming at resolving the conflict between security and efficiency in the design of chaotic image encryption algorithms, an image encryption algorithm based on information hiding is proposed based on the “one-time pad” idea. A random parameter is introduced to ensure a different keystream for each encryption, which has the characteristics of “one-time pad”, improving the security of the algorithm rapidly without significant increase in algorithm complexity. The random parameter is embedded into the ciphered image with information hiding technology, which avoids negotiation for its transport and makes the application of the algorithm easier. Algorithm analysis and experiments show that the algorithm is secure against chosen plaintext attack, differential attack and divide-and-conquer attack, and has good statistical properties in ciphered images.
Mixed raster content (MRC) model for compound image compression

NASA Astrophysics Data System (ADS)

de Queiroz, Ricardo L.; Buckley, Robert R.; Xu, Ming

1998-12-01

This paper will describe the Mixed Raster Content (MRC) method for compressing compound images, containing both binary test and continuous-tone images. A single compression algorithm that simultaneously meets the requirements for both text and image compression has been elusive. MRC takes a different approach. Rather than using a single algorithm, MRC uses a multi-layered imaging model for representing the results of multiple compression algorithms, including ones developed specifically for text and for images. As a result, MRC can combine the best of existing or new compression algorithms and offer different quality-compression ratio tradeoffs. The algorithms used by MRC set the lower bound on its compression performance. Compared to existing algorithms, MRC has some image-processing overhead to manage multiple algorithms and the imaging model. This paper will develop the rationale for the MRC approach by describing the multi-layered imaging model in light of a rate-distortion trade-off. Results will be presented comparing images compressed using MRC, JPEG and state-of-the-art wavelet algorithms such as SPIHT. MRC has been approved or proposed as an architectural model for several standards, including ITU Color Fax, IETF Internet Fax, and JPEG 2000.
Sparsity-constrained PET image reconstruction with learned dictionaries

NASA Astrophysics Data System (ADS)

Tang, Jing; Yang, Bao; Wang, Yanhua; Ying, Leslie

2016-09-01

PET imaging plays an important role in scientific and clinical measurement of biochemical and physiological processes. Model-based PET image reconstruction such as the iterative expectation maximization algorithm seeking the maximum likelihood solution leads to increased noise. The maximum a posteriori (MAP) estimate removes divergence at higher iterations. However, a conventional smoothing prior or a total-variation (TV) prior in a MAP reconstruction algorithm causes over smoothing or blocky artifacts in the reconstructed images. We propose to use dictionary learning (DL) based sparse signal representation in the formation of the prior for MAP PET image reconstruction. The dictionary to sparsify the PET images in the reconstruction process is learned from various training images including the corresponding MR structural image and a self-created hollow sphere. Using simulated and patient brain PET data with corresponding MR images, we study the performance of the DL-MAP algorithm and compare it quantitatively with a conventional MAP algorithm, a TV-MAP algorithm, and a patch-based algorithm. The DL-MAP algorithm achieves improved bias and contrast (or regional mean values) at comparable noise to what the other MAP algorithms acquire. The dictionary learned from the hollow sphere leads to similar results as the dictionary learned from the corresponding MR image. Achieving robust performance in various noise-level simulation and patient studies, the DL-MAP algorithm with a general dictionary demonstrates its potential in quantitative PET imaging.
Medical image registration by combining global and local information: a chain-type diffeomorphic demons algorithm.

PubMed

Liu, Xiaozheng; Yuan, Zhenming; Zhu, Junming; Xu, Dongrong

2013-12-07

The demons algorithm is a popular algorithm for non-rigid image registration because of its computational efficiency and simple implementation. The deformation forces of the classic demons algorithm were derived from image gradients by considering the deformation to decrease the intensity dissimilarity between images. However, the methods using the difference of image intensity for medical image registration are easily affected by image artifacts, such as image noise, non-uniform imaging and partial volume effects. The gradient magnitude image is constructed from the local information of an image, so the difference in a gradient magnitude image can be regarded as more reliable and robust for these artifacts. Then, registering medical images by considering the differences in both image intensity and gradient magnitude is a straightforward selection. In this paper, based on a diffeomorphic demons algorithm, we propose a chain-type diffeomorphic demons algorithm by combining the differences in both image intensity and gradient magnitude for medical image registration. Previous work had shown that the classic demons algorithm can be considered as an approximation of a second order gradient descent on the sum of the squared intensity differences. By optimizing the new dissimilarity criteria, we also present a set of new demons forces which were derived from the gradients of the image and gradient magnitude image. We show that, in controlled experiments, this advantage is confirmed, and yields a fast convergence.
FIVQ algorithm for interference hyper-spectral image compression

NASA Astrophysics Data System (ADS)

Wen, Jia; Ma, Caiwen; Zhao, Junsuo

2014-07-01

Based on the improved vector quantization (IVQ) algorithm [1] which was proposed in 2012, this paper proposes a further improved vector quantization (FIVQ) algorithm for LASIS (Large Aperture Static Imaging Spectrometer) interference hyper-spectral image compression. To get better image quality, IVQ algorithm takes both the mean values and the VQ indices as the encoding rules. Although IVQ algorithm can improve both the bit rate and the image quality, it still can be further improved in order to get much lower bit rate for the LASIS interference pattern with the special optical characteristics based on the pushing and sweeping in LASIS imaging principle. In the proposed algorithm FIVQ, the neighborhood of the encoding blocks of the interference pattern image, which are using the mean value rules, will be checked whether they have the same mean value as the current processing block. Experiments show the proposed algorithm FIVQ can get lower bit rate compared to that of the IVQ algorithm for the LASIS interference hyper-spectral sequences.
Research on the Improved Image Dodging Algorithm Based on Mask Technique

NASA Astrophysics Data System (ADS)

Yao, F.; Hu, H.; Wan, Y.

2012-08-01

The remote sensing image dodging algorithm based on Mask technique is a good method for removing the uneven lightness within a single image. However, there are some problems with this algorithm, such as how to set an appropriate filter size, for which there is no good solution. In order to solve these problems, an improved algorithm is proposed. In this improved algorithm, the original image is divided into blocks, and then the image blocks with different definitions are smoothed using the low-pass filters with different cut-off frequencies to get the background image; for the image after subtraction, the regions with different lightness are processed using different linear transformation models. The improved algorithm can get a better dodging result than the original one, and can make the contrast of the whole image more consistent.
Spatial compression algorithm for the analysis of very large multivariate images

DOEpatents

Keenan, Michael R [Albuquerque, NM

2008-07-15

A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms to provide further computational efficiencies.
Fast perceptual image hash based on cascade algorithm

NASA Astrophysics Data System (ADS)

Ruchay, Alexey; Kober, Vitaly; Yavtushenko, Evgeniya

2017-09-01

In this paper, we propose a perceptual image hash algorithm based on cascade algorithm, which can be applied in image authentication, retrieval, and indexing. Image perceptual hash uses for image retrieval in sense of human perception against distortions caused by compression, noise, common signal processing and geometrical modifications. The main disadvantage of perceptual hash is high time expenses. In the proposed cascade algorithm of image retrieval initializes with short hashes, and then a full hash is applied to the processed results. Computer simulation results show that the proposed hash algorithm yields a good performance in terms of robustness, discriminability, and time expenses.
A novel blinding digital watermark algorithm based on lab color space

NASA Astrophysics Data System (ADS)

Dong, Bing-feng; Qiu, Yun-jie; Lu, Hong-tao

2010-02-01

It is necessary for blinding digital image watermark algorithm to extract watermark information without any extra information except the watermarked image itself. But most of the current blinding watermark algorithms have the same disadvantage: besides the watermarked image, they also need the size and other information about the original image when extracting the watermark. This paper presents an innovative blinding color image watermark algorithm based on Lab color space, which does not have the disadvantages mentioned above. This algorithm first marks the watermark region size and position through embedding some regular blocks called anchor points in image spatial domain, and then embeds the watermark into the image. In doing so, the watermark information can be easily extracted after doing cropping and scale change to the image. Experimental results show that the algorithm is particularly robust against the color adjusting and geometry transformation. This algorithm has already been used in a copyright protecting project and works very well.
High Resolution Image Reconstruction from Projection of Low Resolution Images DIffering in Subpixel Shifts

NASA Technical Reports Server (NTRS)

Mareboyana, Manohar; Le Moigne-Stewart, Jacqueline; Bennett, Jerome

2016-01-01

In this paper, we demonstrate a simple algorithm that projects low resolution (LR) images differing in subpixel shifts on a high resolution (HR) also called super resolution (SR) grid. The algorithm is very effective in accuracy as well as time efficiency. A number of spatial interpolation techniques using nearest neighbor, inverse-distance weighted averages, Radial Basis Functions (RBF) etc. used in projection yield comparable results. For best accuracy of reconstructing SR image by a factor of two requires four LR images differing in four independent subpixel shifts. The algorithm has two steps: i) registration of low resolution images and (ii) shifting the low resolution images to align with reference image and projecting them on high resolution grid based on the shifts of each low resolution image using different interpolation techniques. Experiments are conducted by simulating low resolution images by subpixel shifts and subsampling of original high resolution image and the reconstructing the high resolution images from the simulated low resolution images. The results of accuracy of reconstruction are compared by using mean squared error measure between original high resolution image and reconstructed image. The algorithm was tested on remote sensing images and found to outperform previously proposed techniques such as Iterative Back Projection algorithm (IBP), Maximum Likelihood (ML), and Maximum a posterior (MAP) algorithms. The algorithm is robust and is not overly sensitive to the registration inaccuracies.
Development and Translation of Hybrid Optoacoustic/Ultrasonic Tomography for Early Breast Cancer Detection

DTIC Science & Technology

2014-09-01

to develop an optimized system design and associated image reconstruction algorithms for a hybrid three-dimensional (3D) breast imaging system that...research is to develop an optimized system design and associated image reconstruction algorithms for a hybrid three-dimensional (3D) breast imaging ...i) developed time-of- flight extraction algorithms to perform USCT, (ii) developing image reconstruction algorithms for USCT, (iii) developed
Comparison of subpixel image registration algorithms

NASA Astrophysics Data System (ADS)

Boye, R. R.; Nelson, C. L.

2009-02-01

Research into the use of multiframe superresolution has led to the development of algorithms for providing images with enhanced resolution using several lower resolution copies. An integral component of these algorithms is the determination of the registration of each of the low resolution images to a reference image. Without this information, no resolution enhancement can be attained. We have endeavored to find a suitable method for registering severely undersampled images by comparing several approaches. To test the algorithms, an ideal image is input to a simulated image formation program, creating several undersampled images with known geometric transformations. The registration algorithms are then applied to the set of low resolution images and the estimated registration parameters compared to the actual values. This investigation is limited to monochromatic images (extension to color images is not difficult) and only considers global geometric transformations. Each registration approach will be reviewed and evaluated with respect to the accuracy of the estimated registration parameters as well as the computational complexity required. In addition, the effects of image content, specifically spatial frequency content, as well as the immunity of the registration algorithms to noise will be discussed.
3-D Image Encryption Based on Rubik's Cube and RC6 Algorithm

NASA Astrophysics Data System (ADS)

Helmy, Mai; El-Rabaie, El-Sayed M.; Eldokany, Ibrahim M.; El-Samie, Fathi E. Abd

2017-12-01

A novel encryption algorithm based on the 3-D Rubik's cube is proposed in this paper to achieve 3D encryption of a group of images. This proposed encryption algorithm begins with RC6 as a first step for encrypting multiple images, separately. After that, the obtained encrypted images are further encrypted with the 3-D Rubik's cube. The RC6 encrypted images are used as the faces of the Rubik's cube. From the concepts of image encryption, the RC6 algorithm adds a degree of diffusion, while the Rubik's cube algorithm adds a degree of permutation. The simulation results demonstrate that the proposed encryption algorithm is efficient, and it exhibits strong robustness and security. The encrypted images are further transmitted over wireless Orthogonal Frequency Division Multiplexing (OFDM) system and decrypted at the receiver side. Evaluation of the quality of the decrypted images at the receiver side reveals good results.
SKL algorithm based fabric image matching and retrieval

NASA Astrophysics Data System (ADS)

Cao, Yichen; Zhang, Xueqin; Ma, Guojian; Sun, Rongqing; Dong, Deping

2017-07-01

Intelligent computer image processing technology provides convenience and possibility for designers to carry out designs. Shape analysis can be achieved by extracting SURF feature. However, high dimension of SURF feature causes to lower matching speed. To solve this problem, this paper proposed a fast fabric image matching algorithm based on SURF K-means and LSH algorithm. By constructing the bag of visual words on K-Means algorithm, and forming feature histogram of each image, the dimension of SURF feature is reduced at the first step. Then with the help of LSH algorithm, the features are encoded and the dimension is further reduced. In addition, the indexes of each image and each class of image are created, and the number of matching images is decreased by LSH hash bucket. Experiments on fabric image database show that this algorithm can speed up the matching and retrieval process, the result can satisfy the requirement of dress designers with accuracy and speed.
Denoising of polychromatic CT images based on their own noise properties

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Ji Hye; Chang, Yongjin; Ra, Jong Beom, E-mail: jbra@kaist.ac.kr

Purpose: Because of high diagnostic accuracy and fast scan time, computed tomography (CT) has been widely used in various clinical applications. Since the CT scan introduces radiation exposure to patients, however, dose reduction has recently been recognized as an important issue in CT imaging. However, low-dose CT causes an increase of noise in the image and thereby deteriorates the accuracy of diagnosis. In this paper, the authors develop an efficient denoising algorithm for low-dose CT images obtained using a polychromatic x-ray source. The algorithm is based on two steps: (i) estimation of space variant noise statistics, which are uniquely determinedmore » according to the system geometry and scanned object, and (ii) subsequent novel conversion of the estimated noise to Gaussian noise so that an existing high performance Gaussian noise filtering algorithm can be directly applied to CT images with non-Gaussian noise. Methods: For efficient polychromatic CT image denoising, the authors first reconstruct an image with the iterative maximum-likelihood polychromatic algorithm for CT to alleviate the beam-hardening problem. We then estimate the space-variant noise variance distribution on the image domain. Since there are many high performance denoising algorithms available for the Gaussian noise, image denoising can become much more efficient if they can be used. Hence, the authors propose a novel conversion scheme to transform the estimated space-variant noise to near Gaussian noise. In the suggested scheme, the authors first convert the image so that its mean and variance can have a linear relationship, and then produce a Gaussian image via variance stabilizing transform. The authors then apply a block matching 4D algorithm that is optimized for noise reduction of the Gaussian image, and reconvert the result to obtain a final denoised image. To examine the performance of the proposed method, an XCAT phantom simulation and a physical phantom experiment were conducted. Results: Both simulation and experimental results show that, unlike the existing denoising algorithms, the proposed algorithm can effectively reduce the noise over the whole region of CT images while preventing degradation of image resolution. Conclusions: To effectively denoise polychromatic low-dose CT images, a novel denoising algorithm is proposed. Because this algorithm is based on the noise statistics of a reconstructed polychromatic CT image, the spatially varying noise on the image is effectively reduced so that the denoised image will have homogeneous quality over the image domain. Through a simulation and a real experiment, it is verified that the proposed algorithm can deliver considerably better performance compared to the existing denoising algorithms.« less

Adaptive optics image restoration algorithm based on wavefront reconstruction and adaptive total variation method

NASA Astrophysics Data System (ADS)

Li, Dongming; Zhang, Lijuan; Wang, Ting; Liu, Huan; Yang, Jinhua; Chen, Guifen

2016-11-01

To improve the adaptive optics (AO) image's quality, we study the AO image restoration algorithm based on wavefront reconstruction technology and adaptive total variation (TV) method in this paper. Firstly, the wavefront reconstruction using Zernike polynomial is used for initial estimated for the point spread function (PSF). Then, we develop our proposed iterative solutions for AO images restoration, addressing the joint deconvolution issue. The image restoration experiments are performed to verify the image restoration effect of our proposed algorithm. The experimental results show that, compared with the RL-IBD algorithm and Wiener-IBD algorithm, we can see that GMG measures (for real AO image) from our algorithm are increased by 36.92%, and 27.44% respectively, and the computation time are decreased by 7.2%, and 3.4% respectively, and its estimation accuracy is significantly improved.
Visual performance-based image enhancement methodology: an investigation of contrast enhancement algorithms

NASA Astrophysics Data System (ADS)

Neriani, Kelly E.; Herbranson, Travis J.; Reis, George A.; Pinkus, Alan R.; Goodyear, Charles D.

2006-05-01

While vast numbers of image enhancing algorithms have already been developed, the majority of these algorithms have not been assessed in terms of their visual performance-enhancing effects using militarily relevant scenarios. The goal of this research was to apply a visual performance-based assessment methodology to evaluate six algorithms that were specifically designed to enhance the contrast of digital images. The image enhancing algorithms used in this study included three different histogram equalization algorithms, the Autolevels function, the Recursive Rational Filter technique described in Marsi, Ramponi, and Carrato1 and the multiscale Retinex algorithm described in Rahman, Jobson and Woodell2. The methodology used in the assessment has been developed to acquire objective human visual performance data as a means of evaluating the contrast enhancement algorithms. Objective performance metrics, response time and error rate, were used to compare algorithm enhanced images versus two baseline conditions, original non-enhanced images and contrast-degraded images. Observers completed a visual search task using a spatial-forcedchoice paradigm. Observers searched images for a target (a military vehicle) hidden among foliage and then indicated in which quadrant of the screen the target was located. Response time and percent correct were measured for each observer. Results of the study and future directions are discussed.
The influence of image reconstruction algorithms on linear thorax EIT image analysis of ventilation.

PubMed

Zhao, Zhanqi; Frerichs, Inéz; Pulletz, Sven; Müller-Lisse, Ullrich; Möller, Knut

2014-06-01

Analysis methods of electrical impedance tomography (EIT) images based on different reconstruction algorithms were examined. EIT measurements were performed on eight mechanically ventilated patients with acute respiratory distress syndrome. A maneuver with step increase of airway pressure was performed. EIT raw data were reconstructed offline with (1) filtered back-projection (BP); (2) the Dräger algorithm based on linearized Newton-Raphson (DR); (3) the GREIT (Graz consensus reconstruction algorithm for EIT) reconstruction algorithm with a circular forward model (GR(C)) and (4) GREIT with individual thorax geometry (GR(T)). Individual thorax contours were automatically determined from the routine computed tomography images. Five indices were calculated on the resulting EIT images respectively: (a) the ratio between tidal and deep inflation impedance changes; (b) tidal impedance changes in the right and left lungs; (c) center of gravity; (d) the global inhomogeneity index and (e) ventilation delay at mid-dorsal regions. No significant differences were found in all examined indices among the four reconstruction algorithms (p > 0.2, Kruskal-Wallis test). The examined algorithms used for EIT image reconstruction do not influence the selected indices derived from the EIT image analysis. Indices that validated for images with one reconstruction algorithm are also valid for other reconstruction algorithms.
Study of image matching algorithm and sub-pixel fitting algorithm in target tracking

NASA Astrophysics Data System (ADS)

Yang, Ming-dong; Jia, Jianjun; Qiang, Jia; Wang, Jian-yu

2015-03-01

Image correlation matching is a tracking method that searched a region most approximate to the target template based on the correlation measure between two images. Because there is no need to segment the image, and the computation of this method is little. Image correlation matching is a basic method of target tracking. This paper mainly studies the image matching algorithm of gray scale image, which precision is at sub-pixel level. The matching algorithm used in this paper is SAD (Sum of Absolute Difference) method. This method excels in real-time systems because of its low computation complexity. The SAD method is introduced firstly and the most frequently used sub-pixel fitting algorithms are introduced at the meantime. These fitting algorithms can't be used in real-time systems because they are too complex. However, target tracking often requires high real-time performance, we put forward a fitting algorithm named paraboloidal fitting algorithm based on the consideration above, this algorithm is simple and realized easily in real-time system. The result of this algorithm is compared with that of surface fitting algorithm through image matching simulation. By comparison, the precision difference between these two algorithms is little, it's less than 0.01pixel. In order to research the influence of target rotation on precision of image matching, the experiment of camera rotation was carried on. The detector used in the camera is a CMOS detector. It is fixed to an arc pendulum table, take pictures when the camera rotated different angles. Choose a subarea in the original picture as the template, and search the best matching spot using image matching algorithm mentioned above. The result shows that the matching error is bigger when the target rotation angle is larger. It's an approximate linear relation. Finally, the influence of noise on matching precision was researched. Gaussian noise and pepper and salt noise were added in the image respectively, and the image was processed by mean filter and median filter, then image matching was processed. The result show that when the noise is little, mean filter and median filter can achieve a good result. But when the noise density of salt and pepper noise is bigger than 0.4, or the variance of Gaussian noise is bigger than 0.0015, the result of image matching will be wrong.
Image compression/decompression based on mathematical transform, reduction/expansion, and image sharpening

DOEpatents

Fu, Chi-Yung; Petrich, Loren I.

1997-01-01

An image represented in a first image array of pixels is first decimated in two dimensions before being compressed by a predefined compression algorithm such as JPEG. Another possible predefined compression algorithm can involve a wavelet technique. The compressed, reduced image is then transmitted over the limited bandwidth transmission medium, and the transmitted image is decompressed using an algorithm which is an inverse of the predefined compression algorithm (such as reverse JPEG). The decompressed, reduced image is then interpolated back to its original array size. Edges (contours) in the image are then sharpened to enhance the perceptual quality of the reconstructed image. Specific sharpening techniques are described.
Cloning and characterization of the nagA gene that encodes beta-n-acetylglucosaminidase from Aspergillus nidulans and its expression in Aspergillus oryzae.

PubMed

Kim, Sunhwa; Matsuo, Ichiro; Ajisaka, Katsumi; Nakajima, Harushi; Kitamoto, Katsuhiko

2002-10-01

We isolated a beta-N-acetylglucosaminidase encoding gene and its cDNA from the filamentous fungus Aspergillus nidulans, and designated it nagA. The nagA gene contained no intron and encoded a polypeptide of 603 amino acids with a putative 19-amino acid signal sequence. The deduced amino acid sequence was very similar to the sequence of Candida albicans Hex1 and Trichoderma harzianum Nag1. Yeast cells containing the nagA cDNA under the control of the GAL1 promoter expressed beta-N-acetylglucosaminidase activity. The chromosomal nagA gene of A. nidulans was disrupted by replacement with the argB marker gene. The disruptant strains expressed low levels of beta-N-acetylglucosaminidase activity and showed poor growth on a medium containing chitobiose as a carbon source. Aspergillus oryzae strain carrying the nagA gene under the control of the improved glaA promoter produced large amounts of beta-N-acetylglucosaminidase in a wheat bran solid culture.
An image hiding method based on cascaded iterative Fourier transform and public-key encryption algorithm

NASA Astrophysics Data System (ADS)

Zhang, B.; Sang, Jun; Alam, Mohammad S.

2013-03-01

An image hiding method based on cascaded iterative Fourier transform and public-key encryption algorithm was proposed. Firstly, the original secret image was encrypted into two phase-only masks M1 and M2 via cascaded iterative Fourier transform (CIFT) algorithm. Then, the public-key encryption algorithm RSA was adopted to encrypt M2 into M2' . Finally, a host image was enlarged by extending one pixel into 2×2 pixels and each element in M1 and M2' was multiplied with a superimposition coefficient and added to or subtracted from two different elements in the 2×2 pixels of the enlarged host image. To recover the secret image from the stego-image, the two masks were extracted from the stego-image without the original host image. By applying public-key encryption algorithm, the key distribution was facilitated, and also compared with the image hiding method based on optical interference, the proposed method may reach higher robustness by employing the characteristics of the CIFT algorithm. Computer simulations show that this method has good robustness against image processing.
Multiple-algorithm parallel fusion of infrared polarization and intensity images based on algorithmic complementarity and synergy

NASA Astrophysics Data System (ADS)

Zhang, Lei; Yang, Fengbao; Ji, Linna; Lv, Sheng

2018-01-01

Diverse image fusion methods perform differently. Each method has advantages and disadvantages compared with others. One notion is that the advantages of different image methods can be effectively combined. A multiple-algorithm parallel fusion method based on algorithmic complementarity and synergy is proposed. First, in view of the characteristics of the different algorithms and difference-features among images, an index vector-based feature-similarity is proposed to define the degree of complementarity and synergy. This proposed index vector is a reliable evidence indicator for algorithm selection. Second, the algorithms with a high degree of complementarity and synergy are selected. Then, the different degrees of various features and infrared intensity images are used as the initial weights for the nonnegative matrix factorization (NMF). This avoids randomness of the NMF initialization parameter. Finally, the fused images of different algorithms are integrated using the NMF because of its excellent data fusing performance on independent features. Experimental results demonstrate that the visual effect and objective evaluation index of the fused images obtained using the proposed method are better than those obtained using traditional methods. The proposed method retains all the advantages that individual fusion algorithms have.
Remote sensing image denoising application by generalized morphological component analysis

NASA Astrophysics Data System (ADS)

Yu, Chong; Chen, Xiong

2014-12-01

In this paper, we introduced a remote sensing image denoising method based on generalized morphological component analysis (GMCA). This novel algorithm is the further extension of morphological component analysis (MCA) algorithm to the blind source separation framework. The iterative thresholding strategy adopted by GMCA algorithm firstly works on the most significant features in the image, and then progressively incorporates smaller features to finely tune the parameters of whole model. Mathematical analysis of the computational complexity of GMCA algorithm is provided. Several comparison experiments with state-of-the-art denoising algorithms are reported. In order to make quantitative assessment of algorithms in experiments, Peak Signal to Noise Ratio (PSNR) index and Structural Similarity (SSIM) index are calculated to assess the denoising effect from the gray-level fidelity aspect and the structure-level fidelity aspect, respectively. Quantitative analysis on experiment results, which is consistent with the visual effect illustrated by denoised images, has proven that the introduced GMCA algorithm possesses a marvelous remote sensing image denoising effectiveness and ability. It is even hard to distinguish the original noiseless image from the recovered image by adopting GMCA algorithm through visual effect.
Artifact removal algorithms for stroke detection using a multistatic MIST beamforming algorithm.

PubMed

Ricci, E; Di Domenico, S; Cianca, E; Rossi, T

2015-01-01

Microwave imaging (MWI) has been recently proved as a promising imaging modality for low-complexity, low-cost and fast brain imaging tools, which could play a fundamental role to efficiently manage emergencies related to stroke and hemorrhages. This paper focuses on the UWB radar imaging approach and in particular on the processing algorithms of the backscattered signals. Assuming the use of the multistatic version of the MIST (Microwave Imaging Space-Time) beamforming algorithm, developed by Hagness et al. for the early detection of breast cancer, the paper proposes and compares two artifact removal algorithms. Artifacts removal is an essential step of any UWB radar imaging system and currently considered artifact removal algorithms have been shown not to be effective in the specific scenario of brain imaging. First of all, the paper proposes modifications of a known artifact removal algorithm. These modifications are shown to be effective to achieve good localization accuracy and lower false positives. However, the main contribution is the proposal of an artifact removal algorithm based on statistical methods, which allows to achieve even better performance but with much lower computational complexity.
[An improved medical image fusion algorithm and quality evaluation].

PubMed

Chen, Meiling; Tao, Ling; Qian, Zhiyu

2009-08-01

Medical image fusion is of very important value for application in medical image analysis and diagnosis. In this paper, the conventional method of wavelet fusion is improved,so a new algorithm of medical image fusion is presented and the high frequency and low frequency coefficients are studied respectively. When high frequency coefficients are chosen, the regional edge intensities of each sub-image are calculated to realize adaptive fusion. The choice of low frequency coefficient is based on the edges of images, so that the fused image preserves all useful information and appears more distinctly. We apply the conventional and the improved fusion algorithms based on wavelet transform to fuse two images of human body and also evaluate the fusion results through a quality evaluation method. Experimental results show that this algorithm can effectively retain the details of information on original images and enhance their edge and texture features. This new algorithm is better than the conventional fusion algorithm based on wavelet transform.
SU-E-J-218: Evaluation of CT Images Created Using a New Metal Artifact Reduction Reconstruction Algorithm for Radiation Therapy Treatment Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Niemkiewicz, J; Palmiotti, A; Miner, M

2014-06-01

Purpose: Metal in patients creates streak artifacts in CT images. When used for radiation treatment planning, these artifacts make it difficult to identify internal structures and affects radiation dose calculations, which depend on HU numbers for inhomogeneity correction. This work quantitatively evaluates a new metal artifact reduction (MAR) CT image reconstruction algorithm (GE Healthcare CT-0521-04.13-EN-US DOC1381483) when metal is present. Methods: A Gammex Model 467 Tissue Characterization phantom was used. CT images were taken of this phantom on a GE Optima580RT CT scanner with and without steel and titanium plugs using both the standard and MAR reconstruction algorithms. HU valuesmore » were compared pixel by pixel to determine if the MAR algorithm altered the HUs of normal tissues when no metal is present, and to evaluate the effect of using the MAR algorithm when metal is present. Also, CT images of patients with internal metal objects using standard and MAR reconstruction algorithms were compared. Results: Comparing the standard and MAR reconstructed images of the phantom without metal, 95.0% of pixels were within ±35 HU and 98.0% of pixels were within ±85 HU. Also, the MAR reconstruction algorithm showed significant improvement in maintaining HUs of non-metallic regions in the images taken of the phantom with metal. HU Gamma analysis (2%, 2mm) of metal vs. non-metal phantom imaging using standard reconstruction resulted in an 84.8% pass rate compared to 96.6% for the MAR reconstructed images. CT images of patients with metal show significant artifact reduction when reconstructed with the MAR algorithm. Conclusion: CT imaging using the MAR reconstruction algorithm provides improved visualization of internal anatomy and more accurate HUs when metal is present compared to the standard reconstruction algorithm. MAR reconstructed CT images provide qualitative and quantitative improvements over current reconstruction algorithms, thus improving radiation treatment planning accuracy.« less
Fast Lossless Compression of Multispectral-Image Data

NASA Technical Reports Server (NTRS)

Klimesh, Matthew

2006-01-01

An algorithm that effects fast lossless compression of multispectral-image data is based on low-complexity, proven adaptive-filtering algorithms. This algorithm is intended for use in compressing multispectral-image data aboard spacecraft for transmission to Earth stations. Variants of this algorithm could be useful for lossless compression of three-dimensional medical imagery and, perhaps, for compressing image data in general.
Multivariate statistical model for 3D image segmentation with application to medical images.

PubMed

John, Nigel M; Kabuka, Mansur R; Ibrahim, Mohamed O

2003-12-01

In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probability-based multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).
Research on fast Fourier transforms algorithm of huge remote sensing image technology with GPU and partitioning technology.

PubMed

Yang, Xue; Li, Xue-You; Li, Jia-Guo; Ma, Jun; Zhang, Li; Yang, Jan; Du, Quan-Ye

2014-02-01

Fast Fourier transforms (FFT) is a basic approach to remote sensing image processing. With the improvement of capacity of remote sensing image capture with the features of hyperspectrum, high spatial resolution and high temporal resolution, how to use FFT technology to efficiently process huge remote sensing image becomes the critical step and research hot spot of current image processing technology. FFT algorithm, one of the basic algorithms of image processing, can be used for stripe noise removal, image compression, image registration, etc. in processing remote sensing image. CUFFT function library is the FFT algorithm library based on CPU and FFTW. FFTW is a FFT algorithm developed based on CPU in PC platform, and is currently the fastest CPU based FFT algorithm function library. However there is a common problem that once the available memory or memory is less than the capacity of image, there will be out of memory or memory overflow when using the above two methods to realize image FFT arithmetic. To address this problem, a CPU and partitioning technology based Huge Remote Fast Fourier Transform (HRFFT) algorithm is proposed in this paper. By improving the FFT algorithm in CUFFT function library, the problem of out of memory and memory overflow is solved. Moreover, this method is proved rational by experiment combined with the CCD image of HJ-1A satellite. When applied to practical image processing, it improves effect of the image processing, speeds up the processing, which saves the time of computation and achieves sound result.
Adaptive Residual Interpolation for Color and Multispectral Image Demosaicking †

PubMed Central

Kiku, Daisuke; Okutomi, Masatoshi

2017-01-01

Color image demosaicking for the Bayer color filter array is an essential image processing operation for acquiring high-quality color images. Recently, residual interpolation (RI)-based algorithms have demonstrated superior demosaicking performance over conventional color difference interpolation-based algorithms. In this paper, we propose adaptive residual interpolation (ARI) that improves existing RI-based algorithms by adaptively combining two RI-based algorithms and selecting a suitable iteration number at each pixel. These are performed based on a unified criterion that evaluates the validity of an RI-based algorithm. Experimental comparisons using standard color image datasets demonstrate that ARI can improve existing RI-based algorithms by more than 0.6 dB in the color peak signal-to-noise ratio and can outperform state-of-the-art algorithms based on training images. We further extend ARI for a multispectral filter array, in which more than three spectral bands are arrayed, and demonstrate that ARI can achieve state-of-the-art performance also for the task of multispectral image demosaicking. PMID:29194407
Adaptive Residual Interpolation for Color and Multispectral Image Demosaicking.

PubMed

Monno, Yusuke; Kiku, Daisuke; Tanaka, Masayuki; Okutomi, Masatoshi

2017-12-01

Color image demosaicking for the Bayer color filter array is an essential image processing operation for acquiring high-quality color images. Recently, residual interpolation (RI)-based algorithms have demonstrated superior demosaicking performance over conventional color difference interpolation-based algorithms. In this paper, we propose adaptive residual interpolation (ARI) that improves existing RI-based algorithms by adaptively combining two RI-based algorithms and selecting a suitable iteration number at each pixel. These are performed based on a unified criterion that evaluates the validity of an RI-based algorithm. Experimental comparisons using standard color image datasets demonstrate that ARI can improve existing RI-based algorithms by more than 0.6 dB in the color peak signal-to-noise ratio and can outperform state-of-the-art algorithms based on training images. We further extend ARI for a multispectral filter array, in which more than three spectral bands are arrayed, and demonstrate that ARI can achieve state-of-the-art performance also for the task of multispectral image demosaicking.
Implementation of digital image encryption algorithm using logistic function and DNA encoding

NASA Astrophysics Data System (ADS)

Suryadi, MT; Satria, Yudi; Fauzi, Muhammad

2018-03-01

Cryptography is a method to secure information that might be in form of digital image. Based on past research, in order to increase security level of chaos based encryption algorithm and DNA based encryption algorithm, encryption algorithm using logistic function and DNA encoding was proposed. Digital image encryption algorithm using logistic function and DNA encoding use DNA encoding to scramble the pixel values into DNA base and scramble it in DNA addition, DNA complement, and XOR operation. The logistic function in this algorithm used as random number generator needed in DNA complement and XOR operation. The result of the test show that the PSNR values of cipher images are 7.98-7.99 bits, the entropy values are close to 8, the histogram of cipher images are uniformly distributed and the correlation coefficient of cipher images are near 0. Thus, the cipher image can be decrypted perfectly and the encryption algorithm has good resistance to entropy attack and statistical attack.
Image Registration Algorithm Based on Parallax Constraint and Clustering Analysis

NASA Astrophysics Data System (ADS)

Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

2018-01-01

To resolve the problem of slow computation speed and low matching accuracy in image registration, a new image registration algorithm based on parallax constraint and clustering analysis is proposed. Firstly, Harris corner detection algorithm is used to extract the feature points of two images. Secondly, use Normalized Cross Correlation (NCC) function to perform the approximate matching of feature points, and the initial feature pair is obtained. Then, according to the parallax constraint condition, the initial feature pair is preprocessed by K-means clustering algorithm, which is used to remove the feature point pairs with obvious errors in the approximate matching process. Finally, adopt Random Sample Consensus (RANSAC) algorithm to optimize the feature points to obtain the final feature point matching result, and the fast and accurate image registration is realized. The experimental results show that the image registration algorithm proposed in this paper can improve the accuracy of the image matching while ensuring the real-time performance of the algorithm.
Analysis and improvement of the quantum image matching

NASA Astrophysics Data System (ADS)

Dang, Yijie; Jiang, Nan; Hu, Hao; Zhang, Wenyin

2017-11-01

We investigate the quantum image matching algorithm proposed by Jiang et al. (Quantum Inf Process 15(9):3543-3572, 2016). Although the complexity of this algorithm is much better than the classical exhaustive algorithm, there may be an error in it: After matching the area between two images, only the pixel at the upper left corner of the matched area played part in following steps. That is to say, the paper only matched one pixel, instead of an area. If more than one pixels in the big image are the same as the one at the upper left corner of the small image, the algorithm will randomly measure one of them, which causes the error. In this paper, an improved version is presented which takes full advantage of the whole matched area to locate a small image in a big image. The theoretical analysis indicates that the network complexity is higher than the previous algorithm, but it is still far lower than the classical algorithm. Hence, this algorithm is still efficient.

Autofocus algorithm using one-dimensional Fourier transform and Pearson correlation

NASA Astrophysics Data System (ADS)

Bueno Mario, A.; Alvarez-Borrego, Josue; Acho, L.

2004-10-01

A new autofocus algorithm based on one-dimensional Fourier transform and Pearson correlation for Z automatized microscope is proposed. Our goal is to determine in fast response time and accuracy, the best focused plane through an algorithm. We capture in bright and dark field several images set at different Z distances from biological organism sample. The algorithm uses the one-dimensional Fourier transform to obtain the image frequency content of a vectors pattern previously defined comparing the Pearson correlation of these frequency vectors versus the reference image frequency vector, the most out of focus image, we find the best focusing. Experimental results showed the algorithm has fast response time and accuracy in getting the best focus plane from captured images. In conclusions, the algorithm can be implemented in real time systems due fast response time, accuracy and robustness. The algorithm can be used to get focused images in bright and dark field and it can be extended to include fusion techniques to construct multifocus final images beyond of this paper.
Noisy image magnification with total variation regularization and order-changed dictionary learning

NASA Astrophysics Data System (ADS)

Xu, Jian; Chang, Zhiguo; Fan, Jiulun; Zhao, Xiaoqiang; Wu, Xiaomin; Wang, Yanzi

2015-12-01

Noisy low resolution (LR) images are always obtained in real applications, but many existing image magnification algorithms can not get good result from a noisy LR image. We propose a two-step image magnification algorithm to solve this problem. The proposed algorithm takes the advantages of both regularization-based method and learning-based method. The first step is based on total variation (TV) regularization and the second step is based on sparse representation. In the first step, we add a constraint on the TV regularization model to magnify the LR image and at the same time to suppress the noise in it. In the second step, we propose an order-changed dictionary training algorithm to train the dictionaries which is dominated by texture details. Experimental results demonstrate that the proposed algorithm performs better than many other algorithms when the noise is not serious. The proposed algorithm can also provide better visual quality on natural LR images.
A Compressed Sensing-based Image Reconstruction Algorithm for Solar Flare X-Ray Observations

NASA Astrophysics Data System (ADS)

Felix, Simon; Bolzern, Roman; Battaglia, Marina

2017-11-01

One way of imaging X-ray emission from solar flares is to measure Fourier components of the spatial X-ray source distribution. We present a new compressed sensing-based algorithm named VIS_CS, which reconstructs the spatial distribution from such Fourier components. We demonstrate the application of the algorithm on synthetic and observed solar flare X-ray data from the Reuven Ramaty High Energy Solar Spectroscopic Imager satellite and compare its performance with existing algorithms. VIS_CS produces competitive results with accurate photometry and morphology, without requiring any algorithm- and X-ray-source-specific parameter tuning. Its robustness and performance make this algorithm ideally suited for the generation of quicklook images or large image cubes without user intervention, such as for imaging spectroscopy analysis.
A Compressed Sensing-based Image Reconstruction Algorithm for Solar Flare X-Ray Observations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Felix, Simon; Bolzern, Roman; Battaglia, Marina, E-mail: simon.felix@fhnw.ch, E-mail: roman.bolzern@fhnw.ch, E-mail: marina.battaglia@fhnw.ch

One way of imaging X-ray emission from solar flares is to measure Fourier components of the spatial X-ray source distribution. We present a new compressed sensing-based algorithm named VIS-CS, which reconstructs the spatial distribution from such Fourier components. We demonstrate the application of the algorithm on synthetic and observed solar flare X-ray data from the Reuven Ramaty High Energy Solar Spectroscopic Imager satellite and compare its performance with existing algorithms. VIS-CS produces competitive results with accurate photometry and morphology, without requiring any algorithm- and X-ray-source-specific parameter tuning. Its robustness and performance make this algorithm ideally suited for the generation ofmore » quicklook images or large image cubes without user intervention, such as for imaging spectroscopy analysis.« less
A high-performance spatial database based approach for pathology imaging algorithm evaluation

PubMed Central

Wang, Fusheng; Kong, Jun; Gao, Jingjing; Cooper, Lee A.D.; Kurc, Tahsin; Zhou, Zhengwen; Adler, David; Vergara-Niedermayr, Cristobal; Katigbak, Bryan; Brat, Daniel J.; Saltz, Joel H.

2013-01-01

Background: Algorithm evaluation provides a means to characterize variability across image analysis algorithms, validate algorithms by comparison with human annotations, combine results from multiple algorithms for performance improvement, and facilitate algorithm sensitivity studies. The sizes of images and image analysis results in pathology image analysis pose significant challenges in algorithm evaluation. We present an efficient parallel spatial database approach to model, normalize, manage, and query large volumes of analytical image result data. This provides an efficient platform for algorithm evaluation. Our experiments with a set of brain tumor images demonstrate the application, scalability, and effectiveness of the platform. Context: The paper describes an approach and platform for evaluation of pathology image analysis algorithms. The platform facilitates algorithm evaluation through a high-performance database built on the Pathology Analytic Imaging Standards (PAIS) data model. Aims: (1) Develop a framework to support algorithm evaluation by modeling and managing analytical results and human annotations from pathology images; (2) Create a robust data normalization tool for converting, validating, and fixing spatial data from algorithm or human annotations; (3) Develop a set of queries to support data sampling and result comparisons; (4) Achieve high performance computation capacity via a parallel data management infrastructure, parallel data loading and spatial indexing optimizations in this infrastructure. Materials and Methods: We have considered two scenarios for algorithm evaluation: (1) algorithm comparison where multiple result sets from different methods are compared and consolidated; and (2) algorithm validation where algorithm results are compared with human annotations. We have developed a spatial normalization toolkit to validate and normalize spatial boundaries produced by image analysis algorithms or human annotations. The validated data were formatted based on the PAIS data model and loaded into a spatial database. To support efficient data loading, we have implemented a parallel data loading tool that takes advantage of multi-core CPUs to accelerate data injection. The spatial database manages both geometric shapes and image features or classifications, and enables spatial sampling, result comparison, and result aggregation through expressive structured query language (SQL) queries with spatial extensions. To provide scalable and efficient query support, we have employed a shared nothing parallel database architecture, which distributes data homogenously across multiple database partitions to take advantage of parallel computation power and implements spatial indexing to achieve high I/O throughput. Results: Our work proposes a high performance, parallel spatial database platform for algorithm validation and comparison. This platform was evaluated by storing, managing, and comparing analysis results from a set of brain tumor whole slide images. The tools we develop are open source and available to download. Conclusions: Pathology image algorithm validation and comparison are essential to iterative algorithm development and refinement. One critical component is the support for queries involving spatial predicates and comparisons. In our work, we develop an efficient data model and parallel database approach to model, normalize, manage and query large volumes of analytical image result data. Our experiments demonstrate that the data partitioning strategy and the grid-based indexing result in good data distribution across database nodes and reduce I/O overhead in spatial join queries through parallel retrieval of relevant data and quick subsetting of datasets. The set of tools in the framework provide a full pipeline to normalize, load, manage and query analytical results for algorithm evaluation. PMID:23599905
A comparison of select image-compression algorithms for an electronic still camera

NASA Technical Reports Server (NTRS)

Nerheim, Rosalee

1989-01-01

This effort is a study of image-compression algorithms for an electronic still camera. An electronic still camera can record and transmit high-quality images without the use of film, because images are stored digitally in computer memory. However, high-resolution images contain an enormous amount of information, and will strain the camera's data-storage system. Image compression will allow more images to be stored in the camera's memory. For the electronic still camera, a compression algorithm that produces a reconstructed image of high fidelity is most important. Efficiency of the algorithm is the second priority. High fidelity and efficiency are more important than a high compression ratio. Several algorithms were chosen for this study and judged on fidelity, efficiency and compression ratio. The transform method appears to be the best choice. At present, the method is compressing images to a ratio of 5.3:1 and producing high-fidelity reconstructed images.
Nonrigid Image Registration in Digital Subtraction Angiography Using Multilevel B-Spline

PubMed Central

2013-01-01

We address the problem of motion artifact reduction in digital subtraction angiography (DSA) using image registration techniques. Most of registration algorithms proposed for application in DSA, have been designed for peripheral and cerebral angiography images in which we mainly deal with global rigid motions. These algorithms did not yield good results when applied to coronary angiography images because of complex nonrigid motions that exist in this type of angiography images. Multiresolution and iterative algorithms are proposed to cope with this problem, but these algorithms are associated with high computational cost which makes them not acceptable for real-time clinical applications. In this paper we propose a nonrigid image registration algorithm for coronary angiography images that is significantly faster than multiresolution and iterative blocking methods and outperforms competing algorithms evaluated on the same data sets. This algorithm is based on a sparse set of matched feature point pairs and the elastic registration is performed by means of multilevel B-spline image warping. Experimental results with several clinical data sets demonstrate the effectiveness of our approach. PMID:23971026
Infrared image enhancement using H(infinity) bounds for surveillance applications.

PubMed

Qidwai, Uvais

2008-08-01

In this paper, two algorithms have been presented to enhance the infrared (IR) images. Using the autoregressive moving average model structure and H(infinity) optimal bounds, the image pixels are mapped from the IR pixel space into normal optical image space, thus enhancing the IR image for improved visual quality. Although H(infinity)-based system identification algorithms are very common now, they are not quite suitable for real-time applications owing to their complexity. However, many variants of such algorithms are possible that can overcome this constraint. Two such algorithms have been developed and implemented in this paper. Theoretical and algorithmic results show remarkable enhancement in the acquired images. This will help in enhancing the visual quality of IR images for surveillance applications.
Image reconstruction through thin scattering media by simulated annealing algorithm

NASA Astrophysics Data System (ADS)

Fang, Longjie; Zuo, Haoyi; Pang, Lin; Yang, Zuogang; Zhang, Xicheng; Zhu, Jianhua

2018-07-01

An idea for reconstructing the image of an object behind thin scattering media is proposed by phase modulation. The optimized phase mask is achieved by modulating the scattered light using simulated annealing algorithm. The correlation coefficient is exploited as a fitness function to evaluate the quality of reconstructed image. The reconstructed images optimized from simulated annealing algorithm and genetic algorithm are compared in detail. The experimental results show that our proposed method has better definition and higher speed than genetic algorithm.
Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm.

PubMed

Yang, Zhang; Shufan, Ye; Li, Guo; Weifeng, Ding

2016-01-01

The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method.
Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm

PubMed Central

Yang, Zhang; Li, Guo; Weifeng, Ding

2016-01-01

The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method. PMID:27403428
Research on compressive sensing reconstruction algorithm based on total variation model

NASA Astrophysics Data System (ADS)

Gao, Yu-xuan; Sun, Huayan; Zhang, Tinghua; Du, Lin

2017-12-01

Compressed sensing for breakthrough Nyquist sampling theorem provides a strong theoretical , making compressive sampling for image signals be carried out simultaneously. In traditional imaging procedures using compressed sensing theory, not only can it reduces the storage space, but also can reduce the demand for detector resolution greatly. Using the sparsity of image signal, by solving the mathematical model of inverse reconfiguration, realize the super-resolution imaging. Reconstruction algorithm is the most critical part of compression perception, to a large extent determine the accuracy of the reconstruction of the image.The reconstruction algorithm based on the total variation (TV) model is more suitable for the compression reconstruction of the two-dimensional image, and the better edge information can be obtained. In order to verify the performance of the algorithm, Simulation Analysis the reconstruction result in different coding mode of the reconstruction algorithm based on the TV reconstruction algorithm. The reconstruction effect of the reconfigurable algorithm based on TV based on the different coding methods is analyzed to verify the stability of the algorithm. This paper compares and analyzes the typical reconstruction algorithm in the same coding mode. On the basis of the minimum total variation algorithm, the Augmented Lagrangian function term is added and the optimal value is solved by the alternating direction method.Experimental results show that the reconstruction algorithm is compared with the traditional classical algorithm based on TV has great advantages, under the low measurement rate can be quickly and accurately recovers target image.
GPU implementation of prior image constrained compressed sensing (PICCS)

NASA Astrophysics Data System (ADS)

Nett, Brian E.; Tang, Jie; Chen, Guang-Hong

2010-04-01

The Prior Image Constrained Compressed Sensing (PICCS) algorithm (Med. Phys. 35, pg. 660, 2008) has been applied to several computed tomography applications with both standard CT systems and flat-panel based systems designed for guiding interventional procedures and radiation therapy treatment delivery. The PICCS algorithm typically utilizes a prior image which is reconstructed via the standard Filtered Backprojection (FBP) reconstruction algorithm. The algorithm then iteratively solves for the image volume that matches the measured data, while simultaneously assuring the image is similar to the prior image. The PICCS algorithm has demonstrated utility in several applications including: improved temporal resolution reconstruction, 4D respiratory phase specific reconstructions for radiation therapy, and cardiac reconstruction from data acquired on an interventional C-arm. One disadvantage of the PICCS algorithm, just as other iterative algorithms, is the long computation times typically associated with reconstruction. In order for an algorithm to gain clinical acceptance reconstruction must be achievable in minutes rather than hours. In this work the PICCS algorithm has been implemented on the GPU in order to significantly reduce the reconstruction time of the PICCS algorithm. The Compute Unified Device Architecture (CUDA) was used in this implementation.
A modified approach combining FNEA and watershed algorithms for segmenting remotely-sensed optical images

NASA Astrophysics Data System (ADS)

Liu, Likun

2018-01-01

In the field of remote sensing image processing, remote sensing image segmentation is a preliminary step for later analysis of remote sensing image processing and semi-auto human interpretation, fully-automatic machine recognition and learning. Since 2000, a technique of object-oriented remote sensing image processing method and its basic thought prevails. The core of the approach is Fractal Net Evolution Approach (FNEA) multi-scale segmentation algorithm. The paper is intent on the research and improvement of the algorithm, which analyzes present segmentation algorithms and selects optimum watershed algorithm as an initialization. Meanwhile, the algorithm is modified by modifying an area parameter, and then combining area parameter with a heterogeneous parameter further. After that, several experiments is carried on to prove the modified FNEA algorithm, compared with traditional pixel-based method (FCM algorithm based on neighborhood information) and combination of FNEA and watershed, has a better segmentation result.
Study on Underwater Image Denoising Algorithm Based on Wavelet Transform

NASA Astrophysics Data System (ADS)

Jian, Sun; Wen, Wang

2017-02-01

This paper analyzes the application of MATLAB in underwater image processing, the transmission characteristics of the underwater laser light signal and the kinds of underwater noise has been described, the common noise suppression algorithm: Wiener filter, median filter, average filter algorithm is brought out. Then the advantages and disadvantages of each algorithm in image sharpness and edge protection areas have been compared. A hybrid filter algorithm based on wavelet transform has been proposed which can be used for Color Image Denoising. At last the PSNR and NMSE of each algorithm has been given out, which compares the ability to de-noising
Edge-oriented dual-dictionary guided enrichment (EDGE) for MRI-CT image reconstruction.

PubMed

Li, Liang; Wang, Bigong; Wang, Ge

2016-01-01

In this paper, we formulate the joint/simultaneous X-ray CT and MRI image reconstruction. In particular, a novel algorithm is proposed for MRI image reconstruction from highly under-sampled MRI data and CT images. It consists of two steps. First, a training dataset is generated from a series of well-registered MRI and CT images on the same patients. Then, an initial MRI image of a patient can be reconstructed via edge-oriented dual-dictionary guided enrichment (EDGE) based on the training dataset and a CT image of the patient. Second, an MRI image is reconstructed using the dictionary learning (DL) algorithm from highly under-sampled k-space data and the initial MRI image. Our algorithm can establish a one-to-one correspondence between the two imaging modalities, and obtain a good initial MRI estimation. Both noise-free and noisy simulation studies were performed to evaluate and validate the proposed algorithm. The results with different under-sampling factors show that the proposed algorithm performed significantly better than those reconstructed using the DL algorithm from MRI data alone.
Adaptive Algorithms for Automated Processing of Document Images

DTIC Science & Technology

2011-01-01

ABSTRACT Title of dissertation: ADAPTIVE ALGORITHMS FOR AUTOMATED PROCESSING OF DOCUMENT IMAGES Mudit Agrawal, Doctor of Philosophy, 2011...2011 4. TITLE AND SUBTITLE Adaptive Algorithms for Automated Processing of Document Images 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM...ALGORITHMS FOR AUTOMATED PROCESSING OF DOCUMENT IMAGES by Mudit Agrawal Dissertation submitted to the Faculty of the Graduate School of the University
Magnetic resonance electrical impedance tomography (MREIT): simulation study of J-substitution algorithm.

PubMed

Kwon, Ohin; Woo, Eung Je; Yoon, Jeong-Rock; Seo, Jin Keun

2002-02-01

We developed a new image reconstruction algorithm for magnetic resonance electrical impedance tomography (MREIT). MREIT is a new EIT imaging technique integrated into magnetic resonance imaging (MRI) system. Based on the assumption that internal current density distribution is obtained using magnetic resonance imaging (MRI) technique, the new image reconstruction algorithm called J-substitution algorithm produces cross-sectional static images of resistivity (or conductivity) distributions. Computer simulations show that the spatial resolution of resistivity image is comparable to that of MRI. MREIT provides accurate high-resolution cross-sectional resistivity images making resistivity values of various human tissues available for many biomedical applications.
Color transfer algorithm in medical images

NASA Astrophysics Data System (ADS)

Wang, Weihong; Xu, Yangfa

2007-12-01

In digital virtual human project, image data acquires from the freezing slice of human body specimen. The color and brightness between a group of images of a certain organ could be quite different. The quality of these images could bring great difficulty in edge extraction, segmentation, as well as 3D reconstruction process. Thus it is necessary to unify the color of the images. The color transfer algorithm is a good algorithm to deal with this kind of problem. This paper introduces the principle of this algorithm and uses it in the medical image processing.
Skull removal in MR images using a modified artificial bee colony optimization algorithm.

PubMed

Taherdangkoo, Mohammad

2014-01-01

Removal of the skull from brain Magnetic Resonance (MR) images is an important preprocessing step required for other image analysis techniques such as brain tissue segmentation. In this paper, we propose a new algorithm based on the Artificial Bee Colony (ABC) optimization algorithm to remove the skull region from brain MR images. We modify the ABC algorithm using a different strategy for initializing the coordinates of scout bees and their direction of search. Moreover, we impose an additional constraint to the ABC algorithm to avoid the creation of discontinuous regions. We found that our algorithm successfully removed all bony skull from a sample of de-identified MR brain images acquired from different model scanners. The obtained results of the proposed algorithm compared with those of previously introduced well known optimization algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) demonstrate the superior results and computational performance of our algorithm, suggesting its potential for clinical applications.

Image compression/decompression based on mathematical transform, reduction/expansion, and image sharpening

DOEpatents

Fu, C.Y.; Petrich, L.I.

1997-12-30

An image represented in a first image array of pixels is first decimated in two dimensions before being compressed by a predefined compression algorithm such as JPEG. Another possible predefined compression algorithm can involve a wavelet technique. The compressed, reduced image is then transmitted over the limited bandwidth transmission medium, and the transmitted image is decompressed using an algorithm which is an inverse of the predefined compression algorithm (such as reverse JPEG). The decompressed, reduced image is then interpolated back to its original array size. Edges (contours) in the image are then sharpened to enhance the perceptual quality of the reconstructed image. Specific sharpening techniques are described. 22 figs.
A New Pivoting and Iterative Text Detection Algorithm for Biomedical Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Songhua; Krauthammer, Prof. Michael

2010-01-01

There is interest to expand the reach of literature mining to include the analysis of biomedical images, which often contain a paper's key findings. Examples include recent studies that use Optical Character Recognition (OCR) to extract image text, which is used to boost biomedical image retrieval and classification. Such studies rely on the robust identification of text elements in biomedical images, which is a non-trivial task. In this work, we introduce a new text detection algorithm for biomedical images based on iterative projection histograms. We study the effectiveness of our algorithm by evaluating the performance on a set of manuallymore » labeled random biomedical images, and compare the performance against other state-of-the-art text detection algorithms. We demonstrate that our projection histogram-based text detection approach is well suited for text detection in biomedical images, and that the iterative application of the algorithm boosts performance to an F score of .60. We provide a C++ implementation of our algorithm freely available for academic use.« less
Compressively sampled MR image reconstruction using generalized thresholding iterative algorithm

NASA Astrophysics Data System (ADS)

Elahi, Sana; kaleem, Muhammad; Omer, Hammad

2018-01-01

Compressed sensing (CS) is an emerging area of interest in Magnetic Resonance Imaging (MRI). CS is used for the reconstruction of the images from a very limited number of samples in k-space. This significantly reduces the MRI data acquisition time. One important requirement for signal recovery in CS is the use of an appropriate non-linear reconstruction algorithm. It is a challenging task to choose a reconstruction algorithm that would accurately reconstruct the MR images from the under-sampled k-space data. Various algorithms have been used to solve the system of non-linear equations for better image quality and reconstruction speed in CS. In the recent past, iterative soft thresholding algorithm (ISTA) has been introduced in CS-MRI. This algorithm directly cancels the incoherent artifacts produced because of the undersampling in k -space. This paper introduces an improved iterative algorithm based on p -thresholding technique for CS-MRI image reconstruction. The use of p -thresholding function promotes sparsity in the image which is a key factor for CS based image reconstruction. The p -thresholding based iterative algorithm is a modification of ISTA, and minimizes non-convex functions. It has been shown that the proposed p -thresholding iterative algorithm can be used effectively to recover fully sampled image from the under-sampled data in MRI. The performance of the proposed method is verified using simulated and actual MRI data taken at St. Mary's Hospital, London. The quality of the reconstructed images is measured in terms of peak signal-to-noise ratio (PSNR), artifact power (AP), and structural similarity index measure (SSIM). The proposed approach shows improved performance when compared to other iterative algorithms based on log thresholding, soft thresholding and hard thresholding techniques at different reduction factors.
An algorithm for encryption of secret images into meaningful images

NASA Astrophysics Data System (ADS)

Kanso, A.; Ghebleh, M.

2017-03-01

Image encryption algorithms typically transform a plain image into a noise-like cipher image, whose appearance is an indication of encrypted content. Bao and Zhou [Image encryption: Generating visually meaningful encrypted images, Information Sciences 324, 2015] propose encrypting the plain image into a visually meaningful cover image. This improves security by masking existence of encrypted content. Following their approach, we propose a lossless visually meaningful image encryption scheme which improves Bao and Zhou's algorithm by making the encrypted content, i.e. distortions to the cover image, more difficult to detect. Empirical results are presented to show high quality of the resulting images and high security of the proposed algorithm. Competence of the proposed scheme is further demonstrated by means of comparison with Bao and Zhou's scheme.
Comparison of photo-matching algorithms commonly used for photographic capture-recapture studies.

PubMed

Matthé, Maximilian; Sannolo, Marco; Winiarski, Kristopher; Spitzen-van der Sluijs, Annemarieke; Goedbloed, Daniel; Steinfartz, Sebastian; Stachow, Ulrich

2017-08-01

Photographic capture-recapture is a valuable tool for obtaining demographic information on wildlife populations due to its noninvasive nature and cost-effectiveness. Recently, several computer-aided photo-matching algorithms have been developed to more efficiently match images of unique individuals in databases with thousands of images. However, the identification accuracy of these algorithms can severely bias estimates of vital rates and population size. Therefore, it is important to understand the performance and limitations of state-of-the-art photo-matching algorithms prior to implementation in capture-recapture studies involving possibly thousands of images. Here, we compared the performance of four photo-matching algorithms; Wild-ID, I3S Pattern+, APHIS, and AmphIdent using multiple amphibian databases of varying image quality. We measured the performance of each algorithm and evaluated the performance in relation to database size and the number of matching images in the database. We found that algorithm performance differed greatly by algorithm and image database, with recognition rates ranging from 100% to 22.6% when limiting the review to the 10 highest ranking images. We found that recognition rate degraded marginally with increased database size and could be improved considerably with a higher number of matching images in the database. In our study, the pixel-based algorithm of AmphIdent exhibited superior recognition rates compared to the other approaches. We recommend carefully evaluating algorithm performance prior to using it to match a complete database. By choosing a suitable matching algorithm, databases of sizes that are unfeasible to match "by eye" can be easily translated to accurate individual capture histories necessary for robust demographic estimates.
Infrared traffic image enhancement algorithm based on dark channel prior and gamma correction

NASA Astrophysics Data System (ADS)

Zheng, Lintao; Shi, Hengliang; Gu, Ming

2017-07-01

The infrared traffic image acquired by the intelligent traffic surveillance equipment has low contrast, little hierarchical differences in perceptions of image and the blurred vision effect. Therefore, infrared traffic image enhancement, being an indispensable key step, is applied to nearly all infrared imaging based traffic engineering applications. In this paper, we propose an infrared traffic image enhancement algorithm that is based on dark channel prior and gamma correction. In existing research dark channel prior, known as a famous image dehazing method, here is used to do infrared image enhancement for the first time. Initially, in the proposed algorithm, the original degraded infrared traffic image is transformed with dark channel prior as the initial enhanced result. A further adjustment based on the gamma curve is needed because initial enhanced result has lower brightness. Comprehensive validation experiments reveal that the proposed algorithm outperforms the current state-of-the-art algorithms.
Comparing multiple turbulence restoration algorithms performance on noisy anisoplanatic imagery

NASA Astrophysics Data System (ADS)

Rucci, Michael A.; Hardie, Russell C.; Dapore, Alexander J.

2017-05-01

In this paper, we compare the performance of multiple turbulence mitigation algorithms to restore imagery degraded by atmospheric turbulence and camera noise. In order to quantify and compare algorithm performance, imaging scenes were simulated by applying noise and varying levels of turbulence. For the simulation, a Monte-Carlo wave optics approach is used to simulate the spatially and temporally varying turbulence in an image sequence. A Poisson-Gaussian noise mixture model is then used to add noise to the observed turbulence image set. These degraded image sets are processed with three separate restoration algorithms: Lucky Look imaging, bispectral speckle imaging, and a block matching method with restoration filter. These algorithms were chosen because they incorporate different approaches and processing techniques. The results quantitatively show how well the algorithms are able to restore the simulated degraded imagery.
Object-oriented feature-tracking algorithms for SAR images of the marginal ice zone

NASA Technical Reports Server (NTRS)

Daida, Jason; Samadani, Ramin; Vesecky, John F.

1990-01-01

An unsupervised method that chooses and applies the most appropriate tracking algorithm from among different sea-ice tracking algorithms is reported. In contrast to current unsupervised methods, this method chooses and applies an algorithm by partially examining a sequential image pair to draw inferences about what was examined. Based on these inferences the reported method subsequently chooses which algorithm to apply to specific areas of the image pair where that algorithm should work best.
Design of Restoration Method Based on Compressed Sensing and TwIST Algorithm

NASA Astrophysics Data System (ADS)

Zhang, Fei; Piao, Yan

2018-04-01

In order to improve the subjective and objective quality of degraded images at low sampling rates effectively,save storage space and reduce computational complexity at the same time, this paper proposes a joint restoration algorithm of compressed sensing and two step iterative threshold shrinkage (TwIST). The algorithm applies the TwIST algorithm which used in image restoration to the compressed sensing theory. Then, a small amount of sparse high-frequency information is obtained in frequency domain. The TwIST algorithm based on compressed sensing theory is used to accurately reconstruct the high frequency image. The experimental results show that the proposed algorithm achieves better subjective visual effects and objective quality of degraded images while accurately restoring degraded images.
Range image registration based on hash map and moth-flame optimization

NASA Astrophysics Data System (ADS)

Zou, Li; Ge, Baozhen; Chen, Lei

2018-03-01

Over the past decade, evolutionary algorithms (EAs) have been introduced to solve range image registration problems because of their robustness and high precision. However, EA-based range image registration algorithms are time-consuming. To reduce the computational time, an EA-based range image registration algorithm using hash map and moth-flame optimization is proposed. In this registration algorithm, a hash map is used to avoid over-exploitation in registration process. Additionally, we present a search equation that is better at exploration and a restart mechanism to avoid being trapped in local minima. We compare the proposed registration algorithm with the registration algorithms using moth-flame optimization and several state-of-the-art EA-based registration algorithms. The experimental results show that the proposed algorithm has a lower computational cost than other algorithms and achieves similar registration precision.
SU-F-BRCD-09: Total Variation (TV) Based Fast Convergent Iterative CBCT Reconstruction with GPU Acceleration.

PubMed

Xu, Q; Yang, D; Tan, J; Anastasio, M

2012-06-01

To improve image quality and reduce imaging dose in CBCT for radiation therapy applications and to realize near real-time image reconstruction based on use of a fast convergence iterative algorithm and acceleration by multi-GPUs. An iterative image reconstruction that sought to minimize a weighted least squares cost function that employed total variation (TV) regularization was employed to mitigate projection data incompleteness and noise. To achieve rapid 3D image reconstruction (< 1 min), a highly optimized multiple-GPU implementation of the algorithm was developed. The convergence rate and reconstruction accuracy were evaluated using a modified 3D Shepp-Logan digital phantom and a Catphan-600 physical phantom. The reconstructed images were compared with the clinical FDK reconstruction results. Digital phantom studies showed that only 15 iterations and 60 iterations are needed to achieve algorithm convergence for 360-view and 60-view cases, respectively. The RMSE was reduced to 10-4 and 10-2, respectively, by using 15 iterations for each case. Our algorithm required 5.4s to complete one iteration for the 60-view case using one Tesla C2075 GPU. The few-view study indicated that our iterative algorithm has great potential to reduce the imaging dose and preserve good image quality. For the physical Catphan studies, the images obtained from the iterative algorithm possessed better spatial resolution and higher SNRs than those obtained from by use of a clinical FDK reconstruction algorithm. We have developed a fast convergence iterative algorithm for CBCT image reconstruction. The developed algorithm yielded images with better spatial resolution and higher SNR than those produced by a commercial FDK tool. In addition, from the few-view study, the iterative algorithm has shown great potential for significantly reducing imaging dose. We expect that the developed reconstruction approach will facilitate applications including IGART and patient daily CBCT-based treatment localization. © 2012 American Association of Physicists in Medicine.
The optimal algorithm for Multi-source RS image fusion.

PubMed

Fu, Wei; Huang, Shui-Guang; Li, Zeng-Shun; Shen, Hao; Li, Jun-Shuai; Wang, Peng-Yuan

2016-01-01

In order to solve the issue which the fusion rules cannot be self-adaptively adjusted by using available fusion methods according to the subsequent processing requirements of Remote Sensing (RS) image, this paper puts forward GSDA (genetic-iterative self-organizing data analysis algorithm) by integrating the merit of genetic arithmetic together with the advantage of iterative self-organizing data analysis algorithm for multi-source RS image fusion. The proposed algorithm considers the wavelet transform of the translation invariance as the model operator, also regards the contrast pyramid conversion as the observed operator. The algorithm then designs the objective function by taking use of the weighted sum of evaluation indices, and optimizes the objective function by employing GSDA so as to get a higher resolution of RS image. As discussed above, the bullet points of the text are summarized as follows.•The contribution proposes the iterative self-organizing data analysis algorithm for multi-source RS image fusion.•This article presents GSDA algorithm for the self-adaptively adjustment of the fusion rules.•This text comes up with the model operator and the observed operator as the fusion scheme of RS image based on GSDA. The proposed algorithm opens up a novel algorithmic pathway for multi-source RS image fusion by means of GSDA.
Phase Diversity and Polarization Augmented Techniques for Active Imaging

DTIC Science & Technology

2007-03-01

build up a system model for use in algorithm development. 32 IV. Conventional Imaging and Atmospheric Turbulence With an understanding of scalar...28, 59, 115 Cholesky Factorization, 14, 42 C2n, see Turbulence Coherent Image Model, 36 Complete Data, see EM Algorithm Complex Coherence...Data, see EM Algorithm Homotopic, 62 Impulse Response, 34, 44 Incoherent Image Model, 36 Incomplete Data, see EM Algorithm Lo- Turbulence Outer Scale
Automated Recognition of 3D Features in GPIR Images

NASA Technical Reports Server (NTRS)

Park, Han; Stough, Timothy; Fijany, Amir

2007-01-01

A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a directed-graph data structure. Relative to past approaches, this multiaxis approach offers the advantages of more reliable detections, better discrimination of objects, and provision of redundant information, which can be helpful in filling gaps in feature recognition by one of the component algorithms. The image-processing class also includes postprocessing algorithms that enhance identified features to prepare them for further scrutiny by human analysts (see figure). Enhancement of images as a postprocessing step is a significant departure from traditional practice, in which enhancement of images is a preprocessing step.
Acceleration of image-based resolution modelling reconstruction using an expectation maximization nested algorithm.

PubMed

Angelis, G I; Reader, A J; Markiewicz, P J; Kotasidis, F A; Lionheart, W R; Matthews, J C

2013-08-07

Recent studies have demonstrated the benefits of a resolution model within iterative reconstruction algorithms in an attempt to account for effects that degrade the spatial resolution of the reconstructed images. However, these algorithms suffer from slower convergence rates, compared to algorithms where no resolution model is used, due to the additional need to solve an image deconvolution problem. In this paper, a recently proposed algorithm, which decouples the tomographic and image deconvolution problems within an image-based expectation maximization (EM) framework, was evaluated. This separation is convenient, because more computational effort can be placed on the image deconvolution problem and therefore accelerate convergence. Since the computational cost of solving the image deconvolution problem is relatively small, multiple image-based EM iterations do not significantly increase the overall reconstruction time. The proposed algorithm was evaluated using 2D simulations, as well as measured 3D data acquired on the high-resolution research tomograph. Results showed that bias reduction can be accelerated by interleaving multiple iterations of the image-based EM algorithm solving the resolution model problem, with a single EM iteration solving the tomographic problem. Significant improvements were observed particularly for voxels that were located on the boundaries between regions of high contrast within the object being imaged and for small regions of interest, where resolution recovery is usually more challenging. Minor differences were observed using the proposed nested algorithm, compared to the single iteration normally performed, when an optimal number of iterations are performed for each algorithm. However, using the proposed nested approach convergence is significantly accelerated enabling reconstruction using far fewer tomographic iterations (up to 70% fewer iterations for small regions). Nevertheless, the optimal number of nested image-based EM iterations is hard to be defined and it should be selected according to the given application.
Image defog algorithm based on open close filter and gradient domain recursive bilateral filter

NASA Astrophysics Data System (ADS)

Liu, Daqian; Liu, Wanjun; Zhao, Qingguo; Fei, Bowen

2017-11-01

To solve the problems of fuzzy details, color distortion, low brightness of the image obtained by the dark channel prior defog algorithm, an image defog algorithm based on open close filter and gradient domain recursive bilateral filter, referred to as OCRBF, was put forward. The algorithm named OCRBF firstly makes use of weighted quad tree to obtain more accurate the global atmospheric value, then exploits multiple-structure element morphological open and close filter towards the minimum channel map to obtain a rough scattering map by dark channel prior, makes use of variogram to correct the transmittance map,and uses gradient domain recursive bilateral filter for the smooth operation, finally gets recovery images by image degradation model, and makes contrast adjustment to get bright, clear and no fog image. A large number of experimental results show that the proposed defog method in this paper can be good to remove the fog , recover color and definition of the fog image containing close range image, image perspective, the image including the bright areas very well, compared with other image defog algorithms,obtain more clear and natural fog free images with details of higher visibility, what's more, the relationship between the time complexity of SIDA algorithm and the number of image pixels is a linear correlation.
Ridge-branch-based blood vessel detection algorithm for multimodal retinal images

NASA Astrophysics Data System (ADS)

Li, Y.; Hutchings, N.; Knighton, R. W.; Gregori, G.; Lujan, B. J.; Flanagan, J. G.

2009-02-01

Automatic detection of retinal blood vessels is important to medical diagnoses and imaging. With the development of imaging technologies, various modals of retinal images are available. Few of currently published algorithms are applied to multimodal retinal images. Besides, the performance of algorithms with pathologies is expected to be improved. The purpose of this paper is to propose an automatic Ridge-Branch-Based (RBB) detection algorithm of blood vessel centerlines and blood vessels for multimodal retinal images (color fundus photographs, fluorescein angiograms, fundus autofluorescence images, SLO fundus images and OCT fundus images, for example). Ridges, which can be considered as centerlines of vessel-like patterns, are first extracted. The method uses the connective branching information of image ridges: if ridge pixels are connected, they are more likely to be in the same class, vessel ridge pixels or non-vessel ridge pixels. Thanks to the good distinguishing ability of the designed "Segment-Based Ridge Features", the classifier and its parameters can be easily adapted to multimodal retinal images without ground truth training. We present thorough experimental results on SLO images, color fundus photograph database and other multimodal retinal images, as well as comparison between other published algorithms. Results showed that the RBB algorithm achieved a good performance.
Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics.

PubMed

Li, Dongming; Sun, Changming; Yang, Jinhua; Liu, Huan; Peng, Jiaqi; Zhang, Lijuan

2017-04-06

An adaptive optics (AO) system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image restoration algorithm via maximum likelihood estimation. Our proposed algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed algorithm. Experimental results show that our algorithm produces accurate AO image restoration results and outperforms the current state-of-the-art blind deconvolution methods.
Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics

PubMed Central

Li, Dongming; Sun, Changming; Yang, Jinhua; Liu, Huan; Peng, Jiaqi; Zhang, Lijuan

2017-01-01

An adaptive optics (AO) system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image restoration algorithm via maximum likelihood estimation. Our proposed algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed algorithm. Experimental results show that our algorithm produces accurate AO image restoration results and outperforms the current state-of-the-art blind deconvolution methods. PMID:28383503
Region of Interest Imaging for a General Trajectory with the Rebinned BPF Algorithm*

PubMed Central

Bian, Junguo; Xia, Dan; Sidky, Emil Y; Pan, Xiaochuan

2010-01-01

The back-projection-filtration (BPF) algorithm has been applied to image reconstruction for cone-beam configurations with general source trajectories. The BPF algorithm can reconstruct 3-D region-of-interest (ROI) images from data containing truncations. However, like many other existing algorithms for cone-beam configurations, the BPF algorithm involves a back-projection with a spatially varying weighting factor, which can result in the non-uniform noise levels in reconstructed images and increased computation time. In this work, we propose a BPF algorithm to eliminate the spatially varying weighting factor by using a rebinned geometry for a general scanning trajectory. This proposed BPF algorithm has an improved noise property, while retaining the advantages of the original BPF algorithm such as minimum data requirement. PMID:20617122

Region of Interest Imaging for a General Trajectory with the Rebinned BPF Algorithm.

PubMed

Bian, Junguo; Xia, Dan; Sidky, Emil Y; Pan, Xiaochuan

2010-02-01

The back-projection-filtration (BPF) algorithm has been applied to image reconstruction for cone-beam configurations with general source trajectories. The BPF algorithm can reconstruct 3-D region-of-interest (ROI) images from data containing truncations. However, like many other existing algorithms for cone-beam configurations, the BPF algorithm involves a back-projection with a spatially varying weighting factor, which can result in the non-uniform noise levels in reconstructed images and increased computation time. In this work, we propose a BPF algorithm to eliminate the spatially varying weighting factor by using a rebinned geometry for a general scanning trajectory. This proposed BPF algorithm has an improved noise property, while retaining the advantages of the original BPF algorithm such as minimum data requirement.
A segmentation algorithm based on image projection for complex text layout

NASA Astrophysics Data System (ADS)

Zhu, Wangsheng; Chen, Qin; Wei, Chuanyi; Li, Ziyang

2017-10-01

Segmentation algorithm is an important part of layout analysis, considering the efficiency advantage of the top-down approach and the particularity of the object, a breakdown of projection layout segmentation algorithm. Firstly, the algorithm will algorithm first partitions the text image, and divided into several columns, then for each column scanning projection, the text image is divided into several sub regions through multiple projection. The experimental results show that, this method inherits the projection itself and rapid calculation speed, but also can avoid the effect of arc image information page segmentation, and also can accurate segmentation of the text image layout is complex.
Image-processing algorithms for inspecting characteristics of hybrid rice seed

NASA Astrophysics Data System (ADS)

Cheng, Fang; Ying, Yibin

2004-03-01

Incompletely closed glumes, germ and disease are three characteristics of hybrid rice seed. Image-processing algorithms developed to detect these seed characteristics were presented in this paper. The rice seed used for this study involved five varieties of Jinyou402, Shanyou10, Zhongyou207, Jiayou and IIyou. The algorithms were implemented with a 5*600 images set, a 4*400 images set and the other 5*600 images set respectively. The image sets included black background images, white background images and both sides images of rice seed. Results show that the algorithm for inspecting seeds with incompletely closed glumes based on Radon Transform achieved an accuracy of 96% for normal seeds, 92% for seeds with fine fissure and 87% for seeds with unclosed glumes, the algorithm for inspecting germinated seeds on panicle based on PCA and ANN achieved n average accuracy of 98% for normal seeds, 88% for germinated seeds on panicle and the algorithm for inspecting diseased seeds based on color features achieved an accuracy of 92% for normal and healthy seeds, 95% for spot diseased seeds and 83% for severe diseased seeds.
A metal artifact reduction algorithm in CT using multiple prior images by recursive active contour segmentation

PubMed Central

Nam, Haewon

2017-01-01

We propose a novel metal artifact reduction (MAR) algorithm for CT images that completes a corrupted sinogram along the metal trace region. When metal implants are located inside a field of view, they create a barrier to the transmitted X-ray beam due to the high attenuation of metals, which significantly degrades the image quality. To fill in the metal trace region efficiently, the proposed algorithm uses multiple prior images with residual error compensation in sinogram space. Multiple prior images are generated by applying a recursive active contour (RAC) segmentation algorithm to the pre-corrected image acquired by MAR with linear interpolation, where the number of prior image is controlled by RAC depending on the object complexity. A sinogram basis is then acquired by forward projection of the prior images. The metal trace region of the original sinogram is replaced by the linearly combined sinogram of the prior images. Then, the additional correction in the metal trace region is performed to compensate the residual errors occurred by non-ideal data acquisition condition. The performance of the proposed MAR algorithm is compared with MAR with linear interpolation and the normalized MAR algorithm using simulated and experimental data. The results show that the proposed algorithm outperforms other MAR algorithms, especially when the object is complex with multiple bone objects. PMID:28604794
The role of advanced reconstruction algorithms in cardiac CT

PubMed Central

Halliburton, Sandra S.; Tanabe, Yuki; Partovi, Sasan

2017-01-01

Non-linear iterative reconstruction (IR) algorithms have been increasingly incorporated into clinical cardiac CT protocols at institutions around the world. Multiple IR algorithms are available commercially from various vendors. IR algorithms decrease image noise and are primarily used to enable lower radiation dose protocols. IR can also be used to improve image quality for imaging of obese patients, coronary atherosclerotic plaques, coronary stents, and myocardial perfusion. In this article, we will review the various applications of IR algorithms in cardiac imaging and evaluate how they have changed practice. PMID:29255694
Theory and algorithms for image reconstruction on chords and within regions of interest

NASA Astrophysics Data System (ADS)

Zou, Yu; Pan, Xiaochuan; Sidky, Emilâ Y.

2005-11-01

We introduce a formula for image reconstruction on a chord of a general source trajectory. We subsequently develop three algorithms for exact image reconstruction on a chord from data acquired with the general trajectory. Interestingly, two of the developed algorithms can accommodate data containing transverse truncations. The widely used helical trajectory and other trajectories discussed in literature can be interpreted as special cases of the general trajectory, and the developed theory and algorithms are thus directly applicable to reconstructing images exactly from data acquired with these trajectories. For instance, chords on a helical trajectory are equivalent to the n-PI-line segments. In this situation, the proposed algorithms become the algorithms that we proposed previously for image reconstruction on PI-line segments. We have performed preliminary numerical studies, which include the study on image reconstruction on chords of two-circle trajectory, which is nonsmooth, and on n-PI lines of a helical trajectory, which is smooth. Quantitative results of these studies verify and demonstrate the proposed theory and algorithms.
Adaptive Cross-correlation Algorithm and Experiment of Extended Scene Shack-Hartmann Wavefront Sensing

NASA Technical Reports Server (NTRS)

Sidick, Erkin; Morgan, Rhonda M.; Green, Joseph J.; Ohara, Catherine M.; Redding, David C.

2007-01-01

We have developed a new, adaptive cross-correlation (ACC) algorithm to estimate with high accuracy the shift as large as several pixels in two extended-scene images captured by a Shack-Hartmann wavefront sensor (SH-WFS). It determines the positions of all of the extended-scene image cells relative to a reference cell using an FFT-based iterative image shifting algorithm. It works with both point-source spot images as well as extended scene images. We have also set up a testbed for extended0scene SH-WFS, and tested the ACC algorithm with the measured data of both point-source and extended-scene images. In this paper we describe our algorithm and present out experimental results.
Real-time Imaging Orientation Determination System to Verify Imaging Polarization Navigation Algorithm

PubMed Central

Lu, Hao; Zhao, Kaichun; Wang, Xiaochu; You, Zheng; Huang, Kaoli

2016-01-01

Bio-inspired imaging polarization navigation which can provide navigation information and is capable of sensing polarization information has advantages of high-precision and anti-interference over polarization navigation sensors that use photodiodes. Although all types of imaging polarimeters exist, they may not qualify for the research on the imaging polarization navigation algorithm. To verify the algorithm, a real-time imaging orientation determination system was designed and implemented. Essential calibration procedures for the type of system that contained camera parameter calibration and the inconsistency of complementary metal oxide semiconductor calibration were discussed, designed, and implemented. Calibration results were used to undistort and rectify the multi-camera system. An orientation determination experiment was conducted. The results indicated that the system could acquire and compute the polarized skylight images throughout the calibrations and resolve orientation by the algorithm to verify in real-time. An orientation determination algorithm based on image processing was tested on the system. The performance and properties of the algorithm were evaluated. The rate of the algorithm was over 1 Hz, the error was over 0.313°, and the population standard deviation was 0.148° without any data filter. PMID:26805851
A fuzzy optimal threshold technique for medical images

NASA Astrophysics Data System (ADS)

Thirupathi Kannan, Balaji; Krishnasamy, Krishnaveni; Pradeep Kumar Kenny, S.

2012-01-01

A new fuzzy based thresholding method for medical images especially cervical cytology images having blob and mosaic structures is proposed in this paper. Many existing thresholding algorithms may segment either blob or mosaic images but there aren't any single algorithm that can do both. In this paper, an input cervical cytology image is binarized, preprocessed and the pixel value with minimum Fuzzy Gaussian Index is identified as an optimal threshold value and used for segmentation. The proposed technique is tested on various cervical cytology images having blob or mosaic structures, compared with various existing algorithms and proved better than the existing algorithms.
The algorithm stitching for medical imaging

NASA Astrophysics Data System (ADS)

Semenishchev, E.; Marchuk, V.; Voronin, V.; Pismenskova, M.; Tolstova, I.; Svirin, I.

2016-05-01

In this paper we propose a stitching algorithm of medical images into one. The algorithm is designed to stitching the medical x-ray imaging, biological particles in microscopic images, medical microscopic images and other. Such image can improve the diagnosis accuracy and quality for minimally invasive studies (e.g., laparoscopy, ophthalmology and other). The proposed algorithm is based on the following steps: the searching and selection areas with overlap boundaries; the keypoint and feature detection; the preliminary stitching images and transformation to reduce the visible distortion; the search a single unified borders in overlap area; brightness, contrast and white balance converting; the superimposition into a one image. Experimental results demonstrate the effectiveness of the proposed method in the task of image stitching.
Generation and assessment of turntable SAR data for the support of ATR development

NASA Astrophysics Data System (ADS)

Cohen, Marvin N.; Showman, Gregory A.; Sangston, K. James; Sylvester, Vincent B.; Gostin, Lamar; Scheer, C. Ruby

1998-10-01

Inverse synthetic aperture radar (ISAR) imaging on a turntable-tower test range permits convenient generation of high resolution two-dimensional images of radar targets under controlled conditions for testing SAR image processing and for supporting automatic target recognition (ATR) algorithm development. However, turntable ISAR images are often obtained under near-field geometries and hence may suffer geometric distortions not present in airborne SAR images. In this paper, turntable data collected at Georgia Tech's Electromagnetic Test Facility are used to begin to assess the utility of two- dimensional ISAR imaging algorithms in forming images to support ATR development. The imaging algorithms considered include a simple 2D discrete Fourier transform (DFT), a 2-D DFT with geometric correction based on image domain resampling, and a computationally-intensive geometric matched filter solution. Images formed with the various algorithms are used to develop ATR templates, which are then compared with an eye toward utilization in an ATR algorithm.
Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera

PubMed Central

Xue, Bai; Choi, Stacey S.; Doble, Nathan; Werner, John S.

2008-01-01

A fast and efficient method for quantifying photoreceptor density in images obtained with an en-face flood-illuminated adaptive optics (AO) imaging system is described. To improve accuracy of cone counting, en-face images are analyzed over extended areas. This is achieved with two separate semiautomated algorithms: (1) a montaging algorithm that joins retinal images with overlapping common features without edge effects and (2) a cone density measurement algorithm that counts the individual cones in the montaged image. The accuracy of the cone density measurement algorithm is high, with >97% agreement for a simulated retinal image (of known density, with low contrast) and for AO images from normal eyes when compared with previously reported histological data. Our algorithms do not require spatial regularity in cone packing and are, therefore, useful for counting cones in diseased retinas, as demonstrated for eyes with Stargardt’s macular dystrophy and retinitis pigmentosa. PMID:17429482
Research on Image Encryption Based on DNA Sequence and Chaos Theory

NASA Astrophysics Data System (ADS)

Tian Zhang, Tian; Yan, Shan Jun; Gu, Cheng Yan; Ren, Ran; Liao, Kai Xin

2018-04-01

Nowadays encryption is a common technique to protect image data from unauthorized access. In recent years, many scientists have proposed various encryption algorithms based on DNA sequence to provide a new idea for the design of image encryption algorithm. Therefore, a new method of image encryption based on DNA computing technology is proposed in this paper, whose original image is encrypted by DNA coding and 1-D logistic chaotic mapping. First, the algorithm uses two modules as the encryption key. The first module uses the real DNA sequence, and the second module is made by one-dimensional logistic chaos mapping. Secondly, the algorithm uses DNA complementary rules to encode original image, and uses the key and DNA computing technology to compute each pixel value of the original image, so as to realize the encryption of the whole image. Simulation results show that the algorithm has good encryption effect and security.
Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera

NASA Astrophysics Data System (ADS)

Xue, Bai; Choi, Stacey S.; Doble, Nathan; Werner, John S.

2007-05-01

A fast and efficient method for quantifying photoreceptor density in images obtained with an en-face flood-illuminated adaptive optics (AO) imaging system is described. To improve accuracy of cone counting, en-face images are analyzed over extended areas. This is achieved with two separate semiautomated algorithms: (1) a montaging algorithm that joins retinal images with overlapping common features without edge effects and (2) a cone density measurement algorithm that counts the individual cones in the montaged image. The accuracy of the cone density measurement algorithm is high, with >97% agreement for a simulated retinal image (of known density, with low contrast) and for AO images from normal eyes when compared with previously reported histological data. Our algorithms do not require spatial regularity in cone packing and are, therefore, useful for counting cones in diseased retinas, as demonstrated for eyes with Stargardt's macular dystrophy and retinitis pigmentosa.
The Research on Denoising of SAR Image Based on Improved K-SVD Algorithm

NASA Astrophysics Data System (ADS)

Tan, Linglong; Li, Changkai; Wang, Yueqin

2018-04-01

SAR images often receive noise interference in the process of acquisition and transmission, which can greatly reduce the quality of images and cause great difficulties for image processing. The existing complete DCT dictionary algorithm is fast in processing speed, but its denoising effect is poor. In this paper, the problem of poor denoising, proposed K-SVD (K-means and singular value decomposition) algorithm is applied to the image noise suppression. Firstly, the sparse dictionary structure is introduced in detail. The dictionary has a compact representation and can effectively train the image signal. Then, the sparse dictionary is trained by K-SVD algorithm according to the sparse representation of the dictionary. The algorithm has more advantages in high dimensional data processing. Experimental results show that the proposed algorithm can remove the speckle noise more effectively than the complete DCT dictionary and retain the edge details better.
Interpolation bias for the inverse compositional Gauss-Newton algorithm in digital image correlation

NASA Astrophysics Data System (ADS)

Su, Yong; Zhang, Qingchuan; Xu, Xiaohai; Gao, Zeren; Wu, Shangquan

2018-01-01

It is believed that the classic forward additive Newton-Raphson (FA-NR) algorithm and the recently introduced inverse compositional Gauss-Newton (IC-GN) algorithm give rise to roughly equal interpolation bias. Questioning the correctness of this statement, this paper presents a thorough analysis of interpolation bias for the IC-GN algorithm. A theoretical model is built to analytically characterize the dependence of interpolation bias upon speckle image, target image interpolation, and reference image gradient estimation. The interpolation biases of the FA-NR algorithm and the IC-GN algorithm can be significantly different, whose relative difference can exceed 80%. For the IC-GN algorithm, the gradient estimator can strongly affect the interpolation bias; the relative difference can reach 178%. Since the mean bias errors are insensitive to image noise, the theoretical model proposed remains valid in the presence of noise. To provide more implementation details, source codes are uploaded as a supplement.
High-speed cell recognition algorithm for ultrafast flow cytometer imaging system.

PubMed

Zhao, Wanyue; Wang, Chao; Chen, Hongwei; Chen, Minghua; Yang, Sigang

2018-04-01

An optical time-stretch flow imaging system enables high-throughput examination of cells/particles with unprecedented high speed and resolution. A significant amount of raw image data is produced. A high-speed cell recognition algorithm is, therefore, highly demanded to analyze large amounts of data efficiently. A high-speed cell recognition algorithm consisting of two-stage cascaded detection and Gaussian mixture model (GMM) classification is proposed. The first stage of detection extracts cell regions. The second stage integrates distance transform and the watershed algorithm to separate clustered cells. Finally, the cells detected are classified by GMM. We compared the performance of our algorithm with support vector machine. Results show that our algorithm increases the running speed by over 150% without sacrificing the recognition accuracy. This algorithm provides a promising solution for high-throughput and automated cell imaging and classification in the ultrafast flow cytometer imaging platform. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).
High-speed cell recognition algorithm for ultrafast flow cytometer imaging system

NASA Astrophysics Data System (ADS)

Zhao, Wanyue; Wang, Chao; Chen, Hongwei; Chen, Minghua; Yang, Sigang

2018-04-01

An optical time-stretch flow imaging system enables high-throughput examination of cells/particles with unprecedented high speed and resolution. A significant amount of raw image data is produced. A high-speed cell recognition algorithm is, therefore, highly demanded to analyze large amounts of data efficiently. A high-speed cell recognition algorithm consisting of two-stage cascaded detection and Gaussian mixture model (GMM) classification is proposed. The first stage of detection extracts cell regions. The second stage integrates distance transform and the watershed algorithm to separate clustered cells. Finally, the cells detected are classified by GMM. We compared the performance of our algorithm with support vector machine. Results show that our algorithm increases the running speed by over 150% without sacrificing the recognition accuracy. This algorithm provides a promising solution for high-throughput and automated cell imaging and classification in the ultrafast flow cytometer imaging platform.
Microscopic image analysis for reticulocyte based on watershed algorithm

NASA Astrophysics Data System (ADS)

Wang, J. Q.; Liu, G. F.; Liu, J. G.; Wang, G.

2007-12-01

We present a watershed-based algorithm in the analysis of light microscopic image for reticulocyte (RET), which will be used in an automated recognition system for RET in peripheral blood. The original images, obtained by micrography, are segmented by modified watershed algorithm and are recognized in term of gray entropy and area of connective area. In the process of watershed algorithm, judgment conditions are controlled according to character of the image, besides, the segmentation is performed by morphological subtraction. The algorithm was simulated with MATLAB software. It is similar for automated and manual scoring and there is good correlation(r=0.956) between the methods, which is resulted from 50 pieces of RET images. The result indicates that the algorithm for peripheral blood RETs is comparable to conventional manual scoring, and it is superior in objectivity. This algorithm avoids time-consuming calculation such as ultra-erosion and region-growth, which will speed up the computation consequentially.
Efficient super-resolution image reconstruction applied to surveillance video captured by small unmanned aircraft systems

NASA Astrophysics Data System (ADS)

He, Qiang; Schultz, Richard R.; Chu, Chee-Hung Henry

2008-04-01

The concept surrounding super-resolution image reconstruction is to recover a highly-resolved image from a series of low-resolution images via between-frame subpixel image registration. In this paper, we propose a novel and efficient super-resolution algorithm, and then apply it to the reconstruction of real video data captured by a small Unmanned Aircraft System (UAS). Small UAS aircraft generally have a wingspan of less than four meters, so that these vehicles and their payloads can be buffeted by even light winds, resulting in potentially unstable video. This algorithm is based on a coarse-to-fine strategy, in which a coarsely super-resolved image sequence is first built from the original video data by image registration and bi-cubic interpolation between a fixed reference frame and every additional frame. It is well known that the median filter is robust to outliers. If we calculate pixel-wise medians in the coarsely super-resolved image sequence, we can restore a refined super-resolved image. The primary advantage is that this is a noniterative algorithm, unlike traditional approaches based on highly-computational iterative algorithms. Experimental results show that our coarse-to-fine super-resolution algorithm is not only robust, but also very efficient. In comparison with five well-known super-resolution algorithms, namely the robust super-resolution algorithm, bi-cubic interpolation, projection onto convex sets (POCS), the Papoulis-Gerchberg algorithm, and the iterated back projection algorithm, our proposed algorithm gives both strong efficiency and robustness, as well as good visual performance. This is particularly useful for the application of super-resolution to UAS surveillance video, where real-time processing is highly desired.

Development of a new metal artifact reduction algorithm by using an edge preserving method for CBCT imaging

NASA Astrophysics Data System (ADS)

Kim, Juhye; Nam, Haewon; Lee, Rena

2015-07-01

CT (computed tomography) images, metal materials such as tooth supplements or surgical clips can cause metal artifact and degrade image quality. In severe cases, this may lead to misdiagnosis. In this research, we developed a new MAR (metal artifact reduction) algorithm by using an edge preserving filter and the MATLAB program (Mathworks, version R2012a). The proposed algorithm consists of 6 steps: image reconstruction from projection data, metal segmentation, forward projection, interpolation, applied edge preserving smoothing filter, and new image reconstruction. For an evaluation of the proposed algorithm, we obtained both numerical simulation data and data for a Rando phantom. In the numerical simulation data, four metal regions were added into the Shepp Logan phantom for metal artifacts. The projection data of the metal-inserted Rando phantom were obtained by using a prototype CBCT scanner manufactured by medical engineering and medical physics (MEMP) laboratory research group in medical science at Ewha Womans University. After these had been adopted the proposed algorithm was performed, and the result were compared with the original image (with metal artifact without correction) and with a corrected image based on linear interpolation. Both visual and quantitative evaluations were done. Compared with the original image with metal artifacts and with the image corrected by using linear interpolation, both the numerical and the experimental phantom data demonstrated that the proposed algorithm reduced the metal artifact. In conclusion, the evaluation in this research showed that the proposed algorithm outperformed the interpolation based MAR algorithm. If an optimization and a stability evaluation of the proposed algorithm can be performed, the developed algorithm is expected to be an effective tool for eliminating metal artifacts even in commercial CT systems.
Comparison of a single-view and a double-view aerosol optical depth retrieval algorithm

NASA Astrophysics Data System (ADS)

Henderson, Bradley G.; Chylek, Petr

2003-11-01

We compare the results of a single-view and a double-view aerosol optical depth (AOD) retrieval algorithm applied to image pairs acquired over NASA Stennis Space Center, Mississippi. The image data were acquired by the Department of Energy's (DOE) Multispectral Thermal Imager (MTI), a pushbroom satellite imager with 15 bands from the visible to the thermal infrared. MTI has the ability to acquire imagery in pairs in which the first image is a near-nadir view and the second image is off-nadir with a zenith angle of approximately 60°. A total of 15 image pairs were used in the analysis. For a given image pair, AOD retrieval is performed twice---once using a single-view algorithm applied to the near-nadir image, then again using a double-view algorithm. Errors for both retrievals are computed by comparing the results to AERONET AOD measurements obtained at the same time and place. The single-view algorithm showed an RMS error about the mean of 0.076 in AOD units, whereas the double-view algorithm showed a modest improvement with an RMS error of 0.06. The single-view errors show a positive bias which is presumed to be a result of the empirical relationship used to determine ground reflectance in the visible. A plot of AOD error of the double-view algorithm versus time shows a noticeable trend which is interpreted to be a calibration drift. When this trend is removed, the RMS error of the double-view algorithm drops to 0.030. The single-view algorithm qualitatively appears to perform better during the spring and summer whereas the double-view algorithm seems to be less sensitive to season.
Validation tools for image segmentation

NASA Astrophysics Data System (ADS)

Padfield, Dirk; Ross, James

2009-02-01

A large variety of image analysis tasks require the segmentation of various regions in an image. For example, segmentation is required to generate accurate models of brain pathology that are important components of modern diagnosis and therapy. While the manual delineation of such structures gives accurate information, the automatic segmentation of regions such as the brain and tumors from such images greatly enhances the speed and repeatability of quantifying such structures. The ubiquitous need for such algorithms has lead to a wide range of image segmentation algorithms with various assumptions, parameters, and robustness. The evaluation of such algorithms is an important step in determining their effectiveness. Therefore, rather than developing new segmentation algorithms, we here describe validation methods for segmentation algorithms. Using similarity metrics comparing the automatic to manual segmentations, we demonstrate methods for optimizing the parameter settings for individual cases and across a collection of datasets using the Design of Experiment framework. We then employ statistical analysis methods to compare the effectiveness of various algorithms. We investigate several region-growing algorithms from the Insight Toolkit and compare their accuracy to that of a separate statistical segmentation algorithm. The segmentation algorithms are used with their optimized parameters to automatically segment the brain and tumor regions in MRI images of 10 patients. The validation tools indicate that none of the ITK algorithms studied are able to outperform with statistical significance the statistical segmentation algorithm although they perform reasonably well considering their simplicity.
A Double-function Digital Watermarking Algorithm Based on Chaotic System and LWT

NASA Astrophysics Data System (ADS)

Yuxia, Zhao; Jingbo, Fan

A double- function digital watermarking technology is studied and a double-function digital watermarking algorithm of colored image is presented based on chaotic system and the lifting wavelet transformation (LWT).The algorithm has realized the double aims of the copyright protection and the integrity authentication of image content. Making use of feature of human visual system (HVS), the watermark image is embedded into the color image's low frequency component and middle frequency components by different means. The algorithm has great security by using two kinds chaotic mappings and Arnold to scramble the watermark image at the same time. The algorithm has good efficiency by using LWT. The emulation experiment indicates the algorithm has great efficiency and security, and the effect of concealing is really good.
The cascaded moving k-means and fuzzy c-means clustering algorithms for unsupervised segmentation of malaria images

NASA Astrophysics Data System (ADS)

Abdul-Nasir, Aimi Salihah; Mashor, Mohd Yusoff; Halim, Nurul Hazwani Abd; Mohamed, Zeehaida

2015-05-01

Malaria is a life-threatening parasitic infectious disease that corresponds for nearly one million deaths each year. Due to the requirement of prompt and accurate diagnosis of malaria, the current study has proposed an unsupervised pixel segmentation based on clustering algorithm in order to obtain the fully segmented red blood cells (RBCs) infected with malaria parasites based on the thin blood smear images of P. vivax species. In order to obtain the segmented infected cell, the malaria images are first enhanced by using modified global contrast stretching technique. Then, an unsupervised segmentation technique based on clustering algorithm has been applied on the intensity component of malaria image in order to segment the infected cell from its blood cells background. In this study, cascaded moving k-means (MKM) and fuzzy c-means (FCM) clustering algorithms has been proposed for malaria slide image segmentation. After that, median filter algorithm has been applied to smooth the image as well as to remove any unwanted regions such as small background pixels from the image. Finally, seeded region growing area extraction algorithm has been applied in order to remove large unwanted regions that are still appeared on the image due to their size in which cannot be cleaned by using median filter. The effectiveness of the proposed cascaded MKM and FCM clustering algorithms has been analyzed qualitatively and quantitatively by comparing the proposed cascaded clustering algorithm with MKM and FCM clustering algorithms. Overall, the results indicate that segmentation using the proposed cascaded clustering algorithm has produced the best segmentation performances by achieving acceptable sensitivity as well as high specificity and accuracy values compared to the segmentation results provided by MKM and FCM algorithms.
Least significant qubit algorithm for quantum images

NASA Astrophysics Data System (ADS)

Sang, Jianzhi; Wang, Shen; Li, Qiong

2016-11-01

To study the feasibility of the classical image least significant bit (LSB) information hiding algorithm on quantum computer, a least significant qubit (LSQb) information hiding algorithm of quantum image is proposed. In this paper, we focus on a novel quantum representation for color digital images (NCQI). Firstly, by designing the three qubits comparator and unitary operators, the reasonability and feasibility of LSQb based on NCQI are presented. Then, the concrete LSQb information hiding algorithm is proposed, which can realize the aim of embedding the secret qubits into the least significant qubits of RGB channels of quantum cover image. Quantum circuit of the LSQb information hiding algorithm is also illustrated. Furthermore, the secrets extracting algorithm and circuit are illustrated through utilizing control-swap gates. The two merits of our algorithm are: (1) it is absolutely blind and (2) when extracting secret binary qubits, it does not need any quantum measurement operation or any other help from classical computer. Finally, simulation and comparative analysis show the performance of our algorithm.
Multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement

NASA Astrophysics Data System (ADS)

Yan, Dan; Bai, Lianfa; Zhang, Yi; Han, Jing

2018-02-01

For the problems of missing details and performance of the colorization based on sparse representation, we propose a conceptual model framework for colorizing gray-scale images, and then a multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement (CEMDC) is proposed based on this framework. The algorithm can achieve a natural colorized effect for a gray-scale image, and it is consistent with the human vision. First, the algorithm establishes a multi-sparse dictionary classification colorization model. Then, to improve the accuracy rate of the classification, the corresponding local constraint algorithm is proposed. Finally, we propose a detail enhancement based on Laplacian Pyramid, which is effective in solving the problem of missing details and improving the speed of image colorization. In addition, the algorithm not only realizes the colorization of the visual gray-scale image, but also can be applied to the other areas, such as color transfer between color images, colorizing gray fusion images, and infrared images.
Images Encryption Method using Steganographic LSB Method, AES and RSA algorithm

NASA Astrophysics Data System (ADS)

Moumen, Abdelkader; Sissaoui, Hocine

2017-03-01

Vulnerability of communication of digital images is an extremely important issue nowadays, particularly when the images are communicated through insecure channels. To improve communication security, many cryptosystems have been presented in the image encryption literature. This paper proposes a novel image encryption technique based on an algorithm that is faster than current methods. The proposed algorithm eliminates the step in which the secrete key is shared during the encryption process. It is formulated based on the symmetric encryption, asymmetric encryption and steganography theories. The image is encrypted using a symmetric algorithm, then, the secret key is encrypted by means of an asymmetrical algorithm and it is hidden in the ciphered image using a least significant bits steganographic scheme. The analysis results show that while enjoying the faster computation, our method performs close to optimal in terms of accuracy.
A street rubbish detection algorithm based on Sift and RCNN

NASA Astrophysics Data System (ADS)

Yu, XiPeng; Chen, Zhong; Zhang, Shuo; Zhang, Ting

2018-02-01

This paper presents a street rubbish detection algorithm based on image registration with Sift feature and RCNN. Firstly, obtain the rubbish region proposal on the real-time street image and set up the CNN convolution neural network trained by the rubbish samples set consists of rubbish and non-rubbish images; Secondly, for every clean street image, obtain the Sift feature and do image registration with the real-time street image to obtain the differential image, the differential image filters a lot of background information, obtain the rubbish region proposal rect where the rubbish may appear on the differential image by the selective search algorithm. Then, the CNN model is used to detect the image pixel data in each of the region proposal on the real-time street image. According to the output vector of the CNN, it is judged whether the rubbish is in the region proposal or not. If it is rubbish, the region proposal on the real-time street image is marked. This algorithm avoids the large number of false detection caused by the detection on the whole image because the CNN is used to identify the image only in the region proposal on the real-time street image that may appear rubbish. Different from the traditional object detection algorithm based on the region proposal, the region proposal is obtained on the differential image not whole real-time street image, and the number of the invalid region proposal is greatly reduced. The algorithm has the high mean average precision (mAP).
A model-based approach for detection of runways and other objects in image sequences acquired using an on-board camera

NASA Technical Reports Server (NTRS)

Kasturi, Rangachar; Devadiga, Sadashiva; Tang, Yuan-Liang

1994-01-01

This research was initiated as a part of the Advanced Sensor and Imaging System Technology (ASSIST) program at NASA Langley Research Center. The primary goal of this research is the development of image analysis algorithms for the detection of runways and other objects using an on-board camera. Initial effort was concentrated on images acquired using a passive millimeter wave (PMMW) sensor. The images obtained using PMMW sensors under poor visibility conditions due to atmospheric fog are characterized by very low spatial resolution but good image contrast compared to those images obtained using sensors operating in the visible spectrum. Algorithms developed for analyzing these images using a model of the runway and other objects are described in Part 1 of this report. Experimental verification of these algorithms was limited to a sequence of images simulated from a single frame of PMMW image. Subsequent development and evaluation of algorithms was done using video image sequences. These images have better spatial and temporal resolution compared to PMMW images. Algorithms for reliable recognition of runways and accurate estimation of spatial position of stationary objects on the ground have been developed and evaluated using several image sequences. These algorithms are described in Part 2 of this report. A list of all publications resulting from this work is also included.
Common-mask guided image reconstruction (c-MGIR) for enhanced 4D cone-beam computed tomography

NASA Astrophysics Data System (ADS)

Park, Justin C.; Zhang, Hao; Chen, Yunmei; Fan, Qiyong; Li, Jonathan G.; Liu, Chihray; Lu, Bo

2015-12-01

Compared to 3D cone beam computed tomography (3D CBCT), the image quality of commercially available four-dimensional (4D) CBCT is severely impaired due to the insufficient amount of projection data available for each phase. Since the traditional Feldkamp-Davis-Kress (FDK)-based algorithm is infeasible for reconstructing high quality 4D CBCT images with limited projections, investigators had developed several compress-sensing (CS) based algorithms to improve image quality. The aim of this study is to develop a novel algorithm which can provide better image quality than the FDK and other CS based algorithms with limited projections. We named this algorithm ‘the common mask guided image reconstruction’ (c-MGIR). In c-MGIR, the unknown CBCT volume is mathematically modeled as a combination of phase-specific motion vectors and phase-independent static vectors. The common-mask matrix, which is the key concept behind the c-MGIR algorithm, separates the common static part across all phase images from the possible moving part in each phase image. The moving part and the static part of the volumes were then alternatively updated by solving two sub-minimization problems iteratively. As the novel mathematical transformation allows the static volume and moving volumes to be updated (during each iteration) with global projections and ‘well’ solved static volume respectively, the algorithm was able to reduce the noise and under-sampling artifact (an issue faced by other algorithms) to the maximum extent. To evaluate the performance of our proposed c-MGIR, we utilized imaging data from both numerical phantoms and a lung cancer patient. The qualities of the images reconstructed with c-MGIR were compared with (1) standard FDK algorithm, (2) conventional total variation (CTV) based algorithm, (3) prior image constrained compressed sensing (PICCS) algorithm, and (4) motion-map constrained image reconstruction (MCIR) algorithm, respectively. To improve the efficiency of the algorithm, the code was implemented with a graphic processing unit for parallel processing purposes. Root mean square error (RMSE) between the ground truth and reconstructed volumes of the numerical phantom were in the descending order of FDK, CTV, PICCS, MCIR, and c-MGIR for all phases. Specifically, the means and the standard deviations of the RMSE of FDK, CTV, PICCS, MCIR and c-MGIR for all phases were 42.64 ± 6.5%, 3.63 ± 0.83%, 1.31% ± 0.09%, 0.86% ± 0.11% and 0.52 % ± 0.02%, respectively. The image quality of the patient case also indicated the superiority of c-MGIR compared to other algorithms. The results indicated that clinically viable 4D CBCT images can be reconstructed while requiring no more projection data than a typical clinical 3D CBCT scan. This makes c-MGIR a potential online reconstruction algorithm for 4D CBCT, which can provide much better image quality than other available algorithms, while requiring less dose and potentially less scanning time.
Common-mask guided image reconstruction (c-MGIR) for enhanced 4D cone-beam computed tomography.

PubMed

Park, Justin C; Zhang, Hao; Chen, Yunmei; Fan, Qiyong; Li, Jonathan G; Liu, Chihray; Lu, Bo

2015-12-07

Compared to 3D cone beam computed tomography (3D CBCT), the image quality of commercially available four-dimensional (4D) CBCT is severely impaired due to the insufficient amount of projection data available for each phase. Since the traditional Feldkamp-Davis-Kress (FDK)-based algorithm is infeasible for reconstructing high quality 4D CBCT images with limited projections, investigators had developed several compress-sensing (CS) based algorithms to improve image quality. The aim of this study is to develop a novel algorithm which can provide better image quality than the FDK and other CS based algorithms with limited projections. We named this algorithm 'the common mask guided image reconstruction' (c-MGIR).In c-MGIR, the unknown CBCT volume is mathematically modeled as a combination of phase-specific motion vectors and phase-independent static vectors. The common-mask matrix, which is the key concept behind the c-MGIR algorithm, separates the common static part across all phase images from the possible moving part in each phase image. The moving part and the static part of the volumes were then alternatively updated by solving two sub-minimization problems iteratively. As the novel mathematical transformation allows the static volume and moving volumes to be updated (during each iteration) with global projections and 'well' solved static volume respectively, the algorithm was able to reduce the noise and under-sampling artifact (an issue faced by other algorithms) to the maximum extent. To evaluate the performance of our proposed c-MGIR, we utilized imaging data from both numerical phantoms and a lung cancer patient. The qualities of the images reconstructed with c-MGIR were compared with (1) standard FDK algorithm, (2) conventional total variation (CTV) based algorithm, (3) prior image constrained compressed sensing (PICCS) algorithm, and (4) motion-map constrained image reconstruction (MCIR) algorithm, respectively. To improve the efficiency of the algorithm, the code was implemented with a graphic processing unit for parallel processing purposes.Root mean square error (RMSE) between the ground truth and reconstructed volumes of the numerical phantom were in the descending order of FDK, CTV, PICCS, MCIR, and c-MGIR for all phases. Specifically, the means and the standard deviations of the RMSE of FDK, CTV, PICCS, MCIR and c-MGIR for all phases were 42.64 ± 6.5%, 3.63 ± 0.83%, 1.31% ± 0.09%, 0.86% ± 0.11% and 0.52 % ± 0.02%, respectively. The image quality of the patient case also indicated the superiority of c-MGIR compared to other algorithms.The results indicated that clinically viable 4D CBCT images can be reconstructed while requiring no more projection data than a typical clinical 3D CBCT scan. This makes c-MGIR a potential online reconstruction algorithm for 4D CBCT, which can provide much better image quality than other available algorithms, while requiring less dose and potentially less scanning time.
Decryption of pure-position permutation algorithms.

PubMed

Zhao, Xiao-Yu; Chen, Gang; Zhang, Dan; Wang, Xiao-Hong; Dong, Guang-Chang

2004-07-01

Pure position permutation image encryption algorithms, commonly used as image encryption investigated in this work are unfortunately frail under known-text attack. In view of the weakness of pure position permutation algorithm, we put forward an effective decryption algorithm for all pure-position permutation algorithms. First, a summary of the pure position permutation image encryption algorithms is given by introducing the concept of ergodic matrices. Then, by using probability theory and algebraic principles, the decryption probability of pure-position permutation algorithms is verified theoretically; and then, by defining the operation system of fuzzy ergodic matrices, we improve a specific decryption algorithm. Finally, some simulation results are shown.
Evaluation of Demons- and FEM-Based Registration Algorithms for Lung Cancer.

PubMed

Yang, Juan; Li, Dengwang; Yin, Yong; Zhao, Fen; Wang, Hongjun

2016-04-01

We evaluated and compared the accuracy of 2 deformable image registration algorithms in 4-dimensional computed tomography images for patients with lung cancer. Ten patients with non-small cell lung cancer or small cell lung cancer were enrolled in this institutional review board-approved study. The displacement vector fields relative to a specific reference image were calculated by using the diffeomorphic demons (DD) algorithm and the finite element method (FEM)-based algorithm. The registration accuracy was evaluated by using normalized mutual information (NMI), the sum of squared intensity difference (SSD), modified Hausdorff distance (dH_M), and ratio of gross tumor volume (rGTV) difference between reference image and deformed phase image. We also compared the registration speed of the 2 algorithms. Of all patients, the FEM-based algorithm showed stronger ability in aligning 2 images than the DD algorithm. The means (±standard deviation) of NMI were 0.86 (±0.05) and 0.90 (±0.05) using the DD algorithm and the FEM-based algorithm, respectively. The means of SSD were 0.006 (±0.003) and 0.003 (±0.002) using the DD algorithm and the FEM-based algorithm, respectively. The means of dH_M were 0.04 (±0.02) and 0.03 (±0.03) using the DD algorithm and the FEM-based algorithm, respectively. The means of rGTV were 3.9% (±1.01%) and 2.9% (±1.1%) using the DD algorithm and the FEM-based algorithm, respectively. However, the FEM-based algorithm costs a longer time than the DD algorithm, with the average running time of 31.4 minutes compared to 21.9 minutes for all patients. The preliminary results showed that the FEM-based algorithm was more accurate than the DD algorithm while compromised with the registration speed. © The Author(s) 2015.
Global Linking of Cell Tracks Using the Viterbi Algorithm

PubMed Central

Jaldén, Joakim; Gilbert, Penney M.; Blau, Helen M.

2016-01-01

Automated tracking of living cells in microscopy image sequences is an important and challenging problem. With this application in mind, we propose a global track linking algorithm, which links cell outlines generated by a segmentation algorithm into tracks. The algorithm adds tracks to the image sequence one at a time, in a way which uses information from the complete image sequence in every linking decision. This is achieved by finding the tracks which give the largest possible increases to a probabilistically motivated scoring function, using the Viterbi algorithm. We also present a novel way to alter previously created tracks when new tracks are created, thus mitigating the effects of error propagation. The algorithm can handle mitosis, apoptosis, and migration in and out of the imaged area, and can also deal with false positives, missed detections, and clusters of jointly segmented cells. The algorithm performance is demonstrated on two challenging datasets acquired using bright-field microscopy, but in principle, the algorithm can be used with any cell type and any imaging technique, presuming there is a suitable segmentation algorithm. PMID:25415983
3-Dimensional stereo implementation of photoacoustic imaging based on a new image reconstruction algorithm without using discrete Fourier transform

NASA Astrophysics Data System (ADS)

Ham, Woonchul; Song, Chulgyu

2017-05-01

In this paper, we propose a new three-dimensional stereo image reconstruction algorithm for a photoacoustic medical imaging system. We also introduce and discuss a new theoretical algorithm by using the physical concept of Radon transform. The main key concept of proposed theoretical algorithm is to evaluate the existence possibility of the acoustic source within a searching region by using the geometric distance between each sensor element of acoustic detector and the corresponding searching region denoted by grid. We derive the mathematical equation for the magnitude of the existence possibility which can be used for implementing a new proposed algorithm. We handle and derive mathematical equations of proposed algorithm for the one-dimensional sensing array case as well as two dimensional sensing array case too. A mathematical k-wave simulation data are used for comparing the image quality of the proposed algorithm with that of general conventional algorithm in which the FFT should be necessarily used. From the k-wave Matlab simulation results, we can prove the effectiveness of the proposed reconstruction algorithm.
Color enhancement and image defogging in HSI based on Retinex model

NASA Astrophysics Data System (ADS)

Gao, Han; Wei, Ping; Ke, Jun

2015-08-01

Retinex is a luminance perceptual algorithm based on color consistency. It has a good performance in color enhancement. But in some cases, the traditional Retinex algorithms, both Single-Scale Retinex(SSR) and Multi-Scale Retinex(MSR) in RGB color space, do not work well and will cause color deviation. To solve this problem, we present improved SSR and MSR algorithms. Compared to other Retinex algorithms, we implement Retinex algorithms in HSI(Hue, Saturation, Intensity) color space, and use a parameter αto improve quality of the image. Moreover, the algorithms presented in this paper has a good performance in image defogging. Contrasted with traditional Retinex algorithms, we use intensity channel to obtain reflection information of an image. The intensity channel is processed using a Gaussian center-surround image filter to get light information, which should be removed from intensity channel. After that, we subtract the light information from intensity channel to obtain the reflection image, which only includes the attribute of the objects in image. Using the reflection image and a parameter α, which is an arbitrary scale factor set manually, we improve the intensity channel, and complete the color enhancement. Our experiments show that this approach works well compared with existing methods for color enhancement. Besides a better performance in color deviation problem and image defogging, a visible improvement in the image quality for human contrast perception is also observed.
An improved ASIFT algorithm for indoor panorama image matching

NASA Astrophysics Data System (ADS)

Fu, Han; Xie, Donghai; Zhong, Ruofei; Wu, Yu; Wu, Qiong

2017-07-01

The generation of 3D models for indoor objects and scenes is an attractive tool for digital city, virtual reality and SLAM purposes. Panoramic images are becoming increasingly more common in such applications due to their advantages to capture the complete environment in one single image with large field of view. The extraction and matching of image feature points are important and difficult steps in three-dimensional reconstruction, and ASIFT is a state-of-the-art algorithm to implement these functions. Compared with the SIFT algorithm, more feature points can be generated and the matching accuracy of ASIFT algorithm is higher, even for the panoramic images with obvious distortions. However, the algorithm is really time-consuming because of complex operations and performs not very well for some indoor scenes under poor light or without rich textures. To solve this problem, this paper proposes an improved ASIFT algorithm for indoor panoramic images: firstly, the panoramic images are projected into multiple normal perspective images. Secondly, the original ASIFT algorithm is simplified from the affine transformation of tilt and rotation with the images to the only tilt affine transformation. Finally, the results are re-projected to the panoramic image space. Experiments in different environments show that this method can not only ensure the precision of feature points extraction and matching, but also greatly reduce the computing time.
Image-Data Compression Using Edge-Optimizing Algorithm for WFA Inference.

ERIC Educational Resources Information Center

Culik, Karel II; Kari, Jarkko

1994-01-01

Presents an inference algorithm that produces a weighted finite automata (WFA), in particular, the grayness functions of graytone images. Image-data compression results based on the new inference algorithm produces a WFA with a relatively small number of edges. Image-data compression results alone and in combination with wavelets are discussed.…
A new pivoting and iterative text detection algorithm for biomedical images.

PubMed

Xu, Songhua; Krauthammer, Michael

2010-12-01

There is interest to expand the reach of literature mining to include the analysis of biomedical images, which often contain a paper's key findings. Examples include recent studies that use Optical Character Recognition (OCR) to extract image text, which is used to boost biomedical image retrieval and classification. Such studies rely on the robust identification of text elements in biomedical images, which is a non-trivial task. In this work, we introduce a new text detection algorithm for biomedical images based on iterative projection histograms. We study the effectiveness of our algorithm by evaluating the performance on a set of manually labeled random biomedical images, and compare the performance against other state-of-the-art text detection algorithms. We demonstrate that our projection histogram-based text detection approach is well suited for text detection in biomedical images, and that the iterative application of the algorithm boosts performance to an F score of .60. We provide a C++ implementation of our algorithm freely available for academic use. Copyright © 2010 Elsevier Inc. All rights reserved.

Research on adaptive optics image restoration algorithm based on improved joint maximum a posteriori method

NASA Astrophysics Data System (ADS)

Zhang, Lijuan; Li, Yang; Wang, Junnan; Liu, Ying

2018-03-01

In this paper, we propose a point spread function (PSF) reconstruction method and joint maximum a posteriori (JMAP) estimation method for the adaptive optics image restoration. Using the JMAP method as the basic principle, we establish the joint log likelihood function of multi-frame adaptive optics (AO) images based on the image Gaussian noise models. To begin with, combining the observed conditions and AO system characteristics, a predicted PSF model for the wavefront phase effect is developed; then, we build up iterative solution formulas of the AO image based on our proposed algorithm, addressing the implementation process of multi-frame AO images joint deconvolution method. We conduct a series of experiments on simulated and real degraded AO images to evaluate our proposed algorithm. Compared with the Wiener iterative blind deconvolution (Wiener-IBD) algorithm and Richardson-Lucy IBD algorithm, our algorithm has better restoration effects including higher peak signal-to-noise ratio ( PSNR) and Laplacian sum ( LS) value than the others. The research results have a certain application values for actual AO image restoration.
Image segmentation evaluation for very-large datasets

NASA Astrophysics Data System (ADS)

Reeves, Anthony P.; Liu, Shuang; Xie, Yiting

2016-03-01

With the advent of modern machine learning methods and fully automated image analysis there is a need for very large image datasets having documented segmentations for both computer algorithm training and evaluation. Current approaches of visual inspection and manual markings do not scale well to big data. We present a new approach that depends on fully automated algorithm outcomes for segmentation documentation, requires no manual marking, and provides quantitative evaluation for computer algorithms. The documentation of new image segmentations and new algorithm outcomes are achieved by visual inspection. The burden of visual inspection on large datasets is minimized by (a) customized visualizations for rapid review and (b) reducing the number of cases to be reviewed through analysis of quantitative segmentation evaluation. This method has been applied to a dataset of 7,440 whole-lung CT images for 6 different segmentation algorithms designed to fully automatically facilitate the measurement of a number of very important quantitative image biomarkers. The results indicate that we could achieve 93% to 99% successful segmentation for these algorithms on this relatively large image database. The presented evaluation method may be scaled to much larger image databases.
Metal-induced streak artifact reduction using iterative reconstruction algorithms in x-ray computed tomography image of the dentoalveolar region.

PubMed

Dong, Jian; Hayakawa, Yoshihiko; Kannenberg, Sven; Kober, Cornelia

2013-02-01

The objective of this study was to reduce metal-induced streak artifact on oral and maxillofacial x-ray computed tomography (CT) images by developing the fast statistical image reconstruction system using iterative reconstruction algorithms. Adjacent CT images often depict similar anatomical structures in thin slices. So, first, images were reconstructed using the same projection data of an artifact-free image. Second, images were processed by the successive iterative restoration method where projection data were generated from reconstructed image in sequence. Besides the maximum likelihood-expectation maximization algorithm, the ordered subset-expectation maximization algorithm (OS-EM) was examined. Also, small region of interest (ROI) setting and reverse processing were applied for improving performance. Both algorithms reduced artifacts instead of slightly decreasing gray levels. The OS-EM and small ROI reduced the processing duration without apparent detriments. Sequential and reverse processing did not show apparent effects. Two alternatives in iterative reconstruction methods were effective for artifact reduction. The OS-EM algorithm and small ROI setting improved the performance. Copyright © 2012 Elsevier Inc. All rights reserved.
Imaging reconstruction based on improved wavelet denoising combined with parallel-beam filtered back-projection algorithm

NASA Astrophysics Data System (ADS)

Ren, Zhong; Liu, Guodong; Huang, Zhen

2012-11-01

The image reconstruction is a key step in medical imaging (MI) and its algorithm's performance determinates the quality and resolution of reconstructed image. Although some algorithms have been used, filter back-projection (FBP) algorithm is still the classical and commonly-used algorithm in clinical MI. In FBP algorithm, filtering of original projection data is a key step in order to overcome artifact of the reconstructed image. Since simple using of classical filters, such as Shepp-Logan (SL), Ram-Lak (RL) filter have some drawbacks and limitations in practice, especially for the projection data polluted by non-stationary random noises. So, an improved wavelet denoising combined with parallel-beam FBP algorithm is used to enhance the quality of reconstructed image in this paper. In the experiments, the reconstructed effects were compared between the improved wavelet denoising and others (directly FBP, mean filter combined FBP and median filter combined FBP method). To determine the optimum reconstruction effect, different algorithms, and different wavelet bases combined with three filters were respectively test. Experimental results show the reconstruction effect of improved FBP algorithm is better than that of others. Comparing the results of different algorithms based on two evaluation standards i.e. mean-square error (MSE), peak-to-peak signal-noise ratio (PSNR), it was found that the reconstructed effects of the improved FBP based on db2 and Hanning filter at decomposition scale 2 was best, its MSE value was less and the PSNR value was higher than others. Therefore, this improved FBP algorithm has potential value in the medical imaging.
A Palmprint Recognition Algorithm Using Phase-Only Correlation

NASA Astrophysics Data System (ADS)

Ito, Koichi; Aoki, Takafumi; Nakajima, Hiroshi; Kobayashi, Koji; Higuchi, Tatsuo

This paper presents a palmprint recognition algorithm using Phase-Only Correlation (POC). The use of phase components in 2D (two-dimensional) discrete Fourier transforms of palmprint images makes it possible to achieve highly robust image registration and matching. In the proposed algorithm, POC is used to align scaling, rotation and translation between two palmprint images, and evaluate similarity between them. Experimental evaluation using a palmprint image database clearly demonstrates efficient matching performance of the proposed algorithm.
Robust Global Image Registration Based on a Hybrid Algorithm Combining Fourier and Spatial Domain Techniques

DTIC Science & Technology

2012-09-01

Robust global image registration based on a hybrid algorithm combining Fourier and spatial domain techniques Peter N. Crabtree, Collin Seanor...00-00-2012 to 00-00-2012 4. TITLE AND SUBTITLE Robust global image registration based on a hybrid algorithm combining Fourier and spatial domain...demonstrate performance of a hybrid algorithm . These results are from analysis of a set of images of an ISO 12233 [12] resolution chart captured in the
Efficient iterative image reconstruction algorithm for dedicated breast CT

NASA Astrophysics Data System (ADS)

Antropova, Natalia; Sanchez, Adrian; Reiser, Ingrid S.; Sidky, Emil Y.; Boone, John; Pan, Xiaochuan

2016-03-01

Dedicated breast computed tomography (bCT) is currently being studied as a potential screening method for breast cancer. The X-ray exposure is set low to achieve an average glandular dose comparable to that of mammography, yielding projection data that contains high levels of noise. Iterative image reconstruction (IIR) algorithms may be well-suited for the system since they potentially reduce the effects of noise in the reconstructed images. However, IIR outcomes can be difficult to control since the algorithm parameters do not directly correspond to the image properties. Also, IIR algorithms are computationally demanding and have optimal parameter settings that depend on the size and shape of the breast and positioning of the patient. In this work, we design an efficient IIR algorithm with meaningful parameter specifications and that can be used on a large, diverse sample of bCT cases. The flexibility and efficiency of this method comes from having the final image produced by a linear combination of two separately reconstructed images - one containing gray level information and the other with enhanced high frequency components. Both of the images result from few iterations of separate IIR algorithms. The proposed algorithm depends on two parameters both of which have a well-defined impact on image quality. The algorithm is applied to numerous bCT cases from a dedicated bCT prototype system developed at University of California, Davis.
Image Registration for Stability Testing of MEMS

NASA Technical Reports Server (NTRS)

Memarsadeghi, Nargess; LeMoigne, Jacqueline; Blake, Peter N.; Morey, Peter A.; Landsman, Wayne B.; Chambers, Victor J.; Moseley, Samuel H.

2011-01-01

Image registration, or alignment of two or more images covering the same scenes or objects, is of great interest in many disciplines such as remote sensing, medical imaging. astronomy, and computer vision. In this paper, we introduce a new application of image registration algorithms. We demonstrate how through a wavelet based image registration algorithm, engineers can evaluate stability of Micro-Electro-Mechanical Systems (MEMS). In particular, we applied image registration algorithms to assess alignment stability of the MicroShutters Subsystem (MSS) of the Near Infrared Spectrograph (NIRSpec) instrument of the James Webb Space Telescope (JWST). This work introduces a new methodology for evaluating stability of MEMS devices to engineers as well as a new application of image registration algorithms to computer scientists.
The remote sensing image segmentation mean shift algorithm parallel processing based on MapReduce

NASA Astrophysics Data System (ADS)

Chen, Xi; Zhou, Liqing

2015-12-01

With the development of satellite remote sensing technology and the remote sensing image data, traditional remote sensing image segmentation technology cannot meet the massive remote sensing image processing and storage requirements. This article put cloud computing and parallel computing technology in remote sensing image segmentation process, and build a cheap and efficient computer cluster system that uses parallel processing to achieve MeanShift algorithm of remote sensing image segmentation based on the MapReduce model, not only to ensure the quality of remote sensing image segmentation, improved split speed, and better meet the real-time requirements. The remote sensing image segmentation MeanShift algorithm parallel processing algorithm based on MapReduce shows certain significance and a realization of value.
Guided filtering for solar image/video processing

NASA Astrophysics Data System (ADS)

Xu, Long; Yan, Yihua; Cheng, Jun

2017-06-01

A new image enhancement algorithm employing guided filtering is proposed in this work for the enhancement of solar images and videos so that users can easily figure out important fine structures embedded in the recorded images/movies for solar observation. The proposed algorithm can efficiently remove image noises, including Gaussian and impulse noises. Meanwhile, it can further highlight fibrous structures on/beyond the solar disk. These fibrous structures can clearly demonstrate the progress of solar flare, prominence coronal mass emission, magnetic field, and so on. The experimental results prove that the proposed algorithm gives significant enhancement of visual quality of solar images beyond original input and several classical image enhancement algorithms, thus facilitating easier determination of interesting solar burst activities from recorded images/movies.
Quantum Color Image Encryption Algorithm Based on A Hyper-Chaotic System and Quantum Fourier Transform

NASA Astrophysics Data System (ADS)

Tan, Ru-Chao; Lei, Tong; Zhao, Qing-Min; Gong, Li-Hua; Zhou, Zhi-Hong

2016-12-01

To improve the slow processing speed of the classical image encryption algorithms and enhance the security of the private color images, a new quantum color image encryption algorithm based on a hyper-chaotic system is proposed, in which the sequences generated by the Chen's hyper-chaotic system are scrambled and diffused with three components of the original color image. Sequentially, the quantum Fourier transform is exploited to fulfill the encryption. Numerical simulations show that the presented quantum color image encryption algorithm possesses large key space to resist illegal attacks, sensitive dependence on initial keys, uniform distribution of gray values for the encrypted image and weak correlation between two adjacent pixels in the cipher-image.
Automatic image equalization and contrast enhancement using Gaussian mixture modeling.

PubMed

Celik, Turgay; Tjahjadi, Tardi

2012-01-01

In this paper, we propose an adaptive image equalization algorithm that automatically enhances the contrast in an input image. The algorithm uses the Gaussian mixture model to model the image gray-level distribution, and the intersection points of the Gaussian components in the model are used to partition the dynamic range of the image into input gray-level intervals. The contrast equalized image is generated by transforming the pixels' gray levels in each input interval to the appropriate output gray-level interval according to the dominant Gaussian component and the cumulative distribution function of the input interval. To take account of the hypothesis that homogeneous regions in the image represent homogeneous silences (or set of Gaussian components) in the image histogram, the Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances, and the gray-level distribution is also used to weight the components in the mapping of the input interval to the output interval. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several state-of-the-art algorithms. Unlike the other algorithms, the proposed algorithm is free of parameter setting for a given dynamic range of the enhanced image and can be applied to a wide range of image types.
A fast and automatic fusion algorithm for unregistered multi-exposure image sequence

NASA Astrophysics Data System (ADS)

Liu, Yan; Yu, Feihong

2014-09-01

Human visual system (HVS) can visualize all the brightness levels of the scene through visual adaptation. However, the dynamic range of most commercial digital cameras and display devices are smaller than the dynamic range of human eye. This implies low dynamic range (LDR) images captured by normal digital camera may lose image details. We propose an efficient approach to high dynamic (HDR) image fusion that copes with image displacement and image blur degradation in a computationally efficient manner, which is suitable for implementation on mobile devices. The various image registration algorithms proposed in the previous literatures are unable to meet the efficiency and performance requirements in the application of mobile devices. In this paper, we selected Oriented Brief (ORB) detector to extract local image structures. The descriptor selected in multi-exposure image fusion algorithm has to be fast and robust to illumination variations and geometric deformations. ORB descriptor is the best candidate in our algorithm. Further, we perform an improved RANdom Sample Consensus (RANSAC) algorithm to reject incorrect matches. For the fusion of images, a new approach based on Stationary Wavelet Transform (SWT) is used. The experimental results demonstrate that the proposed algorithm generates high quality images at low computational cost. Comparisons with a number of other feature matching methods show that our method gets better performance.
Optical image hiding based on computational ghost imaging

NASA Astrophysics Data System (ADS)

Wang, Le; Zhao, Shengmei; Cheng, Weiwen; Gong, Longyan; Chen, Hanwu

2016-05-01

Imaging hiding schemes play important roles in now big data times. They provide copyright protections of digital images. In the paper, we propose a novel image hiding scheme based on computational ghost imaging to have strong robustness and high security. The watermark is encrypted with the configuration of a computational ghost imaging system, and the random speckle patterns compose a secret key. Least significant bit algorithm is adopted to embed the watermark and both the second-order correlation algorithm and the compressed sensing (CS) algorithm are used to extract the watermark. The experimental and simulation results show that the authorized users can get the watermark with the secret key. The watermark image could not be retrieved when the eavesdropping ratio is less than 45% with the second-order correlation algorithm, whereas it is less than 20% with the TVAL3 CS reconstructed algorithm. In addition, the proposed scheme is robust against the 'salt and pepper' noise and image cropping degradations.
Semi-blind sparse image reconstruction with application to MRFM.

PubMed

Park, Se Un; Dobigeon, Nicolas; Hero, Alfred O

2012-09-01

We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known. Small perturbations generated from the model are exploited to produce a few principal components explaining the PSF uncertainty in a high-dimensional space. Unlike recent developments on blind deconvolution of natural images, we assume the image is sparse in the pixel basis, a natural sparsity arising in magnetic resonance force microscopy (MRFM). Our approach adopts a Bayesian Metropolis-within-Gibbs sampling framework. The performance of our Bayesian semi-blind algorithm for sparse images is superior to previously proposed semi-blind algorithms such as the alternating minimization algorithm and blind algorithms developed for natural images. We illustrate our myopic algorithm on real MRFM tobacco virus data.
Skeletonization with hollow detection on gray image by gray weighted distance transform

NASA Astrophysics Data System (ADS)

Bhattacharya, Prabir; Qian, Kai; Cao, Siqi; Qian, Yi

1998-10-01

A skeletonization algorithm that could be used to process non-uniformly distributed gray-scale images with hollows was presented. This algorithm is based on the Gray Weighted Distance Transformation. The process includes a preliminary phase of investigation in the hollows in the gray-scale image, whether these hollows are considered as topological constraints for the skeleton structure depending on their statistically significant depth. We then extract the resulting skeleton that has certain meaningful information for understanding the object in the image. This improved algorithm can overcome the possible misinterpretation of some complicated images in the extracted skeleton, especially in images with asymmetric hollows and asymmetric features. This algorithm can be executed on a parallel machine as all the operations are executed in local. Some examples are discussed to illustrate the algorithm.
A hybrid algorithm for the segmentation of books in libraries

NASA Astrophysics Data System (ADS)

Hu, Zilong; Tang, Jinshan; Lei, Liang

2016-05-01

This paper proposes an algorithm for book segmentation based on bookshelves images. The algorithm can be separated into three parts. The first part is pre-processing, aiming at eliminating or decreasing the effect of image noise and illumination conditions. The second part is near-horizontal line detection based on Canny edge detector, and separating a bookshelves image into multiple sub-images so that each sub-image contains an individual shelf. The last part is book segmentation. In each shelf image, near-vertical line is detected, and obtained lines are used for book segmentation. The proposed algorithm was tested with the bookshelf images taken from OPIE library in MTU, and the experimental results demonstrate good performance.
Image contrast enhancement using adjacent-blocks-based modification for local histogram equalization

NASA Astrophysics Data System (ADS)

Wang, Yang; Pan, Zhibin

2017-11-01

Infrared images usually have some non-ideal characteristics such as weak target-to-background contrast and strong noise. Because of these characteristics, it is necessary to apply the contrast enhancement algorithm to improve the visual quality of infrared images. Histogram equalization (HE) algorithm is a widely used contrast enhancement algorithm due to its effectiveness and simple implementation. But a drawback of HE algorithm is that the local contrast of an image cannot be equally enhanced. Local histogram equalization algorithms are proved to be the effective techniques for local image contrast enhancement. However, over-enhancement of noise and artifacts can be easily found in the local histogram equalization enhanced images. In this paper, a new contrast enhancement technique based on local histogram equalization algorithm is proposed to overcome the drawbacks mentioned above. The input images are segmented into three kinds of overlapped sub-blocks using the gradients of them. To overcome the over-enhancement effect, the histograms of these sub-blocks are then modified by adjacent sub-blocks. We pay more attention to improve the contrast of detail information while the brightness of the flat region in these sub-blocks is well preserved. It will be shown that the proposed algorithm outperforms other related algorithms by enhancing the local contrast without introducing over-enhancement effects and additional noise.
Region-of-interest image reconstruction in circular cone-beam microCT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cho, Seungryong; Bian, Junguo; Pelizzari, Charles A.

2007-12-15

Cone-beam microcomputed tomography (microCT) is one of the most popular choices for small animal imaging which is becoming an important tool for studying animal models with transplanted diseases. Region-of-interest (ROI) imaging techniques in CT, which can reconstruct an ROI image from the projection data set of the ROI, can be used not only for reducing imaging-radiation exposure to the subject and scatters to the detector but also for potentially increasing spatial resolution of the reconstructed images. Increasing spatial resolution in microCT images can facilitate improved accuracy in many assessment tasks. A method proposed previously for increasing CT image spatial resolutionmore » entails the exploitation of the geometric magnification in cone-beam CT. Due to finite detector size, however, this method can lead to data truncation for a large geometric magnification. The Feldkamp-Davis-Kress (FDK) algorithm yields images with artifacts when truncated data are used, whereas the recently developed backprojection filtration (BPF) algorithm is capable of reconstructing ROI images without truncation artifacts from truncated cone-beam data. We apply the BPF algorithm to reconstructing ROI images from truncated data of three different objects acquired by our circular cone-beam microCT system. Reconstructed images by use of the FDK and BPF algorithms from both truncated and nontruncated cone-beam data are compared. The results of the experimental studies demonstrate that, from certain truncated data, the BPF algorithm can reconstruct ROI images with quality comparable to that reconstructed from nontruncated data. In contrast, the FDK algorithm yields ROI images with truncation artifacts. Therefore, an implication of the studies is that, when truncated data are acquired with a configuration of a large geometric magnification, the BPF algorithm can be used for effective enhancement of the spatial resolution of a ROI image.« less
Performance evaluation of image segmentation algorithms on microscopic image data.

PubMed

Beneš, Miroslav; Zitová, Barbara

2015-01-01

In our paper, we present a performance evaluation of image segmentation algorithms on microscopic image data. In spite of the existence of many algorithms for image data partitioning, there is no universal and 'the best' method yet. Moreover, images of microscopic samples can be of various character and quality which can negatively influence the performance of image segmentation algorithms. Thus, the issue of selecting suitable method for a given set of image data is of big interest. We carried out a large number of experiments with a variety of segmentation methods to evaluate the behaviour of individual approaches on the testing set of microscopic images (cross-section images taken in three different modalities from the field of art restoration). The segmentation results were assessed by several indices used for measuring the output quality of image segmentation algorithms. In the end, the benefit of segmentation combination approach is studied and applicability of achieved results on another representatives of microscopic data category - biological samples - is shown. © 2014 The Authors Journal of Microscopy © 2014 Royal Microscopical Society.

SU-F-J-76: Evaluation of the Performance of Different Deformable Image Registration Algorithms in Helical, Axial and Cone-Beam CT Images of a Mobile Phantom

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jaskowiak, J; Ahmad, S; Ali, I

Purpose: To investigate quantitatively the performance of different deformable-image-registration algorithms (DIR) with helical (HCT), axial (ACT) and cone-beam CT (CBCT) by evaluating the variations in the CT-numbers and lengths of targets moving with controlled motion-patterns. Methods: Four DIR-algorithms including demons, fast-demons, Horn-Schunk and Locas-Kanade from the DIRART-software are used to register CT-images of a mobile-phantom. A mobile-phantom is scanned with different imaging techniques that include helical, axial and cone-beam CT. The phantom includes three targets with different lengths that are made from water-equivalent material and inserted in low-density-foam which is moved with adjustable motion-amplitudes and frequencies. Results: Most of themore » DIR-algorithms are able to produce the lengths of the stationary-targets, however, they do not produce the CT-number values in CBCT. The image-artifacts induced by motion are more regular in CBCT imaging where the mobile-target elongation increases linearly with motion-amplitude. In ACT and HCT, the motion-artifacts are irregular where some mobile -targets are elongated or shrunk depending on the motion-phase during imaging. The DIR-algorithms are successful in deforming the images of the mobile-targets to the images of the stationary-targets producing the CT-number values and length of the target for motion-amplitudes < 20 mm. Similarly in ACT, all DIR-algorithms produced the actual CT-number and length of the stationary-targets for motion-amplitudes < 15 mm. As stronger motion-artifacts are induced in HCT and ACT, DIR-algorithms fail to produce CT-values and shape of the stationary-targets and fast-demons-algorithm has worst performance. Conclusion: Most of DIR-algorithms produce the CT-number values and lengths of the stationary-targets in HCT and ACT images that has motion-artifacts induced by small motion-amplitudes. As motion-amplitudes increase, the DIR-algorithms fail to deform mobile-target images to the stationary-images in HCT and ACT. In CBCT, DIR-algorithms are successful in producing length and shape of the stationary-targets, however, they fail to produce the accurate CT-number level.« less
Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images.

PubMed

Ruusuvuori, Pekka; Aijö, Tarmo; Chowdhury, Sharif; Garmendia-Torres, Cecilia; Selinummi, Jyrki; Birbaumer, Mirko; Dudley, Aimée M; Pelkmans, Lucas; Yli-Harja, Olli

2010-05-13

Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed. To better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies. These results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.
Compression of multispectral Landsat imagery using the Embedded Zerotree Wavelet (EZW) algorithm

NASA Technical Reports Server (NTRS)

Shapiro, Jerome M.; Martucci, Stephen A.; Czigler, Martin

1994-01-01

The Embedded Zerotree Wavelet (EZW) algorithm has proven to be an extremely efficient and flexible compression algorithm for low bit rate image coding. The embedding algorithm attempts to order the bits in the bit stream in numerical importance and thus a given code contains all lower rate encodings of the same algorithm. Therefore, precise bit rate control is achievable and a target rate or distortion metric can be met exactly. Furthermore, the technique is fully image adaptive. An algorithm for multispectral image compression which combines the spectral redundancy removal properties of the image-dependent Karhunen-Loeve Transform (KLT) with the efficiency, controllability, and adaptivity of the embedded zerotree wavelet algorithm is presented. Results are shown which illustrate the advantage of jointly encoding spectral components using the KLT and EZW.
Target recognition of ladar range images using slice image: comparison of four improved algorithms

NASA Astrophysics Data System (ADS)

Xia, Wenze; Han, Shaokun; Cao, Jingya; Wang, Liang; Zhai, Yu; Cheng, Yang

2017-07-01

Compared with traditional 3-D shape data, ladar range images possess properties of strong noise, shape degeneracy, and sparsity, which make feature extraction and representation difficult. The slice image is an effective feature descriptor to resolve this problem. We propose four improved algorithms on target recognition of ladar range images using slice image. In order to improve resolution invariance of the slice image, mean value detection instead of maximum value detection is applied in these four improved algorithms. In order to improve rotation invariance of the slice image, three new improved feature descriptors-which are feature slice image, slice-Zernike moments, and slice-Fourier moments-are applied to the last three improved algorithms, respectively. Backpropagation neural networks are used as feature classifiers in the last two improved algorithms. The performance of these four improved recognition systems is analyzed comprehensively in the aspects of the three invariances, recognition rate, and execution time. The final experiment results show that the improvements for these four algorithms reach the desired effect, the three invariances of feature descriptors are not directly related to the final recognition performance of recognition systems, and these four improved recognition systems have different performances under different conditions.
Stable and accurate methods for identification of water bodies from Landsat series imagery using meta-heuristic algorithms

NASA Astrophysics Data System (ADS)

Gamshadzaei, Mohammad Hossein; Rahimzadegan, Majid

2017-10-01

Identification of water extents in Landsat images is challenging due to surfaces with similar reflectance to water extents. The objective of this study is to provide stable and accurate methods for identifying water extents in Landsat images based on meta-heuristic algorithms. Then, seven Landsat images were selected from various environmental regions in Iran. Training of the algorithms was performed using 40 water pixels and 40 nonwater pixels in operational land imager images of Chitgar Lake (one of the study regions). Moreover, high-resolution images from Google Earth were digitized to evaluate the results. Two approaches were considered: index-based and artificial intelligence (AI) algorithms. In the first approach, nine common water spectral indices were investigated. AI algorithms were utilized to acquire coefficients of optimal band combinations to extract water extents. Among the AI algorithms, the artificial neural network algorithm and also the ant colony optimization, genetic algorithm, and particle swarm optimization (PSO) meta-heuristic algorithms were implemented. Index-based methods represented different performances in various regions. Among AI methods, PSO had the best performance with average overall accuracy and kappa coefficient of 93% and 98%, respectively. The results indicated the applicability of acquired band combinations to extract accurately and stably water extents in Landsat imagery.
Fast Steerable Principal Component Analysis

PubMed Central

Zhao, Zhizhen; Shkolnisky, Yoel; Singer, Amit

2016-01-01

Cryo-electron microscopy nowadays often requires the analysis of hundreds of thousands of 2-D images as large as a few hundred pixels in each direction. Here, we introduce an algorithm that efficiently and accurately performs principal component analysis (PCA) for a large set of 2-D images, and, for each image, the set of its uniform rotations in the plane and their reflections. For a dataset consisting of n images of size L × L pixels, the computational complexity of our algorithm is O(nL3 + L4), while existing algorithms take O(nL4). The new algorithm computes the expansion coefficients of the images in a Fourier–Bessel basis efficiently using the nonuniform fast Fourier transform. We compare the accuracy and efficiency of the new algorithm with traditional PCA and existing algorithms for steerable PCA. PMID:27570801
Advanced biologically plausible algorithms for low-level image processing

NASA Astrophysics Data System (ADS)

Gusakova, Valentina I.; Podladchikova, Lubov N.; Shaposhnikov, Dmitry G.; Markin, Sergey N.; Golovan, Alexander V.; Lee, Seong-Whan

1999-08-01

At present, in computer vision, the approach based on modeling the biological vision mechanisms is extensively developed. However, up to now, real world image processing has no effective solution in frameworks of both biologically inspired and conventional approaches. Evidently, new algorithms and system architectures based on advanced biological motivation should be developed for solution of computational problems related to this visual task. Basic problems that should be solved for creation of effective artificial visual system to process real world imags are a search for new algorithms of low-level image processing that, in a great extent, determine system performance. In the present paper, the result of psychophysical experiments and several advanced biologically motivated algorithms for low-level processing are presented. These algorithms are based on local space-variant filter, context encoding visual information presented in the center of input window, and automatic detection of perceptually important image fragments. The core of latter algorithm are using local feature conjunctions such as noncolinear oriented segment and composite feature map formation. Developed algorithms were integrated into foveal active vision model, the MARR. It is supposed that proposed algorithms may significantly improve model performance while real world image processing during memorizing, search, and recognition.
Validation of an improved 'diffeomorphic demons' algorithm for deformable image registration in image-guided radiation therapy.

PubMed

Zhou, Lu; Zhou, Linghong; Zhang, Shuxu; Zhen, Xin; Yu, Hui; Zhang, Guoqian; Wang, Ruihao

2014-01-01

Deformable image registration (DIR) was widely used in radiation therapy, such as in automatic contour generation, dose accumulation, tumor growth or regression analysis. To achieve higher registration accuracy and faster convergence, an improved 'diffeomorphic demons' registration algorithm was proposed and validated. Based on Brox et al.'s gradient constancy assumption and Malis's efficient second-order minimization (ESM) algorithm, a grey value gradient similarity term and a transformation error term were added into the demons energy function, and a formula was derived to calculate the update of transformation field. The limited Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm was used to optimize the energy function so that the iteration number could be determined automatically. The proposed algorithm was validated using mathematically deformed images and physically deformed phantom images. Compared with the original 'diffeomorphic demons' algorithm, the registration method proposed achieve a higher precision and a faster convergence speed. Due to the influence of different scanning conditions in fractionated radiation, the density range of the treatment image and the planning image may be different. In such a case, the improved demons algorithm can achieve faster and more accurate radiotherapy.
Remote-sensing image encryption in hybrid domains

NASA Astrophysics Data System (ADS)

Zhang, Xiaoqiang; Zhu, Guiliang; Ma, Shilong

2012-04-01

Remote-sensing technology plays an important role in military and industrial fields. Remote-sensing image is the main means of acquiring information from satellites, which always contain some confidential information. To securely transmit and store remote-sensing images, we propose a new image encryption algorithm in hybrid domains. This algorithm makes full use of the advantages of image encryption in both spatial domain and transform domain. First, the low-pass subband coefficients of image DWT (discrete wavelet transform) decomposition are sorted by a PWLCM system in transform domain. Second, the image after IDWT (inverse discrete wavelet transform) reconstruction is diffused with 2D (two-dimensional) Logistic map and XOR operation in spatial domain. The experiment results and algorithm analyses show that the new algorithm possesses a large key space and can resist brute-force, statistical and differential attacks. Meanwhile, the proposed algorithm has the desirable encryption efficiency to satisfy requirements in practice.
Efficient geometric rectification techniques for spectral analysis algorithm

NASA Technical Reports Server (NTRS)

Chang, C. Y.; Pang, S. S.; Curlander, J. C.

1992-01-01

The spectral analysis algorithm is a viable technique for processing synthetic aperture radar (SAR) data in near real time throughput rates by trading the image resolution. One major challenge of the spectral analysis algorithm is that the output image, often referred to as the range-Doppler image, is represented in the iso-range and iso-Doppler lines, a curved grid format. This phenomenon is known to be the fanshape effect. Therefore, resampling is required to convert the range-Doppler image into a rectangular grid format before the individual images can be overlaid together to form seamless multi-look strip imagery. An efficient algorithm for geometric rectification of the range-Doppler image is presented. The proposed algorithm, realized in two one-dimensional resampling steps, takes into consideration the fanshape phenomenon of the range-Doppler image as well as the high squint angle and updates of the cross-track and along-track Doppler parameters. No ground reference points are required.
The wavenumber algorithm for full-matrix imaging using an ultrasonic array.

PubMed

Hunter, Alan J; Drinkwater, Bruce W; Wilcox, Paul D

2008-11-01

Ultrasonic imaging using full-matrix capture, e.g., via the total focusing method (TFM), has been shown to increase angular inspection coverage and improve sensitivity to small defects in nondestructive evaluation. In this paper, we develop a Fourier-domain approach to full-matrix imaging based on the wavenumber algorithm used in synthetic aperture radar and sonar. The extension to the wavenumber algorithm for full-matrix data is described and the performance of the new algorithm compared with the TFM, which we use as a representative benchmark for the time-domain algorithms. The wavenumber algorithm provides a mathematically rigorous solution to the inverse problem for the assumed forward wave propagation model, whereas the TFM employs heuristic delay-and-sum beamforming. Consequently, the wavenumber algorithm has an improved point-spread function and provides better imagery. However, the major advantage of the wavenumber algorithm is its superior computational performance. For large arrays and images, the wavenumber algorithm is several orders of magnitude faster than the TFM. On the other hand, the key advantage of the TFM is its flexibility. The wavenumber algorithm requires a regularly sampled linear array, while the TFM can handle arbitrary imaging geometries. The TFM and the wavenumber algorithm are compared using simulated and experimental data.
Ship Detection in SAR Image Based on the Alpha-stable Distribution

PubMed Central

Wang, Changcheng; Liao, Mingsheng; Li, Xiaofeng

2008-01-01

This paper describes an improved Constant False Alarm Rate (CFAR) ship detection algorithm in spaceborne synthetic aperture radar (SAR) image based on Alpha-stable distribution model. Typically, the CFAR algorithm uses the Gaussian distribution model to describe statistical characteristics of a SAR image background clutter. However, the Gaussian distribution is only valid for multilook SAR images when several radar looks are averaged. As sea clutter in SAR images shows spiky or heavy-tailed characteristics, the Gaussian distribution often fails to describe background sea clutter. In this study, we replace the Gaussian distribution with the Alpha-stable distribution, which is widely used in impulsive or spiky signal processing, to describe the background sea clutter in SAR images. In our proposed algorithm, an initial step for detecting possible ship targets is employed. Then, similar to the typical two-parameter CFAR algorithm, a local process is applied to the pixel identified as possible target. A RADARSAT-1 image is used to validate this Alpha-stable distribution based algorithm. Meanwhile, known ship location data during the time of RADARSAT-1 SAR image acquisition is used to validate ship detection results. Validation results show improvements of the new CFAR algorithm based on the Alpha-stable distribution over the CFAR algorithm based on the Gaussian distribution. PMID:27873794
An algorithm for calculi segmentation on ureteroscopic images.

PubMed

Rosa, Benoît; Mozer, Pierre; Szewczyk, Jérôme

2011-03-01

The purpose of the study is to develop an algorithm for the segmentation of renal calculi on ureteroscopic images. In fact, renal calculi are common source of urological obstruction, and laser lithotripsy during ureteroscopy is a possible therapy. A laser-based system to sweep the calculus surface and vaporize it was developed to automate a very tedious manual task. The distal tip of the ureteroscope is directed using image guidance, and this operation is not possible without an efficient segmentation of renal calculi on the ureteroscopic images. We proposed and developed a region growing algorithm to segment renal calculi on ureteroscopic images. Using real video images to compute ground truth and compare our segmentation with a reference segmentation, we computed statistics on different image metrics, such as Precision, Recall, and Yasnoff Measure, for comparison with ground truth. The algorithm and its parameters were established for the most likely clinical scenarii. The segmentation results are encouraging: the developed algorithm was able to correctly detect more than 90% of the surface of the calculi, according to an expert observer. Implementation of an algorithm for the segmentation of calculi on ureteroscopic images is feasible. The next step is the integration of our algorithm in the command scheme of a motorized system to build a complete operating prototype.
Self-recovery fragile watermarking algorithm based on SPHIT

NASA Astrophysics Data System (ADS)

Xin, Li Ping

2015-12-01

A fragile watermark algorithm is proposed, based on SPIHT coding, which can recover the primary image itself. The novelty of the algorithm is that it can tamper location and Self-restoration. The recovery has been very good effect. The first, utilizing the zero-tree structure, the algorithm compresses and encodes the image itself, and then gained self correlative watermark data, so as to greatly reduce the quantity of embedding watermark. Then the watermark data is encoded by error correcting code, and the check bits and watermark bits are scrambled and embedded to enhance the recovery ability. At the same time, by embedding watermark into the latter two bit place of gray level image's bit-plane code, the image after embedded watermark can gain nicer visual effect. The experiment results show that the proposed algorithm may not only detect various processing such as noise adding, cropping, and filtering, but also recover tampered image and realize blind-detection. Peak signal-to-noise ratios of the watermark image were higher than other similar algorithm. The attack capability of the algorithm was enhanced.
Robust crop and weed segmentation under uncontrolled outdoor illumination.

PubMed

Jeon, Hong Y; Tian, Lei F; Zhu, Heping

2011-01-01

An image processing algorithm for detecting individual weeds was developed and evaluated. Weed detection processes included were normalized excessive green conversion, statistical threshold value estimation, adaptive image segmentation, median filter, morphological feature calculation and Artificial Neural Network (ANN). The developed algorithm was validated for its ability to identify and detect weeds and crop plants under uncontrolled outdoor illuminations. A machine vision implementing field robot captured field images under outdoor illuminations and the image processing algorithm automatically processed them without manual adjustment. The errors of the algorithm, when processing 666 field images, ranged from 2.1 to 2.9%. The ANN correctly detected 72.6% of crop plants from the identified plants, and considered the rest as weeds. However, the ANN identification rates for crop plants were improved up to 95.1% by addressing the error sources in the algorithm. The developed weed detection and image processing algorithm provides a novel method to identify plants against soil background under the uncontrolled outdoor illuminations, and to differentiate weeds from crop plants. Thus, the proposed new machine vision and processing algorithm may be useful for outdoor applications including plant specific direct applications (PSDA).
Cone-Beam Computed Tomography for Image-Guided Radiation Therapy of Prostate Cancer

DTIC Science & Technology

2008-01-01

forexa t volumetri image re onstru tion. As a onsequense, images re onstru ted by approx-imate algorithms, mostly based on the Feldkamp algorithm...patient dose from CBCT. Reverse heli al CBCT has been developed for exa tre onstru tion of volumetri images, region-of-interest (ROI) re onstru tion...algorithm with a priori informa-tion in few-view CBCT for IGRT. We expe t the proposed algorithm an redu e the numberof proje tions needed for volumetri
SU-F-I-09: Improvement of Image Registration Using Total-Variation Based Noise Reduction Algorithms for Low-Dose CBCT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mukherjee, S; Farr, J; Merchant, T

Purpose: To study the effect of total-variation based noise reduction algorithms to improve the image registration of low-dose CBCT for patient positioning in radiation therapy. Methods: In low-dose CBCT, the reconstructed image is degraded by excessive quantum noise. In this study, we developed a total-variation based noise reduction algorithm and studied the effect of the algorithm on noise reduction and image registration accuracy. To study the effect of noise reduction, we have calculated the peak signal-to-noise ratio (PSNR). To study the improvement of image registration, we performed image registration between volumetric CT and MV- CBCT images of different head-and-neck patientsmore » and calculated the mutual information (MI) and Pearson correlation coefficient (PCC) as a similarity metric. The PSNR, MI and PCC were calculated for both the noisy and noise-reduced CBCT images. Results: The algorithms were shown to be effective in reducing the noise level and improving the MI and PCC for the low-dose CBCT images tested. For the different head-and-neck patients, a maximum improvement of PSNR of 10 dB with respect to the noisy image was calculated. The improvement of MI and PCC was 9% and 2% respectively. Conclusion: Total-variation based noise reduction algorithm was studied to improve the image registration between CT and low-dose CBCT. The algorithm had shown promising results in reducing the noise from low-dose CBCT images and improving the similarity metric in terms of MI and PCC.« less
Application of an enhanced fuzzy algorithm for MR brain tumor image segmentation

NASA Astrophysics Data System (ADS)

Hemanth, D. Jude; Vijila, C. Kezi Selva; Anitha, J.

2010-02-01

Image segmentation is one of the significant digital image processing techniques commonly used in the medical field. One of the specific applications is tumor detection in abnormal Magnetic Resonance (MR) brain images. Fuzzy approaches are widely preferred for tumor segmentation which generally yields superior results in terms of accuracy. But most of the fuzzy algorithms suffer from the drawback of slow convergence rate which makes the system practically non-feasible. In this work, the application of modified Fuzzy C-means (FCM) algorithm to tackle the convergence problem is explored in the context of brain image segmentation. This modified FCM algorithm employs the concept of quantization to improve the convergence rate besides yielding excellent segmentation efficiency. This algorithm is experimented on real time abnormal MR brain images collected from the radiologists. A comprehensive feature vector is extracted from these images and used for the segmentation technique. An extensive feature selection process is performed which reduces the convergence time period and improve the segmentation efficiency. After segmentation, the tumor portion is extracted from the segmented image. Comparative analysis in terms of segmentation efficiency and convergence rate is performed between the conventional FCM and the modified FCM. Experimental results show superior results for the modified FCM algorithm in terms of the performance measures. Thus, this work highlights the application of the modified algorithm for brain tumor detection in abnormal MR brain images.
Denoising and 4D visualization of OCT images

PubMed Central

Gargesha, Madhusudhana; Jenkins, Michael W.; Rollins, Andrew M.; Wilson, David L.

2009-01-01

We are using Optical Coherence Tomography (OCT) to image structure and function of the developing embryonic heart in avian models. Fast OCT imaging produces very large 3D (2D + time) and 4D (3D volumes + time) data sets, which greatly challenge ones ability to visualize results. Noise in OCT images poses additional challenges. We created an algorithm with a quick, data set specific optimization for reduction of both shot and speckle noise and applied it to 3D visualization and image segmentation in OCT. When compared to baseline algorithms (median, Wiener, orthogonal wavelet, basic non-orthogonal wavelet), a panel of experts judged the new algorithm to give much improved volume renderings concerning both noise and 3D visualization. Specifically, the algorithm provided a better visualization of the myocardial and endocardial surfaces, and the interaction of the embryonic heart tube with surrounding tissue. Quantitative evaluation using an image quality figure of merit also indicated superiority of the new algorithm. Noise reduction aided semi-automatic 2D image segmentation, as quantitatively evaluated using a contour distance measure with respect to an expert segmented contour. In conclusion, the noise reduction algorithm should be quite useful for visualization and quantitative measurements (e.g., heart volume, stroke volume, contraction velocity, etc.) in OCT embryo images. With its semi-automatic, data set specific optimization, we believe that the algorithm can be applied to OCT images from other applications. PMID:18679509
Tissue Probability Map Constrained 4-D Clustering Algorithm for Increased Accuracy and Robustness in Serial MR Brain Image Segmentation

PubMed Central

Xue, Zhong; Shen, Dinggang; Li, Hai; Wong, Stephen

2010-01-01

The traditional fuzzy clustering algorithm and its extensions have been successfully applied in medical image segmentation. However, because of the variability of tissues and anatomical structures, the clustering results might be biased by the tissue population and intensity differences. For example, clustering-based algorithms tend to over-segment white matter tissues of MR brain images. To solve this problem, we introduce a tissue probability map constrained clustering algorithm and apply it to serial MR brain image segmentation, i.e., a series of 3-D MR brain images of the same subject at different time points. Using the new serial image segmentation algorithm in the framework of the CLASSIC framework, which iteratively segments the images and estimates the longitudinal deformations, we improved both accuracy and robustness for serial image computing, and at the mean time produced longitudinally consistent segmentation and stable measures. In the algorithm, the tissue probability maps consist of both the population-based and subject-specific segmentation priors. Experimental study using both simulated longitudinal MR brain data and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data confirmed that using both priors more accurate and robust segmentation results can be obtained. The proposed algorithm can be applied in longitudinal follow up studies of MR brain imaging with subtle morphological changes for neurological disorders. PMID:26566399

Geometry correction Algorithm for UAV Remote Sensing Image Based on Improved Neural Network

NASA Astrophysics Data System (ADS)

Liu, Ruian; Liu, Nan; Zeng, Beibei; Chen, Tingting; Yin, Ninghao

2018-03-01

Aiming at the disadvantage of current geometry correction algorithm for UAV remote sensing image, a new algorithm is proposed. Adaptive genetic algorithm (AGA) and RBF neural network are introduced into this algorithm. And combined with the geometry correction principle for UAV remote sensing image, the algorithm and solving steps of AGA-RBF are presented in order to realize geometry correction for UAV remote sensing. The correction accuracy and operational efficiency is improved through optimizing the structure and connection weight of RBF neural network separately with AGA and LMS algorithm. Finally, experiments show that AGA-RBF algorithm has the advantages of high correction accuracy, high running rate and strong generalization ability.
Automatic Detection of Steganographic Content

DTIC Science & Technology

2005-06-30

Practically, it is mostly embedded into the media files, especially the image files. Consequently, a lot of the anti- steganography algorithms work with raw...1: not enough memory * -2: error running the removal algorithm EXPORT IMAGE *StegRemove( IMAGE * image , int *error); 2.8 Steganography Extraction API...researcher just invented a reliable algorithm that can detect the existence of a steganography if it is embedded anywhere in any uncompressed image . The
Shearlet Features for Registration of Remotely Sensed Multitemporal Images

NASA Technical Reports Server (NTRS)

Murphy, James M.; Le Moigne, Jacqueline

2015-01-01

We investigate the role of anisotropic feature extraction methods for automatic image registration of remotely sensed multitemporal images. Building on the classical use of wavelets in image registration, we develop an algorithm based on shearlets, a mathematical generalization of wavelets that offers increased directional sensitivity. Initial experimental results on LANDSAT images are presented, which indicate superior performance of the shearlet algorithm when compared to classical wavelet algorithms.
Fast algorithm of low power image reformation for OLED display

NASA Astrophysics Data System (ADS)

Lee, Myungwoo; Kim, Taewhan

2014-04-01

We propose a fast algorithm of low-power image reformation for organic light-emitting diode (OLED) display. The proposed algorithm scales the image histogram in a way to reduce power consumption in OLED display by remapping the gray levels of the pixels in the image based on the fast analysis of the histogram of the input image while maintaining contrast of the image. The key idea is that a large number of gray levels are never used in the images and these gray levels can be effectively exploited to reduce power consumption. On the other hand, to maintain the image contrast the gray level remapping is performed by taking into account the object size in the image to which each gray level is applied, that is, reforming little for the gray levels in the objects of large size. Through experiments with 24 Kodak images, it is shown that our proposed algorithm is able to reduce the power consumption by 10% even with 9% contrast enhancement. Our algorithm runs in a linear time so that it can be applied to moving pictures with high resolution.
Centroids evaluation of the images obtained with the conical null-screen corneal topographer

NASA Astrophysics Data System (ADS)

Osorio-Infante, Arturo I.; Armengol-Cruz, Victor de Emanuel; Campos-García, Manuel; Cossio-Guerrero, Cesar; Marquez-Flores, Jorge; Díaz-Uribe, José Rufino

2016-09-01

In this work, we propose some algorithms to recover the centroids of the resultant image obtained by a conical nullscreen based corneal topographer. With these algorithms, we obtain the region of interest (roi) of the original image and using an image-processing algorithm, we calculate the geometric centroid of each roi. In order to improve our algorithm performance, we use different settings of null-screen targets, changing their size and number. We also improved the illumination system to avoid inhomogeneous zones in the corneal images. Finally, we report some corneal topographic measurements with the best setting we found.
A sparsity-based iterative algorithm for reconstruction of micro-CT images from highly undersampled projection datasets obtained with a synchrotron X-ray source

NASA Astrophysics Data System (ADS)

Melli, S. Ali; Wahid, Khan A.; Babyn, Paul; Cooper, David M. L.; Gopi, Varun P.

2016-12-01

Synchrotron X-ray Micro Computed Tomography (Micro-CT) is an imaging technique which is increasingly used for non-invasive in vivo preclinical imaging. However, it often requires a large number of projections from many different angles to reconstruct high-quality images leading to significantly high radiation doses and long scan times. To utilize this imaging technique further for in vivo imaging, we need to design reconstruction algorithms that reduce the radiation dose and scan time without reduction of reconstructed image quality. This research is focused on using a combination of gradient-based Douglas-Rachford splitting and discrete wavelet packet shrinkage image denoising methods to design an algorithm for reconstruction of large-scale reduced-view synchrotron Micro-CT images with acceptable quality metrics. These quality metrics are computed by comparing the reconstructed images with a high-dose reference image reconstructed from 1800 equally spaced projections spanning 180°. Visual and quantitative-based performance assessment of a synthetic head phantom and a femoral cortical bone sample imaged in the biomedical imaging and therapy bending magnet beamline at the Canadian Light Source demonstrates that the proposed algorithm is superior to the existing reconstruction algorithms. Using the proposed reconstruction algorithm to reduce the number of projections in synchrotron Micro-CT is an effective way to reduce the overall radiation dose and scan time which improves in vivo imaging protocols.
Image recognition of clipped stigma traces in rice seeds

NASA Astrophysics Data System (ADS)

Cheng, F.; Ying, YB

2005-11-01

The objective of this research is to develop algorithm to recognize clipped stigma traces in rice seeds using image processing. At first, the micro-configuration of clipped stigma traces was observed with electronic scanning microscope. Then images of rice seeds were acquired with a color machine vision system. A digital image-processing algorithm based on morphological operations and Hough transform was developed to inspect the occurrence of clipped stigma traces. Five varieties of Jinyou402, Shanyou10, Zhongyou207, Jiayou and you3207 were evaluated. The algorithm was implemented with all image sets using a Matlab 6.5 procedure. The results showed that the algorithm achieved an average accuracy of 96%. The algorithm was proved to be insensitive to the different rice seed varieties.
Improving Brain Magnetic Resonance Image (MRI) Segmentation via a Novel Algorithm based on Genetic and Regional Growth

PubMed Central

A., Javadpour; A., Mohammadi

2016-01-01

Background Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging. Objective This study describes a new method for brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regional growth. Methods Among medical imaging methods, brains MRI segmentation is important due to high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer’s disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is used to introduce a new method for MRI segmentation. Primary pixels and similarity criterion are automatically by genetic algorithms to maximize the accuracy and validity in image segmentation. Results By using genetic algorithms and defining the fixed function of image segmentation, the initial points for the algorithm were found. The proposed algorithms are applied to the images and results are manually selected by regional growth in which the initial points were compared. The results showed that the proposed algorithm could reduce segmentation error effectively. Conclusion The study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases. PMID:27672629
Effectiveness of a Staged US and Unenhanced MR Imaging Algorithm in the Diagnosis of Pediatric Appendicitis.

PubMed

Dibble, Elizabeth H; Swenson, David W; Cartagena, Claudia; Baird, Grayson L; Herliczek, Thaddeus W

2018-03-01

Purpose To establish, in a large cohort, the diagnostic performance of a staged algorithm involving ultrasonography (US) followed by conditional unenhanced magnetic resonance (MR) imaging for the imaging work-up of pediatric appendicitis. Materials and Methods A staged imaging algorithm in which US and unenhanced MR imaging were performed in pediatric patients suspected of having appendicitis was implemented at the authors' institution on January 1, 2011, with US as the initial modality followed by unenhanced MR imaging when US findings were equivocal. A search of the radiology database revealed 2180 pediatric patients who had undergone imaging for suspected appendicitis from January 1, 2011, through December 31, 2012. Of the 2180 patients, 1982 (90.9%) were evaluated according to the algorithm. The authors reviewed the electronic medical records and imaging reports for all patients. Imaging reports were reviewed and classified as positive, negative, or equivocal for appendicitis and correlated with surgical and pathology reports. Results The frequency of appendicitis was 20.5% (407 of 1982 patients). US alone was performed in 1905 of the 1982 patients (96.1%), yielding a sensitivity of 98.7% (386 of 391 patients) and specificity of 97.1% (1470 of 1514 patients) for appendicitis. Seventy-seven patients underwent unenhanced MR imaging after equivocal US findings, yielding an overall algorithm sensitivity of 98.2% (400 of 407 patients) and specificity of 97.1% (1530 of 1575 patients). Seven of the 1982 patients (0.4%) had false-negative results with the staged algorithm. The negative predictive value of the staged algorithm was 99.5% (1530 of 1537 patients). Conclusion A staged algorithm of US and unenhanced MR imaging for pediatric appendicitis appears to be effective. The results of this study demonstrate that this staged algorithm is 98.2% sensitive and 97.1% specific for the diagnosis of appendicitis in pediatric patients. © RSNA, 2017.
Magnetic resonance image restoration via dictionary learning under spatially adaptive constraints.

PubMed

Wang, Shanshan; Xia, Yong; Dong, Pei; Feng, David Dagan; Luo, Jianhua; Huang, Qiu

2013-01-01

This paper proposes a spatially adaptive constrained dictionary learning (SAC-DL) algorithm for Rician noise removal in magnitude magnetic resonance (MR) images. This algorithm explores both the strength of dictionary learning to preserve image structures and the robustness of local variance estimation to remove signal-dependent Rician noise. The magnitude image is first separated into a number of partly overlapping image patches. The statistics of each patch are collected and analyzed to obtain a local noise variance. To better adapt to Rician noise, a correction factor is formulated with the local signal-to-noise ratio (SNR). Finally, the trained dictionary is used to denoise each image patch under spatially adaptive constraints. The proposed algorithm has been compared to the popular nonlocal means (NLM) filtering and unbiased NLM (UNLM) algorithm on simulated T1-weighted, T2-weighted and PD-weighted MR images. Our results suggest that the SAC-DL algorithm preserves more image structures while effectively removing the noise than NLM and it is also superior to UNLM at low noise levels.
An improved contrast enhancement algorithm for infrared images based on adaptive double plateaus histogram equalization

NASA Astrophysics Data System (ADS)

Li, Shuo; Jin, Weiqi; Li, Li; Li, Yiyang

2018-05-01

Infrared thermal images can reflect the thermal-radiation distribution of a particular scene. However, the contrast of the infrared images is usually low. Hence, it is generally necessary to enhance the contrast of infrared images in advance to facilitate subsequent recognition and analysis. Based on the adaptive double plateaus histogram equalization, this paper presents an improved contrast enhancement algorithm for infrared thermal images. In the proposed algorithm, the normalized coefficient of variation of the histogram, which characterizes the level of contrast enhancement, is introduced as feedback information to adjust the upper and lower plateau thresholds. The experiments on actual infrared images show that compared to the three typical contrast-enhancement algorithms, the proposed algorithm has better scene adaptability and yields better contrast-enhancement results for infrared images with more dark areas or a higher dynamic range. Hence, it has high application value in contrast enhancement, dynamic range compression, and digital detail enhancement for infrared thermal images.
Enhanced encrypted reversible data hiding algorithm with minimum distortion through homomorphic encryption

NASA Astrophysics Data System (ADS)

Bhardwaj, Rupali

2018-03-01

Reversible data hiding means embedding a secret message in a cover image in such a manner, to the point that in the midst of extraction of the secret message, the cover image and, furthermore, the secret message are recovered with no error. The goal of by far most of the reversible data hiding algorithms is to have improved the embedding rate and enhanced visual quality of stego image. An improved encrypted-domain-based reversible data hiding algorithm to embed two binary bits in each gray pixel of original cover image with minimum distortion of stego-pixels is employed in this paper. Highlights of the proposed algorithm are minimum distortion of pixel's value, elimination of underflow and overflow problem, and equivalence of stego image and cover image with a PSNR of ∞ (for Lena, Goldhill, and Barbara image). The experimental outcomes reveal that in terms of average PSNR and embedding rate, for natural images, the proposed algorithm performed better than other conventional ones.
A novel image enhancement algorithm based on stationary wavelet transform for infrared thermography to the de-bonding defect in solid rocket motors

NASA Astrophysics Data System (ADS)

Liu, Tao; Zhang, Wei; Yan, Shaoze

2015-10-01

In this paper, a multi-scale image enhancement algorithm based on low-passing filtering and nonlinear transformation is proposed for infrared testing image of the de-bonding defect in solid propellant rocket motors. Infrared testing images with high-level noise and low contrast are foundations for identifying defects and calculating the defects size. In order to improve quality of the infrared image, according to distribution properties of the detection image, within framework of stationary wavelet transform, the approximation coefficients at suitable decomposition level is processed by index low-passing filtering by using Fourier transform, after that, the nonlinear transformation is applied to further process the figure to improve the picture contrast. To verify validity of the algorithm, the image enhancement algorithm is applied to infrared testing pictures of two specimens with de-bonding defect. Therein, one specimen is made of a type of high-strength steel, and the other is a type of carbon fiber composite. As the result shown, in the images processed by the image enhancement algorithm presented in the paper, most of noises are eliminated, and contrast between defect areas and normal area is improved greatly; in addition, by using the binary picture of the processed figure, the continuous defect edges can be extracted, all of which show the validity of the algorithm. The paper provides a well-performing image enhancement algorithm for the infrared thermography.
Fast and robust wavelet-based dynamic range compression and contrast enhancement model with color restoration

NASA Astrophysics Data System (ADS)

Unaldi, Numan; Asari, Vijayan K.; Rahman, Zia-ur

2009-05-01

Recently we proposed a wavelet-based dynamic range compression algorithm to improve the visual quality of digital images captured from high dynamic range scenes with non-uniform lighting conditions. The fast image enhancement algorithm that provides dynamic range compression, while preserving the local contrast and tonal rendition, is also a good candidate for real time video processing applications. Although the colors of the enhanced images produced by the proposed algorithm are consistent with the colors of the original image, the proposed algorithm fails to produce color constant results for some "pathological" scenes that have very strong spectral characteristics in a single band. The linear color restoration process is the main reason for this drawback. Hence, a different approach is required for the final color restoration process. In this paper the latest version of the proposed algorithm, which deals with this issue is presented. The results obtained by applying the algorithm to numerous natural images show strong robustness and high image quality.
Low-Light Image Enhancement Using Adaptive Digital Pixel Binning

PubMed Central

Yoo, Yoonjong; Im, Jaehyun; Paik, Joonki

2015-01-01

This paper presents an image enhancement algorithm for low-light scenes in an environment with insufficient illumination. Simple amplification of intensity exhibits various undesired artifacts: noise amplification, intensity saturation, and loss of resolution. In order to enhance low-light images without undesired artifacts, a novel digital binning algorithm is proposed that considers brightness, context, noise level, and anti-saturation of a local region in the image. The proposed algorithm does not require any modification of the image sensor or additional frame-memory; it needs only two line-memories in the image signal processor (ISP). Since the proposed algorithm does not use an iterative computation, it can be easily embedded in an existing digital camera ISP pipeline containing a high-resolution image sensor. PMID:26121609
Cryptanalysis of "an improvement over an image encryption method based on total shuffling"

NASA Astrophysics Data System (ADS)

Akhavan, A.; Samsudin, A.; Akhshani, A.

2015-09-01

In the past two decades, several image encryption algorithms based on chaotic systems had been proposed. Many of the proposed algorithms are meant to improve other chaos based and conventional cryptographic algorithms. Whereas, many of the proposed improvement methods suffer from serious security problems. In this paper, the security of the recently proposed improvement method for a chaos-based image encryption algorithm is analyzed. The results indicate the weakness of the analyzed algorithm against chosen plain-text.
Development of adaptive noise reduction filter algorithm for pediatric body images in a multi-detector CT

NASA Astrophysics Data System (ADS)

Nishimaru, Eiji; Ichikawa, Katsuhiro; Okita, Izumi; Ninomiya, Yuuji; Tomoshige, Yukihiro; Kurokawa, Takehiro; Ono, Yutaka; Nakamura, Yuko; Suzuki, Masayuki

2008-03-01

Recently, several kinds of post-processing image filters which reduce the noise of computed tomography (CT) images have been proposed. However, these image filters are mostly for adults. Because these are not very effective in small (< 20 cm) display fields of view (FOV), we cannot use them for pediatric body images (e.g., premature babies and infant children). We have developed a new noise reduction filter algorithm for pediatric body CT images. This algorithm is based on a 3D post-processing in which the output pixel values are calculated by nonlinear interpolation in z-directions on original volumetric-data-sets. This algorithm does not need the in-plane (axial plane) processing, so the spatial resolution does not change. From the phantom studies, our algorithm could reduce SD up to 40% without affecting the spatial resolution of x-y plane and z-axis, and improved the CNR up to 30%. This newly developed filter algorithm will be useful for the diagnosis and radiation dose reduction of the pediatric body CT images.
Acceleration of the direct reconstruction of linear parametric images using nested algorithms.

PubMed

Wang, Guobao; Qi, Jinyi

2010-03-07

Parametric imaging using dynamic positron emission tomography (PET) provides important information for biological research and clinical diagnosis. Indirect and direct methods have been developed for reconstructing linear parametric images from dynamic PET data. Indirect methods are relatively simple and easy to implement because the image reconstruction and kinetic modeling are performed in two separate steps. Direct methods estimate parametric images directly from raw PET data and are statistically more efficient. However, the convergence rate of direct algorithms can be slow due to the coupling between the reconstruction and kinetic modeling. Here we present two fast gradient-type algorithms for direct reconstruction of linear parametric images. The new algorithms decouple the reconstruction and linear parametric modeling at each iteration by employing the principle of optimization transfer. Convergence speed is accelerated by running more sub-iterations of linear parametric estimation because the computation cost of the linear parametric modeling is much less than that of the image reconstruction. Computer simulation studies demonstrated that the new algorithms converge much faster than the traditional expectation maximization (EM) and the preconditioned conjugate gradient algorithms for dynamic PET.
An Automatic Image Processing System for Glaucoma Screening

PubMed Central

Alodhayb, Sami; Lakshminarayanan, Vasudevan

2017-01-01

Horizontal and vertical cup to disc ratios are the most crucial parameters used clinically to detect glaucoma or monitor its progress and are manually evaluated from retinal fundus images of the optic nerve head. Due to the rarity of the glaucoma experts as well as the increasing in glaucoma's population, an automatically calculated horizontal and vertical cup to disc ratios (HCDR and VCDR, resp.) can be useful for glaucoma screening. We report on two algorithms to calculate the HCDR and VCDR. In the algorithms, level set and inpainting techniques were developed for segmenting the disc, while thresholding using Type-II fuzzy approach was developed for segmenting the cup. The results from the algorithms were verified using the manual markings of images from a dataset of glaucomatous images (retinal fundus images for glaucoma analysis (RIGA dataset)) by six ophthalmologists. The algorithm's accuracy for HCDR and VCDR combined was 74.2%. Only the accuracy of manual markings by one ophthalmologist was higher than the algorithm's accuracy. The algorithm's best agreement was with markings by ophthalmologist number 1 in 230 images (41.8%) of the total tested images. PMID:28947898
Psychophysical Comparisons in Image Compression Algorithms.

DTIC Science & Technology

1999-03-01

Leister, M., "Lossy Lempel - Ziv Algorithm for Large Alphabet Sources and Applications to Image Compression ," IEEE Proceedings, v.I, pp. 225-228, September...1623-1642, September 1990. Sanford, M.A., An Analysis of Data Compression Algorithms used in the Transmission of Imagery, Master’s Thesis, Naval...NAVAL POSTGRADUATE SCHOOL Monterey, California THESIS PSYCHOPHYSICAL COMPARISONS IN IMAGE COMPRESSION ALGORITHMS by % Christopher J. Bodine • March

A Rotor Tip Vortex Tracing Algorithm for Image Post-Processing

NASA Technical Reports Server (NTRS)

Overmeyer, Austin D.

2015-01-01

A neurite tracing algorithm, originally developed for medical image processing, was used to trace the location of the rotor tip vortex in density gradient flow visualization images. The tracing algorithm was applied to several representative test images to form case studies. The accuracy of the tracing algorithm was compared to two current methods including a manual point and click method and a cross-correlation template method. It is shown that the neurite tracing algorithm can reduce the post-processing time to trace the vortex by a factor of 10 to 15 without compromising the accuracy of the tip vortex location compared to other methods presented in literature.
Image restoration by minimizing zero norm of wavelet frame coefficients

NASA Astrophysics Data System (ADS)

Bao, Chenglong; Dong, Bin; Hou, Likun; Shen, Zuowei; Zhang, Xiaoqun; Zhang, Xue

2016-11-01

In this paper, we propose two algorithms, namely the extrapolated proximal iterative hard thresholding (EPIHT) algorithm and the EPIHT algorithm with line-search, for solving the {{\\ell }}0-norm regularized wavelet frame balanced approach for image restoration. Under the theoretical framework of Kurdyka-Łojasiewicz property, we show that the sequences generated by the two algorithms converge to a local minimizer with linear convergence rate. Moreover, extensive numerical experiments on sparse signal reconstruction and wavelet frame based image restoration problems including CT reconstruction, image deblur, demonstrate the improvement of {{\\ell }}0-norm based regularization models over some prevailing ones, as well as the computational efficiency of the proposed algorithms.
A difference tracking algorithm based on discrete sine transform

NASA Astrophysics Data System (ADS)

Liu, HaoPeng; Yao, Yong; Lei, HeBing; Wu, HaoKun

2018-04-01

Target tracking is an important field of computer vision. The template matching tracking algorithm based on squared difference matching (SSD) and standard correlation coefficient (NCC) matching is very sensitive to the gray change of image. When the brightness or gray change, the tracking algorithm will be affected by high-frequency information. Tracking accuracy is reduced, resulting in loss of tracking target. In this paper, a differential tracking algorithm based on discrete sine transform is proposed to reduce the influence of image gray or brightness change. The algorithm that combines the discrete sine transform and the difference algorithm maps the target image into a image digital sequence. The Kalman filter predicts the target position. Using the Hamming distance determines the degree of similarity between the target and the template. The window closest to the template is determined the target to be tracked. The target to be tracked updates the template. Based on the above achieve target tracking. The algorithm is tested in this paper. Compared with SSD and NCC template matching algorithms, the algorithm tracks target stably when image gray or brightness change. And the tracking speed can meet the read-time requirement.
An improved dehazing algorithm of aerial high-definition image

NASA Astrophysics Data System (ADS)

Jiang, Wentao; Ji, Ming; Huang, Xiying; Wang, Chao; Yang, Yizhou; Li, Tao; Wang, Jiaoying; Zhang, Ying

2016-01-01

For unmanned aerial vehicle(UAV) images, the sensor can not get high quality images due to fog and haze weather. To solve this problem, An improved dehazing algorithm of aerial high-definition image is proposed. Based on the model of dark channel prior, the new algorithm firstly extracts the edges from crude estimated transmission map and expands the extracted edges. Then according to the expended edges, the algorithm sets a threshold value to divide the crude estimated transmission map into different areas and makes different guided filter on the different areas compute the optimized transmission map. The experimental results demonstrate that the performance of the proposed algorithm is substantially the same as the one based on dark channel prior and guided filter. The average computation time of the new algorithm is around 40% of the one as well as the detection ability of UAV image is improved effectively in fog and haze weather.
Perception for rugged terrain

NASA Technical Reports Server (NTRS)

Kweon, In SO; Hebert, Martial; Kanade, Takeo

1989-01-01

A three-dimensional perception system for building a geometrical description of rugged terrain environments from range image data is presented with reference to the exploration of the rugged terrain of Mars. An intermediate representation consisting of an elevation map that includes an explicit representation of uncertainty and labeling of the occluded regions is proposed. The locus method used to convert range image to an elevation map is introduced, along with an uncertainty model based on this algorithm. Both the elevation map and the locus method are the basis of a terrain matching algorithm which does not assume any correspondences between range images. The two-stage algorithm consists of a feature-based matching algorithm to compute an initial transform and an iconic terrain matching algorithm to merge multiple range images into a uniform representation. Terrain modeling results on real range images of rugged terrain are presented. The algorithms considered are a fundamental part of the perception system for the Ambler, a legged locomotor.
Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images

PubMed Central

Zhao, Haiying; Liu, Yong; Xie, Xiaojia; Liao, Yiyi; Liu, Xixi

2016-01-01

Visual odometry (VO) estimation from blurred image is a challenging problem in practical robot applications, and the blurred images will severely reduce the estimation accuracy of the VO. In this paper, we address the problem of visual odometry estimation from blurred images, and present an adaptive visual odometry estimation framework robust to blurred images. Our approach employs an objective measure of images, named small image gradient distribution (SIGD), to evaluate the blurring degree of the image, then an adaptive blurred image classification algorithm is proposed to recognize the blurred images, finally we propose an anti-blurred key-frame selection algorithm to enable the VO robust to blurred images. We also carried out varied comparable experiments to evaluate the performance of the VO algorithms with our anti-blur framework under varied blurred images, and the experimental results show that our approach can achieve superior performance comparing to the state-of-the-art methods under the condition with blurred images while not increasing too much computation cost to the original VO algorithms. PMID:27399704
Double regions growing algorithm for automated satellite image mosaicking

NASA Astrophysics Data System (ADS)

Tan, Yihua; Chen, Chen; Tian, Jinwen

2011-12-01

Feathering is a most widely used method in seamless satellite image mosaicking. A simple but effective algorithm - double regions growing (DRG) algorithm, which utilizes the shape content of images' valid regions, is proposed for generating robust feathering-line before feathering. It works without any human intervention, and experiment on real satellite images shows the advantages of the proposed method.
A New Pivoting and Iterative Text Detection Algorithm for Biomedical Images

PubMed Central

Xu, Songhua; Krauthammer, Michael

2010-01-01

There is interest to expand the reach of literature mining to include the analysis of biomedical images, which often contain a paper’s key findings. Examples include recent studies that use Optical Character Recognition (OCR) to extract image text, which is used to boost biomedical image retrieval and classification. Such studies rely on the robust identification of text elements in biomedical images, which is a non-trivial task. In this work, we introduce a new text detection algorithm for biomedical images based on iterative projection histograms. We study the effectiveness of our algorithm by evaluating the performance on a set of manually labeled random biomedical images, and compare the performance against other state-of-the-art text detection algorithms. In this paper, we demonstrate that a projection histogram-based text detection approach is well suited for text detection in biomedical images, with a performance of F score of .60. The approach performs better than comparable approaches for text detection. Further, we show that the iterative application of the algorithm is boosting overall detection performance. A C++ implementation of our algorithm is freely available through email request for academic use. PMID:20887803
The development of a 3D mesoscopic model of metallic foam based on an improved watershed algorithm

NASA Astrophysics Data System (ADS)

Zhang, Jinhua; Zhang, Yadong; Wang, Guikun; Fang, Qin

2018-06-01

The watershed algorithm has been used widely in the x-ray computed tomography (XCT) image segmentation. It provides a transformation defined on a grayscale image and finds the lines that separate adjacent images. However, distortion occurs in developing a mesoscopic model of metallic foam based on XCT image data. The cells are oversegmented at some events when the traditional watershed algorithm is used. The improved watershed algorithm presented in this paper can avoid oversegmentation and is composed of three steps. Firstly, it finds all of the connected cells and identifies the junctions of the corresponding cell walls. Secondly, the image segmentation is conducted to separate the adjacent cells. It generates the lost cell walls between the adjacent cells. Optimization is then performed on the segmentation image. Thirdly, this improved algorithm is validated when it is compared with the image of the metallic foam, which shows that it can avoid the image segmentation distortion. A mesoscopic model of metallic foam is thus formed based on the improved algorithm, and the mesoscopic characteristics of the metallic foam, such as cell size, volume and shape, are identified and analyzed.
Advanced synthetic image generation models and their application to multi/hyperspectral algorithm development

NASA Astrophysics Data System (ADS)

Schott, John R.; Brown, Scott D.; Raqueno, Rolando V.; Gross, Harry N.; Robinson, Gary

1999-01-01

The need for robust image data sets for algorithm development and testing has prompted the consideration of synthetic imagery as a supplement to real imagery. The unique ability of synthetic image generation (SIG) tools to supply per-pixel truth allows algorithm writers to test difficult scenarios that would require expensive collection and instrumentation efforts. In addition, SIG data products can supply the user with `actual' truth measurements of the entire image area that are not subject to measurement error thereby allowing the user to more accurately evaluate the performance of their algorithm. Advanced algorithms place a high demand on synthetic imagery to reproduce both the spectro-radiometric and spatial character observed in real imagery. This paper describes a synthetic image generation model that strives to include the radiometric processes that affect spectral image formation and capture. In particular, it addresses recent advances in SIG modeling that attempt to capture the spatial/spectral correlation inherent in real images. The model is capable of simultaneously generating imagery from a wide range of sensors allowing it to generate daylight, low-light-level and thermal image inputs for broadband, multi- and hyper-spectral exploitation algorithms.
Evaluation of security algorithms used for security processing on DICOM images

NASA Astrophysics Data System (ADS)

Chen, Xiaomeng; Shuai, Jie; Zhang, Jianguo; Huang, H. K.

2005-04-01

In this paper, we developed security approach to provide security measures and features in PACS image acquisition and Tele-radiology image transmission. The security processing on medical images was based on public key infrastructure (PKI) and including digital signature and data encryption to achieve the security features of confidentiality, privacy, authenticity, integrity, and non-repudiation. There are many algorithms which can be used in PKI for data encryption and digital signature. In this research, we select several algorithms to perform security processing on different DICOM images in PACS environment, evaluate the security processing performance of these algorithms, and find the relationship between performance with image types, sizes and the implementation methods.
Algorithmic support for graphic images rotation in avionics

NASA Astrophysics Data System (ADS)

Kniga, E. V.; Gurjanov, A. V.; Shukalov, A. V.; Zharinov, I. O.

2018-05-01

The avionics device designing has an actual problem of development and research algorithms to rotate the images which are being shown in the on-board display. The image rotation algorithms are a part of program software of avionics devices, which are parts of the on-board computers of the airplanes and helicopters. Images to be rotated have the flight location map fragments. The image rotation in the display system can be done as a part of software or mechanically. The program option is worse than the mechanic one in its rotation speed. The comparison of some test images of rotation several algorithms is shown which are being realized mechanically with the program environment Altera QuartusII.
Evaluating an image-fusion algorithm with synthetic-image-generation tools

NASA Astrophysics Data System (ADS)

Gross, Harry N.; Schott, John R.

1996-06-01

An algorithm that combines spectral mixing and nonlinear optimization is used to fuse multiresolution images. Image fusion merges images of different spatial and spectral resolutions to create a high spatial resolution multispectral combination. High spectral resolution allows identification of materials in the scene, while high spatial resolution locates those materials. In this algorithm, conventional spectral mixing estimates the percentage of each material (called endmembers) within each low resolution pixel. Three spectral mixing models are compared; unconstrained, partially constrained, and fully constrained. In the partially constrained application, the endmember fractions are required to sum to one. In the fully constrained application, all fractions are additionally required to lie between zero and one. While negative fractions seem inappropriate, they can arise from random spectral realizations of the materials. In the second part of the algorithm, the low resolution fractions are used as inputs to a constrained nonlinear optimization that calculates the endmember fractions for the high resolution pixels. The constraints mirror the low resolution constraints and maintain consistency with the low resolution fraction results. The algorithm can use one or more higher resolution sharpening images to locate the endmembers to high spatial accuracy. The algorithm was evaluated with synthetic image generation (SIG) tools. A SIG developed image can be used to control the various error sources that are likely to impair the algorithm performance. These error sources include atmospheric effects, mismodeled spectral endmembers, and variability in topography and illumination. By controlling the introduction of these errors, the robustness of the algorithm can be studied and improved upon. The motivation for this research is to take advantage of the next generation of multi/hyperspectral sensors. Although the hyperspectral images will be of modest to low resolution, fusing them with high resolution sharpening images will produce a higher spatial resolution land cover or material map.
Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle-Pock algorithm

PubMed Central

Sidky, Emil Y.; Jørgensen, Jakob H.; Pan, Xiaochuan

2012-01-01

The primal-dual optimization algorithm developed in Chambolle and Pock (CP), 2011 is applied to various convex optimization problems of interest in computed tomography (CT) image reconstruction. This algorithm allows for rapid prototyping of optimization problems for the purpose of designing iterative image reconstruction algorithms for CT. The primal-dual algorithm is briefly summarized in the article, and its potential for prototyping is demonstrated by explicitly deriving CP algorithm instances for many optimization problems relevant to CT. An example application modeling breast CT with low-intensity X-ray illumination is presented. PMID:22538474
Algorithm for Automatic Segmentation of Nuclear Boundaries in Cancer Cells in Three-Channel Luminescent Images

NASA Astrophysics Data System (ADS)

Lisitsa, Y. V.; Yatskou, M. M.; Apanasovich, V. V.; Apanasovich, T. V.

2015-09-01

We have developed an algorithm for segmentation of cancer cell nuclei in three-channel luminescent images of microbiological specimens. The algorithm is based on using a correlation between fluorescence signals in the detection channels for object segmentation, which permits complete automation of the data analysis procedure. We have carried out a comparative analysis of the proposed method and conventional algorithms implemented in the CellProfiler and ImageJ software packages. Our algorithm has an object localization uncertainty which is 2-3 times smaller than for the conventional algorithms, with comparable segmentation accuracy.
Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform

NASA Astrophysics Data System (ADS)

Gong, Lihua; Deng, Chengzhi; Pan, Shumin; Zhou, Nanrun

2018-07-01

Based on hyper-chaotic system and discrete fractional random transform, an image compression-encryption algorithm is designed. The original image is first transformed into a spectrum by the discrete cosine transform and the resulting spectrum is compressed according to the method of spectrum cutting. The random matrix of the discrete fractional random transform is controlled by a chaotic sequence originated from the high dimensional hyper-chaotic system. Then the compressed spectrum is encrypted by the discrete fractional random transform. The order of DFrRT and the parameters of the hyper-chaotic system are the main keys of this image compression and encryption algorithm. The proposed algorithm can compress and encrypt image signal, especially can encrypt multiple images once. To achieve the compression of multiple images, the images are transformed into spectra by the discrete cosine transform, and then the spectra are incised and spliced into a composite spectrum by Zigzag scanning. Simulation results demonstrate that the proposed image compression and encryption algorithm is of high security and good compression performance.
Large-scale image region documentation for fully automated image biomarker algorithm development and evaluation.

PubMed

Reeves, Anthony P; Xie, Yiting; Liu, Shuang

2017-04-01

With the advent of fully automated image analysis and modern machine learning methods, there is a need for very large image datasets having documented segmentations for both computer algorithm training and evaluation. This paper presents a method and implementation for facilitating such datasets that addresses the critical issue of size scaling for algorithm validation and evaluation; current evaluation methods that are usually used in academic studies do not scale to large datasets. This method includes protocols for the documentation of many regions in very large image datasets; the documentation may be incrementally updated by new image data and by improved algorithm outcomes. This method has been used for 5 years in the context of chest health biomarkers from low-dose chest CT images that are now being used with increasing frequency in lung cancer screening practice. The lung scans are segmented into over 100 different anatomical regions, and the method has been applied to a dataset of over 20,000 chest CT images. Using this framework, the computer algorithms have been developed to achieve over 90% acceptable image segmentation on the complete dataset.
Motion-adaptive spatio-temporal regularization for accelerated dynamic MRI.

PubMed

Asif, M Salman; Hamilton, Lei; Brummer, Marijn; Romberg, Justin

2013-09-01

Accelerated magnetic resonance imaging techniques reduce signal acquisition time by undersampling k-space. A fundamental problem in accelerated magnetic resonance imaging is the recovery of quality images from undersampled k-space data. Current state-of-the-art recovery algorithms exploit the spatial and temporal structures in underlying images to improve the reconstruction quality. In recent years, compressed sensing theory has helped formulate mathematical principles and conditions that ensure recovery of (structured) sparse signals from undersampled, incoherent measurements. In this article, a new recovery algorithm, motion-adaptive spatio-temporal regularization, is presented that uses spatial and temporal structured sparsity of MR images in the compressed sensing framework to recover dynamic MR images from highly undersampled k-space data. In contrast to existing algorithms, our proposed algorithm models temporal sparsity using motion-adaptive linear transformations between neighboring images. The efficiency of motion-adaptive spatio-temporal regularization is demonstrated with experiments on cardiac magnetic resonance imaging for a range of reduction factors. Results are also compared with k-t FOCUSS with motion estimation and compensation-another recently proposed recovery algorithm for dynamic magnetic resonance imaging. . Copyright © 2012 Wiley Periodicals, Inc.
Possibility Study of Scale Invariant Feature Transform (SIFT) Algorithm Application to Spine Magnetic Resonance Imaging

PubMed Central

Lee, Dong-Hoon; Lee, Do-Wan; Han, Bong-Soo

2016-01-01

The purpose of this study is an application of scale invariant feature transform (SIFT) algorithm to stitch the cervical-thoracic-lumbar (C-T-L) spine magnetic resonance (MR) images to provide a view of the entire spine in a single image. All MR images were acquired with fast spin echo (FSE) pulse sequence using two MR scanners (1.5 T and 3.0 T). The stitching procedures for each part of spine MR image were performed and implemented on a graphic user interface (GUI) configuration. Moreover, the stitching process is performed in two categories; manual point-to-point (mPTP) selection that performed by user specified corresponding matching points, and automated point-to-point (aPTP) selection that performed by SIFT algorithm. The stitched images using SIFT algorithm showed fine registered results and quantitatively acquired values also indicated little errors compared with commercially mounted stitching algorithm in MRI systems. Our study presented a preliminary validation of the SIFT algorithm application to MRI spine images, and the results indicated that the proposed approach can be performed well for the improvement of diagnosis. We believe that our approach can be helpful for the clinical application and extension of other medical imaging modalities for image stitching. PMID:27064404
Optimization-based image reconstruction from sparse-view data in offset-detector CBCT

NASA Astrophysics Data System (ADS)

Bian, Junguo; Wang, Jiong; Han, Xiao; Sidky, Emil Y.; Shao, Lingxiong; Pan, Xiaochuan

2013-01-01

The field of view (FOV) of a cone-beam computed tomography (CBCT) unit in a single-photon emission computed tomography (SPECT)/CBCT system can be increased by offsetting the CBCT detector. Analytic-based algorithms have been developed for image reconstruction from data collected at a large number of densely sampled views in offset-detector CBCT. However, the radiation dose involved in a large number of projections can be of a health concern to the imaged subject. CBCT-imaging dose can be reduced by lowering the number of projections. As analytic-based algorithms are unlikely to reconstruct accurate images from sparse-view data, we investigate and characterize in the work optimization-based algorithms, including an adaptive steepest descent-weighted projection onto convex sets (ASD-WPOCS) algorithms, for image reconstruction from sparse-view data collected in offset-detector CBCT. Using simulated data and real data collected from a physical pelvis phantom and patient, we verify and characterize properties of the algorithms under study. Results of our study suggest that optimization-based algorithms such as ASD-WPOCS may be developed for yielding images of potential utility from a number of projections substantially smaller than those used currently in clinical SPECT/CBCT imaging, thus leading to a dose reduction in CBCT imaging.

Novel approach for image skeleton and distance transformation parallel algorithms

NASA Astrophysics Data System (ADS)

Qing, Kent P.; Means, Robert W.

1994-05-01

Image Understanding is more important in medical imaging than ever, particularly where real-time automatic inspection, screening and classification systems are installed. Skeleton and distance transformations are among the common operations that extract useful information from binary images and aid in Image Understanding. The distance transformation describes the objects in an image by labeling every pixel in each object with the distance to its nearest boundary. The skeleton algorithm starts from the distance transformation and finds the set of pixels that have a locally maximum label. The distance algorithm has to scan the entire image several times depending on the object width. For each pixel, the algorithm must access the neighboring pixels and find the maximum distance from the nearest boundary. It is a computational and memory access intensive procedure. In this paper, we propose a novel parallel approach to the distance transform and skeleton algorithms using the latest VLSI high- speed convolutional chips such as HNC's ViP. The algorithm speed is dependent on the object's width and takes (k + [(k-1)/3]) * 7 milliseconds for a 512 X 512 image with k being the maximum distance of the largest object. All objects in the image will be skeletonized at the same time in parallel.
Comparison of two algorithms in the automatic segmentation of blood vessels in fundus images

NASA Astrophysics Data System (ADS)

LeAnder, Robert; Chowdary, Myneni Sushma; Mokkapati, Swapnasri; Umbaugh, Scott E.

2008-03-01

Effective timing and treatment are critical to saving the sight of patients with diabetes. Lack of screening, as well as a shortage of ophthalmologists, help contribute to approximately 8,000 cases per year of people who lose their sight to diabetic retinopathy, the leading cause of new cases of blindness [1] [2]. Timely treatment for diabetic retinopathy prevents severe vision loss in over 50% of eyes tested [1]. Fundus images can provide information for detecting and monitoring eye-related diseases, like diabetic retinopathy, which if detected early, may help prevent vision loss. Damaged blood vessels can indicate the presence of diabetic retinopathy [9]. So, early detection of damaged vessels in retinal images can provide valuable information about the presence of disease, thereby helping to prevent vision loss. Purpose: The purpose of this study was to compare the effectiveness of two blood vessel segmentation algorithms. Methods: Fifteen fundus images from the STARE database were used to develop two algorithms using the CVIPtools software environment. Another set of fifteen images were derived from the first fifteen and contained ophthalmologists' hand-drawn tracings over the retinal vessels. The ophthalmologists' tracings were used as the "gold standard" for perfect segmentation and compared with the segmented images that were output by the two algorithms. Comparisons between the segmented and the hand-drawn images were made using Pratt's Figure of Merit (FOM), Signal-to-Noise Ratio (SNR) and Root Mean Square (RMS) Error. Results: Algorithm 2 has an FOM that is 10% higher than Algorithm 1. Algorithm 2 has a 6%-higher SNR than Algorithm 1. Algorithm 2 has only 1.3% more RMS error than Algorithm 1. Conclusions: Algorithm 1 extracted most of the blood vessels with some missing intersections and bifurcations. Algorithm 2 extracted all the major blood vessels, but eradicated some vessels as well. Algorithm 2 outperformed Algorithm 1 in terms of visual clarity, FOM and SNR. The performances of these algorithms show that they have an appreciable amount of potential in helping ophthalmologists detect the severity of eye-related diseases and prevent vision loss.
Blind restoration method of three-dimensional microscope image based on RL algorithm

NASA Astrophysics Data System (ADS)

Yao, Jin-li; Tian, Si; Wang, Xiang-rong; Wang, Jing-li

2013-08-01

Thin specimens of biological tissue appear three dimensional transparent under a microscope. The optic slice images can be captured by moving the focal planes at the different locations of the specimen. The captured image has low resolution due to the influence of the out-of-focus information comes from the planes adjacent to the local plane. Using traditional methods can remove the blur in the images at a certain degree, but it needs to know the point spread function (PSF) of the imaging system accurately. The accuracy degree of PSF influences the restoration result greatly. In fact, it is difficult to obtain the accurate PSF of the imaging system. In order to restore the original appearance of the specimen under the conditions of the imaging system parameters are unknown or there is noise and spherical aberration in the system, a blind restoration methods of three-dimensional microscope based on the R-L algorithm is proposed in this paper. On the basis of the exhaustive study of the two-dimension R-L algorithm, according to the theory of the microscopy imaging and the wavelet transform denoising pretreatment, we expand the R-L algorithm to three-dimension space. It is a nonlinear restoration method with the maximum entropy constraint. The method doesn't need to know the PSF of the microscopy imaging system precisely to recover the blur image. The image and PSF converge to the optimum solutions by many alterative iterations and corrections. The matlab simulation and experiments results show that the expansion algorithm is better in visual indicators, peak signal to noise ratio and improved signal to noise ratio when compared with the PML algorithm, and the proposed algorithm can suppress noise, restore more details of target, increase image resolution.
A Novel Image Compression Algorithm for High Resolution 3D Reconstruction

NASA Astrophysics Data System (ADS)

Siddeq, M. M.; Rodrigues, M. A.

2014-06-01

This research presents a novel algorithm to compress high-resolution images for accurate structured light 3D reconstruction. Structured light images contain a pattern of light and shadows projected on the surface of the object, which are captured by the sensor at very high resolutions. Our algorithm is concerned with compressing such images to a high degree with minimum loss without adversely affecting 3D reconstruction. The Compression Algorithm starts with a single level discrete wavelet transform (DWT) for decomposing an image into four sub-bands. The sub-band LL is transformed by DCT yielding a DC-matrix and an AC-matrix. The Minimize-Matrix-Size Algorithm is used to compress the AC-matrix while a DWT is applied again to the DC-matrix resulting in LL2, HL2, LH2 and HH2 sub-bands. The LL2 sub-band is transformed by DCT, while the Minimize-Matrix-Size Algorithm is applied to the other sub-bands. The proposed algorithm has been tested with images of different sizes within a 3D reconstruction scenario. The algorithm is demonstrated to be more effective than JPEG2000 and JPEG concerning higher compression rates with equivalent perceived quality and the ability to more accurately reconstruct the 3D models.
Mapped Landmark Algorithm for Precision Landing

NASA Technical Reports Server (NTRS)

Johnson, Andrew; Ansar, Adnan; Matthies, Larry

2007-01-01

A report discusses a computer vision algorithm for position estimation to enable precision landing during planetary descent. The Descent Image Motion Estimation System for the Mars Exploration Rovers has been used as a starting point for creating code for precision, terrain-relative navigation during planetary landing. The algorithm is designed to be general because it handles images taken at different scales and resolutions relative to the map, and can produce mapped landmark matches for any planetary terrain of sufficient texture. These matches provide a measurement of horizontal position relative to a known landing site specified on the surface map. Multiple mapped landmarks generated per image allow for automatic detection and elimination of bad matches. Attitude and position can be generated from each image; this image-based attitude measurement can be used by the onboard navigation filter to improve the attitude estimate, which will improve the position estimates. The algorithm uses normalized correlation of grayscale images, producing precise, sub-pixel images. The algorithm has been broken into two sub-algorithms: (1) FFT Map Matching (see figure), which matches a single large template by correlation in the frequency domain, and (2) Mapped Landmark Refinement, which matches many small templates by correlation in the spatial domain. Each relies on feature selection, the homography transform, and 3D image correlation. The algorithm is implemented in C++ and is rated at Technology Readiness Level (TRL) 4.
Automatic target detection using binary template matching

NASA Astrophysics Data System (ADS)

Jun, Dong-San; Sun, Sun-Gu; Park, HyunWook

2005-03-01

This paper presents a new automatic target detection (ATD) algorithm to detect targets such as battle tanks and armored personal carriers in ground-to-ground scenarios. Whereas most ATD algorithms were developed for forward-looking infrared (FLIR) images, we have developed an ATD algorithm for charge-coupled device (CCD) images, which have superior quality to FLIR images in daylight. The proposed algorithm uses fast binary template matching with an adaptive binarization, which is robust to various light conditions in CCD images and saves computation time. Experimental results show that the proposed method has good detection performance.
Multi scales based sparse matrix spectral clustering image segmentation

NASA Astrophysics Data System (ADS)

Liu, Zhongmin; Chen, Zhicai; Li, Zhanming; Hu, Wenjin

2018-04-01

In image segmentation, spectral clustering algorithms have to adopt the appropriate scaling parameter to calculate the similarity matrix between the pixels, which may have a great impact on the clustering result. Moreover, when the number of data instance is large, computational complexity and memory use of the algorithm will greatly increase. To solve these two problems, we proposed a new spectral clustering image segmentation algorithm based on multi scales and sparse matrix. We devised a new feature extraction method at first, then extracted the features of image on different scales, at last, using the feature information to construct sparse similarity matrix which can improve the operation efficiency. Compared with traditional spectral clustering algorithm, image segmentation experimental results show our algorithm have better degree of accuracy and robustness.
[Improvement of magnetic resonance phase unwrapping method based on Goldstein Branch-cut algorithm].

PubMed

Guo, Lin; Kang, Lili; Wang, Dandan

2013-02-01

The phase information of magnetic resonance (MR) phase image can be used in many MR imaging techniques, but phase wrapping of the images often results in inaccurate phase information and phase unwrapping is essential for MR imaging techniques. In this paper we analyze the causes of errors in phase unwrapping with the commonly used Goldstein Brunch-cut algorithm and propose an improved algorithm. During the unwrapping process, masking, filtering, dipole- remover preprocessor, and the Prim algorithm of the minimum spanning tree were introduced to optimize the residues essential for the Goldstein Brunch-cut algorithm. Experimental results showed that the residues, branch-cuts and continuous unwrapped phase surface were efficiently reduced and the quality of MR phase images was obviously improved with the proposed method.
Lesion Detection in CT Images Using Deep Learning Semantic Segmentation Technique

NASA Astrophysics Data System (ADS)

Kalinovsky, A.; Liauchuk, V.; Tarasau, A.

2017-05-01

In this paper, the problem of automatic detection of tuberculosis lesion on 3D lung CT images is considered as a benchmark for testing out algorithms based on a modern concept of Deep Learning. For training and testing of the algorithms a domestic dataset of 338 3D CT scans of tuberculosis patients with manually labelled lesions was used. The algorithms which are based on using Deep Convolutional Networks were implemented and applied in three different ways including slice-wise lesion detection in 2D images using semantic segmentation, slice-wise lesion detection in 2D images using sliding window technique as well as straightforward detection of lesions via semantic segmentation in whole 3D CT scans. The algorithms demonstrate superior performance compared to algorithms based on conventional image analysis methods.
Integrating image quality in 2nu-SVM biometric match score fusion.

PubMed

Vatsa, Mayank; Singh, Richa; Noore, Afzel

2007-10-01

This paper proposes an intelligent 2nu-support vector machine based match score fusion algorithm to improve the performance of face and iris recognition by integrating the quality of images. The proposed algorithm applies redundant discrete wavelet transform to evaluate the underlying linear and non-linear features present in the image. A composite quality score is computed to determine the extent of smoothness, sharpness, noise, and other pertinent features present in each subband of the image. The match score and the corresponding quality score of an image are fused using 2nu-support vector machine to improve the verification performance. The proposed algorithm is experimentally validated using the FERET face database and the CASIA iris database. The verification performance and statistical evaluation show that the proposed algorithm outperforms existing fusion algorithms.
New segmentation-based tone mapping algorithm for high dynamic range image

NASA Astrophysics Data System (ADS)

Duan, Weiwei; Guo, Huinan; Zhou, Zuofeng; Huang, Huimin; Cao, Jianzhong

2017-07-01

The traditional tone mapping algorithm for the display of high dynamic range (HDR) image has the drawback of losing the impression of brightness, contrast and color information. To overcome this phenomenon, we propose a new tone mapping algorithm based on dividing the image into different exposure regions in this paper. Firstly, the over-exposure region is determined using the Local Binary Pattern information of HDR image. Then, based on the peak and average gray of the histogram, the under-exposure and normal-exposure region of HDR image are selected separately. Finally, the different exposure regions are mapped by differentiated tone mapping methods to get the final result. The experiment results show that the proposed algorithm achieve the better performance both in visual quality and objective contrast criterion than other algorithms.
Motion Estimation Using the Firefly Algorithm in Ultrasonic Image Sequence of Soft Tissue

PubMed Central

Chao, Chih-Feng; Horng, Ming-Huwi; Chen, Yu-Chan

2015-01-01

Ultrasonic image sequence of the soft tissue is widely used in disease diagnosis; however, the speckle noises usually influenced the image quality. These images usually have a low signal-to-noise ratio presentation. The phenomenon gives rise to traditional motion estimation algorithms that are not suitable to measure the motion vectors. In this paper, a new motion estimation algorithm is developed for assessing the velocity field of soft tissue in a sequence of ultrasonic B-mode images. The proposed iterative firefly algorithm (IFA) searches for few candidate points to obtain the optimal motion vector, and then compares it to the traditional iterative full search algorithm (IFSA) via a series of experiments of in vivo ultrasonic image sequences. The experimental results show that the IFA can assess the vector with better efficiency and almost equal estimation quality compared to the traditional IFSA method. PMID:25873987
Motion estimation using the firefly algorithm in ultrasonic image sequence of soft tissue.

PubMed

Chao, Chih-Feng; Horng, Ming-Huwi; Chen, Yu-Chan

2015-01-01

Ultrasonic image sequence of the soft tissue is widely used in disease diagnosis; however, the speckle noises usually influenced the image quality. These images usually have a low signal-to-noise ratio presentation. The phenomenon gives rise to traditional motion estimation algorithms that are not suitable to measure the motion vectors. In this paper, a new motion estimation algorithm is developed for assessing the velocity field of soft tissue in a sequence of ultrasonic B-mode images. The proposed iterative firefly algorithm (IFA) searches for few candidate points to obtain the optimal motion vector, and then compares it to the traditional iterative full search algorithm (IFSA) via a series of experiments of in vivo ultrasonic image sequences. The experimental results show that the IFA can assess the vector with better efficiency and almost equal estimation quality compared to the traditional IFSA method.
Research on Bayes matting algorithm based on Gaussian mixture model

NASA Astrophysics Data System (ADS)

Quan, Wei; Jiang, Shan; Han, Cheng; Zhang, Chao; Jiang, Zhengang

2015-12-01

The digital matting problem is a classical problem of imaging. It aims at separating non-rectangular foreground objects from a background image, and compositing with a new background image. Accurate matting determines the quality of the compositing image. A Bayesian matting Algorithm Based on Gaussian Mixture Model is proposed to solve this matting problem. Firstly, the traditional Bayesian framework is improved by introducing Gaussian mixture model. Then, a weighting factor is added in order to suppress the noises of the compositing images. Finally, the effect is further improved by regulating the user's input. This algorithm is applied to matting jobs of classical images. The results are compared to the traditional Bayesian method. It is shown that our algorithm has better performance in detail such as hair. Our algorithm eliminates the noise well. And it is very effectively in dealing with the kind of work, such as interested objects with intricate boundaries.
Self-calibration of a noisy multiple-sensor system with genetic algorithms

NASA Astrophysics Data System (ADS)

Brooks, Richard R.; Iyengar, S. Sitharama; Chen, Jianhua

1996-01-01

This paper explores an image processing application of optimization techniques which entails interpreting noisy sensor data. The application is a generalization of image correlation; we attempt to find the optimal gruence which matches two overlapping gray-scale images corrupted with noise. Both taboo search and genetic algorithms are used to find the parameters which match the two images. A genetic algorithm approach using an elitist reproduction scheme is found to provide significantly superior results. The presentation includes a graphic presentation of the paths taken by tabu search and genetic algorithms when trying to find the best possible match between two corrupted images.
Fast image matching algorithm based on projection characteristics

NASA Astrophysics Data System (ADS)

Zhou, Lijuan; Yue, Xiaobo; Zhou, Lijun

2011-06-01

Based on analyzing the traditional template matching algorithm, this paper identified the key factors restricting the speed of matching and put forward a brand new fast matching algorithm based on projection. Projecting the grayscale image, this algorithm converts the two-dimensional information of the image into one-dimensional one, and then matches and identifies through one-dimensional correlation, meanwhile, because of normalization has been done, when the image brightness or signal amplitude increasing in proportion, it could also perform correct matching. Experimental results show that the projection characteristics based image registration method proposed in this article could greatly improve the matching speed, which ensuring the matching accuracy as well.
Research on improved edge extraction algorithm of rectangular piece

NASA Astrophysics Data System (ADS)

He, Yi-Bin; Zeng, Ya-Jun; Chen, Han-Xin; Xiao, San-Xia; Wang, Yan-Wei; Huang, Si-Yu

Traditional edge detection operators such as Prewitt operator, LOG operator and Canny operator, etc. cannot meet the requirements of the modern industrial measurement. This paper proposes a kind of image edge detection algorithm based on improved morphological gradient. It can be detect the image using structural elements, which deals with the characteristic information of the image directly. Choosing different shapes and sizes of structural elements to use together, the ideal image edge information can be detected. The experimental result shows that the algorithm can well extract image edge with noise, which is clearer, and has more detailed edges compared with the previous edge detection algorithm.
Small convolution kernels for high-fidelity image restoration

NASA Technical Reports Server (NTRS)

Reichenbach, Stephen E.; Park, Stephen K.

1991-01-01

An algorithm is developed for computing the mean-square-optimal values for small, image-restoration kernels. The algorithm is based on a comprehensive, end-to-end imaging system model that accounts for the important components of the imaging process: the statistics of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and display reconstruction. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernel values that restore the image with maximum fidelity, that is, the kernel minimizes the expected mean-square restoration error. The algorithm is consistent with the derivation of the spatially unconstrained Wiener filter, but leads to a small, spatially constrained kernel that, unlike the unconstrained filter, can be efficiently implemented by convolution. Simulation experiments demonstrate that for a wide range of imaging systems these small kernels can restore images with fidelity comparable to images restored with the unconstrained Wiener filter.
A Fusion Algorithm for GFP Image and Phase Contrast Image of Arabidopsis Cell Based on SFL-Contourlet Transform

PubMed Central

Feng, Peng; Wang, Jing; Wei, Biao; Mi, Deling

2013-01-01

A hybrid multiscale and multilevel image fusion algorithm for green fluorescent protein (GFP) image and phase contrast image of Arabidopsis cell is proposed in this paper. Combining intensity-hue-saturation (IHS) transform and sharp frequency localization Contourlet transform (SFL-CT), this algorithm uses different fusion strategies for different detailed subbands, which include neighborhood consistency measurement (NCM) that can adaptively find balance between color background and gray structure. Also two kinds of neighborhood classes based on empirical model are taken into consideration. Visual information fidelity (VIF) as an objective criterion is introduced to evaluate the fusion image. The experimental results of 117 groups of Arabidopsis cell image from John Innes Center show that the new algorithm cannot only make the details of original images well preserved but also improve the visibility of the fusion image, which shows the superiority of the novel method to traditional ones. PMID:23476716
The fast iris image clarity evaluation based on Tenengrad and ROI selection

NASA Astrophysics Data System (ADS)

Gao, Shuqin; Han, Min; Cheng, Xu

2018-04-01

In iris recognition system, the clarity of iris image is an important factor that influences recognition effect. In the process of recognition, the blurred image may possibly be rejected by the automatic iris recognition system, which will lead to the failure of identification. Therefore it is necessary to evaluate the iris image definition before recognition. Considered the existing evaluation methods on iris image definition, we proposed a fast algorithm to evaluate the definition of iris image in this paper. In our algorithm, firstly ROI (Region of Interest) is extracted based on the reference point which is determined by using the feature of the light spots within the pupil, then Tenengrad operator is used to evaluate the iris image's definition. Experiment results show that, the iris image definition algorithm proposed in this paper could accurately distinguish the iris images of different clarity, and the algorithm has the merit of low computational complexity and more effectiveness.

Phase retrieval using regularization method in intensity correlation imaging

NASA Astrophysics Data System (ADS)

Li, Xiyu; Gao, Xin; Tang, Jia; Lu, Changming; Wang, Jianli; Wang, Bin

2014-11-01

Intensity correlation imaging(ICI) method can obtain high resolution image with ground-based low precision mirrors, in the imaging process, phase retrieval algorithm should be used to reconstituted the object's image. But the algorithm now used(such as hybrid input-output algorithm) is sensitive to noise and easy to stagnate. However the signal-to-noise ratio of intensity interferometry is low especially in imaging astronomical objects. In this paper, we build the mathematical model of phase retrieval and simplified it into a constrained optimization problem of a multi-dimensional function. New error function was designed by noise distribution and prior information using regularization method. The simulation results show that the regularization method can improve the performance of phase retrieval algorithm and get better image especially in low SNR condition
A MAP blind image deconvolution algorithm with bandwidth over-constrained

NASA Astrophysics Data System (ADS)

Ren, Zhilei; Liu, Jin; Liang, Yonghui; He, Yulong

2018-03-01

We demonstrate a maximum a posteriori (MAP) blind image deconvolution algorithm with bandwidth over-constrained and total variation (TV) regularization to recover a clear image from the AO corrected images. The point spread functions (PSFs) are estimated by bandwidth limited less than the cutoff frequency of the optical system. Our algorithm performs well in avoiding noise magnification. The performance is demonstrated on simulated data.
Robust generative asymmetric GMM for brain MR image segmentation.

PubMed

Ji, Zexuan; Xia, Yong; Zheng, Yuhui

2017-11-01

Accurate segmentation of brain tissues from magnetic resonance (MR) images based on the unsupervised statistical models such as Gaussian mixture model (GMM) has been widely studied during last decades. However, most GMM based segmentation methods suffer from limited accuracy due to the influences of noise and intensity inhomogeneity in brain MR images. To further improve the accuracy for brain MR image segmentation, this paper presents a Robust Generative Asymmetric GMM (RGAGMM) for simultaneous brain MR image segmentation and intensity inhomogeneity correction. First, we develop an asymmetric distribution to fit the data shapes, and thus construct a spatial constrained asymmetric model. Then, we incorporate two pseudo-likelihood quantities and bias field estimation into the model's log-likelihood, aiming to exploit the neighboring priors of within-cluster and between-cluster and to alleviate the impact of intensity inhomogeneity, respectively. Finally, an expectation maximization algorithm is derived to iteratively maximize the approximation of the data log-likelihood function to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously. To demonstrate the performances of the proposed algorithm, we first applied the proposed algorithm to a synthetic brain MR image to show the intermediate illustrations and the estimated distribution of the proposed algorithm. The next group of experiments is carried out in clinical 3T-weighted brain MR images which contain quite serious intensity inhomogeneity and noise. Then we quantitatively compare our algorithm to state-of-the-art segmentation approaches by using Dice coefficient (DC) on benchmark images obtained from IBSR and BrainWeb with different level of noise and intensity inhomogeneity. The comparison results on various brain MR images demonstrate the superior performances of the proposed algorithm in dealing with the noise and intensity inhomogeneity. In this paper, the RGAGMM algorithm is proposed which can simply and efficiently incorporate spatial constraints into an EM framework to simultaneously segment brain MR images and estimate the intensity inhomogeneity. The proposed algorithm is flexible to fit the data shapes, and can simultaneously overcome the influence of noise and intensity inhomogeneity, and hence is capable of improving over 5% segmentation accuracy comparing with several state-of-the-art algorithms. Copyright © 2017 Elsevier B.V. All rights reserved.
Real-time image dehazing using local adaptive neighborhoods and dark-channel-prior

NASA Astrophysics Data System (ADS)

Valderrama, Jesus A.; Díaz-Ramírez, Víctor H.; Kober, Vitaly; Hernandez, Enrique

2015-09-01

A real-time algorithm for single image dehazing is presented. The algorithm is based on calculation of local neighborhoods of a hazed image inside a moving window. The local neighborhoods are constructed by computing rank-order statistics. Next the dark-channel-prior approach is applied to the local neighborhoods to estimate the transmission function of the scene. By using the suggested approach there is no need for applying a refining algorithm to the estimated transmission such as the soft matting algorithm. To achieve high-rate signal processing the proposed algorithm is implemented exploiting massive parallelism on a graphics processing unit (GPU). Computer simulation results are carried out to test the performance of the proposed algorithm in terms of dehazing efficiency and speed of processing. These tests are performed using several synthetic and real images. The obtained results are analyzed and compared with those obtained with existing dehazing algorithms.
Time-frequency analysis-based time-windowing algorithm for the inverse synthetic aperture radar imaging of ships

NASA Astrophysics Data System (ADS)

Zhou, Peng; Zhang, Xi; Sun, Weifeng; Dai, Yongshou; Wan, Yong

2018-01-01

An algorithm based on time-frequency analysis is proposed to select an imaging time window for the inverse synthetic aperture radar imaging of ships. An appropriate range bin is selected to perform the time-frequency analysis after radial motion compensation. The selected range bin is that with the maximum mean amplitude among the range bins whose echoes are confirmed to be contributed by a dominant scatter. The criterion for judging whether the echoes of a range bin are contributed by a dominant scatter is key to the proposed algorithm and is therefore described in detail. When the first range bin that satisfies the judgment criterion is found, a sequence composed of the frequencies that have the largest amplitudes in every moment's time-frequency spectrum corresponding to this range bin is employed to calculate the length and the center moment of the optimal imaging time window. Experiments performed with simulation data and real data show the effectiveness of the proposed algorithm, and comparisons between the proposed algorithm and the image contrast-based algorithm (ICBA) are provided. Similar image contrast and lower entropy are acquired using the proposed algorithm as compared with those values when using the ICBA.
Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination

PubMed Central

Jeon, Hong Y.; Tian, Lei F.; Zhu, Heping

2011-01-01

An image processing algorithm for detecting individual weeds was developed and evaluated. Weed detection processes included were normalized excessive green conversion, statistical threshold value estimation, adaptive image segmentation, median filter, morphological feature calculation and Artificial Neural Network (ANN). The developed algorithm was validated for its ability to identify and detect weeds and crop plants under uncontrolled outdoor illuminations. A machine vision implementing field robot captured field images under outdoor illuminations and the image processing algorithm automatically processed them without manual adjustment. The errors of the algorithm, when processing 666 field images, ranged from 2.1 to 2.9%. The ANN correctly detected 72.6% of crop plants from the identified plants, and considered the rest as weeds. However, the ANN identification rates for crop plants were improved up to 95.1% by addressing the error sources in the algorithm. The developed weed detection and image processing algorithm provides a novel method to identify plants against soil background under the uncontrolled outdoor illuminations, and to differentiate weeds from crop plants. Thus, the proposed new machine vision and processing algorithm may be useful for outdoor applications including plant specific direct applications (PSDA). PMID:22163954
Image denoising via fundamental anisotropic diffusion and wavelet shrinkage: a comparative study

NASA Astrophysics Data System (ADS)

Bayraktar, Bulent; Analoui, Mostafa

2004-05-01

Noise removal faces a challenge: Keeping the image details. Resolving the dilemma of two purposes (smoothing and keeping image features in tact) working inadvertently of each other was an almost impossible task until anisotropic dif-fusion (AD) was formally introduced by Perona and Malik (PM). AD favors intra-region smoothing over inter-region in piecewise smooth images. Many authors regularized the original PM algorithm to overcome its drawbacks. We compared the performance of denoising using such 'fundamental' AD algorithms and one of the most powerful multiresolution tools available today, namely, wavelet shrinkage. The AD algorithms here are called 'fundamental' in the sense that the regularized versions center around the original PM algorithm with minor changes to the logic. The algorithms are tested with different noise types and levels. On top of the visual inspection, two mathematical metrics are used for performance comparison: Signal-to-noise ratio (SNR) and universal image quality index (UIQI). We conclude that some of the regu-larized versions of PM algorithm (AD) perform comparably with wavelet shrinkage denoising. This saves a lot of compu-tational power. With this conclusion, we applied the better-performing fundamental AD algorithms to a new imaging modality: Optical Coherence Tomography (OCT).
Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing.

PubMed

Vatsa, Mayank; Singh, Richa; Noore, Afzel

2008-08-01

This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition. A curve evolution approach is proposed to effectively segment a nonideal iris image using the modified Mumford-Shah functional. Different enhancement algorithms are concurrently applied on the segmented iris image to produce multiple enhanced versions of the iris image. A support-vector-machine-based learning algorithm selects locally enhanced regions from each globally enhanced image and combines these good-quality regions to create a single high-quality iris image. Two distinct features are extracted from the high-quality iris image. The global textural feature is extracted using the 1-D log polar Gabor transform, and the local topological feature is extracted using Euler numbers. An intelligent fusion algorithm combines the textural and topological matching scores to further improve the iris recognition performance and reduce the false rejection rate, whereas an indexing algorithm enables fast and accurate iris identification. The verification and identification performance of the proposed algorithms is validated and compared with other algorithms using the CASIA Version 3, ICE 2005, and UBIRIS iris databases.
Clinical Applications of a CT Window Blending Algorithm: RADIO (Relative Attenuation-Dependent Image Overlay).

PubMed

Mandell, Jacob C; Khurana, Bharti; Folio, Les R; Hyun, Hyewon; Smith, Stacy E; Dunne, Ruth M; Andriole, Katherine P

2017-06-01

A methodology is described using Adobe Photoshop and Adobe Extendscript to process DICOM images with a Relative Attenuation-Dependent Image Overlay (RADIO) algorithm to visualize the full dynamic range of CT in one view, without requiring a change in window and level settings. The potential clinical uses for such an algorithm are described in a pictorial overview, including applications in emergency radiology, oncologic imaging, and nuclear medicine and molecular imaging.
Autonomous subpixel satellite track end point determination for space-based images.

PubMed

Simms, Lance M

2011-08-01

An algorithm for determining satellite track end points with subpixel resolution in spaced-based images is presented. The algorithm allows for significant curvature in the imaged track due to rotation of the spacecraft capturing the image. The motivation behind the subpixel end point determination is first presented, followed by a description of the methodology used. Results from running the algorithm on real ground-based and simulated spaced-based images are shown to highlight its effectiveness.
Fast processing of microscopic images using object-based extended depth of field.

PubMed

Intarapanich, Apichart; Kaewkamnerd, Saowaluck; Pannarut, Montri; Shaw, Philip J; Tongsima, Sissades

2016-12-22

Microscopic analysis requires that foreground objects of interest, e.g. cells, are in focus. In a typical microscopic specimen, the foreground objects may lie on different depths of field necessitating capture of multiple images taken at different focal planes. The extended depth of field (EDoF) technique is a computational method for merging images from different depths of field into a composite image with all foreground objects in focus. Composite images generated by EDoF can be applied in automated image processing and pattern recognition systems. However, current algorithms for EDoF are computationally intensive and impractical, especially for applications such as medical diagnosis where rapid sample turnaround is important. Since foreground objects typically constitute a minor part of an image, the EDoF technique could be made to work much faster if only foreground regions are processed to make the composite image. We propose a novel algorithm called object-based extended depths of field (OEDoF) to address this issue. The OEDoF algorithm consists of four major modules: 1) color conversion, 2) object region identification, 3) good contrast pixel identification and 4) detail merging. First, the algorithm employs color conversion to enhance contrast followed by identification of foreground pixels. A composite image is constructed using only these foreground pixels, which dramatically reduces the computational time. We used 250 images obtained from 45 specimens of confirmed malaria infections to test our proposed algorithm. The resulting composite images with all in-focus objects were produced using the proposed OEDoF algorithm. We measured the performance of OEDoF in terms of image clarity (quality) and processing time. The features of interest selected by the OEDoF algorithm are comparable in quality with equivalent regions in images processed by the state-of-the-art complex wavelet EDoF algorithm; however, OEDoF required four times less processing time. This work presents a modification of the extended depth of field approach for efficiently enhancing microscopic images. This selective object processing scheme used in OEDoF can significantly reduce the overall processing time while maintaining the clarity of important image features. The empirical results from parasite-infected red cell images revealed that our proposed method efficiently and effectively produced in-focus composite images. With the speed improvement of OEDoF, this proposed algorithm is suitable for processing large numbers of microscope images, e.g., as required for medical diagnosis.
A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation.

PubMed

Wang, Rui; Zhou, Yongquan; Zhao, Chengyan; Wu, Haizhou

2015-01-01

Multi-threshold image segmentation is a powerful image processing technique that is used for the preprocessing of pattern recognition and computer vision. However, traditional multilevel thresholding methods are computationally expensive because they involve exhaustively searching the optimal thresholds to optimize the objective functions. To overcome this drawback, this paper proposes a flower pollination algorithm with a randomized location modification. The proposed algorithm is used to find optimal threshold values for maximizing Otsu's objective functions with regard to eight medical grayscale images. When benchmarked against other state-of-the-art evolutionary algorithms, the new algorithm proves itself to be robust and effective through numerical experimental results including Otsu's objective values and standard deviations.
Effects of illumination on image reconstruction via Fourier ptychography

NASA Astrophysics Data System (ADS)

Cao, Xinrui; Sinzinger, Stefan

2017-12-01

The Fourier ptychographic microscopy (FPM) technique provides high-resolution images by combining a traditional imaging system, e.g. a microscope or a 4f-imaging system, with a multiplexing illumination system, e.g. an LED array and numerical image processing for enhanced image reconstruction. In order to numerically combine images that are captured under varying illumination angles, an iterative phase-retrieval algorithm is often applied. However, in practice, the performance of the FPM algorithm degrades due to the imperfections of the optical system, the image noise caused by the camera, etc. To eliminate the influence of the aberrations of the imaging system, an embedded pupil function recovery (EPRY)-FPM algorithm has been proposed [Opt. Express 22, 4960-4972 (2014)]. In this paper, we study how the performance of FPM and EPRY-FPM algorithms are affected by imperfections of the illumination system using both numerical simulations and experiments. The investigated imperfections include varying and non-uniform intensities, and wavefront aberrations. Our study shows that the aberrations of the illumination system significantly affect the performance of both FPM and EPRY-FPM algorithms. Hence, in practice, aberrations in the illumination system gain significant influence on the resulting image quality.
Information theoretic analysis of linear shift-invariant edge-detection operators

NASA Astrophysics Data System (ADS)

Jiang, Bo; Rahman, Zia-ur

2012-06-01

Generally, the designs of digital image processing algorithms and image gathering devices remain separate. Consequently, the performance of digital image processing algorithms is evaluated without taking into account the influences by the image gathering process. However, experiments show that the image gathering process has a profound impact on the performance of digital image processing and the quality of the resulting images. Huck et al. proposed one definitive theoretic analysis of visual communication channels, where the different parts, such as image gathering, processing, and display, are assessed in an integrated manner using Shannon's information theory. We perform an end-to-end information theory based system analysis to assess linear shift-invariant edge-detection algorithms. We evaluate the performance of the different algorithms as a function of the characteristics of the scene and the parameters, such as sampling, additive noise etc., that define the image gathering system. The edge-detection algorithm is regarded as having high performance only if the information rate from the scene to the edge image approaches its maximum possible. This goal can be achieved only by jointly optimizing all processes. Our information-theoretic assessment provides a new tool that allows us to compare different linear shift-invariant edge detectors in a common environment.
Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm.

PubMed

Yang, Mengzhao; Song, Wei; Mei, Haibin

2017-07-23

The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient.
Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm

PubMed Central

Song, Wei; Mei, Haibin

2017-01-01

The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient. PMID:28737699
Content-aware dark image enhancement through channel division.

PubMed

Rivera, Adin Ramirez; Ryu, Byungyong; Chae, Oksam

2012-09-01

The current contrast enhancement algorithms occasionally result in artifacts, overenhancement, and unnatural effects in the processed images. These drawbacks increase for images taken under poor illumination conditions. In this paper, we propose a content-aware algorithm that enhances dark images, sharpens edges, reveals details in textured regions, and preserves the smoothness of flat regions. The algorithm produces an ad hoc transformation for each image, adapting the mapping functions to each image's characteristics to produce the maximum enhancement. We analyze the contrast of the image in the boundary and textured regions, and group the information with common characteristics. These groups model the relations within the image, from which we extract the transformation functions. The results are then adaptively mixed, by considering the human vision system characteristics, to boost the details in the image. Results show that the algorithm can automatically process a wide range of images-e.g., mixed shadow and bright areas, outdoor and indoor lighting, and face images-without introducing artifacts, which is an improvement over many existing methods.
Comparison study of reconstruction algorithms for prototype digital breast tomosynthesis using various breast phantoms.

PubMed

Kim, Ye-seul; Park, Hye-suk; Lee, Haeng-Hwa; Choi, Young-Wook; Choi, Jae-Gu; Kim, Hak Hee; Kim, Hee-Joung

2016-02-01

Digital breast tomosynthesis (DBT) is a recently developed system for three-dimensional imaging that offers the potential to reduce the false positives of mammography by preventing tissue overlap. Many qualitative evaluations of digital breast tomosynthesis were previously performed by using a phantom with an unrealistic model and with heterogeneous background and noise, which is not representative of real breasts. The purpose of the present work was to compare reconstruction algorithms for DBT by using various breast phantoms; validation was also performed by using patient images. DBT was performed by using a prototype unit that was optimized for very low exposures and rapid readout. Three algorithms were compared: a back-projection (BP) algorithm, a filtered BP (FBP) algorithm, and an iterative expectation maximization (EM) algorithm. To compare the algorithms, three types of breast phantoms (homogeneous background phantom, heterogeneous background phantom, and anthropomorphic breast phantom) were evaluated, and clinical images were also reconstructed by using the different reconstruction algorithms. The in-plane image quality was evaluated based on the line profile and the contrast-to-noise ratio (CNR), and out-of-plane artifacts were evaluated by means of the artifact spread function (ASF). Parenchymal texture features of contrast and homogeneity were computed based on reconstructed images of an anthropomorphic breast phantom. The clinical images were studied to validate the effect of reconstruction algorithms. The results showed that the CNRs of masses reconstructed by using the EM algorithm were slightly higher than those obtained by using the BP algorithm, whereas the FBP algorithm yielded much lower CNR due to its high fluctuations of background noise. The FBP algorithm provides the best conspicuity for larger calcifications by enhancing their contrast and sharpness more than the other algorithms; however, in the case of small-size and low-contrast microcalcifications, the FBP reduced detectability due to its increased noise. The EM algorithm yielded high conspicuity for both microcalcifications and masses and yielded better ASFs in terms of the full width at half maximum. The higher contrast and lower homogeneity in terms of texture analysis were shown in FBP algorithm than in other algorithms. The patient images using the EM algorithm resulted in high visibility of low-contrast mass with clear border. In this study, we compared three reconstruction algorithms by using various kinds of breast phantoms and patient cases. Future work using these algorithms and considering the type of the breast and the acquisition techniques used (e.g., angular range, dose distribution) should include the use of actual patients or patient-like phantoms to increase the potential for practical applications.
Spectral compression algorithms for the analysis of very large multivariate images

DOEpatents

Keenan, Michael R.

2007-10-16

A method for spectrally compressing data sets enables the efficient analysis of very large multivariate images. The spectral compression algorithm uses a factored representation of the data that can be obtained from Principal Components Analysis or other factorization technique. Furthermore, a block algorithm can be used for performing common operations more efficiently. An image analysis can be performed on the factored representation of the data, using only the most significant factors. The spectral compression algorithm can be combined with a spatial compression algorithm to provide further computational efficiencies.
Algorithms for differentiating between images of heterogeneous tissue across fluorescence microscopes.

PubMed

Chitalia, Rhea; Mueller, Jenna; Fu, Henry L; Whitley, Melodi Javid; Kirsch, David G; Brown, J Quincy; Willett, Rebecca; Ramanujam, Nimmi

2016-09-01

Fluorescence microscopy can be used to acquire real-time images of tissue morphology and with appropriate algorithms can rapidly quantify features associated with disease. The objective of this study was to assess the ability of various segmentation algorithms to isolate fluorescent positive features (FPFs) in heterogeneous images and identify an approach that can be used across multiple fluorescence microscopes with minimal tuning between systems. Specifically, we show a variety of image segmentation algorithms applied to images of stained tumor and muscle tissue acquired with 3 different fluorescence microscopes. Results indicate that a technique called maximally stable extremal regions followed by thresholding (MSER + Binary) yielded the greatest contrast in FPF density between tumor and muscle images across multiple microscopy systems.

Contour Detection and Completion for Inpainting and Segmentation Based on Topological Gradient and Fast Marching Algorithms

PubMed Central

Auroux, Didier; Cohen, Laurent D.; Masmoudi, Mohamed

2011-01-01

We combine in this paper the topological gradient, which is a powerful method for edge detection in image processing, and a variant of the minimal path method in order to find connected contours. The topological gradient provides a more global analysis of the image than the standard gradient and identifies the main edges of an image. Several image processing problems (e.g., inpainting and segmentation) require continuous contours. For this purpose, we consider the fast marching algorithm in order to find minimal paths in the topological gradient image. This coupled algorithm quickly provides accurate and connected contours. We present then two numerical applications, to image inpainting and segmentation, of this hybrid algorithm. PMID:22194734
A Lossless hybrid wavelet-fractal compression for welding radiographic images.

PubMed

Mekhalfa, Faiza; Avanaki, Mohammad R N; Berkani, Daoud

2016-01-01

In this work a lossless wavelet-fractal image coder is proposed. The process starts by compressing and decompressing the original image using wavelet transformation and fractal coding algorithm. The decompressed image is removed from the original one to obtain a residual image which is coded by using Huffman algorithm. Simulation results show that with the proposed scheme, we achieve an infinite peak signal to noise ratio (PSNR) with higher compression ratio compared to typical lossless method. Moreover, the use of wavelet transform speeds up the fractal compression algorithm by reducing the size of the domain pool. The compression results of several welding radiographic images using the proposed scheme are evaluated quantitatively and compared with the results of Huffman coding algorithm.
A novel computer algorithm for modeling and treating mandibular fractures: A pilot study.

PubMed

Rizzi, Christopher J; Ortlip, Timothy; Greywoode, Jewel D; Vakharia, Kavita T; Vakharia, Kalpesh T

2017-02-01

To describe a novel computer algorithm that can model mandibular fracture repair. To evaluate the algorithm as a tool to model mandibular fracture reduction and hardware selection. Retrospective pilot study combined with cross-sectional survey. A computer algorithm utilizing Aquarius Net (TeraRecon, Inc, Foster City, CA) and Adobe Photoshop CS6 (Adobe Systems, Inc, San Jose, CA) was developed to model mandibular fracture repair. Ten different fracture patterns were selected from nine patients who had already undergone mandibular fracture repair. The preoperative computed tomography (CT) images were processed with the computer algorithm to create virtual images that matched the actual postoperative three-dimensional CT images. A survey comparing the true postoperative image with the virtual postoperative images was created and administered to otolaryngology resident and attending physicians. They were asked to rate on a scale from 0 to 10 (0 = completely different; 10 = identical) the similarity between the two images in terms of the fracture reduction and fixation hardware. Ten mandible fracture cases were analyzed and processed. There were 15 survey respondents. The mean score for overall similarity between the images was 8.41 ± 0.91; the mean score for similarity of fracture reduction was 8.61 ± 0.98; and the mean score for hardware appearance was 8.27 ± 0.97. There were no significant differences between attending and resident responses. There were no significant differences based on fracture location. This computer algorithm can accurately model mandibular fracture repair. Images created by the algorithm are highly similar to true postoperative images. The algorithm can potentially assist a surgeon planning mandibular fracture repair. 4. Laryngoscope, 2016 127:331-336, 2017. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.
Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights

PubMed Central

Akbari, Hamed; Bilello, Michel; Da, Xiao; Davatzikos, Christos

2015-01-01

Evaluating various algorithms for the inter-subject registration of brain magnetic resonance images (MRI) is a necessary topic receiving growing attention. Existing studies evaluated image registration algorithms in specific tasks or using specific databases (e.g., only for skull-stripped images, only for single-site images, etc.). Consequently, the choice of registration algorithms seems task- and usage/parameter-dependent. Nevertheless, recent large-scale, often multi-institutional imaging-related studies create the need and raise the question whether some registration algorithms can 1) generally apply to various tasks/databases posing various challenges; 2) perform consistently well, and while doing so, 3) require minimal or ideally no parameter tuning. In seeking answers to this question, we evaluated 12 general-purpose registration algorithms, for their generality, accuracy and robustness. We fixed their parameters at values suggested by algorithm developers as reported in the literature. We tested them in 7 databases/tasks, which present one or more of 4 commonly-encountered challenges: 1) inter-subject anatomical variability in skull-stripped images; 2) intensity homogeneity, noise and large structural differences in raw images; 3) imaging protocol and field-of-view (FOV) differences in multi-site data; and 4) missing correspondences in pathology-bearing images. Totally 7,562 registrations were performed. Registration accuracies were measured by (multi-)expert-annotated landmarks or regions of interest (ROIs). To ensure reproducibility, we used public software tools, public databases (whenever possible), and we fully disclose the parameter settings. We show evaluation results, and discuss the performances in light of algorithms’ similarity metrics, transformation models and optimization strategies. We also discuss future directions for the algorithm development and evaluations. PMID:24951685
Optimization, evaluation, and comparison of standard algorithms for image reconstruction with the VIP-PET.

PubMed

Mikhaylova, E; Kolstein, M; De Lorenzo, G; Chmeissani, M

2014-07-01

A novel positron emission tomography (PET) scanner design based on a room-temperature pixelated CdTe solid-state detector is being developed within the framework of the Voxel Imaging PET (VIP) Pathfinder project [1]. The simulation results show a great potential of the VIP to produce high-resolution images even in extremely challenging conditions such as the screening of a human head [2]. With unprecedented high channel density (450 channels/cm 3 ) image reconstruction is a challenge. Therefore optimization is needed to find the best algorithm in order to exploit correctly the promising detector potential. The following reconstruction algorithms are evaluated: 2-D Filtered Backprojection (FBP), Ordered Subset Expectation Maximization (OSEM), List-Mode OSEM (LM-OSEM), and the Origin Ensemble (OE) algorithm. The evaluation is based on the comparison of a true image phantom with a set of reconstructed images obtained by each algorithm. This is achieved by calculation of image quality merit parameters such as the bias, the variance and the mean square error (MSE). A systematic optimization of each algorithm is performed by varying the reconstruction parameters, such as the cutoff frequency of the noise filters and the number of iterations. The region of interest (ROI) analysis of the reconstructed phantom is also performed for each algorithm and the results are compared. Additionally, the performance of the image reconstruction methods is compared by calculating the modulation transfer function (MTF). The reconstruction time is also taken into account to choose the optimal algorithm. The analysis is based on GAMOS [3] simulation including the expected CdTe and electronic specifics.
Comparison of algorithms for automatic border detection of melanoma in dermoscopy images

NASA Astrophysics Data System (ADS)

Srinivasa Raghavan, Sowmya; Kaur, Ravneet; LeAnder, Robert

2016-09-01

Melanoma is one of the most rapidly accelerating cancers in the world [1]. Early diagnosis is critical to an effective cure. We propose a new algorithm for more accurately detecting melanoma borders in dermoscopy images. Proper border detection requires eliminating occlusions like hair and bubbles by processing the original image. The preprocessing step involves transforming the RGB image to the CIE L*u*v* color space, in order to decouple brightness from color information, then increasing contrast, using contrast-limited adaptive histogram equalization (CLAHE), followed by artifacts removal using a Gaussian filter. After preprocessing, the Chen-Vese technique segments the preprocessed images to create a lesion mask which undergoes a morphological closing operation. Next, the largest central blob in the lesion is detected, after which, the blob is dilated to generate an image output mask. Finally, the automatically-generated mask is compared to the manual mask by calculating the XOR error [3]. Our border detection algorithm was developed using training and test sets of 30 and 20 images, respectively. This detection method was compared to the SRM method [4] by calculating the average XOR error for each of the two algorithms. Average error for test images was 0.10, using the new algorithm, and 0.99, using SRM method. In comparing the average error values produced by the two algorithms, it is evident that the average XOR error for our technique is lower than the SRM method, thereby implying that the new algorithm detects borders of melanomas more accurately than the SRM algorithm.
A combination chaotic system and application in color image encryption

NASA Astrophysics Data System (ADS)

Parvaz, R.; Zarebnia, M.

2018-05-01

In this paper, by using Logistic, Sine and Tent systems we define a combination chaotic system. Some properties of the chaotic system are studied by using figures and numerical results. A color image encryption algorithm is introduced based on new chaotic system. Also this encryption algorithm can be used for gray scale or binary images. The experimental results of the encryption algorithm show that the encryption algorithm is secure and practical.
Experimental image alignment system

NASA Technical Reports Server (NTRS)

Moyer, A. L.; Kowel, S. T.; Kornreich, P. G.

1980-01-01

A microcomputer-based instrument for image alignment with respect to a reference image is described which uses the DEFT sensor (Direct Electronic Fourier Transform) for image sensing and preprocessing. The instrument alignment algorithm which uses the two-dimensional Fourier transform as input is also described. It generates signals used to steer the stage carrying the test image into the correct orientation. This algorithm has computational advantages over algorithms which use image intensity data as input and is suitable for a microcomputer-based instrument since the two-dimensional Fourier transform is provided by the DEFT sensor.
Depth-resolved analytical model and correction algorithm for photothermal optical coherence tomography

PubMed Central

Lapierre-Landry, Maryse; Tucker-Schwartz, Jason M.; Skala, Melissa C.

2016-01-01

Photothermal OCT (PT-OCT) is an emerging molecular imaging technique that occupies a spatial imaging regime between microscopy and whole body imaging. PT-OCT would benefit from a theoretical model to optimize imaging parameters and test image processing algorithms. We propose the first analytical PT-OCT model to replicate an experimental A-scan in homogeneous and layered samples. We also propose the PT-CLEAN algorithm to reduce phase-accumulation and shadowing, two artifacts found in PT-OCT images, and demonstrate it on phantoms and in vivo mouse tumors. PMID:27446693
Dictionary Pair Learning on Grassmann Manifolds for Image Denoising.

PubMed

Zeng, Xianhua; Bian, Wei; Liu, Wei; Shen, Jialie; Tao, Dacheng

2015-11-01

Image denoising is a fundamental problem in computer vision and image processing that holds considerable practical importance for real-world applications. The traditional patch-based and sparse coding-driven image denoising methods convert 2D image patches into 1D vectors for further processing. Thus, these methods inevitably break down the inherent 2D geometric structure of natural images. To overcome this limitation pertaining to the previous image denoising methods, we propose a 2D image denoising model, namely, the dictionary pair learning (DPL) model, and we design a corresponding algorithm called the DPL on the Grassmann-manifold (DPLG) algorithm. The DPLG algorithm first learns an initial dictionary pair (i.e., the left and right dictionaries) by employing a subspace partition technique on the Grassmann manifold, wherein the refined dictionary pair is obtained through a sub-dictionary pair merging. The DPLG obtains a sparse representation by encoding each image patch only with the selected sub-dictionary pair. The non-zero elements of the sparse representation are further smoothed by the graph Laplacian operator to remove the noise. Consequently, the DPLG algorithm not only preserves the inherent 2D geometric structure of natural images but also performs manifold smoothing in the 2D sparse coding space. We demonstrate that the DPLG algorithm also improves the structural SIMilarity values of the perceptual visual quality for denoised images using the experimental evaluations on the benchmark images and Berkeley segmentation data sets. Moreover, the DPLG also produces the competitive peak signal-to-noise ratio values from popular image denoising algorithms.
Localization of tumors in various organs, using edge detection algorithms

NASA Astrophysics Data System (ADS)

López Vélez, Felipe

2015-09-01

The edge of an image is a set of points organized in a curved line, where in each of these points the brightness of the image changes abruptly, or has discontinuities, in order to find these edges there will be five different mathematical methods to be used and later on compared with its peers, this is with the aim of finding which of the methods is the one that can find the edges of any given image. In this paper these five methods will be used for medical purposes in order to find which one is capable of finding the edges of a scanned image more accurately than the others. The problem consists in analyzing the following two biomedicals images. One of them represents a brain tumor and the other one a liver tumor. These images will be analyzed with the help of the five methods described and the results will be compared in order to determine the best method to be used. It was decided to use different algorithms of edge detection in order to obtain the results shown below; Bessel algorithm, Morse algorithm, Hermite algorithm, Weibull algorithm and Sobel algorithm. After analyzing the appliance of each of the methods to both images it's impossible to determine the most accurate method for tumor detection due to the fact that in each case the best method changed, i.e., for the brain tumor image it can be noticed that the Morse method was the best at finding the edges of the image but for the liver tumor image it was the Hermite method. Making further observations it is found that Hermite and Morse have for these two cases the lowest standard deviations, concluding that these two are the most accurate method to find the edges in analysis of biomedical images.
Application of Deconvolution Algorithm of Point Spread Function in Improving Image Quality: An Observer Preference Study on Chest Radiography.

PubMed

Chae, Kum Ju; Goo, Jin Mo; Ahn, Su Yeon; Yoo, Jin Young; Yoon, Soon Ho

2018-01-01

To evaluate the preference of observers for image quality of chest radiography using the deconvolution algorithm of point spread function (PSF) (TRUVIEW ART algorithm, DRTECH Corp.) compared with that of original chest radiography for visualization of anatomic regions of the chest. Prospectively enrolled 50 pairs of posteroanterior chest radiographs collected with standard protocol and with additional TRUVIEW ART algorithm were compared by four chest radiologists. This algorithm corrects scattered signals generated by a scintillator. Readers independently evaluated the visibility of 10 anatomical regions and overall image quality with a 5-point scale of preference. The significance of the differences in reader's preference was tested with a Wilcoxon's signed rank test. All four readers preferred the images applied with the algorithm to those without algorithm for all 10 anatomical regions (mean, 3.6; range, 3.2-4.0; p < 0.001) and for the overall image quality (mean, 3.8; range, 3.3-4.0; p < 0.001). The most preferred anatomical regions were the azygoesophageal recess, thoracic spine, and unobscured lung. The visibility of chest anatomical structures applied with the deconvolution algorithm of PSF was superior to the original chest radiography.
A Class of Manifold Regularized Multiplicative Update Algorithms for Image Clustering.

PubMed

Yang, Shangming; Yi, Zhang; He, Xiaofei; Li, Xuelong

2015-12-01

Multiplicative update algorithms are important tools for information retrieval, image processing, and pattern recognition. However, when the graph regularization is added to the cost function, different classes of sample data may be mapped to the same subspace, which leads to the increase of data clustering error rate. In this paper, an improved nonnegative matrix factorization (NMF) cost function is introduced. Based on the cost function, a class of novel graph regularized NMF algorithms is developed, which results in a class of extended multiplicative update algorithms with manifold structure regularization. Analysis shows that in the learning, the proposed algorithms can efficiently minimize the rank of the data representation matrix. Theoretical results presented in this paper are confirmed by simulations. For different initializations and data sets, variation curves of cost functions and decomposition data are presented to show the convergence features of the proposed update rules. Basis images, reconstructed images, and clustering results are utilized to present the efficiency of the new algorithms. Last, the clustering accuracies of different algorithms are also investigated, which shows that the proposed algorithms can achieve state-of-the-art performance in applications of image clustering.
Fitting-free algorithm for efficient quantification of collagen fiber alignment in SHG imaging applications.

PubMed

Hall, Gunnsteinn; Liang, Wenxuan; Li, Xingde

2017-10-01

Collagen fiber alignment derived from second harmonic generation (SHG) microscopy images can be important for disease diagnostics. Image processing algorithms are needed to robustly quantify the alignment in images with high sensitivity and reliability. Fourier transform (FT) magnitude, 2D power spectrum, and image autocorrelation have previously been used to extract fiber information from images by assuming a certain mathematical model (e.g. Gaussian distribution of the fiber-related parameters) and fitting. The fitting process is slow and fails to converge when the data is not Gaussian. Herein we present an efficient constant-time deterministic algorithm which characterizes the symmetricity of the FT magnitude image in terms of a single parameter, named the fiber alignment anisotropy R ranging from 0 (randomized fibers) to 1 (perfect alignment). This represents an important improvement of the technology and may bring us one step closer to utilizing the technology for various applications in real time. In addition, we present a digital image phantom-based framework for characterizing and validating the algorithm, as well as assessing the robustness of the algorithm against different perturbations.
Retinal vessel segmentation on SLO image

PubMed Central

Xu, Juan; Ishikawa, Hiroshi; Wollstein, Gadi; Schuman, Joel S.

2010-01-01

A scanning laser ophthalmoscopy (SLO) image, taken from optical coherence tomography (OCT), usually has lower global/local contrast and more noise compared to the traditional retinal photograph, which makes the vessel segmentation challenging work. A hybrid algorithm is proposed to efficiently solve these problems by fusing several designed methods, taking the advantages of each method and reducing the error measurements. The algorithm has several steps consisting of image preprocessing, thresholding probe and weighted fusing. Four different methods are first designed to transform the SLO image into feature response images by taking different combinations of matched filter, contrast enhancement and mathematical morphology operators. A thresholding probe algorithm is then applied on those response images to obtain four vessel maps. Weighted majority opinion is used to fuse these vessel maps and generate a final vessel map. The experimental results showed that the proposed hybrid algorithm could successfully segment the blood vessels on SLO images, by detecting the major and small vessels and suppressing the noises. The algorithm showed substantial potential in various clinical applications. The use of this method can be also extended to medical image registration based on blood vessel location. PMID:19163149
Experimental Evaluation of a Deformable Registration Algorithm for Motion Correction in PET-CT Guided Biopsy.

PubMed

Khare, Rahul; Sala, Guillaume; Kinahan, Paul; Esposito, Giuseppe; Banovac, Filip; Cleary, Kevin; Enquobahrie, Andinet

2013-01-01

Positron emission tomography computed tomography (PET-CT) images are increasingly being used for guidance during percutaneous biopsy. However, due to the physics of image acquisition, PET-CT images are susceptible to problems due to respiratory and cardiac motion, leading to inaccurate tumor localization, shape distortion, and attenuation correction. To address these problems, we present a method for motion correction that relies on respiratory gated CT images aligned using a deformable registration algorithm. In this work, we use two deformable registration algorithms and two optimization approaches for registering the CT images obtained over the respiratory cycle. The two algorithms are the BSpline and the symmetric forces Demons registration. In the first optmization approach, CT images at each time point are registered to a single reference time point. In the second approach, deformation maps are obtained to align each CT time point with its adjacent time point. These deformations are then composed to find the deformation with respect to a reference time point. We evaluate these two algorithms and optimization approaches using respiratory gated CT images obtained from 7 patients. Our results show that overall the BSpline registration algorithm with the reference optimization approach gives the best results.
A fast non-local means algorithm based on integral image and reconstructed similar kernel

NASA Astrophysics Data System (ADS)

Lin, Zheng; Song, Enmin

2018-03-01

Image denoising is one of the essential methods in digital image processing. The non-local means (NLM) denoising approach is a remarkable denoising technique. However, its time complexity of the computation is high. In this paper, we design a fast NLM algorithm based on integral image and reconstructed similar kernel. First, the integral image is introduced in the traditional NLM algorithm. In doing so, it reduces a great deal of repetitive operations in the parallel processing, which will greatly improves the running speed of the algorithm. Secondly, in order to amend the error of the integral image, we construct a similar window resembling the Gaussian kernel in the pyramidal stacking pattern. Finally, in order to eliminate the influence produced by replacing the Gaussian weighted Euclidean distance with Euclidean distance, we propose a scheme to construct a similar kernel with a size of 3 x 3 in a neighborhood window which will reduce the effect of noise on a single pixel. Experimental results demonstrate that the proposed algorithm is about seventeen times faster than the traditional NLM algorithm, yet produce comparable results in terms of Peak Signal-to- Noise Ratio (the PSNR increased 2.9% in average) and perceptual image quality.
A fast global fitting algorithm for fluorescence lifetime imaging microscopy based on image segmentation.

PubMed

Pelet, S; Previte, M J R; Laiho, L H; So, P T C

2004-10-01

Global fitting algorithms have been shown to improve effectively the accuracy and precision of the analysis of fluorescence lifetime imaging microscopy data. Global analysis performs better than unconstrained data fitting when prior information exists, such as the spatial invariance of the lifetimes of individual fluorescent species. The highly coupled nature of global analysis often results in a significantly slower convergence of the data fitting algorithm as compared with unconstrained analysis. Convergence speed can be greatly accelerated by providing appropriate initial guesses. Realizing that the image morphology often correlates with fluorophore distribution, a global fitting algorithm has been developed to assign initial guesses throughout an image based on a segmentation analysis. This algorithm was tested on both simulated data sets and time-domain lifetime measurements. We have successfully measured fluorophore distribution in fibroblasts stained with Hoechst and calcein. This method further allows second harmonic generation from collagen and elastin autofluorescence to be differentiated in fluorescence lifetime imaging microscopy images of ex vivo human skin. On our experimental measurement, this algorithm increased convergence speed by over two orders of magnitude and achieved significantly better fits. Copyright 2004 Biophysical Society
The power of neural nets

NASA Technical Reports Server (NTRS)

Ryan, J. P.; Shah, B. H.

1987-01-01

Implementation of the Hopfield net which is used in the image processing type of applications where only partial information about the image may be available is discussed. The image classification type of algorithm of Hopfield and other learning algorithms, such as the Boltzmann machine and the back-propagation training algorithm, have many vital applications in space.
Parallel algorithm for determining motion vectors in ice floe images by matching edge features

NASA Technical Reports Server (NTRS)

Manohar, M.; Ramapriyan, H. K.; Strong, J. P.

1988-01-01

A parallel algorithm is described to determine motion vectors of ice floes using time sequences of images of the Arctic ocean obtained from the Synthetic Aperture Radar (SAR) instrument flown on-board the SEASAT spacecraft. Researchers describe a parallel algorithm which is implemented on the MPP for locating corresponding objects based on their translationally and rotationally invariant features. The algorithm first approximates the edges in the images by polygons or sets of connected straight-line segments. Each such edge structure is then reduced to a seed point. Associated with each seed point are the descriptions (lengths, orientations and sequence numbers) of the lines constituting the corresponding edge structure. A parallel matching algorithm is used to match packed arrays of such descriptions to identify corresponding seed points in the two images. The matching algorithm is designed such that fragmentation and merging of ice floes are taken into account by accepting partial matches. The technique has been demonstrated to work on synthetic test patterns and real image pairs from SEASAT in times ranging from .5 to 0.7 seconds for 128 x 128 images.

An adaptive clustering algorithm for image matching based on corner feature

NASA Astrophysics Data System (ADS)

Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

2018-04-01

The traditional image matching algorithm always can not balance the real-time and accuracy better, to solve the problem, an adaptive clustering algorithm for image matching based on corner feature is proposed in this paper. The method is based on the similarity of the matching pairs of vector pairs, and the adaptive clustering is performed on the matching point pairs. Harris corner detection is carried out first, the feature points of the reference image and the perceived image are extracted, and the feature points of the two images are first matched by Normalized Cross Correlation (NCC) function. Then, using the improved algorithm proposed in this paper, the matching results are clustered to reduce the ineffective operation and improve the matching speed and robustness. Finally, the Random Sample Consensus (RANSAC) algorithm is used to match the matching points after clustering. The experimental results show that the proposed algorithm can effectively eliminate the most wrong matching points while the correct matching points are retained, and improve the accuracy of RANSAC matching, reduce the computation load of whole matching process at the same time.
The implementation of contour-based object orientation estimation algorithm in FPGA-based on-board vision system

NASA Astrophysics Data System (ADS)

Alpatov, Boris; Babayan, Pavel; Ershov, Maksim; Strotov, Valery

2016-10-01

This paper describes the implementation of the orientation estimation algorithm in FPGA-based vision system. An approach to estimate an orientation of objects lacking axial symmetry is proposed. Suggested algorithm is intended to estimate orientation of a specific known 3D object based on object 3D model. The proposed orientation estimation algorithm consists of two stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.
Implementation of an IMU Aided Image Stacking Algorithm in a Digital Camera for Unmanned Aerial Vehicles

PubMed Central

Audi, Ahmad; Pierrot-Deseilligny, Marc; Meynard, Christophe

2017-01-01

Images acquired with a long exposure time using a camera embedded on UAVs (Unmanned Aerial Vehicles) exhibit motion blur due to the erratic movements of the UAV. The aim of the present work is to be able to acquire several images with a short exposure time and use an image processing algorithm to produce a stacked image with an equivalent long exposure time. Our method is based on the feature point image registration technique. The algorithm is implemented on the light-weight IGN (Institut national de l’information géographique) camera, which has an IMU (Inertial Measurement Unit) sensor and an SoC (System on Chip)/FPGA (Field-Programmable Gate Array). To obtain the correct parameters for the resampling of the images, the proposed method accurately estimates the geometrical transformation between the first and the N-th images. Feature points are detected in the first image using the FAST (Features from Accelerated Segment Test) detector, then homologous points on other images are obtained by template matching using an initial position benefiting greatly from the presence of the IMU sensor. The SoC/FPGA in the camera is used to speed up some parts of the algorithm in order to achieve real-time performance as our ultimate objective is to exclusively write the resulting image to save bandwidth on the storage device. The paper includes a detailed description of the implemented algorithm, resource usage summary, resulting processing time, resulting images and block diagrams of the described architecture. The resulting stacked image obtained for real surveys does not seem visually impaired. An interesting by-product of this algorithm is the 3D rotation estimated by a photogrammetric method between poses, which can be used to recalibrate in real time the gyrometers of the IMU. Timing results demonstrate that the image resampling part of this algorithm is the most demanding processing task and should also be accelerated in the FPGA in future work. PMID:28718788
Implementation of an IMU Aided Image Stacking Algorithm in a Digital Camera for Unmanned Aerial Vehicles.

PubMed

Audi, Ahmad; Pierrot-Deseilligny, Marc; Meynard, Christophe; Thom, Christian

2017-07-18

Images acquired with a long exposure time using a camera embedded on UAVs (Unmanned Aerial Vehicles) exhibit motion blur due to the erratic movements of the UAV. The aim of the present work is to be able to acquire several images with a short exposure time and use an image processing algorithm to produce a stacked image with an equivalent long exposure time. Our method is based on the feature point image registration technique. The algorithm is implemented on the light-weight IGN (Institut national de l'information géographique) camera, which has an IMU (Inertial Measurement Unit) sensor and an SoC (System on Chip)/FPGA (Field-Programmable Gate Array). To obtain the correct parameters for the resampling of the images, the proposed method accurately estimates the geometrical transformation between the first and the N -th images. Feature points are detected in the first image using the FAST (Features from Accelerated Segment Test) detector, then homologous points on other images are obtained by template matching using an initial position benefiting greatly from the presence of the IMU sensor. The SoC/FPGA in the camera is used to speed up some parts of the algorithm in order to achieve real-time performance as our ultimate objective is to exclusively write the resulting image to save bandwidth on the storage device. The paper includes a detailed description of the implemented algorithm, resource usage summary, resulting processing time, resulting images and block diagrams of the described architecture. The resulting stacked image obtained for real surveys does not seem visually impaired. An interesting by-product of this algorithm is the 3D rotation estimated by a photogrammetric method between poses, which can be used to recalibrate in real time the gyrometers of the IMU. Timing results demonstrate that the image resampling part of this algorithm is the most demanding processing task and should also be accelerated in the FPGA in future work.
Information Hiding: an Annotated Bibliography

DTIC Science & Technology

1999-04-13

parameters needed for reconstruction are enciphered using DES . The encrypted image is hidden in a cover image . [153] 074115, ‘Watermarking algorithm ...authors present a block based watermarking algorithm for digital images . The D.C.T. of the block is increased by a certain value. Quality control is...includes evaluation of the watermark robustness and the subjec- tive visual image quality. Two algorithms use the frequency domain while the two others use
Prototype for Meta-Algorithmic, Content-Aware Image Analysis

DTIC Science & Technology

2015-03-01

PROTOTYPE FOR META-ALGORITHMIC, CONTENT-AWARE IMAGE ANALYSIS UNIVERSITY OF VIRGINIA MARCH 2015 FINAL TECHNICAL REPORT...ALGORITHMIC, CONTENT-AWARE IMAGE ANALYSIS 5a. CONTRACT NUMBER FA8750-12-C-0181 5b. GRANT NUMBER N/A 5c. PROGRAM ELEMENT NUMBER 62305E 6. AUTHOR(S) S...approaches were studied in detail and their results on a sample dataset are presented. 15. SUBJECT TERMS Image Analysis , Computer Vision, Content
Architectures and algorithms for digital image processing; Proceedings of the Meeting, Cannes, France, December 5, 6, 1985

NASA Technical Reports Server (NTRS)

Duff, Michael J. B. (Editor); Siegel, Howard J. (Editor); Corbett, Francis J. (Editor)

1986-01-01

The conference presents papers on the architectures, algorithms, and applications of image processing. Particular attention is given to a very large scale integration system for image reconstruction from projections, a prebuffer algorithm for instant display of volume data, and an adaptive image sequence filtering scheme based on motion detection. Papers are also presented on a simple, direct practical method of sensing local motion and analyzing local optical flow, image matching techniques, and an automated biological dosimetry system.
Cone-Beam Computed Tomography for Image-Guided Radiation Therapy of Prostate Cancer

DTIC Science & Technology

2010-01-01

ltered ba kproje tion (FBP) al-gorithm that does not depend upon the hords hastherefore been developed for volumetri image re- onstru tion in a...reproje tion of the rst re onstru ted volumetri image. The NCAT 6 phantom images re onstru ted by the tandem algorithm are shown in Fig. 3. The paper...algorithm has been applied to a ir ular one-beam mi ro-CT for volumetri images of the ROIwith a higher spatial resolution and at a redu edexposure to
Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation.

PubMed

Blessy, S A Praylin Selva; Sulochana, C Helen

2015-01-01

Segmentation of brain tumor from Magnetic Resonance Imaging (MRI) becomes very complicated due to the structural complexities of human brain and the presence of intensity inhomogeneities. To propose a method that effectively segments brain tumor from MR images and to evaluate the performance of unsupervised optimal fuzzy clustering (UOFC) algorithm for segmentation of brain tumor from MR images. Segmentation is done by preprocessing the MR image to standardize intensity inhomogeneities followed by feature extraction, feature fusion and clustering. Different validation measures are used to evaluate the performance of the proposed method using different clustering algorithms. The proposed method using UOFC algorithm produces high sensitivity (96%) and low specificity (4%) compared to other clustering methods. Validation results clearly show that the proposed method with UOFC algorithm effectively segments brain tumor from MR images.
Classification algorithm of lung lobe for lung disease cases based on multislice CT images

NASA Astrophysics Data System (ADS)

Matsuhiro, M.; Kawata, Y.; Niki, N.; Nakano, Y.; Mishima, M.; Ohmatsu, H.; Tsuchida, T.; Eguchi, K.; Kaneko, M.; Moriyama, N.

2011-03-01

With the development of multi-slice CT technology, to obtain an accurate 3D image of lung field in a short time is possible. To support that, a lot of image processing methods need to be developed. In clinical setting for diagnosis of lung cancer, it is important to study and analyse lung structure. Therefore, classification of lung lobe provides useful information for lung cancer analysis. In this report, we describe algorithm which classify lungs into lung lobes for lung disease cases from multi-slice CT images. The classification algorithm of lung lobes is efficiently carried out using information of lung blood vessel, bronchus, and interlobar fissure. Applying the classification algorithms to multi-slice CT images of 20 normal cases and 5 lung disease cases, we demonstrate the usefulness of the proposed algorithms.
Comparison of SeaWinds Backscatter Imaging Algorithms

PubMed Central

Long, David G.

2017-01-01

This paper compares the performance and tradeoffs of various backscatter imaging algorithms for the SeaWinds scatterometer when multiple passes over a target are available. Reconstruction methods are compared with conventional gridding algorithms. In particular, the performance and tradeoffs in conventional ‘drop in the bucket’ (DIB) gridding at the intrinsic sensor resolution are compared to high-spatial-resolution imaging algorithms such as fine-resolution DIB and the scatterometer image reconstruction (SIR) that generate enhanced-resolution backscatter images. Various options for each algorithm are explored, including considering both linear and dB computation. The effects of sampling density and reconstruction quality versus time are explored. Both simulated and actual data results are considered. The results demonstrate the effectiveness of high-resolution reconstruction using SIR as well as its limitations and the limitations of DIB and fDIB. PMID:28828143
Score-Level Fusion of Phase-Based and Feature-Based Fingerprint Matching Algorithms

NASA Astrophysics Data System (ADS)

Ito, Koichi; Morita, Ayumi; Aoki, Takafumi; Nakajima, Hiroshi; Kobayashi, Koji; Higuchi, Tatsuo

This paper proposes an efficient fingerprint recognition algorithm combining phase-based image matching and feature-based matching. In our previous work, we have already proposed an efficient fingerprint recognition algorithm using Phase-Only Correlation (POC), and developed commercial fingerprint verification units for access control applications. The use of Fourier phase information of fingerprint images makes it possible to achieve robust recognition for weakly impressed, low-quality fingerprint images. This paper presents an idea of improving the performance of POC-based fingerprint matching by combining it with feature-based matching, where feature-based matching is introduced in order to improve recognition efficiency for images with nonlinear distortion. Experimental evaluation using two different types of fingerprint image databases demonstrates efficient recognition performance of the combination of the POC-based algorithm and the feature-based algorithm.
An FPGA-based heterogeneous image fusion system design method

NASA Astrophysics Data System (ADS)

Song, Le; Lin, Yu-chi; Chen, Yan-hua; Zhao, Mei-rong

2011-08-01

Taking the advantages of FPGA's low cost and compact structure, an FPGA-based heterogeneous image fusion platform is established in this study. Altera's Cyclone IV series FPGA is adopted as the core processor of the platform, and the visible light CCD camera and infrared thermal imager are used as the image-capturing device in order to obtain dualchannel heterogeneous video images. Tailor-made image fusion algorithms such as gray-scale weighted averaging, maximum selection and minimum selection methods are analyzed and compared. VHDL language and the synchronous design method are utilized to perform a reliable RTL-level description. Altera's Quartus II 9.0 software is applied to simulate and implement the algorithm modules. The contrast experiments of various fusion algorithms show that, preferably image quality of the heterogeneous image fusion can be obtained on top of the proposed system. The applied range of the different fusion algorithms is also discussed.
A novel algorithm of super-resolution image reconstruction based on multi-class dictionaries for natural scene

NASA Astrophysics Data System (ADS)

Wu, Wei; Zhao, Dewei; Zhang, Huan

2015-12-01

Super-resolution image reconstruction is an effective method to improve the image quality. It has important research significance in the field of image processing. However, the choice of the dictionary directly affects the efficiency of image reconstruction. A sparse representation theory is introduced into the problem of the nearest neighbor selection. Based on the sparse representation of super-resolution image reconstruction method, a super-resolution image reconstruction algorithm based on multi-class dictionary is analyzed. This method avoids the redundancy problem of only training a hyper complete dictionary, and makes the sub-dictionary more representatives, and then replaces the traditional Euclidean distance computing method to improve the quality of the whole image reconstruction. In addition, the ill-posed problem is introduced into non-local self-similarity regularization. Experimental results show that the algorithm is much better results than state-of-the-art algorithm in terms of both PSNR and visual perception.
Expansion of the visual angle of a car rear-view image via an image mosaic algorithm

NASA Astrophysics Data System (ADS)

Wu, Zhuangwen; Zhu, Liangrong; Sun, Xincheng

2015-05-01

The rear-view image system is one of the active safety devices in cars and is widely applied in all types of vehicles and traffic safety areas. However, studies made by both domestic and foreign researchers were based on a single image capture device while reversing, so a blind area still remained to drivers. Even if multiple cameras were used to expand the visual angle of the car's rear-view image in some studies, the blind area remained because different source images were not mosaicked together. To acquire an expanded visual angle of a car rear-view image, two charge-coupled device cameras with optical axes angled at 30 deg were mounted below the left and right fenders of a car in three light conditions-sunny outdoors, cloudy outdoors, and an underground garage-to capture rear-view heterologous images of the car. Then these rear-view heterologous images were rapidly registered through the scale invariant feature transform algorithm. Combined with the random sample consensus algorithm, the two heterologous images were finally mosaicked using the linear weighted gradated in-and-out fusion algorithm, and a seamless and visual-angle-expanded rear-view image was acquired. The four-index test results showed that the algorithms can mosaic rear-view images well in the underground garage condition, where the average rate of correct matching was the lowest among the three conditions. The rear-view image mosaic algorithm presented had the best information preservation, the shortest computation time and the most complete preservation of the image detail features compared to the mean value method (MVM) and segmental fusion method (SFM), and it was also able to perform better in real time and provided more comprehensive image details than MVM and SFM. In addition, it had the most complete image preservation from source images among the three algorithms. The method introduced by this paper provided the basis for researching the expansion of the visual angle of a car rear-view image in all-weather conditions.
Evaluation of GMI and PMI diffeomorphic‐based demons algorithms for aligning PET and CT Images

PubMed Central

Yang, Juan; Zhang, You; Yin, Yong

2015-01-01

Fusion of anatomic information in computed tomography (CT) and functional information in F18‐FDG positron emission tomography (PET) is crucial for accurate differentiation of tumor from benign masses, designing radiotherapy treatment plan and staging of cancer. Although current PET and CT images can be acquired from combined F18‐FDG PET/CT scanner, the two acquisitions are scanned separately and take a long time, which may induce potential positional errors in global and local caused by respiratory motion or organ peristalsis. So registration (alignment) of whole‐body PET and CT images is a prerequisite for their meaningful fusion. The purpose of this study was to assess the performance of two multimodal registration algorithms for aligning PET and CT images. The proposed gradient of mutual information (GMI)‐based demons algorithm, which incorporated the GMI between two images as an external force to facilitate the alignment, was compared with the point‐wise mutual information (PMI) diffeomorphic‐based demons algorithm whose external force was modified by replacing the image intensity difference in diffeomorphic demons algorithm with the PMI to make it appropriate for multimodal image registration. Eight patients with esophageal cancer(s) were enrolled in this IRB‐approved study. Whole‐body PET and CT images were acquired from a combined F18‐FDG PET/CT scanner for each patient. The modified Hausdorff distance (dMH) was used to evaluate the registration accuracy of the two algorithms. Of all patients, the mean values and standard deviations (SDs) of dMH were 6.65 (± 1.90) voxels and 6.01 (± 1.90) after the GMI‐based demons and the PMI diffeomorphic‐based demons registration algorithms respectively. Preliminary results on oncological patients showed that the respiratory motion and organ peristalsis in PET/CT esophageal images could not be neglected, although a combined F18‐FDG PET/CT scanner was used for image acquisition. The PMI diffeomorphic‐based demons algorithm was more accurate than the GMI‐based demons algorithm in registering PET/CT esophageal images. PACS numbers: 87.57.nj, 87.57. Q‐, 87.57.uk PMID:26218993
Evaluation of GMI and PMI diffeomorphic-based demons algorithms for aligning PET and CT Images.

PubMed

Yang, Juan; Wang, Hongjun; Zhang, You; Yin, Yong

2015-07-08

Fusion of anatomic information in computed tomography (CT) and functional information in 18F-FDG positron emission tomography (PET) is crucial for accurate differentiation of tumor from benign masses, designing radiotherapy treatment plan and staging of cancer. Although current PET and CT images can be acquired from combined 18F-FDG PET/CT scanner, the two acquisitions are scanned separately and take a long time, which may induce potential positional errors in global and local caused by respiratory motion or organ peristalsis. So registration (alignment) of whole-body PET and CT images is a prerequisite for their meaningful fusion. The purpose of this study was to assess the performance of two multimodal registration algorithms for aligning PET and CT images. The proposed gradient of mutual information (GMI)-based demons algorithm, which incorporated the GMI between two images as an external force to facilitate the alignment, was compared with the point-wise mutual information (PMI) diffeomorphic-based demons algorithm whose external force was modified by replacing the image intensity difference in diffeomorphic demons algorithm with the PMI to make it appropriate for multimodal image registration. Eight patients with esophageal cancer(s) were enrolled in this IRB-approved study. Whole-body PET and CT images were acquired from a combined 18F-FDG PET/CT scanner for each patient. The modified Hausdorff distance (d(MH)) was used to evaluate the registration accuracy of the two algorithms. Of all patients, the mean values and standard deviations (SDs) of d(MH) were 6.65 (± 1.90) voxels and 6.01 (± 1.90) after the GMI-based demons and the PMI diffeomorphic-based demons registration algorithms respectively. Preliminary results on oncological patients showed that the respiratory motion and organ peristalsis in PET/CT esophageal images could not be neglected, although a combined 18F-FDG PET/CT scanner was used for image acquisition. The PMI diffeomorphic-based demons algorithm was more accurate than the GMI-based demons algorithm in registering PET/CT esophageal images.
Eliminating "Hotspots" in Digital Image Processing

NASA Technical Reports Server (NTRS)

Salomon, P. M.

1984-01-01

Signals from defective picture elements rejected. Image processing program for use with charge-coupled device (CCD) or other mosaic imager augmented with algorithm that compensates for common type of electronic defect. Algorithm prevents false interpretation of "hotspots". Used for robotics, image enhancement, image analysis and digital television.
A Comparison of Four-Image Reconstruction Algorithms for 3-D PET Imaging of MDAPET Camera Using Phantom Data

NASA Astrophysics Data System (ADS)

Baghaei, H.; Wong, Wai-Hoi; Uribe, J.; Li, Hongdi; Wang, Yu; Liu, Yaqiang; Xing, Tao; Ramirez, R.; Xie, Shuping; Kim, Soonseok

2004-10-01

We compared two fully three-dimensional (3-D) image reconstruction algorithms and two 3-D rebinning algorithms followed by reconstruction with a two-dimensional (2-D) filtered-backprojection algorithm for 3-D positron emission tomography (PET) imaging. The two 3-D image reconstruction algorithms were ordered-subsets expectation-maximization (3D-OSEM) and 3-D reprojection (3DRP) algorithms. The two rebinning algorithms were Fourier rebinning (FORE) and single slice rebinning (SSRB). The 3-D projection data used for this work were acquired with a high-resolution PET scanner (MDAPET) with an intrinsic transaxial resolution of 2.8 mm. The scanner has 14 detector rings covering an axial field-of-view of 38.5 mm. We scanned three phantoms: 1) a uniform cylindrical phantom with inner diameter of 21.5 cm; 2) a uniform 11.5-cm cylindrical phantom with four embedded small hot lesions with diameters of 3, 4, 5, and 6 mm; and 3) the 3-D Hoffman brain phantom with three embedded small hot lesion phantoms with diameters of 3, 5, and 8.6 mm in a warm background. Lesions were placed at different radial and axial distances. We evaluated the different reconstruction methods for MDAPET camera by comparing the noise level of images, contrast recovery, and hot lesion detection, and visually compared images. We found that overall the 3D-OSEM algorithm, especially when images post filtered with the Metz filter, produced the best results in terms of contrast-noise tradeoff, and detection of hot spots, and reproduction of brain phantom structures. Even though the MDAPET camera has a relatively small maximum axial acceptance (/spl plusmn/5 deg), images produced with the 3DRP algorithm had slightly better contrast recovery and reproduced the structures of the brain phantom slightly better than the faster 2-D rebinning methods.
A new approach to optic disc detection in human retinal images using the firefly algorithm.

PubMed

Rahebi, Javad; Hardalaç, Fırat

2016-03-01

There are various methods and algorithms to detect the optic discs in retinal images. In recent years, much attention has been given to the utilization of the intelligent algorithms. In this paper, we present a new automated method of optic disc detection in human retinal images using the firefly algorithm. The firefly intelligent algorithm is an emerging intelligent algorithm that was inspired by the social behavior of fireflies. The population in this algorithm includes the fireflies, each of which has a specific rate of lighting or fitness. In this method, the insects are compared two by two, and the less attractive insects can be observed to move toward the more attractive insects. Finally, one of the insects is selected as the most attractive, and this insect presents the optimum response to the problem in question. Here, we used the light intensity of the pixels of the retinal image pixels instead of firefly lightings. The movement of these insects due to local fluctuations produces different light intensity values in the images. Because the optic disc is the brightest area in the retinal images, all of the insects move toward brightest area and thus specify the location of the optic disc in the image. The results of implementation show that proposed algorithm could acquire an accuracy rate of 100 % in DRIVE dataset, 95 % in STARE dataset, and 94.38 % in DiaRetDB1 dataset. The results of implementation reveal high capability and accuracy of proposed algorithm in the detection of the optic disc from retinal images. Also, recorded required time for the detection of the optic disc in these images is 2.13 s for DRIVE dataset, 2.81 s for STARE dataset, and 3.52 s for DiaRetDB1 dataset accordingly. These time values are average value.

High resolution x-ray CMT: Reconstruction methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, J.K.

This paper qualitatively discusses the primary characteristics of methods for reconstructing tomographic images from a set of projections. These reconstruction methods can be categorized as either {open_quotes}analytic{close_quotes} or {open_quotes}iterative{close_quotes} techniques. Analytic algorithms are derived from the formal inversion of equations describing the imaging process, while iterative algorithms incorporate a model of the imaging process and provide a mechanism to iteratively improve image estimates. Analytic reconstruction algorithms are typically computationally more efficient than iterative methods; however, analytic algorithms are available for a relatively limited set of imaging geometries and situations. Thus, the framework of iterative reconstruction methods is better suited formore » high accuracy, tomographic reconstruction codes.« less
Accurate 3D reconstruction by a new PDS-OSEM algorithm for HRRT

NASA Astrophysics Data System (ADS)

Chen, Tai-Been; Horng-Shing Lu, Henry; Kim, Hang-Keun; Son, Young-Don; Cho, Zang-Hee

2014-03-01

State-of-the-art high resolution research tomography (HRRT) provides high resolution PET images with full 3D human brain scanning. But, a short time frame in dynamic study causes many problems related to the low counts in the acquired data. The PDS-OSEM algorithm was proposed to reconstruct the HRRT image with a high signal-to-noise ratio that provides accurate information for dynamic data. The new algorithm was evaluated by simulated image, empirical phantoms, and real human brain data. Meanwhile, the time activity curve was adopted to validate a reconstructed performance of dynamic data between PDS-OSEM and OP-OSEM algorithms. According to simulated and empirical studies, the PDS-OSEM algorithm reconstructs images with higher quality, higher accuracy, less noise, and less average sum of square error than those of OP-OSEM. The presented algorithm is useful to provide quality images under the condition of low count rates in dynamic studies with a short scan time.
Dimension-Factorized Range Migration Algorithm for Regularly Distributed Array Imaging

PubMed Central

Guo, Qijia; Wang, Jie; Chang, Tianying

2017-01-01

The two-dimensional planar MIMO array is a popular approach for millimeter wave imaging applications. As a promising practical alternative, sparse MIMO arrays have been devised to reduce the number of antenna elements and transmitting/receiving channels with predictable and acceptable loss in image quality. In this paper, a high precision three-dimensional imaging algorithm is proposed for MIMO arrays of the regularly distributed type, especially the sparse varieties. Termed the Dimension-Factorized Range Migration Algorithm, the new imaging approach factorizes the conventional MIMO Range Migration Algorithm into multiple operations across the sparse dimensions. The thinner the sparse dimensions of the array, the more efficient the new algorithm will be. Advantages of the proposed approach are demonstrated by comparison with the conventional MIMO Range Migration Algorithm and its non-uniform fast Fourier transform based variant in terms of all the important characteristics of the approaches, especially the anti-noise capability. The computation cost is analyzed as well to evaluate the efficiency quantitatively. PMID:29113083
Evolutionary Fuzzy Block-Matching-Based Camera Raw Image Denoising.

PubMed

Yang, Chin-Chang; Guo, Shu-Mei; Tsai, Jason Sheng-Hong

2017-09-01

An evolutionary fuzzy block-matching-based image denoising algorithm is proposed to remove noise from a camera raw image. Recently, a variance stabilization transform is widely used to stabilize the noise variance, so that a Gaussian denoising algorithm can be used to remove the signal-dependent noise in camera sensors. However, in the stabilized domain, the existed denoising algorithm may blur too much detail. To provide a better estimate of the noise-free signal, a new block-matching approach is proposed to find similar blocks by the use of a type-2 fuzzy logic system (FLS). Then, these similar blocks are averaged with the weightings which are determined by the FLS. Finally, an efficient differential evolution is used to further improve the performance of the proposed denoising algorithm. The experimental results show that the proposed denoising algorithm effectively improves the performance of image denoising. Furthermore, the average performance of the proposed method is better than those of two state-of-the-art image denoising algorithms in subjective and objective measures.
Application of the EM algorithm to radiographic images.

PubMed

Brailean, J C; Little, D; Giger, M L; Chen, C T; Sullivan, B J

1992-01-01

The expectation maximization (EM) algorithm has received considerable attention in the area of positron emitted tomography (PET) as a restoration and reconstruction technique. In this paper, the restoration capabilities of the EM algorithm when applied to radiographic images is investigated. This application does not involve reconstruction. The performance of the EM algorithm is quantitatively evaluated using a "perceived" signal-to-noise ratio (SNR) as the image quality metric. This perceived SNR is based on statistical decision theory and includes both the observer's visual response function and a noise component internal to the eye-brain system. For a variety of processing parameters, the relative SNR (ratio of the processed SNR to the original SNR) is calculated and used as a metric to compare quantitatively the effects of the EM algorithm with two other image enhancement techniques: global contrast enhancement (windowing) and unsharp mask filtering. The results suggest that the EM algorithm's performance is superior when compared to unsharp mask filtering and global contrast enhancement for radiographic images which contain objects smaller than 4 mm.
Hybrid Image Fusion for Sharpness Enhancement of Multi-Spectral Lunar Images

NASA Astrophysics Data System (ADS)

Awumah, Anna; Mahanti, Prasun; Robinson, Mark

2016-10-01

Image fusion enhances the sharpness of a multi-spectral (MS) image by incorporating spatial details from a higher-resolution panchromatic (Pan) image [1,2]. Known applications of image fusion for planetary images are rare, although image fusion is well-known for its applications to Earth-based remote sensing. In a recent work [3], six different image fusion algorithms were implemented and their performances were verified with images from the Lunar Reconnaissance Orbiter (LRO) Camera. The image fusion procedure obtained a high-resolution multi-spectral (HRMS) product from the LRO Narrow Angle Camera (used as Pan) and LRO Wide Angle Camera (used as MS) images. The results showed that the Intensity-Hue-Saturation (IHS) algorithm results in a high-spatial quality product while the Wavelet-based image fusion algorithm best preserves spectral quality among all the algorithms. In this work we show the results of a hybrid IHS-Wavelet image fusion algorithm when applied to LROC MS images. The hybrid method provides the best HRMS product - both in terms of spatial resolution and preservation of spectral details. Results from hybrid image fusion can enable new science and increase the science return from existing LROC images.[1] Pohl, Cle, and John L. Van Genderen. "Review article multisensor image fusion in remote sensing: concepts, methods and applications." International journal of remote sensing 19.5 (1998): 823-854.[2] Zhang, Yun. "Understanding image fusion." Photogramm. Eng. Remote Sens 70.6 (2004): 657-661.[3] Mahanti, Prasun et al. "Enhancement of spatial resolution of the LROC Wide Angle Camera images." Archives, XXIII ISPRS Congress Archives (2016).
Text image authenticating algorithm based on MD5-hash function and Henon map

NASA Astrophysics Data System (ADS)

Wei, Jinqiao; Wang, Ying; Ma, Xiaoxue

2017-07-01

In order to cater to the evidentiary requirements of the text image, this paper proposes a fragile watermarking algorithm based on Hash function and Henon map. The algorithm is to divide a text image into parts, get flippable pixels and nonflippable pixels of every lump according to PSD, generate watermark of non-flippable pixels with MD5-Hash, encrypt watermark with Henon map and select embedded blocks. The simulation results show that the algorithm with a good ability in tampering localization can be used to authenticate and forensics the authenticity and integrity of text images
A new image encryption algorithm based on the fractional-order hyperchaotic Lorenz system

NASA Astrophysics Data System (ADS)

Wang, Zhen; Huang, Xia; Li, Yu-Xia; Song, Xiao-Na

2013-01-01

We propose a new image encryption algorithm on the basis of the fractional-order hyperchaotic Lorenz system. While in the process of generating a key stream, the system parameters and the derivative order are embedded in the proposed algorithm to enhance the security. Such an algorithm is detailed in terms of security analyses, including correlation analysis, information entropy analysis, run statistic analysis, mean-variance gray value analysis, and key sensitivity analysis. The experimental results demonstrate that the proposed image encryption scheme has the advantages of large key space and high security for practical image encryption.
Time reversal and phase coherent music techniques for super-resolution ultrasound imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Lianjie; Labyed, Yassin

Systems and methods for super-resolution ultrasound imaging using a windowed and generalized TR-MUSIC algorithm that divides the imaging region into overlapping sub-regions and applies the TR-MUSIC algorithm to the windowed backscattered ultrasound signals corresponding to each sub-region. The algorithm is also structured to account for the ultrasound attenuation in the medium and the finite-size effects of ultrasound transducer elements. A modified TR-MUSIC imaging algorithm is used to account for ultrasound scattering from both density and compressibility contrasts. The phase response of ultrasound transducer elements is accounted for in a PC-MUSIC system.
Tracking tumor boundary in MV-EPID images without implanted markers: A feasibility study.

PubMed

Zhang, Xiaoyong; Homma, Noriyasu; Ichiji, Kei; Takai, Yoshihiro; Yoshizawa, Makoto

2015-05-01

To develop a markerless tracking algorithm to track the tumor boundary in megavoltage (MV)-electronic portal imaging device (EPID) images for image-guided radiation therapy. A level set method (LSM)-based algorithm is developed to track tumor boundary in EPID image sequences. Given an EPID image sequence, an initial curve is manually specified in the first frame. Driven by a region-scalable energy fitting function, the initial curve automatically evolves toward the tumor boundary and stops on the desired boundary while the energy function reaches its minimum. For the subsequent frames, the tracking algorithm updates the initial curve by using the tracking result in the previous frame and reuses the LSM to detect the tumor boundary in the subsequent frame so that the tracking processing can be continued without user intervention. The tracking algorithm is tested on three image datasets, including a 4-D phantom EPID image sequence, four digitally deformable phantom image sequences with different noise levels, and four clinical EPID image sequences acquired in lung cancer treatment. The tracking accuracy is evaluated based on two metrics: centroid localization error (CLE) and volume overlap index (VOI) between the tracking result and the ground truth. For the 4-D phantom image sequence, the CLE is 0.23 ± 0.20 mm, and VOI is 95.6% ± 0.2%. For the digital phantom image sequences, the total CLE and VOI are 0.11 ± 0.08 mm and 96.7% ± 0.7%, respectively. In addition, for the clinical EPID image sequences, the proposed algorithm achieves 0.32 ± 0.77 mm in the CLE and 72.1% ± 5.5% in the VOI. These results demonstrate the effectiveness of the authors' proposed method both in tumor localization and boundary tracking in EPID images. In addition, compared with two existing tracking algorithms, the proposed method achieves a higher accuracy in tumor localization. In this paper, the authors presented a feasibility study of tracking tumor boundary in EPID images by using a LSM-based algorithm. Experimental results conducted on phantom and clinical EPID images demonstrated the effectiveness of the tracking algorithm for visible tumor target. Compared with previous tracking methods, the authors' algorithm has the potential to improve the tracking accuracy in radiation therapy. In addition, real-time tumor boundary information within the irradiation field will be potentially useful for further applications, such as adaptive beam delivery, dose evaluation.
Tracking tumor boundary in MV-EPID images without implanted markers: A feasibility study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Xiaoyong, E-mail: xiaoyong@ieee.org; Homma, Noriyasu, E-mail: homma@ieee.org; Ichiji, Kei, E-mail: ichiji@yoshizawa.ecei.tohoku.ac.jp

2015-05-15

Purpose: To develop a markerless tracking algorithm to track the tumor boundary in megavoltage (MV)-electronic portal imaging device (EPID) images for image-guided radiation therapy. Methods: A level set method (LSM)-based algorithm is developed to track tumor boundary in EPID image sequences. Given an EPID image sequence, an initial curve is manually specified in the first frame. Driven by a region-scalable energy fitting function, the initial curve automatically evolves toward the tumor boundary and stops on the desired boundary while the energy function reaches its minimum. For the subsequent frames, the tracking algorithm updates the initial curve by using the trackingmore » result in the previous frame and reuses the LSM to detect the tumor boundary in the subsequent frame so that the tracking processing can be continued without user intervention. The tracking algorithm is tested on three image datasets, including a 4-D phantom EPID image sequence, four digitally deformable phantom image sequences with different noise levels, and four clinical EPID image sequences acquired in lung cancer treatment. The tracking accuracy is evaluated based on two metrics: centroid localization error (CLE) and volume overlap index (VOI) between the tracking result and the ground truth. Results: For the 4-D phantom image sequence, the CLE is 0.23 ± 0.20 mm, and VOI is 95.6% ± 0.2%. For the digital phantom image sequences, the total CLE and VOI are 0.11 ± 0.08 mm and 96.7% ± 0.7%, respectively. In addition, for the clinical EPID image sequences, the proposed algorithm achieves 0.32 ± 0.77 mm in the CLE and 72.1% ± 5.5% in the VOI. These results demonstrate the effectiveness of the authors’ proposed method both in tumor localization and boundary tracking in EPID images. In addition, compared with two existing tracking algorithms, the proposed method achieves a higher accuracy in tumor localization. Conclusions: In this paper, the authors presented a feasibility study of tracking tumor boundary in EPID images by using a LSM-based algorithm. Experimental results conducted on phantom and clinical EPID images demonstrated the effectiveness of the tracking algorithm for visible tumor target. Compared with previous tracking methods, the authors’ algorithm has the potential to improve the tracking accuracy in radiation therapy. In addition, real-time tumor boundary information within the irradiation field will be potentially useful for further applications, such as adaptive beam delivery, dose evaluation.« less
Large-scale image region documentation for fully automated image biomarker algorithm development and evaluation

PubMed Central

Reeves, Anthony P.; Xie, Yiting; Liu, Shuang

2017-01-01

Abstract. With the advent of fully automated image analysis and modern machine learning methods, there is a need for very large image datasets having documented segmentations for both computer algorithm training and evaluation. This paper presents a method and implementation for facilitating such datasets that addresses the critical issue of size scaling for algorithm validation and evaluation; current evaluation methods that are usually used in academic studies do not scale to large datasets. This method includes protocols for the documentation of many regions in very large image datasets; the documentation may be incrementally updated by new image data and by improved algorithm outcomes. This method has been used for 5 years in the context of chest health biomarkers from low-dose chest CT images that are now being used with increasing frequency in lung cancer screening practice. The lung scans are segmented into over 100 different anatomical regions, and the method has been applied to a dataset of over 20,000 chest CT images. Using this framework, the computer algorithms have been developed to achieve over 90% acceptable image segmentation on the complete dataset. PMID:28612037
Research on HDR image fusion algorithm based on Laplace pyramid weight transform with extreme low-light CMOS

NASA Astrophysics Data System (ADS)

Guan, Wen; Li, Li; Jin, Weiqi; Qiu, Su; Zou, Yan

2015-10-01

Extreme-Low-Light CMOS has been widely applied in the field of night-vision as a new type of solid image sensor. But if the illumination in the scene has drastic changes or the illumination is too strong, Extreme-Low-Light CMOS can't both clearly present the high-light scene and low-light region. According to the partial saturation problem in the field of night-vision, a HDR image fusion algorithm based on the Laplace Pyramid was researched. The overall gray value and the contrast of the low light image is very low. We choose the fusion strategy based on regional average gradient for the top layer of the long exposure image and short exposure image, which has rich brightness and textural features. The remained layers which represent the edge feature information of the target are based on the fusion strategy based on regional energy. In the process of source image reconstruction with Laplacian pyramid image, we compare the fusion results with four kinds of basal images. The algorithm is tested using Matlab and compared with the different fusion strategies. We use information entropy, average gradient and standard deviation these three objective evaluation parameters for the further analysis of the fusion result. Different low illumination environment experiments show that the algorithm in this paper can rapidly get wide dynamic range while keeping high entropy. Through the verification of this algorithm features, there is a further application prospect of the optimized algorithm. Keywords: high dynamic range imaging, image fusion, multi-exposure image, weight coefficient, information fusion, Laplacian pyramid transform.
Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application.

PubMed

Chen, Pengyun; Zhang, Yichen; Jia, Zhenhong; Yang, Jie; Kasabov, Nikola

2017-06-06

Traditional image change detection based on a non-subsampled contourlet transform always ignores the neighborhood information's relationship to the non-subsampled contourlet coefficients, and the detection results are susceptible to noise interference. To address these disadvantages, we propose a denoising method based on the non-subsampled contourlet transform domain that uses the Hidden Markov Tree model (NSCT-HMT) for change detection of remote sensing images. First, the ENVI software is used to calibrate the original remote sensing images. After that, the mean-ratio operation is adopted to obtain the difference image that will be denoised by the NSCT-HMT model. Then, using the Fuzzy Local Information C-means (FLICM) algorithm, the difference image is divided into the change area and unchanged area. The proposed algorithm is applied to a real remote sensing data set. The application results show that the proposed algorithm can effectively suppress clutter noise, and retain more detailed information from the original images. The proposed algorithm has higher detection accuracy than the Markov Random Field-Fuzzy C-means (MRF-FCM), the non-subsampled contourlet transform-Fuzzy C-means clustering (NSCT-FCM), the pointwise approach and graph theory (PA-GT), and the Principal Component Analysis-Nonlocal Means (PCA-NLM) denosing algorithm. Finally, the five algorithms are used to detect the southern boundary of the Gurbantunggut Desert in Xinjiang Uygur Autonomous Region of China, and the results show that the proposed algorithm has the best effect on real remote sensing image change detection.
Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application

PubMed Central

Chen, Pengyun; Zhang, Yichen; Jia, Zhenhong; Yang, Jie; Kasabov, Nikola

2017-01-01

Traditional image change detection based on a non-subsampled contourlet transform always ignores the neighborhood information’s relationship to the non-subsampled contourlet coefficients, and the detection results are susceptible to noise interference. To address these disadvantages, we propose a denoising method based on the non-subsampled contourlet transform domain that uses the Hidden Markov Tree model (NSCT-HMT) for change detection of remote sensing images. First, the ENVI software is used to calibrate the original remote sensing images. After that, the mean-ratio operation is adopted to obtain the difference image that will be denoised by the NSCT-HMT model. Then, using the Fuzzy Local Information C-means (FLICM) algorithm, the difference image is divided into the change area and unchanged area. The proposed algorithm is applied to a real remote sensing data set. The application results show that the proposed algorithm can effectively suppress clutter noise, and retain more detailed information from the original images. The proposed algorithm has higher detection accuracy than the Markov Random Field-Fuzzy C-means (MRF-FCM), the non-subsampled contourlet transform-Fuzzy C-means clustering (NSCT-FCM), the pointwise approach and graph theory (PA-GT), and the Principal Component Analysis-Nonlocal Means (PCA-NLM) denosing algorithm. Finally, the five algorithms are used to detect the southern boundary of the Gurbantunggut Desert in Xinjiang Uygur Autonomous Region of China, and the results show that the proposed algorithm has the best effect on real remote sensing image change detection. PMID:28587299
Inverse consistent non-rigid image registration based on robust point set matching

PubMed Central

2014-01-01

Background Robust point matching (RPM) has been extensively used in non-rigid registration of images to robustly register two sets of image points. However, except for the location at control points, RPM cannot estimate the consistent correspondence between two images because RPM is a unidirectional image matching approach. Therefore, it is an important issue to make an improvement in image registration based on RPM. Methods In our work, a consistent image registration approach based on the point sets matching is proposed to incorporate the property of inverse consistency and improve registration accuracy. Instead of only estimating the forward transformation between the source point sets and the target point sets in state-of-the-art RPM algorithms, the forward and backward transformations between two point sets are estimated concurrently in our algorithm. The inverse consistency constraints are introduced to the cost function of RPM and the fuzzy correspondences between two point sets are estimated based on both the forward and backward transformations simultaneously. A modified consistent landmark thin-plate spline registration is discussed in detail to find the forward and backward transformations during the optimization of RPM. The similarity of image content is also incorporated into point matching in order to improve image matching. Results Synthetic data sets, medical images are employed to demonstrate and validate the performance of our approach. The inverse consistent errors of our algorithm are smaller than RPM. Especially, the topology of transformations is preserved well for our algorithm for the large deformation between point sets. Moreover, the distance errors of our algorithm are similar to that of RPM, and they maintain a downward trend as whole, which demonstrates the convergence of our algorithm. The registration errors for image registrations are evaluated also. Again, our algorithm achieves the lower registration errors in same iteration number. The determinant of the Jacobian matrix of the deformation field is used to analyse the smoothness of the forward and backward transformations. The forward and backward transformations estimated by our algorithm are smooth for small deformation. For registration of lung slices and individual brain slices, large or small determinant of the Jacobian matrix of the deformation fields are observed. Conclusions Results indicate the improvement of the proposed algorithm in bi-directional image registration and the decrease of the inverse consistent errors of the forward and the reverse transformations between two images. PMID:25559889
Applying a visual language for image processing as a graphical teaching tool in medical imaging

NASA Astrophysics Data System (ADS)

Birchman, James J.; Tanimoto, Steven L.; Rowberg, Alan H.; Choi, Hyung-Sik; Kim, Yongmin

1992-05-01

Typical user interaction in image processing is with command line entries, pull-down menus, or text menu selections from a list, and as such is not generally graphical in nature. Although applying these interactive methods to construct more sophisticated algorithms from a series of simple image processing steps may be clear to engineers and programmers, it may not be clear to clinicians. A solution to this problem is to implement a visual programming language using visual representations to express image processing algorithms. Visual representations promote a more natural and rapid understanding of image processing algorithms by providing more visual insight into what the algorithms do than the interactive methods mentioned above can provide. Individuals accustomed to dealing with images will be more likely to understand an algorithm that is represented visually. This is especially true of referring physicians, such as surgeons in an intensive care unit. With the increasing acceptance of picture archiving and communications system (PACS) workstations and the trend toward increasing clinical use of image processing, referring physicians will need to learn more sophisticated concepts than simply image access and display. If the procedures that they perform commonly, such as window width and window level adjustment and image enhancement using unsharp masking, are depicted visually in an interactive environment, it will be easier for them to learn and apply these concepts. The software described in this paper is a visual programming language for imaging processing which has been implemented on the NeXT computer using NeXTstep user interface development tools and other tools in an object-oriented environment. The concept is based upon the description of a visual language titled `Visualization of Vision Algorithms' (VIVA). Iconic representations of simple image processing steps are placed into a workbench screen and connected together into a dataflow path by the user. As the user creates and edits a dataflow path, more complex algorithms can be built on the screen. Once the algorithm is built, it can be executed, its results can be reviewed, and operator parameters can be interactively adjusted until an optimized output is produced. The optimized algorithm can then be saved and added to the system as a new operator. This system has been evaluated as a graphical teaching tool for window width and window level adjustment, image enhancement using unsharp masking, and other techniques.
Segmentation of pomegranate MR images using spatial fuzzy c-means (SFCM) algorithm

NASA Astrophysics Data System (ADS)

Moradi, Ghobad; Shamsi, Mousa; Sedaaghi, M. H.; Alsharif, M. R.

2011-10-01

Segmentation is one of the fundamental issues of image processing and machine vision. It plays a prominent role in a variety of image processing applications. In this paper, one of the most important applications of image processing in MRI segmentation of pomegranate is explored. Pomegranate is a fruit with pharmacological properties such as being anti-viral and anti-cancer. Having a high quality product in hand would be critical factor in its marketing. The internal quality of the product is comprehensively important in the sorting process. The determination of qualitative features cannot be manually made. Therefore, the segmentation of the internal structures of the fruit needs to be performed as accurately as possible in presence of noise. Fuzzy c-means (FCM) algorithm is noise-sensitive and pixels with noise are classified inversely. As a solution, in this paper, the spatial FCM algorithm in pomegranate MR images' segmentation is proposed. The algorithm is performed with setting the spatial neighborhood information in FCM and modification of fuzzy membership function for each class. The segmentation algorithm results on the original and the corrupted Pomegranate MR images by Gaussian, Salt Pepper and Speckle noises show that the SFCM algorithm operates much more significantly than FCM algorithm. Also, after diverse steps of qualitative and quantitative analysis, we have concluded that the SFCM algorithm with 5×5 window size is better than the other windows.
A novel material detection algorithm based on 2D GMM-based power density function and image detail addition scheme in dual energy X-ray images.

PubMed

Pourghassem, Hossein

2012-01-01

Material detection is a vital need in dual energy X-ray luggage inspection systems at security of airport and strategic places. In this paper, a novel material detection algorithm based on statistical trainable models using 2-Dimensional power density function (PDF) of three material categories in dual energy X-ray images is proposed. In this algorithm, the PDF of each material category as a statistical model is estimated from transmission measurement values of low and high energy X-ray images by Gaussian Mixture Models (GMM). Material label of each pixel of object is determined based on dependency probability of its transmission measurement values in the low and high energy to PDF of three material categories (metallic, organic and mixed materials). The performance of material detection algorithm is improved by a maximum voting scheme in a neighborhood of image as a post-processing stage. Using two background removing and denoising stages, high and low energy X-ray images are enhanced as a pre-processing procedure. For improving the discrimination capability of the proposed material detection algorithm, the details of the low and high energy X-ray images are added to constructed color image which includes three colors (orange, blue and green) for representing the organic, metallic and mixed materials. The proposed algorithm is evaluated on real images that had been captured from a commercial dual energy X-ray luggage inspection system. The obtained results show that the proposed algorithm is effective and operative in detection of the metallic, organic and mixed materials with acceptable accuracy.
Quantitative fluorescence microscopy and image deconvolution.

PubMed

Swedlow, Jason R

2013-01-01

Quantitative imaging and image deconvolution have become standard techniques for the modern cell biologist because they can form the basis of an increasing number of assays for molecular function in a cellular context. There are two major types of deconvolution approaches--deblurring and restoration algorithms. Deblurring algorithms remove blur but treat a series of optical sections as individual two-dimensional entities and therefore sometimes mishandle blurred light. Restoration algorithms determine an object that, when convolved with the point-spread function of the microscope, could produce the image data. The advantages and disadvantages of these methods are discussed in this chapter. Image deconvolution in fluorescence microscopy has usually been applied to high-resolution imaging to improve contrast and thus detect small, dim objects that might otherwise be obscured. Their proper use demands some consideration of the imaging hardware, the acquisition process, fundamental aspects of photon detection, and image processing. This can prove daunting for some cell biologists, but the power of these techniques has been proven many times in the works cited in the chapter and elsewhere. Their usage is now well defined, so they can be incorporated into the capabilities of most laboratories. A major application of fluorescence microscopy is the quantitative measurement of the localization, dynamics, and interactions of cellular factors. The introduction of green fluorescent protein and its spectral variants has led to a significant increase in the use of fluorescence microscopy as a quantitative assay system. For quantitative imaging assays, it is critical to consider the nature of the image-acquisition system and to validate its response to known standards. Any image-processing algorithms used before quantitative analysis should preserve the relative signal levels in different parts of the image. A very common image-processing algorithm, image deconvolution, is used to remove blurred signal from an image. There are two major types of deconvolution approaches, deblurring and restoration algorithms. Deblurring algorithms remove blur, but treat a series of optical sections as individual two-dimensional entities, and therefore sometimes mishandle blurred light. Restoration algorithms determine an object that, when convolved with the point-spread function of the microscope, could produce the image data. The advantages and disadvantages of these methods are discussed. Copyright © 1998 Elsevier Inc. All rights reserved.

Comparison among Reconstruction Algorithms for Quantitative Analysis of 11C-Acetate Cardiac PET Imaging.

PubMed

Shi, Ximin; Li, Nan; Ding, Haiyan; Dang, Yonghong; Hu, Guilan; Liu, Shuai; Cui, Jie; Zhang, Yue; Li, Fang; Zhang, Hui; Huo, Li

2018-01-01

Kinetic modeling of dynamic 11 C-acetate PET imaging provides quantitative information for myocardium assessment. The quality and quantitation of PET images are known to be dependent on PET reconstruction methods. This study aims to investigate the impacts of reconstruction algorithms on the quantitative analysis of dynamic 11 C-acetate cardiac PET imaging. Suspected alcoholic cardiomyopathy patients ( N = 24) underwent 11 C-acetate dynamic PET imaging after low dose CT scan. PET images were reconstructed using four algorithms: filtered backprojection (FBP), ordered subsets expectation maximization (OSEM), OSEM with time-of-flight (TOF), and OSEM with both time-of-flight and point-spread-function (TPSF). Standardized uptake values (SUVs) at different time points were compared among images reconstructed using the four algorithms. Time-activity curves (TACs) in myocardium and blood pools of ventricles were generated from the dynamic image series. Kinetic parameters K 1 and k 2 were derived using a 1-tissue-compartment model for kinetic modeling of cardiac flow from 11 C-acetate PET images. Significant image quality improvement was found in the images reconstructed using iterative OSEM-type algorithms (OSME, TOF, and TPSF) compared with FBP. However, no statistical differences in SUVs were observed among the four reconstruction methods at the selected time points. Kinetic parameters K 1 and k 2 also exhibited no statistical difference among the four reconstruction algorithms in terms of mean value and standard deviation. However, for the correlation analysis, OSEM reconstruction presented relatively higher residual in correlation with FBP reconstruction compared with TOF and TPSF reconstruction, and TOF and TPSF reconstruction were highly correlated with each other. All the tested reconstruction algorithms performed similarly for quantitative analysis of 11 C-acetate cardiac PET imaging. TOF and TPSF yielded highly consistent kinetic parameter results with superior image quality compared with FBP. OSEM was relatively less reliable. Both TOF and TPSF were recommended for cardiac 11 C-acetate kinetic analysis.
Real-time image-processing algorithm for markerless tumour tracking using X-ray fluoroscopic imaging.

PubMed

Mori, S

2014-05-01

To ensure accuracy in respiratory-gating treatment, X-ray fluoroscopic imaging is used to detect tumour position in real time. Detection accuracy is strongly dependent on image quality, particularly positional differences between the patient and treatment couch. We developed a new algorithm to improve the quality of images obtained in X-ray fluoroscopic imaging and report the preliminary results. Two oblique X-ray fluoroscopic images were acquired using a dynamic flat panel detector (DFPD) for two patients with lung cancer. The weighting factor was applied to the DFPD image in respective columns, because most anatomical structures, as well as the treatment couch and port cover edge, were aligned in the superior-inferior direction when the patient lay on the treatment couch. The weighting factors for the respective columns were varied until the standard deviation of the pixel values within the image region was minimized. Once the weighting factors were calculated, the quality of the DFPD image was improved by applying the factors to multiframe images. Applying the image-processing algorithm produced substantial improvement in the quality of images, and the image contrast was increased. The treatment couch and irradiation port edge, which were not related to a patient's position, were removed. The average image-processing time was 1.1 ms, showing that this fast image processing can be applied to real-time tumour-tracking systems. These findings indicate that this image-processing algorithm improves the image quality in patients with lung cancer and successfully removes objects not related to the patient. Our image-processing algorithm might be useful in improving gated-treatment accuracy.
An underwater turbulence degraded image restoration algorithm

NASA Astrophysics Data System (ADS)

Furhad, Md. Hasan; Tahtali, Murat; Lambert, Andrew

2017-09-01

Underwater turbulence occurs due to random fluctuations of temperature and salinity in the water. These fluctuations are responsible for variations in water density, refractive index and attenuation. These impose random geometric distortions, spatio-temporal varying blur, limited range visibility and limited contrast on the acquired images. There are some restoration techniques developed to address this problem, such as image registration based, lucky region based and centroid-based image restoration algorithms. Although these methods demonstrate better results in terms of removing turbulence, they require computationally intensive image registration, higher CPU load and memory allocations. Thus, in this paper, a simple patch based dictionary learning algorithm is proposed to restore the image by alleviating the costly image registration step. Dictionary learning is a machine learning technique which builds a dictionary of non-zero atoms derived from the sparse representation of an image or signal. The image is divided into several patches and the sharp patches are detected from them. Next, dictionary learning is performed on these patches to estimate the restored image. Finally, an image deconvolution algorithm is employed on the estimated restored image to remove noise that still exists.
JPEG2000 still image coding quality.

PubMed

Chen, Tzong-Jer; Lin, Sheng-Chieh; Lin, You-Chen; Cheng, Ren-Gui; Lin, Li-Hui; Wu, Wei

2013-10-01

This work demonstrates the image qualities between two popular JPEG2000 programs. Two medical image compression algorithms are both coded using JPEG2000, but they are different regarding the interface, convenience, speed of computation, and their characteristic options influenced by the encoder, quantization, tiling, etc. The differences in image quality and compression ratio are also affected by the modality and compression algorithm implementation. Do they provide the same quality? The qualities of compressed medical images from two image compression programs named Apollo and JJ2000 were evaluated extensively using objective metrics. These algorithms were applied to three medical image modalities at various compression ratios ranging from 10:1 to 100:1. Following that, the quality of the reconstructed images was evaluated using five objective metrics. The Spearman rank correlation coefficients were measured under every metric in the two programs. We found that JJ2000 and Apollo exhibited indistinguishable image quality for all images evaluated using the above five metrics (r > 0.98, p < 0.001). It can be concluded that the image quality of the JJ2000 and Apollo algorithms is statistically equivalent for medical image compression.
GPU accelerated fuzzy connected image segmentation by using CUDA.

PubMed

Zhuge, Ying; Cao, Yong; Miller, Robert W

2009-01-01

Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem of these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays commodity graphics hardware provides high parallel computing power. In this paper, we present a parallel fuzzy connected image segmentation algorithm on Nvidia's Compute Unified Device Architecture (CUDA) platform for segmenting large medical image data sets. Our experiments based on three data sets with small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 7.2x, 7.3x, and 14.4x, correspondingly, for the three data sets over the sequential implementation of fuzzy connected image segmentation algorithm on CPU.
A hybrid algorithm for speckle noise reduction of ultrasound images.

PubMed

Singh, Karamjeet; Ranade, Sukhjeet Kaur; Singh, Chandan

2017-09-01

Medical images are contaminated by multiplicative speckle noise which significantly reduce the contrast of ultrasound images and creates a negative effect on various image interpretation tasks. In this paper, we proposed a hybrid denoising approach which collaborate the both local and nonlocal information in an efficient manner. The proposed hybrid algorithm consist of three stages in which at first stage the use of local statistics in the form of guided filter is used to reduce the effect of speckle noise initially. Then, an improved speckle reducing bilateral filter (SRBF) is developed to further reduce the speckle noise from the medical images. Finally, to reconstruct the diffused edges we have used the efficient post-processing technique which jointly considered the advantages of both bilateral and nonlocal mean (NLM) filter for the attenuation of speckle noise efficiently. The performance of proposed hybrid algorithm is evaluated on synthetic, simulated and real ultrasound images. The experiments conducted on various test images demonstrate that our proposed hybrid approach outperforms the various traditional speckle reduction approaches included recently proposed NLM and optimized Bayesian-based NLM. The results of various quantitative, qualitative measures and by visual inspection of denoise synthetic and real ultrasound images demonstrate that the proposed hybrid algorithm have strong denoising capability and able to preserve the fine image details such as edge of a lesion better than previously developed methods for speckle noise reduction. The denoising and edge preserving capability of hybrid algorithm is far better than existing traditional and recently proposed speckle reduction (SR) filters. The success of proposed algorithm would help in building the lay foundation for inventing the hybrid algorithms for denoising of ultrasound images. Copyright © 2017 Elsevier B.V. All rights reserved.
Locally adaptive vector quantization: Data compression with feature preservation

NASA Technical Reports Server (NTRS)

Cheung, K. M.; Sayano, M.

1992-01-01

A study of a locally adaptive vector quantization (LAVQ) algorithm for data compression is presented. This algorithm provides high-speed one-pass compression and is fully adaptable to any data source and does not require a priori knowledge of the source statistics. Therefore, LAVQ is a universal data compression algorithm. The basic algorithm and several modifications to improve performance are discussed. These modifications are nonlinear quantization, coarse quantization of the codebook, and lossless compression of the output. Performance of LAVQ on various images using irreversible (lossy) coding is comparable to that of the Linde-Buzo-Gray algorithm, but LAVQ has a much higher speed; thus this algorithm has potential for real-time video compression. Unlike most other image compression algorithms, LAVQ preserves fine detail in images. LAVQ's performance as a lossless data compression algorithm is comparable to that of Lempel-Ziv-based algorithms, but LAVQ uses far less memory during the coding process.
Automated algorithm for CBCT-based dose calculations of prostate radiotherapy with bilateral hip prostheses.

PubMed

Almatani, Turki; Hugtenburg, Richard P; Lewis, Ryan D; Barley, Susan E; Edwards, Mark A

2016-10-01

Cone beam CT (CBCT) images contain more scatter than a conventional CT image and therefore provide inaccurate Hounsfield units (HUs). Consequently, CBCT images cannot be used directly for radiotherapy dose calculation. The aim of this study is to enable dose calculations to be performed with the use of CBCT images taken during radiotherapy and evaluate the necessity of replanning. A patient with prostate cancer with bilateral metallic prosthetic hip replacements was imaged using both CT and CBCT. The multilevel threshold (MLT) algorithm was used to categorize pixel values in the CBCT images into segments of homogeneous HU. The variation in HU with position in the CBCT images was taken into consideration. This segmentation method relies on the operator dividing the CBCT data into a set of volumes where the variation in the relationship between pixel values and HUs is small. An automated MLT algorithm was developed to reduce the operator time associated with the process. An intensity-modulated radiation therapy plan was generated from CT images of the patient. The plan was then copied to the segmented CBCT (sCBCT) data sets with identical settings, and the doses were recalculated and compared. Gamma evaluation showed that the percentage of points in the rectum with γ < 1 (3%/3 mm) were 98.7% and 97.7% in the sCBCT using MLT and the automated MLT algorithms, respectively. Compared with the planning CT (pCT) plan, the MLT algorithm showed -0.46% dose difference with 8 h operator time while the automated MLT algorithm showed -1.3%, which are both considered to be clinically acceptable, when using collapsed cone algorithm. The segmentation of CBCT images using the method in this study can be used for dose calculation. For a patient with prostate cancer with bilateral hip prostheses and the associated issues with CT imaging, the MLT algorithms achieved a sufficient dose calculation accuracy that is clinically acceptable. The automated MLT algorithm reduced the operator time associated with implementing the MLT algorithm to achieve clinically acceptable accuracy. This saved time makes the automated MLT algorithm superior and easier to implement in the clinical setting. The MLT algorithm has been extended to the complex example of a patient with bilateral hip prostheses, which with the introduction of automation is feasible for use in adaptive radiotherapy, as an alternative to obtaining a new pCT and reoutlining the structures.
A new automatic algorithm for quantification of myocardial infarction imaged by late gadolinium enhancement cardiovascular magnetic resonance: experimental validation and comparison to expert delineations in multi-center, multi-vendor patient data.

PubMed

Engblom, Henrik; Tufvesson, Jane; Jablonowski, Robert; Carlsson, Marcus; Aletras, Anthony H; Hoffmann, Pavel; Jacquier, Alexis; Kober, Frank; Metzler, Bernhard; Erlinge, David; Atar, Dan; Arheden, Håkan; Heiberg, Einar

2016-05-04

Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) using magnitude inversion recovery (IR) or phase sensitive inversion recovery (PSIR) has become clinical standard for assessment of myocardial infarction (MI). However, there is no clinical standard for quantification of MI even though multiple methods have been proposed. Simple thresholds have yielded varying results and advanced algorithms have only been validated in single center studies. Therefore, the aim of this study was to develop an automatic algorithm for MI quantification in IR and PSIR LGE images and to validate the new algorithm experimentally and compare it to expert delineations in multi-center, multi-vendor patient data. The new automatic algorithm, EWA (Expectation Maximization, weighted intensity, a priori information), was implemented using an intensity threshold by Expectation Maximization (EM) and a weighted summation to account for partial volume effects. The EWA algorithm was validated in-vivo against triphenyltetrazolium-chloride (TTC) staining (n = 7 pigs with paired IR and PSIR images) and against ex-vivo high resolution T1-weighted images (n = 23 IR and n = 13 PSIR images). The EWA algorithm was also compared to expert delineation in 124 patients from multi-center, multi-vendor clinical trials 2-6 days following first time ST-elevation myocardial infarction (STEMI) treated with percutaneous coronary intervention (PCI) (n = 124 IR and n = 49 PSIR images). Infarct size by the EWA algorithm in vivo in pigs showed a bias to ex-vivo TTC of -1 ± 4%LVM (R = 0.84) in IR and -2 ± 3%LVM (R = 0.92) in PSIR images and a bias to ex-vivo T1-weighted images of 0 ± 4%LVM (R = 0.94) in IR and 0 ± 5%LVM (R = 0.79) in PSIR images. In multi-center patient studies, infarct size by the EWA algorithm showed a bias to expert delineation of -2 ± 6 %LVM (R = 0.81) in IR images (n = 124) and 0 ± 5%LVM (R = 0.89) in PSIR images (n = 49). The EWA algorithm was validated experimentally and in patient data with a low bias in both IR and PSIR LGE images. Thus, the use of EM and a weighted intensity as in the EWA algorithm, may serve as a clinical standard for the quantification of myocardial infarction in LGE CMR images. CHILL-MI: NCT01379261 . NCT01374321 .
Evaluating some computer exhancement algorithms that improve the visibility of cometary morphology

NASA Technical Reports Server (NTRS)

Larson, Stephen M.; Slaughter, Charles D.

1992-01-01

Digital enhancement of cometary images is a necessary tool in studying cometary morphology. Many image processing algorithms, some developed specifically for comets, have been used to enhance the subtle, low contrast coma and tail features. We compare some of the most commonly used algorithms on two different images to evaluate their strong and weak points, and conclude that there currently exists no single 'ideal' algorithm, although the radial gradient spatial filter gives the best overall result. This comparison should aid users in selecting the best algorithm to enhance particular features of interest.
A general Bayesian image reconstruction algorithm with entropy prior: Preliminary application to HST data

NASA Astrophysics Data System (ADS)

Nunez, Jorge; Llacer, Jorge

1993-10-01

This paper describes a general Bayesian iterative algorithm with entropy prior for image reconstruction. It solves the cases of both pure Poisson data and Poisson data with Gaussian readout noise. The algorithm maintains positivity of the solution; it includes case-specific prior information (default map) and flatfield corrections; it removes background and can be accelerated to be faster than the Richardson-Lucy algorithm. In order to determine the hyperparameter that balances the entropy and liklihood terms in the Bayesian approach, we have used a liklihood cross-validation technique. Cross-validation is more robust than other methods because it is less demanding in terms of the knowledge of exact data characteristics and of the point-spread function. We have used the algorithm to reconstruct successfully images obtained in different space-and ground-based imaging situations. It has been possible to recover most of the original intended capabilities of the Hubble Space Telescope (HST) wide field and planetary camera (WFPC) and faint object camera (FOC) from images obtained in their present state. Semireal simulations for the future wide field planetary camera 2 show that even after the repair of the spherical abberration problem, image reconstruction can play a key role in improving the resolution of the cameras, well beyond the design of the Hubble instruments. We also show that ground-based images can be reconstructed successfully with the algorithm. A technique which consists of dividing the CCD observations into two frames, with one-half the exposure time each, emerges as a recommended procedure for the utilization of the described algorithms. We have compared our technique with two commonly used reconstruction algorithms: the Richardson-Lucy and the Cambridge maximum entropy algorithms.
LiveWire interactive boundary extraction algorithm based on Haar wavelet transform and control point set direction search

NASA Astrophysics Data System (ADS)

Cheng, Jun; Zhang, Jun; Tian, Jinwen

2015-12-01

Based on deep analysis of the LiveWire interactive boundary extraction algorithm, a new algorithm focusing on improving the speed of LiveWire algorithm is proposed in this paper. Firstly, the Haar wavelet transform is carried on the input image, and the boundary is extracted on the low resolution image obtained by the wavelet transform of the input image. Secondly, calculating LiveWire shortest path is based on the control point set direction search by utilizing the spatial relationship between the two control points users provide in real time. Thirdly, the search order of the adjacent points of the starting node is set in advance. An ordinary queue instead of a priority queue is taken as the storage pool of the points when optimizing their shortest path value, thus reducing the complexity of the algorithm from O[n2] to O[n]. Finally, A region iterative backward projection method based on neighborhood pixel polling has been used to convert dual-pixel boundary of the reconstructed image to single-pixel boundary after Haar wavelet inverse transform. The algorithm proposed in this paper combines the advantage of the Haar wavelet transform and the advantage of the optimal path searching method based on control point set direction search. The former has fast speed of image decomposition and reconstruction and is more consistent with the texture features of the image and the latter can reduce the time complexity of the original algorithm. So that the algorithm can improve the speed in interactive boundary extraction as well as reflect the boundary information of the image more comprehensively. All methods mentioned above have a big role in improving the execution efficiency and the robustness of the algorithm.
Mixture of learners for cancer stem cell detection using CD13 and H and E stained images

NASA Astrophysics Data System (ADS)

Oǧuz, Oǧuzhan; Akbaş, Cem Emre; Mallah, Maen; Taşdemir, Kasım.; Akhan Güzelcan, Ece; Muenzenmayer, Christian; Wittenberg, Thomas; Üner, Ayşegül; Cetin, A. E.; ćetin Atalay, Rengül

2016-03-01

In this article, algorithms for cancer stem cell (CSC) detection in liver cancer tissue images are developed. Conventionally, a pathologist examines of cancer cell morphologies under microscope. Computer aided diagnosis systems (CAD) aims to help pathologists in this tedious and repetitive work. The first algorithm locates CSCs in CD13 stained liver tissue images. The method has also an online learning algorithm to improve the accuracy of detection. The second family of algorithms classify the cancer tissues stained with H and E which is clinically routine and cost effective than immunohistochemistry (IHC) procedure. The algorithms utilize 1D-SIFT and Eigen-analysis based feature sets as descriptors. Normal and cancerous tissues can be classified with 92.1% accuracy in H and E stained images. Classification accuracy of low and high-grade cancerous tissue images is 70.4%. Therefore, this study paves the way for diagnosing the cancerous tissue and grading the level of it using H and E stained microscopic tissue images.
Multichannel blind iterative image restoration.

PubMed

Sroubek, Filip; Flusser, Jan

2003-01-01

Blind image deconvolution is required in many applications of microscopy imaging, remote sensing, and astronomical imaging. Unfortunately in a single-channel framework, serious conceptual and numerical problems are often encountered. Very recently, an eigenvector-based method (EVAM) was proposed for a multichannel framework which determines perfectly convolution masks in a noise-free environment if channel disparity, called co-primeness, is satisfied. We propose a novel iterative algorithm based on recent anisotropic denoising techniques of total variation and a Mumford-Shah functional with the EVAM restoration condition included. A linearization scheme of half-quadratic regularization together with a cell-centered finite difference discretization scheme is used in the algorithm and provides a unified approach to the solution of total variation or Mumford-Shah. The algorithm performs well even on very noisy images and does not require an exact estimation of mask orders. We demonstrate capabilities of the algorithm on synthetic data. Finally, the algorithm is applied to defocused images taken with a digital camera and to data from astronomical ground-based observations of the Sun.
Complex-based OCT angiography algorithm recovers microvascular information better than amplitude- or phase-based algorithms in phase-stable systems

NASA Astrophysics Data System (ADS)

Xu, Jingjiang; Song, Shaozhen; Li, Yuandong; Wang, Ruikang K.

2018-01-01

Optical coherence tomography angiography (OCTA) is increasingly becoming a popular inspection tool for biomedical imaging applications. By exploring the amplitude, phase and complex information available in OCT signals, numerous algorithms have been proposed that contrast functional vessel networks within microcirculatory tissue beds. However, it is not clear which algorithm delivers optimal imaging performance. Here, we investigate systematically how amplitude and phase information have an impact on the OCTA imaging performance, to establish the relationship of amplitude and phase stability with OCT signal-to-noise ratio (SNR), time interval and particle dynamics. With either repeated A-scan or repeated B-scan imaging protocols, the amplitude noise increases with the increase of OCT SNR; however, the phase noise does the opposite, i.e. it increases with the decrease of OCT SNR. Coupled with experimental measurements, we utilize a simple Monte Carlo (MC) model to simulate the performance of amplitude-, phase- and complex-based algorithms for OCTA imaging, the results of which suggest that complex-based algorithms deliver the best performance when the phase noise is < ~40 mrad. We also conduct a series of in vivo vascular imaging in animal models and human retina to verify the findings from the MC model through assessing the OCTA performance metrics of vessel connectivity, image SNR and contrast-to-noise ratio. We show that for all the metrics assessed, the complex-based algorithm delivers better performance than either the amplitude- or phase-based algorithms for both the repeated A-scan and the B-scan imaging protocols, which agrees well with the conclusion drawn from the MC simulations.
Inter-slice bidirectional registration-based segmentation of the prostate gland in MR and CT image sequences

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khalvati, Farzad, E-mail: farzad.khalvati@uwaterloo.ca; Tizhoosh, Hamid R.; Salmanpour, Aryan

Purpose: Accurate segmentation and volume estimation of the prostate gland in magnetic resonance (MR) and computed tomography (CT) images are necessary steps in diagnosis, treatment, and monitoring of prostate cancer. This paper presents an algorithm for the prostate gland volume estimation based on the semiautomated segmentation of individual slices in T2-weighted MR and CT image sequences. Methods: The proposedInter-Slice Bidirectional Registration-based Segmentation (iBRS) algorithm relies on interslice image registration of volume data to segment the prostate gland without the use of an anatomical atlas. It requires the user to mark only three slices in a given volume dataset, i.e., themore » first, middle, and last slices. Next, the proposed algorithm uses a registration algorithm to autosegment the remaining slices. We conducted comprehensive experiments to measure the performance of the proposed algorithm using three registration methods (i.e., rigid, affine, and nonrigid techniques). Results: The results with the proposed technique were compared with manual marking using prostate MR and CT images from 117 patients. Manual marking was performed by an expert user for all 117 patients. The median accuracies for individual slices measured using the Dice similarity coefficient (DSC) were 92% and 91% for MR and CT images, respectively. The iBRS algorithm was also evaluated regarding user variability, which confirmed that the algorithm was robust to interuser variability when marking the prostate gland. Conclusions: The proposed algorithm exploits the interslice data redundancy of the images in a volume dataset of MR and CT images and eliminates the need for an atlas, minimizing the computational cost while producing highly accurate results which are robust to interuser variability.« less
Inter-slice bidirectional registration-based segmentation of the prostate gland in MR and CT image sequences

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khalvati, Farzad, E-mail: farzad.khalvati@uwaterloo.ca; Tizhoosh, Hamid R.; Salmanpour, Aryan

2013-12-15

Purpose: Accurate segmentation and volume estimation of the prostate gland in magnetic resonance (MR) and computed tomography (CT) images are necessary steps in diagnosis, treatment, and monitoring of prostate cancer. This paper presents an algorithm for the prostate gland volume estimation based on the semiautomated segmentation of individual slices in T2-weighted MR and CT image sequences. Methods: The proposedInter-Slice Bidirectional Registration-based Segmentation (iBRS) algorithm relies on interslice image registration of volume data to segment the prostate gland without the use of an anatomical atlas. It requires the user to mark only three slices in a given volume dataset, i.e., themore » first, middle, and last slices. Next, the proposed algorithm uses a registration algorithm to autosegment the remaining slices. We conducted comprehensive experiments to measure the performance of the proposed algorithm using three registration methods (i.e., rigid, affine, and nonrigid techniques). Results: The results with the proposed technique were compared with manual marking using prostate MR and CT images from 117 patients. Manual marking was performed by an expert user for all 117 patients. The median accuracies for individual slices measured using the Dice similarity coefficient (DSC) were 92% and 91% for MR and CT images, respectively. The iBRS algorithm was also evaluated regarding user variability, which confirmed that the algorithm was robust to interuser variability when marking the prostate gland. Conclusions: The proposed algorithm exploits the interslice data redundancy of the images in a volume dataset of MR and CT images and eliminates the need for an atlas, minimizing the computational cost while producing highly accurate results which are robust to interuser variability.« less
Complex-based OCT angiography algorithm recovers microvascular information better than amplitude- or phase-based algorithms in phase-stable systems.

PubMed

Xu, Jingjiang; Song, Shaozhen; Li, Yuandong; Wang, Ruikang K

2017-12-19

Optical coherence tomography angiography (OCTA) is increasingly becoming a popular inspection tool for biomedical imaging applications. By exploring the amplitude, phase and complex information available in OCT signals, numerous algorithms have been proposed that contrast functional vessel networks within microcirculatory tissue beds. However, it is not clear which algorithm delivers optimal imaging performance. Here, we investigate systematically how amplitude and phase information have an impact on the OCTA imaging performance, to establish the relationship of amplitude and phase stability with OCT signal-to-noise ratio (SNR), time interval and particle dynamics. With either repeated A-scan or repeated B-scan imaging protocols, the amplitude noise increases with the increase of OCT SNR; however, the phase noise does the opposite, i.e. it increases with the decrease of OCT SNR. Coupled with experimental measurements, we utilize a simple Monte Carlo (MC) model to simulate the performance of amplitude-, phase- and complex-based algorithms for OCTA imaging, the results of which suggest that complex-based algorithms deliver the best performance when the phase noise is < ~40 mrad. We also conduct a series of in vivo vascular imaging in animal models and human retina to verify the findings from the MC model through assessing the OCTA performance metrics of vessel connectivity, image SNR and contrast-to-noise ratio. We show that for all the metrics assessed, the complex-based algorithm delivers better performance than either the amplitude- or phase-based algorithms for both the repeated A-scan and the B-scan imaging protocols, which agrees well with the conclusion drawn from the MC simulations.
A Demons algorithm for image registration with locally adaptive regularization.

PubMed

Cahill, Nathan D; Noble, J Alison; Hawkes, David J

2009-01-01

Thirion's Demons is a popular algorithm for nonrigid image registration because of its linear computational complexity and ease of implementation. It approximately solves the diffusion registration problem by successively estimating force vectors that drive the deformation toward alignment and smoothing the force vectors by Gaussian convolution. In this article, we show how the Demons algorithm can be generalized to allow image-driven locally adaptive regularization in a manner that preserves both the linear complexity and ease of implementation of the original Demons algorithm. We show that the proposed algorithm exhibits lower target registration error and requires less computational effort than the original Demons algorithm on the registration of serial chest CT scans of patients with lung nodules.
Optimisation and evaluation of hyperspectral imaging system using machine learning algorithm

NASA Astrophysics Data System (ADS)

Suthar, Gajendra; Huang, Jung Y.; Chidangil, Santhosh

2017-10-01

Hyperspectral imaging (HSI), also called imaging spectrometer, originated from remote sensing. Hyperspectral imaging is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the objects physiology, morphology, and composition. The present work involves testing and evaluating the performance of the hyperspectral imaging system. The methodology involved manually taking reflectance of the object in many images or scan of the object. The object used for the evaluation of the system was cabbage and tomato. The data is further converted to the required format and the analysis is done using machine learning algorithm. The machine learning algorithms applied were able to distinguish between the object present in the hypercube obtain by the scan. It was concluded from the results that system was working as expected. This was observed by the different spectra obtained by using the machine-learning algorithm.

Saliency detection algorithm based on LSC-RC

NASA Astrophysics Data System (ADS)

Wu, Wei; Tian, Weiye; Wang, Ding; Luo, Xin; Wu, Yingfei; Zhang, Yu

2018-02-01

Image prominence is the most important region in an image, which can cause the visual attention and response of human beings. Preferentially allocating the computer resources for the image analysis and synthesis by the significant region is of great significance to improve the image area detecting. As a preprocessing of other disciplines in image processing field, the image prominence has widely applications in image retrieval and image segmentation. Among these applications, the super-pixel segmentation significance detection algorithm based on linear spectral clustering (LSC) has achieved good results. The significance detection algorithm proposed in this paper is better than the regional contrast ratio by replacing the method of regional formation in the latter with the linear spectral clustering image is super-pixel block. After combining with the latest depth learning method, the accuracy of the significant region detecting has a great promotion. At last, the superiority and feasibility of the super-pixel segmentation detection algorithm based on linear spectral clustering are proved by the comparative test.
Improved Seam-Line Searching Algorithm for UAV Image Mosaic with Optical Flow.

PubMed

Zhang, Weilong; Guo, Bingxuan; Li, Ming; Liao, Xuan; Li, Wenzhuo

2018-04-16

Ghosting and seams are two major challenges in creating unmanned aerial vehicle (UAV) image mosaic. In response to these problems, this paper proposes an improved method for UAV image seam-line searching. First, an image matching algorithm is used to extract and match the features of adjacent images, so that they can be transformed into the same coordinate system. Then, the gray scale difference, the gradient minimum, and the optical flow value of pixels in adjacent image overlapped area in a neighborhood are calculated, which can be applied to creating an energy function for seam-line searching. Based on that, an improved dynamic programming algorithm is proposed to search the optimal seam-lines to complete the UAV image mosaic. This algorithm adopts a more adaptive energy aggregation and traversal strategy, which can find a more ideal splicing path for adjacent UAV images and avoid the ground objects better. The experimental results show that the proposed method can effectively solve the problems of ghosting and seams in the panoramic UAV images.
Speckle-reduction algorithm for ultrasound images in complex wavelet domain using genetic algorithm-based mixture model.

PubMed

Uddin, Muhammad Shahin; Tahtali, Murat; Lambert, Andrew J; Pickering, Mark R; Marchese, Margaret; Stuart, Iain

2016-05-20

Compared with other medical-imaging modalities, ultrasound (US) imaging is a valuable way to examine the body's internal organs, and two-dimensional (2D) imaging is currently the most common technique used in clinical diagnoses. Conventional 2D US imaging systems are highly flexible cost-effective imaging tools that permit operators to observe and record images of a large variety of thin anatomical sections in real time. Recently, 3D US imaging has also been gaining popularity due to its considerable advantages over 2D US imaging. It reduces dependency on the operator and provides better qualitative and quantitative information for an effective diagnosis. Furthermore, it provides a 3D view, which allows the observation of volume information. The major shortcoming of any type of US imaging is the presence of speckle noise. Hence, speckle reduction is vital in providing a better clinical diagnosis. The key objective of any speckle-reduction algorithm is to attain a speckle-free image while preserving the important anatomical features. In this paper we introduce a nonlinear multi-scale complex wavelet-diffusion based algorithm for speckle reduction and sharp-edge preservation of 2D and 3D US images. In the proposed method we use a Rayleigh and Maxwell-mixture model for 2D and 3D US images, respectively, where a genetic algorithm is used in combination with an expectation maximization method to estimate mixture parameters. Experimental results using both 2D and 3D synthetic, physical phantom, and clinical data demonstrate that our proposed algorithm significantly reduces speckle noise while preserving sharp edges without discernible distortions. The proposed approach performs better than the state-of-the-art approaches in both qualitative and quantitative measures.
Iterative pixelwise approach applied to computer-generated holograms and diffractive optical elements.

PubMed

Hsu, Wei-Feng; Lin, Shih-Chih

2018-01-01

This paper presents a novel approach to optimizing the design of phase-only computer-generated holograms (CGH) for the creation of binary images in an optical Fourier transform system. Optimization begins by selecting an image pixel with a temporal change in amplitude. The modulated image function undergoes an inverse Fourier transform followed by the imposition of a CGH constraint and the Fourier transform to yield an image function associated with the change in amplitude of the selected pixel. In iterations where the quality of the image is improved, that image function is adopted as the input for the next iteration. In cases where the image quality is not improved, the image function before the pixel changed is used as the input. Thus, the proposed approach is referred to as the pixelwise hybrid input-output (PHIO) algorithm. The PHIO algorithm was shown to achieve image quality far exceeding that of the Gerchberg-Saxton (GS) algorithm. The benefits were particularly evident when the PHIO algorithm was equipped with a dynamic range of image intensities equivalent to the amplitude freedom of the image signal. The signal variation of images reconstructed from the GS algorithm was 1.0223, but only 0.2537 when using PHIO, i.e., a 75% improvement. Nonetheless, the proposed scheme resulted in a 10% degradation in diffraction efficiency and signal-to-noise ratio.
A Novel Binarization Algorithm for Ballistics Firearm Identification

NASA Astrophysics Data System (ADS)

Li, Dongguang

The identification of ballistics specimens from imaging systems is of paramount importance in criminal investigation. Binarization plays a key role in preprocess of recognizing cartridges in the ballistic imaging systems. Unfortunately, it is very difficult to get the satisfactory binary image using existing binary algorithms. In this paper, we utilize the global and local thresholds to enhance the image binarization. Importantly, we present a novel criterion for effectively detecting edges in the images. Comprehensive experiments have been conducted over sample ballistic images. The empirical results demonstrate the proposed method can provide a better solution than existing binary algorithms.
Robust mosiacs of close-range high-resolution images

NASA Astrophysics Data System (ADS)

Song, Ran; Szymanski, John E.

2008-03-01

This paper presents a robust algorithm which relies only on the information contained within the captured images for the construction of massive composite mosaic images from close-range and high-resolution originals, such as those obtained when imaging architectural and heritage structures. We first apply Harris algorithm to extract a selection of corners and, then, employ both the intensity correlation and the spatial correlation between the corresponding corners for matching them. Then we estimate the eight-parameter projective transformation matrix by the genetic algorithm. Lastly, image fusion using a weighted blending function together with intensity compensation produces an effective seamless mosaic image.
Automatic macroscopic characterization of diesel sprays by means of a new image processing algorithm

NASA Astrophysics Data System (ADS)

Rubio-Gómez, Guillermo; Martínez-Martínez, S.; Rua-Mojica, Luis F.; Gómez-Gordo, Pablo; de la Garza, Oscar A.

2018-05-01

A novel algorithm is proposed for the automatic segmentation of diesel spray images and the calculation of their macroscopic parameters. The algorithm automatically detects each spray present in an image, and therefore it is able to work with diesel injectors with a different number of nozzle holes without any modification. The main characteristic of the algorithm is that it splits each spray into three different regions and then segments each one with an individually calculated binarization threshold. Each threshold level is calculated from the analysis of a representative luminosity profile of each region. This approach makes it robust to irregular light distribution along a single spray and between different sprays of an image. Once the sprays are segmented, the macroscopic parameters of each one are calculated. The algorithm is tested with two sets of diesel spray images taken under normal and irregular illumination setups.
Image-algebraic design of multispectral target recognition algorithms

NASA Astrophysics Data System (ADS)

Schmalz, Mark S.; Ritter, Gerhard X.

1994-06-01

In this paper, we discuss methods for multispectral ATR (Automated Target Recognition) of small targets that are sensed under suboptimal conditions, such as haze, smoke, and low light levels. In particular, we discuss our ongoing development of algorithms and software that effect intelligent object recognition by selecting ATR filter parameters according to ambient conditions. Our algorithms are expressed in terms of IA (image algebra), a concise, rigorous notation that unifies linear and nonlinear mathematics in the image processing domain. IA has been implemented on a variety of parallel computers, with preprocessors available for the Ada and FORTRAN languages. An image algebra C++ class library has recently been made available. Thus, our algorithms are both feasible implementationally and portable to numerous machines. Analyses emphasize the aspects of image algebra that aid the design of multispectral vision algorithms, such as parameterized templates that facilitate the flexible specification of ATR filters.
An improved wavelet neural network medical image segmentation algorithm with combined maximum entropy

NASA Astrophysics Data System (ADS)

Hu, Xiaoqian; Tao, Jinxu; Ye, Zhongfu; Qiu, Bensheng; Xu, Jinzhang

2018-05-01

In order to solve the problem of medical image segmentation, a wavelet neural network medical image segmentation algorithm based on combined maximum entropy criterion is proposed. Firstly, we use bee colony algorithm to optimize the network parameters of wavelet neural network, get the parameters of network structure, initial weights and threshold values, and so on, we can quickly converge to higher precision when training, and avoid to falling into relative extremum; then the optimal number of iterations is obtained by calculating the maximum entropy of the segmented image, so as to achieve the automatic and accurate segmentation effect. Medical image segmentation experiments show that the proposed algorithm can reduce sample training time effectively and improve convergence precision, and segmentation effect is more accurate and effective than traditional BP neural network (back propagation neural network : a multilayer feed forward neural network which trained according to the error backward propagation algorithm.
Vectorized Rebinning Algorithm for Fast Data Down-Sampling

NASA Technical Reports Server (NTRS)

Dean, Bruce; Aronstein, David; Smith, Jeffrey

2013-01-01

A vectorized rebinning (down-sampling) algorithm, applicable to N-dimensional data sets, has been developed that offers a significant reduction in computer run time when compared to conventional rebinning algorithms. For clarity, a two-dimensional version of the algorithm is discussed to illustrate some specific details of the algorithm content, and using the language of image processing, 2D data will be referred to as "images," and each value in an image as a "pixel." The new approach is fully vectorized, i.e., the down-sampling procedure is done as a single step over all image rows, and then as a single step over all image columns. Data rebinning (or down-sampling) is a procedure that uses a discretely sampled N-dimensional data set to create a representation of the same data, but with fewer discrete samples. Such data down-sampling is fundamental to digital signal processing, e.g., for data compression applications.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Stassi, D.; Ma, H.; Schmidt, T. G., E-mail: taly.gilat-schmidt@marquette.edu

Purpose: Reconstructing a low-motion cardiac phase is expected to improve coronary artery visualization in coronary computed tomography angiography (CCTA) exams. This study developed an automated algorithm for selecting the optimal cardiac phase for CCTA reconstruction. The algorithm uses prospectively gated, single-beat, multiphase data made possible by wide cone-beam imaging. The proposed algorithm differs from previous approaches because the optimal phase is identified based on vessel image quality (IQ) directly, compared to previous approaches that included motion estimation and interphase processing. Because there is no processing of interphase information, the algorithm can be applied to any sampling of image phases, makingmore » it suited for prospectively gated studies where only a subset of phases are available. Methods: An automated algorithm was developed to select the optimal phase based on quantitative IQ metrics. For each reconstructed slice at each reconstructed phase, an image quality metric was calculated based on measures of circularity and edge strength of through-plane vessels. The image quality metric was aggregated across slices, while a metric of vessel-location consistency was used to ignore slices that did not contain through-plane vessels. The algorithm performance was evaluated using two observer studies. Fourteen single-beat cardiac CT exams (Revolution CT, GE Healthcare, Chalfont St. Giles, UK) reconstructed at 2% intervals were evaluated for best systolic (1), diastolic (6), or systolic and diastolic phases (7) by three readers and the algorithm. Pairwise inter-reader and reader-algorithm agreement was evaluated using the mean absolute difference (MAD) and concordance correlation coefficient (CCC) between the reader and algorithm-selected phases. A reader-consensus best phase was determined and compared to the algorithm selected phase. In cases where the algorithm and consensus best phases differed by more than 2%, IQ was scored by three readers using a five point Likert scale. Results: There was no statistically significant difference between inter-reader and reader-algorithm agreement for either MAD or CCC metrics (p > 0.1). The algorithm phase was within 2% of the consensus phase in 15/21 of cases. The average absolute difference between consensus and algorithm best phases was 2.29% ± 2.47%, with a maximum difference of 8%. Average image quality scores for the algorithm chosen best phase were 4.01 ± 0.65 overall, 3.33 ± 1.27 for right coronary artery (RCA), 4.50 ± 0.35 for left anterior descending (LAD) artery, and 4.50 ± 0.35 for left circumflex artery (LCX). Average image quality scores for the consensus best phase were 4.11 ± 0.54 overall, 3.44 ± 1.03 for RCA, 4.39 ± 0.39 for LAD, and 4.50 ± 0.18 for LCX. There was no statistically significant difference (p > 0.1) between the image quality scores of the algorithm phase and the consensus phase. Conclusions: The proposed algorithm was statistically equivalent to a reader in selecting an optimal cardiac phase for CCTA exams. When reader and algorithm phases differed by >2%, image quality as rated by blinded readers was statistically equivalent. By detecting the optimal phase for CCTA reconstruction, the proposed algorithm is expected to improve coronary artery visualization in CCTA exams.« less
Image processing meta-algorithm development via genetic manipulation of existing algorithm graphs

NASA Astrophysics Data System (ADS)

Schalkoff, Robert J.; Shaaban, Khaled M.

1999-07-01

Automatic algorithm generation for image processing applications is not a new idea, however previous work is either restricted to morphological operates or impractical. In this paper, we show recent research result in the development and use of meta-algorithms, i.e. algorithms which lead to new algorithms. Although the concept is generally applicable, the application domain in this work is restricted to image processing. The meta-algorithm concept described in this paper is based upon out work in dynamic algorithm. The paper first present the concept of dynamic algorithms which, on the basis of training and archived algorithmic experience embedded in an algorithm graph (AG), dynamically adjust the sequence of operations applied to the input image data. Each node in the tree-based representation of a dynamic algorithm with out degree greater than 2 is a decision node. At these nodes, the algorithm examines the input data and determines which path will most likely achieve the desired results. This is currently done using nearest-neighbor classification. The details of this implementation are shown. The constrained perturbation of existing algorithm graphs, coupled with a suitable search strategy, is one mechanism to achieve meta-algorithm an doffers rich potential for the discovery of new algorithms. In our work, a meta-algorithm autonomously generates new dynamic algorithm graphs via genetic recombination of existing algorithm graphs. The AG representation is well suited to this genetic-like perturbation, using a commonly- employed technique in artificial neural network synthesis, namely the blueprint representation of graphs. A number of exam. One of the principal limitations of our current approach is the need for significant human input in the learning phase. Efforts to overcome this limitation are discussed. Future research directions are indicated.
Anatomy assisted PET image reconstruction incorporating multi-resolution joint entropy

NASA Astrophysics Data System (ADS)

Tang, Jing; Rahmim, Arman

2015-01-01

A promising approach in PET image reconstruction is to incorporate high resolution anatomical information (measured from MR or CT) taking the anato-functional similarity measures such as mutual information or joint entropy (JE) as the prior. These similarity measures only classify voxels based on intensity values, while neglecting structural spatial information. In this work, we developed an anatomy-assisted maximum a posteriori (MAP) reconstruction algorithm wherein the JE measure is supplied by spatial information generated using wavelet multi-resolution analysis. The proposed wavelet-based JE (WJE) MAP algorithm involves calculation of derivatives of the subband JE measures with respect to individual PET image voxel intensities, which we have shown can be computed very similarly to how the inverse wavelet transform is implemented. We performed a simulation study with the BrainWeb phantom creating PET data corresponding to different noise levels. Realistically simulated T1-weighted MR images provided by BrainWeb modeling were applied in the anatomy-assisted reconstruction with the WJE-MAP algorithm and the intensity-only JE-MAP algorithm. Quantitative analysis showed that the WJE-MAP algorithm performed similarly to the JE-MAP algorithm at low noise level in the gray matter (GM) and white matter (WM) regions in terms of noise versus bias tradeoff. When noise increased to medium level in the simulated data, the WJE-MAP algorithm started to surpass the JE-MAP algorithm in the GM region, which is less uniform with smaller isolated structures compared to the WM region. In the high noise level simulation, the WJE-MAP algorithm presented clear improvement over the JE-MAP algorithm in both the GM and WM regions. In addition to the simulation study, we applied the reconstruction algorithms to real patient studies involving DPA-173 PET data and Florbetapir PET data with corresponding T1-MPRAGE MRI images. Compared to the intensity-only JE-MAP algorithm, the WJE-MAP algorithm resulted in comparable regional mean values to those from the maximum likelihood algorithm while reducing noise. Achieving robust performance in various noise-level simulation and patient studies, the WJE-MAP algorithm demonstrates its potential in clinical quantitative PET imaging.
Intelligent fuzzy approach for fast fractal image compression

NASA Astrophysics Data System (ADS)

Nodehi, Ali; Sulong, Ghazali; Al-Rodhaan, Mznah; Al-Dhelaan, Abdullah; Rehman, Amjad; Saba, Tanzila

2014-12-01

Fractal image compression (FIC) is recognized as a NP-hard problem, and it suffers from a high number of mean square error (MSE) computations. In this paper, a two-phase algorithm was proposed to reduce the MSE computation of FIC. In the first phase, based on edge property, range and domains are arranged. In the second one, imperialist competitive algorithm (ICA) is used according to the classified blocks. For maintaining the quality of the retrieved image and accelerating algorithm operation, we divided the solutions into two groups: developed countries and undeveloped countries. Simulations were carried out to evaluate the performance of the developed approach. Promising results thus achieved exhibit performance better than genetic algorithm (GA)-based and Full-search algorithms in terms of decreasing the number of MSE computations. The number of MSE computations was reduced by the proposed algorithm for 463 times faster compared to the Full-search algorithm, although the retrieved image quality did not have a considerable change.
Increasing the object recognition distance of compact open air on board vision system

NASA Astrophysics Data System (ADS)

Kirillov, Sergey; Kostkin, Ivan; Strotov, Valery; Dmitriev, Vladimir; Berdnikov, Vadim; Akopov, Eduard; Elyutin, Aleksey

2016-10-01

The aim of this work was developing an algorithm eliminating the atmospheric distortion and improves image quality. The proposed algorithm is entirely software without using additional hardware photographic equipment. . This algorithm does not required preliminary calibration. It can work equally effectively with the images obtained at a distances from 1 to 500 meters. An algorithm for the open air images improve designed for Raspberry Pi model B on-board vision systems is proposed. The results of experimental examination are given.
An Approach towards Ultrasound Kidney Cysts Detection using Vector Graphic Image Analysis

NASA Astrophysics Data System (ADS)

Mahmud, Wan Mahani Hafizah Wan; Supriyanto, Eko

2017-08-01

This study develops new approach towards detection of kidney ultrasound image for both with single cyst as well as multiple cysts. 50 single cyst images and 25 multiple cysts images were used to test the developed algorithm. Steps involved in developing this algorithm were vector graphic image formation and analysis, thresholding, binarization, filtering as well as roundness test. Performance evaluation to 50 single cyst images gave accuracy of 92%, while for multiple cysts images, the accuracy was about 86.89% when tested to 25 multiple cysts images. This developed algorithm may be used in developing a computerized system such as computer aided diagnosis system to help medical experts in diagnosis of kidney cysts.
Automatic elastic image registration by interpolation of 3D rotations and translations from discrete rigid-body transformations.

PubMed

Walimbe, Vivek; Shekhar, Raj

2006-12-01

We present an algorithm for automatic elastic registration of three-dimensional (3D) medical images. Our algorithm initially recovers the global spatial mismatch between the reference and floating images, followed by hierarchical octree-based subdivision of the reference image and independent registration of the floating image with the individual subvolumes of the reference image at each hierarchical level. Global as well as local registrations use the six-parameter full rigid-body transformation model and are based on maximization of normalized mutual information (NMI). To ensure robustness of the subvolume registration with low voxel counts, we calculate NMI using a combination of current and prior mutual histograms. To generate a smooth deformation field, we perform direct interpolation of six-parameter rigid-body subvolume transformations obtained at the last subdivision level. Our interpolation scheme involves scalar interpolation of the 3D translations and quaternion interpolation of the 3D rotational pose. We analyzed the performance of our algorithm through experiments involving registration of synthetically deformed computed tomography (CT) images. Our algorithm is general and can be applied to image pairs of any two modalities of most organs. We have demonstrated successful registration of clinical whole-body CT and positron emission tomography (PET) images using this algorithm. The registration accuracy for this application was evaluated, based on validation using expert-identified anatomical landmarks in 15 CT-PET image pairs. The algorithm's performance was comparable to the average accuracy observed for three expert-determined registrations in the same 15 image pairs.
Hybrid wavefront sensing and image correction algorithm for imaging through turbulent media

NASA Astrophysics Data System (ADS)

Wu, Chensheng; Robertson Rzasa, John; Ko, Jonathan; Davis, Christopher C.

2017-09-01

It is well known that passive image correction of turbulence distortions often involves using geometry-dependent deconvolution algorithms. On the other hand, active imaging techniques using adaptive optic correction should use the distorted wavefront information for guidance. Our work shows that a hybrid hardware-software approach is possible to obtain accurate and highly detailed images through turbulent media. The processing algorithm also takes much fewer iteration steps in comparison with conventional image processing algorithms. In our proposed approach, a plenoptic sensor is used as a wavefront sensor to guide post-stage image correction on a high-definition zoomable camera. Conversely, we show that given the ground truth of the highly detailed image and the plenoptic imaging result, we can generate an accurate prediction of the blurred image on a traditional zoomable camera. Similarly, the ground truth combined with the blurred image from the zoomable camera would provide the wavefront conditions. In application, our hybrid approach can be used as an effective way to conduct object recognition in a turbulent environment where the target has been significantly distorted or is even unrecognizable.
Improved Wallis Dodging Algorithm for Large-Scale Super-Resolution Reconstruction Remote Sensing Images.

PubMed

Fan, Chong; Chen, Xushuai; Zhong, Lei; Zhou, Min; Shi, Yun; Duan, Yulin

2017-03-18

A sub-block algorithm is usually applied in the super-resolution (SR) reconstruction of images because of limitations in computer memory. However, the sub-block SR images can hardly achieve a seamless image mosaicking because of the uneven distribution of brightness and contrast among these sub-blocks. An effectively improved weighted Wallis dodging algorithm is proposed, aiming at the characteristic that SR reconstructed images are gray images with the same size and overlapping region. This algorithm can achieve consistency of image brightness and contrast. Meanwhile, a weighted adjustment sequence is presented to avoid the spatial propagation and accumulation of errors and the loss of image information caused by excessive computation. A seam line elimination method can share the partial dislocation in the seam line to the entire overlapping region with a smooth transition effect. Subsequently, the improved method is employed to remove the uneven illumination for 900 SR reconstructed images of ZY-3. Then, the overlapping image mosaic method is adopted to accomplish a seamless image mosaic based on the optimal seam line.
A robust firearm identification algorithm of forensic ballistics specimens

NASA Astrophysics Data System (ADS)

Chuan, Z. L.; Jemain, A. A.; Liong, C.-Y.; Ghani, N. A. M.; Tan, L. K.

2017-09-01

There are several inherent difficulties in the existing firearm identification algorithms, include requiring the physical interpretation and time consuming. Therefore, the aim of this study is to propose a robust algorithm for a firearm identification based on extracting a set of informative features from the segmented region of interest (ROI) using the simulated noisy center-firing pin impression images. The proposed algorithm comprises Laplacian sharpening filter, clustering-based threshold selection, unweighted least square estimator, and segment a square ROI from the noisy images. A total of 250 simulated noisy images collected from five different pistols of the same make, model and caliber are used to evaluate the robustness of the proposed algorithm. This study found that the proposed algorithm is able to perform the identical task on the noisy images with noise levels as high as 70%, while maintaining a firearm identification accuracy rate of over 90%.

DIRBoost-an algorithm for boosting deformable image registration: application to lung CT intra-subject registration.

PubMed

Muenzing, Sascha E A; van Ginneken, Bram; Viergever, Max A; Pluim, Josien P W

2014-04-01

We introduce a boosting algorithm to improve on existing methods for deformable image registration (DIR). The proposed DIRBoost algorithm is inspired by the theory on hypothesis boosting, well known in the field of machine learning. DIRBoost utilizes a method for automatic registration error detection to obtain estimates of local registration quality. All areas detected as erroneously registered are subjected to boosting, i.e. undergo iterative registrations by employing boosting masks on both the fixed and moving image. We validated the DIRBoost algorithm on three different DIR methods (ANTS gSyn, NiftyReg, and DROP) on three independent reference datasets of pulmonary image scan pairs. DIRBoost reduced registration errors significantly and consistently on all reference datasets for each DIR algorithm, yielding an improvement of the registration accuracy by 5-34% depending on the dataset and the registration algorithm employed. Copyright © 2014 Elsevier B.V. All rights reserved.
A comparison of the fractal and JPEG algorithms

NASA Technical Reports Server (NTRS)

Cheung, K.-M.; Shahshahani, M.

1991-01-01

A proprietary fractal image compression algorithm and the Joint Photographic Experts Group (JPEG) industry standard algorithm for image compression are compared. In every case, the JPEG algorithm was superior to the fractal method at a given compression ratio according to a root mean square criterion and a peak signal to noise criterion.
Multimodal molecular 3D imaging for the tumoral volumetric distribution assessment of folate-based biosensors.

PubMed

Ramírez-Nava, Gerardo J; Santos-Cuevas, Clara L; Chairez, Isaac; Aranda-Lara, Liliana

2017-12-01

The aim of this study was to characterize the in vivo volumetric distribution of three folate-based biosensors by different imaging modalities (X-ray, fluorescence, Cerenkov luminescence, and radioisotopic imaging) through the development of a tridimensional image reconstruction algorithm. The preclinical and multimodal Xtreme imaging system, with a Multimodal Animal Rotation System (MARS), was used to acquire bidimensional images, which were processed to obtain the tridimensional reconstruction. Images of mice at different times (biosensor distribution) were simultaneously obtained from the four imaging modalities. The filtered back projection and inverse Radon transformation were used as main image-processing techniques. The algorithm developed in Matlab was able to calculate the volumetric profiles of 99m Tc-Folate-Bombesin (radioisotopic image), 177 Lu-Folate-Bombesin (Cerenkov image), and FolateRSense™ 680 (fluorescence image) in tumors and kidneys of mice, and no significant differences were detected in the volumetric quantifications among measurement techniques. The imaging tridimensional reconstruction algorithm can be easily extrapolated to different 2D acquisition-type images. This characteristic flexibility of the algorithm developed in this study is a remarkable advantage in comparison to similar reconstruction methods.
Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging

PubMed Central

Mo, Changyeun; Kim, Giyoung; Lim, Jongguk; Kim, Moon S.; Cho, Hyunjeong; Cho, Byoung-Kwan

2015-01-01

Rapid visible/near-infrared (VNIR) hyperspectral imaging methods, employing both a single waveband algorithm and multi-spectral algorithms, were developed in order to discrimination between sound and discolored lettuce. Reflectance spectra for sound and discolored lettuce surfaces were extracted from hyperspectral reflectance images obtained in the 400–1000 nm wavelength range. The optimal wavebands for discriminating between discolored and sound lettuce surfaces were determined using one-way analysis of variance. Multi-spectral imaging algorithms developed using ratio and subtraction functions resulted in enhanced classification accuracy of above 99.9% for discolored and sound areas on both adaxial and abaxial lettuce surfaces. Ratio imaging (RI) and subtraction imaging (SI) algorithms at wavelengths of 552/701 nm and 557–701 nm, respectively, exhibited better classification performances compared to results obtained for all possible two-waveband combinations. These results suggest that hyperspectral reflectance imaging techniques can potentially be used to discriminate between discolored and sound fresh-cut lettuce. PMID:26610510
Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging.

PubMed

Mo, Changyeun; Kim, Giyoung; Lim, Jongguk; Kim, Moon S; Cho, Hyunjeong; Cho, Byoung-Kwan

2015-11-20

Rapid visible/near-infrared (VNIR) hyperspectral imaging methods, employing both a single waveband algorithm and multi-spectral algorithms, were developed in order to discrimination between sound and discolored lettuce. Reflectance spectra for sound and discolored lettuce surfaces were extracted from hyperspectral reflectance images obtained in the 400-1000 nm wavelength range. The optimal wavebands for discriminating between discolored and sound lettuce surfaces were determined using one-way analysis of variance. Multi-spectral imaging algorithms developed using ratio and subtraction functions resulted in enhanced classification accuracy of above 99.9% for discolored and sound areas on both adaxial and abaxial lettuce surfaces. Ratio imaging (RI) and subtraction imaging (SI) algorithms at wavelengths of 552/701 nm and 557-701 nm, respectively, exhibited better classification performances compared to results obtained for all possible two-waveband combinations. These results suggest that hyperspectral reflectance imaging techniques can potentially be used to discriminate between discolored and sound fresh-cut lettuce.
Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images.

PubMed

Marchetti, Michael A; Codella, Noel C F; Dusza, Stephen W; Gutman, David A; Helba, Brian; Kalloo, Aadi; Mishra, Nabin; Carrera, Cristina; Celebi, M Emre; DeFazio, Jennifer L; Jaimes, Natalia; Marghoob, Ashfaq A; Quigley, Elizabeth; Scope, Alon; Yélamos, Oriol; Halpern, Allan C

2018-02-01

Computer vision may aid in melanoma detection. We sought to compare melanoma diagnostic accuracy of computer algorithms to dermatologists using dermoscopic images. We conducted a cross-sectional study using 100 randomly selected dermoscopic images (50 melanomas, 44 nevi, and 6 lentigines) from an international computer vision melanoma challenge dataset (n = 379), along with individual algorithm results from 25 teams. We used 5 methods (nonlearned and machine learning) to combine individual automated predictions into "fusion" algorithms. In a companion study, 8 dermatologists classified the lesions in the 100 images as either benign or malignant. The average sensitivity and specificity of dermatologists in classification was 82% and 59%. At 82% sensitivity, dermatologist specificity was similar to the top challenge algorithm (59% vs. 62%, P = .68) but lower than the best-performing fusion algorithm (59% vs. 76%, P = .02). Receiver operating characteristic area of the top fusion algorithm was greater than the mean receiver operating characteristic area of dermatologists (0.86 vs. 0.71, P = .001). The dataset lacked the full spectrum of skin lesions encountered in clinical practice, particularly banal lesions. Readers and algorithms were not provided clinical data (eg, age or lesion history/symptoms). Results obtained using our study design cannot be extrapolated to clinical practice. Deep learning computer vision systems classified melanoma dermoscopy images with accuracy that exceeded some but not all dermatologists. Copyright © 2017 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.
Prior image constrained scatter correction in cone-beam computed tomography image-guided radiation therapy.

PubMed

Brunner, Stephen; Nett, Brian E; Tolakanahalli, Ranjini; Chen, Guang-Hong

2011-02-21

X-ray scatter is a significant problem in cone-beam computed tomography when thicker objects and larger cone angles are used, as scattered radiation can lead to reduced contrast and CT number inaccuracy. Advances have been made in x-ray computed tomography (CT) by incorporating a high quality prior image into the image reconstruction process. In this paper, we extend this idea to correct scatter-induced shading artifacts in cone-beam CT image-guided radiation therapy. Specifically, this paper presents a new scatter correction algorithm which uses a prior image with low scatter artifacts to reduce shading artifacts in cone-beam CT images acquired under conditions of high scatter. The proposed correction algorithm begins with an empirical hypothesis that the target image can be written as a weighted summation of a series of basis images that are generated by raising the raw cone-beam projection data to different powers, and then, reconstructing using the standard filtered backprojection algorithm. The weight for each basis image is calculated by minimizing the difference between the target image and the prior image. The performance of the scatter correction algorithm is qualitatively and quantitatively evaluated through phantom studies using a Varian 2100 EX System with an on-board imager. Results show that the proposed scatter correction algorithm using a prior image with low scatter artifacts can substantially mitigate scatter-induced shading artifacts in both full-fan and half-fan modes.
Quantitative Image Quality and Histogram-Based Evaluations of an Iterative Reconstruction Algorithm at Low-to-Ultralow Radiation Dose Levels: A Phantom Study in Chest CT

PubMed Central

Lee, Ki Baek

2018-01-01

Objective To describe the quantitative image quality and histogram-based evaluation of an iterative reconstruction (IR) algorithm in chest computed tomography (CT) scans at low-to-ultralow CT radiation dose levels. Materials and Methods In an adult anthropomorphic phantom, chest CT scans were performed with 128-section dual-source CT at 70, 80, 100, 120, and 140 kVp, and the reference (3.4 mGy in volume CT Dose Index [CTDIvol]), 30%-, 60%-, and 90%-reduced radiation dose levels (2.4, 1.4, and 0.3 mGy). The CT images were reconstructed by using filtered back projection (FBP) algorithms and IR algorithm with strengths 1, 3, and 5. Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were statistically compared between different dose levels, tube voltages, and reconstruction algorithms. Moreover, histograms of subtraction images before and after standardization in x- and y-axes were visually compared. Results Compared with FBP images, IR images with strengths 1, 3, and 5 demonstrated image noise reduction up to 49.1%, SNR increase up to 100.7%, and CNR increase up to 67.3%. Noteworthy image quality degradations on IR images including a 184.9% increase in image noise, 63.0% decrease in SNR, and 51.3% decrease in CNR, and were shown between 60% and 90% reduced levels of radiation dose (p < 0.0001). Subtraction histograms between FBP and IR images showed progressively increased dispersion with increased IR strength and increased dose reduction. After standardization, the histograms appeared deviated and ragged between FBP images and IR images with strength 3 or 5, but almost normally-distributed between FBP images and IR images with strength 1. Conclusion The IR algorithm may be used to save radiation doses without substantial image quality degradation in chest CT scanning of the adult anthropomorphic phantom, down to approximately 1.4 mGy in CTDIvol (60% reduced dose). PMID:29354008
Automated detection using natural language processing of radiologists recommendations for additional imaging of incidental findings.

PubMed

Dutta, Sayon; Long, William J; Brown, David F M; Reisner, Andrew T

2013-08-01

As use of radiology studies increases, there is a concurrent increase in incidental findings (eg, lung nodules) for which the radiologist issues recommendations for additional imaging for follow-up. Busy emergency physicians may be challenged to carefully communicate recommendations for additional imaging not relevant to the patient's primary evaluation. The emergence of electronic health records and natural language processing algorithms may help address this quality gap. We seek to describe recommendations for additional imaging from our institution and develop and validate an automated natural language processing algorithm to reliably identify recommendations for additional imaging. We developed a natural language processing algorithm to detect recommendations for additional imaging, using 3 iterative cycles of training and validation. The third cycle used 3,235 radiology reports (1,600 for algorithm training and 1,635 for validation) of discharged emergency department (ED) patients from which we determined the incidence of discharge-relevant recommendations for additional imaging and the frequency of appropriate discharge documentation. The test characteristics of the 3 natural language processing algorithm iterations were compared, using blinded chart review as the criterion standard. Discharge-relevant recommendations for additional imaging were found in 4.5% (95% confidence interval [CI] 3.5% to 5.5%) of ED radiology reports, but 51% (95% CI 43% to 59%) of discharge instructions failed to note those findings. The final natural language processing algorithm had 89% (95% CI 82% to 94%) sensitivity and 98% (95% CI 97% to 98%) specificity for detecting recommendations for additional imaging. For discharge-relevant recommendations for additional imaging, sensitivity improved to 97% (95% CI 89% to 100%). Recommendations for additional imaging are common, and failure to document relevant recommendations for additional imaging in ED discharge instructions occurs frequently. The natural language processing algorithm's performance improved with each iteration and offers a promising error-prevention tool. Copyright © 2013 American College of Emergency Physicians. Published by Mosby, Inc. All rights reserved.
Architecture and data processing alternatives for the tse computer. Volume 4: Image rotation using tse operations

NASA Technical Reports Server (NTRS)

Kao, M. H.; Bodenheimer, R. E.

1976-01-01

The tse computer's capability of achieving image congruence between temporal and multiple images with misregistration due to rotational differences is reported. The coordinate transformations are obtained and a general algorithms is devised to perform image rotation using tse operations very efficiently. The details of this algorithm as well as its theoretical implications are presented. Step by step procedures of image registration are described in detail. Numerous examples are also employed to demonstrate the correctness and the effectiveness of the algorithms and conclusions and recommendations are made.
An Example-Based Super-Resolution Algorithm for Selfie Images

PubMed Central

William, Jino Hans; Venkateswaran, N.; Narayanan, Srinath; Ramachandran, Sandeep

2016-01-01

A selfie is typically a self-portrait captured using the front camera of a smartphone. Most state-of-the-art smartphones are equipped with a high-resolution (HR) rear camera and a low-resolution (LR) front camera. As selfies are captured by front camera with limited pixel resolution, the fine details in it are explicitly missed. This paper aims to improve the resolution of selfies by exploiting the fine details in HR images captured by rear camera using an example-based super-resolution (SR) algorithm. HR images captured by rear camera carry significant fine details and are used as an exemplar to train an optimal matrix-value regression (MVR) operator. The MVR operator serves as an image-pair priori which learns the correspondence between the LR-HR patch-pairs and is effectively used to super-resolve LR selfie images. The proposed MVR algorithm avoids vectorization of image patch-pairs and preserves image-level information during both learning and recovering process. The proposed algorithm is evaluated for its efficiency and effectiveness both qualitatively and quantitatively with other state-of-the-art SR algorithms. The results validate that the proposed algorithm is efficient as it requires less than 3 seconds to super-resolve LR selfie and is effective as it preserves sharp details without introducing any counterfeit fine details. PMID:27064500
Regularization iteration imaging algorithm for electrical capacitance tomography

NASA Astrophysics Data System (ADS)

Tong, Guowei; Liu, Shi; Chen, Hongyan; Wang, Xueyao

2018-03-01

The image reconstruction method plays a crucial role in real-world applications of the electrical capacitance tomography technique. In this study, a new cost function that simultaneously considers the sparsity and low-rank properties of the imaging targets is proposed to improve the quality of the reconstruction images, in which the image reconstruction task is converted into an optimization problem. Within the framework of the split Bregman algorithm, an iterative scheme that splits a complicated optimization problem into several simpler sub-tasks is developed to solve the proposed cost function efficiently, in which the fast-iterative shrinkage thresholding algorithm is introduced to accelerate the convergence. Numerical experiment results verify the effectiveness of the proposed algorithm in improving the reconstruction precision and robustness.
Locating and decoding barcodes in fuzzy images captured by smart phones

NASA Astrophysics Data System (ADS)

Deng, Wupeng; Hu, Jiwei; Liu, Quan; Lou, Ping

2017-07-01

With the development of barcodes for commercial use, people's requirements for detecting barcodes by smart phone become increasingly pressing. The low quality of barcode image captured by mobile phone always affects the decoding and recognition rates. This paper focuses on locating and decoding EAN-13 barcodes in fuzzy images. We present a more accurate locating algorithm based on segment length and high fault-tolerant rate algorithm for decoding barcodes. Unlike existing approaches, location algorithm is based on the edge segment length of EAN -13 barcodes, while our decoding algorithm allows the appearance of fuzzy region in barcode image. Experimental results are performed on damaged, contaminated and scratched digital images, and provide a quite promising result for EAN -13 barcode location and decoding.
A spectrum fractal feature classification algorithm for agriculture crops with hyper spectrum image

NASA Astrophysics Data System (ADS)

Su, Junying

2011-11-01

A fractal dimension feature analysis method in spectrum domain for hyper spectrum image is proposed for agriculture crops classification. Firstly, a fractal dimension calculation algorithm in spectrum domain is presented together with the fast fractal dimension value calculation algorithm using the step measurement method. Secondly, the hyper spectrum image classification algorithm and flowchart is presented based on fractal dimension feature analysis in spectrum domain. Finally, the experiment result of the agricultural crops classification with FCL1 hyper spectrum image set with the proposed method and SAM (spectral angle mapper). The experiment results show it can obtain better classification result than the traditional SAM feature analysis which can fulfill use the spectrum information of hyper spectrum image to realize precision agricultural crops classification.
A joint watermarking/encryption algorithm for verifying medical image integrity and authenticity in both encrypted and spatial domains.

PubMed

Bouslimi, D; Coatrieux, G; Roux, Ch

2011-01-01

In this paper, we propose a new joint watermarking/encryption algorithm for the purpose of verifying the reliability of medical images in both encrypted and spatial domains. It combines a substitutive watermarking algorithm, the quantization index modulation (QIM), with a block cipher algorithm, the Advanced Encryption Standard (AES), in CBC mode of operation. The proposed solution gives access to the outcomes of the image integrity and of its origins even though the image is stored encrypted. Experimental results achieved on 8 bits encoded Ultrasound images illustrate the overall performances of the proposed scheme. By making use of the AES block cipher in CBC mode, the proposed solution is compliant with or transparent to the DICOM standard.
Space-variant restoration of images degraded by camera motion blur.

PubMed

Sorel, Michal; Flusser, Jan

2008-02-01

We examine the problem of restoration from multiple images degraded by camera motion blur. We consider scenes with significant depth variations resulting in space-variant blur. The proposed algorithm can be applied if the camera moves along an arbitrary curve parallel to the image plane, without any rotations. The knowledge of camera trajectory and camera parameters is not necessary. At the input, the user selects a region where depth variations are negligible. The algorithm belongs to the group of variational methods that estimate simultaneously a sharp image and a depth map, based on the minimization of a cost functional. To initialize the minimization, it uses an auxiliary window-based depth estimation algorithm. Feasibility of the algorithm is demonstrated by three experiments with real images.
Detection of low-contrast images in film-grain noise.

PubMed

Naderi, F; Sawchuk, A A

1978-09-15

When low contrast photographic images are digitized by a very small aperture, extreme film-grain noise almost completely obliterates the image information. Using a large aperture to average out the noise destroys the fine details of the image. In these situations conventional statistical restoration techniques have little effect, and well chosen heuristic algorithms have yielded better results. In this paper we analyze the noisecheating algorithm of Zweig et al. [J. Opt. Soc. Am. 65, 1347 (1975)] and show that it can be justified by classical maximum-likelihood detection theory. A more general algorithm applicable to a broader class of images is then developed by considering the signal-dependent nature of film-grain noise. Finally, a Bayesian detection algorithm with improved performance is presented.
An improved feature extraction algorithm based on KAZE for multi-spectral image

NASA Astrophysics Data System (ADS)

Yang, Jianping; Li, Jun

2018-02-01

Multi-spectral image contains abundant spectral information, which is widely used in all fields like resource exploration, meteorological observation and modern military. Image preprocessing, such as image feature extraction and matching, is indispensable while dealing with multi-spectral remote sensing image. Although the feature matching algorithm based on linear scale such as SIFT and SURF performs strong on robustness, the local accuracy cannot be guaranteed. Therefore, this paper proposes an improved KAZE algorithm, which is based on nonlinear scale, to raise the number of feature and to enhance the matching rate by using the adjusted-cosine vector. The experiment result shows that the number of feature and the matching rate of the improved KAZE are remarkably than the original KAZE algorithm.
Image-classification-based global dimming algorithm for LED backlights in LCDs

NASA Astrophysics Data System (ADS)

Qibin, Feng; Huijie, He; Dong, Han; Lei, Zhang; Guoqiang, Lv

2015-07-01

Backlight dimming can help LCDs reduce power consumption and improve CR. With fixed parameters, dimming algorithm cannot achieve satisfied effects for all kinds of images. The paper introduces an image-classification-based global dimming algorithm. The proposed classification method especially for backlight dimming is based on luminance and CR of input images. The parameters for backlight dimming level and pixel compensation are adaptive with image classifications. The simulation results show that the classification based dimming algorithm presents 86.13% power reduction improvement compared with dimming without classification, with almost same display quality. The prototype is developed. There are no perceived distortions when playing videos. The practical average power reduction of the prototype TV is 18.72%, compared with common TV without dimming.
Axial Cone-Beam Reconstruction by Weighted BPF/DBPF and Orthogonal Butterfly Filtering.

PubMed

Tang, Shaojie; Tang, Xiangyang

2016-09-01

The backprojection-filtration (BPF) and the derivative backprojection filtered (DBPF) algorithms, in which Hilbert filtering is the common algorithmic feature, are originally derived for exact helical reconstruction from cone-beam (CB) scan data and axial reconstruction from fan beam data, respectively. These two algorithms can be heuristically extended for image reconstruction from axial CB scan data, but induce severe artifacts in images located away from the central plane, determined by the circular source trajectory. We propose an algorithmic solution herein to eliminate the artifacts. The solution is an integration of three-dimensional (3-D) weighted axial CB-BPF/DBPF algorithm with orthogonal butterfly filtering, namely axial CB-BPF/DBPF cascaded with orthogonal butterfly filtering. Using the computer simulated Forbild head and thoracic phantoms that are rigorous in inspecting the reconstruction accuracy, and an anthropomorphic thoracic phantom with projection data acquired by a CT scanner, we evaluate the performance of the proposed algorithm. Preliminary results show that the orthogonal butterfly filtering can eliminate the severe streak artifacts existing in the images reconstructed by the 3-D weighted axial CB-BPF/DBPF algorithm located at off-central planes. Integrated with orthogonal butterfly filtering, the 3-D weighted CB-BPF/DBPF algorithm can perform at least as well as the 3-D weighted CB-FBP algorithm in image reconstruction from axial CB scan data. The proposed 3-D weighted axial CB-BPF/DBPF cascaded with orthogonal butterfly filtering can be an algorithmic solution for CT imaging in extensive clinical and preclinical applications.

Ray Tracing Through Non-Imaging Concentrators

NASA Astrophysics Data System (ADS)

Greynolds, Alan W.

1984-01-01

A generalized algorithm for tracing rays through both imaging and non-imaging radiation collectors is presented. A computer program based on the algorithm is then applied to analyzing various two-stage Winston concentrators.
Quantitative assessment of the impact of biomedical image acquisition on the results obtained from image analysis and processing.

PubMed

Koprowski, Robert

2014-07-04

Dedicated, automatic algorithms for image analysis and processing are becoming more and more common in medical diagnosis. When creating dedicated algorithms, many factors must be taken into consideration. They are associated with selecting the appropriate algorithm parameters and taking into account the impact of data acquisition on the results obtained. An important feature of algorithms is the possibility of their use in other medical units by other operators. This problem, namely operator's (acquisition) impact on the results obtained from image analysis and processing, has been shown on a few examples. The analysed images were obtained from a variety of medical devices such as thermal imaging, tomography devices and those working in visible light. The objects of imaging were cellular elements, the anterior segment and fundus of the eye, postural defects and others. In total, almost 200'000 images coming from 8 different medical units were analysed. All image analysis algorithms were implemented in C and Matlab. For various algorithms and methods of medical imaging, the impact of image acquisition on the results obtained is different. There are different levels of algorithm sensitivity to changes in the parameters, for example: (1) for microscope settings and the brightness assessment of cellular elements there is a difference of 8%; (2) for the thyroid ultrasound images there is a difference in marking the thyroid lobe area which results in a brightness assessment difference of 2%. The method of image acquisition in image analysis and processing also affects: (3) the accuracy of determining the temperature in the characteristic areas on the patient's back for the thermal method - error of 31%; (4) the accuracy of finding characteristic points in photogrammetric images when evaluating postural defects - error of 11%; (5) the accuracy of performing ablative and non-ablative treatments in cosmetology - error of 18% for the nose, 10% for the cheeks, and 7% for the forehead. Similarly, when: (7) measuring the anterior eye chamber - there is an error of 20%; (8) measuring the tooth enamel thickness - error of 15%; (9) evaluating the mechanical properties of the cornea during pressure measurement - error of 47%. The paper presents vital, selected issues occurring when assessing the accuracy of designed automatic algorithms for image analysis and processing in bioengineering. The impact of acquisition of images on the problems arising in their analysis has been shown on selected examples. It has also been indicated to which elements of image analysis and processing special attention should be paid in their design.
Information extraction and transmission techniques for spaceborne synthetic aperture radar images

NASA Technical Reports Server (NTRS)

Frost, V. S.; Yurovsky, L.; Watson, E.; Townsend, K.; Gardner, S.; Boberg, D.; Watson, J.; Minden, G. J.; Shanmugan, K. S.

1984-01-01

Information extraction and transmission techniques for synthetic aperture radar (SAR) imagery were investigated. Four interrelated problems were addressed. An optimal tonal SAR image classification algorithm was developed and evaluated. A data compression technique was developed for SAR imagery which is simple and provides a 5:1 compression with acceptable image quality. An optimal textural edge detector was developed. Several SAR image enhancement algorithms have been proposed. The effectiveness of each algorithm was compared quantitatively.
An algorithm for 4D CT image sorting using spatial continuity.

PubMed

Li, Chen; Liu, Jie

2013-01-01

4D CT, which could locate the position of the movement of the tumor in the entire respiratory cycle and reduce image artifacts effectively, has been widely used in making radiation therapy of tumors. The current 4D CT methods required external surrogates of respiratory motion obtained from extra instruments. However, respiratory signals recorded by these external makers may not always accurately represent the internal tumor and organ movements, especially when irregular breathing patterns happened. In this paper we have proposed a novel automatic 4D CT sorting algorithm that performs without these external surrogates. The sorting algorithm requires collecting the image data with a cine scan protocol. Beginning with the first couch position, images from the adjacent couch position are selected out according to spatial continuity. The process is continued until images from all couch positions are sorted and the entire 3D volume is produced. The algorithm is verified by respiratory phantom image data and clinical image data. The primary test results show that the 4D CT images created by our algorithm have eliminated the motion artifacts effectively and clearly demonstrated the movement of tumor and organ in the breath period.
Automated detection of jet contrails using the AVHRR split window

NASA Technical Reports Server (NTRS)

Engelstad, M.; Sengupta, S. K.; Lee, T.; Welch, R. M.

1992-01-01

This paper investigates the automated detection of jet contrails using data from the Advanced Very High Resolution Radiometer. A preliminary algorithm subtracts the 11.8-micron image from the 10.8-micron image, creating a difference image on which contrails are enhanced. Then a three-stage algorithm searches the difference image for the nearly-straight line segments which characterize contrails. First, the algorithm searches for elevated, linear patterns called 'ridges'. Second, it applies a Hough transform to the detected ridges to locate nearly-straight lines. Third, the algorithm determines which of the nearly-straight lines are likely to be contrails. The paper applies this technique to several test scenes.
An algorithm for automated ROI definition in water or epoxy-filled NEMA NU-2 image quality phantoms.

PubMed

Pierce, Larry A; Byrd, Darrin W; Elston, Brian F; Karp, Joel S; Sunderland, John J; Kinahan, Paul E

2016-01-08

Drawing regions of interest (ROIs) in positron emission tomography/computed tomography (PET/CT) scans of the National Electrical Manufacturers Association (NEMA) NU-2 Image Quality (IQ) phantom is a time-consuming process that allows for interuser variability in the measurements. In order to reduce operator effort and allow batch processing of IQ phantom images, we propose a fast, robust, automated algorithm for performing IQ phantom sphere localization and analysis. The algorithm is easily altered to accommodate different configurations of the IQ phantom. The proposed algorithm uses information from both the PET and CT image volumes in order to overcome the challenges of detecting the smallest spheres in the PET volume. This algorithm has been released as an open-source plug-in to the Osirix medical image viewing software package. We test the algorithm under various noise conditions, positions within the scanner, air bubbles in the phantom spheres, and scanner misalignment conditions. The proposed algorithm shows run-times between 3 and 4 min and has proven to be robust under all tested conditions, with expected sphere localization deviations of less than 0.2 mm and variations of PET ROI mean and maximum values on the order of 0.5% and 2%, respectively, over multiple PET acquisitions. We conclude that the proposed algorithm is stable when challenged with a variety of physical and imaging anomalies, and that the algorithm can be a valuable tool for those who use the NEMA NU-2 IQ phantom for PET/CT scanner acceptance testing and QA/QC.
Fast registration and reconstruction of aliased low-resolution frames by use of a modified maximum-likelihood approach.

PubMed

Alam, M S; Bognar, J G; Cain, S; Yasuda, B J

1998-03-10

During the process of microscanning a controlled vibrating mirror typically is used to produce subpixel shifts in a sequence of forward-looking infrared (FLIR) images. If the FLIR is mounted on a moving platform, such as an aircraft, uncontrolled random vibrations associated with the platform can be used to generate the shifts. Iterative techniques such as the expectation-maximization (EM) approach by means of the maximum-likelihood algorithm can be used to generate high-resolution images from multiple randomly shifted aliased frames. In the maximum-likelihood approach the data are considered to be Poisson random variables and an EM algorithm is developed that iteratively estimates an unaliased image that is compensated for known imager-system blur while it simultaneously estimates the translational shifts. Although this algorithm yields high-resolution images from a sequence of randomly shifted frames, it requires significant computation time and cannot be implemented for real-time applications that use the currently available high-performance processors. The new image shifts are iteratively calculated by evaluation of a cost function that compares the shifted and interlaced data frames with the corresponding values in the algorithm's latest estimate of the high-resolution image. We present a registration algorithm that estimates the shifts in one step. The shift parameters provided by the new algorithm are accurate enough to eliminate the need for iterative recalculation of translational shifts. Using this shift information, we apply a simplified version of the EM algorithm to estimate a high-resolution image from a given sequence of video frames. The proposed modified EM algorithm has been found to reduce significantly the computational burden when compared with the original EM algorithm, thus making it more attractive for practical implementation. Both simulation and experimental results are presented to verify the effectiveness of the proposed technique.
A BPF-FBP tandem algorithm for image reconstruction in reverse helical cone-beam CT

PubMed Central

Cho, Seungryong; Xia, Dan; Pellizzari, Charles A.; Pan, Xiaochuan

2010-01-01

Purpose: Reverse helical cone-beam computed tomography (CBCT) is a scanning configuration for potential applications in image-guided radiation therapy in which an accurate anatomic image of the patient is needed for image-guidance procedures. The authors previously developed an algorithm for image reconstruction from nontruncated data of an object that is completely within the reverse helix. The purpose of this work is to develop an image reconstruction approach for reverse helical CBCT of a long object that extends out of the reverse helix and therefore constitutes data truncation. Methods: The proposed approach comprises of two reconstruction steps. In the first step, a chord-based backprojection-filtration (BPF) algorithm reconstructs a volumetric image of an object from the original cone-beam data. Because there exists a chordless region in the middle of the reverse helix, the image obtained in the first step contains an unreconstructed central-gap region. In the second step, the gap region is reconstructed by use of a Pack–Noo-formula-based filteredbackprojection (FBP) algorithm from the modified cone-beam data obtained by subtracting from the original cone-beam data the reprojection of the image reconstructed in the first step. Results: The authors have performed numerical studies to validate the proposed approach in image reconstruction from reverse helical cone-beam data. The results confirm that the proposed approach can reconstruct accurate images of a long object without suffering from data-truncation artifacts or cone-angle artifacts. Conclusions: They developed and validated a BPF-FBP tandem algorithm to reconstruct images of a long object from reverse helical cone-beam data. The chord-based BPF algorithm was utilized for converting the long-object problem into a short-object problem. The proposed approach is applicable to other scanning configurations such as reduced circular sinusoidal trajectories. PMID:20175463
A BPF-FBP tandem algorithm for image reconstruction in reverse helical cone-beam CT.

PubMed

Cho, Seungryong; Xia, Dan; Pellizzari, Charles A; Pan, Xiaochuan

2010-01-01

Reverse helical cone-beam computed tomography (CBCT) is a scanning configuration for potential applications in image-guided radiation therapy in which an accurate anatomic image of the patient is needed for image-guidance procedures. The authors previously developed an algorithm for image reconstruction from nontruncated data of an object that is completely within the reverse helix. The purpose of this work is to develop an image reconstruction approach for reverse helical CBCT of a long object that extends out of the reverse helix and therefore constitutes data truncation. The proposed approach comprises of two reconstruction steps. In the first step, a chord-based backprojection-filtration (BPF) algorithm reconstructs a volumetric image of an object from the original cone-beam data. Because there exists a chordless region in the middle of the reverse helix, the image obtained in the first step contains an unreconstructed central-gap region. In the second step, the gap region is reconstructed by use of a Pack-Noo-formula-based filteredback-projection (FBP) algorithm from the modified cone-beam data obtained by subtracting from the original cone-beam data the reprojection of the image reconstructed in the first step. The authors have performed numerical studies to validate the proposed approach in image reconstruction from reverse helical cone-beam data. The results confirm that the proposed approach can reconstruct accurate images of a long object without suffering from data-truncation artifacts or cone-angle artifacts. They developed and validated a BPF-FBP tandem algorithm to reconstruct images of a long object from reverse helical cone-beam data. The chord-based BPF algorithm was utilized for converting the long-object problem into a short-object problem. The proposed approach is applicable to other scanning configurations such as reduced circular sinusoidal trajectories.
Image stack alignment in full-field X-ray absorption spectroscopy using SIFT_PyOCL.

PubMed

Paleo, Pierre; Pouyet, Emeline; Kieffer, Jérôme

2014-03-01

Full-field X-ray absorption spectroscopy experiments allow the acquisition of millions of spectra within minutes. However, the construction of the hyperspectral image requires an image alignment procedure with sub-pixel precision. While the image correlation algorithm has originally been used for image re-alignment using translations, the Scale Invariant Feature Transform (SIFT) algorithm (which is by design robust versus rotation, illumination change, translation and scaling) presents an additional advantage: the alignment can be limited to a region of interest of any arbitrary shape. In this context, a Python module, named SIFT_PyOCL, has been developed. It implements a parallel version of the SIFT algorithm in OpenCL, providing high-speed image registration and alignment both on processors and graphics cards. The performance of the algorithm allows online processing of large datasets.
Finite grade pheromone ant colony optimization for image segmentation

NASA Astrophysics Data System (ADS)

Yuanjing, F.; Li, Y.; Liangjun, K.

2008-06-01

By combining the decision process of ant colony optimization (ACO) with the multistage decision process of image segmentation based on active contour model (ACM), an algorithm called finite grade ACO (FACO) for image segmentation is proposed. This algorithm classifies pheromone into finite grades and updating of the pheromone is achieved by changing the grades and the updated quantity of pheromone is independent from the objective function. The algorithm that provides a new approach to obtain precise contour is proved to converge to the global optimal solutions linearly by means of finite Markov chains. The segmentation experiments with ultrasound heart image show the effectiveness of the algorithm. Comparing the results for segmentation of left ventricle images shows that the ACO for image segmentation is more effective than the GA approach and the new pheromone updating strategy appears good time performance in optimization process.
Three-dimensional near-field MIMO array imaging using range migration techniques.

PubMed

Zhuge, Xiaodong; Yarovoy, Alexander G

2012-06-01

This paper presents a 3-D near-field imaging algorithm that is formulated for 2-D wideband multiple-input-multiple-output (MIMO) imaging array topology. The proposed MIMO range migration technique performs the image reconstruction procedure in the frequency-wavenumber domain. The algorithm is able to completely compensate the curvature of the wavefront in the near-field through a specifically defined interpolation process and provides extremely high computational efficiency by the application of the fast Fourier transform. The implementation aspects of the algorithm and the sampling criteria of a MIMO aperture are discussed. The image reconstruction performance and computational efficiency of the algorithm are demonstrated both with numerical simulations and measurements using 2-D MIMO arrays. Real-time 3-D near-field imaging can be achieved with a real-aperture array by applying the proposed MIMO range migration techniques.
Application of Curved MPR Algorithm to High Resolution 3 Dimensional T2 Weighted CISS Images for Virtual Uncoiling of Membranous Cochlea as an Aid for Cochlear Morphometry.

PubMed

Kumar, Joish Upendra; Kavitha, Y

2017-02-01

With the use of various surgical techniques, types of implants, the preoperative assessment of cochlear dimensions is becoming increasingly relevant prior to cochlear implantation. High resolution CISS protocol MRI gives a better assessment of membranous cochlea, cochlear nerve, and membranous labyrinth. Curved Multiplanar Reconstruction (MPR) algorithm provides better images that can be used for measuring dimensions of membranous cochlea. To ascertain the value of curved multiplanar reconstruction algorithm in high resolution 3-Dimensional T2 Weighted Gradient Echo Constructive Interference Steady State (3D T2W GRE CISS) imaging for accurate morphometry of membranous cochlea. Fourteen children underwent MRI for inner ear assessment. High resolution 3D T2W GRE CISS sequence was used to obtain images of cochlea. Curved MPR reconstruction algorithm was used to virtually uncoil the membranous cochlea on the volume images and cochlear measurements were done. Virtually uncoiled images of membranous cochlea of appropriate resolution were obtained from the volume data obtained from the high resolution 3D T2W GRE CISS images, after using curved MPR reconstruction algorithm mean membranous cochlear length in the children was 27.52 mm. Maximum apical turn diameter of membranous cochlea was 1.13 mm, mid turn diameter was 1.38 mm, basal turn diameter was 1.81 mm. Curved MPR reconstruction algorithm applied to CISS protocol images facilitates in getting appropriate quality images of membranous cochlea for accurate measurements.
Accounting for hardware imperfections in EIT image reconstruction algorithms.

PubMed

Hartinger, Alzbeta E; Gagnon, Hervé; Guardo, Robert

2007-07-01

Electrical impedance tomography (EIT) is a non-invasive technique for imaging the conductivity distribution of a body section. Different types of EIT images can be reconstructed: absolute, time difference and frequency difference. Reconstruction algorithms are sensitive to many errors which translate into image artefacts. These errors generally result from incorrect modelling or inaccurate measurements. Every reconstruction algorithm incorporates a model of the physical set-up which must be as accurate as possible since any discrepancy with the actual set-up will cause image artefacts. Several methods have been proposed in the literature to improve the model realism, such as creating anatomical-shaped meshes, adding a complete electrode model and tracking changes in electrode contact impedances and positions. Absolute and frequency difference reconstruction algorithms are particularly sensitive to measurement errors and generally assume that measurements are made with an ideal EIT system. Real EIT systems have hardware imperfections that cause measurement errors. These errors translate into image artefacts since the reconstruction algorithm cannot properly discriminate genuine measurement variations produced by the medium under study from those caused by hardware imperfections. We therefore propose a method for eliminating these artefacts by integrating a model of the system hardware imperfections into the reconstruction algorithms. The effectiveness of the method has been evaluated by reconstructing absolute, time difference and frequency difference images with and without the hardware model from data acquired on a resistor mesh phantom. Results have shown that artefacts are smaller for images reconstructed with the model, especially for frequency difference imaging.
Improved space object detection using short-exposure image data with daylight background.

PubMed

Becker, David; Cain, Stephen

2018-05-10

Space object detection is of great importance in the highly dependent yet competitive and congested space domain. The detection algorithms employed play a crucial role in fulfilling the detection component in the space situational awareness mission to detect, track, characterize, and catalog unknown space objects. Many current space detection algorithms use a matched filter or a spatial correlator on long-exposure data to make a detection decision at a single pixel point of a spatial image based on the assumption that the data follow a Gaussian distribution. Long-exposure imaging is critical to detection performance in these algorithms; however, for imaging under daylight conditions, it becomes necessary to create a long-exposure image as the sum of many short-exposure images. This paper explores the potential for increasing detection capabilities for small and dim space objects in a stack of short-exposure images dominated by a bright background. The algorithm proposed in this paper improves the traditional stack and average method of forming a long-exposure image by selectively removing short-exposure frames of data that do not positively contribute to the overall signal-to-noise ratio of the averaged image. The performance of the algorithm is compared to a traditional matched filter detector using data generated in MATLAB as well as laboratory-collected data. The results are illustrated on a receiver operating characteristic curve to highlight the increased probability of detection associated with the proposed algorithm.
Information theoretic analysis of edge detection in visual communication

NASA Astrophysics Data System (ADS)

Jiang, Bo; Rahman, Zia-ur

2010-08-01

Generally, the designs of digital image processing algorithms and image gathering devices remain separate. Consequently, the performance of digital image processing algorithms is evaluated without taking into account the artifacts introduced into the process by the image gathering process. However, experiments show that the image gathering process profoundly impacts the performance of digital image processing and the quality of the resulting images. Huck et al. proposed one definitive theoretic analysis of visual communication channels, where the different parts, such as image gathering, processing, and display, are assessed in an integrated manner using Shannon's information theory. In this paper, we perform an end-to-end information theory based system analysis to assess edge detection methods. We evaluate the performance of the different algorithms as a function of the characteristics of the scene, and the parameters, such as sampling, additive noise etc., that define the image gathering system. The edge detection algorithm is regarded to have high performance only if the information rate from the scene to the edge approaches the maximum possible. This goal can be achieved only by jointly optimizing all processes. People generally use subjective judgment to compare different edge detection methods. There is not a common tool that can be used to evaluate the performance of the different algorithms, and to give people a guide for selecting the best algorithm for a given system or scene. Our information-theoretic assessment becomes this new tool to which allows us to compare the different edge detection operators in a common environment.
Self-recovery reversible image watermarking algorithm

PubMed Central

Sun, He; Gao, Shangbing; Jin, Shenghua

2018-01-01

The integrity of image content is essential, although most watermarking algorithms can achieve image authentication but not automatically repair damaged areas or restore the original image. In this paper, a self-recovery reversible image watermarking algorithm is proposed to recover the tampered areas effectively. First of all, the original image is divided into homogeneous blocks and non-homogeneous blocks through multi-scale decomposition, and the feature information of each block is calculated as the recovery watermark. Then, the original image is divided into 4×4 non-overlapping blocks classified into smooth blocks and texture blocks according to image textures. Finally, the recovery watermark generated by homogeneous blocks and error-correcting codes is embedded into the corresponding smooth block by mapping; watermark information generated by non-homogeneous blocks and error-correcting codes is embedded into the corresponding non-embedded smooth block and the texture block via mapping. The correlation attack is detected by invariant moments when the watermarked image is attacked. To determine whether a sub-block has been tampered with, its feature is calculated and the recovery watermark is extracted from the corresponding block. If the image has been tampered with, it can be recovered. The experimental results show that the proposed algorithm can effectively recover the tampered areas with high accuracy and high quality. The algorithm is characterized by sound visual quality and excellent image restoration. PMID:29920528
Methodology for the Elimination of Reflection and System Vibration Effects in Particle Image Velocimetry Data Processing

NASA Technical Reports Server (NTRS)

Bremmer, David M.; Hutcheson, Florence V.; Stead, Daniel J.

2005-01-01

A methodology to eliminate model reflection and system vibration effects from post processed particle image velocimetry data is presented. Reflection and vibration lead to loss of data, and biased velocity calculations in PIV processing. A series of algorithms were developed to alleviate these problems. Reflections emanating from the model surface caused by the laser light sheet are removed from the PIV images by subtracting an image in which only the reflections are visible from all of the images within a data acquisition set. The result is a set of PIV images where only the seeded particles are apparent. Fiduciary marks painted on the surface of the test model were used as reference points in the images. By locating the centroids of these marks it was possible to shift all of the images to a common reference frame. This image alignment procedure as well as the subtraction of model reflection are performed in a first algorithm. Once the images have been shifted, they are compared with a background image that was recorded under no flow conditions. The second and third algorithms find the coordinates of fiduciary marks in the acquisition set images and the background image and calculate the displacement between these images. The final algorithm shifts all of the images so that fiduciary mark centroids lie in the same location as the background image centroids. This methodology effectively eliminated the effects of vibration so that unbiased data could be used for PIV processing. The PIV data used for this work was generated at the NASA Langley Research Center Quiet Flow Facility. The experiment entailed flow visualization near the flap side edge region of an airfoil model. Commercial PIV software was used for data acquisition and processing. In this paper, the experiment and the PIV acquisition of the data are described. The methodology used to develop the algorithms for reflection and system vibration removal is stated, and the implementation, testing and validation of these algorithms are presented.
A memory-efficient staining algorithm in 3D seismic modelling and imaging

NASA Astrophysics Data System (ADS)

Jia, Xiaofeng; Yang, Lu

2017-08-01

The staining algorithm has been proven to generate high signal-to-noise ratio (S/N) images in poorly illuminated areas in two-dimensional cases. In the staining algorithm, the stained wavefield relevant to the target area and the regular source wavefield forward propagate synchronously. Cross-correlating these two wavefields with the backward propagated receiver wavefield separately, we obtain two images: the local image of the target area and the conventional reverse time migration (RTM) image. This imaging process costs massive computer memory for wavefield storage, especially in large scale three-dimensional cases. To make the staining algorithm applicable to three-dimensional RTM, we develop a method to implement the staining algorithm in three-dimensional acoustic modelling in a standard staggered grid finite difference (FD) scheme. The implementation is adaptive to the order of spatial accuracy of the FD operator. The method can be applied to elastic, electromagnetic, and other wave equations. Taking the memory requirement into account, we adopt a random boundary condition (RBC) to backward extrapolate the receiver wavefield and reconstruct it by reverse propagation using the final wavefield snapshot only. Meanwhile, we forward simulate the stained wavefield and source wavefield simultaneously using the nearly perfectly matched layer (NPML) boundary condition. Experiments on a complex geologic model indicate that the RBC-NPML collaborative strategy not only minimizes the memory consumption but also guarantees high quality imaging results. We apply the staining algorithm to three-dimensional RTM via the proposed strategy. Numerical results show that our staining algorithm can produce high S/N images in the target areas with other structures effectively muted.
Reconstructing liver shape and position from MR image slices using an active shape model

NASA Astrophysics Data System (ADS)

Fenchel, Matthias; Thesen, Stefan; Schilling, Andreas

2008-03-01

We present an algorithm for fully automatic reconstruction of 3D position, orientation and shape of the human liver from a sparsely covering set of n 2D MR slice images. Reconstructing the shape of an organ from slice images can be used for scan planning, for surgical planning or other purposes where 3D anatomical knowledge has to be inferred from sparse slices. The algorithm is based on adapting an active shape model of the liver surface to a given set of slice images. The active shape model is created from a training set of liver segmentations from a group of volunteers. The training set is set up with semi-manual segmentations of T1-weighted volumetric MR images. Searching for the optimal shape model that best fits to the image data is done by maximizing a similarity measure based on local appearance at the surface. Two different algorithms for the active shape model search are proposed and compared: both algorithms seek to maximize the a-posteriori probability of the grey level appearance around the surface while constraining the surface to the space of valid shapes. The first algorithm works by using grey value profile statistics in normal direction. The second algorithm uses average and variance images to calculate the local surface appearance on the fly. Both algorithms are validated by fitting the active shape model to abdominal 2D slice images and comparing the shapes, which have been reconstructed, to the manual segmentations and to the results of active shape model searches from 3D image data. The results turn out to be promising and competitive to active shape model segmentations from 3D data.

GPU-based parallel algorithm for blind image restoration using midfrequency-based methods

NASA Astrophysics Data System (ADS)

Xie, Lang; Luo, Yi-han; Bao, Qi-liang

2013-08-01

GPU-based general-purpose computing is a new branch of modern parallel computing, so the study of parallel algorithms specially designed for GPU hardware architecture is of great significance. In order to solve the problem of high computational complexity and poor real-time performance in blind image restoration, the midfrequency-based algorithm for blind image restoration was analyzed and improved in this paper. Furthermore, a midfrequency-based filtering method is also used to restore the image hardly with any recursion or iteration. Combining the algorithm with data intensiveness, data parallel computing and GPU execution model of single instruction and multiple threads, a new parallel midfrequency-based algorithm for blind image restoration is proposed in this paper, which is suitable for stream computing of GPU. In this algorithm, the GPU is utilized to accelerate the estimation of class-G point spread functions and midfrequency-based filtering. Aiming at better management of the GPU threads, the threads in a grid are scheduled according to the decomposition of the filtering data in frequency domain after the optimization of data access and the communication between the host and the device. The kernel parallelism structure is determined by the decomposition of the filtering data to ensure the transmission rate to get around the memory bandwidth limitation. The results show that, with the new algorithm, the operational speed is significantly increased and the real-time performance of image restoration is effectively improved, especially for high-resolution images.
Optimized algorithm for the spatial nonuniformity correction of an imaging system based on a charge-coupled device color camera.

PubMed

de Lasarte, Marta; Pujol, Jaume; Arjona, Montserrat; Vilaseca, Meritxell

2007-01-10

We present an optimized linear algorithm for the spatial nonuniformity correction of a CCD color camera's imaging system and the experimental methodology developed for its implementation. We assess the influence of the algorithm's variables on the quality of the correction, that is, the dark image, the base correction image, and the reference level, and the range of application of the correction using a uniform radiance field provided by an integrator cube. The best spatial nonuniformity correction is achieved by having a nonzero dark image, by using an image with a mean digital level placed in the linear response range of the camera as the base correction image and taking the mean digital level of the image as the reference digital level. The response of the CCD color camera's imaging system to the uniform radiance field shows a high level of spatial uniformity after the optimized algorithm has been applied, which also allows us to achieve a high-quality spatial nonuniformity correction of captured images under different exposure conditions.
Machine learning for a Toolkit for Image Mining

NASA Technical Reports Server (NTRS)

Delanoy, Richard L.

1995-01-01

A prototype user environment is described that enables a user with very limited computer skills to collaborate with a computer algorithm to develop search tools (agents) that can be used for image analysis, creating metadata for tagging images, searching for images in an image database on the basis of image content, or as a component of computer vision algorithms. Agents are learned in an ongoing, two-way dialogue between the user and the algorithm. The user points to mistakes made in classification. The algorithm, in response, attempts to discover which image attributes are discriminating between objects of interest and clutter. It then builds a candidate agent and applies it to an input image, producing an 'interest' image highlighting features that are consistent with the set of objects and clutter indicated by the user. The dialogue repeats until the user is satisfied. The prototype environment, called the Toolkit for Image Mining (TIM) is currently capable of learning spectral and textural patterns. Learning exhibits rapid convergence to reasonable levels of performance and, when thoroughly trained, Fo appears to be competitive in discrimination accuracy with other classification techniques.
Locality constrained joint dynamic sparse representation for local matching based face recognition.

PubMed

Wang, Jianzhong; Yi, Yugen; Zhou, Wei; Shi, Yanjiao; Qi, Miao; Zhang, Ming; Zhang, Baoxue; Kong, Jun

2014-01-01

Recently, Sparse Representation-based Classification (SRC) has attracted a lot of attention for its applications to various tasks, especially in biometric techniques such as face recognition. However, factors such as lighting, expression, pose and disguise variations in face images will decrease the performances of SRC and most other face recognition techniques. In order to overcome these limitations, we propose a robust face recognition method named Locality Constrained Joint Dynamic Sparse Representation-based Classification (LCJDSRC) in this paper. In our method, a face image is first partitioned into several smaller sub-images. Then, these sub-images are sparsely represented using the proposed locality constrained joint dynamic sparse representation algorithm. Finally, the representation results for all sub-images are aggregated to obtain the final recognition result. Compared with other algorithms which process each sub-image of a face image independently, the proposed algorithm regards the local matching-based face recognition as a multi-task learning problem. Thus, the latent relationships among the sub-images from the same face image are taken into account. Meanwhile, the locality information of the data is also considered in our algorithm. We evaluate our algorithm by comparing it with other state-of-the-art approaches. Extensive experiments on four benchmark face databases (ORL, Extended YaleB, AR and LFW) demonstrate the effectiveness of LCJDSRC.
Fast and automatic algorithm for optic disc extraction in retinal images using principle-component-analysis-based preprocessing and curvelet transform.

PubMed

Shahbeig, Saleh; Pourghassem, Hossein

2013-01-01

Optic disc or optic nerve (ON) head extraction in retinal images has widespread applications in retinal disease diagnosis and human identification in biometric systems. This paper introduces a fast and automatic algorithm for detecting and extracting the ON region accurately from the retinal images without the use of the blood-vessel information. In this algorithm, to compensate for the destructive changes of the illumination and also enhance the contrast of the retinal images, we estimate the illumination of background and apply an adaptive correction function on the curvelet transform coefficients of retinal images. In other words, we eliminate the fault factors and pave the way to extract the ON region exactly. Then, we detect the ON region from retinal images using the morphology operators based on geodesic conversions, by applying a proper adaptive correction function on the reconstructed image's curvelet transform coefficients and a novel powerful criterion. Finally, using a local thresholding on the detected area of the retinal images, we extract the ON region. The proposed algorithm is evaluated on available images of DRIVE and STARE databases. The experimental results indicate that the proposed algorithm obtains an accuracy rate of 100% and 97.53% for the ON extractions on DRIVE and STARE databases, respectively.
Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms

PubMed Central

Kandaswamy, Umasankar; Rotman, Ziv; Watt, Dana; Schillebeeckx, Ian; Cavalli, Valeria; Klyachko, Vitaly

2013-01-01

High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation. PMID:23261652
The algorithm of motion blur image restoration based on PSF half-blind estimation

NASA Astrophysics Data System (ADS)

Chen, Da-Ke; Lin, Zhe

2011-08-01

A novel algorithm of motion blur image restoration based on PSF half-blind estimation with Hough transform was introduced on the basis of full analysis of the principle of TDICCD camera, with the problem that vertical uniform linear motion estimation used by IBD algorithm as the original value of PSF led to image restoration distortion. Firstly, the mathematical model of image degradation was established with the transcendental information of multi-frame images, and then two parameters (movement blur length and angle) that have crucial influence on PSF estimation was set accordingly. Finally, the ultimate restored image can be acquired through multiple iterative of the initial value of PSF estimation in Fourier domain, which the initial value was gained by the above method. Experimental results show that the proposal algorithm can not only effectively solve the image distortion problem caused by relative motion between TDICCD camera and movement objects, but also the details characteristics of original image are clearly restored.
Image reconstruction from few-view CT data by gradient-domain dictionary learning.

PubMed

Hu, Zhanli; Liu, Qiegen; Zhang, Na; Zhang, Yunwan; Peng, Xi; Wu, Peter Z; Zheng, Hairong; Liang, Dong

2016-05-21

Decreasing the number of projections is an effective way to reduce the radiation dose exposed to patients in medical computed tomography (CT) imaging. However, incomplete projection data for CT reconstruction will result in artifacts and distortions. In this paper, a novel dictionary learning algorithm operating in the gradient-domain (Grad-DL) is proposed for few-view CT reconstruction. Specifically, the dictionaries are trained from the horizontal and vertical gradient images, respectively and the desired image is reconstructed subsequently from the sparse representations of both gradients by solving the least-square method. Since the gradient images are sparser than the image itself, the proposed approach could lead to sparser representations than conventional DL methods in the image-domain, and thus a better reconstruction quality is achieved. To evaluate the proposed Grad-DL algorithm, both qualitative and quantitative studies were employed through computer simulations as well as real data experiments on fan-beam and cone-beam geometry. The results show that the proposed algorithm can yield better images than the existing algorithms.
SU-D-201-06: Random Walk Algorithm Seed Localization Parameters in Lung Positron Emission Tomography (PET) Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Soufi, M; Asl, A Kamali; Geramifar, P

2015-06-15

Purpose: The objective of this study was to find the best seed localization parameters in random walk algorithm application to lung tumor delineation in Positron Emission Tomography (PET) images. Methods: PET images suffer from statistical noise and therefore tumor delineation in these images is a challenging task. Random walk algorithm, a graph based image segmentation technique, has reliable image noise robustness. Also its fast computation and fast editing characteristics make it powerful for clinical purposes. We implemented the random walk algorithm using MATLAB codes. The validation and verification of the algorithm have been done by 4D-NCAT phantom with spherical lungmore » lesions in different diameters from 20 to 90 mm (with incremental steps of 10 mm) and different tumor to background ratios of 4:1 and 8:1. STIR (Software for Tomographic Image Reconstruction) has been applied to reconstruct the phantom PET images with different pixel sizes of 2×2×2 and 4×4×4 mm{sup 3}. For seed localization, we selected pixels with different maximum Standardized Uptake Value (SUVmax) percentages, at least (70%, 80%, 90% and 100%) SUVmax for foreground seeds and up to (20% to 55%, 5% increment) SUVmax for background seeds. Also, for investigation of algorithm performance on clinical data, 19 patients with lung tumor were studied. The resulted contours from algorithm have been compared with nuclear medicine expert manual contouring as ground truth. Results: Phantom and clinical lesion segmentation have shown that the best segmentation results obtained by selecting the pixels with at least 70% SUVmax as foreground seeds and pixels up to 30% SUVmax as background seeds respectively. The mean Dice Similarity Coefficient of 94% ± 5% (83% ± 6%) and mean Hausdorff Distance of 1 (2) pixels have been obtained for phantom (clinical) study. Conclusion: The accurate results of random walk algorithm in PET image segmentation assure its application for radiation treatment planning and diagnosis.« less
Medical image reconstruction algorithm based on the geometric information between sensor detector and ROI

NASA Astrophysics Data System (ADS)

Ham, Woonchul; Song, Chulgyu; Lee, Kangsan; Roh, Seungkuk

2016-05-01

In this paper, we propose a new image reconstruction algorithm considering the geometric information of acoustic sources and senor detector and review the two-step reconstruction algorithm which was previously proposed based on the geometrical information of ROI(region of interest) considering the finite size of acoustic sensor element. In a new image reconstruction algorithm, not only mathematical analysis is very simple but also its software implementation is very easy because we don't need to use the FFT. We verify the effectiveness of the proposed reconstruction algorithm by showing the simulation results by using Matlab k-wave toolkit.
Phi-s correlation and dynamic time warping - Two methods for tracking ice floes in SAR images

NASA Technical Reports Server (NTRS)

Mcconnell, Ross; Kober, Wolfgang; Kwok, Ronald; Curlander, John C.; Pang, Shirley S.

1991-01-01

The authors present two algorithms for performing shape matching on ice floe boundaries in SAR (synthetic aperture radar) images. These algorithms quickly produce a set of ice motion and rotation vectors that can be used to guide a pixel value correlator. The algorithms match a shape descriptor known as the Phi-s curve. The first algorithm uses normalized correlation to match the Phi-s curves, while the second uses dynamic programming to compute an elastic match that better accommodates ice floe deformation. Some empirical data on the performance of the algorithms on Seasat SAR images are presented.
Removal of impulse noise clusters from color images with local order statistics

NASA Astrophysics Data System (ADS)

Ruchay, Alexey; Kober, Vitaly

2017-09-01

This paper proposes a novel algorithm for restoring images corrupted with clusters of impulse noise. The noise clusters often occur when the probability of impulse noise is very high. The proposed noise removal algorithm consists of detection of bulky impulse noise in three color channels with local order statistics followed by removal of the detected clusters by means of vector median filtering. With the help of computer simulation we show that the proposed algorithm is able to effectively remove clustered impulse noise. The performance of the proposed algorithm is compared in terms of image restoration metrics with that of common successful algorithms.
TU-A-12A-07: CT-Based Biomarkers to Characterize Lung Lesion: Effects of CT Dose, Slice Thickness and Reconstruction Algorithm Based Upon a Phantom Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, B; Tan, Y; Tsai, W

2014-06-15

Purpose: Radiogenomics promises the ability to study cancer tumor genotype from the phenotype obtained through radiographic imaging. However, little attention has been paid to the sensitivity of image features, the image-based biomarkers, to imaging acquisition techniques. This study explores the impact of CT dose, slice thickness and reconstruction algorithm on measuring image features using a thorax phantom. Methods: Twentyfour phantom lesions of known volume (1 and 2mm), shape (spherical, elliptical, lobular and spicular) and density (-630, -10 and +100 HU) were scanned on a GE VCT at four doses (25, 50, 100, and 200 mAs). For each scan, six imagemore » series were reconstructed at three slice thicknesses of 5, 2.5 and 1.25mm with continuous intervals, using the lung and standard reconstruction algorithms. The lesions were segmented with an in-house 3D algorithm. Fifty (50) image features representing lesion size, shape, edge, and density distribution/texture were computed. Regression method was employed to analyze the effect of CT dose, slice of thickness and reconstruction algorithm on these features adjusting 3 confounding factors (size, density and shape of phantom lesions). Results: The coefficients of CT dose, slice thickness and reconstruction algorithm are presented in Table 1 in the supplementary material. No significant difference was found between the image features calculated on low dose CT scans (25mAs and 50mAs). About 50% texture features were found statistically different between low doses and high doses (100 and 200mAs). Significant differences were found for almost all features when calculated on 1.25mm, 2.5mm, and 5mm slice thickness images. Reconstruction algorithms significantly affected all density-based image features, but not morphological features. Conclusions: There is a great need to standardize the CT imaging protocols for radiogenomics study because CT dose, slice thickness and reconstruction algorithm impact quantitative image features to various degrees as our study has shown.« less
Research and implementation of finger-vein recognition algorithm

NASA Astrophysics Data System (ADS)

Pang, Zengyao; Yang, Jie; Chen, Yilei; Liu, Yin

2017-06-01

In finger vein image preprocessing, finger angle correction and ROI extraction are important parts of the system. In this paper, we propose an angle correction algorithm based on the centroid of the vein image, and extract the ROI region according to the bidirectional gray projection method. Inspired by the fact that features in those vein areas have similar appearance as valleys, a novel method was proposed to extract center and width of palm vein based on multi-directional gradients, which is easy-computing, quick and stable. On this basis, an encoding method was designed to determine the gray value distribution of texture image. This algorithm could effectively overcome the edge of the texture extraction error. Finally, the system was equipped with higher robustness and recognition accuracy by utilizing fuzzy threshold determination and global gray value matching algorithm. Experimental results on pairs of matched palm images show that, the proposed method has a EER with 3.21% extracts features at the speed of 27ms per image. It can be concluded that the proposed algorithm has obvious advantages in grain extraction efficiency, matching accuracy and algorithm efficiency.
Photoacoustic image reconstruction via deep learning

NASA Astrophysics Data System (ADS)

Antholzer, Stephan; Haltmeier, Markus; Nuster, Robert; Schwab, Johannes

2018-02-01

Applying standard algorithms to sparse data problems in photoacoustic tomography (PAT) yields low-quality images containing severe under-sampling artifacts. To some extent, these artifacts can be reduced by iterative image reconstruction algorithms which allow to include prior knowledge such as smoothness, total variation (TV) or sparsity constraints. These algorithms tend to be time consuming as the forward and adjoint problems have to be solved repeatedly. Further, iterative algorithms have additional drawbacks. For example, the reconstruction quality strongly depends on a-priori model assumptions about the objects to be recovered, which are often not strictly satisfied in practical applications. To overcome these issues, in this paper, we develop direct and efficient reconstruction algorithms based on deep learning. As opposed to iterative algorithms, we apply a convolutional neural network, whose parameters are trained before the reconstruction process based on a set of training data. For actual image reconstruction, a single evaluation of the trained network yields the desired result. Our presented numerical results (using two different network architectures) demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative reconstruction methods.
Super-resolution reconstruction of MR image with a novel residual learning network algorithm

NASA Astrophysics Data System (ADS)

Shi, Jun; Liu, Qingping; Wang, Chaofeng; Zhang, Qi; Ying, Shihui; Xu, Haoyu

2018-04-01

Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The image super-resolution (SR) technique offers an alternative approach to improve the spatial resolution of MRI due to its simplicity. Convolutional neural networks (CNN)-based SR algorithms have achieved state-of-the-art performance, in which the global residual learning (GRL) strategy is now commonly used due to its effectiveness for learning image details for SR. However, the partial loss of image details usually happens in a very deep network due to the degradation problem. In this work, we propose a novel residual learning-based SR algorithm for MRI, which combines both multi-scale GRL and shallow network block-based local residual learning (LRL). The proposed LRL module works effectively in capturing high-frequency details by learning local residuals. One simulated MRI dataset and two real MRI datasets have been used to evaluate our algorithm. The experimental results show that the proposed SR algorithm achieves superior performance to all of the other compared CNN-based SR algorithms in this work.
An Evolutionary Algorithm for Fast Intensity Based Image Matching Between Optical and SAR Satellite Imagery

NASA Astrophysics Data System (ADS)

Fischer, Peter; Schuegraf, Philipp; Merkle, Nina; Storch, Tobias

2018-04-01

This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR) optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search) and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.
A photoacoustic imaging reconstruction method based on directional total variation with adaptive directivity.

PubMed

Wang, Jin; Zhang, Chen; Wang, Yuanyuan

2017-05-30

In photoacoustic tomography (PAT), total variation (TV) based iteration algorithm is reported to have a good performance in PAT image reconstruction. However, classical TV based algorithm fails to preserve the edges and texture details of the image because it is not sensitive to the direction of the image. Therefore, it is of great significance to develop a new PAT reconstruction algorithm to effectively solve the drawback of TV. In this paper, a directional total variation with adaptive directivity (DDTV) model-based PAT image reconstruction algorithm, which weightedly sums the image gradients based on the spatially varying directivity pattern of the image is proposed to overcome the shortcomings of TV. The orientation field of the image is adaptively estimated through a gradient-based approach. The image gradients are weighted at every pixel based on both its anisotropic direction and another parameter, which evaluates the estimated orientation field reliability. An efficient algorithm is derived to solve the iteration problem associated with DDTV and possessing directivity of the image adaptively updated for each iteration step. Several texture images with various directivity patterns are chosen as the phantoms for the numerical simulations. The 180-, 90- and 30-view circular scans are conducted. Results obtained show that the DDTV-based PAT reconstructed algorithm outperforms the filtered back-projection method (FBP) and TV algorithms in the quality of reconstructed images with the peak signal-to-noise rations (PSNR) exceeding those of TV and FBP by about 10 and 18 dB, respectively, for all cases. The Shepp-Logan phantom is studied with further discussion of multimode scanning, convergence speed, robustness and universality aspects. In-vitro experiments are performed for both the sparse-view circular scanning and linear scanning. The results further prove the effectiveness of the DDTV, which shows better results than that of the TV with sharper image edges and clearer texture details. Both numerical simulation and in vitro experiments confirm that the DDTV provides a significant quality improvement of PAT reconstructed images for various directivity patterns.
A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT.

PubMed

Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa; Hu, Yanle

2016-03-08

On-board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real-time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image-guided radiotherapy (MR-IGRT) system. Manual con-tours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR-TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR-TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD-LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP-TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high-contrast images (i.e., kidney), the thresholding method provided the best speed (< 1 ms) with a satisfying accuracy (Dice = 0.95). When the image contrast was low, the VR-TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on-board MR-IGRT system.
An Interferometry Imaging Beauty Contest

NASA Technical Reports Server (NTRS)

Lawson, Peter R.; Cotton, William D.; Hummel, Christian A.; Monnier, John D.; Zhaod, Ming; Young, John S.; Thorsteinsson, Hrobjartur; Meimon, Serge C.; Mugnier, Laurent; LeBesnerais, Guy;

2004-01-01

We present a formal comparison of the performance of algorithms used for synthesis imaging with optical/infrared long-baseline interferometers. Six different algorithms are evaluated based on their performance with simulated test data. Each set of test data is formated in the interferometry Data Exchange Standard and is designed to simulate a specific problem relevant to long-baseline imaging. The data are calibrated power spectra and bispectra measured with a ctitious array, intended to be typical of existing imaging interferometers. The strengths and limitations of each algorithm are discussed.

Color reproduction and processing algorithm based on real-time mapping for endoscopic images.

PubMed

Khan, Tareq H; Mohammed, Shahed K; Imtiaz, Mohammad S; Wahid, Khan A

2016-01-01

In this paper, we present a real-time preprocessing algorithm for image enhancement for endoscopic images. A novel dictionary based color mapping algorithm is used for reproducing the color information from a theme image. The theme image is selected from a nearby anatomical location. A database of color endoscopy image for different location is prepared for this purpose. The color map is dynamic as its contents change with the change of the theme image. This method is used on low contrast grayscale white light images and raw narrow band images to highlight the vascular and mucosa structures and to colorize the images. It can also be applied to enhance the tone of color images. The statistic visual representation and universal image quality measures show that the proposed method can highlight the mucosa structure compared to other methods. The color similarity has been verified using Delta E color difference, structure similarity index, mean structure similarity index and structure and hue similarity. The color enhancement was measured using color enhancement factor that shows considerable improvements. The proposed algorithm has low and linear time complexity, which results in higher execution speed than other related works.
A super resolution framework for low resolution document image OCR

NASA Astrophysics Data System (ADS)

Ma, Di; Agam, Gady

2013-01-01

Optical character recognition is widely used for converting document images into digital media. Existing OCR algorithms and tools produce good results from high resolution, good quality, document images. In this paper, we propose a machine learning based super resolution framework for low resolution document image OCR. Two main techniques are used in our proposed approach: a document page segmentation algorithm and a modified K-means clustering algorithm. Using this approach, by exploiting coherence in the document, we reconstruct from a low resolution document image a better resolution image and improve OCR results. Experimental results show substantial gain in low resolution documents such as the ones captured from video.
Self-localization for an autonomous mobile robot based on an omni-directional vision system

NASA Astrophysics Data System (ADS)

Chiang, Shu-Yin; Lin, Kuang-Yu; Chia, Tsorng-Lin

2013-12-01

In this study, we designed an autonomous mobile robot based on the rules of the Federation of International Robotsoccer Association (FIRA) RoboSot category, integrating the techniques of computer vision, real-time image processing, dynamic target tracking, wireless communication, self-localization, motion control, path planning, and control strategy to achieve the contest goal. The self-localization scheme of the mobile robot is based on the algorithms featured in the images from its omni-directional vision system. In previous works, we used the image colors of the field goals as reference points, combining either dual-circle or trilateration positioning of the reference points to achieve selflocalization of the autonomous mobile robot. However, because the image of the game field is easily affected by ambient light, positioning systems exclusively based on color model algorithms cause errors. To reduce environmental effects and achieve the self-localization of the robot, the proposed algorithm is applied in assessing the corners of field lines by using an omni-directional vision system. Particularly in the mid-size league of the RobotCup soccer competition, selflocalization algorithms based on extracting white lines from the soccer field have become increasingly popular. Moreover, white lines are less influenced by light than are the color model of the goals. Therefore, we propose an algorithm that transforms the omni-directional image into an unwrapped transformed image, enhancing the extraction features. The process is described as follows: First, radical scan-lines were used to process omni-directional images, reducing the computational load and improving system efficiency. The lines were radically arranged around the center of the omni-directional camera image, resulting in a shorter computational time compared with the traditional Cartesian coordinate system. However, the omni-directional image is a distorted image, which makes it difficult to recognize the position of the robot. Therefore, image transformation was required to implement self-localization. Second, we used an approach to transform the omni-directional images into panoramic images. Hence, the distortion of the white line can be fixed through the transformation. The interest points that form the corners of the landmark were then located using the features from accelerated segment test (FAST) algorithm. In this algorithm, a circle of sixteen pixels surrounding the corner candidate is considered and is a high-speed feature detector in real-time frame rate applications. Finally, the dual-circle, trilateration, and cross-ratio projection algorithms were implemented in choosing the corners obtained from the FAST algorithm and localizing the position of the robot. The results demonstrate that the proposed algorithm is accurate, exhibiting a 2-cm position error in the soccer field measuring 600 cm2 x 400 cm2.
Automatic Image Registration of Multimodal Remotely Sensed Data with Global Shearlet Features

NASA Technical Reports Server (NTRS)

Murphy, James M.; Le Moigne, Jacqueline; Harding, David J.

2015-01-01

Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard, but sometimes are not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy, and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets. Our algorithm computes shearlet and wavelet features for the images to be registered, then performs least squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, though approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared to wavelet features alone.
Automatic Image Registration of Multi-Modal Remotely Sensed Data with Global Shearlet Features

PubMed Central

Murphy, James M.; Le Moigne, Jacqueline; Harding, David J.

2017-01-01

Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard, but sometimes are not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy, and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets. Our algorithm computes shearlet and wavelet features for the images to be registered, then performs least squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, though approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared to wavelet features alone. PMID:29123329
Data Compression Techniques for Maps

DTIC Science & Technology

1989-01-01

Lempel - Ziv compression is applied to the classified and unclassified images as also to the output of the compression algorithms . The algorithms ...resulted in a compression of 7:1. The output of the quadtree coding algorithm was then compressed using Lempel - Ziv coding. The compression ratio achieved...using Lempel - Ziv coding. The unclassified image gave a compression ratio of only 1.4:1. The K means classified image
Statistical Signal Models and Algorithms for Image Analysis

DTIC Science & Technology

1984-10-25

In this report, two-dimensional stochastic linear models are used in developing algorithms for image analysis such as classification, segmentation, and object detection in images characterized by textured backgrounds. These models generate two-dimensional random processes as outputs to which statistical inference procedures can naturally be applied. A common thread throughout our algorithms is the interpretation of the inference procedures in terms of linear prediction
Heterogeneous Vision Data Fusion for Independently Moving Cameras

DTIC Science & Technology

2010-03-01

target detection , tracking , and identification over a large terrain. The goal of the project is to investigate and evaluate the existing image...fusion algorithms, develop new real-time algorithms for Category-II image fusion, and apply these algorithms in moving target detection and tracking . The...moving target detection and classification. 15. SUBJECT TERMS Image Fusion, Target Detection , Moving Cameras, IR Camera, EO Camera 16. SECURITY
Windowed time-reversal music technique for super-resolution ultrasound imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Lianjie; Labyed, Yassin

Systems and methods for super-resolution ultrasound imaging using a windowed and generalized TR-MUSIC algorithm that divides the imaging region into overlapping sub-regions and applies the TR-MUSIC algorithm to the windowed backscattered ultrasound signals corresponding to each sub-region. The algorithm is also structured to account for the ultrasound attenuation in the medium and the finite-size effects of ultrasound transducer elements.
Accelerated fast iterative shrinkage thresholding algorithms for sparsity-regularized cone-beam CT image reconstruction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Qiaofeng; Sawatzky, Alex; Anastasio, Mark A., E-mail: anastasio@wustl.edu

Purpose: The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. Methods: Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that ismore » solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. Results: The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. Conclusions: The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated accelerated FISTAs for use with two nonsmooth penalty functions that will lead to further reductions in image reconstruction times while preserving image quality. Moreover, with the help of a mixed sparsity-regularization, better preservation of soft-tissue structures can be potentially obtained. The algorithms were systematically evaluated by use of computer-simulated and clinical data sets.« less
Accelerated fast iterative shrinkage thresholding algorithms for sparsity-regularized cone-beam CT image reconstruction.

PubMed

Xu, Qiaofeng; Yang, Deshan; Tan, Jun; Sawatzky, Alex; Anastasio, Mark A

2016-04-01

The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that is solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated accelerated FISTAs for use with two nonsmooth penalty functions that will lead to further reductions in image reconstruction times while preserving image quality. Moreover, with the help of a mixed sparsity-regularization, better preservation of soft-tissue structures can be potentially obtained. The algorithms were systematically evaluated by use of computer-simulated and clinical data sets.
Accelerated fast iterative shrinkage thresholding algorithms for sparsity-regularized cone-beam CT image reconstruction

PubMed Central

Xu, Qiaofeng; Yang, Deshan; Tan, Jun; Sawatzky, Alex; Anastasio, Mark A.

2016-01-01

Purpose: The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. Methods: Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that is solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. Results: The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. Conclusions: The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated accelerated FISTAs for use with two nonsmooth penalty functions that will lead to further reductions in image reconstruction times while preserving image quality. Moreover, with the help of a mixed sparsity-regularization, better preservation of soft-tissue structures can be potentially obtained. The algorithms were systematically evaluated by use of computer-simulated and clinical data sets. PMID:27036582
Hierarchical layered and semantic-based image segmentation using ergodicity map

NASA Astrophysics Data System (ADS)

Yadegar, Jacob; Liu, Xiaoqing

2010-04-01

Image segmentation plays a foundational role in image understanding and computer vision. Although great strides have been made and progress achieved on automatic/semi-automatic image segmentation algorithms, designing a generic, robust, and efficient image segmentation algorithm is still challenging. Human vision is still far superior compared to computer vision, especially in interpreting semantic meanings/objects in images. We present a hierarchical/layered semantic image segmentation algorithm that can automatically and efficiently segment images into hierarchical layered/multi-scaled semantic regions/objects with contextual topological relationships. The proposed algorithm bridges the gap between high-level semantics and low-level visual features/cues (such as color, intensity, edge, etc.) through utilizing a layered/hierarchical ergodicity map, where ergodicity is computed based on a space filling fractal concept and used as a region dissimilarity measurement. The algorithm applies a highly scalable, efficient, and adaptive Peano- Cesaro triangulation/tiling technique to decompose the given image into a set of similar/homogenous regions based on low-level visual cues in a top-down manner. The layered/hierarchical ergodicity map is built through a bottom-up region dissimilarity analysis. The recursive fractal sweep associated with the Peano-Cesaro triangulation provides efficient local multi-resolution refinement to any level of detail. The generated binary decomposition tree also provides efficient neighbor retrieval mechanisms for contextual topological object/region relationship generation. Experiments have been conducted within the maritime image environment where the segmented layered semantic objects include the basic level objects (i.e. sky/land/water) and deeper level objects in the sky/land/water surfaces. Experimental results demonstrate the proposed algorithm has the capability to robustly and efficiently segment images into layered semantic objects/regions with contextual topological relationships.
Multimodal medical image fusion by combining gradient minimization smoothing filter and non-subsampled directional filter bank

NASA Astrophysics Data System (ADS)

Zhang, Cheng; Wenbo, Mei; Huiqian, Du; Zexian, Wang

2018-04-01

A new algorithm was proposed for medical images fusion in this paper, which combined gradient minimization smoothing filter (GMSF) with non-sampled directional filter bank (NSDFB). In order to preserve more detail information, a multi scale edge preserving decomposition framework (MEDF) was used to decompose an image into a base image and a series of detail images. For the fusion of base images, the local Gaussian membership function is applied to construct the fusion weighted factor. For the fusion of detail images, NSDFB was applied to decompose each detail image into multiple directional sub-images that are fused by pulse coupled neural network (PCNN) respectively. The experimental results demonstrate that the proposed algorithm is superior to the compared algorithms in both visual effect and objective assessment.
Pediatric chest HRCT using the iDose4 Hybrid Iterative Reconstruction Algorithm: Which iDose level to choose?

NASA Astrophysics Data System (ADS)

Smarda, M.; Alexopoulou, E.; Mazioti, A.; Kordolaimi, S.; Ploussi, A.; Priftis, K.; Efstathopoulos, E.

2015-09-01

Purpose of the study is to determine the appropriate iterative reconstruction (IR) algorithm level that combines image quality and diagnostic confidence, for pediatric patients undergoing high-resolution computed tomography (HRCT). During the last 2 years, a total number of 20 children up to 10 years old with a clinical presentation of chronic bronchitis underwent HRCT in our department's 64-detector row CT scanner using the iDose IR algorithm, with almost similar image settings (80kVp, 40-50 mAs). CT images were reconstructed with all iDose levels (level 1 to 7) as well as with filtered-back projection (FBP) algorithm. Subjective image quality was evaluated by 2 experienced radiologists in terms of image noise, sharpness, contrast and diagnostic acceptability using a 5-point scale (1=excellent image, 5=non-acceptable image). Artifacts existance was also pointed out. All mean scores from both radiologists corresponded to satisfactory image quality (score ≤3), even with the FBP algorithm use. Almost excellent (score <2) overall image quality was achieved with iDose levels 5 to 7, but oversmoothing artifacts appearing with iDose levels 6 and 7 affected the diagnostic confidence. In conclusion, the use of iDose level 5 enables almost excellent image quality without considerable artifacts affecting the diagnosis. Further evaluation is needed in order to draw more precise conclusions.
A biological phantom for evaluation of CT image reconstruction algorithms

NASA Astrophysics Data System (ADS)

Cammin, J.; Fung, G. S. K.; Fishman, E. K.; Siewerdsen, J. H.; Stayman, J. W.; Taguchi, K.

2014-03-01

In recent years, iterative algorithms have become popular in diagnostic CT imaging to reduce noise or radiation dose to the patient. The non-linear nature of these algorithms leads to non-linearities in the imaging chain. However, the methods to assess the performance of CT imaging systems were developed assuming the linear process of filtered backprojection (FBP). Those methods may not be suitable any longer when applied to non-linear systems. In order to evaluate the imaging performance, a phantom is typically scanned and the image quality is measured using various indices. For reasons of practicality, cost, and durability, those phantoms often consist of simple water containers with uniform cylinder inserts. However, these phantoms do not represent the rich structure and patterns of real tissue accurately. As a result, the measured image quality or detectability performance for lesions may not reflect the performance on clinical images. The discrepancy between estimated and real performance may be even larger for iterative methods which sometimes produce "plastic-like", patchy images with homogeneous patterns. Consequently, more realistic phantoms should be used to assess the performance of iterative algorithms. We designed and constructed a biological phantom consisting of porcine organs and tissue that models a human abdomen, including liver lesions. We scanned the phantom on a clinical CT scanner and compared basic image quality indices between filtered backprojection and an iterative reconstruction algorithm.
Imaging metallic samples using electrical capacitance tomography: forward modelling and reconstruction algorithms

NASA Astrophysics Data System (ADS)

Hosani, E. Al; Zhang, M.; Abascal, J. F. P. J.; Soleimani, M.

2016-11-01

Electrical capacitance tomography (ECT) is an imaging technology used to reconstruct the permittivity distribution within the sensing region. So far, ECT has been primarily used to image non-conductive media only, since if the conductivity of the imaged object is high, the capacitance measuring circuit will be almost shortened by the conductivity path and a clear image cannot be produced using the standard image reconstruction approaches. This paper tackles the problem of imaging metallic samples using conventional ECT systems by investigating the two main aspects of image reconstruction algorithms, namely the forward problem and the inverse problem. For the forward problem, two different methods to model the region of high conductivity in ECT is presented. On the other hand, for the inverse problem, three different algorithms to reconstruct the high contrast images are examined. The first two methods are the linear single step Tikhonov method and the iterative total variation regularization method, and use two sets of ECT data to reconstruct the image in time difference mode. The third method, namely the level set method, uses absolute ECT measurements and was developed using a metallic forward model. The results indicate that the applications of conventional ECT systems can be extended to metal samples using the suggested algorithms and forward model, especially using a level set algorithm to find the boundary of the metal.
Status report: Data management program algorithm evaluation activity at Marshall Space Flight Center

NASA Technical Reports Server (NTRS)

Jayroe, R. R., Jr.

1977-01-01

An algorithm evaluation activity was initiated to study the problems associated with image processing by assessing the independent and interdependent effects of registration, compression, and classification techniques on LANDSAT data for several discipline applications. The objective of the activity was to make recommendations on selected applicable image processing algorithms in terms of accuracy, cost, and timeliness or to propose alternative ways of processing the data. As a means of accomplishing this objective, an Image Coding Panel was established. The conduct of the algorithm evaluation is described.
Optimized atom position and coefficient coding for matching pursuit-based image compression.

PubMed

Shoa, Alireza; Shirani, Shahram

2009-12-01

In this paper, we propose a new encoding algorithm for matching pursuit image coding. We show that coding performance is improved when correlations between atom positions and atom coefficients are both used in encoding. We find the optimum tradeoff between efficient atom position coding and efficient atom coefficient coding and optimize the encoder parameters. Our proposed algorithm outperforms the existing coding algorithms designed for matching pursuit image coding. Additionally, we show that our algorithm results in better rate distortion performance than JPEG 2000 at low bit rates.
A MULTICORE BASED PARALLEL IMAGE REGISTRATION METHOD

PubMed Central

Yang, Lin; Gong, Leiguang; Zhang, Hong; Nosher, John L.; Foran, David J.

2012-01-01

Image registration is a crucial step for many image-assisted clinical applications such as surgery planning and treatment evaluation. In this paper we proposed a landmark based nonlinear image registration algorithm for matching 2D image pairs. The algorithm was shown to be effective and robust under conditions of large deformations. In landmark based registration, the most important step is establishing the correspondence among the selected landmark points. This usually requires an extensive search which is often computationally expensive. We introduced a nonregular data partition algorithm using the K-means clustering algorithm to group the landmarks based on the number of available processing cores. The step optimizes the memory usage and data transfer. We have tested our method using IBM Cell Broadband Engine (Cell/B.E.) platform. PMID:19964921

Deep Marginalized Sparse Denoising Auto-Encoder for Image Denoising

NASA Astrophysics Data System (ADS)

Ma, Hongqiang; Ma, Shiping; Xu, Yuelei; Zhu, Mingming

2018-01-01

Stacked Sparse Denoising Auto-Encoder (SSDA) has been successfully applied to image denoising. As a deep network, the SSDA network with powerful data feature learning ability is superior to the traditional image denoising algorithms. However, the algorithm has high computational complexity and slow convergence rate in the training. To address this limitation, we present a method of image denoising based on Deep Marginalized Sparse Denoising Auto-Encoder (DMSDA). The loss function of Sparse Denoising Auto-Encoder is marginalized so that it satisfies both sparseness and marginality. The experimental results show that the proposed algorithm can not only outperform SSDA in the convergence speed and training time, but also has better denoising performance than the current excellent denoising algorithms, including both the subjective and objective evaluation of image denoising.
Image analysis of multiple moving wood pieces in real time

NASA Astrophysics Data System (ADS)

Wang, Weixing

2006-02-01

This paper presents algorithms for image processing and image analysis of wood piece materials. The algorithms were designed for auto-detection of wood piece materials on a moving conveyor belt or a truck. When wood objects on moving, the hard task is to trace the contours of the objects in n optimal way. To make the algorithms work efficiently in the plant, a flexible online system was designed and developed, which mainly consists of image acquisition, image processing, object delineation and analysis. A number of newly-developed algorithms can delineate wood objects with high accuracy and high speed, and in the wood piece analysis part, each wood piece can be characterized by a number of visual parameters which can also be used for constructing experimental models directly in the system.
Automated selection of the optimal cardiac phase for single-beat coronary CT angiography reconstruction.

PubMed

Stassi, D; Dutta, S; Ma, H; Soderman, A; Pazzani, D; Gros, E; Okerlund, D; Schmidt, T G

2016-01-01

Reconstructing a low-motion cardiac phase is expected to improve coronary artery visualization in coronary computed tomography angiography (CCTA) exams. This study developed an automated algorithm for selecting the optimal cardiac phase for CCTA reconstruction. The algorithm uses prospectively gated, single-beat, multiphase data made possible by wide cone-beam imaging. The proposed algorithm differs from previous approaches because the optimal phase is identified based on vessel image quality (IQ) directly, compared to previous approaches that included motion estimation and interphase processing. Because there is no processing of interphase information, the algorithm can be applied to any sampling of image phases, making it suited for prospectively gated studies where only a subset of phases are available. An automated algorithm was developed to select the optimal phase based on quantitative IQ metrics. For each reconstructed slice at each reconstructed phase, an image quality metric was calculated based on measures of circularity and edge strength of through-plane vessels. The image quality metric was aggregated across slices, while a metric of vessel-location consistency was used to ignore slices that did not contain through-plane vessels. The algorithm performance was evaluated using two observer studies. Fourteen single-beat cardiac CT exams (Revolution CT, GE Healthcare, Chalfont St. Giles, UK) reconstructed at 2% intervals were evaluated for best systolic (1), diastolic (6), or systolic and diastolic phases (7) by three readers and the algorithm. Pairwise inter-reader and reader-algorithm agreement was evaluated using the mean absolute difference (MAD) and concordance correlation coefficient (CCC) between the reader and algorithm-selected phases. A reader-consensus best phase was determined and compared to the algorithm selected phase. In cases where the algorithm and consensus best phases differed by more than 2%, IQ was scored by three readers using a five point Likert scale. There was no statistically significant difference between inter-reader and reader-algorithm agreement for either MAD or CCC metrics (p > 0.1). The algorithm phase was within 2% of the consensus phase in 15/21 of cases. The average absolute difference between consensus and algorithm best phases was 2.29% ± 2.47%, with a maximum difference of 8%. Average image quality scores for the algorithm chosen best phase were 4.01 ± 0.65 overall, 3.33 ± 1.27 for right coronary artery (RCA), 4.50 ± 0.35 for left anterior descending (LAD) artery, and 4.50 ± 0.35 for left circumflex artery (LCX). Average image quality scores for the consensus best phase were 4.11 ± 0.54 overall, 3.44 ± 1.03 for RCA, 4.39 ± 0.39 for LAD, and 4.50 ± 0.18 for LCX. There was no statistically significant difference (p > 0.1) between the image quality scores of the algorithm phase and the consensus phase. The proposed algorithm was statistically equivalent to a reader in selecting an optimal cardiac phase for CCTA exams. When reader and algorithm phases differed by >2%, image quality as rated by blinded readers was statistically equivalent. By detecting the optimal phase for CCTA reconstruction, the proposed algorithm is expected to improve coronary artery visualization in CCTA exams.
Block-Based Connected-Component Labeling Algorithm Using Binary Decision Trees

PubMed Central

Chang, Wan-Yu; Chiu, Chung-Cheng; Yang, Jia-Horng

2015-01-01

In this paper, we propose a fast labeling algorithm based on block-based concepts. Because the number of memory access points directly affects the time consumption of the labeling algorithms, the aim of the proposed algorithm is to minimize neighborhood operations. Our algorithm utilizes a block-based view and correlates a raster scan to select the necessary pixels generated by a block-based scan mask. We analyze the advantages of a sequential raster scan for the block-based scan mask, and integrate the block-connected relationships using two different procedures with binary decision trees to reduce unnecessary memory access. This greatly simplifies the pixel locations of the block-based scan mask. Furthermore, our algorithm significantly reduces the number of leaf nodes and depth levels required in the binary decision tree. We analyze the labeling performance of the proposed algorithm alongside that of other labeling algorithms using high-resolution images and foreground images. The experimental results from synthetic and real image datasets demonstrate that the proposed algorithm is faster than other methods. PMID:26393597
Linear feature detection algorithm for astronomical surveys - I. Algorithm description

NASA Astrophysics Data System (ADS)

Bektešević, Dino; Vinković, Dejan

2017-11-01

Computer vision algorithms are powerful tools in astronomical image analyses, especially when automation of object detection and extraction is required. Modern object detection algorithms in astronomy are oriented towards detection of stars and galaxies, ignoring completely the detection of existing linear features. With the emergence of wide-field sky surveys, linear features attract scientific interest as possible trails of fast flybys of near-Earth asteroids and meteors. In this work, we describe a new linear feature detection algorithm designed specifically for implementation in big data astronomy. The algorithm combines a series of algorithmic steps that first remove other objects (stars and galaxies) from the image and then enhance the line to enable more efficient line detection with the Hough algorithm. The rate of false positives is greatly reduced thanks to a step that replaces possible line segments with rectangles and then compares lines fitted to the rectangles with the lines obtained directly from the image. The speed of the algorithm and its applicability in astronomical surveys are also discussed.
Medical Image Encryption: An Application for Improved Padding Based GGH Encryption Algorithm

PubMed Central

Sokouti, Massoud; Zakerolhosseini, Ali; Sokouti, Babak

2016-01-01

Medical images are regarded as important and sensitive data in the medical informatics systems. For transferring medical images over an insecure network, developing a secure encryption algorithm is necessary. Among the three main properties of security services (i.e., confidentiality, integrity, and availability), the confidentiality is the most essential feature for exchanging medical images among physicians. The Goldreich Goldwasser Halevi (GGH) algorithm can be a good choice for encrypting medical images as both the algorithm and sensitive data are represented by numeric matrices. Additionally, the GGH algorithm does not increase the size of the image and hence, its complexity will remain as simple as O(n2). However, one of the disadvantages of using the GGH algorithm is the Chosen Cipher Text attack. In our strategy, this shortcoming of GGH algorithm has been taken in to consideration and has been improved by applying the padding (i.e., snail tour XORing), before the GGH encryption process. For evaluating their performances, three measurement criteria are considered including (i) Number of Pixels Change Rate (NPCR), (ii) Unified Average Changing Intensity (UACI), and (iii) Avalanche effect. The results on three different sizes of images showed that padding GGH approach has improved UACI, NPCR, and Avalanche by almost 100%, 35%, and 45%, respectively, in comparison to the standard GGH algorithm. Also, the outcomes will make the padding GGH resist against the cipher text, the chosen cipher text, and the statistical attacks. Furthermore, increasing the avalanche effect of more than 50% is a promising achievement in comparison to the increased complexities of the proposed method in terms of encryption and decryption processes. PMID:27857824
Sci-Fri PM: Radiation Therapy, Planning, Imaging, and Special Techniques - 11: Quantification of chest wall motion during deep inspiration breast hold treatments using cine EPID images and a physics based algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alpuche Aviles, Jorge E.; VanBeek, Timothy

Purpose: This work presents an algorithm used to quantify intra-fraction motion for patients treated using deep inspiration breath hold (DIBH). The algorithm quantifies the position of the chest wall in breast tangent fields using electronic portal images. Methods: The algorithm assumes that image profiles, taken along a direction perpendicular to the medial border of the field, follow a monotonically and smooth decreasing function. This assumption is invalid in the presence of lung and can be used to calculate chest wall position. The algorithm was validated by determining the position of the chest wall for varying field edge positions in portalmore » images of a thoracic phantom. The algorithm was used to quantify intra-fraction motion in cine images for 7 patients treated with DIBH. Results: Phantom results show that changes in the distance between chest wall and field edge were accurate within 0.1 mm on average. For a fixed field edge, the algorithm calculates the position of the chest wall with a 0.2 mm standard deviation. Intra-fraction motion for DIBH patients was within 1 mm 91.4% of the time and within 1.5 mm 97.9% of the time. The maximum intra-fraction motion was 3.0 mm. Conclusions: A physics based algorithm was developed and can be used to quantify the position of chest wall irradiated in tangent portal images with an accuracy of 0.1 mm and precision of 0.6 mm. Intra-fraction motion for patients treated with DIBH at our clinic is less than 3 mm.« less
Development of a control algorithm for the ultrasound scanning robot (NCCUSR) using ultrasound image and force feedback.

PubMed

Kim, Yeoun Jae; Seo, Jong Hyun; Kim, Hong Rae; Kim, Kwang Gi

2017-06-01

Clinicians who frequently perform ultrasound scanning procedures often suffer from musculoskeletal disorders, arthritis, and myalgias. To minimize their occurrence and to assist clinicians, ultrasound scanning robots have been developed worldwide. Although, to date, there is still no commercially available ultrasound scanning robot, many control methods have been suggested and researched. These control algorithms are either image based or force based. If the ultrasound scanning robot control algorithm was a combination of the two algorithms, it could benefit from the advantage of each one. However, there are no existing control methods for ultrasound scanning robots that combine force control and image analysis. Therefore, in this work, a control algorithm is developed for an ultrasound scanning robot using force feedback and ultrasound image analysis. A manipulator-type ultrasound scanning robot named 'NCCUSR' is developed and a control algorithm for this robot is suggested and verified. First, conventional hybrid position-force control is implemented for the robot and the hybrid position-force control algorithm is combined with ultrasound image analysis to fully control the robot. The control method is verified using a thyroid phantom. It was found that the proposed algorithm can be applied to control the ultrasound scanning robot and experimental outcomes suggest that the images acquired using the proposed control method can yield a rating score that is equivalent to images acquired directly by the clinicians. The proposed control method can be applied to control the ultrasound scanning robot. However, more work must be completed to verify the proposed control method in order to become clinically feasible. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Implementation of dictionary pair learning algorithm for image quality improvement

NASA Astrophysics Data System (ADS)

Vimala, C.; Aruna Priya, P.

2018-04-01

This paper proposes an image denoising on dictionary pair learning algorithm. Visual information is transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmissions is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image.
Objective performance assessment of five computed tomography iterative reconstruction algorithms.

PubMed

Omotayo, Azeez; Elbakri, Idris

2016-11-22

Iterative algorithms are gaining clinical acceptance in CT. We performed objective phantom-based image quality evaluation of five commercial iterative reconstruction algorithms available on four different multi-detector CT (MDCT) scanners at different dose levels as well as the conventional filtered back-projection (FBP) reconstruction. Using the Catphan500 phantom, we evaluated image noise, contrast-to-noise ratio (CNR), modulation transfer function (MTF) and noise-power spectrum (NPS). The algorithms were evaluated over a CTDIvol range of 0.75-18.7 mGy on four major MDCT scanners: GE DiscoveryCT750HD (algorithms: ASIR™ and VEO™); Siemens Somatom Definition AS+ (algorithm: SAFIRE™); Toshiba Aquilion64 (algorithm: AIDR3D™); and Philips Ingenuity iCT256 (algorithm: iDose4™). Images were reconstructed using FBP and the respective iterative algorithms on the four scanners. Use of iterative algorithms decreased image noise and increased CNR, relative to FBP. In the dose range of 1.3-1.5 mGy, noise reduction using iterative algorithms was in the range of 11%-51% on GE DiscoveryCT750HD, 10%-52% on Siemens Somatom Definition AS+, 49%-62% on Toshiba Aquilion64, and 13%-44% on Philips Ingenuity iCT256. The corresponding CNR increase was in the range 11%-105% on GE, 11%-106% on Siemens, 85%-145% on Toshiba and 13%-77% on Philips respectively. Most algorithms did not affect the MTF, except for VEO™ which produced an increase in the limiting resolution of up to 30%. A shift in the peak of the NPS curve towards lower frequencies and a decrease in NPS amplitude were obtained with all iterative algorithms. VEO™ required long reconstruction times, while all other algorithms produced reconstructions in real time. Compared to FBP, iterative algorithms reduced image noise and increased CNR. The iterative algorithms available on different scanners achieved different levels of noise reduction and CNR increase while spatial resolution improvements were obtained only with VEO™. This study is useful in that it provides performance assessment of the iterative algorithms available from several mainstream CT manufacturers.
[A study on medical image fusion].

PubMed

Zhang, Er-hu; Bian, Zheng-zhong

2002-09-01

Five algorithms with its advantages and disadvantage for medical image fusion are analyzed. Four kinds of quantitative evaluation criteria for the quality of image fusion algorithms are proposed and these will give us some guidance for future research.
Efficient image enhancement using sparse source separation in the Retinex theory

NASA Astrophysics Data System (ADS)

Yoon, Jongsu; Choi, Jangwon; Choe, Yoonsik

2017-11-01

Color constancy is the feature of the human vision system (HVS) that ensures the relative constancy of the perceived color of objects under varying illumination conditions. The Retinex theory of machine vision systems is based on the HVS. Among Retinex algorithms, the physics-based algorithms are efficient; however, they generally do not satisfy the local characteristics of the original Retinex theory because they eliminate global illumination from their optimization. We apply the sparse source separation technique to the Retinex theory to present a physics-based algorithm that satisfies the locality characteristic of the original Retinex theory. Previous Retinex algorithms have limited use in image enhancement because the total variation Retinex results in an overly enhanced image and the sparse source separation Retinex cannot completely restore the original image. In contrast, our proposed method preserves the image edge and can very nearly replicate the original image without any special operation.
Advances in multi-sensor data fusion: algorithms and applications.

PubMed

Dong, Jiang; Zhuang, Dafang; Huang, Yaohuan; Fu, Jingying

2009-01-01

With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1) Improvements of fusion algorithms; (2) Development of "algorithm fusion" methods; (3) Establishment of an automatic quality assessment scheme.
Image Segmentation Method Using Fuzzy C Mean Clustering Based on Multi-Objective Optimization

NASA Astrophysics Data System (ADS)

Chen, Jinlin; Yang, Chunzhi; Xu, Guangkui; Ning, Li

2018-04-01

Image segmentation is not only one of the hottest topics in digital image processing, but also an important part of computer vision applications. As one kind of image segmentation algorithms, fuzzy C-means clustering is an effective and concise segmentation algorithm. However, the drawback of FCM is that it is sensitive to image noise. To solve the problem, this paper designs a novel fuzzy C-mean clustering algorithm based on multi-objective optimization. We add a parameter λ to the fuzzy distance measurement formula to improve the multi-objective optimization. The parameter λ can adjust the weights of the pixel local information. In the algorithm, the local correlation of neighboring pixels is added to the improved multi-objective mathematical model to optimize the clustering cent. Two different experimental results show that the novel fuzzy C-means approach has an efficient performance and computational time while segmenting images by different type of noises.
Optic disc segmentation: level set methods and blood vessels inpainting

NASA Astrophysics Data System (ADS)

Almazroa, A.; Sun, Weiwei; Alodhayb, Sami; Raahemifar, Kaamran; Lakshminarayanan, Vasudevan

2017-03-01

Segmenting the optic disc (OD) is an important and essential step in creating a frame of reference for diagnosing optic nerve head (ONH) pathology such as glaucoma. Therefore, a reliable OD segmentation technique is necessary for automatic screening of ONH abnormalities. The main contribution of this paper is in presenting a novel OD segmentation algorithm based on applying a level set method on a localized OD image. To prevent the blood vessels from interfering with the level set process, an inpainting technique is applied. The algorithm is evaluated using a new retinal fundus image dataset called RIGA (Retinal Images for Glaucoma Analysis). In the case of low quality images, a double level set is applied in which the first level set is considered to be a localization for the OD. Five hundred and fifty images are used to test the algorithm accuracy as well as its agreement with manual markings by six ophthalmologists. The accuracy of the algorithm in marking the optic disc area and centroid is 83.9%, and the best agreement is observed between the results of the algorithm and manual markings in 379 images.
A Hybrid Shared-Memory Parallel Max-Tree Algorithm for Extreme Dynamic-Range Images.

PubMed

Moschini, Ugo; Meijster, Arnold; Wilkinson, Michael H F

2018-03-01

Max-trees, or component trees, are graph structures that represent the connected components of an image in a hierarchical way. Nowadays, many application fields rely on images with high-dynamic range or floating point values. Efficient sequential algorithms exist to build trees and compute attributes for images of any bit depth. However, we show that the current parallel algorithms perform poorly already with integers at bit depths higher than 16 bits per pixel. We propose a parallel method combining the two worlds of flooding and merging max-tree algorithms. First, a pilot max-tree of a quantized version of the image is built in parallel using a flooding method. Later, this structure is used in a parallel leaf-to-root approach to compute efficiently the final max-tree and to drive the merging of the sub-trees computed by the threads. We present an analysis of the performance both on simulated and actual 2D images and 3D volumes. Execution times are about better than the fastest sequential algorithm and speed-up goes up to on 64 threads.
An Efficient Correction Algorithm for Eliminating Image Misalignment Effects on Co-Phasing Measurement Accuracy for Segmented Active Optics Systems

PubMed Central

Yue, Dan; Xu, Shuyan; Nie, Haitao; Wang, Zongyang

2016-01-01

The misalignment between recorded in-focus and out-of-focus images using the Phase Diversity (PD) algorithm leads to a dramatic decline in wavefront detection accuracy and image recovery quality for segmented active optics systems. This paper demonstrates the theoretical relationship between the image misalignment and tip-tilt terms in Zernike polynomials of the wavefront phase for the first time, and an efficient two-step alignment correction algorithm is proposed to eliminate these misalignment effects. This algorithm processes a spatial 2-D cross-correlation of the misaligned images, revising the offset to 1 or 2 pixels and narrowing the search range for alignment. Then, it eliminates the need for subpixel fine alignment to achieve adaptive correction by adding additional tip-tilt terms to the Optical Transfer Function (OTF) of the out-of-focus channel. The experimental results demonstrate the feasibility and validity of the proposed correction algorithm to improve the measurement accuracy during the co-phasing of segmented mirrors. With this alignment correction, the reconstructed wavefront is more accurate, and the recovered image is of higher quality. PMID:26934045
Contour-based object orientation estimation

NASA Astrophysics Data System (ADS)

Alpatov, Boris; Babayan, Pavel

2016-04-01

Real-time object orientation estimation is an actual problem of computer vision nowadays. In this paper we propose an approach to estimate an orientation of objects lacking axial symmetry. Proposed algorithm is intended to estimate orientation of a specific known 3D object, so 3D model is required for learning. The proposed orientation estimation algorithm consists of 2 stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. It minimizes the training image set. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy (mean error value less than 6°) in all case studies. The real-time performance of the algorithm was also demonstrated.
The performance analysis of three-dimensional track-before-detect algorithm based on Fisher-Tippett-Gnedenko theorem

NASA Astrophysics Data System (ADS)

Cho, Hoonkyung; Chun, Joohwan; Song, Sungchan

2016-09-01

The dim moving target tracking from the infrared image sequence in the presence of high clutter and noise has been recently under intensive investigation. The track-before-detect (TBD) algorithm processing the image sequence over a number of frames before decisions on the target track and existence is known to be especially attractive in very low SNR environments (⩽ 3 dB). In this paper, we shortly present a three-dimensional (3-D) TBD with dynamic programming (TBD-DP) algorithm using multiple IR image sensors. Since traditional two-dimensional TBD algorithm cannot track and detect the along the viewing direction, we use 3-D TBD with multiple sensors and also strictly analyze the detection performance (false alarm and detection probabilities) based on Fisher-Tippett-Gnedenko theorem. The 3-D TBD-DP algorithm which does not require a separate image registration step uses the pixel intensity values jointly read off from multiple image frames to compute the merit function required in the DP process. Therefore, we also establish the relationship between the pixel coordinates of image frame and the reference coordinates.
The development of a line-scan imaging algorithm for the detection of fecal contamination on leafy geens

NASA Astrophysics Data System (ADS)

Yang, Chun-Chieh; Kim, Moon S.; Chuang, Yung-Kun; Lee, Hoyoung

2013-05-01

This paper reports the development of a multispectral algorithm, using the line-scan hyperspectral imaging system, to detect fecal contamination on leafy greens. Fresh bovine feces were applied to the surfaces of washed loose baby spinach leaves. A hyperspectral line-scan imaging system was used to acquire hyperspectral fluorescence images of the contaminated leaves. Hyperspectral image analysis resulted in the selection of the 666 nm and 688 nm wavebands for a multispectral algorithm to rapidly detect feces on leafy greens, by use of the ratio of fluorescence intensities measured at those two wavebands (666 nm over 688 nm). The algorithm successfully distinguished most of the lowly diluted fecal spots (0.05 g feces/ml water and 0.025 g feces/ml water) and some of the highly diluted spots (0.0125 g feces/ml water and 0.00625 g feces/ml water) from the clean spinach leaves. The results showed the potential of the multispectral algorithm with line-scan imaging system for application to automated food processing lines for food safety inspection of leafy green vegetables.

Axial Cone Beam Reconstruction by Weighted BPF/DBPF and Orthogonal Butterfly Filtering

PubMed Central

Tang, Shaojie; Tang, Xiangyang

2016-01-01

Goal The backprojection-filtration (BPF) and the derivative backprojection filtered (DBPF) algorithms, in which Hilbert filtering is the common algorithmic feature, are originally derived for exact helical reconstruction from cone beam (CB) scan data and axial reconstruction from fan beam data, respectively. These two algorithms can be heuristically extended for image reconstruction from axial CB scan data, but induce severe artifacts in images located away from the central plane determined by the circular source trajectory. We propose an algorithmic solution herein to eliminate the artifacts. Methods The solution is an integration of three-dimensional (3D) weighted axial CB-BPF/ DBPF algorithm with orthogonal butterfly filtering, namely axial CB-BPF/DBPF cascaded with orthogonal butterfly filtering. Using the computer simulated Forbild head and thoracic phantoms that are rigorous in inspecting reconstruction accuracy and an anthropomorphic thoracic phantom with projection data acquired by a CT scanner, we evaluate performance of the proposed algorithm. Results Preliminary results show that the orthogonal butterfly filtering can eliminate the severe streak artifacts existing in the images reconstructed by the 3D weighted axial CB-BPF/DBPF algorithm located at off-central planes. Conclusion Integrated with orthogonal butterfly filtering, the 3D weighted CB-BPF/DBPF algorithm can perform at least as well as the 3D weighted CB-FBP algorithm in image reconstruction from axial CB scan data. Significance The proposed 3D weighted axial CB-BPF/DBPF cascaded with orthogonal butterfly filtering can be an algorithmic solution for CT imaging in extensive clinical and preclinical applications. PMID:26660512
Implementation of a Real-Time Stacking Algorithm in a Photogrammetric Digital Camera for Uavs

NASA Astrophysics Data System (ADS)

Audi, A.; Pierrot-Deseilligny, M.; Meynard, C.; Thom, C.

2017-08-01

In the recent years, unmanned aerial vehicles (UAVs) have become an interesting tool in aerial photography and photogrammetry activities. In this context, some applications (like cloudy sky surveys, narrow-spectral imagery and night-vision imagery) need a longexposure time where one of the main problems is the motion blur caused by the erratic camera movements during image acquisition. This paper describes an automatic real-time stacking algorithm which produces a high photogrammetric quality final composite image with an equivalent long-exposure time using several images acquired with short-exposure times. Our method is inspired by feature-based image registration technique. The algorithm is implemented on the light-weight IGN camera, which has an IMU sensor and a SoC/FPGA. To obtain the correct parameters for the resampling of images, the presented method accurately estimates the geometrical relation between the first and the Nth image, taking into account the internal parameters and the distortion of the camera. Features are detected in the first image by the FAST detector, than homologous points on other images are obtained by template matching aided by the IMU sensors. The SoC/FPGA in the camera is used to speed up time-consuming parts of the algorithm such as features detection and images resampling in order to achieve a real-time performance as we want to write only the resulting final image to save bandwidth on the storage device. The paper includes a detailed description of the implemented algorithm, resource usage summary, resulting processing time, resulting images, as well as block diagrams of the described architecture. The resulting stacked image obtained on real surveys doesn't seem visually impaired. Timing results demonstrate that our algorithm can be used in real-time since its processing time is less than the writing time of an image in the storage device. An interesting by-product of this algorithm is the 3D rotation estimated by a photogrammetric method between poses, which can be used to recalibrate in real-time the gyrometers of the IMU.
Automatic extraction of via in the CT image of PCB

NASA Astrophysics Data System (ADS)

Liu, Xifeng; Hu, Yuwei

2018-04-01

In modern industry, the nondestructive testing of printed circuit board (PCB) can prevent effectively the system failure and is becoming more and more important. In order to detect the via in the PCB base on the CT image automatically accurately and reliably, a novel algorithm for via extraction based on weighting stack combining the morphologic character of via is designed. Every slice data in the vertical direction of the PCB is superimposed to enhanced vias target. The OTSU algorithm is used to segment the slice image. OTSU algorithm of thresholding gray level images is efficient for separating an image into two classes where two types of fairly distinct classes exist in the image. Randomized Hough Transform was used to locate the region of via in the segmented binary image. Then the 3D reconstruction of via based on sequence slice images was done by volume rendering. The accuracy of via positioning and detecting from a CT images of PCB was demonstrated by proposed algorithm. It was found that the method is good in veracity and stability for detecting of via in three dimensional.
Improved Seam-Line Searching Algorithm for UAV Image Mosaic with Optical Flow

PubMed Central

Zhang, Weilong; Guo, Bingxuan; Liao, Xuan; Li, Wenzhuo

2018-01-01

Ghosting and seams are two major challenges in creating unmanned aerial vehicle (UAV) image mosaic. In response to these problems, this paper proposes an improved method for UAV image seam-line searching. First, an image matching algorithm is used to extract and match the features of adjacent images, so that they can be transformed into the same coordinate system. Then, the gray scale difference, the gradient minimum, and the optical flow value of pixels in adjacent image overlapped area in a neighborhood are calculated, which can be applied to creating an energy function for seam-line searching. Based on that, an improved dynamic programming algorithm is proposed to search the optimal seam-lines to complete the UAV image mosaic. This algorithm adopts a more adaptive energy aggregation and traversal strategy, which can find a more ideal splicing path for adjacent UAV images and avoid the ground objects better. The experimental results show that the proposed method can effectively solve the problems of ghosting and seams in the panoramic UAV images. PMID:29659526
Adaptive image coding based on cubic-spline interpolation

NASA Astrophysics Data System (ADS)

Jiang, Jian-Xing; Hong, Shao-Hua; Lin, Tsung-Ching; Wang, Lin; Truong, Trieu-Kien

2014-09-01

It has been investigated that at low bit rates, downsampling prior to coding and upsampling after decoding can achieve better compression performance than standard coding algorithms, e.g., JPEG and H. 264/AVC. However, at high bit rates, the sampling-based schemes generate more distortion. Additionally, the maximum bit rate for the sampling-based scheme to outperform the standard algorithm is image-dependent. In this paper, a practical adaptive image coding algorithm based on the cubic-spline interpolation (CSI) is proposed. This proposed algorithm adaptively selects the image coding method from CSI-based modified JPEG and standard JPEG under a given target bit rate utilizing the so called ρ-domain analysis. The experimental results indicate that compared with the standard JPEG, the proposed algorithm can show better performance at low bit rates and maintain the same performance at high bit rates.
Hardware architecture design of image restoration based on time-frequency domain computation

NASA Astrophysics Data System (ADS)

Wen, Bo; Zhang, Jing; Jiao, Zipeng

2013-10-01

The image restoration algorithms based on time-frequency domain computation is high maturity and applied widely in engineering. To solve the high-speed implementation of these algorithms, the TFDC hardware architecture is proposed. Firstly, the main module is designed, by analyzing the common processing and numerical calculation. Then, to improve the commonality, the iteration control module is planed for iterative algorithms. In addition, to reduce the computational cost and memory requirements, the necessary optimizations are suggested for the time-consuming module, which include two-dimensional FFT/IFFT and the plural calculation. Eventually, the TFDC hardware architecture is adopted for hardware design of real-time image restoration system. The result proves that, the TFDC hardware architecture and its optimizations can be applied to image restoration algorithms based on TFDC, with good algorithm commonality, hardware realizability and high efficiency.
Wavelet tree structure based speckle noise removal for optical coherence tomography

NASA Astrophysics Data System (ADS)

Yuan, Xin; Liu, Xuan; Liu, Yang

2018-02-01

We report a new speckle noise removal algorithm in optical coherence tomography (OCT). Though wavelet domain thresholding algorithms have demonstrated superior advantages in suppressing noise magnitude and preserving image sharpness in OCT, the wavelet tree structure has not been investigated in previous applications. In this work, we propose an adaptive wavelet thresholding algorithm via exploiting the tree structure in wavelet coefficients to remove the speckle noise in OCT images. The threshold for each wavelet band is adaptively selected following a special rule to retain the structure of the image across different wavelet layers. Our results demonstrate that the proposed algorithm outperforms conventional wavelet thresholding, with significant advantages in preserving image features.
Accelerating Families of Fuzzy K-Means Algorithms for Vector Quantization Codebook Design

PubMed Central

Mata, Edson; Bandeira, Silvio; de Mattos Neto, Paulo; Lopes, Waslon; Madeiro, Francisco

2016-01-01

The performance of signal processing systems based on vector quantization depends on codebook design. In the image compression scenario, the quality of the reconstructed images depends on the codebooks used. In this paper, alternatives are proposed for accelerating families of fuzzy K-means algorithms for codebook design. The acceleration is obtained by reducing the number of iterations of the algorithms and applying efficient nearest neighbor search techniques. Simulation results concerning image vector quantization have shown that the acceleration obtained so far does not decrease the quality of the reconstructed images. Codebook design time savings up to about 40% are obtained by the accelerated versions with respect to the original versions of the algorithms. PMID:27886061
Accelerating Families of Fuzzy K-Means Algorithms for Vector Quantization Codebook Design.

PubMed

Mata, Edson; Bandeira, Silvio; de Mattos Neto, Paulo; Lopes, Waslon; Madeiro, Francisco

2016-11-23

The performance of signal processing systems based on vector quantization depends on codebook design. In the image compression scenario, the quality of the reconstructed images depends on the codebooks used. In this paper, alternatives are proposed for accelerating families of fuzzy K-means algorithms for codebook design. The acceleration is obtained by reducing the number of iterations of the algorithms and applying efficient nearest neighbor search techniques. Simulation results concerning image vector quantization have shown that the acceleration obtained so far does not decrease the quality of the reconstructed images. Codebook design time savings up to about 40% are obtained by the accelerated versions with respect to the original versions of the algorithms.
Nonrigid synthetic aperture radar and optical image coregistration by combining local rigid transformations using a Kohonen network.

PubMed

Salehpour, Mehdi; Behrad, Alireza

2017-10-01

This study proposes a new algorithm for nonrigid coregistration of synthetic aperture radar (SAR) and optical images. The proposed algorithm employs point features extracted by the binary robust invariant scalable keypoints algorithm and a new method called weighted bidirectional matching for initial correspondence. To refine false matches, we assume that the transformation between SAR and optical images is locally rigid. This property is used to refine false matches by assigning scores to matched pairs and clustering local rigid transformations using a two-layer Kohonen network. Finally, the thin plate spline algorithm and mutual information are used for nonrigid coregistration of SAR and optical images.
Algorithm of pulmonary emphysema extraction using thoracic 3D CT images

NASA Astrophysics Data System (ADS)

Saita, Shinsuke; Kubo, Mitsuru; Kawata, Yoshiki; Niki, Noboru; Nakano, Yasutaka; Ohmatsu, Hironobu; Tominaga, Keigo; Eguchi, Kenji; Moriyama, Noriyuki

2007-03-01

Recently, due to aging and smoking, emphysema patients are increasing. The restoration of alveolus which was destroyed by emphysema is not possible, thus early detection of emphysema is desired. We describe a quantitative algorithm for extracting emphysematous lesions and quantitatively evaluate their distribution patterns using low dose thoracic 3-D CT images. The algorithm identified lung anatomies, and extracted low attenuation area (LAA) as emphysematous lesion candidates. Applying the algorithm to thoracic 3-D CT images and then by follow-up 3-D CT images, we demonstrate its potential effectiveness to assist radiologists and physicians to quantitatively evaluate the emphysematous lesions distribution and their evolution in time interval changes.
Algorithm of pulmonary emphysema extraction using low dose thoracic 3D CT images

NASA Astrophysics Data System (ADS)

Saita, S.; Kubo, M.; Kawata, Y.; Niki, N.; Nakano, Y.; Omatsu, H.; Tominaga, K.; Eguchi, K.; Moriyama, N.

2006-03-01

Recently, due to aging and smoking, emphysema patients are increasing. The restoration of alveolus which was destroyed by emphysema is not possible, thus early detection of emphysema is desired. We describe a quantitative algorithm for extracting emphysematous lesions and quantitatively evaluate their distribution patterns using low dose thoracic 3-D CT images. The algorithm identified lung anatomies, and extracted low attenuation area (LAA) as emphysematous lesion candidates. Applying the algorithm to 100 thoracic 3-D CT images and then by follow-up 3-D CT images, we demonstrate its potential effectiveness to assist radiologists and physicians to quantitatively evaluate the emphysematous lesions distribution and their evolution in time interval changes.
Characterization of Moving Dust Particles

NASA Technical Reports Server (NTRS)

Bos, Brent J.; Antonille, Scott R.; Memarsadeghi, Nargess

2010-01-01

A large depth-of-field Particle Image Velocimeter (PIV) has been developed at NASA GSFC to characterize dynamic dust environments on planetary surfaces. This instrument detects and senses lofted dust particles. We have been developing an autonomous image analysis algorithm architecture for the PIV instrument to greatly reduce the amount of data that it has to store and downlink. The algorithm analyzes PIV images and reduces the image information down to only the particle measurement data we are interested in receiving on the ground - typically reducing the amount of data to be handled by more than two orders of magnitude. We give a general description of PIV algorithms and describe only the algorithm for estimating the velocity of the traveling particles.
Computational segmentation of collagen fibers from second-harmonic generation images of breast cancer

NASA Astrophysics Data System (ADS)

Bredfeldt, Jeremy S.; Liu, Yuming; Pehlke, Carolyn A.; Conklin, Matthew W.; Szulczewski, Joseph M.; Inman, David R.; Keely, Patricia J.; Nowak, Robert D.; Mackie, Thomas R.; Eliceiri, Kevin W.

2014-01-01

Second-harmonic generation (SHG) imaging can help reveal interactions between collagen fibers and cancer cells. Quantitative analysis of SHG images of collagen fibers is challenged by the heterogeneity of collagen structures and low signal-to-noise ratio often found while imaging collagen in tissue. The role of collagen in breast cancer progression can be assessed post acquisition via enhanced computation. To facilitate this, we have implemented and evaluated four algorithms for extracting fiber information, such as number, length, and curvature, from a variety of SHG images of collagen in breast tissue. The image-processing algorithms included a Gaussian filter, SPIRAL-TV filter, Tubeness filter, and curvelet-denoising filter. Fibers are then extracted using an automated tracking algorithm called fiber extraction (FIRE). We evaluated the algorithm performance by comparing length, angle and position of the automatically extracted fibers with those of manually extracted fibers in twenty-five SHG images of breast cancer. We found that the curvelet-denoising filter followed by FIRE, a process we call CT-FIRE, outperforms the other algorithms under investigation. CT-FIRE was then successfully applied to track collagen fiber shape changes over time in an in vivo mouse model for breast cancer.
Image reconstruction and scan configurations enabled by optimization-based algorithms in multispectral CT

NASA Astrophysics Data System (ADS)

Chen, Buxin; Zhang, Zheng; Sidky, Emil Y.; Xia, Dan; Pan, Xiaochuan

2017-11-01

Optimization-based algorithms for image reconstruction in multispectral (or photon-counting) computed tomography (MCT) remains a topic of active research. The challenge of optimization-based image reconstruction in MCT stems from the inherently non-linear data model that can lead to a non-convex optimization program for which no mathematically exact solver seems to exist for achieving globally optimal solutions. In this work, based upon a non-linear data model, we design a non-convex optimization program, derive its first-order-optimality conditions, and propose an algorithm to solve the program for image reconstruction in MCT. In addition to consideration of image reconstruction for the standard scan configuration, the emphasis is on investigating the algorithm’s potential for enabling non-standard scan configurations with no or minimum hardware modification to existing CT systems, which has potential practical implications for lowered hardware cost, enhanced scanning flexibility, and reduced imaging dose/time in MCT. Numerical studies are carried out for verification of the algorithm and its implementation, and for a preliminary demonstration and characterization of the algorithm in reconstructing images and in enabling non-standard configurations with varying scanning angular range and/or x-ray illumination coverage in MCT.
A Distributed Compressive Sensing Scheme for Event Capture in Wireless Visual Sensor Networks

NASA Astrophysics Data System (ADS)

Hou, Meng; Xu, Sen; Wu, Weiling; Lin, Fei

2018-01-01

Image signals which acquired by wireless visual sensor network can be used for specific event capture. This event capture is realized by image processing at the sink node. A distributed compressive sensing scheme is used for the transmission of these image signals from the camera nodes to the sink node. A measurement and joint reconstruction algorithm for these image signals are proposed in this paper. Make advantage of spatial correlation between images within a sensing area, the cluster head node which as the image decoder can accurately co-reconstruct these image signals. The subjective visual quality and the reconstruction error rate are used for the evaluation of reconstructed image quality. Simulation results show that the joint reconstruction algorithm achieves higher image quality at the same image compressive rate than the independent reconstruction algorithm.
Time-of-flight PET image reconstruction using origin ensembles.

PubMed

Wülker, Christian; Sitek, Arkadiusz; Prevrhal, Sven

2015-03-07

The origin ensemble (OE) algorithm is a novel statistical method for minimum-mean-square-error (MMSE) reconstruction of emission tomography data. This method allows one to perform reconstruction entirely in the image domain, i.e. without the use of forward and backprojection operations. We have investigated the OE algorithm in the context of list-mode (LM) time-of-flight (TOF) PET reconstruction. In this paper, we provide a general introduction to MMSE reconstruction, and a statistically rigorous derivation of the OE algorithm. We show how to efficiently incorporate TOF information into the reconstruction process, and how to correct for random coincidences and scattered events. To examine the feasibility of LM-TOF MMSE reconstruction with the OE algorithm, we applied MMSE-OE and standard maximum-likelihood expectation-maximization (ML-EM) reconstruction to LM-TOF phantom data with a count number typically registered in clinical PET examinations. We analyzed the convergence behavior of the OE algorithm, and compared reconstruction time and image quality to that of the EM algorithm. In summary, during the reconstruction process, MMSE-OE contrast recovery (CRV) remained approximately the same, while background variability (BV) gradually decreased with an increasing number of OE iterations. The final MMSE-OE images exhibited lower BV and a slightly lower CRV than the corresponding ML-EM images. The reconstruction time of the OE algorithm was approximately 1.3 times longer. At the same time, the OE algorithm can inherently provide a comprehensive statistical characterization of the acquired data. This characterization can be utilized for further data processing, e.g. in kinetic analysis and image registration, making the OE algorithm a promising approach in a variety of applications.
Time-of-flight PET image reconstruction using origin ensembles

NASA Astrophysics Data System (ADS)

Wülker, Christian; Sitek, Arkadiusz; Prevrhal, Sven

2015-03-01

The origin ensemble (OE) algorithm is a novel statistical method for minimum-mean-square-error (MMSE) reconstruction of emission tomography data. This method allows one to perform reconstruction entirely in the image domain, i.e. without the use of forward and backprojection operations. We have investigated the OE algorithm in the context of list-mode (LM) time-of-flight (TOF) PET reconstruction. In this paper, we provide a general introduction to MMSE reconstruction, and a statistically rigorous derivation of the OE algorithm. We show how to efficiently incorporate TOF information into the reconstruction process, and how to correct for random coincidences and scattered events. To examine the feasibility of LM-TOF MMSE reconstruction with the OE algorithm, we applied MMSE-OE and standard maximum-likelihood expectation-maximization (ML-EM) reconstruction to LM-TOF phantom data with a count number typically registered in clinical PET examinations. We analyzed the convergence behavior of the OE algorithm, and compared reconstruction time and image quality to that of the EM algorithm. In summary, during the reconstruction process, MMSE-OE contrast recovery (CRV) remained approximately the same, while background variability (BV) gradually decreased with an increasing number of OE iterations. The final MMSE-OE images exhibited lower BV and a slightly lower CRV than the corresponding ML-EM images. The reconstruction time of the OE algorithm was approximately 1.3 times longer. At the same time, the OE algorithm can inherently provide a comprehensive statistical characterization of the acquired data. This characterization can be utilized for further data processing, e.g. in kinetic analysis and image registration, making the OE algorithm a promising approach in a variety of applications.
A robust correspondence matching algorithm of ground images along the optic axis

NASA Astrophysics Data System (ADS)

Jia, Fengman; Kang, Zhizhong

2013-10-01

Facing challenges of nontraditional geometry, multiple resolutions and the same features sensed from different angles, there are more difficulties of robust correspondence matching for ground images along the optic axis. A method combining SIFT algorithm and the geometric constraint of the ratio of coordinate differences between image point and image principal point is proposed in this paper. As it can provide robust matching across a substantial range of affine distortion addition of change in 3D viewpoint and noise, we use SIFT algorithm to tackle the problem of image distortion. By analyzing the nontraditional geometry of ground image along the optic axis, this paper derivates that for one correspondence pair, the ratio of distances between image point and image principal point in an image pair should be a value not far from 1. Therefore, a geometric constraint for gross points detection is formed. The proposed approach is tested with real image data acquired by Kodak. The results show that with SIFT and the proposed geometric constraint, the robustness of correspondence matching on the ground images along the optic axis can be effectively improved, and thus prove the validity of the proposed algorithm.
Cross contrast multi-channel image registration using image synthesis for MR brain images.

PubMed

Chen, Min; Carass, Aaron; Jog, Amod; Lee, Junghoon; Roy, Snehashis; Prince, Jerry L

2017-02-01

Multi-modal deformable registration is important for many medical image analysis tasks such as atlas alignment, image fusion, and distortion correction. Whereas a conventional method would register images with different modalities using modality independent features or information theoretic metrics such as mutual information, this paper presents a new framework that addresses the problem using a two-channel registration algorithm capable of using mono-modal similarity measures such as sum of squared differences or cross-correlation. To make it possible to use these same-modality measures, image synthesis is used to create proxy images for the opposite modality as well as intensity-normalized images from each of the two available images. The new deformable registration framework was evaluated by performing intra-subject deformation recovery, intra-subject boundary alignment, and inter-subject label transfer experiments using multi-contrast magnetic resonance brain imaging data. Three different multi-channel registration algorithms were evaluated, revealing that the framework is robust to the multi-channel deformable registration algorithm that is used. With a single exception, all results demonstrated improvements when compared against single channel registrations using the same algorithm with mutual information. Copyright © 2016 Elsevier B.V. All rights reserved.

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