Unique Neural Characteristics of Atypical Lateralization of Language in Healthy Individuals

PubMed Central

Biduła, Szymon P.; Przybylski, Łukasz; Pawlak, Mikołaj A.; Króliczak, Gregory

2017-01-01

Using functional magnetic resonance imaging (fMRI) in 63 healthy participants, including left-handed and ambidextrous individuals, we tested how atypical lateralization of language—i. e., bilateral or right hemispheric language representation—differs from the typical left-hemisphere dominance. Although regardless of their handedness, all 11 participants from the atypical group engaged classical language centers, i.e., Broca's and Wernicke's areas, the right-hemisphere components of the default mode network (DMN), including the angular gyrus and middle temporal gyrus, were also critically involved during the verbal fluency task. Importantly, activity in these regions could not be explained in terms of mirroring the typical language pattern because left-hemisphere dominant individuals did not exhibit similar significant signal modulations. Moreover, when spatial extent of language-related activity across whole brain was considered, the bilateral language organization entailed more diffuse functional processing. Finally, we detected significant differences between the typical and atypical group in the resting-state connectivity at the global and local level. These findings suggest that the atypical lateralization of language has unique features, and is not a simple mirror image of the typical left hemispheric language representation. PMID:28983238

Nonlesional atypical mesial temporal epilepsy

PubMed Central

Alexopoulos, Andreas V.; Busch, Robyn M.; Wehner, Tim; Nair, Dileep; Bingaman, William E.; Najm, Imad M.

2013-01-01

Objective: Misleading manifestations of common epilepsy syndromes might account for some epilepsy surgery failures, thus we sought to characterize patients with difficult to diagnose (atypical) mesial temporal lobe epilepsy (mTLE). Methods: We retrospectively reviewed our surgical database over 12 years to identify patients who underwent a standard anterior temporal lobectomy after undergoing intracranial EEG (ICEEG) evaluation with a combination of depth and subdural electrodes. We carefully studied electroclinical manifestations, neuroimaging data, neuropsychological findings, and indications for ICEEG. Results: Of 835 patients who underwent anterior temporal lobectomy, 55 were investigated with ICEEG. Ten of these had atypical mTLE features and were not considered to have mTLE preoperatively. All of them had Engel class I outcome for 3 to 7 years (median 3.85). Five reported uncommon auras, and 3 had no auras. Scalp-EEG and nuclear imaging studies failed to provide adequate localization. None had MRI evidence of hippocampal sclerosis. However, ICEEG demonstrated exclusive mesial temporal seizure onset in all patients. Clues suggesting the possibility of mTLE were typical auras when present, anterior temporal epileptiform discharges or ictal patterns, small hippocampi, asymmetrical or ipsilateral temporal hypometabolism on PET, anterior temporal hyperperfusion on ictal SPECT, and asymmetry of memory scores. Histopathology revealed hippocampal sclerosis in 6 patients and gliosis in 2. Conclusions: Atypical electroclinical presentation may be deceptive in some patients with mTLE. We emphasize the importance of searching for typical mTLE features to guide ICEEG study of mesial temporal structures in such patients, who may otherwise mistakenly undergo extramesial temporal resections or be denied surgery. PMID:24174582

[Diagnosis and treatment of heparin-induced thrombocytopenia (HIT) based on its atypical immunological features].

PubMed

Miyata, Shigeki; Maeda, Takuma

2016-03-01

Heparin-induced thrombocytopenia (HIT) is a prothrombotic side effect of heparin therapy caused by HIT antibodies, i.e., anti-platelet factor 4 (PF4)/heparin IgG with platelet-activating properties. For serological diagnosis, antigen immunoassays are commonly used worldwide. However, such assays do not indicate their platelet-activating properties, leading to low specificity for the HIT diagnosis. Therefore, over-diagnosis is currently the most serious problem associated with HIT. The detection of platelet-activating antibodies using a washed platelet activation assay is crucial for appropriate HIT diagnosis. Recent advances in our understanding of the pathogenesis of HIT include it having several clinical features atypical for an immune-mediated disease. Heparin-naïve patients can develop IgG antibodies as early as day 4, as in a secondary immune response. Evidence for an anamnestic response on heparin re-exposure is lacking. In addition, HIT antibodies are relatively short-lived, unlike those in a secondary immune response. These lines of evidence suggest that the mechanisms underlying HIT antibody formation may be compatible with a non-T cell-dependent immune reaction. These atypical clinical and serological features should be carefully considered while endeavoring to accurately diagnose HIT, which leads to appropriate therapies such as immediate administration of an alternative anticoagulant to prevent thromboembolic events and re-administration of heparin during surgery involving cardiopulmonary bypass when HIT antibodies are no longer detectable.

Perianal atypical leiomyoma: A case report.

PubMed

Sun, Pingliang; Ou, Hailing; Huang, Shen; Wei, Longxiang; Zhang, Sen; Liu, Jiali; Geng, Shuguang; Yang, Kun

2017-12-01

Reports on perianal atypical leiomyoma, a perianal tumor, are rare. We confirmed a perianal atypical leiomyoma by its clinical presentation, magnetic resonance imaging findings, and immunohistochemistry. A 28-year-old female with a perianal mass found more than 4 years ago. The 5cm_4cm_4cm sized mass was located on the left side of the anus and vagina; The magnetic resonance imaging (MRI) scan revealed: A 4.1cm × 5.2cm × 4.9cm sized round mass was observed on the left side of the circumference. Perianal atypical leiomyoma. anal peripheral mass resection was performed under lumbar anesthesia. The postoperative course was uneventful, healing, the patient was discharged. Perianal atypical leiomyomas are benign tumors, but with the clinically atypical leiomyoma, it is sometimes difficult to distinguish between potential malignant smooth muscle tumors,and there may be malignant changes. Surgery should ensure complete resection, and to avoid postoperative recurrence, there should be a regular follow-up.

Characterization of atypical language activation patterns in focal epilepsy.

PubMed

Berl, Madison M; Zimmaro, Lauren A; Khan, Omar I; Dustin, Irene; Ritzl, Eva; Duke, Elizabeth S; Sepeta, Leigh N; Sato, Susumu; Theodore, William H; Gaillard, William D

2014-01-01

Functional magnetic resonance imaging is sensitive to the variation in language network patterns. Large populations are needed to rigorously assess atypical patterns, which, even in neurological populations, are a minority. We studied 220 patients with focal epilepsy and 118 healthy volunteers who performed an auditory description decision task. We compared a data-driven hierarchical clustering approach to the commonly used a priori laterality index (LI) threshold (LI < 0.20 as atypical) to classify language patterns within frontal and temporal regions of interest. We explored (n = 128) whether IQ varied with different language activation patterns. The rate of atypical language among healthy volunteers (2.5%) and patients (24.5%) agreed with previous studies; however, we found 6 patterns of atypical language: a symmetrically bilateral, 2 unilaterally crossed, and 3 right dominant patterns. There was high agreement between classification methods, yet the cluster analysis revealed novel correlations with clinical features. Beyond the established association of left-handedness, early seizure onset, and vascular pathology with atypical language, cluster analysis identified an association of handedness with frontal lateralization, early seizure onset with temporal lateralization, and left hemisphere focus with a unilateral right pattern. Intelligence quotient was not significantly different among patterns. Language dominance is a continuum; however, our results demonstrate meaningful thresholds in classifying laterality. Atypical language patterns are less frequent but more variable than typical language patterns, posing challenges for accurate presurgical planning. Language dominance should be assessed on a regional rather than hemispheric basis, and clinical characteristics should inform evaluation of atypical language dominance. Reorganization of language is not uniformly detrimental to language functioning. © 2014 American Neurological Association.

Psychopathology in patients with endogenous Cushing's syndrome: 'atypical' or melancholic features.

PubMed

Dorn, L D; Burgess, E S; Dubbert, B; Simpson, S E; Friedman, T; Kling, M; Gold, P W; Chrousos, G P

1995-10-01

Prolonged elevations of glucocorticoids have been linked to the effective disturbances experienced by patients with Cushing's syndrome. Major depression has been most commonly reported in patients with endogenous Cushing's syndrome. The purpose of this study was to determine whether these patients experience melancholic or 'atypical' subtype depression and to determine relations between current psychological functioning and factors such as duration and severity of Cushing's syndrome. We examined 33 adult patients with documented Cushing's syndrome and 17 hospitalized, matched controls, using standardized structured interviews and tests. During the active phase of Cushing's syndrome (prior to and/or on admission), 66.7% of all patients reported histories meeting criteria for a psychiatric diagnosis. Of those with a diagnosis during Cushing's syndrome, 50% reported major depression. Upon presentation to this institution, atypical depression was the most common diagnosis involving 51.5% (n =17) of all enrolled patients. Of the 17 with atypical depression, 8 reported a co-morbid psychiatric disorder. The duration of Cushing's syndrome was an important factor in predicting whether patients sought psychological intervention or met criteria for psychiatric diagnosis. Patients with active endogenous Cushing's syndrome exhibit significant psychopathology expressed primarily by atypical depression. Longer duration of Cushing's syndrome may place them at increased risk of such psychopathology.

Textural features for radar image analysis

NASA Technical Reports Server (NTRS)

Shanmugan, K. S.; Narayanan, V.; Frost, V. S.; Stiles, J. A.; Holtzman, J. C.

1981-01-01

Texture is seen as an important spatial feature useful for identifying objects or regions of interest in an image. While textural features have been widely used in analyzing a variety of photographic images, they have not been used in processing radar images. A procedure for extracting a set of textural features for characterizing small areas in radar images is presented, and it is shown that these features can be used in classifying segments of radar images corresponding to different geological formations.

[Immunofluorescence assay with Crithidia luciliae for the detection of anti-DNA antibodies. Atypical images and their relationship with Chagas' disease and leishmaniasis].

PubMed

Griemberg, Gloria; Ferrarotti, Nidia F; Svibel, Graciela; Ravelli, Maria R; Taranto, Nestor J; Malchiodi, Emilio L; Pizzimenti, Maria C

2006-01-01

Anti-native DNA antibodies can be detected by indirect immunofluorescence assay with Crithidia luciliae, displaying an annular image due to a kinetoplast containing double stranded DNA. Other structures such as membrane, flagellum and basal corpuscle can be stained as well, showing what is called atypical fluorescent images. As C. luciliae belongs to the Trypanosomatidae family, which include the human pathogens Trypanosoma cruzi and Leishmania spp., it was considered that these atypical images could be caused by cross-reactions. Serological studies for Chagas' disease were performed in 105 serum samples displaying atypical images. Sixty four percent of the samples from non endemic and 78.3% from endemic areas for Chagas' disease showed fluorescence in both, membrane and flagellum (joint image). Fifty samples from normal blood donors and 57 samples from patients with conective tissue diseases were tested with C. luciliae. None of them presented the joint image except for two patients with lupus who were also chagasic. In addition, 54 samples from chagasic patients were studied and all of them presented the joint image. We also studied 46 samples from patients with leishmaniasis from whom 28 were coinfected with T. cruzi. The joint image was observed in 88.0% of the samples with leishmaniasis and in 89.3% of the co-infected samples. The results suggest that C. luciliae could be used as an economical, and of low risk, alternative substrate for the serological diagnosis of Chagas' disease, even though it does not discriminate for Leishmania spp. infection. This study also suggests that whenever atypical images are observed in C. luciliae during the search for anti-DNA antibodies, it would be convenient to submit the patient to clinical and serological tests for the diagnosis of leishmaniosis and Chagas' disease.

Saliency image of feature building for image quality assessment

NASA Astrophysics Data System (ADS)

Ju, Xinuo; Sun, Jiyin; Wang, Peng

2011-11-01

The purpose and method of image quality assessment are quite different for automatic target recognition (ATR) and traditional application. Local invariant feature detectors, mainly including corner detectors, blob detectors and region detectors etc., are widely applied for ATR. A saliency model of feature was proposed to evaluate feasibility of ATR in this paper. The first step consisted of computing the first-order derivatives on horizontal orientation and vertical orientation, and computing DoG maps in different scales respectively. Next, saliency images of feature were built based auto-correlation matrix in different scale. Then, saliency images of feature of different scales amalgamated. Experiment were performed on a large test set, including infrared images and optical images, and the result showed that the salient regions computed by this model were consistent with real feature regions computed by mostly local invariant feature extraction algorithms.

Atypical Cities

ERIC Educational Resources Information Center

DiJulio, Betsy

2011-01-01

In this creative challenge, Surrealism and one-point perspective combine to produce images that not only go "beyond the real" but also beyond the ubiquitous "imaginary city" assignment often used to teach one-point perspective. Perhaps the difference is that in the "atypical cities challenge," an understanding of one-point perspective is a means…

Predictive features of chronic kidney disease in atypical haemolytic uremic syndrome

PubMed Central

Jamme, Matthieu; Raimbourg, Quentin; Chauveau, Dominique; Seguin, Amélie; Presne, Claire; Perez, Pierre; Gobert, Pierre; Wynckel, Alain; Provôt, François; Delmas, Yahsou; Mousson, Christiane; Servais, Aude; Vrigneaud, Laurence; Veyradier, Agnès

2017-01-01

Chronic kidney disease (CKD) is a frequent and serious complication of atypical haemolytic uremic syndrome (aHUS). We aimed to develop a simple accurate model to predict the risk of renal dysfunction in aHUS based on clinical and biological features available at hospital admission. Renal function at 1-year follow-up, based on an estimated glomerular filtration rate < 60mL/min/1.73m2 as assessed by the Modification of Diet in Renal Disease equation, was used as an indicator of significant CKD. Prospectively collected data from a cohort of 156 aHUS patients who did not receive eculizumab were used to identify predictors of CKD. Covariates associated with renal impairment were identified by multivariate analysis. The model performance was assessed and a scoring system for clinical practice was constructed from the regression coefficient. Multivariate analyses identified three predictors of CKD: a high serum creatinine level, a high mean arterial pressure and a mildly decreased platelet count. The prognostic model had a good discriminative ability (area under the curve = .84). The scoring system ranged from 0 to 5, with corresponding risks of CKD ranging from 18% to 100%. This model accurately predicts development of 1-year CKD in patients with aHUS using clinical and biological features available on admission. After further validation, this model may assist in clinical decision making. PMID:28542627

Can PET-CT imaging and radiokinetic analyses provide useful clinical information on atypical femoral shaft fracture in osteoporotic patients?

PubMed

Chesnut, C Haile; Chesnut, Charles H

2012-03-01

Atypical femoral shaft fractures are associated with the extended usage of nitrogen-containing bisphosphonates as therapy for osteoporosis. For such fractures, the positron emission tomography (PET) procedure, coupled with computerized tomography (CT), provides a potential imaging modality for defining aspects of the pathogenesis, site specificity, and possible prodromal abnormalities prior to fracture. PET-CT may assess the radiokinetic variables K1 (a putative marker for skeletal blood flow) and Ki (a putative marker for skeletal bone formation), and when combined with PET imaging modalities and CT skeletal site localization, may define the site of such radiokinetic findings. Further studies into the clinical usage of PET-CT in patients with atypical femoral shaft fractures are warranted.

Atypical preeclampsia – Gestational proteinuria

PubMed Central

Stevens, Amy B.; Brasuell, Diane M.; Higdon, Rebecca N.

2017-01-01

There are many rural areas where obstetric care is predominately performed by family medicine physicians. As such, it is important for family medicine physicians to stay up to date with the latest obstetric guidelines. Preeclampsia is a well-established disorder and the guidelines for screening and treatment are well known. However, atypical presentations of preeclampsia have been less studied. Notably, what constitutes atypical preeclampsia and when to be concerned for increased morbidity and mortality in the mother and neonate. This report describes a unique case in which a woman with proteinuria of pregnancy developed atypical preeclampsia with severe features. This report discusses the care that was given by a practicing family medicine physician and the reasoning behind it. PMID:29417031

Atypical presentations of subacute sclerosing panencephalitis in two neurologically handicapped cases.

PubMed

Demir, E; Ozcelik, A; Arhan, E; Serdaroglu, A; Gucuyener, K

2009-08-01

Subacute sclerosing panencephalitis (SSPE) is a neurodegenerative disorder caused by persistent measles infection. Here, we report two neurologically handicapped cases presenting with atypical features of SSPE. Patient 1 who had mild mental retardation manifested acute encephalopathy with partial seizures and hemiplegia, mimicking encephalitis. He showed a fulminant course without myoclonia or a periodic electroencephalogram complex. Although SSPE is usually associated with an increased diffusion pattern, diffusion-weighted imaging of our patient showed decreased diffusion in the right hippocampus. Patient 2 with infantile hemiparesis presented with secondary generalized seizures, followed by asymettrical myoclonias involving the side contralateral to the hemiparesis. A periodic electroencephalogram complex was absent on the previously damaged brain regions. Our findings show that preexisting neurological disorders may modify the clinical or electrophysiological findings of SSPE, leading to atypical presentations. SSPE should be considered in the differential diagnosis of acute encephalopathy with lateralizing signs or unidentified seizures. Decreased diffusion resolution in diffusion-weighted-imaging may correlate with rapid clinical progression in SSPE. Georg Thieme Verlag KG Stuttgart New York.

Hemorrhage detection in MRI brain images using images features

NASA Astrophysics Data System (ADS)

Moraru, Luminita; Moldovanu, Simona; Bibicu, Dorin; Stratulat (Visan), Mirela

2013-11-01

The abnormalities appear frequently on Magnetic Resonance Images (MRI) of brain in elderly patients presenting either stroke or cognitive impairment. Detection of brain hemorrhage lesions in MRI is an important but very time-consuming task. This research aims to develop a method to extract brain tissue features from T2-weighted MR images of the brain using a selection of the most valuable texture features in order to discriminate between normal and affected areas of the brain. Due to textural similarity between normal and affected areas in brain MR images these operation are very challenging. A trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection, but they could be detected by using a texture analysis. The proposed analysis is developed in five steps: i) in the pre-processing step: the de-noising operation is performed using the Daubechies wavelets; ii) the original images were transformed in image features using the first order descriptors; iii) the regions of interest (ROIs) were cropped from images feature following up the axial symmetry properties with respect to the mid - sagittal plan; iv) the variation in the measurement of features was quantified using the two descriptors of the co-occurrence matrix, namely energy and homogeneity; v) finally, the meaningful of the image features is analyzed by using the t-test method. P-value has been applied to the pair of features in order to measure they efficacy.

[Clinico-pathologic Characteristics of Adult Patients with Atypical Infectious Mononucleosis].

PubMed

Yu, Ya-Ping; Song, Ping; An, Zhi-Ming; Zhou, Xiao-Gang; Li, Feng; Wang, Li-Ping; Mei, Jian-Gang; Zhai, Yong-Ping

2016-12-01

To investigate the clinicopathologic characteristics of adult patients with atypical infectious mononucleosis(IM). From January 2003 to December 2013, a total of 5 cases of atypical IM misdiagnosed as lymphoma were selected, and the clinico-pathological characteristics and efficacy of treatment were analyzed. Biopsy of lymph node or tonsil was performed to evaluate the possibility of lymphoma. Peripheral blood EBV antibody and EBV-DNA were examined by ELISA and real-time fluorescence quantitative PCR, respectively. All the cases were considered as lymphoma on the basis of morphological features in initial evaluation before relapse. These features included a florid immunoblastic proliferation, distortion of the underlying nodal or tonsillar architecture and the presence of necrosis. The immunophenotypic features, EBV encoded RNA (EBER) in situ hybridization and the gene rearrangement of immunoglobulin or T cell receptor may be helpful for the distinction of atypical IM from lymphoma. IM as EBV-related lymphoproliferative process shows marked clinical and histological diversity. Atypical case of IM may mimic many different type of lymphoma in clinical and pathologic features, and the misdiagnosis should be avoided by using molecular and pathological examination.

Feature-Based Retinal Image Registration Using D-Saddle Feature

PubMed Central

Hasikin, Khairunnisa; A. Karim, Noor Khairiah; Ahmedy, Fatimah

2017-01-01

Retinal image registration is important to assist diagnosis and monitor retinal diseases, such as diabetic retinopathy and glaucoma. However, registering retinal images for various registration applications requires the detection and distribution of feature points on the low-quality region that consists of vessels of varying contrast and sizes. A recent feature detector known as Saddle detects feature points on vessels that are poorly distributed and densely positioned on strong contrast vessels. Therefore, we propose a multiresolution difference of Gaussian pyramid with Saddle detector (D-Saddle) to detect feature points on the low-quality region that consists of vessels with varying contrast and sizes. D-Saddle is tested on Fundus Image Registration (FIRE) Dataset that consists of 134 retinal image pairs. Experimental results show that D-Saddle successfully registered 43% of retinal image pairs with average registration accuracy of 2.329 pixels while a lower success rate is observed in other four state-of-the-art retinal image registration methods GDB-ICP (28%), Harris-PIIFD (4%), H-M (16%), and Saddle (16%). Furthermore, the registration accuracy of D-Saddle has the weakest correlation (Spearman) with the intensity uniformity metric among all methods. Finally, the paired t-test shows that D-Saddle significantly improved the overall registration accuracy of the original Saddle. PMID:29204257

Atypical depression is more common than melancholic in fibromyalgia: an observational cohort study.

PubMed

Ross, Rebecca L; Jones, Kim D; Ward, Rachel L; Wood, Lisa J; Bennett, Robert M

2010-06-14

It has been postulated that atypical and melancholic depression subtypes exist in depressed fibromyalgia (FM) patients, yet no study has empirically tested this hypothesis. The purpose of this study is to determine whether major depressive disorder (MDD) with atypical features and MDD with melancholic features occurs in a FM sample and to describe their demographic, clinical and diagnostic characteristics. An observational cohort study using a descriptive cross-sectional design recruited a convenience sample of 76 outpatients with FM from an academic rheumatology clinic and a community mental health practice. Diagnoses of FM were confirmed using the 1990 ACR classification guidelines. Diagnoses of MDD and diagnostic subtypes were determined using the DSM-IV-TR criteria. Clinical characteristics were measured using the Fibromyalgia Impact Questionnaire, Structured Interview Guide for the Hamilton Depression Rating Scale with Atypical Depression Supplement and other standardized instruments. Odds ratios were computed on subtype-specific diagnostic criteria. Correlations assessed associations between subtype diagnoses and diagnostic criteria. Of the 76 subjects with FM, 11.8% (n = 9) were euthymic, 52.6% (n = 40) met diagnostic criteria for MDD with atypical features and 35.6% (n = 27) for MDD with melancholic features. Groups did not differ on demographic characteristics except for gender (p = 0.01). The non-depressed and atypical groups trended toward having a longer duration of FM symptoms (18.05 yrs. +/- 12.83; 20.36 yrs. +/- 15.07) compared to the melancholic group (14.11 yrs. +/- 8.82; p = 0.09). The two depressed groups experienced greater severity on all clinical features compared to the non-depressed group. The atypical group did not differ clinically from the melancholic group except the latter experienced greater depression severity (p = 0.001). The atypical group demonstrated the highest prevalence and correlations with atypical-specific diagnostic

Image segmentation using association rule features.

PubMed

Rushing, John A; Ranganath, Heggere; Hinke, Thomas H; Graves, Sara J

2002-01-01

A new type of texture feature based on association rules is described. Association rules have been used in applications such as market basket analysis to capture relationships present among items in large data sets. It is shown that association rules can be adapted to capture frequently occurring local structures in images. The frequency of occurrence of these structures can be used to characterize texture. Methods for segmentation of textured images based on association rule features are described. Simulation results using images consisting of man made and natural textures show that association rule features perform well compared to other widely used texture features. Association rule features are used to detect cumulus cloud fields in GOES satellite images and are found to achieve higher accuracy than other statistical texture features for this problem.

Quantitative imaging features: extension of the oncology medical image database

NASA Astrophysics Data System (ADS)

Patel, M. N.; Looney, P. T.; Young, K. C.; Halling-Brown, M. D.

2015-03-01

Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to be able to harness this large influx of data is of paramount importance. The Oncology Medical Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, and annotations and where applicable expert determined ground truths describing features of interest. Medical imaging provides the ability to detect and localize many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in anatomic, physiologic, biochemical or molecular processes. Quantitative imaging features are sensitive, specific, accurate and reproducible imaging measures of these changes. Here, we describe an extension to the OMI-DB whereby a range of imaging features and descriptors are pre-calculated using a high throughput approach. The ability to calculate multiple imaging features and data from the acquired images would be valuable and facilitate further research applications investigating detection, prognosis, and classification. The resultant data store contains more than 10 million quantitative features as well as features derived from CAD predictions. Theses data can be used to build predictive models to aid image classification, treatment response assessment as well as to identify prognostic imaging biomarkers.

Histological features associated with diagnostic agreement in atypical ductal hyperplasia of the breast: illustrative cases from the B-Path study.

PubMed

Allison, Kimberly H; Rendi, Mara H; Peacock, Sue; Morgan, Tom; Elmore, Joann G; Weaver, Donald L

2016-12-01

This study examined the case-specific characteristics associated with interobserver diagnostic agreement in atypical ductal hyperplasia (ADH) of the breast. Seventy-two test set cases with a consensus diagnosis of ADH from the B-Path study were evaluated. Cases were scored for 17 histological features, which were then correlated with the participant agreement with the consensus ADH diagnosis. Participating pathologists' perceptions of case difficulty, borderline features or whether they would obtain a second opinion were also examined for associations with agreement. Of the 2070 participant interpretations of the 72 consensus ADH cases, 48% were scored by participants as difficult and 45% as borderline between two diagnoses; the presence of both of these features was significantly associated with increased agreement (P < 0.001). A second opinion would have been obtained in 80% of interpretations, and this was associated with increased agreement (P < 0.001). Diagnostic agreement ranged from 10% to 89% on a case-by-case basis. Cases with papillary lesions, cribriform architecture and obvious cytological monotony were associated with higher agreement. Lower agreement rates were associated with solid or micropapillary architecture, borderline cytological monotony, or cases without a diagnostic area that was obvious on low power. The results of this study suggest that pathologists frequently recognize the challenge of ADH cases, with some cases being more prone to diagnostic variability. In addition, there are specific histological features associated with diagnostic agreement on ADH cases. Multiple example images from cases in this test set are provided to serve as educational illustrations of these challenges. © 2016 John Wiley & Sons Ltd.

Histologic Features associated with Diagnostic Agreement in Atypical Ductal Hyperplasia of the Breast: Illustrative Cases from the B-Path Study

PubMed Central

Allison, Kimberly H.; Rendi, Mara H.; Peacock, Sue; Morgan, Tom; Elmore, Joann G.; Weaver, Donald L.

2016-01-01

Background Case specific characteristics associated with interobserver diagnostic agreement in atypical ductal hyperplasia (ADH) of the breast are poorly understood. Methods Seventy-two test set cases with a consensus diagnosis of ADH from the B-Path study were evaluated. Cases were scored for 17 histologic features which were then correlated with the participant agreement with the consensus ADH diagnosis. Participating pathologists’ perceptions of case difficulty, borderline features, or if they would obtain a second opinion were also examined for associations with agreement. Results Of the 2,070 participant interpretations on the 72 consensus ADH cases, 48% were scored by participants as difficult and 45% as borderline between two diagnoses; the presence of both of these features was significantly associated with increased agreement (p < 0.001). A second opinion would have been obtained in 80% of interpretations, and this was associated with increased agreement (p < 0.001). Diagnostic agreement ranged from 10–89% on a case-by-case basis. Cases with papillary lesions, cribriform architecture and obvious cytologic monotony were associated with higher agreement. Lower agreement rates were associated with solid or micro-papillary architecture, borderline cytologic monotony or cases without a diagnostic area that was obvious on low power. Conclusions The results of this study suggest that pathologists frequently recognize the challenge of ADH cases with some cases more prone to diagnostic variability. In addition, there are specific histologic features associated with diagnostic agreement on ADH cases. Multiple example images from cases in this test set are provided to serve as educational illustrations of these challenges. PMID:27398812

Analysis of the Origin of Atypical Scanning Laser Polarimetry Patterns by Polarization-Sensitive Optical Coherence Tomography

PubMed Central

Götzinger, Erich; Pircher, Michael; Baumann, Bernhard; Hirn, Cornelia; Vass, Clemens; Hitzenberger, Christoph K.

2010-01-01

Purpose To analyze the physical origin of atypical scanning laser polarimetry (SLP) patterns. To compare polarization-sensitive optical coherence tomography (PS-OCT) scans to SLP images. To present a method to obtain pseudo-SLP images by PS-OCT that are free of atypical artifacts. Methods Forty-one eyes of healthy subjects, subjects with suspected glaucoma, and patients with glaucoma were imaged by SLP (GDx VCC) and a prototype spectral domain PS-OCT system. The PS-OCT system acquires three-dimensional (3D) datasets of intensity, retardation, and optic axis orientation simultaneously within 3 seconds. B-scans of intensity and retardation and en face maps of retinal nerve fiber layer (RNFL) retardation were derived from the 3D PS-OCT datasets. Results were compared with those obtained by SLP. Results Twenty-two eyes showed atypical retardation patterns, and 19 eyes showed normal patterns. From the 22 atypical eyes, 15 showed atypical patterns in both imaging modalities, five were atypical only in SLP images, and two were atypical only in PS-OCT images. In most (15 of 22) atypical cases, an increased penetration of the probing beam into the birefringent sclera was identified as the source of atypical patterns. In such cases, the artifacts could be eliminated in PS-OCT images by depth segmentation and exclusion of scleral signals. Conclusions PS-OCT provides deeper insight into the contribution of different fundus layers to SLP images. Increased light penetration into the sclera can distort SLP retardation patterns of the RNFL. PMID:19036999

Image segmentation-based robust feature extraction for color image watermarking

NASA Astrophysics Data System (ADS)

Li, Mianjie; Deng, Zeyu; Yuan, Xiaochen

2018-04-01

This paper proposes a local digital image watermarking method based on Robust Feature Extraction. The segmentation is achieved by Simple Linear Iterative Clustering (SLIC) based on which an Image Segmentation-based Robust Feature Extraction (ISRFE) method is proposed for feature extraction. Our method can adaptively extract feature regions from the blocks segmented by SLIC. This novel method can extract the most robust feature region in every segmented image. Each feature region is decomposed into low-frequency domain and high-frequency domain by Discrete Cosine Transform (DCT). Watermark images are then embedded into the coefficients in the low-frequency domain. The Distortion-Compensated Dither Modulation (DC-DM) algorithm is chosen as the quantization method for embedding. The experimental results indicate that the method has good performance under various attacks. Furthermore, the proposed method can obtain a trade-off between high robustness and good image quality.

How atypical is atypical language dominance?

PubMed

Knecht, S; Jansen, A; Frank, A; van Randenborgh, J; Sommer, J; Kanowski, M; Heinze, H J

2003-04-01

Atypical, right-hemisphere language dominance is poorly understood. It is often observed in patients with brain reorganization due to lesions early in life. It can also be encountered in seemingly normal individuals. We compared the patterns of neural language activation in 7 individuals with left- and 7 with right-hemisphere language dominance, none of whom had any evidence of brain lesions. We speculated that incongruencies in the activation patterns in atypical, right-hemisphere language dominance could indicate a reorganized neural language system after undetected early brain damage. Functional magnetic resonance imaging analysis of brain activation during phonetic word generation demonstrated (1). no increased activation in the subdominant hemisphere in right compared to left language dominance, (2). a similar variability in the pattern of activation in both groups, and (3). a mirror reverse pattern of activation in right- compared to left-hemisphere dominant subjects. These findings support the view that in individuals with an unrevealing medical history right-hemispheric dominance constitutes a natural rather than an abortive variant of language lateralization.

Evidence for Broadening Criteria for Atypical Depression Which May Define a Reactive Depressive Disorder.

PubMed

Silverstein, Brett; Angst, Jules

2015-01-01

Objective. Arguing that additional symptoms should be added to the criteria for atypical depression. Method. Published research articles on atypical depression are reviewed. Results. (1) The original studies upon which the criteria for atypical depression were based cited fatigue, insomnia, pain, and loss of weight as characteristic symptoms. (2) Several studies of DSM depressive criteria found patients with atypical depression to exhibit high levels of insomnia, fatigue, and loss of appetite/weight. (3) Several studies have found atypical depression to be comorbid with headaches, bulimia, and body image issues. (4) Most probands who report atypical depression meet criteria for "somatic depression," defined as depression associated with several of disordered eating, poor body image, headaches, fatigue, and insomnia. The gender difference in prevalence of atypical depression results from its overlap with somatic depression. Somatic depression is associated with psychosocial measures related to gender, linking it with the descriptions of atypical depression as "reactive" appearing in the studies upon which the original criteria for atypical depression were based. Conclusion. Insomnia, disordered eating, poor body image, and aches/pains should be added as criteria for atypical depression matching criteria for somatic depression defining a reactive depressive disorder possibly distinct from endogenous melancholic depression.

Robust image features: concentric contrasting circles and their image extraction

NASA Astrophysics Data System (ADS)

Gatrell, Lance B.; Hoff, William A.; Sklair, Cheryl W.

1992-03-01

Many computer vision tasks can be simplified if special image features are placed on the objects to be recognized. A review of special image features that have been used in the past is given and then a new image feature, the concentric contrasting circle, is presented. The concentric contrasting circle image feature has the advantages of being easily manufactured, easily extracted from the image, robust extraction (true targets are found, while few false targets are found), it is a passive feature, and its centroid is completely invariant to the three translational and one rotational degrees of freedom and nearly invariant to the remaining two rotational degrees of freedom. There are several examples of existing parallel implementations which perform most of the extraction work. Extraction robustness was measured by recording the probability of correct detection and the false alarm rate in a set of images of scenes containing mockups of satellites, fluid couplings, and electrical components. A typical application of concentric contrasting circle features is to place them on modeled objects for monocular pose estimation or object identification. This feature is demonstrated on a visually challenging background of a specular but wrinkled surface similar to a multilayered insulation spacecraft thermal blanket.

Image ratio features for facial expression recognition application.

PubMed

Song, Mingli; Tao, Dacheng; Liu, Zicheng; Li, Xuelong; Zhou, Mengchu

2010-06-01

Video-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e.g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University Cohn-Kanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system.

Osteoblastoma of body of the talus--Report of a rare case with atypical radiological features.

PubMed

Mir, Naseer Ahmed; Baba, Asif Nazir; Maajid, Saheel; Badoo, Abdul Rashid; Mir, Ghulam Rasool

2010-06-01

Osteoblastoma is a benign bone tumour found commonly in the spine and long tubular bones. Involvement of the talus is uncommon, and when present, is found in the neck of the talus. Osteoblastoma of the body of talus is a very rare entity. We report a young male, presenting as chronic ankle pain, with a radiolucent lesion with a thick periosteal shell in the body of the talus. Analysis of clinical, radiological and histological findings confirmed the diagnosis of osteoblastoma. The case is reported for the rarity of the site and atypical radiological features that osteoblastoma can present with. Copyright 2009 European Foot and Ankle Society. Published by Elsevier Ltd. All rights reserved.

Unsupervised feature learning for autonomous rock image classification

NASA Astrophysics Data System (ADS)

Shu, Lei; McIsaac, Kenneth; Osinski, Gordon R.; Francis, Raymond

2017-09-01

Autonomous rock image classification can enhance the capability of robots for geological detection and enlarge the scientific returns, both in investigation on Earth and planetary surface exploration on Mars. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. In our tests, rock image classification using the learned features shows that the learned features can outperform manually selected features. Self-taught learning is also proposed to learn the feature representation from a large database of unlabelled rock images of mixed class. The learned features can then be used repeatedly for classification of any subclass. This takes advantage of the large dataset of unlabelled rock images and learns a general feature representation for many kinds of rocks. We show experimental results supporting the feasibility of self-taught learning on rock images.

A rare case of atypical pleomorphic adenoma arising from periocular ectopic lacrimal gland.

PubMed

Wajda, Brynn N; Mancini, Ronald; Evers, Bret; Nick Hogan, R

2018-06-23

To describe features of atypical pleomorphic adenoma, a rare clinical entity, particularly when found in ectopic periocular lacrimal gland tissue. Case report of biopsy-confirmed periocular atypical pleomorphic adenoma. A 35-year-old female presented with a unique orbital lesion found to be ectopic lacrimal gland demonstrating atypical pleomorphic adenoma on formal histopathologic review. Pleomorphic adenoma is pathologically characterized as an epithelial lesion intermixed with mesenchymal elements. It is further classified as atypical with the presence of features such as hypercellularity, regions of necrosis or hyalinization, cellular dysplasia, capsular violation, and malignant characteristics without frank local extension or distant metastases. Due to its rarity, the natural history and prognosis of atypical pleomorphic adenoma is unclear. Physicians need to recognize this entity, and complete surgical excision with strict follow-up regimens are likely warranted.

Automatic Extraction of Planetary Image Features

NASA Technical Reports Server (NTRS)

Troglio, G.; LeMoigne, J.; Moser, G.; Serpico, S. B.; Benediktsson, J. A.

2009-01-01

With the launch of several Lunar missions such as the Lunar Reconnaissance Orbiter (LRO) and Chandrayaan-1, a large amount of Lunar images will be acquired and will need to be analyzed. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to Lunar data that often present low contrast and uneven illumination characteristics. In this paper, we propose a new method for the extraction of Lunar features (that can be generalized to other planetary images), based on the combination of several image processing techniques, a watershed segmentation and the generalized Hough Transform. This feature extraction has many applications, among which image registration.

Categorizing biomedicine images using novel image features and sparse coding representation

PubMed Central

2013-01-01

Background Images embedded in biomedical publications carry rich information that often concisely summarize key hypotheses adopted, methods employed, or results obtained in a published study. Therefore, they offer valuable clues for understanding main content in a biomedical publication. Prior studies have pointed out the potential of mining images embedded in biomedical publications for automatically understanding and retrieving such images' associated source documents. Within the broad area of biomedical image processing, categorizing biomedical images is a fundamental step for building many advanced image analysis, retrieval, and mining applications. Similar to any automatic categorization effort, discriminative image features can provide the most crucial aid in the process. Method We observe that many images embedded in biomedical publications carry versatile annotation text. Based on the locations of and the spatial relationships between these text elements in an image, we thus propose some novel image features for image categorization purpose, which quantitatively characterize the spatial positions and distributions of text elements inside a biomedical image. We further adopt a sparse coding representation (SCR) based technique to categorize images embedded in biomedical publications by leveraging our newly proposed image features. Results we randomly selected 990 images of the JPG format for use in our experiments where 310 images were used as training samples and the rest were used as the testing cases. We first segmented 310 sample images following the our proposed procedure. This step produced a total of 1035 sub-images. We then manually labeled all these sub-images according to the two-level hierarchical image taxonomy proposed by [1]. Among our annotation results, 316 are microscopy images, 126 are gel electrophoresis images, 135 are line charts, 156 are bar charts, 52 are spot charts, 25 are tables, 70 are flow charts, and the remaining 155 images are

Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features

PubMed Central

Iyappan, Anandhi; Younesi, Erfan; Redolfi, Alberto; Vrooman, Henri; Khanna, Shashank; Frisoni, Giovanni B.; Hofmann-Apitius, Martin

2017-01-01

Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes. PMID:28731430

Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features.

PubMed

Iyappan, Anandhi; Younesi, Erfan; Redolfi, Alberto; Vrooman, Henri; Khanna, Shashank; Frisoni, Giovanni B; Hofmann-Apitius, Martin

2017-01-01

Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes.

Improved parallel image reconstruction using feature refinement.

PubMed

Cheng, Jing; Jia, Sen; Ying, Leslie; Liu, Yuanyuan; Wang, Shanshan; Zhu, Yanjie; Li, Ye; Zou, Chao; Liu, Xin; Liang, Dong

2018-07-01

The aim of this study was to develop a novel feature refinement MR reconstruction method from highly undersampled multichannel acquisitions for improving the image quality and preserve more detail information. The feature refinement technique, which uses a feature descriptor to pick up useful features from residual image discarded by sparsity constrains, is applied to preserve the details of the image in compressed sensing and parallel imaging in MRI (CS-pMRI). The texture descriptor and structure descriptor recognizing different types of features are required for forming the feature descriptor. Feasibility of the feature refinement was validated using three different multicoil reconstruction methods on in vivo data. Experimental results show that reconstruction methods with feature refinement improve the quality of reconstructed image and restore the image details more accurately than the original methods, which is also verified by the lower values of the root mean square error and high frequency error norm. A simple and effective way to preserve more useful detailed information in CS-pMRI is proposed. This technique can effectively improve the reconstruction quality and has superior performance in terms of detail preservation compared with the original version without feature refinement. Magn Reson Med 80:211-223, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Image counter-forensics based on feature injection

NASA Astrophysics Data System (ADS)

Iuliani, M.; Rossetto, S.; Bianchi, T.; De Rosa, Alessia; Piva, A.; Barni, M.

2014-02-01

Starting from the concept that many image forensic tools are based on the detection of some features revealing a particular aspect of the history of an image, in this work we model the counter-forensic attack as the injection of a specific fake feature pointing to the same history of an authentic reference image. We propose a general attack strategy that does not rely on a specific detector structure. Given a source image x and a target image y, the adversary processes x in the pixel domain producing an attacked image ~x, perceptually similar to x, whose feature f(~x) is as close as possible to f(y) computed on y. Our proposed counter-forensic attack consists in the constrained minimization of the feature distance Φ(z) =│ f(z) - f(y)│ through iterative methods based on gradient descent. To solve the intrinsic limit due to the numerical estimation of the gradient on large images, we propose the application of a feature decomposition process, that allows the problem to be reduced into many subproblems on the blocks the image is partitioned into. The proposed strategy has been tested by attacking three different features and its performance has been compared to state-of-the-art counter-forensic methods.

Typical and atypical metastatic sites of recurrent endometrial carcinoma

PubMed Central

Krajewski, Katherine M.; Jagannathan, Jyothi; Giardino, Angela; Berlin, Suzanne; Ramaiya, Nikhil

2013-01-01

Abstract The purpose of this article is to illustrate the imaging findings of typical and atypical metastatic sites of recurrent endometrial carcinoma. Typical sites include local pelvic recurrence, pelvic and para-aortic nodes, peritoneum, and lungs. Atypical sites include extra-abdominal lymph nodes, liver, adrenals, brain, bones and soft tissue. It is important for radiologists to recognize the typical and atypical sites of metastases in patients with recurrent endometrial carcinoma to facilitate earlier diagnosis and treatment. PMID:23545091

A-type granites and related rocks: Evolution of a concept, problems and prospects

NASA Astrophysics Data System (ADS)

Bonin, Bernard

2007-08-01

Although A-type granites have long been recognized as a distinct group of granites, the term A-type was coined first less than thirty years ago. A-type suites occur in geodynamic contexts ranging from within-plate settings to plate boundaries, locations and times of emplacement are not random. Rare in the lower crust, as some charnockite suites, they are fairly common at shallower depths, especially at the subvolcanic level where they form ring complexes rooting caldera volcanoes. Characteristic features include hypersolvus to transsolvus to subsolvus alkali feldspar textures, iron-rich mafic mineralogy, bulk-rock compositions yielding ferroan, alkali-calcic to alkaline affinities, high LILE+HFSE abundances, and pronounced anomalies due to high degrees of mineral fractionation. Isotopic features evidence sources containing a large mantle input. Experimental data show that A-type magmas contain dissolved OH F-bearing fluids, crystallised under reduced and oxidized conditions, and yield high-temperature liquidus, favouring early crystallisation of anhydrous iron minerals, such as fayalite. Though many petrogenetic models imply solely crustal derivation, no convincing A-type liquids were produced experimentally from crustal materials, nor have any leucosomes of A-type composition been detected within migmatitic terranes. As it occurs in association with mafic igneous rocks in continents as well as on the ocean floor, A-type granite is likely to come from mantle-derived transitional to alkaline mafic to intermediate magmas. Rare felsic materials found in the meteoritic and lunar record yield dominantly A-type features. Contrary to the more common types of granite, A-type granite is, therefore, not typical of Earth and was produced in planetary environments differing from those prevailing on Earth.

Automatic Feature Extraction from Planetary Images

NASA Technical Reports Server (NTRS)

Troglio, Giulia; Le Moigne, Jacqueline; Benediktsson, Jon A.; Moser, Gabriele; Serpico, Sebastiano B.

2010-01-01

With the launch of several planetary missions in the last decade, a large amount of planetary images has already been acquired and much more will be available for analysis in the coming years. The image data need to be analyzed, preferably by automatic processing techniques because of the huge amount of data. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to planetary data that often present low contrast and uneven illumination characteristics. Different methods have already been presented for crater extraction from planetary images, but the detection of other types of planetary features has not been addressed yet. Here, we propose a new unsupervised method for the extraction of different features from the surface of the analyzed planet, based on the combination of several image processing techniques, including a watershed segmentation and the generalized Hough Transform. The method has many applications, among which image registration and can be applied to arbitrary planetary images.

Feature hashing for fast image retrieval

NASA Astrophysics Data System (ADS)

Yan, Lingyu; Fu, Jiarun; Zhang, Hongxin; Yuan, Lu; Xu, Hui

2018-03-01

Currently, researches on content based image retrieval mainly focus on robust feature extraction. However, due to the exponential growth of online images, it is necessary to consider searching among large scale images, which is very timeconsuming and unscalable. Hence, we need to pay much attention to the efficiency of image retrieval. In this paper, we propose a feature hashing method for image retrieval which not only generates compact fingerprint for image representation, but also prevents huge semantic loss during the process of hashing. To generate the fingerprint, an objective function of semantic loss is constructed and minimized, which combine the influence of both the neighborhood structure of feature data and mapping error. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. Experimental results show good performance of our approach.

Feature-based Alignment of Volumetric Multi-modal Images

PubMed Central

Toews, Matthew; Zöllei, Lilla; Wells, William M.

2014-01-01

This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology. PMID:24683955

A comparison study of image features between FFDM and film mammogram images

PubMed Central

Jing, Hao; Yang, Yongyi; Wernick, Miles N.; Yarusso, Laura M.; Nishikawa, Robert M.

2012-01-01

Purpose: This work is to provide a direct, quantitative comparison of image features measured by film and full-field digital mammography (FFDM). The purpose is to investigate whether there is any systematic difference between film and FFDM in terms of quantitative image features and their influence on the performance of a computer-aided diagnosis (CAD) system. Methods: The authors make use of a set of matched film-FFDM image pairs acquired from cadaver breast specimens with simulated microcalcifications consisting of bone and teeth fragments using both a GE digital mammography system and a screen-film system. To quantify the image features, the authors consider a set of 12 textural features of lesion regions and six image features of individual microcalcifications (MCs). The authors first conduct a direct comparison on these quantitative features extracted from film and FFDM images. The authors then study the performance of a CAD classifier for discriminating between MCs and false positives (FPs) when the classifier is trained on images of different types (film, FFDM, or both). Results: For all the features considered, the quantitative results show a high degree of correlation between features extracted from film and FFDM, with the correlation coefficients ranging from 0.7326 to 0.9602 for the different features. Based on a Fisher sign rank test, there was no significant difference observed between the features extracted from film and those from FFDM. For both MC detection and discrimination of FPs from MCs, FFDM had a slight but statistically significant advantage in performance; however, when the classifiers were trained on different types of images (acquired with FFDM or SFM) for discriminating MCs from FPs, there was little difference. Conclusions: The results indicate good agreement between film and FFDM in quantitative image features. While FFDM images provide better detection performance in MCs, FFDM and film images may be interchangeable for the purposes of

Feature maps driven no-reference image quality prediction of authentically distorted images

NASA Astrophysics Data System (ADS)

Ghadiyaram, Deepti; Bovik, Alan C.

2015-03-01

Current blind image quality prediction models rely on benchmark databases comprised of singly and synthetically distorted images, thereby learning image features that are only adequate to predict human perceived visual quality on such inauthentic distortions. However, real world images often contain complex mixtures of multiple distortions. Rather than a) discounting the effect of these mixtures of distortions on an image's perceptual quality and considering only the dominant distortion or b) using features that are only proven to be efficient for singly distorted images, we deeply study the natural scene statistics of authentically distorted images, in different color spaces and transform domains. We propose a feature-maps-driven statistical approach which avoids any latent assumptions about the type of distortion(s) contained in an image, and focuses instead on modeling the remarkable consistencies in the scene statistics of real world images in the absence of distortions. We design a deep belief network that takes model-based statistical image features derived from a very large database of authentically distorted images as input and discovers good feature representations by generalizing over different distortion types, mixtures, and severities, which are later used to learn a regressor for quality prediction. We demonstrate the remarkable competence of our features for improving automatic perceptual quality prediction on a benchmark database and on the newly designed LIVE Authentic Image Quality Challenge Database and show that our approach of combining robust statistical features and the deep belief network dramatically outperforms the state-of-the-art.

Clinical, pathological, and molecular features of classical and L-type atypical-BSE in goats

PubMed Central

D’Angelo, Antonio; Mazza, Maria; Meloni, Daniela; Baioni, Elisa; Maurella, Cristiana; Colussi, Silvia; Martinelli, Nicola; Lo Faro, Monica; Favole, Alessandra; Grifoni, Silvia; Gallo, Marina; Lombardi, Guerino; Iulini, Barbara; Casalone, Cristina; Corona, Cristiano

2018-01-01

Monitoring of small ruminants for transmissible spongiform encephalopathies (TSEs) has recently become more relevant after two natural scrapie suspected cases of goats were found to be positive for classical BSE (C-BSE). C-BSE probably established itself in this species unrecognized, undermining disease control measures. This opens the possibility that TSEs in goats may remain an animal source for human prion diseases. Currently, there are no data regarding the natural presence of the atypical BSE in caprines. Here we report that C-BSE and L-type atypical BSE (L-BSE) isolates from bovine species are intracerebrally transmissible to goats, with a 100% attack rate and a significantly shorter incubation period and survival time after C-BSE than after L-BSE experimental infection, suggesting a lower species barrier for classical agentin goat. All animals showed nearly the same clinical features of disease characterized by skin lesions, including broken hair and alopecia, and abnormal mental status. Histology and immunohistochemistry showed several differences between C-BSE and L-BSE infection, allowing discrimination between the two different strains. The lymphoreticular involvement we observed in the C-BSE positive goats argues in favour of a peripheral distribution of PrPSc similar to classical scrapie. Western blot and other currently approved screening tests detected both strains in the goats and were able to classify negative control animals. These data demonstrate that active surveillance of small ruminants, as applied to fallen stock and/or healthy slaughter populations in European countries, is able to correctly identify and classify classical and L-BSE and ultimately protect public health. PMID:29795663

Tracing the associations between sex, the atypical and the combined atypical-melancholic depression subtypes: A path analysis.

PubMed

Rodgers, Stephanie; Vandeleur, Caroline L; Ajdacic-Gross, Vladeta; Aleksandrowicz, Aleksandra A; Strippoli, Marie-Pierre F; Castelao, Enrique; Glaus, Jennifer; Lasserre, Aurélie M; Müller, Mario; Rössler, Wulf; Angst, Jules; Preisig, Martin

2016-01-15

Numerous studies have examined determinants leading to preponderance of women in major depressive disorder (MDD), which is particularly accentuated for the atypical depression subtype. It is thus of interest to explore the specific indirect effects influencing the association between sex and established depression subtypes. The data of 1624 subjects with a lifetime diagnosis of MDD derived from the population-based PsyCoLaus data were used. An atypical (n=256), a melancholic (n=422), a combined atypical and melancholic features subtype (n=198), and an unspecified MDD group (n=748) were constructed according to the DSM-IV specifiers. Path models with direct and indirect effects were applied to the data. Partial mediation of the female-related atypical and combined atypical-melancholic depression subtypes was found. Early anxiety disorders and high emotion-orientated coping acted as mediating variables between sex and the atypical depression subtype. In contrast, high Body Mass Index (BMI) served as a suppression variable, also concerning the association between sex and the combined atypical-melancholic subtype. The latter association was additionally mediated by an early age of MDD onset and early/late anxiety disorders. The use of cross-sectional data does not allow causal conclusions. This is the first study that provides evidence for a differentiation of the general mechanisms explaining sex differences of overall MDD by depression subtypes. Determinants affecting the pathways begin early in life. Since some of them are primarily of behavioral nature, the present findings could be a valuable target in mental health care. Copyright © 2015 Elsevier B.V. All rights reserved.

Dual Tracer PET Imaging (68Ga-DOTATATE and 18F-FDG) Features in Pulmonary Carcinoid: Correlation with Tumor Proliferation Index.

PubMed

Bhatkar, Dhiraj; Utpat, Ketaki; Basu, Sandip; Joshi, Jyotsna M

2017-01-01

Pulmonary carcinoid tumors are rare group of lung neoplasms representing 1% of all the lung tumors. The typical bronchial carcinoids showed higher and more selective uptake of 68 Ga-DOTATATE than of 18 F-FDG on PET-CT. The Ki-67(MIB-1), a tumor proliferation index is a prognostic marker in neuroendocrine tumors for estimating tumor progression. Atypical carcinoids have higher Ki-67 index and have an increased propensity to metastasize as compared to typical ones. 68 Ga-DOTATATE PET imaging along with Ki-67 can be correlated for better management of patients with neuroendocrine tumors. We describe the dual tracer imaging features in a patient of pulmonary carcinoid with avid 68 Ga-DOTATATE and minimal 18 FDG ( 18 Flurodeoxyglucose) uptake diagnosed on the basis of imaging and bronchoscopic biopsy and its correlation with tumor proliferation index.

A short feature vector for image matching: The Log-Polar Magnitude feature descriptor

PubMed Central

Hast, Anders; Wählby, Carolina; Sintorn, Ida-Maria

2017-01-01

The choice of an optimal feature detector-descriptor combination for image matching often depends on the application and the image type. In this paper, we propose the Log-Polar Magnitude feature descriptor—a rotation, scale, and illumination invariant descriptor that achieves comparable performance to SIFT on a large variety of image registration problems but with much shorter feature vectors. The descriptor is based on the Log-Polar Transform followed by a Fourier Transform and selection of the magnitude spectrum components. Selecting different frequency components allows optimizing for image patterns specific for a particular application. In addition, by relying only on coordinates of the found features and (optionally) feature sizes our descriptor is completely detector independent. We propose 48- or 56-long feature vectors that potentially can be shortened even further depending on the application. Shorter feature vectors result in better memory usage and faster matching. This combined with the fact that the descriptor does not require a time-consuming feature orientation estimation (the rotation invariance is achieved solely by using the magnitude spectrum of the Log-Polar Transform) makes it particularly attractive to applications with limited hardware capacity. Evaluation is performed on the standard Oxford dataset and two different microscopy datasets; one with fluorescence and one with transmission electron microscopy images. Our method performs better than SURF and comparable to SIFT on the Oxford dataset, and better than SIFT on both microscopy datasets indicating that it is particularly useful in applications with microscopy images. PMID:29190737

Image fusion using sparse overcomplete feature dictionaries

DOEpatents

Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt

2015-10-06

Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

Melorheostosis: Two atypical cases.

PubMed

Sureka, Binit; Mittal, Mahesh Kumar; Udhaya, Kk; Sinha, Mukul; Mittal, Aliza; Thukral, Brij Bhushan

2014-04-01

Melorheostosis is an uncommon mesenchymal dysplasia that rarely affects the axial skeleton. We describe two atypical cases of melorheostosis with classical imaging findings - the first one involving the cervico-dorsal spine with encroachment of left vertebral artery canal causing attenuation of the left vertebral artery and the second one of mixed sclerosing bony dysplasia (monomelic involvement coexisting with osteopoikilosis).

Atypical psychotic symptoms and Dandy-Walker variant.

PubMed

Williams, Aislinn J; Wang, Zhenni; Taylor, Stephan F

2016-10-01

New-onset psychotic symptoms often respond well to antipsychotic treatment; however, symptoms may be difficult to treat when an underlying brain malformation is present. Here, we present a case of atypical psychotic symptoms in the context of a congenital cerebellar malformation (Dandy-Walker variant). The patient ultimately improved with paliperidone palmitate after multiple antipsychotic medication trials (both oral and one long-acting injectable) were ineffective. Neuroimaging may provide valuable diagnostic and prognostic information in cases of new-onset psychosis with atypical features and treatment resistance, even in the absence of neurologic signs and symptoms.

Correlative feature analysis of FFDM images

NASA Astrophysics Data System (ADS)

Yuan, Yading; Giger, Maryellen L.; Li, Hui; Sennett, Charlene

2008-03-01

Identifying the corresponding image pair of a lesion is an essential step for combining information from different views of the lesion to improve the diagnostic ability of both radiologists and CAD systems. Because of the non-rigidity of the breasts and the 2D projective property of mammograms, this task is not trivial. In this study, we present a computerized framework that differentiates the corresponding images from different views of a lesion from non-corresponding ones. A dual-stage segmentation method, which employs an initial radial gradient index(RGI) based segmentation and an active contour model, was initially applied to extract mass lesions from the surrounding tissues. Then various lesion features were automatically extracted from each of the two views of each lesion to quantify the characteristics of margin, shape, size, texture and context of the lesion, as well as its distance to nipple. We employed a two-step method to select an effective subset of features, and combined it with a BANN to obtain a discriminant score, which yielded an estimate of the probability that the two images are of the same physical lesion. ROC analysis was used to evaluate the performance of the individual features and the selected feature subset in the task of distinguishing between corresponding and non-corresponding pairs. By using a FFDM database with 124 corresponding image pairs and 35 non-corresponding pairs, the distance feature yielded an AUC (area under the ROC curve) of 0.8 with leave-one-out evaluation by lesion, and the feature subset, which includes distance feature, lesion size and lesion contrast, yielded an AUC of 0.86. The improvement by using multiple features was statistically significant as compared to single feature performance. (p<0.001)

The Efficacy of Acute Electroconvulsive Therapy in Atypical Depression

PubMed Central

Husain, Mustafa M.; McClintock, Shawn M.; Rush, A. John; Knapp, Rebecca G.; Fink, Max; Rummans, Teresa A.; Rasmussen, Keith; Claassen, Cynthia; Petrides, Georgios; Biggs, Melanie M.; Mueller, Martina; Sampson, Shirlene; Bailine, Samuel H.; Lisanby, Sarah H.; Kellner, Charles H.

2013-01-01

Objective This study examined the characteristics and outcomes of patients with major depressive disorder (MDD), with or without atypical features, who were treated with acute bilateral electroconvulsive therapy (ECT). Method Analyses were conducted with 489 patients who met DSM-IV criteria for MDD. Subjects were identified as typical or atypical on the basis of the Structured Clinical Interview for DSM-IV obtained at baseline prior to ECT. Depression symptom severity was measured by the 24-item Hamilton Rating Scale for Depression (HAM-D24) and the 30-item Inventory of Depressive Symptomatology–Self-Report (IDS-SR30). Remission was defined as at least a 60% decrease from baseline in HAM-D24 score and a total score of 10 or below on the last 2 consecutive HAM-D24 ratings. The randomized controlled trial was performed from 1997 to 2004. Results The typical (N = 453) and atypical (N = 36) groups differed in several sociodemographic and clinical variables including gender (p = .0071), age (p = .0005), treatment resistance (p = .0014), and age at first illness onset (p < .0001) and onset of current episode (p = .0008). Following an acute course of bilateral ECT, a considerable portion of both the typical (67.1%) and the atypical (80.6%) groups reached remission. The atypical group was 2.6 (95% CI = 1.1 to 6.2) times more likely to remit than the typical group after adjustment for age, psychosis, gender, clinical site, and depression severity based on the HAM-D24. Conclusion Acute ECT is an efficacious treatment for depressed patients with typical or atypical symptom features. PMID:18278988

Hepatic CT image query using Gabor features

NASA Astrophysics Data System (ADS)

Zhao, Chenguang; Cheng, Hongyan; Zhuang, Tiange

2004-07-01

A retrieval scheme for liver computerize tomography (CT) images based on Gabor texture is presented. For each hepatic CT image, we manually delineate abnormal regions within liver area. Then, a continuous Gabor transform is utilized to analyze the texture of the pathology bearing region and extract the corresponding feature vectors. For a given sample image, we compare its feature vector with those of other images. Similar images with the highest rank are retrieved. In experiments, 45 liver CT images are collected, and the effectiveness of Gabor texture for content based retrieval is verified.

Self-limiting Atypical Antipsychotics-induced Edema: Clinical Cases and Systematic Review.

PubMed

Umar, Musa Usman; Abdullahi, Aminu Taura

2016-01-01

A number of atypical antipsychotics have been associated with peripheral edema. The exact cause is not known. We report two cases of olanzapine-induced edema and a brief review of atypical antipsychotic-induced edema, possible risk factors, etiology, and clinical features. The recommendation is given on different methods of managing this side effect.

Self-limiting Atypical Antipsychotics-induced Edema: Clinical Cases and Systematic Review

PubMed Central

Umar, Musa Usman; Abdullahi, Aminu Taura

2016-01-01

A number of atypical antipsychotics have been associated with peripheral edema. The exact cause is not known. We report two cases of olanzapine-induced edema and a brief review of atypical antipsychotic-induced edema, possible risk factors, etiology, and clinical features. The recommendation is given on different methods of managing this side effect. PMID:27335511

Melorheostosis: Two atypical cases

PubMed Central

Sureka, Binit; Mittal, Mahesh Kumar; Udhaya, KK; Sinha, Mukul; Mittal, Aliza; Thukral, Brij Bhushan

2014-01-01

Melorheostosis is an uncommon mesenchymal dysplasia that rarely affects the axial skeleton. We describe two atypical cases of melorheostosis with classical imaging findings – the first one involving the cervico-dorsal spine with encroachment of left vertebral artery canal causing attenuation of the left vertebral artery and the second one of mixed sclerosing bony dysplasia (monomelic involvement coexisting with osteopoikilosis). PMID:25024532

Atypical hemispheric dominance for attention: functional MRI topography.

PubMed

Flöel, Agnes; Jansen, Andreas; Deppe, Michael; Kanowski, Martin; Konrad, Carsten; Sommer, Jens; Knecht, Stefan

2005-09-01

The right hemisphere is predominantly involved in tasks associated with spatial attention. However, left hemispheric dominance for spatial attention can be found in healthy individuals, and both spatial attention and language can be lateralized to the same hemisphere. Little is known about the underlying regional distribution of neural activation in these 'atypical' individuals. Previously a large number of healthy subjects were screened for hemispheric dominance of visuospatial attention and language, using functional Doppler ultrasonography. From this group, subjects were chosen who were 'atypical' for hemispheric dominance of visuospatial attention and language, and their pattern of brain activation was studied with functional magnetic resonance imaging during a task probing spatial attention. Right-handed subjects with the 'typical' pattern of brain organization served as control subjects. It was found that subjects with an inverted lateralization of language and spatial attention (language right, attention left) recruited left-hemispheric areas in the attention task, homotopic to those recruited by control subjects in the right hemisphere. Subjects with lateralization of both language and attention to the right hemisphere activated an attentional network in the right hemisphere that was comparable to control subjects. The present findings suggest that not the hemispheric side, but the intrahemispheric pattern of activation is the distinct feature for the neural processes underlying language and attention.

Semantic image segmentation with fused CNN features

NASA Astrophysics Data System (ADS)

Geng, Hui-qiang; Zhang, Hua; Xue, Yan-bing; Zhou, Mian; Xu, Guang-ping; Gao, Zan

2017-09-01

Semantic image segmentation is a task to predict a category label for every image pixel. The key challenge of it is to design a strong feature representation. In this paper, we fuse the hierarchical convolutional neural network (CNN) features and the region-based features as the feature representation. The hierarchical features contain more global information, while the region-based features contain more local information. The combination of these two kinds of features significantly enhances the feature representation. Then the fused features are used to train a softmax classifier to produce per-pixel label assignment probability. And a fully connected conditional random field (CRF) is used as a post-processing method to improve the labeling consistency. We conduct experiments on SIFT flow dataset. The pixel accuracy and class accuracy are 84.4% and 34.86%, respectively.

Natural image statistics and low-complexity feature selection.

PubMed

Vasconcelos, Manuela; Vasconcelos, Nuno

2009-02-01

Low-complexity feature selection is analyzed in the context of visual recognition. It is hypothesized that high-order dependences of bandpass features contain little information for discrimination of natural images. This hypothesis is characterized formally by the introduction of the concepts of conjunctive interference and decomposability order of a feature set. Necessary and sufficient conditions for the feasibility of low-complexity feature selection are then derived in terms of these concepts. It is shown that the intrinsic complexity of feature selection is determined by the decomposability order of the feature set and not its dimension. Feature selection algorithms are then derived for all levels of complexity and are shown to be approximated by existing information-theoretic methods, which they consistently outperform. The new algorithms are also used to objectively test the hypothesis of low decomposability order through comparison of classification performance. It is shown that, for image classification, the gain of modeling feature dependencies has strongly diminishing returns: best results are obtained under the assumption of decomposability order 1. This suggests a generic law for bandpass features extracted from natural images: that the effect, on the dependence of any two features, of observing any other feature is constant across image classes.

A novel examination of atypical major depressive disorder based on attachment theory.

PubMed

Levitan, Robert D; Atkinson, Leslie; Pedersen, Rebecca; Buis, Tom; Kennedy, Sidney H; Chopra, Kevin; Leung, Eman M; Segal, Zindel V

2009-06-01

While a large body of descriptive work has thoroughly investigated the clinical correlates of atypical depression, little is known about its fundamental origins. This study examined atypical depression from an attachment theory framework. Our hypothesis was that, compared to adults with melancholic depression, those with atypical depression would report more anxious-ambivalent attachment and less secure attachment. As gender has been an important consideration in prior work on atypical depression, this same hypothesis was further tested in female subjects only. One hundred ninety-nine consecutive adults presenting to a tertiary mood disorders clinic with major depressive disorder with either atypical or melancholic features according to the Structured Clinical Interview for DSM-IV Axis-I Disorders were administered a self-report adult attachment questionnaire to assess the core dimensions of secure, anxious-ambivalent, and avoidant attachment. Attachment scores were compared across the 2 depressed groups defined by atypical and melancholic features using multivariate analysis of variance. The study was conducted between 1999 and 2004. When men and women were considered together, the multivariate test comparing attachment scores by depressive group was statistically significant at p < .05. Between-subjects testing indicated that atypical depression was associated with significantly lower secure attachment scores, with a trend toward higher anxious-ambivalent attachment scores, than was melancholia. When women were analyzed separately, the multivariate test was statistically significant at p < .01, with both secure and anxious-ambivalent attachment scores differing significantly across depressive groups. These preliminary findings suggest that attachment theory, and insecure and anxious-ambivalent attachment in particular, may be a useful framework from which to study the origins, clinical correlates, and treatment of atypical depression. Gender may be an important

Understanding the Uncanny: Both Atypical Features and Category Ambiguity Provoke Aversion toward Humanlike Robots

PubMed Central

Strait, Megan K.; Floerke, Victoria A.; Ju, Wendy; Maddox, Keith; Remedios, Jessica D.; Jung, Malte F.; Urry, Heather L.

2017-01-01

Robots intended for social contexts are often designed with explicit humanlike attributes in order to facilitate their reception by (and communication with) people. However, observation of an “uncanny valley”—a phenomenon in which highly humanlike entities provoke aversion in human observers—has lead some to caution against this practice. Both of these contrasting perspectives on the anthropomorphic design of social robots find some support in empirical investigations to date. Yet, owing to outstanding empirical limitations and theoretical disputes, the uncanny valley and its implications for human-robot interaction remains poorly understood. We thus explored the relationship between human similarity and people's aversion toward humanlike robots via manipulation of the agents' appearances. To that end, we employed a picture-viewing task (Nagents = 60) to conduct an experimental test (Nparticipants = 72) of the uncanny valley's existence and the visual features that cause certain humanlike robots to be unnerving. Across the levels of human similarity, we further manipulated agent appearance on two dimensions, typicality (prototypic, atypical, and ambiguous) and agent identity (robot, person), and measured participants' aversion using both subjective and behavioral indices. Our findings were as follows: (1) Further substantiating its existence, the data show a clear and consistent uncanny valley in the current design space of humanoid robots. (2) Both category ambiguity, and more so, atypicalities provoke aversive responding, thus shedding light on the visual factors that drive people's discomfort. (3) Use of the Negative Attitudes toward Robots Scale did not reveal any significant relationships between people's pre-existing attitudes toward humanlike robots and their aversive responding—suggesting positive exposure and/or additional experience with robots is unlikely to affect the occurrence of an uncanny valley effect in humanoid robotics. This work

Understanding the Uncanny: Both Atypical Features and Category Ambiguity Provoke Aversion toward Humanlike Robots.

PubMed

Strait, Megan K; Floerke, Victoria A; Ju, Wendy; Maddox, Keith; Remedios, Jessica D; Jung, Malte F; Urry, Heather L

2017-01-01

Robots intended for social contexts are often designed with explicit humanlike attributes in order to facilitate their reception by (and communication with) people. However, observation of an "uncanny valley"-a phenomenon in which highly humanlike entities provoke aversion in human observers-has lead some to caution against this practice. Both of these contrasting perspectives on the anthropomorphic design of social robots find some support in empirical investigations to date. Yet, owing to outstanding empirical limitations and theoretical disputes, the uncanny valley and its implications for human-robot interaction remains poorly understood. We thus explored the relationship between human similarity and people's aversion toward humanlike robots via manipulation of the agents' appearances. To that end, we employed a picture-viewing task ( N agents = 60) to conduct an experimental test ( N participants = 72) of the uncanny valley's existence and the visual features that cause certain humanlike robots to be unnerving. Across the levels of human similarity, we further manipulated agent appearance on two dimensions, typicality (prototypic, atypical, and ambiguous) and agent identity (robot, person), and measured participants' aversion using both subjective and behavioral indices. Our findings were as follows: (1) Further substantiating its existence, the data show a clear and consistent uncanny valley in the current design space of humanoid robots. (2) Both category ambiguity, and more so, atypicalities provoke aversive responding, thus shedding light on the visual factors that drive people's discomfort. (3) Use of the Negative Attitudes toward Robots Scale did not reveal any significant relationships between people's pre-existing attitudes toward humanlike robots and their aversive responding-suggesting positive exposure and/or additional experience with robots is unlikely to affect the occurrence of an uncanny valley effect in humanoid robotics. This work furthers

"Atypical" Pleomorphic Lipomatous Tumor: A Clinicopathologic, Immunohistochemical and Molecular Study of 21 Cases, Emphasizing its Relationship to Atypical Spindle Cell Lipomatous Tumor and Suggesting a Morphologic Spectrum (Atypical Spindle Cell/Pleomorphic Lipomatous Tumor).

PubMed

Creytens, David; Mentzel, Thomas; Ferdinande, Liesbeth; Lecoutere, Evelyne; van Gorp, Joost; Atanesyan, Lilit; de Groot, Karel; Savola, Suvi; Van Roy, Nadine; Van Dorpe, Jo; Flucke, Uta

2017-11-01

The classification of the until recently poorly explored group of atypical adipocytic neoplasms with spindle cell features, for which recently the term atypical spindle cell lipomatous tumor (ASLT) has been proposed, remains challenging. Recent studies have proposed ASLT as a unique entity with (in at least a significant subset of cases) a specific genetic background, namely deletions/losses of 13q14, including RB1 and its flanking genes RCBTB2, DLEU1, and ITM2B. Similar genetic aberrations have been reported in pleomorphic liposarcomas (PLSs). This prompted us to investigate a series of 21 low-grade adipocytic neoplasms with a pleomorphic lipoma-like appearance, but with atypical morphologic features (including atypical spindle cells, pleomorphic [multinucleated] cells, pleomorphic lipoblasts and poor circumscription), for which we propose the term "atypical" pleomorphic lipomatous tumor (APLT). Five cases of PLS were also included in this study. We used multiplex ligation-dependent probe amplification to evaluate genetic changes of 13q14. In addition, array-based comparative genomic hybridization was performed on 4 APLTs and all PLSs. Multiplex ligation-dependent probe amplification showed consistent loss of RB1 and its flanking gene RCBTB2 in all cases of APLT. This genetic alteration was also present in all PLSs, suggesting genetic overlap, in addition to morphologic overlap, with APLTs. However, array-based comparative genomic hybridization demonstrated more complex genetic alterations with more losses and gains in PLSs compared with APLTs. APLTs arose in the subcutis (67%) more frequently than in the deep (subfascial) soft tissues (33%). With a median follow-up of 42 months, recurrences were documented in 2 of 12 APLTs for which a long follow-up was available. Herein, we also demonstrate that APLTs share obvious overlapping morphologic, immunohistochemical, genetic and clinical characteristics with the recently defined ASLT, suggesting that they are related

Atypical centromeres in plants-what they can tell us.

PubMed

Cuacos, Maria; H Franklin, F Chris; Heckmann, Stefan

2015-01-01

The centromere, visible as the primary constriction of condensed metaphase chromosomes, is a defined chromosomal locus essential for genome stability. It mediates transient assembly of a multi-protein complex, the kinetochore, which enables interaction with spindle fibers and thus faithful segregation of the genetic information during nuclear divisions. Centromeric DNA varies in extent and sequence composition among organisms, but a common feature of almost all active eukaryotic centromeres is the presence of the centromeric histone H3 variant cenH3 (a.k.a. CENP-A). These typical centromere features apply to most studied species. However, a number of species display "atypical" centromeres, such as holocentromeres (centromere extension along almost the entire chromatid length) or neocentromeres (ectopic centromere activity). In this review, we provide an overview of different atypical centromere types found in plants including holocentromeres, de novo formed centromeres and terminal neocentromeres as well as di-, tri- and metapolycentromeres (more than one centromere per chromosomes). We discuss their specific and common features and compare them to centromere types found in other eukaryotic species. We also highlight new insights into centromere biology gained in plants with atypical centromeres such as distinct mechanisms to define a holocentromere, specific adaptations in species with holocentromeres during meiosis or various scenarios leading to neocentromere formation.

Characterization of the Autophagy Marker Protein Atg8 Reveals Atypical Features of Autophagy in Plasmodium falciparum

PubMed Central

Allanki, Aparna Devi; Sijwali, Puran Singh

2014-01-01

Conventional autophagy is a lysosome-dependent degradation process that has crucial homeostatic and regulatory functions in eukaryotic organisms. As malaria parasites must dispose a number of self and host cellular contents, we investigated if autophagy in malaria parasites is similar to the conventional autophagy. Genome wide analysis revealed a partial autophagy repertoire in Plasmodium, as homologs for only 15 of the 33 yeast autophagy proteins could be identified, including the autophagy marker Atg8. To gain insights into autophagy in malaria parasites, we investigated Plasmodium falciparum Atg8 (PfAtg8) employing techniques and conditions that are routinely used to study autophagy. Atg8 was similarly expressed and showed punctate localization throughout the parasite in both asexual and sexual stages; it was exclusively found in the pellet fraction as an integral membrane protein, which is in contrast to the yeast or mammalian Atg8 that is distributed among cytosolic and membrane fractions, and suggests for a constitutive autophagy. Starvation, the best known autophagy inducer, decreased PfAtg8 level by almost 3-fold compared to the normally growing parasites. Neither the Atg8-associated puncta nor the Atg8 expression level was significantly altered by treatment of parasites with routinely used autophagy inhibitors (cysteine (E64) and aspartic (pepstatin) protease inhibitors, the kinase inhibitor 3-methyladenine, and the lysosomotropic agent chloroquine), indicating an atypical feature of autophagy. Furthermore, prolonged inhibition of the major food vacuole protease activity by E64 and pepstatin did not cause accumulation of the Atg8-associated puncta in the food vacuole, suggesting that autophagy is primarily not meant for degradative function in malaria parasites. Atg8 showed partial colocalization with the apicoplast; doxycycline treatment, which disrupts apicoplast, did not affect Atg8 localization, suggesting a role, but not exclusive, in apicoplast

Multimodal imaging of temporal processing in typical and atypical language development.

PubMed

Kovelman, Ioulia; Wagley, Neelima; Hay, Jessica S F; Ugolini, Margaret; Bowyer, Susan M; Lajiness-O'Neill, Renee; Brennan, Jonathan

2015-03-01

New approaches to understanding language and reading acquisition propose that the human brain's ability to synchronize its neural firing rate to syllable-length linguistic units may be important to children's ability to acquire human language. Yet, little evidence from brain imaging studies has been available to support this proposal. Here, we summarize three recent brain imaging (functional near-infrared spectroscopy (fNIRS), functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG)) studies from our laboratories with young English-speaking children (aged 6-12 years). In the first study (fNIRS), we used an auditory beat perception task to show that, in children, the left superior temporal gyrus (STG) responds preferentially to rhythmic beats at 1.5 Hz. In the second study (fMRI), we found correlations between children's amplitude rise-time sensitivity, phonological awareness, and brain activation in the left STG. In the third study (MEG), typically developing children outperformed children with autism spectrum disorder in extracting words from rhythmically rich foreign speech and displayed different brain activation during the learning phase. The overall findings suggest that the efficiency with which left temporal regions process slow temporal (rhythmic) information may be important for gains in language and reading proficiency. These findings carry implications for better understanding of the brain's mechanisms that support language and reading acquisition during both typical and atypical development. © 2014 New York Academy of Sciences.

Image Recommendation Algorithm Using Feature-Based Collaborative Filtering

NASA Astrophysics Data System (ADS)

Kim, Deok-Hwan

As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.

Periosteal ganglia: CT and MR imaging features.

PubMed

Abdelwahab, I F; Kenan, S; Hermann, G; Klein, M J; Lewis, M M

1993-07-01

The imaging features of four cases of periosteal ganglia were studied. Three lesions were located over the proximal shaft of the tibia, in proximity to the pes anserinus. The fourth lesion involved the distal shaft of the ulna. Three lesions had different degrees of external cortical erosion, scalloping, and thick spicules of periosteal bone on plain radiographs. The bone adjacent to the fourth lesion was not involved. Computed tomography (CT) showed these lesions to be sharply defined soft-tissue masses abutting the periosteum. All of the lesions had the same attenuation as fluid. Magnetic resonance (MR) imaging revealed the ganglia to be sharply defined masses that were isointense compared with neighboring muscles on T1-weighted images. There was markedly increased signal intensity compared with that of fat on T2-weighted images. The signal intensity on both types of images was homogeneous. The MR imaging features were consistent with the fluid nature of the lesions. Under the appropriate clinical circumstances, the MR imaging and CT features of periosteal ganglia are diagnostic.

The atypical pneumonias: clinical diagnosis and importance.

PubMed

Cunha, B A

2006-05-01

The most common atypical pneumonias are caused by three zoonotic pathogens, Chlamydia psittaci (psittacosis), Francisella tularensis (tularemia), and Coxiella burnetii (Q fever), and three nonzoonotic pathogens, Chlamydia pneumoniae, Mycoplasma pneumoniae, and Legionella. These atypical agents, unlike the typical pathogens, often cause extrapulmonary manifestations. Atypical CAPs are systemic infectious diseases with a pulmonary component and may be differentiated clinically from typical CAPs by the pattern of extrapulmonary organ involvement which is characteristic for each atypical CAP. Zoonotic pneumonias may be eliminated from diagnostic consideration with a negative contact history. The commonest clinical problem is to differentiate legionnaire's disease from typical CAP as well as from C. pneumoniae or M. pneumonia infection. Legionella is the most important atypical pathogen in terms of severity. It may be clinically differentiated from typical CAP and other atypical pathogens by the use of a weighted point system of syndromic diagnosis based on the characteristic pattern of extrapulmonary features. Because legionnaire's disease often presents as severe CAP, a presumptive diagnosis of Legionella should prompt specific testing and empirical anti-Legionella therapy such as the Winthrop-University Hospital Infectious Disease Division's weighted point score system. Most atypical pathogens are difficult or dangerous to isolate and a definitive laboratory diagnosis is usually based on indirect, i.e., direct flourescent antibody (DFA), indirect flourescent antibody (IFA). Atypical CAP is virtually always monomicrobial; increased IFA IgG tests indicate past exposure and not concurrent infection. Anti-Legionella antibiotics include macrolides, doxycycline, rifampin, quinolones, and telithromycin. The drugs with the highest level of anti-Legionella activity are quinolones and telithromycin. Therapy is usually continued for 2 weeks if potent anti-Legionella drugs are

A study of T2-weighted MR image texture features and diffusion-weighted MR image features for computer-aided diagnosis of prostate cancer

NASA Astrophysics Data System (ADS)

Peng, Yahui; Jiang, Yulei; Antic, Tatjana; Giger, Maryellen L.; Eggener, Scott; Oto, Aytekin

2013-02-01

The purpose of this study was to study T2-weighted magnetic resonance (MR) image texture features and diffusionweighted (DW) MR image features in distinguishing prostate cancer (PCa) from normal tissue. We collected two image datasets: 23 PCa patients (25 PCa and 23 normal tissue regions of interest [ROIs]) imaged with Philips MR scanners, and 30 PCa patients (41 PCa and 26 normal tissue ROIs) imaged with GE MR scanners. A radiologist drew ROIs manually via consensus histology-MR correlation conference with a pathologist. A number of T2-weighted texture features and apparent diffusion coefficient (ADC) features were investigated, and linear discriminant analysis (LDA) was used to combine select strong image features. Area under the receiver operating characteristic (ROC) curve (AUC) was used to characterize feature effectiveness in distinguishing PCa from normal tissue ROIs. Of the features studied, ADC 10th percentile, ADC average, and T2-weighted sum average yielded AUC values (+/-standard error) of 0.95+/-0.03, 0.94+/-0.03, and 0.85+/-0.05 on the Phillips images, and 0.91+/-0.04, 0.89+/-0.04, and 0.70+/-0.06 on the GE images, respectively. The three-feature combination yielded AUC values of 0.94+/-0.03 and 0.89+/-0.04 on the Phillips and GE images, respectively. ADC 10th percentile, ADC average, and T2-weighted sum average, are effective in distinguishing PCa from normal tissue, and appear robust in images acquired from Phillips and GE MR scanners.

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

Adapting Local Features for Face Detection in Thermal Image.

PubMed

Ma, Chao; Trung, Ngo Thanh; Uchiyama, Hideaki; Nagahara, Hajime; Shimada, Atsushi; Taniguchi, Rin-Ichiro

2017-11-27

A thermal camera captures the temperature distribution of a scene as a thermal image. In thermal images, facial appearances of different people under different lighting conditions are similar. This is because facial temperature distribution is generally constant and not affected by lighting condition. This similarity in face appearances is advantageous for face detection. To detect faces in thermal images, cascade classifiers with Haar-like features are generally used. However, there are few studies exploring the local features for face detection in thermal images. In this paper, we introduce two approaches relying on local features for face detection in thermal images. First, we create new feature types by extending Multi-Block LBP. We consider a margin around the reference and the generally constant distribution of facial temperature. In this way, we make the features more robust to image noise and more effective for face detection in thermal images. Second, we propose an AdaBoost-based training method to get cascade classifiers with multiple types of local features. These feature types have different advantages. In this way we enhance the description power of local features. We did a hold-out validation experiment and a field experiment. In the hold-out validation experiment, we captured a dataset from 20 participants, comprising 14 males and 6 females. For each participant, we captured 420 images with 10 variations in camera distance, 21 poses, and 2 appearances (participant with/without glasses). We compared the performance of cascade classifiers trained by different sets of the features. The experiment results showed that the proposed approaches effectively improve the performance of face detection in thermal images. In the field experiment, we compared the face detection performance in realistic scenes using thermal and RGB images, and gave discussion based on the results.

The target sign in colorectal liver metastases: an atypical Gd-EOB-DTPA "uptake" on the hepatobiliary phase of MR imaging.

PubMed

Granata, Vincenza; Catalano, Orlando; Fusco, Roberta; Tatangelo, Fabiana; Rega, Daniela; Nasti, Guglielmo; Avallone, Antonio; Piccirillo, Mauro; Izzo, Francesco; Petrillo, Antonella

2015-10-01

To describe the MRI findings in colorectal cancer liver metastases using gadoxetic acid (Gd-EOB-DTPA), with special emphasis on the target feature seen on the hepatobiliary phase. The medical records of 45 colorectal cancer patients with an overall number of 150 liver metastases were reviewed. All patients underwent Gd-EOB-DTPA-enhanced MRI before any kind of treatment. We retrospectively evaluated, for each lesion, the signal intensity on the T1-weighted, T2-weighted, and diffusion-weighted images. Additionally, the enhancement pattern during the arterial-, portal-, equilibrium-, and hepatobiliary-phase was assessed. Fourteen lesions had a pathological correlation. Lesions size was 5-40 mm (mean 15 mm). All metastases were hypointense on T1-w imaging. Ninety-nine lesions (66%) had a central area of very high signal intensity on T2-w imaging. Fifty-one metastases (34%) were hyperintense on the T2-w images. In DWI, all lesions had a restricted diffusion. The mean ADC value was 1.31 × 10(-3) mm(2)/s (range 1.10-1.45 × 10(-3) mm(2)/s). During the arterial-phase imaging, 61 lesions (41%) showed a rim enhancement, while 89 lesions (59%) appeared as hypointense. All lesions had low signal intensity in the portal and equilibrium phase. Thirty-nine percent of the lesions also showed an enhancing rim on the portal-phase images. During the hepatobiliary phase, 80 lesions (53.3%) were hypointense, while 70 lesions (46.7%) had a target appearance. A number of metastases show an atypical contrast medium uptake during the hepatobiliary phase of gadoxetic acid-enhanced MRI, consisting in a target appearance.

Magnetic resonance imaging spectroscopy in pediatric atypical teratoid rhabdoid tumors of the brain.

PubMed

Bruggers, Carol S; Moore, Kevin

2014-08-01

Pediatric central nervous system (CNS) atypical teratoid rhabdoid tumors (ATRT) are highly malignant tumors characterized by SMARCB1 gene abnormalities. Despite chemoradiation responsiveness, most children die of disease. No imaging findings distinguish ATRT from other malignant brain tumors. This study sought to describe magnetic resonance spectroscopy (MRS) of childhood CNS ATRT and identify metabolite patterns for diagnosis and disease status monitoring. Data from 7 children diagnosed with CNS ATRT from 2007 to 2010, whose imaging included MRS, were retrospectively reviewed. Age at diagnosis ranged from 2.5 to 54 months. Tumors were large with calcium and cysts and avid gadolinium enhancement. All were isointense on T1-weighted imaging and mildly hyperintense on T2-weighted imaging. Short-TE MRS showed prominent lactate+lipid and choline, minimal N-acetyl acetate (NAA), and rarely minimal myoinositol and low creatine peaks. Long TE showed prominent choline, minimal NAA, and rarely low lactate peaks. The combination of prominent choline and lactate+lipids peaks, and generally absent NAA and myoinositol peaks by MRS in this panel of ATRT expands existing information and provides a potentially distinct metabolite profile from other malignant pediatric brain tumors, including medulloblastoma. Prospective, comparative quantitative MRS of ATRT with other pediatric CNS tumors is warranted.

Intrinsic feature-based pose measurement for imaging motion compensation

DOEpatents

Baba, Justin S.; Goddard, Jr., James Samuel

2014-08-19

Systems and methods for generating motion corrected tomographic images are provided. A method includes obtaining first images of a region of interest (ROI) to be imaged and associated with a first time, where the first images are associated with different positions and orientations with respect to the ROI. The method also includes defining an active region in the each of the first images and selecting intrinsic features in each of the first images based on the active region. Second, identifying a portion of the intrinsic features temporally and spatially matching intrinsic features in corresponding ones of second images of the ROI associated with a second time prior to the first time and computing three-dimensional (3D) coordinates for the portion of the intrinsic features. Finally, the method includes computing a relative pose for the first images based on the 3D coordinates.

World Wide Web Based Image Search Engine Using Text and Image Content Features

NASA Astrophysics Data System (ADS)

Luo, Bo; Wang, Xiaogang; Tang, Xiaoou

2003-01-01

Using both text and image content features, a hybrid image retrieval system for Word Wide Web is developed in this paper. We first use a text-based image meta-search engine to retrieve images from the Web based on the text information on the image host pages to provide an initial image set. Because of the high-speed and low cost nature of the text-based approach, we can easily retrieve a broad coverage of images with a high recall rate and a relatively low precision. An image content based ordering is then performed on the initial image set. All the images are clustered into different folders based on the image content features. In addition, the images can be re-ranked by the content features according to the user feedback. Such a design makes it truly practical to use both text and image content for image retrieval over the Internet. Experimental results confirm the efficiency of the system.

The analysis of image feature robustness using cometcloud

PubMed Central

Qi, Xin; Kim, Hyunjoo; Xing, Fuyong; Parashar, Manish; Foran, David J.; Yang, Lin

2012-01-01

The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval. PMID:23248759

Detecting Image Splicing Using Merged Features in Chroma Space

PubMed Central

Liu, Guangjie; Dai, Yuewei

2014-01-01

Image splicing is an image editing method to copy a part of an image and paste it onto another image, and it is commonly followed by postprocessing such as local/global blurring, compression, and resizing. To detect this kind of forgery, the image rich models, a feature set successfully used in the steganalysis is evaluated on the splicing image dataset at first, and the dominant submodel is selected as the first kind of feature. The selected feature and the DCT Markov features are used together to detect splicing forgery in the chroma channel, which is convinced effective in splicing detection. The experimental results indicate that the proposed method can detect splicing forgeries with lower error rate compared to the previous literature. PMID:24574877

Detecting image splicing using merged features in chroma space.

PubMed

Xu, Bo; Liu, Guangjie; Dai, Yuewei

2014-01-01

Image splicing is an image editing method to copy a part of an image and paste it onto another image, and it is commonly followed by postprocessing such as local/global blurring, compression, and resizing. To detect this kind of forgery, the image rich models, a feature set successfully used in the steganalysis is evaluated on the splicing image dataset at first, and the dominant submodel is selected as the first kind of feature. The selected feature and the DCT Markov features are used together to detect splicing forgery in the chroma channel, which is convinced effective in splicing detection. The experimental results indicate that the proposed method can detect splicing forgeries with lower error rate compared to the previous literature.

Diagnostic role of (99)Tc(m)-MDP SPECT/CT combined SPECT/MRI Multi modality imaging for early and atypical bone metastases.

PubMed

Chen, Xiao-Liang; Li, Qian; Cao, Lin; Jiang, Shi-Xi

2014-01-01

The bone metastasis appeared early before the bone imaging for most of the above patients. (99)Tc(m)-MDP ((99)Tc(m) marked methylene diphosphonate) bone imaging could diagnosis the bone metastasis with highly sensitivity, but with lower specificity. The aim of this study is to explore the diagnostic value of (99)Tc(m)-MDP SPECT/CT combined SPECT/MRI Multi modality imaging for the early period atypical bone metastases. 15 to 30 mCi (99)Tc(m)-MDP was intravenously injected to the 34 malignant patients diagnosed as doubtful early bone metastases. SPECT, CT and SPECT/CT images were captured and analyzed consequently. For the patients diagnosed as early period atypical bone metastases by SPECT/CT, combining the SPECT/CT and MRI together as the SPECT/MRI integrated image. The obtained SPECT/MRI image was analyzed and compared with the pathogenic results of patients. The results indicated that 34 early period doubtful metastatic focus, including 34 SPECT positive focus, 17 focus without special changes by using CT method, 11 bone metastases focus by using SPECT/CT method, 23 doubtful bone metastases focus, 8 doubtful bone metastases focus, 14 doubtful bone metastases focus and 2 focus without clear image. Totally, SPECT/CT combined with SPECT/MRI method diagnosed 30 bone metastatic focus and 4 doubtfully metastatic focus. In conclusion, (99)Tc(m)-MDP SPECT/CT combined SPECT/MRI Multi modality imaging shows a higher diagnostic value for the early period bone metastases, which also enhances the diagnostic accuracy rate.

Caribbean parkinsonism and other atypical parkinsonian disorders.

PubMed

Tolosa, Eduardo; Calandrella, Daniela; Gallardo, Marisol

2004-05-01

Atypical parkinsonism (AP) is a term applied to disorders characterized by parkinsonism that evolves rapidly, with poor or transient response to levodopa, or has other associated features such as early falls and postural instability, early autonomic failure, supranuclear gaze palsy, pyramidal or cerebellar signs, alien hand syndrome or severe ideomotor apraxia. The most common AP are multiple system atrophy, progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). Other APs include Caribbean parkinsonism (CP) and parkinsonism-dementia complex of Guam (PDC). In this review we provide an update in etiology, neuropathology, diagnosis and treatment of atypical parkinsonian disorders associated with protein tau deposit, also known as tauopathies.

Observing Behavior and Atypically Restricted Stimulus Control

ERIC Educational Resources Information Center

Dube, William V.; Dickson, Chata A.; Balsamo, Lyn M.; O'Donnell, Kristin Lombard; Tomanari, Gerson Y.; Farren, Kevin M.; Wheeler, Emily E.; McIlvane, William J.

2010-01-01

Restricted stimulus control refers to discrimination learning with atypical limitations in the range of controlling stimuli or stimulus features. In the study reported here, 4 normally capable individuals and 10 individuals with intellectual disabilities (ID) performed two-sample delayed matching to sample. Sample-stimulus observing was recorded…

Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection.

PubMed

Li, Baopu; Meng, Max Q-H

2012-05-01

Tumor in digestive tract is a common disease and wireless capsule endoscopy (WCE) is a relatively new technology to examine diseases for digestive tract especially for small intestine. This paper addresses the problem of automatic recognition of tumor for WCE images. Candidate color texture feature that integrates uniform local binary pattern and wavelet is proposed to characterize WCE images. The proposed features are invariant to illumination change and describe multiresolution characteristics of WCE images. Two feature selection approaches based on support vector machine, sequential forward floating selection and recursive feature elimination, are further employed to refine the proposed features for improving the detection accuracy. Extensive experiments validate that the proposed computer-aided diagnosis system achieves a promising tumor recognition accuracy of 92.4% in WCE images on our collected data.

Morphological Feature Extraction for Automatic Registration of Multispectral Images

NASA Technical Reports Server (NTRS)

Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

2007-01-01

The task of image registration can be divided into two major components, i.e., the extraction of control points or features from images, and the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual extraction of control features can be subjective and extremely time consuming, and often results in few usable points. On the other hand, automated feature extraction allows using invariant target features such as edges, corners, and line intersections as relevant landmarks for registration purposes. In this paper, we present an extension of a recently developed morphological approach for automatic extraction of landmark chips and corresponding windows in a fully unsupervised manner for the registration of multispectral images. Once a set of chip-window pairs is obtained, a (hierarchical) robust feature matching procedure, based on a multiresolution overcomplete wavelet decomposition scheme, is used for registration purposes. The proposed method is validated on a pair of remotely sensed scenes acquired by the Advanced Land Imager (ALI) multispectral instrument and the Hyperion hyperspectral instrument aboard NASA's Earth Observing-1 satellite.

Atypical/Nor98 Scrapie Infectivity in Sheep Peripheral Tissues

PubMed Central

Andréoletti, Olivier; Orge, Leonor; Benestad, Sylvie L.; Beringue, Vincent; Litaise, Claire; Simon, Stéphanie; Le Dur, Annick; Laude, Hubert; Simmons, Hugh; Lugan, Séverine; Corbière, Fabien; Costes, Pierrette; Morel, Nathalie; Schelcher, François; Lacroux, Caroline

2011-01-01

Atypical/Nor98 scrapie was first identified in 1998 in Norway. It is now considered as a worldwide disease of small ruminants and currently represents a significant part of the detected transmissible spongiform encephalopathies (TSE) cases in Europe. Atypical/Nor98 scrapie cases were reported in ARR/ARR sheep, which are highly resistant to BSE and other small ruminants TSE agents. The biology and pathogenesis of the Atypical/Nor98 scrapie agent in its natural host is still poorly understood. However, based on the absence of detectable abnormal PrP in peripheral tissues of affected individuals, human and animal exposure risk to this specific TSE agent has been considered low. In this study we demonstrate that infectivity can accumulate, even if no abnormal PrP is detectable, in lymphoid tissues, nerves, and muscles from natural and/or experimental Atypical/Nor98 scrapie cases. Evidence is provided that, in comparison to other TSE agents, samples containing Atypical/Nor98 scrapie infectivity could remain PrPSc negative. This feature will impact detection of Atypical/Nor98 scrapie cases in the field, and highlights the need to review current evaluations of the disease prevalence and potential transmissibility. Finally, an estimate is made of the infectivity loads accumulating in peripheral tissues in both Atypical/Nor98 and classical scrapie cases that currently enter the food chain. The results obtained indicate that dietary exposure risk to small ruminants TSE agents may be higher than commonly believed. PMID:21347349

Invariant Feature Matching for Image Registration Application Based on New Dissimilarity of Spatial Features

PubMed Central

Mousavi Kahaki, Seyed Mostafa; Nordin, Md Jan; Ashtari, Amir H.; J. Zahra, Sophia

2016-01-01

An invariant feature matching method is proposed as a spatially invariant feature matching approach. Deformation effects, such as affine and homography, change the local information within the image and can result in ambiguous local information pertaining to image points. New method based on dissimilarity values, which measures the dissimilarity of the features through the path based on Eigenvector properties, is proposed. Evidence shows that existing matching techniques using similarity metrics—such as normalized cross-correlation, squared sum of intensity differences and correlation coefficient—are insufficient for achieving adequate results under different image deformations. Thus, new descriptor’s similarity metrics based on normalized Eigenvector correlation and signal directional differences, which are robust under local variation of the image information, are proposed to establish an efficient feature matching technique. The method proposed in this study measures the dissimilarity in the signal frequency along the path between two features. Moreover, these dissimilarity values are accumulated in a 2D dissimilarity space, allowing accurate corresponding features to be extracted based on the cumulative space using a voting strategy. This method can be used in image registration applications, as it overcomes the limitations of the existing approaches. The output results demonstrate that the proposed technique outperforms the other methods when evaluated using a standard dataset, in terms of precision-recall and corner correspondence. PMID:26985996

On the appropriate feature for general SAR image registration

NASA Astrophysics Data System (ADS)

Li, Dong; Zhang, Yunhua

2012-09-01

An investigation to the appropriate feature for SAR image registration is conducted. The commonly-used features such as tie points, Harris corner, the scale invariant feature transform (SIFT), and the speeded up robust feature (SURF) are comprehensively evaluated in terms of several criteria such as the geometrical invariance of feature, the extraction speed, the localization accuracy, the geometrical invariance of descriptor, the matching speed, the robustness to decorrelation, and the flexibility to image speckling. It is shown that SURF outperforms others. It is particularly indicated that SURF has good flexibility to image speckling because the Fast-Hessian detector of SURF has a potential relation with the refined Lee filter. It is recommended to perform SURF on the oversampled image with unaltered sampling step so as to improve the subpixel registration accuracy and speckle immunity. Thus SURF is more appropriate and competent for general SAR image registration.

[A report of atypical hypomyelinating leukodystrophy with atrophy of the basal ganglia and cerebellum caused by a de novo mutation in tubulin beta 4A (TUBB4A) gene and literature review].

PubMed

Du, Y; Li, C; Guo, J; Guo, P; Li, Z Y; Zhang, W

2017-06-01

Objective: To explore the clinical symptoms and neuroimaging features of a patient with atypical hypomyelinating leukodystrophy with atrophy of the basal ganglia and cerebellum (H-ABC) caused by a novel TUBB4A mutation. Methods: We analyzed the clinical data, imaging features and the result of genetic testing of a case diagnosed as atypical H-ABC. Results: The initial symptoms were progressive spasticity, mild cerebellar ataxia and mild cognitive impairment. MRI showed regional blurring of slight high signal on T(2)-weight and FLAIR image in white matter of the bilateral midbrain ventral, internal capsule, posteior horn of lateral ventricle and centrum semiovale, with normal bilateral cerebellar and caudoputamen nucleus. Compared with normal subjects of the same age and gender, hypometabolism was found by (18)F-FDG-PET in brainstem, cerebellar and caudoputamen nucleus in the patient. Genetic testing revealed a de novo pathogenic exome missense heterozygous mutations c. 70G>A in TUBB4A, which was not reported in the human gene mutation database (HGMDpro) and was assessed to be a pathogenic mutation by pathogenic mutation prediction software. Conclusions: The diversity of TUBB4A gene mutations may cause different functional and/or structural impairment in subcortical white matter, cerebellar and caudoputamen nucleus, leading to atypical symptoms and neuroimaging features. Genetic testing for pathogenic mutation in TUBB 4A gene is a key for the diagnosis of H - ABC .

IgLON5-Associated Encephalitis With Atypical Brain Magnetic Resonance Imaging and Cerebrospinal Fluid Changes.

PubMed

Montagna, Massimiliano; Amir, Rizvana; De Volder, Ilse; Lammens, Martin; Huyskens, Jef; Willekens, Barbara

2018-01-01

IgLON5-associated encephalitis is a syndrome with different clinical presentations consisting of sleep dysfunction, bulbar dysfunction, chorea, and progressive supranuclear palsy-like symptoms whereas dysautonomy and cognitive decline usually appear in later stages of the disease. We report a case of a patient with IgLON5-associated encephalitis presenting with rapidly progressive cognitive decline and atypical inflammatory lesions on brain magnetic resonance imaging, oligoclonal bands on cerebrospinal fluid, anti-IgLON5 antibodies exclusively of the IgG1 class, and a fierce inflammatory reaction on brain biopsy, who responded favorably to immunotherapy.

Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction

NASA Astrophysics Data System (ADS)

Attallah, Bilal; Serir, Amina; Chahir, Youssef; Boudjelal, Abdelwahhab

2017-11-01

Palmprint recognition systems are dependent on feature extraction. A method of feature extraction using higher discrimination information was developed to characterize palmprint images. In this method, two individual feature extraction techniques are applied to a discrete wavelet transform of a palmprint image, and their outputs are fused. The two techniques used in the fusion are the histogram of gradient and the binarized statistical image features. They are then evaluated using an extreme learning machine classifier before selecting a feature based on principal component analysis. Three palmprint databases, the Hong Kong Polytechnic University (PolyU) Multispectral Palmprint Database, Hong Kong PolyU Palmprint Database II, and the Delhi Touchless (IIDT) Palmprint Database, are used in this study. The study shows that our method effectively identifies and verifies palmprints and outperforms other methods based on feature extraction.

Image search engine with selective filtering and feature-element-based classification

NASA Astrophysics Data System (ADS)

Li, Qing; Zhang, Yujin; Dai, Shengyang

2001-12-01

With the growth of Internet and storage capability in recent years, image has become a widespread information format in World Wide Web. However, it has become increasingly harder to search for images of interest, and effective image search engine for the WWW needs to be developed. We propose in this paper a selective filtering process and a novel approach for image classification based on feature element in the image search engine we developed for the WWW. First a selective filtering process is embedded in a general web crawler to filter out the meaningless images with GIF format. Two parameters that can be obtained easily are used in the filtering process. Our classification approach first extract feature elements from images instead of feature vectors. Compared with feature vectors, feature elements can better capture visual meanings of the image according to subjective perception of human beings. Different from traditional image classification method, our classification approach based on feature element doesn't calculate the distance between two vectors in the feature space, while trying to find associations between feature element and class attribute of the image. Experiments are presented to show the efficiency of the proposed approach.

Content-based cell pathology image retrieval by combining different features

NASA Astrophysics Data System (ADS)

Zhou, Guangquan; Jiang, Lu; Luo, Limin; Bao, Xudong; Shu, Huazhong

2004-04-01

Content Based Color Cell Pathology Image Retrieval is one of the newest computer image processing applications in medicine. Recently, some algorithms have been developed to achieve this goal. Because of the particularity of cell pathology images, the result of the image retrieval based on single characteristic is not satisfactory. A new method for pathology image retrieval by combining color, texture and morphologic features to search cell images is proposed. Firstly, nucleus regions of leukocytes in images are automatically segmented by K-mean clustering method. Then single leukocyte region is detected by utilizing thresholding algorithm segmentation and mathematics morphology. The features that include color, texture and morphologic features are extracted from single leukocyte to represent main attribute in the search query. The features are then normalized because the numerical value range and physical meaning of extracted features are different. Finally, the relevance feedback system is introduced. So that the system can automatically adjust the weights of different features and improve the results of retrieval system according to the feedback information. Retrieval results using the proposed method fit closely with human perception and are better than those obtained with the methods based on single feature.

Atypical pneumonia

MedlinePlus

Walking pneumonia; Community-acquired pneumonia - atypical ... Bacteria that cause atypical pneumonia include: Mycoplasma pneumonia is caused by the bacteria Mycoplasma pneumoniae . It often affects people younger than age 40. Pneumonia due ...

Learning about the internal structure of categories through classification and feature inference.

PubMed

Jee, Benjamin D; Wiley, Jennifer

2014-01-01

Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a "partial inference" condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.

Shared atypical default mode and salience network functional connectivity between autism and schizophrenia.

PubMed

Chen, Heng; Uddin, Lucina Q; Duan, Xujun; Zheng, Junjie; Long, Zhiliang; Zhang, Youxue; Guo, Xiaonan; Zhang, Yan; Zhao, Jingping; Chen, Huafu

2017-11-01

Schizophrenia and autism spectrum disorder (ASD) are two prevalent neurodevelopmental disorders sharing some similar genetic basis and clinical features. The extent to which they share common neural substrates remains unclear. Resting-state fMRI data were collected from 35 drug-naïve adolescent participants with first-episode schizophrenia (15.6 ± 1.8 years old) and 31 healthy controls (15.4 ± 1.6 years old). Data from 22 participants with ASD (13.1 ± 3.1 years old) and 21 healthy controls (12.9 ± 2.9 years old) were downloaded from the Autism Brain Imaging Data Exchange. Resting-state functional networks were constructed using predefined regions of interest. Multivariate pattern analysis combined with multi-task regression feature selection methods were conducted in two datasets separately. Classification between individuals with disorders and controls was achieved with high accuracy (schizophrenia dataset: accuracy = 83%; ASD dataset: accuracy = 80%). Shared atypical brain connections contributing to classification were mostly present in the default mode network (DMN) and salience network (SN). These functional connections were further related to severity of social deficits in ASD (p = 0.002). Distinct atypical connections were also more related to the DMN and SN, but showed different atypical connectivity patterns between the two disorders. These results suggest some common neural mechanisms contributing to schizophrenia and ASD, and may aid in understanding the pathology of these two neurodevelopmental disorders. Autism Res 2017, 10: 1776-1786. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Autism spectrum disorder (ASD) and schizophrenia are two common neurodevelopmental disorders which share several genetic and behavioral features. The present study identified common neural mechanisms contributing to ASD and schizophrenia using resting-state functional MRI data. The results may help to understand

Cognitive Function and Depression in Symptom Resolution in Schizophrenia Patients Treated with an Atypical Antipsychotic

ERIC Educational Resources Information Center

Stip, Emmanuel; Mancini-Marie, Adham

2004-01-01

Objective: To investigate which cognitive and affective features contribute most to responder/non-responder group separation during a switching trial with atypical antipsychotic. Design: A prospective open trial with an atypical antipsychotic (olanzapine). Patients: One hundred and thirty-four patients meeting diagnostic criteria for…

Model-Based Learning of Local Image Features for Unsupervised Texture Segmentation

NASA Astrophysics Data System (ADS)

Kiechle, Martin; Storath, Martin; Weinmann, Andreas; Kleinsteuber, Martin

2018-04-01

Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this work, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images.

Web Image Retrieval Using Self-Organizing Feature Map.

ERIC Educational Resources Information Center

Wu, Qishi; Iyengar, S. Sitharama; Zhu, Mengxia

2001-01-01

Provides an overview of current image retrieval systems. Describes the architecture of the SOFM (Self Organizing Feature Maps) based image retrieval system, discussing the system architecture and features. Introduces the Kohonen model, and describes the implementation details of SOFM computation and its learning algorithm. Presents a test example…

Spectral-Spatial Scale Invariant Feature Transform for Hyperspectral Images.

PubMed

Al-Khafaji, Suhad Lateef; Jun Zhou; Zia, Ali; Liew, Alan Wee-Chung

2018-02-01

Spectral-spatial feature extraction is an important task in hyperspectral image processing. In this paper we propose a novel method to extract distinctive invariant features from hyperspectral images for registration of hyperspectral images with different spectral conditions. Spectral condition means images are captured with different incident lights, viewing angles, or using different hyperspectral cameras. In addition, spectral condition includes images of objects with the same shape but different materials. This method, which is named spectral-spatial scale invariant feature transform (SS-SIFT), explores both spectral and spatial dimensions simultaneously to extract spectral and geometric transformation invariant features. Similar to the classic SIFT algorithm, SS-SIFT consists of keypoint detection and descriptor construction steps. Keypoints are extracted from spectral-spatial scale space and are detected from extrema after 3D difference of Gaussian is applied to the data cube. Two descriptors are proposed for each keypoint by exploring the distribution of spectral-spatial gradient magnitude in its local 3D neighborhood. The effectiveness of the SS-SIFT approach is validated on images collected in different light conditions, different geometric projections, and using two hyperspectral cameras with different spectral wavelength ranges and resolutions. The experimental results show that our method generates robust invariant features for spectral-spatial image matching.

Skin self-examinations and visual identification of atypical nevi: comparing individual and crowdsourcing approaches.

PubMed

King, Andy J; Gehl, Robert W; Grossman, Douglas; Jensen, Jakob D

2013-12-01

Skin self-examination (SSE) is one method for identifying atypical nevi among members of the general public. Unfortunately, past research has shown that SSE has low sensitivity in detecting atypical nevi. The current study investigates whether crowdsourcing (collective effort) can improve SSE identification accuracy. Collective effort is potentially useful for improving people's visual identification of atypical nevi during SSE because, even when a single person has low reliability at a task, the pattern of the group can overcome the limitations of each individual. Adults (N=500) were recruited from a shopping mall in the Midwest. Participants viewed educational pamphlets about SSE and then completed a mole identification task. For the task, participants were asked to circle mole images that appeared atypical. Forty nevi images were provided; nine of the images were of nevi that were later diagnosed as melanoma. Consistent with past research, individual effort exhibited modest sensitivity (.58) for identifying atypical nevi in the mole identification task. As predicted, collective effort overcame the limitations of individual effort. Specifically, a 19% collective effort identification threshold exhibited superior sensitivity (.90). The results of the current study suggest that limitations of SSE can be countered by collective effort, a finding that supports the pursuit of interventions promoting early melanoma detection that contain crowdsourced visual identification components. Copyright © 2013 Elsevier Ltd. All rights reserved.

Histological Image Feature Mining Reveals Emergent Diagnostic Properties for Renal Cancer

PubMed Central

Kothari, Sonal; Phan, John H.; Young, Andrew N.; Wang, May D.

2016-01-01

Computer-aided histological image classification systems are important for making objective and timely cancer diagnostic decisions. These systems use combinations of image features that quantify a variety of image properties. Because researchers tend to validate their diagnostic systems on specific cancer endpoints, it is difficult to predict which image features will perform well given a new cancer endpoint. In this paper, we define a comprehensive set of common image features (consisting of 12 distinct feature subsets) that quantify a variety of image properties. We use a data-mining approach to determine which feature subsets and image properties emerge as part of an “optimal” diagnostic model when applied to specific cancer endpoints. Our goal is to assess the performance of such comprehensive image feature sets for application to a wide variety of diagnostic problems. We perform this study on 12 endpoints including 6 renal tumor subtype endpoints and 6 renal cancer grade endpoints. Keywords-histology, image mining, computer-aided diagnosis PMID:28163980

Feature Matching of Historical Images Based on Geometry of Quadrilaterals

NASA Astrophysics Data System (ADS)

Maiwald, F.; Schneider, D.; Henze, F.; Münster, S.; Niebling, F.

2018-05-01

This contribution shows an approach to match historical images from the photo library of the Saxon State and University Library Dresden (SLUB) in the context of a historical three-dimensional city model of Dresden. In comparison to recent images, historical photography provides diverse factors which make an automatical image analysis (feature detection, feature matching and relative orientation of images) difficult. Due to e.g. film grain, dust particles or the digitalization process, historical images are often covered by noise interfering with the image signal needed for a robust feature matching. The presented approach uses quadrilaterals in image space as these are commonly available in man-made structures and façade images (windows, stones, claddings). It is explained how to generally detect quadrilaterals in images. Consequently, the properties of the quadrilaterals as well as the relationship to neighbouring quadrilaterals are used for the description and matching of feature points. The results show that most of the matches are robust and correct but still small in numbers.

Diffusion Tensor Image Registration Using Hybrid Connectivity and Tensor Features

PubMed Central

Wang, Qian; Yap, Pew-Thian; Wu, Guorong; Shen, Dinggang

2014-01-01

Most existing diffusion tensor imaging (DTI) registration methods estimate structural correspondences based on voxelwise matching of tensors. The rich connectivity information that is given by DTI, however, is often neglected. In this article, we propose to integrate complementary information given by connectivity features and tensor features for improved registration accuracy. To utilize connectivity information, we place multiple anchors representing different brain anatomies in the image space, and define the connectivity features for each voxel as the geodesic distances from all anchors to the voxel under consideration. The geodesic distance, which is computed in relation to the tensor field, encapsulates information of brain connectivity. We also extract tensor features for every voxel to reflect the local statistics of tensors in its neighborhood. We then combine both connectivity features and tensor features for registration of tensor images. From the images, landmarks are selected automatically and their correspondences are determined based on their connectivity and tensor feature vectors. The deformation field that deforms one tensor image to the other is iteratively estimated and optimized according to the landmarks and their associated correspondences. Experimental results show that, by using connectivity features and tensor features simultaneously, registration accuracy is increased substantially compared with the cases using either type of features alone. PMID:24293159

[Atypical Guillain-Barre syndrome clustering: is it necessary to reconsider the diagnostic criteria and microbiological protocol?

PubMed

Dominguez-Mayoral, A; Gutierrez, C; Lopez-Dominguez, J M; Eichau, S; Abril, J; Navarro-Mascarell, G; Quesada-Garcia, M A; Ramos, M; Alvarez-Lopez, M; Menendez-De Leon, C; Izquierdo, G

2017-05-01

Guillain-Barre syndrome is classically defined as a symmetrical ascending acute polyradiculoneuropathy, although there are atypical variants that make diagnosis difficult. The medical data of six patients in our hospital area are collected during the first quarter of 2013. Lumbar punctures, imaging, neurophysiological studies, ganglioside antibodies and serologies have been proposed in all cases. We focus on the atypical features as late hyporeflexia, increased frequency of asymmetry and distal paresis and initial fever. From a neurophysiological point of view, all patients presented sensorimotor axonal forms. The most consistent datas in early studies is the F wave's alteration. A Miller Fisher variant associated with faciocervicobraquial paresis and cerebral reversible vasoconstriction syndrome has been detected. A bilateral brachial paresis and lumbar polyradiculopathy in the context of influenza A infection is other interesting case. The saltatory variant with cranial nerve involvement and lower limbs paresis has been demonstrated in one patient. Bands in cerebrospinal fluid are positive in three cases and anti-ganglioside antibodies in one patient. The syndrome of inappropriate secretion of antidiuretic hormone may explain some of the hyponatremias registered. The first line of treatment are inmunoglobulins in all patients. Plasmapheresis exchanges has been used as an additional therapy in four cases. These clusters of six axonal cases with atypical clinical features justifies the need for knowledge of these variants in order to achieve an early treatment. Late hyporeflexia and brachialfaciocervico, saltatory and lumbar forms should be considered in the spectrum of Guillain-Barre syndrome. The etiological study should rule out a lots of pathogens as influenza A.

Robust Feature Matching in Terrestrial Image Sequences

NASA Astrophysics Data System (ADS)

Abbas, A.; Ghuffar, S.

2018-04-01

From the last decade, the feature detection, description and matching techniques are most commonly exploited in various photogrammetric and computer vision applications, which includes: 3D reconstruction of scenes, image stitching for panoramic creation, image classification, or object recognition etc. However, in terrestrial imagery of urban scenes contains various issues, which include duplicate and identical structures (i.e. repeated windows and doors) that cause the problem in feature matching phase and ultimately lead to failure of results specially in case of camera pose and scene structure estimation. In this paper, we will address the issue related to ambiguous feature matching in urban environment due to repeating patterns.

Differences in the structural features of atypical adenomatous hyperplasia and low-grade prostatic adenocarcinoma.

PubMed

Midi, Ahmet; Tecimer, Tülay; Bozkurt, Süheyla; Ozkan, Naziye

2008-04-01

Atypical adenomatous hyperplasia (AAH) is a small glandular proliferation that has histological similarities with Gleason grade 1 and 2 prostatic adenocarcinoma (PACG1,2). There are no distinct histomorphological criteria distinguishing these two lesions from each other and other small glandular proliferations. Because treatment approaches are different for these lesions, it is necessary to determine histological criteria. The aim of this study is to review the histological features of these two lesions and to define new histological criteria distinguishing AAH from PACG1,2. We, therefore, assessed 18 anatomical and structural parameters. We found 11 AAH (22 foci) and 15 PACG1,2 (22 foci) cases in 105 radical prostatectomy specimens. Basal cell-specific antikeratin was applied to these lesions. We assumed that PACG1,2 lesions did have not basal cells and we grouped the lesions as AAH and PACG1,2 based on this assumption. We found differences between AAH and PACG1,2 lesions for some parameters including the number of glands, structures such as the main ductus and basal cells. We found similar properties in the two lesions for the following parameters: localization, multiplicity, diameter of the lesion, focus asymmetry, distance between glands, inflammatory cells in and out of the lesions, secretory cell shape on the luminal side, papillary projection towards the luminal side of gland, the shape of the outer gland, the infiltrative pattern of the gland, glandular pleomorphism, biggest gland diameter and median gland diameter. We determined that concurrent evaluation of histomorphological features was important to differentiate between AAH and PACG1,2.

Skin image retrieval using Gabor wavelet texture feature.

PubMed

Ou, X; Pan, W; Zhang, X; Xiao, P

2016-12-01

Skin imaging plays a key role in many clinical studies. We have used many skin imaging techniques, including the recently developed capacitive contact skin imaging based on fingerprint sensors. The aim of this study was to develop an effective skin image retrieval technique using Gabor wavelet transform, which can be used on different types of skin images, but with a special focus on skin capacitive contact images. Content-based image retrieval (CBIR) is a useful technology to retrieve stored images from database by supplying query images. In a typical CBIR, images are retrieved based on colour, shape, texture, etc. In this study, texture feature is used for retrieving skin images, and Gabor wavelet transform is used for texture feature description and extraction. The results show that the Gabor wavelet texture features can work efficiently on different types of skin images. Although Gabor wavelet transform is slower compared with other image retrieval techniques, such as principal component analysis (PCA) and grey-level co-occurrence matrix (GLCM), Gabor wavelet transform is the best for retrieving skin capacitive contact images and facial images with different orientations. Gabor wavelet transform can also work well on facial images with different expressions and skin cancer/disease images. We have developed an effective skin image retrieval method based on Gabor wavelet transform, that it is useful for retrieving different types of images, namely digital colour face images, digital colour skin cancer and skin disease images, and particularly greyscale skin capacitive contact images. Gabor wavelet transform can also be potentially useful for face recognition (with different orientation and expressions) and skin cancer/disease diagnosis. © 2016 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

Uniform competency-based local feature extraction for remote sensing images

NASA Astrophysics Data System (ADS)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

[Clinical features of four atypical pediatric cases of endemic typhus with pneumonia].

PubMed

Liu, Jin-rong; Xu, Bao-ping; Li, Shao-gang; Liu, Jun; Tian, Bao-lin; Zhao, Shun-ying

2013-10-01

To analyze clinical manifestations, treatment and prognosis of 4 cases with endemic typhus. The clinical data of four endemic typhus patients in prognosis were retrospectively analyzed. These four atypical cases of endemic typhus with pneumonia were treated in our department from October 2011 to March 2012. They were all male, with an age range of 15 months to 7 years. The four patients had long history, mild respiratory symptom and no improvement was found after treatment with cephalosporins. There were no evidences of bacterial, viral, or fungal infections and we thought they might have infection with other pathogen. Three were from rural areas. Routine blood tests, Weil-Felix reaction, blood smear (Giemsa staining) , and indirect immunofluorescence assay were performed. Blood smear and IFA tests showed evidences for endemic typhus. The clinical presentations were atypical, the patients had no headache, but all had fever, rash, and pneumonia of varying severity. None of the patients had a severe cough, but bronchial casts were observed in one case. Recurrent fever was reported in three cases. Physical examinations showed no eschars, but one patient had a subconjunctival hemorrhage, and one had skin scratches, cervical lymphadenopathy, pleural effusion, pericardial effusion, and cardiac dilatation. Two patients had remarkably increased peripheral blood leukocyte counts; both these patients also had high alanine aminotransferase (ALT) levels and one had a high C-reactive protein (CRP) level. Weil-Felix testing was negative or the OX19 titer was low. The peripheral blood smear (Giemsa stain) showed intracellular pathogens in all four cases. After combined therapy with doxycycline and macrolide antibiotics, all four patients recovered well. The endemic typhus children often come from rural areas. The clinical presentations were atypical, they usually have no headache, but have fever (often Periodic fever) , rash, and pneumonia of varying severity in these four cases

Comparison of Endoscopic and Histological Findings between Typical and Atypical Celiac Disease in Children.

PubMed

Semwal, Pooja; Gupta, Raj Kumar; Sharma, Rahul; Garg, Kapil

2018-04-01

Celiac disease is a common non-communicable disease with varied presentations. Purpose of this study was to find the duodeno-endoscopic features in celiac disease and to compare duodeno-endoscopic and histological findings between typical and atypical celiac disease in children. Hospital based observational study was conducted at Sir Padampat Mother and Child Health Institute, Jaipur from June 2015 to May 2016. Patients were selected and divided in two groups- typical and atypical celiac disease based upon the presenting symptoms. Upper gastrointestinal endoscopy and duodenal biopsy was performed for serology positive patients. Results were analysed using appropriate statistical test of significance. Out of 101 enrolled patients, 47.5% were male. Age ranged from 1 to 18 years. Study showed that 54.5% were typical and 45.5% were atypical. Patients presenting with atypical symptoms were predominantly of older age group. On endoscopy, scalloping, mosaic pattern, reduced fold height and absent fold height; and in histology, advanced Marsh stage were significantly higher in the typical group. Awareness of atypical presentations as well as duodeno-endoscopic features may have considerable practical importance for the diagnosis of celiac disease in children. Scalloping, mosaic pattern, reduced fold height and nodularity are main endoscopic markers of celiac disease in children. Endoscopic markers of duodenal mucosa may be important in early diagnosis of celiac disease, in children subjected to endoscopy for atypical presentations or indication other than suspected celiac disease.

Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

PubMed Central

Wu, Guorong; Kim, Minjeong; Wang, Qian; Munsell, Brent C.

2015-01-01

Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data,, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked auto-encoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework image registration experiments were conducted on 7.0-tesla brain MR images. In all experiments, the results showed the new image registration framework consistently demonstrated more accurate registration results when compared to state-of-the-art. PMID:26552069

Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning.

PubMed

Wu, Guorong; Kim, Minjeong; Wang, Qian; Munsell, Brent C; Shen, Dinggang

2016-07-01

Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked autoencoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework, image registration experiments were conducted on 7.0-T brain MR images. In all experiments, the results showed that the new image registration framework consistently demonstrated more accurate registration results when compared to state of the art.

Combined Papillated Bowen Disease and Clear Cell Atypical Fibroxanthoma

PubMed Central

Suárez-Vilela, Dimas; Izquierdo-García, Francisco; Domínguez-Iglesias, Francisco; Méndez-Álvarez, Jose Ramón

2010-01-01

We describe a case of papillated Bowen disease (PBD), associated with a clear cell atypical fibroxanthoma (CCAFXA). The epidermal lesion showed a bowenoid papillomatous growth pattern with histologic features suggestive of infection by human papilloma virus (HPV). In the dermis a neoplasm made up by spindled or polygonal cells with wide clear cytoplasm and moderate nuclear pleomorphism was found. Immunohistochemical characteristics of these two lesions were clearly different. The atypical cells of the intraepidermal proliferation were positive for AE1-AE3 anticytokeratin antibody, EMA, p16, p53 and p63. The dermal tumor was positive for vimentin, CD10, CD68, CD99, alpha-1-antitrypsin and c-kit. Histological features and immunohistochemical profile of the dermal tumor corresponded to a CCAFXA, a very uncommon neoplasm of which only 10 cases have been reported. In situ hybridization for numerous types of HPVs was negative in both lesions. PMID:21103191

Atypical combinations and scientific impact.

PubMed

Uzzi, Brian; Mukherjee, Satyam; Stringer, Michael; Jones, Ben

2013-10-25

Novelty is an essential feature of creative ideas, yet the building blocks of new ideas are often embodied in existing knowledge. From this perspective, balancing atypical knowledge with conventional knowledge may be critical to the link between innovativeness and impact. Our analysis of 17.9 million papers spanning all scientific fields suggests that science follows a nearly universal pattern: The highest-impact science is primarily grounded in exceptionally conventional combinations of prior work yet simultaneously features an intrusion of unusual combinations. Papers of this type were twice as likely to be highly cited works. Novel combinations of prior work are rare, yet teams are 37.7% more likely than solo authors to insert novel combinations into familiar knowledge domains.

Automatic extraction of planetary image features

NASA Technical Reports Server (NTRS)

LeMoigne-Stewart, Jacqueline J. (Inventor); Troglio, Giulia (Inventor); Benediktsson, Jon A. (Inventor); Serpico, Sebastiano B. (Inventor); Moser, Gabriele (Inventor)

2013-01-01

A method for the extraction of Lunar data and/or planetary features is provided. The feature extraction method can include one or more image processing techniques, including, but not limited to, a watershed segmentation and/or the generalized Hough Transform. According to some embodiments, the feature extraction method can include extracting features, such as, small rocks. According to some embodiments, small rocks can be extracted by applying a watershed segmentation algorithm to the Canny gradient. According to some embodiments, applying a watershed segmentation algorithm to the Canny gradient can allow regions that appear as close contours in the gradient to be segmented.

Late-Onset Hepatic Veno-Occlusive Disease after Allografting: Report of Two Cases with Atypical Clinical Features Successfully Treated with Defibrotide.

PubMed

Castellino, Alessia; Guidi, Stefano; Dellacasa, Chiara Maria; Gozzini, Antonella; Donnini, Irene; Nozzoli, Chiara; Manetta, Sara; Aydin, Semra; Giaccone, Luisa; Festuccia, Moreno; Brunello, Lucia; Maffini, Enrico; Bruno, Benedetto; David, Ezio; Busca, Alessandro

2018-01-01

Hepatic Veno-Occlusive Disease (VOD) is a potentially severe complication of hematopoietic stem cell transplantation (HSCT). Here we report two patients receiving an allogeneic HSCT who developed late onset VOD with atypical clinical features. The two patients presented with only few risk factors, namely, advanced acute leukemia, a myeloablative busulphan-containing regimen and received grafts from an unrelated donor. The first patient did not experience painful hepatomegaly and weight gain and both patients showed only a mild elevation in total serum bilirubin level. Most importantly, the two patients developed clinical signs beyond day 21 post-HSCT. Hepatic transjugular biopsy confirmed the diagnosis of VOD. Intravenous defibrotide was promptly started leading to a marked clinical improvement. Based on our experience, liver biopsy may represent a useful diagnostic tool when the clinical features of VOD are ambiguous. Early therapeutic intervention with defibrotide represents a crucial issue for the successful outcome of patients with VOD.

Late-Onset Hepatic Veno-Occlusive Disease after Allografting: Report of Two Cases with Atypical Clinical Features Successfully Treated with Defibrotide

PubMed Central

Castellino, Alessia; Guidi, Stefano; Dellacasa, Chiara Maria; Gozzini, Antonella; Donnini, Irene; Nozzoli, Chiara; Manetta, Sara; Aydin, Semra; Giaccone, Luisa; Festuccia, Moreno; Brunello, Lucia; Maffini, Enrico; Bruno, Benedetto; David, Ezio; Busca, Alessandro

2018-01-01

Hepatic Veno-Occlusive Disease (VOD) is a potentially severe complication of hematopoietic stem cell transplantation (HSCT). Here we report two patients receiving an allogeneic HSCT who developed late onset VOD with atypical clinical features. The two patients presented with only few risk factors, namely, advanced acute leukemia, a myeloablative busulphan-containing regimen and received grafts from an unrelated donor. The first patient did not experience painful hepatomegaly and weight gain and both patients showed only a mild elevation in total serum bilirubin level. Most importantly, the two patients developed clinical signs beyond day 21 post-HSCT. Hepatic transjugular biopsy confirmed the diagnosis of VOD. Intravenous defibrotide was promptly started leading to a marked clinical improvement. Based on our experience, liver biopsy may represent a useful diagnostic tool when the clinical features of VOD are ambiguous. Early therapeutic intervention with defibrotide represents a crucial issue for the successful outcome of patients with VOD. PMID:29326798

Modulation of A-type potassium channels by a family of calcium sensors.

PubMed

An, W F; Bowlby, M R; Betty, M; Cao, J; Ling, H P; Mendoza, G; Hinson, J W; Mattsson, K I; Strassle, B W; Trimmer, J S; Rhodes, K J

2000-02-03

In the brain and heart, rapidly inactivating (A-type) voltage-gated potassium (Kv) currents operate at subthreshold membrane potentials to control the excitability of neurons and cardiac myocytes. Although pore-forming alpha-subunits of the Kv4, or Shal-related, channel family form A-type currents in heterologous cells, these differ significantly from native A-type currents. Here we describe three Kv channel-interacting proteins (KChIPs) that bind to the cytoplasmic amino termini of Kv4 alpha-subunits. We find that expression of KChIP and Kv4 together reconstitutes several features of native A-type currents by modulating the density, inactivation kinetics and rate of recovery from inactivation of Kv4 channels in heterologous cells. All three KChIPs co-localize and co-immunoprecipitate with brain Kv4 alpha-subunits, and are thus integral components of native Kv4 channel complexes. The KChIPs have four EF-hand-like domains and bind calcium ions. As the activity and density of neuronal A-type currents tightly control responses to excitatory synaptic inputs, these KChIPs may regulate A-type currents, and hence neuronal excitability, in response to changes in intracellular calcium.

Atypical Manifestations of Hyperthyroidism

PubMed Central

Boxall, E. A.; Lauener, R. W.; McIntosh, H. W.

1964-01-01

Patients with hyperthyroidism usually present with symptoms of hypermetabolism with or without goitre and/or eye signs. Occasionally, however, the chief complaints are not immediately suggestive of hyperthyroidism. Patients with hyperthyroidism are described who presented with such atypical manifestations as periodic muscular paralysis, myasthenia, myopathy, encephalopathy, psychosis, angina pectoris, atrial fibrillation, heart failure without underlying heart disease, skeletal demineralization, pretibial myxedema, unilateral eye signs, and pitting edema of the ankles. ImagesFig. 2Fig. 3Fig. 5Fig. 7Fig. 8Fig. 9Fig. 10 PMID:14178405

Automated feature extraction and classification from image sources

USGS Publications Warehouse

,

1995-01-01

The U.S. Department of the Interior, U.S. Geological Survey (USGS), and Unisys Corporation have completed a cooperative research and development agreement (CRADA) to explore automated feature extraction and classification from image sources. The CRADA helped the USGS define the spectral and spatial resolution characteristics of airborne and satellite imaging sensors necessary to meet base cartographic and land use and land cover feature classification requirements and help develop future automated geographic and cartographic data production capabilities. The USGS is seeking a new commercial partner to continue automated feature extraction and classification research and development.

Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning

PubMed Central

Vu, Tiep Huu; Mousavi, Hojjat Seyed; Monga, Vishal; Rao, Ganesh; Rao, UK Arvind

2016-01-01

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available. PMID:26513781

An adaptive multi-feature segmentation model for infrared image

NASA Astrophysics Data System (ADS)

Zhang, Tingting; Han, Jin; Zhang, Yi; Bai, Lianfa

2016-04-01

Active contour models (ACM) have been extensively applied to image segmentation, conventional region-based active contour models only utilize global or local single feature information to minimize the energy functional to drive the contour evolution. Considering the limitations of original ACMs, an adaptive multi-feature segmentation model is proposed to handle infrared images with blurred boundaries and low contrast. In the proposed model, several essential local statistic features are introduced to construct a multi-feature signed pressure function (MFSPF). In addition, we draw upon the adaptive weight coefficient to modify the level set formulation, which is formed by integrating MFSPF with local statistic features and signed pressure function with global information. Experimental results demonstrate that the proposed method can make up for the inadequacy of the original method and get desirable results in segmenting infrared images.

Face processing in Williams syndrome is already atypical in infancy.

PubMed

D'Souza, Dean; Cole, Victoria; Farran, Emily K; Brown, Janice H; Humphreys, Kate; Howard, John; Rodic, Maja; Dekker, Tessa M; D'Souza, Hana; Karmiloff-Smith, Annette

2015-01-01

Face processing is a crucial socio-cognitive ability. Is it acquired progressively or does it constitute an innately-specified, face-processing module? The latter would be supported if some individuals with seriously impaired intelligence nonetheless showed intact face-processing abilities. Some theorists claim that Williams syndrome (WS) provides such evidence since, despite IQs in the 50s, adolescents/adults with WS score in the normal range on standardized face-processing tests. Others argue that atypical neural and cognitive processes underlie WS face-processing proficiencies. But what about infants with WS? Do they start with typical face-processing abilities, with atypicality developing later, or are atypicalities already evident in infancy? We used an infant familiarization/novelty design and compared infants with WS to typically developing controls as well as to a group of infants with Down syndrome matched on both mental and chronological age. Participants were familiarized with a schematic face, after which they saw a novel face in which either the features (eye shape) were changed or just the configuration of the original features. Configural changes were processed successfully by controls, but not by infants with WS who were only sensitive to featural changes and who showed syndrome-specific profiles different from infants with the other neurodevelopmental disorder. Our findings indicate that theorists can no longer use the case of WS to support claims that evolution has endowed the human brain with an independent face-processing module.

Generating description with multi-feature fusion and saliency maps of image

NASA Astrophysics Data System (ADS)

Liu, Lisha; Ding, Yuxuan; Tian, Chunna; Yuan, Bo

2018-04-01

Generating description for an image can be regard as visual understanding. It is across artificial intelligence, machine learning, natural language processing and many other areas. In this paper, we present a model that generates description for images based on RNN (recurrent neural network) with object attention and multi-feature of images. The deep recurrent neural networks have excellent performance in machine translation, so we use it to generate natural sentence description for images. The proposed method uses single CNN (convolution neural network) that is trained on ImageNet to extract image features. But we think it can not adequately contain the content in images, it may only focus on the object area of image. So we add scene information to image feature using CNN which is trained on Places205. Experiments show that model with multi-feature extracted by two CNNs perform better than which with a single feature. In addition, we make saliency weights on images to emphasize the salient objects in images. We evaluate our model on MSCOCO based on public metrics, and the results show that our model performs better than several state-of-the-art methods.

A novel content-based medical image retrieval method based on query topic dependent image features (QTDIF)

NASA Astrophysics Data System (ADS)

Xiong, Wei; Qiu, Bo; Tian, Qi; Mueller, Henning; Xu, Changsheng

2005-04-01

Medical image retrieval is still mainly a research domain with a large variety of applications and techniques. With the ImageCLEF 2004 benchmark, an evaluation framework has been created that includes a database, query topics and ground truth data. Eleven systems (with a total of more than 50 runs) compared their performance in various configurations. The results show that there is not any one feature that performs well on all query tasks. Key to successful retrieval is rather the selection of features and feature weights based on a specific set of input features, thus on the query task. In this paper we propose a novel method based on query topic dependent image features (QTDIF) for content-based medical image retrieval. These feature sets are designed to capture both inter-category and intra-category statistical variations to achieve good retrieval performance in terms of recall and precision. We have used Gaussian Mixture Models (GMM) and blob representation to model medical images and construct the proposed novel QTDIF for CBIR. Finally, trained multi-class support vector machines (SVM) are used for image similarity ranking. The proposed methods have been tested over the Casimage database with around 9000 images, for the given 26 image topics, used for imageCLEF 2004. The retrieval performance has been compared with the medGIFT system, which is based on the GNU Image Finding Tool (GIFT). The experimental results show that the proposed QTDIF-based CBIR can provide significantly better performance than systems based general features only.

Relative Pose Estimation Using Image Feature Triplets

NASA Astrophysics Data System (ADS)

Chuang, T. Y.; Rottensteiner, F.; Heipke, C.

2015-03-01

A fully automated reconstruction of the trajectory of image sequences using point correspondences is turning into a routine practice. However, there are cases in which point features are hardly detectable, cannot be localized in a stable distribution, and consequently lead to an insufficient pose estimation. This paper presents a triplet-wise scheme for calibrated relative pose estimation from image point and line triplets, and investigates the effectiveness of the feature integration upon the relative pose estimation. To this end, we employ an existing point matching technique and propose a method for line triplet matching in which the relative poses are resolved during the matching procedure. The line matching method aims at establishing hypotheses about potential minimal line matches that can be used for determining the parameters of relative orientation (pose estimation) of two images with respect to the reference one; then, quantifying the agreement using the estimated orientation parameters. Rather than randomly choosing the line candidates in the matching process, we generate an associated lookup table to guide the selection of potential line matches. In addition, we integrate the homologous point and line triplets into a common adjustment procedure. In order to be able to also work with image sequences the adjustment is formulated in an incremental manner. The proposed scheme is evaluated with both synthetic and real datasets, demonstrating its satisfactory performance and revealing the effectiveness of image feature integration.

An Evaluation of Feature Learning Methods for High Resolution Image Classification

NASA Astrophysics Data System (ADS)

Tokarczyk, P.; Montoya, J.; Schindler, K.

2012-07-01

Automatic image classification is one of the fundamental problems of remote sensing research. The classification problem is even more challenging in high-resolution images of urban areas, where the objects are small and heterogeneous. Two questions arise, namely which features to extract from the raw sensor data to capture the local radiometry and image structure at each pixel or segment, and which classification method to apply to the feature vectors. While classifiers are nowadays well understood, selecting the right features remains a largely empirical process. Here we concentrate on the features. Several methods are evaluated which allow one to learn suitable features from unlabelled image data by analysing the image statistics. In a comparative study, we evaluate unsupervised feature learning with different linear and non-linear learning methods, including principal component analysis (PCA) and deep belief networks (DBN). We also compare these automatically learned features with popular choices of ad-hoc features including raw intensity values, standard combinations like the NDVI, a few PCA channels, and texture filters. The comparison is done in a unified framework using the same images, the target classes, reference data and a Random Forest classifier.

Medical image retrieval system using multiple features from 3D ROIs

NASA Astrophysics Data System (ADS)

Lu, Hongbing; Wang, Weiwei; Liao, Qimei; Zhang, Guopeng; Zhou, Zhiming

2012-02-01

Compared to a retrieval using global image features, features extracted from regions of interest (ROIs) that reflect distribution patterns of abnormalities would benefit more for content-based medical image retrieval (CBMIR) systems. Currently, most CBMIR systems have been designed for 2D ROIs, which cannot reflect 3D anatomical features and region distribution of lesions comprehensively. To further improve the accuracy of image retrieval, we proposed a retrieval method with 3D features including both geometric features such as Shape Index (SI) and Curvedness (CV) and texture features derived from 3D Gray Level Co-occurrence Matrix, which were extracted from 3D ROIs, based on our previous 2D medical images retrieval system. The system was evaluated with 20 volume CT datasets for colon polyp detection. Preliminary experiments indicated that the integration of morphological features with texture features could improve retrieval performance greatly. The retrieval result using features extracted from 3D ROIs accorded better with the diagnosis from optical colonoscopy than that based on features from 2D ROIs. With the test database of images, the average accuracy rate for 3D retrieval method was 76.6%, indicating its potential value in clinical application.

[Psychotic forms of atypical autism in children].

PubMed

Simashkova, N V

2006-01-01

The aim of the study was to determine clinical borders of psychotic forms of atypical autism in children, its psychopathological and age-specific manifestations as well as nosological peculiarities and to specify its pathogenetic features. Eighty patients with childhood endogenous autism, Rett syndrome, fragile X syndrome, Down syndrome have been studied during 14 years. The study showed that psychoses similar by symptoms and course, which are characterized by attacks and regressive-catatonic disorders, may develop in the course of atypical autism. These psychoses develop on the background of dysontogenesis with consequent replacement of the following stages: autistic, regressive, catatonic, with returning to the autistic stage between attacks. Psychopathological similarity of these psychoses in different disorders correlated with EEG changes of the same type (appearance of the marked I-rhythm at the regressive stage of psychosis).

Special feature on imaging systems and techniques

NASA Astrophysics Data System (ADS)

Yang, Wuqiang; Giakos, George

2013-07-01

The IEEE International Conference on Imaging Systems and Techniques (IST'2012) was held in Manchester, UK, on 16-17 July 2012. The participants came from 26 countries or regions: Austria, Brazil, Canada, China, Denmark, France, Germany, Greece, India, Iran, Iraq, Italy, Japan, Korea, Latvia, Malaysia, Norway, Poland, Portugal, Sweden, Switzerland, Taiwan, Tunisia, UAE, UK and USA. The technical program of the conference consisted of a series of scientific and technical sessions, exploring physical principles, engineering and applications of new imaging systems and techniques, as reflected by the diversity of the submitted papers. Following a rigorous review process, a total of 123 papers were accepted, and they were organized into 30 oral presentation sessions and a poster session. In addition, six invited keynotes were arranged. The conference not only provided the participants with a unique opportunity to exchange ideas and disseminate research outcomes but also paved a way to establish global collaboration. Following the IST'2012, a total of 55 papers, which were technically extended substantially from their versions in the conference proceeding, were submitted as regular papers to this special feature of Measurement Science and Technology . Following a rigorous reviewing process, 25 papers have been finally accepted for publication in this special feature and they are organized into three categories: (1) industrial tomography, (2) imaging systems and techniques and (3) image processing. These papers not only present the latest developments in the field of imaging systems and techniques but also offer potential solutions to existing problems. We hope that this special feature provides a good reference for researchers who are active in the field and will serve as a catalyst to trigger further research. It has been our great pleasure to be the guest editors of this special feature. We would like to thank the authors for their contributions, without which it would

Differences in the structural features of atypical adenomatous hyperplasia and low-grade prostatic adenocarcinoma

PubMed Central

Midi, Ahmet; Tecimer, Tülay; Bozkurt, Süheyla; Özkan, Naziye

2008-01-01

Aim Atypical adenomatous hyperplasia (AAH) is a small glandular proliferation that has histological similarities with Gleason grade 1 and 2 prostatic adenocarcinoma (PACG1,2). There are no distinct histomorphological criteria distinguishing these two lesions from each other and other small glandular proliferations. Because treatment approaches are different for these lesions, it is necessary to determine histological criteria. The aim of this study is to review the histological features of these two lesions and to define new histological criteria distinguishing AAH from PACG1,2. We, therefore, assessed 18 anatomical and structural parameters. Materials and Methods We found 11 AAH (22 foci) and 15 PACG1,2 (22 foci) cases in 105 radical prostatectomy specimens. Basal cell-specific antikeratin was applied to these lesions. We assumed that PACG1,2 lesions did have not basal cells and we grouped the lesions as AAH and PACG1,2 based on this assumption. Results We found differences between AAH and PACG1,2 lesions for some parameters including the number of glands, structures such as the main ductus and basal cells. We found similar properties in the two lesions for the following parameters: localization, multiplicity, diameter of the lesion, focus asymmetry, distance between glands, inflammatory cells in and out of the lesions, secretory cell shape on the luminal side, papillary projection towards the luminal side of gland, the shape of the outer gland, the infiltrative pattern of the gland, glandular pleomorphism, biggest gland diameter and median gland diameter. Conclusion We determined that concurrent evaluation of histomorphological features was important to differentiate between AAH and PACG1,2. PMID:19468392

Evaluation of image features and classification methods for Barrett's cancer detection using VLE imaging

NASA Astrophysics Data System (ADS)

Klomp, Sander; van der Sommen, Fons; Swager, Anne-Fré; Zinger, Svitlana; Schoon, Erik J.; Curvers, Wouter L.; Bergman, Jacques J.; de With, Peter H. N.

2017-03-01

Volumetric Laser Endomicroscopy (VLE) is a promising technique for the detection of early neoplasia in Barrett's Esophagus (BE). VLE generates hundreds of high resolution, grayscale, cross-sectional images of the esophagus. However, at present, classifying these images is a time consuming and cumbersome effort performed by an expert using a clinical prediction model. This paper explores the feasibility of using computer vision techniques to accurately predict the presence of dysplastic tissue in VLE BE images. Our contribution is threefold. First, a benchmarking is performed for widely applied machine learning techniques and feature extraction methods. Second, three new features based on the clinical detection model are proposed, having superior classification accuracy and speed, compared to earlier work. Third, we evaluate automated parameter tuning by applying simple grid search and feature selection methods. The results are evaluated on a clinically validated dataset of 30 dysplastic and 30 non-dysplastic VLE images. Optimal classification accuracy is obtained by applying a support vector machine and using our modified Haralick features and optimal image cropping, obtaining an area under the receiver operating characteristic of 0.95 compared to the clinical prediction model at 0.81. Optimal execution time is achieved using a proposed mean and median feature, which is extracted at least factor 2.5 faster than alternative features with comparable performance.

Hdr Imaging for Feature Detection on Detailed Architectural Scenes

NASA Astrophysics Data System (ADS)

Kontogianni, G.; Stathopoulou, E. K.; Georgopoulos, A.; Doulamis, A.

2015-02-01

3D reconstruction relies on accurate detection, extraction, description and matching of image features. This is even truer for complex architectural scenes that pose needs for 3D models of high quality, without any loss of detail in geometry or color. Illumination conditions influence the radiometric quality of images, as standard sensors cannot depict properly a wide range of intensities in the same scene. Indeed, overexposed or underexposed pixels cause irreplaceable information loss and degrade digital representation. Images taken under extreme lighting environments may be thus prohibitive for feature detection/extraction and consequently for matching and 3D reconstruction. High Dynamic Range (HDR) images could be helpful for these operators because they broaden the limits of illumination range that Standard or Low Dynamic Range (SDR/LDR) images can capture and increase in this way the amount of details contained in the image. Experimental results of this study prove this assumption as they examine state of the art feature detectors applied both on standard dynamic range and HDR images.

Automated Image Registration Using Morphological Region of Interest Feature Extraction

NASA Technical Reports Server (NTRS)

Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

2005-01-01

With the recent explosion in the amount of remotely sensed imagery and the corresponding interest in temporal change detection and modeling, image registration has become increasingly important as a necessary first step in the integration of multi-temporal and multi-sensor data for applications such as the analysis of seasonal and annual global climate changes, as well as land use/cover changes. The task of image registration can be divided into two major components: (1) the extraction of control points or features from images; and (2) the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual control feature extraction can be subjective and extremely time consuming, and often results in few usable points. Automated feature extraction is a solution to this problem, where desired target features are invariant, and represent evenly distributed landmarks such as edges, corners and line intersections. In this paper, we develop a novel automated registration approach based on the following steps. First, a mathematical morphology (MM)-based method is used to obtain a scale-orientation morphological profile at each image pixel. Next, a spectral dissimilarity metric such as the spectral information divergence is applied for automated extraction of landmark chips, followed by an initial approximate matching. This initial condition is then refined using a hierarchical robust feature matching (RFM) procedure. Experimental results reveal that the proposed registration technique offers a robust solution in the presence of seasonal changes and other interfering factors. Keywords-Automated image registration, multi-temporal imagery, mathematical morphology, robust feature matching.

Textural features for image classification

NASA Technical Reports Server (NTRS)

Haralick, R. M.; Dinstein, I.; Shanmugam, K.

1973-01-01

Description of some easily computable textural features based on gray-tone spatial dependances, and illustration of their application in category-identification tasks of three different kinds of image data - namely, photomicrographs of five kinds of sandstones, 1:20,000 panchromatic aerial photographs of eight land-use categories, and ERTS multispectral imagery containing several land-use categories. Two kinds of decision rules are used - one for which the decision regions are convex polyhedra (a piecewise-linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89% for the photomicrographs, 82% for the aerial photographic imagery, and 83% for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

Spinal infections: clinical and imaging features.

PubMed

Arbelaez, Andres; Restrepo, Feliza; Castillo, Mauricio

2014-10-01

Spinal infections represent a group of rare conditions affecting vertebral bodies, intervertebral discs, paraspinal soft tissues, epidural space, meninges, and spinal cord. The causal factors, clinical presentations, and imaging features are a challenge because the difficulty to differentiate them from other conditions, such as degenerative and inflammatory disorders and spinal neoplasm. They require early recognition because delay diagnosis, imaging, and intervention may have devastating consequences especially in children and the elderly. This article reviews the most common spinal infections, their pathophysiologic, clinical manifestation, and their imaging findings.

Predictors of underestimation of malignancy after image-guided core needle biopsy diagnosis of flat epithelial atypia or atypical ductal hyperplasia.

PubMed

Yu, Chi-Chang; Ueng, Shir-Hwa; Cheung, Yun-Chung; Shen, Shih-Che; Kuo, Wen-Lin; Tsai, Hsiu-Pei; Lo, Yung-Feng; Chen, Shin-Cheh

2015-01-01

Flat epithelial atypia (FEA) and atypical ductal hyperplasia (ADH) are precursors of breast malignancy. Management of FEA or ADH after image-guided core needle biopsy (CNB) remains controversial. The aim of this study was to evaluate malignancy underestimation rates after FEA or ADH diagnosis using image-guided CNB and to identify clinical characteristics and imaging features associated with malignancy as well as identify cases with low underestimation rates that may be treatable by observation only. We retrospectively reviewed 2,875 consecutive image-guided CNBs recorded in an electronic data base from January 2010 to December 2011 and identified 128 (4.5%) FEA and 83 (2.9%) ADH diagnoses (211 total cases). Of these, 64 (30.3%) were echo-guided CNB procedures and 147 (69.7%) mammography-guided CNBs. Twenty patients (9.5%) were upgraded to malignancy. Multivariate analysis indicated that age (OR = 1.123, p = 0.002, increase of 1 year), mass-type lesion with calcifications (OR = 8.213, p = 0.006), and ADH in CNB specimens (OR = 8.071, p = 0.003) were independent predictors of underestimation. In univariate analysis of echo-guided CNB (n = 64), mass with calcifications had the highest underestimation rate (p < 0.001). Multivariate analysis of 147 mammography-guided CNBs revealed that age (OR = 1.122, p = 0.040, increase of 1 year) and calcification distribution were significant independent predictors of underestimation. No FEA case in which, complete calcification retrieval was recorded after CNB was upgraded to malignancy. Older age at diagnosis on image-guided CNB was a predictor of malignancy underestimation. Mass with calcifications was more likely to be associated with malignancy, and in cases presenting as calcifications only, segmental distribution or linear shapes were significantly associated with upgrading. Excision after FEA or ADH diagnosis by image-guided CNB is warranted except for FEA diagnosed using mammography-guided CNB with complete calcification

Hyperspectral remote sensing image retrieval system using spectral and texture features.

PubMed

Zhang, Jing; Geng, Wenhao; Liang, Xi; Li, Jiafeng; Zhuo, Li; Zhou, Qianlan

2017-06-01

Although many content-based image retrieval systems have been developed, few studies have focused on hyperspectral remote sensing images. In this paper, a hyperspectral remote sensing image retrieval system based on spectral and texture features is proposed. The main contributions are fourfold: (1) considering the "mixed pixel" in the hyperspectral image, endmembers as spectral features are extracted by an improved automatic pixel purity index algorithm, then the texture features are extracted with the gray level co-occurrence matrix; (2) similarity measurement is designed for the hyperspectral remote sensing image retrieval system, in which the similarity of spectral features is measured with the spectral information divergence and spectral angle match mixed measurement and in which the similarity of textural features is measured with Euclidean distance; (3) considering the limited ability of the human visual system, the retrieval results are returned after synthesizing true color images based on the hyperspectral image characteristics; (4) the retrieval results are optimized by adjusting the feature weights of similarity measurements according to the user's relevance feedback. The experimental results on NASA data sets can show that our system can achieve comparable superior retrieval performance to existing hyperspectral analysis schemes.

Image Mosaic Method Based on SIFT Features of Line Segment

PubMed Central

Zhu, Jun; Ren, Mingwu

2014-01-01

This paper proposes a novel image mosaic method based on SIFT (Scale Invariant Feature Transform) feature of line segment, aiming to resolve incident scaling, rotation, changes in lighting condition, and so on between two images in the panoramic image mosaic process. This method firstly uses Harris corner detection operator to detect key points. Secondly, it constructs directed line segments, describes them with SIFT feature, and matches those directed segments to acquire rough point matching. Finally, Ransac method is used to eliminate wrong pairs in order to accomplish image mosaic. The results from experiment based on four pairs of images show that our method has strong robustness for resolution, lighting, rotation, and scaling. PMID:24511326

SU-E-J-237: Image Feature Based DRR and Portal Image Registration

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

Wang, X; Chang, J

Purpose: Two-dimensional (2D) matching of the kV X-ray and digitally reconstructed radiography (DRR) images is an important setup technique for image-guided radiotherapy (IGRT). In our clinics, mutual information based methods are used for this purpose on commercial linear accelerators, but with often needs for manual corrections. This work proved the feasibility that feature based image transform can be used to register kV and DRR images. Methods: The scale invariant feature transform (SIFT) method was implemented to detect the matching image details (or key points) between the kV and DRR images. These key points represent high image intensity gradients, and thusmore » the scale invariant features. Due to the poor image contrast from our kV image, direct application of the SIFT method yielded many detection errors. To assist the finding of key points, the center coordinates of the kV and DRR images were read from the DICOM header, and the two groups of key points with similar relative positions to their corresponding centers were paired up. Using these points, a rigid transform (with scaling, horizontal and vertical shifts) was estimated. We also artificially introduced vertical and horizontal shifts to test the accuracy of our registration method on anterior-posterior (AP) and lateral pelvic images. Results: The results provided a satisfactory overlay of the transformed kV onto the DRR image. The introduced vs. detected shifts were fit into a linear regression. In the AP image experiments, linear regression analysis showed a slope of 1.15 and 0.98 with an R2 of 0.89 and 0.99 for the horizontal and vertical shifts, respectively. The results are 1.2 and 1.3 with R2 of 0.72 and 0.82 for the lateral image shifts. Conclusion: This work provided an alternative technique for kV to DRR alignment. Further improvements in the estimation accuracy and image contrast tolerance are underway.« less

Feature detection in satellite images using neural network technology

NASA Technical Reports Server (NTRS)

Augusteijn, Marijke F.; Dimalanta, Arturo S.

1992-01-01

A feasibility study of automated classification of satellite images is described. Satellite images were characterized by the textures they contain. In particular, the detection of cloud textures was investigated. The method of second-order gray level statistics, using co-occurrence matrices, was applied to extract feature vectors from image segments. Neural network technology was employed to classify these feature vectors. The cascade-correlation architecture was successfully used as a classifier. The use of a Kohonen network was also investigated but this architecture could not reliably classify the feature vectors due to the complicated structure of the classification problem. The best results were obtained when data from different spectral bands were fused.

Scattering features for lung cancer detection in fibered confocal fluorescence microscopy images.

PubMed

Rakotomamonjy, Alain; Petitjean, Caroline; Salaün, Mathieu; Thiberville, Luc

2014-06-01

To assess the feasibility of lung cancer diagnosis using fibered confocal fluorescence microscopy (FCFM) imaging technique and scattering features for pattern recognition. FCFM imaging technique is a new medical imaging technique for which interest has yet to be established for diagnosis. This paper addresses the problem of lung cancer detection using FCFM images and, as a first contribution, assesses the feasibility of computer-aided diagnosis through these images. Towards this aim, we have built a pattern recognition scheme which involves a feature extraction stage and a classification stage. The second contribution relies on the features used for discrimination. Indeed, we have employed the so-called scattering transform for extracting discriminative features, which are robust to small deformations in the images. We have also compared and combined these features with classical yet powerful features like local binary patterns (LBP) and their variants denoted as local quinary patterns (LQP). We show that scattering features yielded to better recognition performances than classical features like LBP and their LQP variants for the FCFM image classification problems. Another finding is that LBP-based and scattering-based features provide complementary discriminative information and, in some situations, we empirically establish that performance can be improved when jointly using LBP, LQP and scattering features. In this work we analyze the joint capability of FCFM images and scattering features for lung cancer diagnosis. The proposed method achieves a good recognition rate for such a diagnosis problem. It also performs well when used in conjunction with other features for other classical medical imaging classification problems. Copyright © 2014 Elsevier B.V. All rights reserved.

Blind image quality assessment based on aesthetic and statistical quality-aware features

NASA Astrophysics Data System (ADS)

Jenadeleh, Mohsen; Masaeli, Mohammad Masood; Moghaddam, Mohsen Ebrahimi

2017-07-01

The main goal of image quality assessment (IQA) methods is the emulation of human perceptual image quality judgments. Therefore, the correlation between objective scores of these methods with human perceptual scores is considered as their performance metric. Human judgment of the image quality implicitly includes many factors when assessing perceptual image qualities such as aesthetics, semantics, context, and various types of visual distortions. The main idea of this paper is to use a host of features that are commonly employed in image aesthetics assessment in order to improve blind image quality assessment (BIQA) methods accuracy. We propose an approach that enriches the features of BIQA methods by integrating a host of aesthetics image features with the features of natural image statistics derived from multiple domains. The proposed features have been used for augmenting five different state-of-the-art BIQA methods, which use statistical natural scene statistics features. Experiments were performed on seven benchmark image quality databases. The experimental results showed significant improvement of the accuracy of the methods.

Syllabic patterns in typical and atypical phonological development: ultrasonographic analysis.

PubMed

Vassoler, Aline Mara de Oliveira; Berti, Larissa Cristina

2018-01-01

Objective The present study aims to compare the production of syllabic patterns of the CVC and CV types performed by Brazilian children with typical and atypical phonological development through ultrasonography of tongue. Methods Ten children (five with typical and with five atypical phonological development) recorded nine pairs of words from the syllables: CCV and CV. The images and audios were captured simultaneously by the Articulate Assistant Advanced software. The data were submitted to perceptive analysis and ultrasonographic articulatory analysis (the area between the tip and the blade of the tongue). The area measurements were submitted to one-way repeated measures ANOVA. Results ANOVA demonstrated a significant effect for the clinical condition (typical and atypical), (F (1.8) = 172.48, p> 0.000) forthe area measurements. In both syllabic patterns (CCV and CV) the atypical children showed greater values ​​of the area between the tip and the blade of the tongue. Regarding the syllabic patterns analyzed, the statistical test showed no significant effect (F (1.8)=0.19, p>0.658). Conclusion The use of a greater area of ​​the tongue by children with atypical phonological development suggests the non-differentiation of the tip and the anterior body gestures of the tongue in the production of CV and CCV.

Convolutional neural network features based change detection in satellite images

NASA Astrophysics Data System (ADS)

Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

2016-07-01

With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

Investigation of atypical molten pool dynamics in tungsten carbide-cobalt during laser deposition using in-situ thermal imaging

NASA Astrophysics Data System (ADS)

Xiong, Yuhong; Hofmeister, William H.; Smugeresky, John E.; Delplanque, Jean-Pierre; Schoenung, Julie M.

2012-01-01

An atypical "swirling" phenomenon observed during the laser deposition of tungsten carbide-cobalt cermets by laser engineered net shaping (LENS®) was studied using in-situ high-speed thermal imaging. To provide fundamental insight into this phenomenon, the thermal behavior of pure cobalt during LENS was also investigated for comparison. Several factors were considered as the possible source of the observed differences. Of those, phase difference, material emissivity, momentum transfer, and free surface disruption from the powder jets, and, to a lesser extent, Marangoni convection were identified as the relevant mechanisms.

Feature-based registration of historical aerial images by Area Minimization

NASA Astrophysics Data System (ADS)

Nagarajan, Sudhagar; Schenk, Toni

2016-06-01

The registration of historical images plays a significant role in assessing changes in land topography over time. By comparing historical aerial images with recent data, geometric changes that have taken place over the years can be quantified. However, the lack of ground control information and precise camera parameters has limited scientists' ability to reliably incorporate historical images into change detection studies. Other limitations include the methods of determining identical points between recent and historical images, which has proven to be a cumbersome task due to continuous land cover changes. Our research demonstrates a method of registering historical images using Time Invariant Line (TIL) features. TIL features are different representations of the same line features in multi-temporal data without explicit point-to-point or straight line-to-straight line correspondence. We successfully determined the exterior orientation of historical images by minimizing the area formed between corresponding TIL features in recent and historical images. We then tested the feasibility of the approach with synthetic and real data and analyzed the results. Based on our analysis, this method shows promise for long-term 3D change detection studies.

Discriminative feature representation: an effective postprocessing solution to low dose CT imaging

NASA Astrophysics Data System (ADS)

Chen, Yang; Liu, Jin; Hu, Yining; Yang, Jian; Shi, Luyao; Shu, Huazhong; Gui, Zhiguo; Coatrieux, Gouenou; Luo, Limin

2017-03-01

This paper proposes a concise and effective approach termed discriminative feature representation (DFR) for low dose computerized tomography (LDCT) image processing, which is currently a challenging problem in medical imaging field. This DFR method assumes LDCT images as the superposition of desirable high dose CT (HDCT) 3D features and undesirable noise-artifact 3D features (the combined term of noise and artifact features induced by low dose scan protocols), and the decomposed HDCT features are used to provide the processed LDCT images with higher quality. The target HDCT features are solved via the DFR algorithm using a featured dictionary composed by atoms representing HDCT features and noise-artifact features. In this study, the featured dictionary is efficiently built using physical phantom images collected from the same CT scanner as the target clinical LDCT images to process. The proposed DFR method also has good robustness in parameter setting for different CT scanner types. This DFR method can be directly applied to process DICOM formatted LDCT images, and has good applicability to current CT systems. Comparative experiments with abdomen LDCT data validate the good performance of the proposed approach. This research was supported by National Natural Science Foundation under grants (81370040, 81530060), the Fundamental Research Funds for the Central Universities, and the Qing Lan Project in Jiangsu Province.

Magnetic resonance imaging-guided core needle breast biopsies resulting in high-risk histopathologic findings: upstage frequency and lesion characteristics.

PubMed

Weinfurtner, R Jared; Patel, Bhavika; Laronga, Christine; Lee, Marie C; Falcon, Shannon L; Mooney, Blaise P; Yue, Binglin; Drukteinis, Jennifer S

2015-06-01

Analysis of magnetic resonance imaging-guided breast biopsies yielding high-risk histopathologic features at a single institution found an overall upstage rate to malignancy of 14% at surgical excision. All upstaged lesions were associated with atypical ductal hyperplasia. Flat epithelial atypia and atypical lobular hyperplasia alone or with lobular carcinoma in situ were not associated with an upstage to malignancy. The purpose of the present study w as to determine the malignancy upstage rates and imaging features of high-risk histopathologic findings resulting from magnetic resonance imaging (MRI)-guided core needle breast biopsies. These features include atypical ductal hyperplasia (ADH), atypical lobular hyperplasia (ALH), flat epithelial atypia (FEA), and lobular carcinoma in situ (LCIS). A retrospective medical record review was performed on all MRI-guided core needle breast biopsies at a single institution from June 1, 2007 to December 1, 2013 to select biopsies yielding high-risk histopathologic findings. The patient demographics, MRI lesion characteristics, and histopathologic features at biopsy and surgical excision were analyzed. A total of 257 MRI-guided biopsies had been performed, and 50 yielded high-risk histopathologic features (19%). Biopsy site and surgical excision site correlation was confirmed in 29 of 50 cases. Four of 29 lesions (14%) were upstaged: 1 case to invasive ductal carcinoma and 3 cases to ductal carcinoma in situ. ADH alone had an overall upstage rate of 7% (1 of 14), mixed ADH/ALH a rate of 75% (3 of 4), ALH alone or with LCIS a rate of 0% (0 of 7), and FEA a rate of 0% (0 of 4). Only mixed ADH/ALH had a statistically significant upstage rate to malignancy compared with the other high-risk histopathologic subtypes combined. No specific imaging characteristics on MRI were associated with an upstage to malignancy on the statistical analysis. MRI-guided breast biopsies yielding high-risk histopathologic features were associated with

Texture Feature Analysis for Different Resolution Level of Kidney Ultrasound Images

NASA Astrophysics Data System (ADS)

Kairuddin, Wan Nur Hafsha Wan; Mahmud, Wan Mahani Hafizah Wan

2017-08-01

Image feature extraction is a technique to identify the characteristic of the image. The objective of this work is to discover the texture features that best describe a tissue characteristic of a healthy kidney from ultrasound (US) image. Three ultrasound machines that have different specifications are used in order to get a different quality (different resolution) of the image. Initially, the acquired images are pre-processed to de-noise the speckle to ensure the image preserve the pixels in a region of interest (ROI) for further extraction. Gaussian Low- pass Filter is chosen as the filtering method in this work. 150 of enhanced images then are segmented by creating a foreground and background of image where the mask is created to eliminate some unwanted intensity values. Statistical based texture features method is used namely Intensity Histogram (IH), Gray-Level Co-Occurance Matrix (GLCM) and Gray-level run-length matrix (GLRLM).This method is depends on the spatial distribution of intensity values or gray levels in the kidney region. By using One-Way ANOVA in SPSS, the result indicated that three features (Contrast, Difference Variance and Inverse Difference Moment Normalized) from GLCM are not statistically significant; this concludes that these three features describe a healthy kidney characteristics regardless of the ultrasound image quality.

Multispectral image fusion based on fractal features

NASA Astrophysics Data System (ADS)

Tian, Jie; Chen, Jie; Zhang, Chunhua

2004-01-01

Imagery sensors have been one indispensable part of the detection and recognition systems. They are widely used to the field of surveillance, navigation, control and guide, et. However, different imagery sensors depend on diverse imaging mechanisms, and work within diverse range of spectrum. They also perform diverse functions and have diverse circumstance requires. So it is unpractical to accomplish the task of detection or recognition with a single imagery sensor under the conditions of different circumstances, different backgrounds and different targets. Fortunately, the multi-sensor image fusion technique emerged as important route to solve this problem. So image fusion has been one of the main technical routines used to detect and recognize objects from images. While, loss of information is unavoidable during fusion process, so it is always a very important content of image fusion how to preserve the useful information to the utmost. That is to say, it should be taken into account before designing the fusion schemes how to avoid the loss of useful information or how to preserve the features helpful to the detection. In consideration of these issues and the fact that most detection problems are actually to distinguish man-made objects from natural background, a fractal-based multi-spectral fusion algorithm has been proposed in this paper aiming at the recognition of battlefield targets in the complicated backgrounds. According to this algorithm, source images are firstly orthogonally decomposed according to wavelet transform theories, and then fractal-based detection is held to each decomposed image. At this step, natural background and man-made targets are distinguished by use of fractal models that can well imitate natural objects. Special fusion operators are employed during the fusion of area that contains man-made targets so that useful information could be preserved and features of targets could be extruded. The final fused image is reconstructed from the

Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion

PubMed Central

Zhao, Yuanshen; Gong, Liang; Huang, Yixiang; Liu, Chengliang

2016-01-01

Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost. PMID:26840313

Automatic parameter selection for feature-based multi-sensor image registration

NASA Astrophysics Data System (ADS)

DelMarco, Stephen; Tom, Victor; Webb, Helen; Chao, Alan

2006-05-01

Accurate image registration is critical for applications such as precision targeting, geo-location, change-detection, surveillance, and remote sensing. However, the increasing volume of image data is exceeding the current capacity of human analysts to perform manual registration. This image data glut necessitates the development of automated approaches to image registration, including algorithm parameter value selection. Proper parameter value selection is crucial to the success of registration techniques. The appropriate algorithm parameters can be highly scene and sensor dependent. Therefore, robust algorithm parameter value selection approaches are a critical component of an end-to-end image registration algorithm. In previous work, we developed a general framework for multisensor image registration which includes feature-based registration approaches. In this work we examine the problem of automated parameter selection. We apply the automated parameter selection approach of Yitzhaky and Peli to select parameters for feature-based registration of multisensor image data. The approach consists of generating multiple feature-detected images by sweeping over parameter combinations and using these images to generate estimated ground truth. The feature-detected images are compared to the estimated ground truth images to generate ROC points associated with each parameter combination. We develop a strategy for selecting the optimal parameter set by choosing the parameter combination corresponding to the optimal ROC point. We present numerical results showing the effectiveness of the approach using registration of collected SAR data to reference EO data.

Large Margin Multi-Modal Multi-Task Feature Extraction for Image Classification.

PubMed

Yong Luo; Yonggang Wen; Dacheng Tao; Jie Gui; Chao Xu

2016-01-01

The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We, therefore, propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features, so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization, and each subproblem can be efficiently solved. Experiments on two challenging real-world image data sets demonstrate the effectiveness and superiority of the proposed method.

Classification of radiolarian images with hand-crafted and deep features

NASA Astrophysics Data System (ADS)

Keçeli, Ali Seydi; Kaya, Aydın; Keçeli, Seda Uzunçimen

2017-12-01

Radiolarians are planktonic protozoa and are important biostratigraphic and paleoenvironmental indicators for paleogeographic reconstructions. Radiolarian paleontology still remains as a low cost and the one of the most convenient way to obtain dating of deep ocean sediments. Traditional methods for identifying radiolarians are time-consuming and cannot scale to the granularity or scope necessary for large-scale studies. Automated image classification will allow making these analyses promptly. In this study, a method for automatic radiolarian image classification is proposed on Scanning Electron Microscope (SEM) images of radiolarians to ease species identification of fossilized radiolarians. The proposed method uses both hand-crafted features like invariant moments, wavelet moments, Gabor features, basic morphological features and deep features obtained from a pre-trained Convolutional Neural Network (CNN). Feature selection is applied over deep features to reduce high dimensionality. Classification outcomes are analyzed to compare hand-crafted features, deep features, and their combinations. Results show that the deep features obtained from a pre-trained CNN are more discriminative comparing to hand-crafted ones. Additionally, feature selection utilizes to the computational cost of classification algorithms and have no negative effect on classification accuracy.

Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image.

PubMed

Singh, Anushikha; Dutta, Malay Kishore; ParthaSarathi, M; Uher, Vaclav; Burget, Radim

2016-02-01

Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image. In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings. Unlike the existing research works where the features are considered from the complete fundus or a sub image of the fundus, this work is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification. The experimental results presented in this paper indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image. Accuracy of glaucoma identification achieved in this work is 94.7% and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Onboard Image Registration from Invariant Features

NASA Technical Reports Server (NTRS)

Wang, Yi; Ng, Justin; Garay, Michael J.; Burl, Michael C

2008-01-01

This paper describes a feature-based image registration technique that is potentially well-suited for onboard deployment. The overall goal is to provide a fast, robust method for dynamically combining observations from multiple platforms into sensors webs that respond quickly to short-lived events and provide rich observations of objects that evolve in space and time. The approach, which has enjoyed considerable success in mainstream computer vision applications, uses invariant SIFT descriptors extracted at image interest points together with the RANSAC algorithm to robustly estimate transformation parameters that relate one image to another. Experimental results for two satellite image registration tasks are presented: (1) automatic registration of images from the MODIS instrument on Terra to the MODIS instrument on Aqua and (2) automatic stabilization of a multi-day sequence of GOES-West images collected during the October 2007 Southern California wildfires.

Gross feature recognition of Anatomical Images based on Atlas grid (GAIA): Incorporating the local discrepancy between an atlas and a target image to capture the features of anatomic brain MRI.

PubMed

Qin, Yuan-Yuan; Hsu, Johnny T; Yoshida, Shoko; Faria, Andreia V; Oishi, Kumiko; Unschuld, Paul G; Redgrave, Graham W; Ying, Sarah H; Ross, Christopher A; van Zijl, Peter C M; Hillis, Argye E; Albert, Marilyn S; Lyketsos, Constantine G; Miller, Michael I; Mori, Susumu; Oishi, Kenichi

2013-01-01

We aimed to develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gross feature recognition of Anatomical Images based on Atlas grid (GAIA), in which the local intensity alteration, caused by pathological (e.g., ischemia) or physiological (development and aging) intensity changes, as well as by atlas-image misregistration, is used to capture the anatomical features of target images. As a proof-of-concept, the GAIA was applied for pattern recognition of the neuroanatomical features of multiple stages of Alzheimer's disease, Huntington's disease, spinocerebellar ataxia type 6, and four subtypes of primary progressive aphasia. For each of these diseases, feature vectors based on a training dataset were applied to a test dataset to evaluate the accuracy of pattern recognition. The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified. The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which should enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes.

Atypical fibrosarcomas derived from cutaneous ganglion cell-like cells in 2 domestic Djungarian hamsters (Phodopus sungorus).

PubMed

Kondo, Hirotaka; Onuma, Mamoru; Shibuya, Hisashi; Sato, Tsuneo; Abbott, Jeffrey R

2011-07-01

Androgen-dependent atypical fibromas are benign tumors derived from ganglion-cell-like cells that are particular to Djungarian hamsters (Phodopus sungorus). Masses excised from 2 hamsters were composed of pleomorphic ganglion cell-like cells supported by small to moderate amounts of collagenous matrix. Intracytoplasmic fibrils were present in silver-stained sections, and immunohistochemistry showed that the cells expressed vimentin, androgen receptor, and, in one case, estrogen receptor α. In contrast to previously reported atypical fibromas, these tumors had features of anaplasia and were locally invasive. We diagnosed the tumors as atypical fibrosarcomas and consider them an unusual malignant counterpart of atypical fibroma. Copyright 2011 by the American Association for Laboratory Animal Science

SU-D-BRA-04: Computerized Framework for Marker-Less Localization of Anatomical Feature Points in Range Images Based On Differential Geometry Features for Image-Guided Radiation Therapy

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

Soufi, M; Arimura, H; Toyofuku, F

Purpose: To propose a computerized framework for localization of anatomical feature points on the patient surface in infrared-ray based range images by using differential geometry (curvature) features. Methods: The general concept was to reconstruct the patient surface by using a mathematical modeling technique for the computation of differential geometry features that characterize the local shapes of the patient surfaces. A region of interest (ROI) was firstly extracted based on a template matching technique applied on amplitude (grayscale) images. The extracted ROI was preprocessed for reducing temporal and spatial noises by using Kalman and bilateral filters, respectively. Next, a smooth patientmore » surface was reconstructed by using a non-uniform rational basis spline (NURBS) model. Finally, differential geometry features, i.e. the shape index and curvedness features were computed for localizing the anatomical feature points. The proposed framework was trained for optimizing shape index and curvedness thresholds and tested on range images of an anthropomorphic head phantom. The range images were acquired by an infrared ray-based time-of-flight (TOF) camera. The localization accuracy was evaluated by measuring the mean of minimum Euclidean distances (MMED) between reference (ground truth) points and the feature points localized by the proposed framework. The evaluation was performed for points localized on convex regions (e.g. apex of nose) and concave regions (e.g. nasofacial sulcus). Results: The proposed framework has localized anatomical feature points on convex and concave anatomical landmarks with MMEDs of 1.91±0.50 mm and 3.70±0.92 mm, respectively. A statistically significant difference was obtained between the feature points on the convex and concave regions (P<0.001). Conclusion: Our study has shown the feasibility of differential geometry features for localization of anatomical feature points on the patient surface in range images. The proposed

Borderline Personality Disorder: Bipolarity, Mood Stabilizers and Atypical Antipsychotics in Treatment

PubMed Central

Belli, Hasan; Ural, Cenk; Akbudak, Mahir

2012-01-01

In this article, it is aimed to review the efficacies of mood stabilizers and atypical antipsychotics, which are used commonly in psychopharmacological treatments of bipolar and borderline personality disorders. In this context, common phenomenology between borderline personality and bipolar disorders and differential features of clinical diagnosis will be reviewed in line with the literature. Both disorders can demonstrate common features in the diagnostic aspect, and can overlap phenomenologically. Concomitance rate of both disorders is quite high. In order to differentiate these two disorders from each other, quality of mood fluctuations, impulsivity types and linear progression of disorders should be carefully considered. There are various studies in mood stabilizer use, like lithium, carbamazepine, oxcarbazepine, sodium valproate and lamotrigine, in the treatment of borderline personality disorder. Moreover, there are also studies, which have revealed efficacies of risperidone, olanzapine and quetiapine as atypical antipsychotics. It is not easy to differentiate borderline personality disorder from the bipolar disorders. An intensively careful evaluation should be performed. This differentiation may be helpful also for the treatment. There are many studies about efficacy of valproate and lamotrigine in treatment of borderline personality disorder. However, findings related to other mood stabilizers are inadequate. Olanzapine and quetiapine are reported to be more effective among atypical antipsychotics. No drug is approved for the treatment of borderline personality disorder by the entitled authorities, yet. Psychotherapeutic approaches have preserved their significant places in treatment of borderline personality disorder. Moreover, symptom based approach is recommended in use of mood stabilizers and atypical antipsychotics. PMID:23024731

Image Recognition and Feature Detection in Solar Physics

NASA Astrophysics Data System (ADS)

Martens, Petrus C.

2012-05-01

The Solar Dynamics Observatory (SDO) data repository will dwarf the archives of all previous solar physics missions put together. NASA recognized early on that the traditional methods of analyzing the data -- solar scientists and grad students in particular analyzing the images by hand -- would simply not work and tasked our Feature Finding Team (FFT) with developing automated feature recognition modules for solar events and phenomena likely to be observed by SDO. Having these metadata available on-line will enable solar scientist to conduct statistical studies involving large sets of events that would be impossible now with traditional means. We have followed a two-track approach in our project: we have been developing some existing task-specific solar feature finding modules to be "pipe-line" ready for the stream of SDO data, plus we are designing a few new modules. Secondly, we took it upon us to develop an entirely new "trainable" module that would be capable of identifying different types of solar phenomena starting from a limited number of user-provided examples. Both approaches are now reaching fruition, and I will show examples and movies with results from several of our feature finding modules. In the second part of my presentation I will focus on our “trainable” module, which is the most innovative in character. First, there is the strong similarity between solar and medical X-ray images with regard to their texture, which has allowed us to apply some advances made in medical image recognition. Second, we have found that there is a strong similarity between the way our trainable module works and the way our brain recognizes images. The brain can quickly recognize similar images from key characteristics, just as our code does. We conclude from that that our approach represents the beginning of a more human-like procedure for computer image recognition.

Neuroinflammation is increased in the parietal cortex of atypical Alzheimer's disease.

PubMed

Boon, Baayla D C; Hoozemans, Jeroen J M; Lopuhaä, Boaz; Eigenhuis, Kristel N; Scheltens, Philip; Kamphorst, Wouter; Rozemuller, Annemieke J M; Bouwman, Femke H

2018-05-29

While most patients with Alzheimer's disease (AD) present with memory complaints, 30% of patients with early disease onset present with non-amnestic symptoms. This atypical presentation is thought to be caused by a different spreading of neurofibrillary tangles (NFT) than originally proposed by Braak and Braak. Recent studies suggest a prominent role for neuroinflammation in the spreading of tau pathology. We aimed to explore whether an atypical spreading of pathology in AD is associated with an atypical distribution of neuroinflammation. Typical and atypical AD cases were selected based on both NFT distribution and amnestic or non-amnestic clinical presentation. Immunohistochemistry was performed on the temporal pole and superior parietal lobe of 10 typical and 9 atypical AD cases. The presence of amyloid-beta (N-terminal; IC16), pTau (AT8), reactive astrocytes (GFAP), microglia (Iba1, CD68, and HLA-DP/DQ/DR), and complement factors (C1q, C3d, C4b, and C5b-9) was quantified by image analysis. Differences in lobar distribution patterns of immunoreactivity were statistically assessed using a linear mixed model. We found a temporal dominant distribution for amyloid-beta, GFAP, and Iba1 in both typical and atypical AD. Distribution of pTau, CD68, HLA-DP/DQ/DR, C3d, and C4b differed between AD variants. Typical AD cases showed a temporal dominant distribution of these markers, whereas atypical AD cases showed a parietal dominant distribution. Interestingly, when quantifying for the number of amyloid-beta plaques instead of stained surface area, atypical AD cases differed in distribution pattern from typical AD cases. Remarkably, plaque morphology and localization of neuroinflammation within the plaques was different between the two phenotypes. Our data show a different localization of neuroinflammatory markers and amyloid-beta plaques between AD phenotypes. In addition, these markers reflect the atypical distribution of tau pathology in atypical AD, suggesting that

Atypical band keratopathy following long-term pilocarpine treatment.

PubMed Central

Brazier, D J; Hitchings, R A

1989-01-01

Two patients with an atypical form of band keratopathy following long-term pilocarpine treatment are described. The keratopathy is thought to have resulted from the presence of the preservative phenylmercuric nitrate in the pilocarpine drops. Symptoms of reduced acuity, visual haloes, and recurrent epithelial erosions were relieved by removal of the opacities. Images PMID:2713309

Reproducibility and Prognosis of Quantitative Features Extracted from CT Images12

PubMed Central

Balagurunathan, Yoganand; Gu, Yuhua; Wang, Hua; Kumar, Virendra; Grove, Olya; Hawkins, Sam; Kim, Jongphil; Goldgof, Dmitry B; Hall, Lawrence O; Gatenby, Robert A; Gillies, Robert J

2014-01-01

We study the reproducibility of quantitative imaging features that are used to describe tumor shape, size, and texture from computed tomography (CT) scans of non-small cell lung cancer (NSCLC). CT images are dependent on various scanning factors. We focus on characterizing image features that are reproducible in the presence of variations due to patient factors and segmentation methods. Thirty-two NSCLC nonenhanced lung CT scans were obtained from the Reference Image Database to Evaluate Response data set. The tumors were segmented using both manual (radiologist expert) and ensemble (software-automated) methods. A set of features (219 three-dimensional and 110 two-dimensional) was computed, and quantitative image features were statistically filtered to identify a subset of reproducible and nonredundant features. The variability in the repeated experiment was measured by the test-retest concordance correlation coefficient (CCCTreT). The natural range in the features, normalized to variance, was measured by the dynamic range (DR). In this study, there were 29 features across segmentation methods found with CCCTreT and DR ≥ 0.9 and R2Bet ≥ 0.95. These reproducible features were tested for predicting radiologist prognostic score; some texture features (run-length and Laws kernels) had an area under the curve of 0.9. The representative features were tested for their prognostic capabilities using an independent NSCLC data set (59 lung adenocarcinomas), where one of the texture features, run-length gray-level nonuniformity, was statistically significant in separating the samples into survival groups (P ≤ .046). PMID:24772210

Homozygous TREM2 mutation in a family with atypical frontotemporal dementia.

PubMed

Le Ber, Isabelle; De Septenville, Anne; Guerreiro, Rita; Bras, José; Camuzat, Agnès; Caroppo, Paola; Lattante, Serena; Couarch, Philippe; Kabashi, Edor; Bouya-Ahmed, Kawtar; Dubois, Bruno; Brice, Alexis

2014-10-01

TREM2 mutations were first identified in Nasu-Hakola disease, a rare autosomal recessive disease characterized by recurrent fractures because of bone cysts and presenile dementia. Recently, homozygous and compound heterozygous TREM2 mutations were identified in rare families with frontotemporal lobar degeneration (FTLD) but without bone involvement. We identified a p.Thr66Met heterozygous mutation in a new consanguineous Italian family. Two sibs had early onset autosomal recessive FTLD without severe bone disorders. Atypical signs were present in this family: early parietal and hippocampus involvement, parkinsonism, epilepsy, and corpus callosum thickness on brain magnetic resonance imaging. This study further demonstrates the implication of TREM2 mutations in FTLD phenotypes. It illustrates the variability of bone phenotype and underlines the frequency of atypical signs in TREM2 carriers. This and previous studies evidence that TREM2 mutation screening should be limited to autosomal recessive FTLD with atypical phenotypes characterized by: (1) a very young age at onset (20-50 years); (2) early parietal and hippocampal deficits; (3) the presence of seizures and parkinsonism; (4) suggestive extensive white matter lesions and corpus callosum thickness on brain magnetic resonance imaging. Copyright © 2014 Elsevier Inc. All rights reserved.

Homozygous TREM2 mutation in a family with atypical frontotemporal dementia

PubMed Central

Bras, José; Camuzat, Agnès; Caroppo, Paola; Lattante, Serena; Couarch, Philippe; Kabashi, Edor; Bouya-Ahmed, Kawtar; Dubois, Bruno; Brice, Alexis

2014-01-01

TREM2 mutations were first identified in Nasu-Hakola disease, a rare autosomal recessive disease characterized by recurrent fractures because of bone cysts and presenile dementia. Recently, homozygous and compound heterozygous TREM2 mutations were identified in rare families with frontotemporal lobar degeneration (FTLD) but without bone involvement. We identified a p.Thr66Met heterozygous mutation in a new consanguineous Italian family. Two sibs had early onset autosomal recessive FTLD without severe bone disorders. Atypical signs were present in this family: early parietal and hippocampus involvement, parkinsonism, epilepsy, and corpus callosum thickness on brain magnetic resonance imaging. This study further demonstrates the implication of TREM2 mutations in FTLD phenotypes. It illustrates the variability of bone phenotype and underlines the frequency of atypical signs in TREM2 carriers. This and previous studies evidence that TREM2 mutation screening should be limited to autosomal recessive FTLD with atypical phenotypes characterized by: (1) a very young age at onset (20–50 years); (2) early parietal and hippocampal deficits; (3) the presence of seizures and parkinsonism; (4) suggestive extensive white matter lesions and corpus callosum thickness on brain magnetic resonance imaging. PMID:24910390

Classification of yeast cells from image features to evaluate pathogen conditions

NASA Astrophysics Data System (ADS)

van der Putten, Peter; Bertens, Laura; Liu, Jinshuo; Hagen, Ferry; Boekhout, Teun; Verbeek, Fons J.

2007-01-01

Morphometrics from images, image analysis, may reveal differences between classes of objects present in the images. We have performed an image-features-based classification for the pathogenic yeast Cryptococcus neoformans. Building and analyzing image collections from the yeast under different environmental or genetic conditions may help to diagnose a new "unseen" situation. Diagnosis here means that retrieval of the relevant information from the image collection is at hand each time a new "sample" is presented. The basidiomycetous yeast Cryptococcus neoformans can cause infections such as meningitis or pneumonia. The presence of an extra-cellular capsule is known to be related to virulence. This paper reports on the approach towards developing classifiers for detecting potentially more or less virulent cells in a sample, i.e. an image, by using a range of features derived from the shape or density distribution. The classifier can henceforth be used for automating screening and annotating existing image collections. In addition we will present our methods for creating samples, collecting images, image preprocessing, identifying "yeast cells" and creating feature extraction from the images. We compare various expertise based and fully automated methods of feature selection and benchmark a range of classification algorithms and illustrate successful application to this particular domain.

Iris recognition based on key image feature extraction.

PubMed

Ren, X; Tian, Q; Zhang, J; Wu, S; Zeng, Y

2008-01-01

In iris recognition, feature extraction can be influenced by factors such as illumination and contrast, and thus the features extracted may be unreliable, which can cause a high rate of false results in iris pattern recognition. In order to obtain stable features, an algorithm was proposed in this paper to extract key features of a pattern from multiple images. The proposed algorithm built an iris feature template by extracting key features and performed iris identity enrolment. Simulation results showed that the selected key features have high recognition accuracy on the CASIA Iris Set, where both contrast and illumination variance exist.

Atypical activation during the Embedded Figures Task as a functional magnetic resonance imaging endophenotype of autism

PubMed Central

Holt, Rosemary J.; Chura, Lindsay R.; Calder, Andrew J.; Suckling, John; Bullmore, Edward T.; Baron-Cohen, Simon

2012-01-01

Atypical activation during the Embedded Figures Task has been demonstrated in autism, but has not been investigated in siblings or related to measures of clinical severity. We identified atypical activation during the Embedded Figures Task in participants with autism and unaffected siblings compared with control subjects in a number of temporal and frontal brain regions. Autism and sibling groups, however, did not differ in terms of activation during this task. This suggests that the pattern of atypical activation identified may represent a functional endophenotype of autism, related to familial risk for the condition shared between individuals with autism and their siblings. We also found that reduced activation in autism relative to control subjects in regions including associative visual and face processing areas was strongly correlated with the clinical severity of impairments in reciprocal social interaction. Behavioural performance was intact in autism and sibling groups. Results are discussed in terms of atypical information processing styles or of increased activation in temporal and frontal regions in autism and the broader phenotype. By separating the aspects of atypical activation as markers of familial risk for the condition from those that are autism-specific, our findings offer new insight into the factors that might cause the expression of autism in families, affecting some children but not others. PMID:23065480

Atypical centromeres in plants—what they can tell us

PubMed Central

Cuacos, Maria; H. Franklin, F. Chris; Heckmann, Stefan

2015-01-01

The centromere, visible as the primary constriction of condensed metaphase chromosomes, is a defined chromosomal locus essential for genome stability. It mediates transient assembly of a multi-protein complex, the kinetochore, which enables interaction with spindle fibers and thus faithful segregation of the genetic information during nuclear divisions. Centromeric DNA varies in extent and sequence composition among organisms, but a common feature of almost all active eukaryotic centromeres is the presence of the centromeric histone H3 variant cenH3 (a.k.a. CENP-A). These typical centromere features apply to most studied species. However, a number of species display “atypical” centromeres, such as holocentromeres (centromere extension along almost the entire chromatid length) or neocentromeres (ectopic centromere activity). In this review, we provide an overview of different atypical centromere types found in plants including holocentromeres, de novo formed centromeres and terminal neocentromeres as well as di-, tri- and metapolycentromeres (more than one centromere per chromosomes). We discuss their specific and common features and compare them to centromere types found in other eukaryotic species. We also highlight new insights into centromere biology gained in plants with atypical centromeres such as distinct mechanisms to define a holocentromere, specific adaptations in species with holocentromeres during meiosis or various scenarios leading to neocentromere formation. PMID:26579160

Computer-aided Classification of Mammographic Masses Using Visually Sensitive Image Features

PubMed Central

Wang, Yunzhi; Aghaei, Faranak; Zarafshani, Ali; Qiu, Yuchen; Qian, Wei; Zheng, Bin

2017-01-01

Purpose To develop a new computer-aided diagnosis (CAD) scheme that computes visually sensitive image features routinely used by radiologists to develop a machine learning classifier and distinguish between the malignant and benign breast masses detected from digital mammograms. Methods An image dataset including 301 breast masses was retrospectively selected. From each segmented mass region, we computed image features that mimic five categories of visually sensitive features routinely used by radiologists in reading mammograms. We then selected five optimal features in the five feature categories and applied logistic regression models for classification. A new CAD interface was also designed to show lesion segmentation, computed feature values and classification score. Results Areas under ROC curves (AUC) were 0.786±0.026 and 0.758±0.027 when to classify mass regions depicting on two view images, respectively. By fusing classification scores computed from two regions, AUC increased to 0.806±0.025. Conclusion This study demonstrated a new approach to develop CAD scheme based on 5 visually sensitive image features. Combining with a “visual aid” interface, CAD results may be much more easily explainable to the observers and increase their confidence to consider CAD generated classification results than using other conventional CAD approaches, which involve many complicated and visually insensitive texture features. PMID:27911353

Image processing tool for automatic feature recognition and quantification

DOEpatents

Chen, Xing; Stoddard, Ryan J.

2017-05-02

A system for defining structures within an image is described. The system includes reading of an input file, preprocessing the input file while preserving metadata such as scale information and then detecting features of the input file. In one version the detection first uses an edge detector followed by identification of features using a Hough transform. The output of the process is identified elements within the image.

Random forest feature selection approach for image segmentation

NASA Astrophysics Data System (ADS)

Lefkovits, László; Lefkovits, Szidónia; Emerich, Simina; Vaida, Mircea Florin

2017-03-01

In the field of image segmentation, discriminative models have shown promising performance. Generally, every such model begins with the extraction of numerous features from annotated images. Most authors create their discriminative model by using many features without using any selection criteria. A more reliable model can be built by using a framework that selects the important variables, from the point of view of the classification, and eliminates the unimportant once. In this article we present a framework for feature selection and data dimensionality reduction. The methodology is built around the random forest (RF) algorithm and its variable importance evaluation. In order to deal with datasets so large as to be practically unmanageable, we propose an algorithm based on RF that reduces the dimension of the database by eliminating irrelevant features. Furthermore, this framework is applied to optimize our discriminative model for brain tumor segmentation.

Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters.

PubMed

Brynolfsson, Patrik; Nilsson, David; Torheim, Turid; Asklund, Thomas; Karlsson, Camilla Thellenberg; Trygg, Johan; Nyholm, Tufve; Garpebring, Anders

2017-06-22

In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.

[Research Progress of Multi-Model Medical Image Fusion at Feature Level].

PubMed

Zhang, Junjie; Zhou, Tao; Lu, Huiling; Wang, Huiqun

2016-04-01

Medical image fusion realizes advantage integration of functional images and anatomical images.This article discusses the research progress of multi-model medical image fusion at feature level.We firstly describe the principle of medical image fusion at feature level.Then we analyze and summarize fuzzy sets,rough sets,D-S evidence theory,artificial neural network,principal component analysis and other fusion methods’ applications in medical image fusion and get summery.Lastly,we in this article indicate present problems and the research direction of multi-model medical images in the future.

Development of an Expert System as a Diagnostic Support of Cervical Cancer in Atypical Glandular Cells, Based on Fuzzy Logics and Image Interpretation

PubMed Central

Domínguez Hernández, Karem R.; Aguilar Lasserre, Alberto A.; Posada Gómez, Rubén; Palet Guzmán, José A.; González Sánchez, Blanca E.

2013-01-01

Cervical cancer is the second largest cause of death among women worldwide. Nowadays, this disease is preventable and curable at low cost and low risk when an accurate diagnosis is done in due time, since it is the neoplasm with the highest prevention potential. This work describes the development of an expert system able to provide a diagnosis to cervical neoplasia (CN) precursor injuries through the integration of fuzzy logics and image interpretation techniques. The key contribution of this research focuses on atypical cases, specifically on atypical glandular cells (AGC). The expert system consists of 3 phases: (1) risk diagnosis which consists of the interpretation of a patient's clinical background and the risks for contracting CN according to specialists; (2) cytology images detection which consists of image interpretation (IM) and the Bethesda system for cytology interpretation, and (3) determination of cancer precursor injuries which consists of in retrieving the information from the prior phases and integrating the expert system by means of a fuzzy logics (FL) model. During the validation stage of the system, 21 already diagnosed cases were tested with a positive correlation in which 100% effectiveness was obtained. The main contribution of this work relies on the reduction of false positives and false negatives by providing a more accurate diagnosis for CN. PMID:23690881

A high performance parallel computing architecture for robust image features

NASA Astrophysics Data System (ADS)

Zhou, Renyan; Liu, Leibo; Wei, Shaojun

2014-03-01

A design of parallel architecture for image feature detection and description is proposed in this article. The major component of this architecture is a 2D cellular network composed of simple reprogrammable processors, enabling the Hessian Blob Detector and Haar Response Calculation, which are the most computing-intensive stage of the Speeded Up Robust Features (SURF) algorithm. Combining this 2D cellular network and dedicated hardware for SURF descriptors, this architecture achieves real-time image feature detection with minimal software in the host processor. A prototype FPGA implementation of the proposed architecture achieves 1318.9 GOPS general pixel processing @ 100 MHz clock and achieves up to 118 fps in VGA (640 × 480) image feature detection. The proposed architecture is stand-alone and scalable so it is easy to be migrated into VLSI implementation.

Atypical Activation during the Embedded Figures Task as a Functional Magnetic Resonance Imaging Endophenotype of Autism

ERIC Educational Resources Information Center

Spencer, Michael D.; Holt, Rosemary J.; Chura, Lindsay R.; Calder, Andrew J.; Suckling, John; Bullmore, Edward T.; Baron-Cohen, Simon

2012-01-01

Atypical activation during the Embedded Figures Task has been demonstrated in autism, but has not been investigated in siblings or related to measures of clinical severity. We identified atypical activation during the Embedded Figures Task in participants with autism and unaffected siblings compared with control subjects in a number of temporal…

Clinical features and roentgenograms of symbrachydactyly.

PubMed

Ogino, T; Minami, A; Kato, H

1989-08-01

Müller and Blauth have suggested that short webbed fingers, atypical cleft hand and acheiria develop as morphological variants of symbrachydactyly. The clinical features and roentgenograms of our 76 patients were analysed. The common features of all types of symbrachydactyly were that all cases were unilateral, various degrees of bone hypoplasia existed in the affected limbs, and in every grade there were some cases with pectoral muscle absence. There seems to be a successive process of formation of atypical cleft hand. Monodactyly and peromelia occur as a result of more severe reduction occurring in the central finger rays of symbrachydactyly of short finger type.

The relationship study between image features and detection probability based on psychology experiments

NASA Astrophysics Data System (ADS)

Lin, Wei; Chen, Yu-hua; Wang, Ji-yuan; Gao, Hong-sheng; Wang, Ji-jun; Su, Rong-hua; Mao, Wei

2011-04-01

Detection probability is an important index to represent and estimate target viability, which provides basis for target recognition and decision-making. But it will expend a mass of time and manpower to obtain detection probability in reality. At the same time, due to the different interpretation of personnel practice knowledge and experience, a great difference will often exist in the datum obtained. By means of studying the relationship between image features and perception quantity based on psychology experiments, the probability model has been established, in which the process is as following.Firstly, four image features have been extracted and quantified, which affect directly detection. Four feature similarity degrees between target and background were defined. Secondly, the relationship between single image feature similarity degree and perception quantity was set up based on psychological principle, and psychological experiments of target interpretation were designed which includes about five hundred people for interpretation and two hundred images. In order to reduce image features correlativity, a lot of artificial synthesis images have been made which include images with single brightness feature difference, images with single chromaticity feature difference, images with single texture feature difference and images with single shape feature difference. By analyzing and fitting a mass of experiments datum, the model quantitys have been determined. Finally, by applying statistical decision theory and experimental results, the relationship between perception quantity with target detection probability has been found. With the verification of a great deal of target interpretation in practice, the target detection probability can be obtained by the model quickly and objectively.

Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images.

PubMed

Echegaray, Sebastian; Bakr, Shaimaa; Rubin, Daniel L; Napel, Sandy

2017-10-06

The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.

A novel image registration approach via combining local features and geometric invariants

PubMed Central

Lu, Yan; Gao, Kun; Zhang, Tinghua; Xu, Tingfa

2018-01-01

Image registration is widely used in many fields, but the adaptability of the existing methods is limited. This work proposes a novel image registration method with high precision for various complex applications. In this framework, the registration problem is divided into two stages. First, we detect and describe scale-invariant feature points using modified computer vision-oriented fast and rotated brief (ORB) algorithm, and a simple method to increase the performance of feature points matching is proposed. Second, we develop a new local constraint of rough selection according to the feature distances. Evidence shows that the existing matching techniques based on image features are insufficient for the images with sparse image details. Then, we propose a novel matching algorithm via geometric constraints, and establish local feature descriptions based on geometric invariances for the selected feature points. Subsequently, a new price function is constructed to evaluate the similarities between points and obtain exact matching pairs. Finally, we employ the progressive sample consensus method to remove wrong matches and calculate the space transform parameters. Experimental results on various complex image datasets verify that the proposed method is more robust and significantly reduces the rate of false matches while retaining more high-quality feature points. PMID:29293595

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.

More than one kind of inference: re-examining what's learned in feature inference and classification.

PubMed

Sweller, Naomi; Hayes, Brett K

2010-08-01

Three studies examined how task demands that impact on attention to typical or atypical category features shape the category representations formed through classification learning and inference learning. During training categories were learned via exemplar classification or by inferring missing exemplar features. In the latter condition inferences were made about missing typical features alone (typical feature inference) or about both missing typical and atypical features (mixed feature inference). Classification and mixed feature inference led to the incorporation of typical and atypical features into category representations, with both kinds of features influencing inferences about familiar (Experiments 1 and 2) and novel (Experiment 3) test items. Those in the typical inference condition focused primarily on typical features. Together with formal modelling, these results challenge previous accounts that have characterized inference learning as producing a focus on typical category features. The results show that two different kinds of inference learning are possible and that these are subserved by different kinds of category representations.

A flower image retrieval method based on ROI feature.

PubMed

Hong, An-Xiang; Chen, Gang; Li, Jun-Li; Chi, Zhe-Ru; Zhang, Dan

2004-07-01

Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).

Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

NASA Astrophysics Data System (ADS)

Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

2017-01-01

We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

Featured Image: Diamonds in a Meteorite

NASA Astrophysics Data System (ADS)

Kohler, Susanna

2018-04-01

This unique image which measures only 60 x 80 micrometers across reveals details in the Kapoeta meteorite, an 11-kg stone that fell in South Sudan in 1942. The sparkle in the image? A cluster of nanodiamonds discovered embedded in the stone in a recent study led by Yassir Abdu (University of Sharjah, United Arab Emirates). Abdu and collaborators showed that these nanodiamonds have similar spectral features to the interiors of dense interstellar clouds and they dont show any signs of shock features. This may suggest that the nanodiamonds were formed by condensation of nebular gases early in the history of the solar system. The diamonds were trapped in the surface material of the Kapoeta meteorites parent body, thought to be the asteroid Vesta. To read more about the authors study, check out the original article below.CitationYassir A. Abdu et al 2018 ApJL 856 L9. doi:10.3847/2041-8213/aab433

No-reference image quality assessment based on statistics of convolution feature maps

NASA Astrophysics Data System (ADS)

Lv, Xiaoxin; Qin, Min; Chen, Xiaohui; Wei, Guo

2018-04-01

We propose a Convolutional Feature Maps (CFM) driven approach to accurately predict image quality. Our motivation bases on the finding that the Nature Scene Statistic (NSS) features on convolution feature maps are significantly sensitive to distortion degree of an image. In our method, a Convolutional Neural Network (CNN) is trained to obtain kernels for generating CFM. We design a forward NSS layer which performs on CFM to better extract NSS features. The quality aware features derived from the output of NSS layer is effective to describe the distortion type and degree an image suffered. Finally, a Support Vector Regression (SVR) is employed in our No-Reference Image Quality Assessment (NR-IQA) model to predict a subjective quality score of a distorted image. Experiments conducted on two public databases demonstrate the promising performance of the proposed method is competitive to state of the art NR-IQA methods.

Clinical characterisation of pneumonia caused by atypical pathogens combining classic and novel predictors.

PubMed

Masiá, M; Gutiérrez, F; Padilla, S; Soldán, B; Mirete, C; Shum, C; Hernández, I; Royo, G; Martin-Hidalgo, A

2007-02-01

The aim of this study was to characterise community-acquired pneumonia (CAP) caused by atypical pathogens by combining distinctive clinical and epidemiological features and novel biological markers. A population-based prospective study of consecutive patients with CAP included investigation of biomarkers of bacterial infection, e.g., procalcitonin, C-reactive protein and lipopolysaccharide-binding protein (LBP) levels. Clinical, radiological and laboratory data for patients with CAP caused by atypical pathogens were compared by univariate and multivariate analysis with data for patients with typical pathogens and patients from whom no organisms were identified. Two predictive scoring models were developed with the most discriminatory variables from multivariate analysis. Of 493 patients, 94 had CAP caused by atypical pathogens. According to multivariate analysis, patients with atypical pneumonia were more likely to have normal white blood cell counts, have repetitive air-conditioning exposure, be aged <65 years, have elevated aspartate aminotransferase levels, have been exposed to birds, and have lower serum levels of LBP. Two different scoring systems were developed that predicted atypical pathogens with sensitivities of 35.2% and 48.8%, and specificities of 93% and 91%, respectively. The combination of selected patient characteristics and laboratory data identified up to half of the cases of atypical pneumonia with high specificity, which should help clinicians to optimise initial empirical therapy for CAP.

A blur-invariant local feature for motion blurred image matching

NASA Astrophysics Data System (ADS)

Tong, Qiang; Aoki, Terumasa

2017-07-01

Image matching between a blurred (caused by camera motion, out of focus, etc.) image and a non-blurred image is a critical task for many image/video applications. However, most of the existing local feature schemes fail to achieve this work. This paper presents a blur-invariant descriptor and a novel local feature scheme including the descriptor and the interest point detector based on moment symmetry - the authors' previous work. The descriptor is based on a new concept - center peak moment-like element (CPME) which is robust to blur and boundary effect. Then by constructing CPMEs, the descriptor is also distinctive and suitable for image matching. Experimental results show our scheme outperforms state of the art methods for blurred image matching

Atypical hemispheric specialization for faces in infants at risk for autism spectrum disorder.

PubMed

Keehn, Brandon; Vogel-Farley, Vanessa; Tager-Flusberg, Helen; Nelson, Charles A

2015-04-01

Among the many experimental findings that tend to distinguish those with and without autism spectrum disorder (ASD) are face processing deficits, reduced hemispheric specialization, and atypical neurostructural and functional connectivity. To investigate the earliest manifestations of these features, we examined lateralization of event-related gamma-band coherence to faces during the first year of life in infants at high risk for autism (HRA; defined as having an older sibling with ASD) who were compared with low-risk comparison (LRC) infants, defined as having no family history of ASD. Participants included 49 HRA and 46 LRC infants who contributed a total of 127 data sets at 6 and 12 months. Electroencephalography was recorded while infants viewed images of familiar/unfamiliar faces. Event-related gamma-band (30-50 Hz) phase coherence between anterior-posterior electrode pairs for left and right hemispheres was computed. Developmental trajectories for lateralization of intra-hemispheric coherence were significantly different in HRA and LRC infants: by 12 months, HRA infants showed significantly greater leftward lateralization compared with LRC infants who showed rightward lateralization. Preliminary results indicate that infants who later met criteria for ASD were those that showed the greatest leftward lateralization. HRA infants demonstrate an aberrant pattern of leftward lateralization of intra-hemispheric coherence by the end of the first year of life, suggesting that the network specialized for face processing may develop atypically. Further, infants with the greatest leftward asymmetry at 12 months where those that later met criteria for ASD, providing support to the growing body of evidence that atypical hemispheric specialization may be an early neurobiological marker for ASD. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.

A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer.

PubMed

Neofytou, Marios S; Tanos, Vasilis; Pattichis, Marios S; Pattichis, Constantinos S; Kyriacou, Efthyvoulos C; Koutsouris, Dimitris D

2007-11-29

In the development of tissue classification methods, classifiers rely on significant differences between texture features extracted from normal and abnormal regions. Yet, significant differences can arise due to variations in the image acquisition method. For endoscopic imaging of the endometrium, we propose a standardized image acquisition protocol to eliminate significant statistical differences due to variations in: (i) the distance from the tissue (panoramic vs close up), (ii) difference in viewing angles and (iii) color correction. We investigate texture feature variability for a variety of targets encountered in clinical endoscopy. All images were captured at clinically optimum illumination and focus using 720 x 576 pixels and 24 bits color for: (i) a variety of testing targets from a color palette with a known color distribution, (ii) different viewing angles, (iv) two different distances from a calf endometrial and from a chicken cavity. Also, human images from the endometrium were captured and analysed. For texture feature analysis, three different sets were considered: (i) Statistical Features (SF), (ii) Spatial Gray Level Dependence Matrices (SGLDM), and (iii) Gray Level Difference Statistics (GLDS). All images were gamma corrected and the extracted texture feature values were compared against the texture feature values extracted from the uncorrected images. Statistical tests were applied to compare images from different viewing conditions so as to determine any significant differences. For the proposed acquisition procedure, results indicate that there is no significant difference in texture features between the panoramic and close up views and between angles. For a calibrated target image, gamma correction provided an acquired image that was a significantly better approximation to the original target image. In turn, this implies that the texture features extracted from the corrected images provided for better approximations to the original images

Efficient and Robust Model-to-Image Alignment using 3D Scale-Invariant Features

PubMed Central

Toews, Matthew; Wells, William M.

2013-01-01

This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a-posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down. PMID:23265799

Improved image retrieval based on fuzzy colour feature vector

NASA Astrophysics Data System (ADS)

Ben-Ahmeida, Ahlam M.; Ben Sasi, Ahmed Y.

2013-03-01

One of Image indexing techniques is the Content-Based Image Retrieval which is an efficient way for retrieving images from the image database automatically based on their visual contents such as colour, texture, and shape. In this paper will be discuss how using content-based image retrieval (CBIR) method by colour feature extraction and similarity checking. By dividing the query image and all images in the database into pieces and extract the features of each part separately and comparing the corresponding portions in order to increase the accuracy in the retrieval. The proposed approach is based on the use of fuzzy sets, to overcome the problem of curse of dimensionality. The contribution of colour of each pixel is associated to all the bins in the histogram using fuzzy-set membership functions. As a result, the Fuzzy Colour Histogram (FCH), outperformed the Conventional Colour Histogram (CCH) in image retrieving, due to its speedy results, where were images represented as signatures that took less size of memory, depending on the number of divisions. The results also showed that FCH is less sensitive and more robust to brightness changes than the CCH with better retrieval recall values.

An image-processing methodology for extracting bloodstain pattern features.

PubMed

Arthur, Ravishka M; Humburg, Philomena J; Hoogenboom, Jerry; Baiker, Martin; Taylor, Michael C; de Bruin, Karla G

2017-08-01

There is a growing trend in forensic science to develop methods to make forensic pattern comparison tasks more objective. This has generally involved the application of suitable image-processing methods to provide numerical data for identification or comparison. This paper outlines a unique image-processing methodology that can be utilised by analysts to generate reliable pattern data that will assist them in forming objective conclusions about a pattern. A range of features were defined and extracted from a laboratory-generated impact spatter pattern. These features were based in part on bloodstain properties commonly used in the analysis of spatter bloodstain patterns. The values of these features were consistent with properties reported qualitatively for such patterns. The image-processing method developed shows considerable promise as a way to establish measurable discriminating pattern criteria that are lacking in current bloodstain pattern taxonomies. Copyright © 2017 Elsevier B.V. All rights reserved.

A unified framework for image retrieval using keyword and visual features.

PubMed

Jing, Feng; Li, Mingling; Zhang, Hong-Jiang; Zhang, Bo

2005-07-01

In this paper, a unified image retrieval framework based on both keyword annotations and visual features is proposed. In this framework, a set of statistical models are built based on visual features of a small set of manually labeled images to represent semantic concepts and used to propagate keywords to other unlabeled images. These models are updated periodically when more images implicitly labeled by users become available through relevance feedback. In this sense, the keyword models serve the function of accumulation and memorization of knowledge learned from user-provided relevance feedback. Furthermore, two sets of effective and efficient similarity measures and relevance feedback schemes are proposed for query by keyword scenario and query by image example scenario, respectively. Keyword models are combined with visual features in these schemes. In particular, a new, entropy-based active learning strategy is introduced to improve the efficiency of relevance feedback for query by keyword. Furthermore, a new algorithm is proposed to estimate the keyword features of the search concept for query by image example. It is shown to be more appropriate than two existing relevance feedback algorithms. Experimental results demonstrate the effectiveness of the proposed framework.

Dynamic feature analysis for Voyager at the Image Processing Laboratory

NASA Technical Reports Server (NTRS)

Yagi, G. M.; Lorre, J. J.; Jepsen, P. L.

1978-01-01

Voyager 1 and 2 were launched from Cape Kennedy to Jupiter, Saturn, and beyond on September 5, 1977 and August 20, 1977. The role of the Image Processing Laboratory is to provide the Voyager Imaging Team with the necessary support to identify atmospheric features (tiepoints) for Jupiter and Saturn data, and to analyze and display them in a suitable form. This support includes the software needed to acquire and store tiepoints, the hardware needed to interactively display images and tiepoints, and the general image processing environment necessary for decalibration and enhancement of the input images. The objective is an understanding of global circulation in the atmospheres of Jupiter and Saturn. Attention is given to the Voyager imaging subsystem, the Voyager imaging science objectives, hardware, software, display monitors, a dynamic feature study, decalibration, navigation, and data base.

Atypical Lymphocytes and Cellular Cannibalism: A Phenomenon, First of its Kind to be Discovered in Chronic Periapical Lesions.

PubMed

Kalele, Ketki P; Patil, Kaustubh P; Nayyar, Abhishek Singh; Sasane, Rutuparna S

2016-04-01

Lymphocytes are often termed to be isomorphic, having a monotonous light microscopic appearance. Morphological aspects of lymphocytes in tissue sections thereby are not routinely taken notice of as their morphology seems to vary only in case of lymphoid malignancies, hematological malignancies apart from certain viral infections. Atypical lymphocytes are the lymphocytes with unusual shape, size or overall structure. These are more commonly known as reactive lymphocytes. The unusual histomorphological feature of these cells include larger size than normal lymphocytes; in some cells the size exceeds even 30 microns. The large size is the result of antigenic stimulation of the cell. Alongwith these, the other rare feature which is recently coming under light is "Cellular Cannibalism" which is defined as a large cell enclosing a slightly smaller one within its cytoplasm. Previously, this feature was noted only in cases of malignant tumors. The objectives of this study were to determine the proportion of atypical lymphocytes in chronic periapical granulomas and cysts; to determine the proportionate cellular cannibalism in these periapical lesions. This was a descriptive, observational study conducted in the Department of Oral Medicine and Radiology and Oral Pathology and Microbiology. Haematoxylin and eosin stained 30 slides of chronic periapical granulomas and 20 slides of cysts reported in the year 2014-15 and the clinical proformas of the patients were retrieved from the files of the Department of Oral Medicine and Radiology and Oral Pathology and Microbiology. These slides were evaluated by 3 experts from the specialization of Oral Pathology and Microbiology to determine the presence of atypical lymphocytes and cellular cannibalism under high power magnification (400X). Out of the 30 slides of chronic periapical granulomas, about 12 slides (40%) revealed presence of atypical lymphocytes. In case of slides of chronic periapical cysts, however, only 4 out of the 20

Face detection on distorted images using perceptual quality-aware features

NASA Astrophysics Data System (ADS)

Gunasekar, Suriya; Ghosh, Joydeep; Bovik, Alan C.

2014-02-01

We quantify the degradation in performance of a popular and effective face detector when human-perceived image quality is degraded by distortions due to additive white gaussian noise, gaussian blur or JPEG compression. It is observed that, within a certain range of perceived image quality, a modest increase in image quality can drastically improve face detection performance. These results can be used to guide resource or bandwidth allocation in a communication/delivery system that is associated with face detection tasks. A new face detector based on QualHOG features is also proposed that augments face-indicative HOG features with perceptual quality-aware spatial Natural Scene Statistics (NSS) features, yielding improved tolerance against image distortions. The new detector provides statistically significant improvements over a strong baseline on a large database of face images representing a wide range of distortions. To facilitate this study, we created a new Distorted Face Database, containing face and non-face patches from images impaired by a variety of common distortion types and levels. This new dataset is available for download and further experimentation at www.ideal.ece.utexas.edu/˜suriya/DFD/.

Feature-based fusion of medical imaging data.

PubMed

Calhoun, Vince D; Adali, Tülay

2009-09-01

The acquisition of multiple brain imaging types for a given study is a very common practice. There have been a number of approaches proposed for combining or fusing multitask or multimodal information. These can be roughly divided into those that attempt to study convergence of multimodal imaging, for example, how function and structure are related in the same region of the brain, and those that attempt to study the complementary nature of modalities, for example, utilizing temporal EEG information and spatial functional magnetic resonance imaging information. Within each of these categories, one can attempt data integration (the use of one imaging modality to improve the results of another) or true data fusion (in which multiple modalities are utilized to inform one another). We review both approaches and present a recent computational approach that first preprocesses the data to compute features of interest. The features are then analyzed in a multivariate manner using independent component analysis. We describe the approach in detail and provide examples of how it has been used for different fusion tasks. We also propose a method for selecting which combination of modalities provides the greatest value in discriminating groups. Finally, we summarize and describe future research topics.

Chorea in Late-Infantile Neuronal Ceroid Lipofuscinosis: An Atypical Presentation.

PubMed

Saini, Arushi Gahlot; Sankhyan, Naveen; Singhi, Pratibha

2016-07-01

Classic late-infantile neuronal ceroid lipofuscinosis is characterized by progressive intellectual and motor deterioration, seizures, vision loss, and early death. Prominent chorea is an atypical feature and is rarely described in children. A four-year-old girl with seizures followed by a year-long progressive cognitive decline and a three month history of intermittent chorea leading to rapid motor deterioration. The onset of illness was marked by seizures occurring as generalized tonic-clonic seizures and myoclonic jerks. There was gradual regression of cognitive milestones with increasing forgetfulness and impaired quality and content of speech. Nine months later, she developed chorea. These movements were associated with clumsiness, incoordination, and progressive loss of motor milestones. She was unable to perform manual tasks or maintain antigravity posture resulting in unsteadiness and frequent falls. The movements were aggravated by action or excitement and were absent in sleep. Magnetic resonance imaging depicted diffuse cerebral and cerebellar atrophy. Sequencing analysis of TPP1 gene showed a novel, homozygous, splice site mutation c.89+1G>A which resulted in nil enzyme activity and a severe phenotype with onset of disease symptoms at an early age of three years. The presence of chorea in late-infantile neuronal ceroid lipofuscinoses is atypical but does not exclude the diagnosis of late-infantile neuronal ceroid lipofuscinoses, especially in children with psychomotor regression, seizures and diffuse brain atrophy. Copyright © 2016 Elsevier Inc. All rights reserved.

[Influence of vaginal microflora on the presence of persistent atypical squamous cells and atypical glandular cells in pap smear--a 3-year study].

PubMed

Ludwin, Inga; Ludwin, Artur; Basta, Antoni

2010-05-01

the evaluation of influence of abnormal vaginal biocoenosis on presence and maintenance ASC and AGC in Pap smears. The study group consisted of 242 non-pregnant women (25-65 years of age): 207 women (4.96%) with atypical sqamous cells and 35 (0.7%) with atypical glandular cells. In all women the vaginal flora was assessed by Nugent scale. Vaginal flora was normal in 157 (75.8%) and pathological in 50 (24.1%) women with ASC. In the ASC subgroup, the highest proportion of physiological vaginal flora was observed in 151 patients (77.4%) with ASC-US, in comparison to 44 (22.6%) with ASC-H, in which the percentage of women with normal or abnormal flora was the same (50% vs 50%). This difference was statistically significant. In case of AGC, vaginal culture was physiological in 23 (65.7%) women, and in 12 (34.3%) abnormal vaginal flora with features of the inflammation. The statistically significant influence of abnormal vaginal flora on the presence of atypical endometrial and endocervical cells was not observed. We did not observed any influence of abnormal vaginal flora on the presence, regression and progression of ASC and AGC.

Robust image matching via ORB feature and VFC for mismatch removal

NASA Astrophysics Data System (ADS)

Ma, Tao; Fu, Wenxing; Fang, Bin; Hu, Fangyu; Quan, Siwen; Ma, Jie

2018-03-01

Image matching is at the base of many image processing and computer vision problems, such as object recognition or structure from motion. Current methods rely on good feature descriptors and mismatch removal strategies for detection and matching. In this paper, we proposed a robust image match approach based on ORB feature and VFC for mismatch removal. ORB (Oriented FAST and Rotated BRIEF) is an outstanding feature, it has the same performance as SIFT with lower cost. VFC (Vector Field Consensus) is a state-of-the-art mismatch removing method. The experiment results demonstrate that our method is efficient and robust.

Efficient and robust model-to-image alignment using 3D scale-invariant features.

PubMed

Toews, Matthew; Wells, William M

2013-04-01

This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down. Copyright © 2012 Elsevier B.V. All rights reserved.

Atypical Hemispheric Specialization for Faces in Infants At-Risk for Autism Spectrum Disorder

PubMed Central

Keehn, Brandon; Vogel-Farley, Vanessa; Tager-Flusberg, Helen; Nelson, Charles A.

2014-01-01

Behavioral and neuroimaging findings from typically developing infants and children have demonstrated that the right hemisphere becomes specialized for processing faces. Face processing impairments and atypical hemispheric specialization have previously been reported in individuals with autism spectrum disorder (ASD). The goal of this study was to examine the emergence of the right-lateralized face processing network in infants at high-risk for autism (HRA; defined as having an older sibling with ASD) and low-risk comparison (LRC) infants, defined as having no family history of ASD. To investigate the earliest appearance of these features, we examined lateralization of event-related gamma-band coherence (a measure of intra-hemispheric connectivity) to faces during the first year of life. Forty-nine HRA and 46 LRC infants contributed a total of 127 data sets at 6- and/or 12-months. EEG was recorded while infants viewed pictures of either their mother or a stranger. Event-related gamma-band (30-50Hz) phase coherence between anterior-posterior regions for left and right hemispheres was computed. HRA infants showed an aberrant pattern of leftward lateralization of intra-hemispheric coherence by the end of the first year of life, suggesting that the network specialized for face processing may develop atypically in these infants. Further, infants with the greatest leftward asymmetry at 12-months were those that later met diagnostic criteria for ASD, providing support to the growing body of evidence that atypical hemispheric specialization may be an early neurobiological marker for ASD. Among the many experimental findings that tend to distinguish those with and without autism spectrum disorder (ASD) are face processing deficits, reduced hemispheric specialization, and atypical neurostructural and functional connectivity. To investigate the earliest manifestations of these features, we examined lateralization of event-related gamma-band coherence to faces during the first

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.

Automatic plankton image classification combining multiple view features via multiple kernel learning.

PubMed

Zheng, Haiyong; Wang, Ruchen; Yu, Zhibin; Wang, Nan; Gu, Zhaorui; Zheng, Bing

2017-12-28

Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system

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.

Atypical epithelial changes in the uterine cervix

PubMed Central

Kirkland, James A.

1963-01-01

Atypical epithelium, i.e., epithelium showing changes just insufficient to warrant a diagnosis of carcinoma in situ, was re-studied in surgical material from 66 patients. Most of these specimens had originally been reported as suspicious or potentially malignant. Of the 66 patients, 62 were alive and 46 of these were `untreated' having had no treatment to the cervix since the original operation. Thirty-seven of the 66 were examined personally, 28 of these being `untreated'; nineteen were found to have gynaecological abnormalities. Cytological examination was performed on 36, with only one suspicious smear; none of these patients was found to have invasive carcinoma or carcinoma in situ. Of the remaining 25 patients not seen personally, all were considered by their doctors to be free of any significant cervical lesion. The incidence of progression from atypical epithelium to carcinoma in situ is so different in the published reports that the definitions must surely be different. Sections from 15 cases of carcinoma in situ were therefore submitted to seven skilled pathologists, and as only 70 out of 105 diagnoses came into this category, the need for agreed definition is obvious. The present study shows a depressing persistence of the original or similar complaint. The follow-up (average 7·4 years) suggests that atypical epithelial change is unlikely to progress to carcinoma. Images PMID:14033012

The impact of subdividing the "atypical" category for urinary cytology on patient management.

PubMed

Glass, Ryan; Cocker, Rubina; Rosen, Lisa; Coutsouvelis, Constantinos; Chau, Karen; Slim, Farah; Brenkert, Ryan; Sheikh-Fayyaz, Silvat; Farmer, Peter; Das, Kasturi

2016-06-01

The purpose of the study is to determine the impact of subdividing the "atypical" cytology interpretation into two groups: Atypical urothelial cells of uncertain significance (AUC-US) and Atypical urothelial cells suspicious for high-grade urothelial carcinoma (AUC-H/SHGUC), on management of patients with no prior history of UC. This is a retrospective study of "atypical" urine cytology with subsequent tissue examination occurring within six months. Cytology reports with "atypical" interpretation were reclassified into AUS-UC and AUC-H based on morphologic features identified by the Johns Hopkins system and the Paris system for urine cytology. Follow-up and categorical outcomes were compared between the reclassified AUC-US and AUC-H groups. There was no significant difference (P < 0.4539) in the rate of cytology follow-up, the follow-up cytology result (P < 0.1845), or time between follow-up cytologies (P < 0.0869) between the reclassified atypical group of AUC-H and AUC-US. There was a significant association (P < 0.0001) of rate of malignancy with the reclassified AUC-H (87.18%) compared to the AUC-US (58.68%) groups. There was no difference in follow-up between the AUC-H and AUC-US, however there was a difference in the rates of malignancy in the two groups. The AUC-H group is similar to the SHGUC group of the Paris system and can be considered as such, whereas the AUC-US group should continue to be considered atypical. We conclude that reclassification of the "atypical" category into AUC-US and AUC-H/SHGUC can reduce the rate of atypia and help in focused follow-up and targeted management. Diagn. Cytopathol. 2016;44:477-482. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Feature Extraction in Sequential Multimedia Images: with Applications in Satellite Images and On-line Videos

NASA Astrophysics Data System (ADS)

Liang, Yu-Li

Multimedia data is increasingly important in scientific discovery and people's daily lives. Content of massive multimedia is often diverse and noisy, and motion between frames is sometimes crucial in analyzing those data. Among all, still images and videos are commonly used formats. Images are compact in size but do not contain motion information. Videos record motion but are sometimes too big to be analyzed. Sequential images, which are a set of continuous images with low frame rate, stand out because they are smaller than videos and still maintain motion information. This thesis investigates features in different types of noisy sequential images, and the proposed solutions that intelligently combined multiple features to successfully retrieve visual information from on-line videos and cloudy satellite images. The first task is detecting supraglacial lakes above ice sheet in sequential satellite images. The dynamics of supraglacial lakes on the Greenland ice sheet deeply affect glacier movement, which is directly related to sea level rise and global environment change. Detecting lakes above ice is suffering from diverse image qualities and unexpected clouds. A new method is proposed to efficiently extract prominent lake candidates with irregular shapes, heterogeneous backgrounds, and in cloudy images. The proposed system fully automatize the procedure that track lakes with high accuracy. We further cooperated with geoscientists to examine the tracked lakes and found new scientific findings. The second one is detecting obscene content in on-line video chat services, such as Chatroulette, that randomly match pairs of users in video chat sessions. A big problem encountered in such systems is the presence of flashers and obscene content. Because of various obscene content and unstable qualities of videos capture by home web-camera, detecting misbehaving users is a highly challenging task. We propose SafeVchat, which is the first solution that achieves satisfactory

Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.

PubMed

Lee, Hansang; Hong, Helen; Kim, Junmo; Jung, Dae Chul

2018-04-01

To develop an automatic deep feature classification (DFC) method for distinguishing benign angiomyolipoma without visible fat (AMLwvf) from malignant clear cell renal cell carcinoma (ccRCC) from abdominal contrast-enhanced computer tomography (CE CT) images. A dataset including 80 abdominal CT images of 39 AMLwvf and 41 ccRCC patients was used. We proposed a DFC method for differentiating the small renal masses (SRM) into AMLwvf and ccRCC using the combination of hand-crafted and deep features, and machine learning classifiers. First, 71-dimensional hand-crafted features (HCF) of texture and shape were extracted from the SRM contours. Second, 1000-4000-dimensional deep features (DF) were extracted from the ImageNet pretrained deep learning model with the SRM image patches. In DF extraction, we proposed the texture image patches (TIP) to emphasize the texture information inside the mass in DFs and reduce the mass size variability. Finally, the two features were concatenated and the random forest (RF) classifier was trained on these concatenated features to classify the types of SRMs. The proposed method was tested on our dataset using leave-one-out cross-validation and evaluated using accuracy, sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and area under receiver operating characteristics curve (AUC). In experiments, the combinations of four deep learning models, AlexNet, VGGNet, GoogleNet, and ResNet, and four input image patches, including original, masked, mass-size, and texture image patches, were compared and analyzed. In qualitative evaluation, we observed the change in feature distributions between the proposed and comparative methods using tSNE method. In quantitative evaluation, we evaluated and compared the classification results, and observed that (a) the proposed HCF + DF outperformed HCF-only and DF-only, (b) AlexNet showed generally the best performances among the CNN models, and (c) the proposed TIPs

Building Facade Modeling Under Line Feature Constraint Based on Close-Range Images

NASA Astrophysics Data System (ADS)

Liang, Y.; Sheng, Y. H.

2018-04-01

To solve existing problems in modeling facade of building merely with point feature based on close-range images , a new method for modeling building facade under line feature constraint is proposed in this paper. Firstly, Camera parameters and sparse spatial point clouds data were restored using the SFM , and 3D dense point clouds were generated with MVS; Secondly, the line features were detected based on the gradient direction , those detected line features were fit considering directions and lengths , then line features were matched under multiple types of constraints and extracted from multi-image sequence. At last, final facade mesh of a building was triangulated with point cloud and line features. The experiment shows that this method can effectively reconstruct the geometric facade of buildings using the advantages of combining point and line features of the close - range image sequence, especially in restoring the contour information of the facade of buildings.

Learning to rank using user clicks and visual features for image retrieval.

PubMed

Yu, Jun; Tao, Dacheng; Wang, Meng; Rui, Yong

2015-04-01

The inconsistency between textual features and visual contents can cause poor image search results. To solve this problem, click features, which are more reliable than textual information in justifying the relevance between a query and clicked images, are adopted in image ranking model. However, the existing ranking model cannot integrate visual features, which are efficient in refining the click-based search results. In this paper, we propose a novel ranking model based on the learning to rank framework. Visual features and click features are simultaneously utilized to obtain the ranking model. Specifically, the proposed approach is based on large margin structured output learning and the visual consistency is integrated with the click features through a hypergraph regularizer term. In accordance with the fast alternating linearization method, we design a novel algorithm to optimize the objective function. This algorithm alternately minimizes two different approximations of the original objective function by keeping one function unchanged and linearizing the other. We conduct experiments on a large-scale dataset collected from the Microsoft Bing image search engine, and the results demonstrate that the proposed learning to rank models based on visual features and user clicks outperforms state-of-the-art algorithms.

Face recognition via sparse representation of SIFT feature on hexagonal-sampling image

NASA Astrophysics Data System (ADS)

Zhang, Daming; Zhang, Xueyong; Li, Lu; Liu, Huayong

2018-04-01

This paper investigates a face recognition approach based on Scale Invariant Feature Transform (SIFT) feature and sparse representation. The approach takes advantage of SIFT which is local feature other than holistic feature in classical Sparse Representation based Classification (SRC) algorithm and possesses strong robustness to expression, pose and illumination variations. Since hexagonal image has more inherit merits than square image to make recognition process more efficient, we extract SIFT keypoint in hexagonal-sampling image. Instead of matching SIFT feature, firstly the sparse representation of each SIFT keypoint is given according the constructed dictionary; secondly these sparse vectors are quantized according dictionary; finally each face image is represented by a histogram and these so-called Bag-of-Words vectors are classified by SVM. Due to use of local feature, the proposed method achieves better result even when the number of training sample is small. In the experiments, the proposed method gave higher face recognition rather than other methods in ORL and Yale B face databases; also, the effectiveness of the hexagonal-sampling in the proposed method is verified.

Radiomics: Extracting more information from medical images using advanced feature analysis

PubMed Central

Lambin, Philippe; Rios-Velazquez, Emmanuel; Leijenaar, Ralph; Carvalho, Sara; van Stiphout, Ruud G.P.M.; Granton, Patrick; Zegers, Catharina M.L.; Gillies, Robert; Boellard, Ronald; Dekker, André; Aerts, Hugo J.W.L.

2015-01-01

Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics – the high-throughput extraction of large amounts of image features from radiographic images – addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory. PMID:22257792

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

Atypical autoerotic deaths

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

Gowitt, G.T.; Hanzlick, R.L.

1992-06-01

So-called typical' autoerotic fatalities are the result of asphyxia due to mechanical compression of the neck, chest, or abdomen, whereas atypical' autoeroticism involves sexual self-stimulation by other means. The authors present five atypical autoerotic fatalities that involved the use of dichlorodifluoromethane, nitrous oxide, isobutyl nitrite, cocaine, or compounds containing 1-1-1-trichloroethane. Mechanisms of death are discussed in each case and the pertinent literature is reviewed.

Glioma grading using cell nuclei morphologic features in digital pathology images

NASA Astrophysics Data System (ADS)

Reza, Syed M. S.; Iftekharuddin, Khan M.

2016-03-01

This work proposes a computationally efficient cell nuclei morphologic feature analysis technique to characterize the brain gliomas in tissue slide images. In this work, our contributions are two-fold: 1) obtain an optimized cell nuclei segmentation method based on the pros and cons of the existing techniques in literature, 2) extract representative features by k-mean clustering of nuclei morphologic features to include area, perimeter, eccentricity, and major axis length. This clustering based representative feature extraction avoids shortcomings of extensive tile [1] [2] and nuclear score [3] based methods for brain glioma grading in pathology images. Multilayer perceptron (MLP) is used to classify extracted features into two tumor types: glioblastoma multiforme (GBM) and low grade glioma (LGG). Quantitative scores such as precision, recall, and accuracy are obtained using 66 clinical patients' images from The Cancer Genome Atlas (TCGA) [4] dataset. On an average ~94% accuracy from 10 fold crossvalidation confirms the efficacy of the proposed method.

Atypicalities in cortical structure, handedness, and functional lateralization for language in autism spectrum disorders.

PubMed

Lindell, Annukka K; Hudry, Kristelle

2013-09-01

Language is typically a highly lateralized function, with atypically reduced or reversed lateralization linked to language impairments. Given the diagnostic and prognostic role of impaired language for autism spectrum disorders (ASDs), this paper reviews the growing body of literature that examines patterns of lateralization in individuals with ASDs. Including research from structural and functional imaging paradigms, and behavioral evidence from investigations of handedness, the review confirms that atypical lateralization is common in people with ASDs. The evidence indicates reduced structural asymmetry in fronto-temporal language regions, attenuated functional activation in response to language and pre-linguistic stimuli, and more ambiguous (mixed) hand preferences, in individuals with ASDs. Critically, the evidence emphasizes an intimate relationship between atypical lateralization and language impairment, with more atypical asymmetries linked to more substantive language impairment. Such evidence highlights opportunities for the identification of structural and functional biomarkers of ASDs, affording the potential for earlier diagnosis and intervention implementation.

Simultenious binary hash and features learning for image retrieval

NASA Astrophysics Data System (ADS)

Frantc, V. A.; Makov, S. V.; Voronin, V. V.; Marchuk, V. I.; Semenishchev, E. A.; Egiazarian, K. O.; Agaian, S.

2016-05-01

Content-based image retrieval systems have plenty of applications in modern world. The most important one is the image search by query image or by semantic description. Approaches to this problem are employed in personal photo-collection management systems, web-scale image search engines, medical systems, etc. Automatic analysis of large unlabeled image datasets is virtually impossible without satisfactory image-retrieval technique. It's the main reason why this kind of automatic image processing has attracted so much attention during recent years. Despite rather huge progress in the field, semantically meaningful image retrieval still remains a challenging task. The main issue here is the demand to provide reliable results in short amount of time. This paper addresses the problem by novel technique for simultaneous learning of global image features and binary hash codes. Our approach provide mapping of pixel-based image representation to hash-value space simultaneously trying to save as much of semantic image content as possible. We use deep learning methodology to generate image description with properties of similarity preservation and statistical independence. The main advantage of our approach in contrast to existing is ability to fine-tune retrieval procedure for very specific application which allow us to provide better results in comparison to general techniques. Presented in the paper framework for data- dependent image hashing is based on use two different kinds of neural networks: convolutional neural networks for image description and autoencoder for feature to hash space mapping. Experimental results confirmed that our approach has shown promising results in compare to other state-of-the-art methods.

Synthetic aperture radar target detection, feature extraction, and image formation techniques

NASA Technical Reports Server (NTRS)

Li, Jian

1994-01-01

This report presents new algorithms for target detection, feature extraction, and image formation with the synthetic aperture radar (SAR) technology. For target detection, we consider target detection with SAR and coherent subtraction. We also study how the image false alarm rates are related to the target template false alarm rates when target templates are used for target detection. For feature extraction from SAR images, we present a computationally efficient eigenstructure-based 2D-MODE algorithm for two-dimensional frequency estimation. For SAR image formation, we present a robust parametric data model for estimating high resolution range signatures of radar targets and for forming high resolution SAR images.

Prostate segmentation in MR images using discriminant boundary features.

PubMed

Yang, Meijuan; Li, Xuelong; Turkbey, Baris; Choyke, Peter L; Yan, Pingkun

2013-02-01

Segmentation of the prostate in magnetic resonance image has become more in need for its assistance to diagnosis and surgical planning of prostate carcinoma. Due to the natural variability of anatomical structures, statistical shape model has been widely applied in medical image segmentation. Robust and distinctive local features are critical for statistical shape model to achieve accurate segmentation results. The scale invariant feature transformation (SIFT) has been employed to capture the information of the local patch surrounding the boundary. However, when SIFT feature being used for segmentation, the scale and variance are not specified with the location of the point of interest. To deal with it, the discriminant analysis in machine learning is introduced to measure the distinctiveness of the learned SIFT features for each landmark directly and to make the scale and variance adaptive to the locations. As the gray values and gradients vary significantly over the boundary of the prostate, separate appearance descriptors are built for each landmark and then optimized. After that, a two stage coarse-to-fine segmentation approach is carried out by incorporating the local shape variations. Finally, the experiments on prostate segmentation from MR image are conducted to verify the efficiency of the proposed algorithms.

[Image Feature Extraction and Discriminant Analysis of Xinjiang Uygur Medicine Based on Color Histogram].

PubMed

Hamit, Murat; Yun, Weikang; Yan, Chuanbo; Kutluk, Abdugheni; Fang, Yang; Alip, Elzat

2015-06-01

Image feature extraction is an important part of image processing and it is an important field of research and application of image processing technology. Uygur medicine is one of Chinese traditional medicine and researchers pay more attention to it. But large amounts of Uygur medicine data have not been fully utilized. In this study, we extracted the image color histogram feature of herbal and zooid medicine of Xinjiang Uygur. First, we did preprocessing, including image color enhancement, size normalizition and color space transformation. Then we extracted color histogram feature and analyzed them with statistical method. And finally, we evaluated the classification ability of features by Bayes discriminant analysis. Experimental results showed that high accuracy for Uygur medicine image classification was obtained by using color histogram feature. This study would have a certain help for the content-based medical image retrieval for Xinjiang Uygur medicine.

Automatic brain MR image denoising based on texture feature-based artificial neural networks.

PubMed

Chang, Yu-Ning; Chang, Herng-Hua

2015-01-01

Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

Neural network-based feature point descriptors for registration of optical and SAR images

NASA Astrophysics Data System (ADS)

Abulkhanov, Dmitry; Konovalenko, Ivan; Nikolaev, Dmitry; Savchik, Alexey; Shvets, Evgeny; Sidorchuk, Dmitry

2018-04-01

Registration of images of different nature is an important technique used in image fusion, change detection, efficient information representation and other problems of computer vision. Solving this task using feature-based approaches is usually more complex than registration of several optical images because traditional feature descriptors (SIFT, SURF, etc.) perform poorly when images have different nature. In this paper we consider the problem of registration of SAR and optical images. We train neural network to build feature point descriptors and use RANSAC algorithm to align found matches. Experimental results are presented that confirm the method's effectiveness.

Characterisation of Feature Points in Eye Fundus Images

NASA Astrophysics Data System (ADS)

Calvo, D.; Ortega, M.; Penedo, M. G.; Rouco, J.

The retinal vessel tree adds decisive knowledge in the diagnosis of numerous opthalmologic pathologies such as hypertension or diabetes. One of the problems in the analysis of the retinal vessel tree is the lack of information in terms of vessels depth as the image acquisition usually leads to a 2D image. This situation provokes a scenario where two different vessels coinciding in a point could be interpreted as a vessel forking into a bifurcation. That is why, for traking and labelling the retinal vascular tree, bifurcations and crossovers of vessels are considered feature points. In this work a novel method for these retinal vessel tree feature points detection and classification is introduced. The method applies image techniques such as filters or thinning to obtain the adequate structure to detect the points and sets a classification of these points studying its environment. The methodology is tested using a standard database and the results show high classification capabilities.

Feature-aided multiple target tracking in the image plane

NASA Astrophysics Data System (ADS)

Brown, Andrew P.; Sullivan, Kevin J.; Miller, David J.

2006-05-01

Vast quantities of EO and IR data are collected on airborne platforms (manned and unmanned) and terrestrial platforms (including fixed installations, e.g., at street intersections), and can be exploited to aid in the global war on terrorism. However, intelligent preprocessing is required to enable operator efficiency and to provide commanders with actionable target information. To this end, we have developed an image plane tracker which automatically detects and tracks multiple targets in image sequences using both motion and feature information. The effects of platform and camera motion are compensated via image registration, and a novel change detection algorithm is applied for accurate moving target detection. The contiguous pixel blob on each moving target is segmented for use in target feature extraction and model learning. Feature-based target location measurements are used for tracking through move-stop-move maneuvers, close target spacing, and occlusion. Effective clutter suppression is achieved using joint probabilistic data association (JPDA), and confirmed target tracks are indicated for further processing or operator review. In this paper we describe the algorithms implemented in the image plane tracker and present performance results obtained with video clips from the DARPA VIVID program data collection and from a miniature unmanned aerial vehicle (UAV) flight.

WE-G-207-05: Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis

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

Lee, J; Nishikawa, R; Reiser, I

Purpose: Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis. Methods: A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benignmore » or malignant. Twenty-three image features were extracted after segmentation using a semi-automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave-one-out-cross-validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist’s drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared. Results: Moderate correlation (Pearson’s rho = 0.43, p-value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance. Conclusion: There are certain images that yield better segmentation or

Unusual cardiac paraganglioma mimicking an atypical carcinoid tumor of the lung

PubMed Central

Evans, Mark; Wang, Beverly; Delrosario, J. Lawrence; Cheng, Timmy; Milliken, Jeffrey

2018-01-01

We present a case of unusual cardiac paraganglioma (PG) initially misdiagnosed as atypical carcinoid tumor of the lung and discuss key clinical and pathologic characteristics that guide surgical management of these rare chromaffin cell tumors. A 64-year-old female with persistent cough and back pain was found to have a 4 cm × 3 cm mass abutting multiple cardiopulmonary structures. A biopsy was performed at an outside institution and pathology reported “atypical neuroendocrine carcinoma, consistent with carcinoid”. The patient was transferred to our institution and pericardial resection with right pneumonectomy was performed to excise the tumor. Histology of the mass was that of PG with multiple ethanol embolizations. Immunohistochemical examination revealed that type I (chief) cells were positive for neuroendocrine markers (chromogranin A and synaptophysin), while type II (sustentacular) cells were positive for S100. There was no evidence of atypical carcinoid tumor in the lung. PG is an entity of chromaffin cell tumors that often affects the adrenal glands and carotid body. PG rarely occurs in the thoracic region, accounting for just 1–2% of all PG. Proper diagnosis of cardiac PG is challenging owing to its rare prevalence, subtle symptoms of presentation, and the neuroendocrine histopathological features it shares with atypical carcinoids. These tumors are typically benign and are best treated by surgical resection. Our report examines the approach to appropriate diagnosis of cardiac PG vs. atypical carcinoid, preoperative management, and surgical treatment by describing successful resection through thoracotomy without the use of cardiopulmonary bypass. PMID:29600100

Atypical Pityriasis rosea in a black child: a case report

PubMed Central

2009-01-01

Introduction Pityriasis rosea is a self-limited inflammatory condition of the skin that mostly affects healthy children and adolescents. Atypical cases of Pityriasis rosea are fairly common and less readily recognized than typical eruptions, and may pose a diagnostic challenge. Case presentation We report the case of a 12-year-old black child that developed an intense pruritic papular eruption with intense facial involvement that was diagnosed of Pityriasis rosea and resolved after five weeks leaving a slight hyperpigmentation. Conclusion Facial and scalp involvement, post-inflammatory disorders of pigmentation and papular lesions are characteristics typically associated to black patients with Pityriasis rosea. The knowledge of features found more frequently in dark-skinned population may be helpful to physicians for diagnosing an atypical Pityriasis rosea in these patients. PMID:20181179

Atypical anorexia nervosa is not related to brain structural changes in newly diagnosed adolescent patients.

PubMed

Olivo, Gaia; Solstrand Dahlberg, Linda; Wiemerslage, Lyle; Swenne, Ingemar; Zhukovsky, Christina; Salonen-Ros, Helena; Larsson, Elna-Marie; Gaudio, Santino; Brooks, Samantha J; Schiöth, Helgi B

2018-01-01

Patients with atypical anorexia nervosa (AN) have many features overlapping with AN in terms of genetic risk, age of onset, psychopathology and prognosis of outcome, although the weight loss may not be a core factor. While brain structural alterations have been reported in AN, there are currently no data regarding atypical AN patients. We investigated brain structure through a voxel-based morphometry analysis in 22 adolescent females newly-diagnosed with atypical AN, and 38 age- and sex-matched healthy controls (HC). ED-related psychopathology, impulsiveness and obsessive-compulsive traits were assessed with the Eating Disorder Examination Questionnaire (EDE-Q), Barratt Impulsiveness Scale (BIS-11) and Obsessive-compulsive Inventory Revised (OCI-R), respectively. Body mass index (BMI) was also calculated. Patients and HC differed significantly on BMI (p < .002), EDE-Q total score (p < .000) and OCI-R total score (p < .000). No differences could be detected in grey matter (GM) regional volume between groups. The ED-related cognitions in atypical AN patients would suggest that atypical AN and AN could be part of the same spectrum of restrictive-ED. However, contrary to previous reports in AN, our atypical AN patients did not show any GM volume reduction. The different degree of weight loss might play a role in determining such discrepancy. Alternatively, the preservation of GM volume might indeed differentiate atypical AN from AN. © 2017 Wiley Periodicals, Inc.

Identification of important image features for pork and turkey ham classification using colour and wavelet texture features and genetic selection.

PubMed

Jackman, Patrick; Sun, Da-Wen; Allen, Paul; Valous, Nektarios A; Mendoza, Fernando; Ward, Paddy

2010-04-01

A method to discriminate between various grades of pork and turkey ham was developed using colour and wavelet texture features. Image analysis methods originally developed for predicting the palatability of beef were applied to rapidly identify the ham grade. With high quality digital images of 50-94 slices per ham it was possible to identify the greyscale that best expressed the differences between the various ham grades. The best 10 discriminating image features were then found with a genetic algorithm. Using the best 10 image features, simple linear discriminant analysis models produced 100% correct classifications for both pork and turkey on both calibration and validation sets. 2009 Elsevier Ltd. All rights reserved.

The algorithm of fast image stitching based on multi-feature extraction

NASA Astrophysics Data System (ADS)

Yang, Chunde; Wu, Ge; Shi, Jing

2018-05-01

This paper proposed an improved image registration method combining Hu-based invariant moment contour information and feature points detection, aiming to solve the problems in traditional image stitching algorithm, such as time-consuming feature points extraction process, redundant invalid information overload and inefficiency. First, use the neighborhood of pixels to extract the contour information, employing the Hu invariant moment as similarity measure to extract SIFT feature points in those similar regions. Then replace the Euclidean distance with Hellinger kernel function to improve the initial matching efficiency and get less mismatching points, further, estimate affine transformation matrix between the images. Finally, local color mapping method is adopted to solve uneven exposure, using the improved multiresolution fusion algorithm to fuse the mosaic images and realize seamless stitching. Experimental results confirm high accuracy and efficiency of method proposed in this paper.

Multi-Modal Ultra-Widefield Imaging Features in Waardenburg Syndrome

PubMed Central

Choudhry, Netan; Rao, Rajesh C.

2015-01-01

Background Waardenburg syndrome is characterized by a group of features including; telecanthus, a broad nasal root, synophrys of the eyebrows, piedbaldism, heterochromia irides, and deaf-mutism. Hypopigmentation of the choroid is a unique feature of this condition examined with multi-modal Ultra-Widefield Imaging in this report. Material/Methods Report of a single case. Results Bilateral symmetric choroidal hypopigmentation was observed with hypoautofluorescence in the region of hypopigmentation. Fluorescein angiography revealed a normal vasculature, however a thickened choroid was seen on Enhanced-Depth Imaging Spectral-Domain OCT (EDI SD-OCT). Conclusion(s) Choroidal hypopigmentation is a unique feature of Waardenburg syndrome, which can be visualized with ultra-widefield fundus autofluorescence. The choroid may also be thickened in this condition and its thickness measured with EDI SD-OCT. PMID:26114849

Quality assessment of remote sensing image fusion using feature-based fourth-order correlation coefficient

NASA Astrophysics Data System (ADS)

Ma, Dan; Liu, Jun; Chen, Kai; Li, Huali; Liu, Ping; Chen, Huijuan; Qian, Jing

2016-04-01

In remote sensing fusion, the spatial details of a panchromatic (PAN) image and the spectrum information of multispectral (MS) images will be transferred into fused images according to the characteristics of the human visual system. Thus, a remote sensing image fusion quality assessment called feature-based fourth-order correlation coefficient (FFOCC) is proposed. FFOCC is based on the feature-based coefficient concept. Spatial features related to spatial details of the PAN image and spectral features related to the spectrum information of MS images are first extracted from the fused image. Then, the fourth-order correlation coefficient between the spatial and spectral features is calculated and treated as the assessment result. FFOCC was then compared with existing widely used indices, such as Erreur Relative Globale Adimensionnelle de Synthese, and quality assessed with no reference. Results of the fusion and distortion experiments indicate that the FFOCC is consistent with subjective evaluation. FFOCC significantly outperforms the other indices in evaluating fusion images that are produced by different fusion methods and that are distorted in spatial and spectral features by blurring, adding noise, and changing intensity. All the findings indicate that the proposed method is an objective and effective quality assessment for remote sensing image fusion.

Automatic detection of diabetic retinopathy features in ultra-wide field retinal images

NASA Astrophysics Data System (ADS)

Levenkova, Anastasia; Sowmya, Arcot; Kalloniatis, Michael; Ly, Angelica; Ho, Arthur

2017-03-01

Diabetic retinopathy (DR) is a major cause of irreversible vision loss. DR screening relies on retinal clinical signs (features). Opportunities for computer-aided DR feature detection have emerged with the development of Ultra-WideField (UWF) digital scanning laser technology. UWF imaging covers 82% greater retinal area (200°), against 45° in conventional cameras3 , allowing more clinically relevant retinopathy to be detected4 . UWF images also provide a high resolution of 3078 x 2702 pixels. Currently DR screening uses 7 overlapping conventional fundus images, and the UWF images provide similar results1,4. However, in 40% of cases, more retinopathy was found outside the 7-field ETDRS) fields by UWF and in 10% of cases, retinopathy was reclassified as more severe4 . This is because UWF imaging allows examination of both the central retina and more peripheral regions, with the latter implicated in DR6 . We have developed an algorithm for automatic recognition of DR features, including bright (cotton wool spots and exudates) and dark lesions (microaneurysms and blot, dot and flame haemorrhages) in UWF images. The algorithm extracts features from grayscale (green "red-free" laser light) and colour-composite UWF images, including intensity, Histogram-of-Gradient and Local binary patterns. Pixel-based classification is performed with three different classifiers. The main contribution is the automatic detection of DR features in the peripheral retina. The method is evaluated by leave-one-out cross-validation on 25 UWF retinal images with 167 bright lesions, and 61 other images with 1089 dark lesions. The SVM classifier performs best with AUC of 94.4% / 95.31% for bright / dark lesions.

Wavelet-based energy features for glaucomatous image classification.

PubMed

Dua, Sumeet; Acharya, U Rajendra; Chowriappa, Pradeep; Sree, S Vinitha

2012-01-01

Texture features within images are actively pursued for accurate and efficient glaucoma classification. Energy distribution over wavelet subbands is applied to find these important texture features. In this paper, we investigate the discriminatory potential of wavelet features obtained from the daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. We propose a novel technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies. We have gauged the effectiveness of the resultant ranked and selected subsets of features using a support vector machine, sequential minimal optimization, random forest, and naïve Bayes classification strategies. We observed an accuracy of around 93% using tenfold cross validations to demonstrate the effectiveness of these methods.

Tissue feature-based intra-fractional motion tracking for stereoscopic x-ray image guided radiotherapy

NASA Astrophysics Data System (ADS)

Xie, Yaoqin; Xing, Lei; Gu, Jia; Liu, Wu

2013-06-01

Real-time knowledge of tumor position during radiation therapy is essential to overcome the adverse effect of intra-fractional organ motion. The goal of this work is to develop a tumor tracking strategy by effectively utilizing the inherent image features of stereoscopic x-ray images acquired during dose delivery. In stereoscopic x-ray image guided radiation delivery, two orthogonal x-ray images are acquired either simultaneously or sequentially. The essence of markerless tumor tracking is the reliable identification of inherent points with distinct tissue features on each projection image and their association between two images. The identification of the feature points on a planar x-ray image is realized by searching for points with high intensity gradient. The feature points are associated by using the scale invariance features transform descriptor. The performance of the proposed technique is evaluated by using images of a motion phantom and four archived clinical cases acquired using either a CyberKnife equipped with a stereoscopic x-ray imaging system, or a LINAC equipped with an onboard kV imager and an electronic portal imaging device. In the phantom study, the results obtained using the proposed method agree with the measurements to within 2 mm in all three directions. In the clinical study, the mean error is 0.48 ± 0.46 mm for four patient data with 144 sequential images. In this work, a tissue feature-based tracking method for stereoscopic x-ray image guided radiation therapy is developed. The technique avoids the invasive procedure of fiducial implantation and may greatly facilitate the clinical workflow.

Atypical location of an osteoid osteoma with atypical anterior knee pain

PubMed Central

Harun, Mutlu; Hayrettin, Yaldız; Serhat, Mutlu; Engin, Cetinkaya; Kamil, Cepni; Armagan, Arslan; Sancar, Parmaksızoglu Atilla

2014-01-01

INTRODUCTION An osteoid osteoma is a painful tumor that most commonly affects the extra-articular parts of the long bones. An intra-articular location of an osteoid osteoma is rare. Various differential diagnoses may arise in connection with such an unusual location because it causes atypical clinical signs. PRESENTATION OF CASE A 24-year-old male developed pain in the central region of the right knee. Magnetic resonance imaging (MRI) showed no clear pathology in the knee joint. A technetium bone scan and computed tomography (CT) were then ordered and confirmed the presence of an osteoid osteoma in the knee joint. The patient was treated through an anteromedial approach to the knee, and the lesion was removed by excisional biopsy under fluoroscopy. DISCUSSION The diagnosis of intra-articular osteoid osteoma is challenging because the clinical presentation can be misleading. MRI is often requested as the first imaging method when dealing with knee symptoms, and radiologists are often unaware of the clinical presentation. Edema seen on MRI can be misleading with respect to the location of the nidus. CT is considered to be the best imaging method because it usually allows for clear visualization of the nidus. Different treatments have been proposed, ranging from open excision to arthroscopic resection. CONCLUSION Osteoid osteoma should be considered in young adult patients with chronic knee pain and no history of trauma. PMID:25462055

Atypical scrapie in sheep from a UK research flock which is free from classical scrapie

PubMed Central

Simmons, Hugh A; Simmons, Marion M; Spencer, Yvonne I; Chaplin, Melanie J; Povey, Gill; Davis, Andrew; Ortiz-Pelaez, Angel; Hunter, Nora; Matthews, Danny; Wrathall, Anthony E

2009-01-01

Background In the wake of the epidemic of bovine spongiform encephalopathy the British government established a flock of sheep from which scrapie-free animals are supplied to laboratories for research. Three breeds of sheep carrying a variety of different genotypes associated with scrapie susceptibility/resistance were imported in 1998 and 2001 from New Zealand, a country regarded as free from scrapie. They are kept in a purpose-built Sheep Unit under strict disease security and are monitored clinically and post mortem for evidence of scrapie. It is emphasised that atypical scrapie, as distinct from classical scrapie, has been recognised only relatively recently and differs from classical scrapie in its clinical, neuropathological and biochemical features. Most cases are detected in apparently healthy sheep by post mortem examination. Results The occurrence of atypical scrapie in three sheep in (or derived from) the Sheep Unit is reported. Significant features of the affected sheep included their relatively high ages (6 y 1 mo, 7 y 9 mo, 9 y 7 mo respectively), their breed (all Cheviots) and their similar PRNP genotypes (AFRQ/AFRQ, AFRQ/ALRQ, and AFRQ/AFRQ, respectively). Two of the three sheep showed no clinical signs prior to death but all were confirmed as having atypical scrapie by immunohistochemistry and Western immunoblotting. Results of epidemiological investigations are presented and possible aetiologies of the cases are discussed. Conclusion By process of exclusion, a likely explanation for the three cases of atypical scrapie is that they arose spontaneously and were not infected from an exterior source. If correct, this raises challenging issues for countries which are currently regarded as free from scrapie. It would mean that atypical scrapie is liable to occur in flocks worldwide, especially in older sheep of susceptible genotypes. To state confidently that both the classical and atypical forms of scrapie are absent from a population it is necessary

Computer-assisted cytologic diagnosis in pancreatic FNA: An application of neural networks to image analysis.

PubMed

Momeni-Boroujeni, Amir; Yousefi, Elham; Somma, Jonathan

2017-12-01

Fine-needle aspiration (FNA) biopsy is an accurate method for the diagnosis of solid pancreatic masses. However, a significant number of cases still pose a diagnostic challenge. The authors have attempted to design a computer model to aid in the diagnosis of these biopsies. Images were captured of cell clusters on ThinPrep slides from 75 pancreatic FNA cases (20 malignant, 24 benign, and 31 atypical). A K-means clustering algorithm was used to segment the cell clusters into separable regions of interest before extracting features similar to those used for cytomorphologic assessment. A multilayer perceptron neural network (MNN) was trained and then tested for its ability to distinguish benign from malignant cases. A total of 277 images of cell clusters were obtained. K-means clustering identified 68,301 possible regions of interest overall. Features such as contour, perimeter, and area were found to be significantly different between malignant and benign images (P <.05). The MNN was 100% accurate for benign and malignant categories. The model's predictions from the atypical data set were 77% accurate. The results of the current study demonstrate that computer models can be used successfully to distinguish benign from malignant pancreatic cytology. The fact that the model can categorize atypical cases into benign or malignant with 77% accuracy highlights the great potential of this technology. Although further study is warranted to validate its clinical applications in pancreatic and perhaps other areas of cytology as well, the potential for improved patient outcomes using MNN for image analysis in pathology is significant. Cancer Cytopathol 2017;125:926-33. © 2017 American Cancer Society. © 2017 American Cancer Society.

Computer-aided diagnostic method for classification of Alzheimer's disease with atrophic image features on MR images

NASA Astrophysics Data System (ADS)

Arimura, Hidetaka; Yoshiura, Takashi; Kumazawa, Seiji; Tanaka, Kazuhiro; Koga, Hiroshi; Mihara, Futoshi; Honda, Hiroshi; Sakai, Shuji; Toyofuku, Fukai; Higashida, Yoshiharu

2008-03-01

Our goal for this study was to attempt to develop a computer-aided diagnostic (CAD) method for classification of Alzheimer's disease (AD) with atrophic image features derived from specific anatomical regions in three-dimensional (3-D) T1-weighted magnetic resonance (MR) images. Specific regions related to the cerebral atrophy of AD were white matter and gray matter regions, and CSF regions in this study. Cerebral cortical gray matter regions were determined by extracting a brain and white matter regions based on a level set based method, whose speed function depended on gradient vectors in an original image and pixel values in grown regions. The CSF regions in cerebral sulci and lateral ventricles were extracted by wrapping the brain tightly with a zero level set determined from a level set function. Volumes of the specific regions and the cortical thickness were determined as atrophic image features. Average cortical thickness was calculated in 32 subregions, which were obtained by dividing each brain region. Finally, AD patients were classified by using a support vector machine, which was trained by the image features of AD and non-AD cases. We applied our CAD method to MR images of whole brains obtained from 29 clinically diagnosed AD cases and 25 non-AD cases. As a result, the area under a receiver operating characteristic (ROC) curve obtained by our computerized method was 0.901 based on a leave-one-out test in identification of AD cases among 54 cases including 8 AD patients at early stages. The accuracy for discrimination between 29 AD patients and 25 non-AD subjects was 0.840, which was determined at the point where the sensitivity was the same as the specificity on the ROC curve. This result showed that our CAD method based on atrophic image features may be promising for detecting AD patients by using 3-D MR images.

Identification of the structural mutation responsible for the dibucaine-resistant (atypical) variant form of human serum cholinesterase.

PubMed Central

McGuire, M C; Nogueira, C P; Bartels, C F; Lightstone, H; Hajra, A; Van der Spek, A F; Lockridge, O; La Du, B N

1989-01-01

A point mutation in the gene for human serum cholinesterase was identified that changes Asp-70 to Gly in the atypical form of serum cholinesterase. The mutation in nucleotide 209, which changes codon 70 from GAT to GGT, was found by sequencing a genomic clone and sequencing selected regions of DNA amplified by the polymerase chain reaction. The entire coding sequences for usual and atypical cholinesterases were compared, and no other consistent base differences were found. A polymorphic site near the C terminus of the coded region was detected, but neither allele at this locus segregated consistently with the atypical trait. The nucleotide-209 mutation was detected in all five atypical cholinesterase families examined. There was complete concordance between this mutation and serum cholinesterase phenotypes for all 14 heterozygous and 6 homozygous atypical subjects tested. The mutation causes the loss of a Sau3A1 restriction site; the resulting DNA fragment length polymorphism was verified by electrophoresis of 32P-labeled DNA restriction fragments from usual and atypical subjects. Dot-blot hybridization analysis with a 19-mer allele-specific probe to the DNA amplified by the polymerase chain reaction distinguished between the usual and atypical genotypes. We conclude that the Asp-70----Gly mutation (acidic to neutral amino acid substitution) accounts for reduced affinity of atypical cholinesterase for choline esters and that Asp-70 must be an important component of the anionic site. Heterogeneity in atypical alleles may exist, but the Asp-70 point mutation may represent an appreciable portion of the atypical gene pool. Images PMID:2915989

Clinical Utility of Amyloid PET Imaging in the Differential Diagnosis of Atypical Dementias and Its Impact on Caregivers.

PubMed

Bensaïdane, Mohamed Reda; Beauregard, Jean-Mathieu; Poulin, Stéphane; Buteau, François-Alexandre; Guimond, Jean; Bergeron, David; Verret, Louis; Fortin, Marie-Pierre; Houde, Michèle; Bouchard, Rémi W; Soucy, Jean-Paul; Laforce, Robert

2016-04-18

Recent studies have supported a role for amyloid positron emission tomography (PET) imaging in distinguishing Alzheimer's disease (AD) pathology from other pathological protein accumulations leading to dementia. We investigated the clinical utility of amyloid PET in the differential diagnosis of atypical dementia cases and its impact on caregivers. Using the amyloid tracer 18F-NAV4694, we prospectively scanned 28 patients (mean age 59.3 y, s.d. 5.8; mean MMSE 21.4, s.d. 6.0) with an atypical dementia syndrome. Following a comprehensive diagnostic workup (i.e., history taking, neurological examination, blood tests, neuropsychological evaluation, MRI, and FDG-PET), no certain diagnosis could be arrived at. Amyloid PET was then conducted and classified as positive or negative. Attending physicians were asked to evaluate whether this result led to a change in diagnosis or altered management. They also reported their degree of confidence in the diagnosis. Caregivers were met after disclosure of amyloid PET results and completed a questionnaire/interview to assess the impact of the scan. Our cohort was evenly divided between positive (14/28) and negative (14/28) 18F-NAV4694 cases. Amyloid PET resulted in a diagnostic change in 9/28 cases (32.1%: 17.8% changed from AD to non-AD, 14.3% from non-AD to AD). There was a 44% increase in diagnostic confidence. Altered management occurred in 71.4% (20/28) of cases. Knowledge of amyloid status improved caregivers' outcomes in all domains (anxiety, depression, disease perception, future anticipation, and quality of life). This study suggests a useful additive role for amyloid PET in atypical cases with an unclear diagnosis beyond the extensive workup of a tertiary memory clinic. Amyloid PET increased diagnostic confidence and led to clinically significant alterations in management. The information gained from that test was well received by caregivers and encouraged spending quality time with their loved ones.

The Extracellular Domain of Myelin Oligodendrocyte Glycoprotein Elicits Atypical Experimental Autoimmune Encephalomyelitis in Rat and Macaque Species

PubMed Central

Curtis, Alan D.; Taslim, Najla; Reece, Shaun P.; Grebenciucova, Elena; Ray, Richard H.; Rosenbaum, Matthew D.; Wardle, Robert L.; Van Scott, Michael R.; Mannie, Mark D.

2014-01-01

Atypical models of experimental autoimmune encephalomyelitis (EAE) are advantageous in that the heterogeneity of clinical signs appears more reflective of those in multiple sclerosis (MS). Conversely, models of classical EAE feature stereotypic progression of an ascending flaccid paralysis that is not a characteristic of MS. The study of atypical EAE however has been limited due to the relative lack of suitable models that feature reliable disease incidence and severity, excepting mice deficient in gamma-interferon signaling pathways. In this study, atypical EAE was induced in Lewis rats, and a related approach was effective for induction of an unusual neurologic syndrome in a cynomolgus macaque. Lewis rats were immunized with the rat immunoglobulin variable (IgV)-related extracellular domain of myelin oligodendrocyte glycoprotein (IgV-MOG) in complete Freund’s adjuvant (CFA) followed by one or more injections of rat IgV-MOG in incomplete Freund’s adjuvant (IFA). The resulting disease was marked by torticollis, unilateral rigid paralysis, forelimb weakness, and high titers of anti-MOG antibody against conformational epitopes of MOG, as well as other signs of atypical EAE. A similar strategy elicited a distinct atypical form of EAE in a cynomolgus macaque. By day 36 in the monkey, titers of IgG against conformational epitopes of extracellular MOG were evident, and on day 201, the macaque had an abrupt onset of an unusual form of EAE that included a pronounced arousal-dependent, transient myotonia. The disease persisted for 6–7 weeks and was marked by a gradual, consistent improvement and an eventual full recovery without recurrence. These data indicate that one or more boosters of IgV-MOG in IFA represent a key variable for induction of atypical or unusual forms of EAE in rat and Macaca species. These studies also reveal a close correlation between humoral immunity against conformational epitopes of MOG, extended confluent demyelinating plaques in spinal cord

The extracellular domain of myelin oligodendrocyte glycoprotein elicits atypical experimental autoimmune encephalomyelitis in rat and Macaque species.

PubMed

Curtis, Alan D; Taslim, Najla; Reece, Shaun P; Grebenciucova, Elena; Ray, Richard H; Rosenbaum, Matthew D; Wardle, Robert L; Van Scott, Michael R; Mannie, Mark D

2014-01-01

Atypical models of experimental autoimmune encephalomyelitis (EAE) are advantageous in that the heterogeneity of clinical signs appears more reflective of those in multiple sclerosis (MS). Conversely, models of classical EAE feature stereotypic progression of an ascending flaccid paralysis that is not a characteristic of MS. The study of atypical EAE however has been limited due to the relative lack of suitable models that feature reliable disease incidence and severity, excepting mice deficient in gamma-interferon signaling pathways. In this study, atypical EAE was induced in Lewis rats, and a related approach was effective for induction of an unusual neurologic syndrome in a cynomolgus macaque. Lewis rats were immunized with the rat immunoglobulin variable (IgV)-related extracellular domain of myelin oligodendrocyte glycoprotein (IgV-MOG) in complete Freund's adjuvant (CFA) followed by one or more injections of rat IgV-MOG in incomplete Freund's adjuvant (IFA). The resulting disease was marked by torticollis, unilateral rigid paralysis, forelimb weakness, and high titers of anti-MOG antibody against conformational epitopes of MOG, as well as other signs of atypical EAE. A similar strategy elicited a distinct atypical form of EAE in a cynomolgus macaque. By day 36 in the monkey, titers of IgG against conformational epitopes of extracellular MOG were evident, and on day 201, the macaque had an abrupt onset of an unusual form of EAE that included a pronounced arousal-dependent, transient myotonia. The disease persisted for 6-7 weeks and was marked by a gradual, consistent improvement and an eventual full recovery without recurrence. These data indicate that one or more boosters of IgV-MOG in IFA represent a key variable for induction of atypical or unusual forms of EAE in rat and Macaca species. These studies also reveal a close correlation between humoral immunity against conformational epitopes of MOG, extended confluent demyelinating plaques in spinal cord and

Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

PubMed

Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep

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.

Difet: Distributed Feature Extraction Tool for High Spatial Resolution Remote Sensing Images

NASA Astrophysics Data System (ADS)

Eken, S.; Aydın, E.; Sayar, A.

2017-11-01

In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

The importance of biochemical and genetic findings in the diagnosis of atypical Norrie disease.

PubMed

Rodríguez-Muñoz, Ana; García-García, Gema; Menor, Francisco; Millán, José M; Tomás-Vila, Miguel; Jaijo, Teresa

2018-01-26

Norrie disease (ND) is a rare X-linked disorder characterized by bilateral congenital blindness. ND is caused by a mutation in the Norrie disease pseudoglioma (NDP) gene, which encodes a 133-amino acid protein called norrin. Intragenic deletions including NDP and adjacent genes have been identified in ND patients with a more severe neurologic phenotype. We report the biochemical, molecular, clinical and radiological features of two unrelated affected males with a deletion including NDP and MAO genes. Biochemical and genetic analyses were performed to understand the atypical phenotype and radiological findings. Biogenic amines in cerebrospinal fluid (CSF) were measured by high-performance liquid chromatography. The coding exons of NDP gene were amplified by polymerase chain reaction. Multiplex ligation-dependent probe amplification and chromosomal microarray were carried out on both affected males. Computed tomography and magnetic resonance imaging were performed on the two patients. In one patient, the serotonin and catecholamine metabolite levels in CSF were virtually undetectable. In both patients, genetic studies revealed microdeletions in the Xp11.3 region, involving the NDP, MAOA and MAOB genes. Radiological examination demonstrated brain and cerebellar atrophy. We suggest that alterations caused by MAO deficit may remain during the first years of life. Clinical phenotype, biochemical findings and neuroimaging can guide the genetic study in patients with atypical ND and help us to a better understanding of this disease.

SU-E-J-261: The Importance of Appropriate Image Preprocessing to Augment the Information of Radiomics Image Features

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

Zhang, L; Fried, D; Fave, X

Purpose: To investigate how different image preprocessing techniques, their parameters, and the different boundary handling techniques can augment the information of features and improve feature’s differentiating capability. Methods: Twenty-seven NSCLC patients with a solid tumor volume and no visually obvious necrotic regions in the simulation CT images were identified. Fourteen of these patients had a necrotic region visible in their pre-treatment PET images (necrosis group), and thirteen had no visible necrotic region in the pre-treatment PET images (non-necrosis group). We investigated how image preprocessing can impact the ability of radiomics image features extracted from the CT to differentiate between twomore » groups. It is expected the histogram in the necrosis group is more negatively skewed, and the uniformity from the necrosis group is less. Therefore, we analyzed two first order features, skewness and uniformity, on the image inside the GTV in the intensity range [−20HU, 180HU] under the combination of several image preprocessing techniques: (1) applying the isotropic Gaussian or anisotropic diffusion smoothing filter with a range of parameter(Gaussian smoothing: size=11, sigma=0:0.1:2.3; anisotropic smoothing: iteration=4, kappa=0:10:110); (2) applying the boundaryadapted Laplacian filter; and (3) applying the adaptive upper threshold for the intensity range. A 2-tailed T-test was used to evaluate the differentiating capability of CT features on pre-treatment PT necrosis. Result: Without any preprocessing, no differences in either skewness or uniformity were observed between two groups. After applying appropriate Gaussian filters (sigma>=1.3) or anisotropic filters(kappa >=60) with the adaptive upper threshold, skewness was significantly more negative in the necrosis group(p<0.05). By applying the boundary-adapted Laplacian filtering after the appropriate Gaussian filters (0.5 <=sigma<=1.1) or anisotropic filters(20<=kappa <=50), the uniformity

CYTOPATHIC EFFECT OF THE ATYPICAL PNEUMONIA ORGANISM IN CULTURES OF HUMAN TISSUE

PubMed Central

Eaton, Monroe D.; Farnham, Ann E.; Levinthal, Jeana D.; Scala, Anthony R.

1962-01-01

Eaton, Monroe D. (Harvard Medical School, Boston, Mass.), Ann E. Farnham, Jeana D. Levinthal, and Anthony R. Scala. Cytopathic effect of the atypical pneumonia organism in cultures of human tissue. J. Bacteriol. 84:1330–1337. 1962.—Three strains of the atypical pneumonia agent were adapted to grow in continuous cell cultures of human amnion or human embryonic lung, with production of initial increased acidity followed by destruction of the cells. Evidence is presented that cytopathic effects of the organism were associated with intracellular growth and formation of microcolonies. Clumps of organisms stained specifically with fluorescein-labeled antibody, and showed distinctive tinctorial reactions with the May Grünwald-Giemsa stain. The cytopathic effect was prevented by fresh serum from a rabbit immunized with an egg-passage strain of the atypical pneumonia agent. Heating the immune serum to 56 C for 30 min abolished the neutralizing effect. The significance of heat-labile serum constituents in killing or inhibition of mycoplasma is discussed. Images PMID:16561984

Orientation Modeling for Amateur Cameras by Matching Image Line Features and Building Vector Data

NASA Astrophysics Data System (ADS)

Hung, C. H.; Chang, W. C.; Chen, L. C.

2016-06-01

With the popularity of geospatial applications, database updating is getting important due to the environmental changes over time. Imagery provides a lower cost and efficient way to update the database. Three dimensional objects can be measured by space intersection using conjugate image points and orientation parameters of cameras. However, precise orientation parameters of light amateur cameras are not always available due to their costliness and heaviness of precision GPS and IMU. To automatize data updating, the correspondence of object vector data and image may be built to improve the accuracy of direct georeferencing. This study contains four major parts, (1) back-projection of object vector data, (2) extraction of image feature lines, (3) object-image feature line matching, and (4) line-based orientation modeling. In order to construct the correspondence of features between an image and a building model, the building vector features were back-projected onto the image using the initial camera orientation from GPS and IMU. Image line features were extracted from the imagery. Afterwards, the matching procedure was done by assessing the similarity between the extracted image features and the back-projected ones. Then, the fourth part utilized line features in orientation modeling. The line-based orientation modeling was performed by the integration of line parametric equations into collinearity condition equations. The experiment data included images with 0.06 m resolution acquired by Canon EOS Mark 5D II camera on a Microdrones MD4-1000 UAV. Experimental results indicate that 2.1 pixel accuracy may be reached, which is equivalent to 0.12 m in the object space.

Discriminatively learning for representing local image features with quadruplet model

NASA Astrophysics Data System (ADS)

Zhang, Da-long; Zhao, Lei; Xu, Duan-qing; Lu, Dong-ming

2017-11-01

Traditional hand-crafted features for representing local image patches are evolving into current data-driven and learning-based image feature, but learning a robust and discriminative descriptor which is capable of controlling various patch-level computer vision tasks is still an open problem. In this work, we propose a novel deep convolutional neural network (CNN) to learn local feature descriptors. We utilize the quadruplets with positive and negative training samples, together with a constraint to restrict the intra-class variance, to learn good discriminative CNN representations. Compared with previous works, our model reduces the overlap in feature space between corresponding and non-corresponding patch pairs, and mitigates margin varying problem caused by commonly used triplet loss. We demonstrate that our method achieves better embedding result than some latest works, like PN-Net and TN-TG, on benchmark dataset.

Atypical Clinical Manifestations of Graves' Disease: An Analysis in Depth

PubMed Central

Hegazi, Mohamed Osama; Ahmed, Sherif

2012-01-01

Over the past few decades, there has been an increase in the number of reports about newly recognized (atypical or unusual) manifestations of Graves' disease (GD), that are related to various body systems. One of these manifestations is sometimes the main presenting feature of GD. Some of the atypical manifestations are specifically related to GD, while others are also similarly seen in patients with other forms of hyperthyroidism. Lack of knowledge of the association between these findings and GD may lead to delay in diagnosis, misdiagnosis, or unnecessary investigations. The atypical clinical presentations of GD include anemia, vomiting, jaundice, and right heart failure. There is one type of anemia that is not explained by any of the known etiological factors and responds well to hyperthyroidism treatment. This type of anemia resembles anemia of chronic disease and may be termed GD anemia. Other forms of anemia that are associated with GD include pernicious anemia, iron deficiency anemia of celiac disease, and autoimmune hemolytic anemia. Vomiting has been reported as a presenting feature of Graves' disease. Some cases had the typical findings of hyperthyroidism initially masked, and the vomiting did not improve until hyperthyroidism has been detected and treated. Hyperthyroidism may present with jaundice, and on the other hand, deep jaundice may develop with the onset of overt hyperthyroidism in previously compensated chronic liver disease patients. Pulmonary hypertension is reported to be associated with GD and to respond to its treatment. GD-related pulmonary hypertension may be so severe to produce isolated right-sided heart failure that is occasionally found as the presenting manifestation of GD. PMID:22132347

Classifying Acute Ischemic Stroke Onset Time using Deep Imaging Features

PubMed Central

Ho, King Chung; Speier, William; El-Saden, Suzie; Arnold, Corey W.

2017-01-01

Models have been developed to predict stroke outcomes (e.g., mortality) in attempt to provide better guidance for stroke treatment. However, there is little work in developing classification models for the problem of unknown time-since-stroke (TSS), which determines a patient’s treatment eligibility based on a clinical defined cutoff time point (i.e., <4.5hrs). In this paper, we construct and compare machine learning methods to classify TSS<4.5hrs using magnetic resonance (MR) imaging features. We also propose a deep learning model to extract hidden representations from the MR perfusion-weighted images and demonstrate classification improvement by incorporating these additional imaging features. Finally, we discuss a strategy to visualize the learned features from the proposed deep learning model. The cross-validation results show that our best classifier achieved an area under the curve of 0.68, which improves significantly over current clinical methods (0.58), demonstrating the potential benefit of using advanced machine learning methods in TSS classification. PMID:29854156

Hierarchical Feature Extraction With Local Neural Response for Image Recognition.

PubMed

Li, Hong; Wei, Yantao; Li, Luoqing; Chen, C L P

2013-04-01

In this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model. We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms.

Research based on the SoPC platform of feature-based image registration

NASA Astrophysics Data System (ADS)

Shi, Yue-dong; Wang, Zhi-hui

2015-12-01

This paper focuses on the study of implementing feature-based image registration by System on a Programmable Chip (SoPC) hardware platform. We solidify the image registration algorithm on the FPGA chip, in which embedded soft core processor Nios II can speed up the image processing system. In this way, we can make image registration technology get rid of the PC. And, consequently, this kind of technology will be got an extensive use. The experiment result indicates that our system shows stable performance, particularly in terms of matching processing which noise immunity is good. And feature points of images show a reasonable distribution.

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images.

PubMed

Zhang, Lefei; Zhang, Qian; Du, Bo; Huang, Xin; Tang, Yuan Yan; Tao, Dacheng

2018-01-01

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature, and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation has not efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient.

Image Retrieval using Integrated Features of Binary Wavelet Transform

NASA Astrophysics Data System (ADS)

Agarwal, Megha; Maheshwari, R. P.

2011-12-01

In this paper a new approach for image retrieval is proposed with the application of binary wavelet transform. This new approach facilitates the feature calculation with the integration of histogram and correlogram features extracted from binary wavelet subbands. Experiments are performed to evaluate and compare the performance of proposed method with the published literature. It is verified that average precision and average recall of proposed method (69.19%, 41.78%) is significantly improved compared to optimal quantized wavelet correlogram (OQWC) [6] (64.3%, 38.00%) and Gabor wavelet correlogram (GWC) [10] (64.1%, 40.6%). All the experiments are performed on Corel 1000 natural image database [20].

Dysfunctional metacognition and drive for thinness in typical and atypical anorexia nervosa.

PubMed

Davenport, Emily; Rushford, Nola; Soon, Siew; McDermott, Cressida

2015-01-01

Anorexia nervosa is complex and difficult to treat. In cognitive therapies the focus has been on cognitive content rather than process. Process-oriented therapies may modify the higher level cognitive processes of metacognition, reported as dysfunctional in adult anorexia nervosa. Their association with clinical features of anorexia nervosa, however, is unclear. With reclassification of anorexia nervosa by DSM-5 into typical and atypical groups, comparability of metacognition and drive for thinness across groups and relationships within groups is also unclear. Main objectives were to determine whether metacognitive factors differ across typical and atypical anorexia nervosa and a non-clinical community sample, and to explore a process model by determining whether drive for thinness is concurrently predicted by metacognitive factors. Women receiving treatment for anorexia nervosa (n = 119) and non-clinical community participants (n = 100), aged between 18 and 46 years, completed the Eating Disorders Inventory (3(rd) Edition) and Metacognitions Questionnaire (Brief Version). Body Mass Index (BMI) of 18.5 kg/m(2) differentiated between typical (n = 75) and atypical (n = 44) anorexia nervosa. Multivariate analyses of variance and regression analyses were conducted. Metacognitive profiles were similar in both typical and atypical anorexia nervosa and confirmed as more dysfunctional than in the non-clinical group. Drive for thinness was concurrently predicted in the typical patients by the metacognitive factors, positive beliefs about worry, and need to control thoughts; in the atypical patients by negative beliefs about worry and, inversely, by cognitive self-consciousness, and in the non-clinical group by cognitive self-consciousness. Despite having a healthier weight, the atypical group was as severely affected by dysfunctional metacognitions and drive for thinness as the typical group. Because metacognition concurrently predicted drive for thinness

Perceptual quality prediction on authentically distorted images using a bag of features approach

PubMed Central

Ghadiyaram, Deepti; Bovik, Alan C.

2017-01-01

Current top-performing blind perceptual image quality prediction models are generally trained on legacy databases of human quality opinion scores on synthetically distorted images. Therefore, they learn image features that effectively predict human visual quality judgments of inauthentic and usually isolated (single) distortions. However, real-world images usually contain complex composite mixtures of multiple distortions. We study the perceptually relevant natural scene statistics of such authentically distorted images in different color spaces and transform domains. We propose a “bag of feature maps” approach that avoids assumptions about the type of distortion(s) contained in an image and instead focuses on capturing consistencies—or departures therefrom—of the statistics of real-world images. Using a large database of authentically distorted images, human opinions of them, and bags of features computed on them, we train a regressor to conduct image quality prediction. We demonstrate the competence of the features toward improving automatic perceptual quality prediction by testing a learned algorithm using them on a benchmark legacy database as well as on a newly introduced distortion-realistic resource called the LIVE In the Wild Image Quality Challenge Database. We extensively evaluate the perceptual quality prediction model and algorithm and show that it is able to achieve good-quality prediction power that is better than other leading models. PMID:28129417

Research on Remote Sensing Image Classification Based on Feature Level Fusion

NASA Astrophysics Data System (ADS)

Yuan, L.; Zhu, G.

2018-04-01

Remote sensing image classification, as an important direction of remote sensing image processing and application, has been widely studied. However, in the process of existing classification algorithms, there still exists the phenomenon of misclassification and missing points, which leads to the final classification accuracy is not high. In this paper, we selected Sentinel-1A and Landsat8 OLI images as data sources, and propose a classification method based on feature level fusion. Compare three kind of feature level fusion algorithms (i.e., Gram-Schmidt spectral sharpening, Principal Component Analysis transform and Brovey transform), and then select the best fused image for the classification experimental. In the classification process, we choose four kinds of image classification algorithms (i.e. Minimum distance, Mahalanobis distance, Support Vector Machine and ISODATA) to do contrast experiment. We use overall classification precision and Kappa coefficient as the classification accuracy evaluation criteria, and the four classification results of fused image are analysed. The experimental results show that the fusion effect of Gram-Schmidt spectral sharpening is better than other methods. In four kinds of classification algorithms, the fused image has the best applicability to Support Vector Machine classification, the overall classification precision is 94.01 % and the Kappa coefficients is 0.91. The fused image with Sentinel-1A and Landsat8 OLI is not only have more spatial information and spectral texture characteristics, but also enhances the distinguishing features of the images. The proposed method is beneficial to improve the accuracy and stability of remote sensing image classification.

Research of image retrieval technology based on color feature

NASA Astrophysics Data System (ADS)

Fu, Yanjun; Jiang, Guangyu; Chen, Fengying

2009-10-01

Recently, with the development of the communication and the computer technology and the improvement of the storage technology and the capability of the digital image equipment, more and more image resources are given to us than ever. And thus the solution of how to locate the proper image quickly and accurately is wanted.The early method is to set up a key word for searching in the database, but now the method has become very difficult when we search much more picture that we need. In order to overcome the limitation of the traditional searching method, content based image retrieval technology was aroused. Now, it is a hot research subject.Color image retrieval is the important part of it. Color is the most important feature for color image retrieval. Three key questions on how to make use of the color characteristic are discussed in the paper: the expression of color, the abstraction of color characteristic and the measurement of likeness based on color. On the basis, the extraction technology of the color histogram characteristic is especially discussed. Considering the advantages and disadvantages of the overall histogram and the partition histogram, a new method based the partition-overall histogram is proposed. The basic thought of it is to divide the image space according to a certain strategy, and then calculate color histogram of each block as the color feature of this block. Users choose the blocks that contain important space information, confirming the right value. The system calculates the distance between the corresponding blocks that users choosed. Other blocks merge into part overall histograms again, and the distance should be calculated. Then accumulate all the distance as the real distance between two pictures. The partition-overall histogram comprehensive utilizes advantages of two methods above, by choosing blocks makes the feature contain more spatial information which can improve performance; the distances between partition-overall histogram

Thermal feature extraction of servers in a datacenter using thermal image registration

NASA Astrophysics Data System (ADS)

Liu, Hang; Ran, Jian; Xie, Ting; Gao, Shan

2017-09-01

Thermal cameras provide fine-grained thermal information that enhances monitoring and enables automatic thermal management in large datacenters. Recent approaches employing mobile robots or thermal camera networks can already identify the physical locations of hot spots. Other distribution information used to optimize datacenter management can also be obtained automatically using pattern recognition technology. However, most of the features extracted from thermal images, such as shape and gradient, may be affected by changes in the position and direction of the thermal camera. This paper presents a method for extracting the thermal features of a hot spot or a server in a container datacenter. First, thermal and visual images are registered based on textural characteristics extracted from images acquired in datacenters. Then, the thermal distribution of each server is standardized. The features of a hot spot or server extracted from the standard distribution can reduce the impact of camera position and direction. The results of experiments show that image registration is efficient for aligning the corresponding visual and thermal images in the datacenter, and the standardization procedure reduces the impacts of camera position and direction on hot spot or server features.

Illumination invariant feature point matching for high-resolution planetary remote sensing images

NASA Astrophysics Data System (ADS)

Wu, Bo; Zeng, Hai; Hu, Han

2018-03-01

Despite its success with regular close-range and remote-sensing images, the scale-invariant feature transform (SIFT) algorithm is essentially not invariant to illumination differences due to the use of gradients for feature description. In planetary remote sensing imagery, which normally lacks sufficient textural information, salient regions are generally triggered by the shadow effects of keypoints, reducing the matching performance of classical SIFT. Based on the observation of dual peaks in a histogram of the dominant orientations of SIFT keypoints, this paper proposes an illumination-invariant SIFT matching method for high-resolution planetary remote sensing images. First, as the peaks in the orientation histogram are generally aligned closely with the sub-solar azimuth angle at the time of image collection, an adaptive suppression Gaussian function is tuned to level the histogram and thereby alleviate the differences in illumination caused by a changing solar angle. Next, the suppression function is incorporated into the original SIFT procedure for obtaining feature descriptors, which are used for initial image matching. Finally, as the distribution of feature descriptors changes after anisotropic suppression, and the ratio check used for matching and outlier removal in classical SIFT may produce inferior results, this paper proposes an improved matching procedure based on cross-checking and template image matching. The experimental results for several high-resolution remote sensing images from both the Moon and Mars, with illumination differences of 20°-180°, reveal that the proposed method retrieves about 40%-60% more matches than the classical SIFT method. The proposed method is of significance for matching or co-registration of planetary remote sensing images for their synergistic use in various applications. It also has the potential to be useful for flyby and rover images by integrating with the affine invariant feature detectors.

The role of cyclothymia in atypical depression: toward a data-based reconceptualization of the borderline-bipolar II connection.

PubMed

Perugi, Giulio; Toni, Cristina; Travierso, Maria Chiara; Akiskal, Hagop S

2003-01-01

Recent data, including our own, indicate significant overlap between atypical depression and bipolar II. Furthermore, the affective fluctuations of patients with these disorders are difficult to separate, on clinical grounds, from cyclothymic temperamental and borderline personality disorders. The present analyses are part of an ongoing Pisa-San Diego investigation to examine whether interpersonal sensitivity, mood reactivity and cyclothymic mood swings constitute a common diathesis underlying the atypical depression-bipolar II-borderline personality constructs. We examined in a semi-structured format 107 consecutive patients who met criteria for major depressive episode with DSM-IV atypical features. Patients were further evaluated on the basis of the Atypical Depression Diagnostic Scale (ADDS), the Hopkins Symptoms Check-list (HSCL-90), and the Hamilton Rating Scale for Depression (HRSD), coupled with its modified form for reverse vegetative features as well as Axis I and SCID-II evaluated Axis II comorbidity, and cyclothymic dispositions ('APA Review', American Psychiatric Press, Washington DC, 1992). Seventy-eight percent of atypical depressives met criteria for bipolar spectrum-principally bipolar II-disorder. Forty-five patients who met the criteria for cyclothymic temperament, compared with the 62 who did not, were indistinguishable on demographic, familial and clinical features, but were significantly higher in lifetime comorbidity for panic disorder with agoraphobia, alcohol abuse, bulimia nervosa, as well as borderline and dependent personality disorders. Cyclothymic atypical depressives also scored higher on the ADDS items of maximum reactivity of mood, interpersonal sensitivity, functional impairment, avoidance of relationships, other rejection avoidance, and on the interpersonal sensitivity, phobic anxiety, paranoid ideation and psychoticism of the HSCL-90 factors. The total number of cyclothymic traits was significantly correlated with 'maximum

Change Detection in Uav Video Mosaics Combining a Feature Based Approach and Extended Image Differencing

NASA Astrophysics Data System (ADS)

Saur, Günter; Krüger, Wolfgang

2016-06-01

Change detection is an important task when using unmanned aerial vehicles (UAV) for video surveillance. We address changes of short time scale using observations in time distances of a few hours. Each observation (previous and current) is a short video sequence acquired by UAV in near-Nadir view. Relevant changes are, e.g., recently parked or moved vehicles. Examples for non-relevant changes are parallaxes caused by 3D structures of the scene, shadow and illumination changes, and compression or transmission artifacts. In this paper we present (1) a new feature based approach to change detection, (2) a combination with extended image differencing (Saur et al., 2014), and (3) the application to video sequences using temporal filtering. In the feature based approach, information about local image features, e.g., corners, is extracted in both images. The label "new object" is generated at image points, where features occur in the current image and no or weaker features are present in the previous image. The label "vanished object" corresponds to missing or weaker features in the current image and present features in the previous image. This leads to two "directed" change masks and differs from image differencing where only one "undirected" change mask is extracted which combines both label types to the single label "changed object". The combination of both algorithms is performed by merging the change masks of both approaches. A color mask showing the different contributions is used for visual inspection by a human image interpreter.

TU-F-CAMPUS-J-05: Effect of Uncorrelated Noise Texture On Computed Tomography Quantitative Image Features

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

Oliver, J; Budzevich, M; Moros, E

Purpose: To investigate the relationship between quantitative image features (i.e. radiomics) and statistical fluctuations (i.e. electronic noise) in clinical Computed Tomography (CT) using the standardized American College of Radiology (ACR) CT accreditation phantom and patient images. Methods: Three levels of uncorrelated Gaussian noise were added to CT images of phantom and patients (20) acquired in static mode and respiratory tracking mode. We calculated the noise-power spectrum (NPS) of the original CT images of the phantom, and of the phantom images with added Gaussian noise with means of 50, 80, and 120 HU. Concurrently, on patient images (original and noise-added images),more » image features were calculated: 14 shape, 19 intensity (1st order statistics from intensity volume histograms), 18 GLCM features (2nd order statistics from grey level co-occurrence matrices) and 11 RLM features (2nd order statistics from run-length matrices). These features provide the underlying structural information of the images. GLCM (size 128x128) was calculated with a step size of 1 voxel in 13 directions and averaged. RLM feature calculation was performed in 13 directions with grey levels binning into 128 levels. Results: Adding the electronic noise to the images modified the quality of the NPS, shifting the noise from mostly correlated to mostly uncorrelated voxels. The dramatic increase in noise texture did not affect image structure/contours significantly for patient images. However, it did affect the image features and textures significantly as demonstrated by GLCM differences. Conclusion: Image features are sensitive to acquisition factors (simulated by adding uncorrelated Gaussian noise). We speculate that image features will be more difficult to detect in the presence of electronic noise (an uncorrelated noise contributor) or, for that matter, any other highly correlated image noise. This work focuses on the effect of electronic, uncorrelated, noise and future work

Edge enhancement and noise suppression for infrared image based on feature analysis

NASA Astrophysics Data System (ADS)

Jiang, Meng

2018-06-01

Infrared images are often suffering from background noise, blurred edges, few details and low signal-to-noise ratios. To improve infrared image quality, it is essential to suppress noise and enhance edges simultaneously. To realize it in this paper, we propose a novel algorithm based on feature analysis in shearlet domain. Firstly, as one of multi-scale geometric analysis (MGA), we introduce the theory and superiority of shearlet transform. Secondly, after analyzing the defects of traditional thresholding technique to suppress noise, we propose a novel feature extraction distinguishing image structures from noise well and use it to improve the traditional thresholding technique. Thirdly, with computing the correlations between neighboring shearlet coefficients, the feature attribute maps identifying the weak detail and strong edges are completed to improve the generalized unsharped masking (GUM). At last, experiment results with infrared images captured in different scenes demonstrate that the proposed algorithm suppresses noise efficiently and enhances image edges adaptively.

Laser interference effect evaluation method based on character of laser-spot and image feature

NASA Astrophysics Data System (ADS)

Tang, Jianfeng; Luo, Xiaolin; Wu, Lingxia

2016-10-01

Evaluating the laser interference effect to CCD objectively and accurately has great research value. Starting from the change of the image's feature before and after interference, meanwhile, considering the influence of the laser-spot distribution character on the masking degree of the image feature information, a laser interference effect evaluation method based on character of laser-spot and image feature was proposed. It reflected the laser-spot distribution character using the distance between the center of the laser-spot and center of the target. It reflected the change of the global image feature using the changes of image's sparse coefficient matrix, which was obtained by the SSIM-inspired orthogonal matching pursuit (OMP) sparse coding algorithm. What's more, the assessment method reflected the change of the local image feature using the changes of the image's edge sharpness, which could be obtained by the change of the image's gradient magnitude. Taken together, the laser interference effect can be evaluated accurately. In terms of the laser interference experiment results, the proposed method shows good rationality and feasibility under the disturbing condition of different laser powers, and it can also overcome the inaccuracy caused by the change of the laser-spot position, realizing the evaluation of the laser interference effect objectively and accurately.

Feature selection from a facial image for distinction of sasang constitution.

PubMed

Koo, Imhoi; Kim, Jong Yeol; Kim, Myoung Geun; Kim, Keun Ho

2009-09-01

Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here.

Enhancement of morphological and vascular features in OCT images using a modified Bayesian residual transform

PubMed Central

Tan, Bingyao; Wong, Alexander; Bizheva, Kostadinka

2018-01-01

A novel image processing algorithm based on a modified Bayesian residual transform (MBRT) was developed for the enhancement of morphological and vascular features in optical coherence tomography (OCT) and OCT angiography (OCTA) images. The MBRT algorithm decomposes the original OCT image into multiple residual images, where each image presents information at a unique scale. Scale selective residual adaptation is used subsequently to enhance morphological features of interest, such as blood vessels and tissue layers, and to suppress irrelevant image features such as noise and motion artefacts. The performance of the proposed MBRT algorithm was tested on a series of cross-sectional and enface OCT and OCTA images of retina and brain tissue that were acquired in-vivo. Results show that the MBRT reduces speckle noise and motion-related imaging artefacts locally, thus improving significantly the contrast and visibility of morphological features in the OCT and OCTA images. PMID:29760996

Image feature extraction based on the camouflage effectiveness evaluation

NASA Astrophysics Data System (ADS)

Yuan, Xin; Lv, Xuliang; Li, Ling; Wang, Xinzhu; Zhang, Zhi

2018-04-01

The key step of camouflage effectiveness evaluation is how to combine the human visual physiological features, psychological features to select effectively evaluation indexes. Based on the predecessors' camo comprehensive evaluation method, this paper chooses the suitable indexes combining with the image quality awareness, and optimizes those indexes combining with human subjective perception. Thus, it perfects the theory of index extraction.

Atypical autoantibodies in patients with primary Sjögren syndrome: clinical characteristics and follow-up of 82 cases.

PubMed

Ramos-Casals, Manuel; Nardi, Norma; Brito-Zerón, Pilar; Aguiló, Sira; Gil, Victor; Delgado, German; Bové, Albert; Font, Josep

2006-04-01

To analyze the clinical characteristics, follow-up, and fulfillment of classification criteria for other systemic autoimmune diseases (SAD) in patients with primary Sjögren syndrome (SS) and atypical autoantibodies. We studied 402 patients diagnosed with primary SS seen consecutively in our Department since 1994. We considered anti-DNA, anti-Sm, anti-RNP, anti-topoisomerase1/Scl70, anticentromere (ACA), anti-Jo1, anti-neutrophil cytoplasmic antibodies (ANCA), anticardiolipin antibodies (aPL), and lupus anticoagulant as atypical autoantibodies. The patients were prospectively followed after inclusion into the protocol, focusing on the development of features that might lead to the fulfillment of classification criteria for additional SAD. As a control group, we selected an age-sex-matched subset of patients with primary SS without atypical autoantibodies. Eighty-two (20%) patients showed atypical autoantibodies (36 had aPL, 21 anti-DNA, 13 ANCA, 10 anti-RNP, 8 ACA, 6 anti-Sm, 2 anti-Scl70, and 1 anti-Jo-1 antibodies). There were 77 (94%) women and 5 (6%) men, with a mean age of 57 years. Patients with atypical autoantibodies had no statistical differences in the prevalence of the main sicca features, extraglandular manifestations (except for a higher prevalence of Raynaud's phenomenon, 28% versus 7%, P=0.001), immunological markers, and in the fulfillment of the 2002 classification criteria, compared with the control group. After a follow-up of 534 patient-years, 13 (16%) of the 82 patients with atypical autoantibodies developed an additional SAD (systemic lupus erythematosus in 5 cases, antiphospholipid syndrome in 4, limited scleroderma in 3, and microscopic polyangiitis in 1) compared with none in the control group (P<0.001). This study shows an immunological overlap (defined by the presence of autoantibodies considered typical of other SAD) in 20% of our patients with primary SS. However, the clinical significance of these atypical autoantibodies varies widely

Effectiveness of image features and similarity measures in cluster-based approaches for content-based image retrieval

NASA Astrophysics Data System (ADS)

Du, Hongbo; Al-Jubouri, Hanan; Sellahewa, Harin

2014-05-01

Content-based image retrieval is an automatic process of retrieving images according to image visual contents instead of textual annotations. It has many areas of application from automatic image annotation and archive, image classification and categorization to homeland security and law enforcement. The key issues affecting the performance of such retrieval systems include sensible image features that can effectively capture the right amount of visual contents and suitable similarity measures to find similar and relevant images ranked in a meaningful order. Many different approaches, methods and techniques have been developed as a result of very intensive research in the past two decades. Among many existing approaches, is a cluster-based approach where clustering methods are used to group local feature descriptors into homogeneous regions, and search is conducted by comparing the regions of the query image against those of the stored images. This paper serves as a review of works in this area. The paper will first summarize the existing work reported in the literature and then present the authors' own investigations in this field. The paper intends to highlight not only achievements made by recent research but also challenges and difficulties still remaining in this area.

A Feature-based Approach to Big Data Analysis of Medical Images

PubMed Central

Toews, Matthew; Wachinger, Christian; Estepar, Raul San Jose; Wells, William M.

2015-01-01

This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches in O(log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct. PMID:26221685

A Feature-Based Approach to Big Data Analysis of Medical Images.

PubMed

Toews, Matthew; Wachinger, Christian; Estepar, Raul San Jose; Wells, William M

2015-01-01

This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches-in O (log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods.. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct.

Regional shape-based feature space for segmenting biomedical images using neural networks

NASA Astrophysics Data System (ADS)

Sundaramoorthy, Gopal; Hoford, John D.; Hoffman, Eric A.

1993-07-01

In biomedical images, structure of interest, particularly the soft tissue structures, such as the heart, airways, bronchial and arterial trees often have grey-scale and textural characteristics similar to other structures in the image, making it difficult to segment them using only gray- scale and texture information. However, these objects can be visually recognized by their unique shapes and sizes. In this paper we discuss, what we believe to be, a novel, simple scheme for extracting features based on regional shapes. To test the effectiveness of these features for image segmentation (classification), we use an artificial neural network and a statistical cluster analysis technique. The proposed shape-based feature extraction algorithm computes regional shape vectors (RSVs) for all pixels that meet a certain threshold criteria. The distance from each such pixel to a boundary is computed in 8 directions (or in 26 directions for a 3-D image). Together, these 8 (or 26) values represent the pixel's (or voxel's) RSV. All RSVs from an image are used to train a multi-layered perceptron neural network which uses these features to 'learn' a suitable classification strategy. To clearly distinguish the desired object from other objects within an image, several examples from inside and outside the desired object are used for training. Several examples are presented to illustrate the strengths and weaknesses of our algorithm. Both synthetic and actual biomedical images are considered. Future extensions to this algorithm are also discussed.

Thin plate spline feature point matching for organ surfaces in minimally invasive surgery imaging

NASA Astrophysics Data System (ADS)

Lin, Bingxiong; Sun, Yu; Qian, Xiaoning

2013-03-01

Robust feature point matching for images with large view angle changes in Minimally Invasive Surgery (MIS) is a challenging task due to low texture and specular reflections in these images. This paper presents a new approach that can improve feature matching performance by exploiting the inherent geometric property of the organ surfaces. Recently, intensity based template image tracking using a Thin Plate Spline (TPS) model has been extended for 3D surface tracking with stereo cameras. The intensity based tracking is also used here for 3D reconstruction of internal organ surfaces. To overcome the small displacement requirement of intensity based tracking, feature point correspondences are used for proper initialization of the nonlinear optimization in the intensity based method. Second, we generate simulated images from the reconstructed 3D surfaces under all potential view positions and orientations, and then extract feature points from these simulated images. The obtained feature points are then filtered and re-projected to the common reference image. The descriptors of the feature points under different view angles are stored to ensure that the proposed method can tolerate a large range of view angles. We evaluate the proposed method with silicon phantoms and in vivo images. The experimental results show that our method is much more robust with respect to the view angle changes than other state-of-the-art methods.

Histological Image Processing Features Induce a Quantitative Characterization of Chronic Tumor Hypoxia

PubMed Central

Grabocka, Elda; Bar-Sagi, Dafna; Mishra, Bud

2016-01-01

Hypoxia in tumors signifies resistance to therapy. Despite a wealth of tumor histology data, including anti-pimonidazole staining, no current methods use these data to induce a quantitative characterization of chronic tumor hypoxia in time and space. We use image-processing algorithms to develop a set of candidate image features that can formulate just such a quantitative description of xenographed colorectal chronic tumor hypoxia. Two features in particular give low-variance measures of chronic hypoxia near a vessel: intensity sampling that extends radially away from approximated blood vessel centroids, and multithresholding to segment tumor tissue into normal, hypoxic, and necrotic regions. From these features we derive a spatiotemporal logical expression whose truth value depends on its predicate clauses that are grounded in this histological evidence. As an alternative to the spatiotemporal logical formulation, we also propose a way to formulate a linear regression function that uses all of the image features to learn what chronic hypoxia looks like, and then gives a quantitative similarity score once it is trained on a set of histology images. PMID:27093539

ALS-Plus Syndrome: Non-Pyramidal Features in a Large ALS Cohort

PubMed Central

McCluskey, Leo; Vandriel, Shannon; Elman, Lauren; Van Deerlin, Vivianna M.; Powers, John; Boller, Ashley; Wood, Elisabeth McCarty; Woo, John; McMillan, Corey T.; Rascovsky, Katya; Grossman, Murray

2014-01-01

Objective Autopsy studies show widespread pathology in amyotrophic lateral sclerosis (ALS), but clinical surveys of multisystem disease in ALS are rare. We investigated ALS-Plus syndrome, an understudied group of patients with clinical features extending beyond pyramidal and neuromuscular systems with or without cognitive/behavioral deficits. Methods In a large, consecutively-ascertained cohort of 550 patients with ALS, we documented atypical clinical manifestations. Genetic screening for C9orf72 hexanucleotide expansions was performed in 343 patients, and SOD1, TARDBP, and VCP were tested in the subgroup of patients with a family history of ALS. Gray matter and white matter imaging was available in a subgroup of 30 patients. Results Seventy-five (13.6%) patients were identified with ALS-Plus syndrome. We found disorders of ocular motility, cerebellar, extrapyramidal and autonomic functioning. Relative to those without ALS-Plus, cognitive impairment (8.0% vs 2.9%, p=0.029), bulbar-onset (49.3% vs 23.2%, p<0.001), and pathogenic mutations (20.0% vs 8.4%, p=0.015) were more than twice as common in ALS-Plus. Survival was significantly shorter in ALS-Plus (29.66 months vs 42.50 months, p=0.02), regardless of bulbar-onset or mutation status. Imaging revealed significantly greater cerebellar and cerebral disease in ALS-Plus compared to those without ALS-Plus. Conclusions ALS-Plus syndrome is not uncommon, and the presence of these atypical features is consistent with neuropathological observations that ALS is a multisystem disorder. ALS-Plus syndrome is associated with increased risk for poor survival and the presence of a pathogenic mutation. PMID:25086858

Cytological analysis of atypical squamous epithelial cells of undetermined significance using the world wide web.

PubMed

Washiya, Kiyotada; Abe, Ichinosuke; Ambo, Junichi; Iwai, Muneo; Okusawa, Estuko; Asanuma, Kyousuke; Watanabe, Jun

2011-01-01

The low-level consistency of the cytodiagnosis of uterine cervical atypical squamous epithelial cells of undetermined significance (ASC-US) employing the Bethesda System has been reported, suggesting the necessity of a wide survey. We presented cases judged as ASC-US on the Web and analyzed the voting results to investigate ASC-US cytologically. Cytology samples from 129 patients diagnosed with ASC-US were used. Images of several atypical cells observed in these cases were presented on the Web. The study, based on the voting results, was presented and opinions were exchanged at the meeting of the Japanese Society of Clinical Cytology. The final diagnosis of ASC-US was benign lesions in 76 cases and low- and high-grade squamous intraepithelial lesions in 44, but no definite diagnosis could be made for the remaining 9. The total number of votes was 17,884 with a 36.5% consistency of cases judged as ASC-US. Benign cases were divided into 6 categories. Four categories not corresponding to the features of koilocytosis and small abnormal keratinized cells were judged as negative for an intraepithelial lesion or malignancy at a high rate. A Web-based survey would be useful which could be viewed at any time and thereby facilitate the sharing of cases to increase consistency. Copyright © 2011 S. Karger AG, Basel.

Tunable filters for multispectral imaging of aeronomical features

NASA Astrophysics Data System (ADS)

Goenka, C.; Semeter, J. L.; Noto, J.; Dahlgren, H.; Marshall, R.; Baumgardner, J.; Riccobono, J.; Migliozzi, M.

2013-10-01

Multispectral imaging of optical emissions in the Earth's upper atmosphere unravels vital information about dynamic phenomena in the Earth-space environment. Wavelength tunable filters allow us to accomplish this without using filter wheels or multiple imaging setups, but with identifiable caveats and trade-offs. We evaluate one such filter, a liquid crystal Fabry-Perot etalon, as a potential candidate for the next generation of imagers for aeronomy. The tunability of such a filter can be exploited in imaging features such as the 6300-6364 Å oxygen emission doublet, or studying the rotational temperature of N2+ in the 4200-4300 Å range, observations which typically require multiple instruments. We further discuss the use of this filter in an optical instrument, called the Liquid Crystal Hyperspectral Imager (LiCHI), which will be developed to make simultaneous measurements in various wavelength ranges.

Multimodal Image Alignment via Linear Mapping between Feature Modalities.

PubMed

Jiang, Yanyun; Zheng, Yuanjie; Hou, Sujuan; Chang, Yuchou; Gee, James

2017-01-01

We propose a novel landmark matching based method for aligning multimodal images, which is accomplished uniquely by resolving a linear mapping between different feature modalities. This linear mapping results in a new measurement on similarity of images captured from different modalities. In addition, our method simultaneously solves this linear mapping and the landmark correspondences by minimizing a convex quadratic function. Our method can estimate complex image relationship between different modalities and nonlinear nonrigid spatial transformations even in the presence of heavy noise, as shown in our experiments carried out by using a variety of image modalities.

Feature Tracking for High Speed AFM Imaging of Biopolymers.

PubMed

Hartman, Brett; Andersson, Sean B

2018-03-31

The scanning speed of atomic force microscopes continues to advance with some current commercial microscopes achieving on the order of one frame per second and at least one reaching 10 frames per second. Despite the success of these instruments, even higher frame rates are needed with scan ranges larger than are currently achievable. Moreover, there is a significant installed base of slower instruments that would benefit from algorithmic approaches to increasing their frame rate without requiring significant hardware modifications. In this paper, we present an experimental demonstration of high speed scanning on an existing, non-high speed instrument, through the use of a feedback-based, feature-tracking algorithm that reduces imaging time by focusing on features of interest to reduce the total imaging area. Experiments on both circular and square gratings, as well as silicon steps and DNA strands show a reduction in imaging time by a factor of 3-12 over raster scanning, depending on the parameters chosen.

Features and limitations of mobile tablet devices for viewing radiological images.

PubMed

Grunert, J H

2015-03-01

Mobile radiological image display systems are becoming increasingly common, necessitating a comparison of the features of these systems, specifically the operating system employed, connection to stationary PACS, data security and rang of image display and image analysis functions. In the fall of 2013, a total of 17 PACS suppliers were surveyed regarding the technical features of 18 mobile radiological image display systems using a standardized questionnaire. The study also examined to what extent the technical specifications of the mobile image display systems satisfy the provisions of the Germany Medical Devices Act as well as the provisions of the German X-ray ordinance (RöV). There are clear differences in terms of how the mobile systems connected to the stationary PACS. Web-based solutions allow the mobile image display systems to function independently of their operating systems. The examined systems differed very little in terms of image display and image analysis functions. Mobile image display systems complement stationary PACS and can be used to view images. The impacts of the new quality assurance guidelines (QS-RL) as well as the upcoming new standard DIN 6868 - 157 on the acceptance testing of mobile image display units for the purpose of image evaluation are discussed. © Georg Thieme Verlag KG Stuttgart · New York.

Image features dependant correlation-weighting function for efficient PRNU based source camera identification.

PubMed

Tiwari, Mayank; Gupta, Bhupendra

2018-04-01

For source camera identification (SCI), photo response non-uniformity (PRNU) has been widely used as the fingerprint of the camera. The PRNU is extracted from the image by applying a de-noising filter then taking the difference between the original image and the de-noised image. However, it is observed that intensity-based features and high-frequency details (edges and texture) of the image, effect quality of the extracted PRNU. This effects correlation calculation and creates problems in SCI. For solving this problem, we propose a weighting function based on image features. We have experimentally identified image features (intensity and high-frequency contents) effect on the estimated PRNU, and then develop a weighting function which gives higher weights to image regions which give reliable PRNU and at the same point it gives comparatively less weights to the image regions which do not give reliable PRNU. Experimental results show that the proposed weighting function is able to improve the accuracy of SCI up to a great extent. Copyright © 2018 Elsevier B.V. All rights reserved.

Robust and efficient method for matching features in omnidirectional images

NASA Astrophysics Data System (ADS)

Zhu, Qinyi; Zhang, Zhijiang; Zeng, Dan

2018-04-01

Binary descriptors have been widely used in many real-time applications due to their efficiency. These descriptors are commonly designed for perspective images but perform poorly on omnidirectional images, which are severely distorted. To address this issue, this paper proposes tangent plane BRIEF (TPBRIEF) and adapted log polar grid-based motion statistics (ALPGMS). TPBRIEF projects keypoints to a unit sphere and applies the fixed test set in BRIEF descriptor on the tangent plane of the unit sphere. The fixed test set is then backprojected onto the original distorted images to construct the distortion invariant descriptor. TPBRIEF directly enables keypoint detecting and feature describing on original distorted images, whereas other approaches correct the distortion through image resampling, which introduces artifacts and adds time cost. With ALPGMS, omnidirectional images are divided into circular arches named adapted log polar grids. Whether a match is true or false is then determined by simply thresholding the match numbers in a grid pair where the two matched points located. Experiments show that TPBRIEF greatly improves the feature matching accuracy and ALPGMS robustly removes wrong matches. Our proposed method outperforms the state-of-the-art methods.

Detection and clustering of features in aerial images by neuron network-based algorithm

NASA Astrophysics Data System (ADS)

Vozenilek, Vit

2015-12-01

The paper presents the algorithm for detection and clustering of feature in aerial photographs based on artificial neural networks. The presented approach is not focused on the detection of specific topographic features, but on the combination of general features analysis and their use for clustering and backward projection of clusters to aerial image. The basis of the algorithm is a calculation of the total error of the network and a change of weights of the network to minimize the error. A classic bipolar sigmoid was used for the activation function of the neurons and the basic method of backpropagation was used for learning. To verify that a set of features is able to represent the image content from the user's perspective, the web application was compiled (ASP.NET on the Microsoft .NET platform). The main achievements include the knowledge that man-made objects in aerial images can be successfully identified by detection of shapes and anomalies. It was also found that the appropriate combination of comprehensive features that describe the colors and selected shapes of individual areas can be useful for image analysis.

Feature Detection of Curve Traffic Sign Image on The Bandung - Jakarta Highway

NASA Astrophysics Data System (ADS)

Naseer, M.; Supriadi, I.; Supangkat, S. H.

2018-03-01

Unsealed roadside and problems with the road surface are common causes of road crashes, particularly when those are combined with curves. Curve traffic sign is an important component for giving early warning to driver on traffic, especially on high-speed traffic like on the highway. Traffic sign detection has became a very interesting research now, and in this paper will be discussed about the detection of curve traffic sign. There are two types of curve signs are discussed, namely the curve turn to the left and the curve turn to the right and the all data sample used are the curves taken / recorded from some signs on the Bandung - Jakarta Highway. Feature detection of the curve signs use Speed Up Robust Feature (SURF) method, where the detected scene image is 800x450. From 45 curve turn to the right images, the system can detect the feature well to 35 images, where the success rate is 77,78%, while from the 45 curve turn to the left images, the system can detect the feature well to 34 images and the success rate is 75,56%, so the average accuracy in the detection process is 76,67%. While the average time for the detection process is 0.411 seconds.

Feature selection and classification of multiparametric medical images using bagging and SVM

NASA Astrophysics Data System (ADS)

Fan, Yong; Resnick, Susan M.; Davatzikos, Christos

2008-03-01

This paper presents a framework for brain classification based on multi-parametric medical images. This method takes advantage of multi-parametric imaging to provide a set of discriminative features for classifier construction by using a regional feature extraction method which takes into account joint correlations among different image parameters; in the experiments herein, MRI and PET images of the brain are used. Support vector machine classifiers are then trained based on the most discriminative features selected from the feature set. To facilitate robust classification and optimal selection of parameters involved in classification, in view of the well-known "curse of dimensionality", base classifiers are constructed in a bagging (bootstrap aggregating) framework for building an ensemble classifier and the classification parameters of these base classifiers are optimized by means of maximizing the area under the ROC (receiver operating characteristic) curve estimated from their prediction performance on left-out samples of bootstrap sampling. This classification system is tested on a sex classification problem, where it yields over 90% classification rates for unseen subjects. The proposed classification method is also compared with other commonly used classification algorithms, with favorable results. These results illustrate that the methods built upon information jointly extracted from multi-parametric images have the potential to perform individual classification with high sensitivity and specificity.

Acousto-Optic Technology for Topographic Feature Extraction and Image Analysis.

DTIC Science & Technology

1981-03-01

This report contains all findings of the acousto - optic technology study for feature extraction conducted by Deft Laboratories Inc. for the U.S. Army...topographic feature extraction and image analysis using acousto - optic (A-O) technology. A conclusion of this study was that A-O devices are potentially

Significance of the impact of motion compensation on the variability of PET image features

NASA Astrophysics Data System (ADS)

Carles, M.; Bach, T.; Torres-Espallardo, I.; Baltas, D.; Nestle, U.; Martí-Bonmatí, L.

2018-03-01

In lung cancer, quantification by positron emission tomography/computed tomography (PET/CT) imaging presents challenges due to respiratory movement. Our primary aim was to study the impact of motion compensation implied by retrospectively gated (4D)-PET/CT on the variability of PET quantitative parameters. Its significance was evaluated by comparison with the variability due to (i) the voxel size in image reconstruction and (ii) the voxel size in image post-resampling. The method employed for feature extraction was chosen based on the analysis of (i) the effect of discretization of the standardized uptake value (SUV) on complementarity between texture features (TF) and conventional indices, (ii) the impact of the segmentation method on the variability of image features, and (iii) the variability of image features across the time-frame of 4D-PET. Thirty-one PET-features were involved. Three SUV discretization methods were applied: a constant width (SUV resolution) of the resampling bin (method RW), a constant number of bins (method RN) and RN on the image obtained after histogram equalization (method EqRN). The segmentation approaches evaluated were 40% of SUVmax and the contrast oriented algorithm (COA). Parameters derived from 4D-PET images were compared with values derived from the PET image obtained for (i) the static protocol used in our clinical routine (3D) and (ii) the 3D image post-resampled to the voxel size of the 4D image and PET image derived after modifying the reconstruction of the 3D image to comprise the voxel size of the 4D image. Results showed that TF complementarity with conventional indices was sensitive to the SUV discretization method. In the comparison of COA and 40% contours, despite the values not being interchangeable, all image features showed strong linear correlations (r  >  0.91, p\\ll 0.001 ). Across the time-frames of 4D-PET, all image features followed a normal distribution in most patients. For our patient cohort, the

Digital image analysis supports a nuclear-to-cytoplasmic ratio cutoff value of 0.5 for atypical urothelial cells.

PubMed

Hang, Jen-Fan; Charu, Vivek; Zhang, M Lisa; VandenBussche, Christopher J

2017-09-01

An elevated nuclear-to-cytoplasmic (N:C) ratio of ≥0.5 is a required criterion for the diagnosis of atypical urothelial cells (AUC) in The Paris System for Reporting Urinary Cytology. To validate the N:C ratio cutoff value and its predictive power for high-grade urothelial carcinoma (HGUC), the authors retrospectively reviewed the urinary tract cytology specimens of 15 cases of AUC with HGUC on follow-up (AUC-HGUC) and 33 cases of AUC without HGUC on follow-up (AUC-N-HGUC). The number of atypical cells in each case was recorded, and each atypical cell was photographed and digitally examined to calculate the nuclear size and N:C ratio. On average, the maximum N:C ratios of atypical cells were significantly different between the AUC-HGUC and AUC-N-HGUC cohorts (0.53 vs 0.43; P =.00009), whereas the maximum nuclear sizes of atypical cells (153.43 μM 2 vs 201.47 μM 2 ; P = .69) and the number of atypical cells per case (10.13 vs 7.88; P = .12) were not found to be significantly different. Receiver operating characteristic analysis demonstrated that the maximum N:C ratio alone had high discriminatory capacity (area under the curve, 79.19%; 95% confidence interval, 64.19%-94.19%). The optimal maximum N:C ratio threshold was 0.486, giving a sensitivity of 73.3% and a specificity of 84.8% for predicting HGUC on follow-up. The identification of AUC with an N:C ratio >0.486 has a high predictive power for HGUC on follow-up in AUC specimens. This justifies using the N:C ratio as a required criterion for the AUC category. Individual laboratories using different cytopreparation methods may require independent validation of the N:C ratio cutoff value. Cancer Cytopathol 2017;125:710-6. © 2017 American Cancer Society. © 2017 American Cancer Society.

Atypical Activations of Fronto-Cerebellar Regions During Forethought in Parents of Children With ADHD.

PubMed

Rapin, Lucile; Poissant, Hélène; Mendrek, Adrianna

2017-10-01

Although several studies suggest heritability of ADHD, only a few investigations of possible associations between people at risk and neural abnormalities in ADHD exist. In this study, we tested whether parents of children with ADHD would show atypical patterns of cerebral activations during forethought, a feature of working memory. Using Functional Magnetic Resonance Imaging (fMRI), we compared 12 parents of children with ADHD and 9 parents of control children during a forethought task. Parents of children with ADHD exhibited significantly increased neural activations in the posterior lobes of the cerebellum and in the left inferior frontal gyrus, relative to parents of control children. These findings are consistent with previous reports in children and suggest the fronto-cerebellar circuit's abnormalities during forethought in parents of children with ADHD. Future studies of people at risk of ADHD are needed to fully understand the extent of the fronto-cerebellar heritability.

Image feature detection and extraction techniques performance evaluation for development of panorama under different light conditions

NASA Astrophysics Data System (ADS)

Patil, Venkat P.; Gohatre, Umakant B.

2018-04-01

The technique of obtaining a wider field-of-view of an image to get high resolution integrated image is normally required for development of panorama of a photographic images or scene from a sequence of part of multiple views. There are various image stitching methods developed recently. For image stitching five basic steps are adopted stitching which are Feature detection and extraction, Image registration, computing homography, image warping and Blending. This paper provides review of some of the existing available image feature detection and extraction techniques and image stitching algorithms by categorizing them into several methods. For each category, the basic concepts are first described and later on the necessary modifications made to the fundamental concepts by different researchers are elaborated. This paper also highlights about the some of the fundamental techniques for the process of photographic image feature detection and extraction methods under various illumination conditions. The Importance of Image stitching is applicable in the various fields such as medical imaging, astrophotography and computer vision. For comparing performance evaluation of the techniques used for image features detection three methods are considered i.e. ORB, SURF, HESSIAN and time required for input images feature detection is measured. Results obtained finally concludes that for daylight condition, ORB algorithm found better due to the fact that less tome is required for more features extracted where as for images under night light condition it shows that SURF detector performs better than ORB/HESSIAN detectors.

Feature Selection from a Facial Image for Distinction of Sasang Constitution

PubMed Central

Koo, Imhoi; Kim, Jong Yeol; Kim, Myoung Geun

2009-01-01

Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here. PMID:19745013

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.

Atypical postcesarean epithelioid trophoblastic lesion with cyst formation: a case report and literature review.

PubMed

Zhou, Feng; Lin, Kaiqing; Shi, Haiyan; Qin, Jiale; Lu, Bingjian; Huang, Lili

2015-07-01

We report an extremely rare case of atypical postcesarean epithelioid trophoblastic lesion with cyst formation. A 41-year-old Chinese woman presented with lower abdominal pain and menstrual disorder. Her serum human chorionic gonadotropin (hCG) was low (0.373 IU/L), and her urine hCG was negative. Ultrasound images showed a 3.7×2.8×2.5 cm(3) mass on the surface of the lower uterine segment, and a laparoscopy indicated a cystic mass in the serosal surface of the lower uterine segment. Histology indicated a cystic lesion consisting of epithelioid trophoblastic cells with an intermediate pattern between a classical placental site nodule and an epithelioid trophoblastic tumor; thus, the term atypical postcesarean epithelioid trophoblastic lesion with cyst formation was appropriate. As in atypical placental site nodule, serum hCG monitoring after treatment is necessary. Copyright © 2015. Published by Elsevier Inc.

Application of Fuzzy c-Means and Joint-Feature-Clustering to Detect Redundancies of Image-Features in Drug Combinations Studies of Breast Cancer

NASA Astrophysics Data System (ADS)

Brandl, Miriam B.; Beck, Dominik; Pham, Tuan D.

2011-06-01

The high dimensionality of image-based dataset can be a drawback for classification accuracy. In this study, we propose the application of fuzzy c-means clustering, cluster validity indices and the notation of a joint-feature-clustering matrix to find redundancies of image-features. The introduced matrix indicates how frequently features are grouped in a mutual cluster. The resulting information can be used to find data-derived feature prototypes with a common biological meaning, reduce data storage as well as computation times and improve the classification accuracy.

a Statistical Texture Feature for Building Collapse Information Extraction of SAR Image

NASA Astrophysics Data System (ADS)

Li, L.; Yang, H.; Chen, Q.; Liu, X.

2018-04-01

Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G0 distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.

Imaging features of orbital myxosarcoma in dogs.

PubMed

Dennis, Ruth

2008-01-01

Myxomas and myxosarcomas are infiltrative connective tissue tumors of fibroblastic origin that can be distinguished by the presence of abundant mucinous stroma. This paper describes the clinical and imaging features of orbital myxosarcoma in five dogs and suggests a predilection for the orbit. The main clinical signs were slowly progressive exophthalmos with soft swelling of the pterygopalatine fossa, and in two dogs, of the periorbital area. No pain was associated with the eye or orbit but one dog had pain on opening the mouth. The dogs were imaged using combinations of ultrasonography, radiography, and magnetic resonance imaging. In four dogs, extensive fluid-filled cavities in the orbit and fascial planes were seen and in the fifth dog, the tumor appeared more solid with small, peripheral cystic areas. In all dogs, the lesion extended along fascial planes to involve the temporomandibular joint, with osteolysis demonstrable in two dogs. Fluid aspirated from the cystic areas was viscous and sticky, mimicking that from a salivary mucocoele. Myxomas and myxosarcomas are known to be infiltrative and not readily amenable to surgical removal but their clinical course seems to be slow, with a reasonable survival time with palliative treatment. In humans, a juxta-articular form is recognized in which a prominent feature is the presence of dilated, cyst-like spaces filled with mucinous material. It is postulated that orbital myxosarcoma in dogs may be similar to the juxta-articular form in man, and may arise from the temporomandibular joint.

SU-E-QI-17: Dependence of 3D/4D PET Quantitative Image Features On Noise

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

Oliver, J; Budzevich, M; Zhang, G

2014-06-15

Purpose: Quantitative imaging is a fast evolving discipline where a large number of features are extracted from images; i.e., radiomics. Some features have been shown to have diagnostic, prognostic and predictive value. However, they are sensitive to acquisition and processing factors; e.g., noise. In this study noise was added to positron emission tomography (PET) images to determine how features were affected by noise. Methods: Three levels of Gaussian noise were added to 8 lung cancer patients PET images acquired in 3D mode (static) and using respiratory tracking (4D); for the latter images from one of 10 phases were used. Amore » total of 62 features: 14 shape, 19 intensity (1stO), 18 GLCM textures (2ndO; from grey level co-occurrence matrices) and 11 RLM textures (2ndO; from run-length matrices) features were extracted from segmented tumors. Dimensions of GLCM were 256×256, calculated using 3D images with a step size of 1 voxel in 13 directions. Grey levels were binned into 256 levels for RLM and features were calculated in all 13 directions. Results: Feature variation generally increased with noise. Shape features were the most stable while RLM were the most unstable. Intensity and GLCM features performed well; the latter being more robust. The most stable 1stO features were compactness, maximum and minimum length, standard deviation, root-mean-squared, I30, V10-V90, and entropy. The most stable 2ndO features were entropy, sum-average, sum-entropy, difference-average, difference-variance, difference-entropy, information-correlation-2, short-run-emphasis, long-run-emphasis, and run-percentage. In general, features computed from images from one of the phases of 4D scans were more stable than from 3D scans. Conclusion: This study shows the need to characterize image features carefully before they are used in research and medical applications. It also shows that the performance of features, and thereby feature selection, may be assessed in part by noise

Treatment of Low-Risk Endometrial Cancer and Complex Atypical Hyperplasia With the Levonorgestrel-Releasing Intrauterine Device.

PubMed

Pal, Navdeep; Broaddus, Russell R; Urbauer, Diana L; Balakrishnan, Nyla; Milbourne, Andrea; Schmeler, Kathleen M; Meyer, Larissa A; Soliman, Pamela T; Lu, Karen H; Ramirez, Pedro T; Ramondetta, Lois; Bodurka, Diane C; Westin, Shannon N

2018-01-01

To assess efficacy of the levonorgestrel-releasing intrauterine device (LNG-IUD) for treatment of complex atypical hyperplasia or low-grade endometrial cancer. This retrospective case series included all patients treated with the LNG-IUD for complex atypical hyperplasia or early-grade endometrial cancer from January 2003 to June 2013. Response rates were calculated and the association of response with clinicopathologic factors, including age, body mass index, and uterine size, was determined. Forty-six patients diagnosed with complex atypical hyperplasia or early-grade endometrial cancer were treated with the LNG-IUD. Of 32 evaluable patients at the 6-month time point, 15 had complex atypical hyperplasia (47%), nine had G1 endometrial cancer (28%), and eight had grade 2 endometrial cancer (25%). Overall response rate was 75% (95% CI 57-89) at 6 months; 80% (95% CI 52-96) in complex atypical hyperplasia, 67% (95% CI 30-93) in grade 1 endometrial cancer, and 75% (CI 35-97) in grade 2 endometrial cancer. Of the clinicopathologic features evaluated, there was a trend toward the association of lack of exogenous progesterone effect in the pathology specimen with nonresponse to the IUD (P=.05). Median uterine diameter was 1.3 cm larger in women who did not respond to the IUD (P=.04). Levonorgestrel-releasing IUD therapy for the conservative treatment of complex atypical hyperplasia or early-grade endometrial cancer resulted in return to normal histology in a majority of patients.

Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

NASA Astrophysics Data System (ADS)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

2018-04-01

In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

Human gait recognition by pyramid of HOG feature on silhouette images

NASA Astrophysics Data System (ADS)

Yang, Guang; Yin, Yafeng; Park, Jeanrok; Man, Hong

2013-03-01

As a uncommon biometric modality, human gait recognition has a great advantage of identify people at a distance without high resolution images. It has attracted much attention in recent years, especially in the fields of computer vision and remote sensing. In this paper, we propose a human gait recognition framework that consists of a reliable background subtraction method followed by the pyramid of Histogram of Gradient (pHOG) feature extraction on the silhouette image, and a Hidden Markov Model (HMM) based classifier. Through background subtraction, the silhouette of human gait in each frame is extracted and normalized from the raw video sequence. After removing the shadow and noise in each region of interest (ROI), pHOG feature is computed on the silhouettes images. Then the pHOG features of each gait class will be used to train a corresponding HMM. In the test stage, pHOG feature will be extracted from each test sequence and used to calculate the posterior probability toward each trained HMM model. Experimental results on the CASIA Gait Dataset B1 demonstrate that with our proposed method can achieve very competitive recognition rate.

Multiple modes of a-type potassium current regulation.

PubMed

Cai, Shi-Qing; Li, Wenchao; Sesti, Federico

2007-01-01

Voltage-dependent potassium (K+) channels (Kv) regulate cell excitability by controlling the movement of K+ ions across the membrane in response to changes in the cell voltage. The Kv family, which includes A-type channels, constitute the largest group of K+ channel genes within the superfamily of Na+, Ca2+ and K+ voltage-gated channels. The name "A-type" stems from the typical profile of these currents that results form the opposing effects of fast activation and inactivation. In neuronal cells, A-type currents (I(A)), determine the interval between two consecutive action potentials during repetitive firing. In cardiac muscle, A-type currents (I(to)), control the initial repolarization of the myocardium. Structurally, A-type channels are tetramers of alpha-subunits each containing six putative transmembrane domains including a voltage-sensor. A-type channels can be modulated by means of protein-protein interactions with so-called beta-subunits that control inactivation voltage sensitivity and other properties, and by post-transcriptional modifications such as phosphorylation or oxidation. Recently a new mode of A-type regulation has been discovered in the form of a class of hybrid beta-subunits that posses their own enzymatic activity. Here, we review the biophysical and physiological properties of these multiple modes of A-type channel regulation.

An Open Source Agenda for Research Linking Text and Image Content Features.

ERIC Educational Resources Information Center

Goodrum, Abby A.; Rorvig, Mark E.; Jeong, Ki-Tai; Suresh, Chitturi

2001-01-01

Proposes methods to utilize image primitives to support term assignment for image classification. Proposes to release code for image analysis in a common tool set for other researchers to use. Of particular focus is the expansion of work by researchers in image indexing to include image content-based feature extraction capabilities in their work.…

Image feature based GPS trace filtering for road network generation and road segmentation

DOE PAGES

Yuan, Jiangye; Cheriyadat, Anil M.

2015-10-19

We propose a new method to infer road networks from GPS trace data and accurately segment road regions in high-resolution aerial images. Unlike previous efforts that rely on GPS traces alone, we exploit image features to infer road networks from noisy trace data. The inferred road network is used to guide road segmentation. We show that the number of image segments spanned by the traces and the trace orientation validated with image features are important attributes for identifying GPS traces on road regions. Based on filtered traces , we construct road networks and integrate them with image features to segmentmore » road regions. Lastly, our experiments show that the proposed method produces more accurate road networks than the leading method that uses GPS traces alone, and also achieves high accuracy in segmenting road regions even with very noisy GPS data.« less

Image feature based GPS trace filtering for road network generation and road segmentation

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

Yuan, Jiangye; Cheriyadat, Anil M.

We propose a new method to infer road networks from GPS trace data and accurately segment road regions in high-resolution aerial images. Unlike previous efforts that rely on GPS traces alone, we exploit image features to infer road networks from noisy trace data. The inferred road network is used to guide road segmentation. We show that the number of image segments spanned by the traces and the trace orientation validated with image features are important attributes for identifying GPS traces on road regions. Based on filtered traces , we construct road networks and integrate them with image features to segmentmore » road regions. Lastly, our experiments show that the proposed method produces more accurate road networks than the leading method that uses GPS traces alone, and also achieves high accuracy in segmenting road regions even with very noisy GPS data.« less

Breast cancer mitosis detection in histopathological images with spatial feature extraction

NASA Astrophysics Data System (ADS)

Albayrak, Abdülkadir; Bilgin, Gökhan

2013-12-01

In this work, cellular mitosis detection in histopathological images has been investigated. Mitosis detection is very expensive and time consuming process. Development of digital imaging in pathology has enabled reasonable and effective solution to this problem. Segmentation of digital images provides easier analysis of cell structures in histopathological data. To differentiate normal and mitotic cells in histopathological images, feature extraction step is very crucial step for the system accuracy. A mitotic cell has more distinctive textural dissimilarities than the other normal cells. Hence, it is important to incorporate spatial information in feature extraction or in post-processing steps. As a main part of this study, Haralick texture descriptor has been proposed with different spatial window sizes in RGB and La*b* color spaces. So, spatial dependencies of normal and mitotic cellular pixels can be evaluated within different pixel neighborhoods. Extracted features are compared with various sample sizes by Support Vector Machines using k-fold cross validation method. According to the represented results, it has been shown that separation accuracy on mitotic and non-mitotic cellular pixels gets better with the increasing size of spatial window.

Blood vessel segmentation in color fundus images based on regional and Hessian features.

PubMed

Shah, Syed Ayaz Ali; Tang, Tong Boon; Faye, Ibrahima; Laude, Augustinus

2017-08-01

To propose a new algorithm of blood vessel segmentation based on regional and Hessian features for image analysis in retinal abnormality diagnosis. Firstly, color fundus images from the publicly available database DRIVE were converted from RGB to grayscale. To enhance the contrast of the dark objects (blood vessels) against the background, the dot product of the grayscale image with itself was generated. To rectify the variation in contrast, we used a 5 × 5 window filter on each pixel. Based on 5 regional features, 1 intensity feature and 2 Hessian features per scale using 9 scales, we extracted a total of 24 features. A linear minimum squared error (LMSE) classifier was trained to classify each pixel into a vessel or non-vessel pixel. The DRIVE dataset provided 20 training and 20 test color fundus images. The proposed algorithm achieves a sensitivity of 72.05% with 94.79% accuracy. Our proposed algorithm achieved higher accuracy (0.9206) at the peripapillary region, where the ocular manifestations in the microvasculature due to glaucoma, central retinal vein occlusion, etc. are most obvious. This supports the proposed algorithm as a strong candidate for automated vessel segmentation.

Feature detection on 3D images of dental imprints

NASA Astrophysics Data System (ADS)

Mokhtari, Marielle; Laurendeau, Denis

1994-09-01

A computer vision approach for the extraction of feature points on 3D images of dental imprints is presented. The position of feature points are needed for the measurement of a set of parameters for automatic diagnosis of malocclusion problems in orthodontics. The system for the acquisition of the 3D profile of the imprint, the procedure for the detection of the interstices between teeth, and the approach for the identification of the type of tooth are described, as well as the algorithm for the reconstruction of the surface of each type of tooth. A new approach for the detection of feature points, called the watershed algorithm, is described in detail. The algorithm is a two-stage procedure which tracks the position of local minima at four different scales and produces a final map of the position of the minima. Experimental results of the application of the watershed algorithm on actual 3D images of dental imprints are presented for molars, premolars and canines. The segmentation approach for the analysis of the shape of incisors is also described in detail.

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

PubMed Central

Huo, Guanying

2017-01-01

As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

Contrast in Terahertz Images of Archival Documents—Part II: Influence of Topographic Features

NASA Astrophysics Data System (ADS)

Bardon, Tiphaine; May, Robert K.; Taday, Philip F.; Strlič, Matija

2017-04-01

We investigate the potential of terahertz time-domain imaging in reflection mode to reveal archival information in documents in a non-invasive way. In particular, this study explores the parameters and signal processing tools that can be used to produce well-contrasted terahertz images of topographic features commonly found in archival documents, such as indentations left by a writing tool, as well as sieve lines. While the amplitude of the waveforms at a specific time delay can provide the most contrasted and legible images of topographic features on flat paper or parchment sheets, this parameter may not be suitable for documents that have a highly irregular surface, such as water- or fire-damaged documents. For analysis of such documents, cross-correlation of the time-domain signals can instead yield images with good contrast. Analysis of the frequency-domain representation of terahertz waveforms can also provide well-contrasted images of topographic features, with improved spatial resolution when utilising high-frequency content. Finally, we point out some of the limitations of these means of analysis for extracting information relating to topographic features of interest from documents.

Imaging mass spectrometry data reduction: automated feature identification and extraction.

PubMed

McDonnell, Liam A; van Remoortere, Alexandra; de Velde, Nico; van Zeijl, René J M; Deelder, André M

2010-12-01

Imaging MS now enables the parallel analysis of hundreds of biomolecules, spanning multiple molecular classes, which allows tissues to be described by their molecular content and distribution. When combined with advanced data analysis routines, tissues can be analyzed and classified based solely on their molecular content. Such molecular histology techniques have been used to distinguish regions with differential molecular signatures that could not be distinguished using established histologic tools. However, its potential to provide an independent, complementary analysis of clinical tissues has been limited by the very large file sizes and large number of discrete variables associated with imaging MS experiments. Here we demonstrate data reduction tools, based on automated feature identification and extraction, for peptide, protein, and lipid imaging MS, using multiple imaging MS technologies, that reduce data loads and the number of variables by >100×, and that highlight highly-localized features that can be missed using standard data analysis strategies. It is then demonstrated how these capabilities enable multivariate analysis on large imaging MS datasets spanning multiple tissues. Copyright © 2010 American Society for Mass Spectrometry. Published by Elsevier Inc. All rights reserved.

Tracking features in retinal images of adaptive optics confocal scanning laser ophthalmoscope using KLT-SIFT algorithm

PubMed Central

Li, Hao; Lu, Jing; Shi, Guohua; Zhang, Yudong

2010-01-01

With the use of adaptive optics (AO), high-resolution microscopic imaging of living human retina in the single cell level has been achieved. In an adaptive optics confocal scanning laser ophthalmoscope (AOSLO) system, with a small field size (about 1 degree, 280 μm), the motion of the eye severely affects the stabilization of the real-time video images and results in significant distortions of the retina images. In this paper, Scale-Invariant Feature Transform (SIFT) is used to abstract stable point features from the retina images. Kanade-Lucas-Tomasi(KLT) algorithm is applied to track the features. With the tracked features, the image distortion in each frame is removed by the second-order polynomial transformation, and 10 successive frames are co-added to enhance the image quality. Features of special interest in an image can also be selected manually and tracked by KLT. A point on a cone is selected manually, and the cone is tracked from frame to frame. PMID:21258443

SU-E-J-242: Volume-Dependence of Quantitative Imaging Features From CT and CE-CT Images of NSCLC

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

Fave, X; Fried, D; UT Health Science Center Graduate School of Biomedical Sciences, Houston, TX

Purpose: To determine whether tumor volume plays a significant role in the values obtained for texture features when they are extracted from computed tomography (CT) images of non-small cell lung cancer (NSCLC). We also sought to identify whether features can be reliably measured at all volumes or if a minimum volume threshold should be recommended. Methods: Eleven features were measured on 40 CT and 32 contrast-enhanced CT (CECT) patient images for this study. Features were selected for their prognostic/diagnostic value in previous publications. Direct correlations between these textures and volume were evaluated using the Spearman correlation coefficient. Any texture thatmore » the Wilcoxon rank-sum test was used to compare the variation above and below a volume cutoff. Four different volume thresholds (5, 10, 15, and 20 cm{sup 3}) were tested. Results: Four textures were found to be significantly correlated with volume in both the CT and CE-CT images. These were busyness, coarseness, gray-level nonuniformity, and run-length nonuniformity with correlation coefficients of 0.92, −0.96, 0.94, and 0.98 for the CT images and 0.95, −0.97, 0.98, and 0.98 for the CE-CT images. After volume normalization, the correlation coefficients decreased substantially. For the data obtained from the CT images, the results of the Wilcoxon rank-sum test were significant when volume thresholds of 5–15 cm3 were used. No volume threshold was shown to be significant for the CE-CT data. Conclusion: Equations for four features that have been used in several published studies were found to be volume-dependent. Future studies should consider implementing normalization factors or removing these features entirely to prevent this potential source of redundancy or bias. This work was supported in part by National Cancer Institute grant R03CA178495-01. Xenia Fave is a recipient of the American Association of Physicists in Medicine Graduate Fellowship.« less

A-type potassium currents in smooth muscle.

PubMed

Amberg, Gregory C; Koh, Sang Don; Imaizumi, Yuji; Ohya, Susumu; Sanders, Kenton M

2003-03-01

A-type currents are voltage-gated, calcium-independent potassium (Kv) currents that undergo rapid activation and inactivation. Commonly associated with neuronal and cardiac cell-types, A-type currents have also been identified and characterized in vascular, genitourinary, and gastrointestinal smooth muscle cells. This review examines the molecular identity, biophysical properties, pharmacology, regulation, and physiological function of smooth muscle A-type currents. In general, this review is intended to facilitate the comparison of A-type currents present in different smooth muscles by providing a comprehensive report of the literature to date. This approach should also aid in the identification of areas of research requiring further attention.

Mineralogy and Temperature-induced Spectral Investigations of A-type Asteroids 246 Asporina and 446 Aeternitas

NASA Technical Reports Server (NTRS)

Reddy, V.; Hardersen, P. S.; Gaffey, M. J.; Abell, P. A.

2005-01-01

A-type asteroids are a relatively rare taxonomic class with no more than 17 known objects. They were first identified as a separate group of R-type asteroids based on broadband spectrophotometry by, and were later classified based on ECAS data by Tholen (1984). These asteroids have moderately high albedos (0.13-0.39), extremely reddish slopes shortward of 0.7 m and a strong absorption feature centered at approx. 1.05 m. More recent surveys like the Small Main-Belt Asteroid Spectroscopic Survey (SMASS) and SMASS II have expanded the taxonomic classes including the A-type, adding 12 new asteroids to the original five.

The ship edge feature detection based on high and low threshold for remote sensing image

NASA Astrophysics Data System (ADS)

Li, Xuan; Li, Shengyang

2018-05-01

In this paper, a method based on high and low threshold is proposed to detect the ship edge feature due to the low accuracy rate caused by the noise. Analyze the relationship between human vision system and the target features, and to determine the ship target by detecting the edge feature. Firstly, using the second-order differential method to enhance the quality of image; Secondly, to improvement the edge operator, we introduction of high and low threshold contrast to enhancement image edge and non-edge points, and the edge as the foreground image, non-edge as a background image using image segmentation to achieve edge detection, and remove the false edges; Finally, the edge features are described based on the result of edge features detection, and determine the ship target. The experimental results show that the proposed method can effectively reduce the number of false edges in edge detection, and has the high accuracy of remote sensing ship edge detection.

Combined optimization of image-gathering and image-processing systems for scene feature detection

NASA Technical Reports Server (NTRS)

Halyo, Nesim; Arduini, Robert F.; Samms, Richard W.

1987-01-01

The relationship between the image gathering and image processing systems for minimum mean squared error estimation of scene characteristics is investigated. A stochastic optimization problem is formulated where the objective is to determine a spatial characteristic of the scene rather than a feature of the already blurred, sampled and noisy image data. An analytical solution for the optimal characteristic image processor is developed. The Wiener filter for the sampled image case is obtained as a special case, where the desired characteristic is scene restoration. Optimal edge detection is investigated using the Laplacian operator x G as the desired characteristic, where G is a two dimensional Gaussian distribution function. It is shown that the optimal edge detector compensates for the blurring introduced by the image gathering optics, and notably, that it is not circularly symmetric. The lack of circular symmetry is largely due to the geometric effects of the sampling lattice used in image acquisition. The optimal image gathering optical transfer function is also investigated and the results of a sensitivity analysis are shown.

Atypical antipsychotics for psychosis in adolescents.

PubMed

Kumar, Ajit; Datta, Soumitra S; Wright, Stephen D; Furtado, Vivek A; Russell, Paul S

2013-10-15

Schizophrenia often presents in adolescence, but current treatment guidelines are based largely on studies of adults with psychosis. Over the past decade, the number of studies on treatment of adolescent-onset psychosis has increased. The current systematic review collates and critiques evidence obtained on the use of various atypical antipsychotic medications for adolescents with psychosis. To investigate the effects of atypical antipsychotic medications in adolescents with psychosis. We reviewed in separate analyses various comparisons of atypical antipsychotic medications with placebo or a typical antipsychotic medication or another atypical antipsychotic medication or the same atypical antipsychotic medication but at a lower dose. We searched the Cochrane Schizophrenia Group Register (October 2011), which is based on regular searches of BIOSIS, CENTRAL, CINAHL, EMBASE, MEDLINE and PsycINFO. We inspected references of all identified studies and contacted study authors and relevant pharmaceutical companies to ask for more information. We included all relevant randomised controlled trials (RCTs) that compared atypical antipsychotic medication with placebo or another pharmacological intervention or with psychosocial interventions, standard psychiatric treatment or no intervention in children and young people aged 13 to 18 years with a diagnosis of schizophrenia, schizoaffective disorder, acute and transient psychoses or unspecified psychosis. We included studies published in English and in other languages that were available in standardised databases. Review authors AK and SSD selected the studies, rated the quality of the studies and performed data extraction. For dichotomous data, we estimated risk ratios (RRs) with 95% confidence intervals (CIs) using a fixed-effect model. When possible, for binary data presented in the 'Summary of findings' table, we calculated illustrative comparative risks. We summated continuous data using the mean difference (MD). Risk of

Accuracy and variability of texture-based radiomics features of lung lesions across CT imaging conditions

NASA Astrophysics Data System (ADS)

Zheng, Yuese; Solomon, Justin; Choudhury, Kingshuk; Marin, Daniele; Samei, Ehsan

2017-03-01

Texture analysis for lung lesions is sensitive to changing imaging conditions but these effects are not well understood, in part, due to a lack of ground-truth phantoms with realistic textures. The purpose of this study was to explore the accuracy and variability of texture features across imaging conditions by comparing imaged texture features to voxel-based 3D printed textured lesions for which the true values are known. The seven features of interest were based on the Grey Level Co-Occurrence Matrix (GLCM). The lesion phantoms were designed with three shapes (spherical, lobulated, and spiculated), two textures (homogenous and heterogeneous), and two sizes (diameter < 1.5 cm and 1.5 cm < diameter < 3 cm), resulting in 24 lesions (with a second replica of each). The lesions were inserted into an anthropomorphic thorax phantom (Multipurpose Chest Phantom N1, Kyoto Kagaku) and imaged using a commercial CT system (GE Revolution) at three CTDI levels (0.67, 1.42, and 5.80 mGy), three reconstruction algorithms (FBP, IR-2, IR-4), four reconstruction kernel types (standard, soft, edge), and two slice thicknesses (0.6 mm and 5 mm). Another repeat scan was performed. Texture features from these images were extracted and compared to the ground truth feature values by percent relative error. The variability across imaging conditions was calculated by standard deviation across a certain imaging condition for all heterogeneous lesions. The results indicated that the acquisition method has a significant influence on the accuracy and variability of extracted features and as such, feature quantities are highly susceptible to imaging parameter choices. The most influential parameters were slice thickness and reconstruction kernels. Thin slice thickness and edge reconstruction kernel overall produced more accurate and more repeatable results. Some features (e.g., Contrast) were more accurately quantified under conditions that render higher spatial frequencies (e.g., thinner slice

Identification of natural images and computer-generated graphics based on statistical and textural features.

PubMed

Peng, Fei; Li, Jiao-ting; Long, Min

2015-03-01

To discriminate the acquisition pipelines of digital images, a novel scheme for the identification of natural images and computer-generated graphics is proposed based on statistical and textural features. First, the differences between them are investigated from the view of statistics and texture, and 31 dimensions of feature are acquired for identification. Then, LIBSVM is used for the classification. Finally, the experimental results are presented. The results show that it can achieve an identification accuracy of 97.89% for computer-generated graphics, and an identification accuracy of 97.75% for natural images. The analyses also demonstrate the proposed method has excellent performance, compared with some existing methods based only on statistical features or other features. The method has a great potential to be implemented for the identification of natural images and computer-generated graphics. © 2014 American Academy of Forensic Sciences.

Segmenting texts from outdoor images taken by mobile phones using color features

NASA Astrophysics Data System (ADS)

Liu, Zongyi; Zhou, Hanning

2011-01-01

Recognizing texts from images taken by mobile phones with low resolution has wide applications. It has been shown that a good image binarization can substantially improve the performances of OCR engines. In this paper, we present a framework to segment texts from outdoor images taken by mobile phones using color features. The framework consists of three steps: (i) the initial process including image enhancement, binarization and noise filtering, where we binarize the input images in each RGB channel, and apply component level noise filtering; (ii) grouping components into blocks using color features, where we compute the component similarities by dynamically adjusting the weights of RGB channels, and merge groups hierachically, and (iii) blocks selection, where we use the run-length features and choose the Support Vector Machine (SVM) as the classifier. We tested the algorithm using 13 outdoor images taken by an old-style LG-64693 mobile phone with 640x480 resolution. We compared the segmentation results with Tsar's algorithm, a state-of-the-art camera text detection algorithm, and show that our algorithm is more robust, particularly in terms of the false alarm rates. In addition, we also evaluated the impacts of our algorithm on the Abbyy's FineReader, one of the most popular commercial OCR engines in the market.

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

PubMed Central

Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

2017-01-01

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images. PMID:28335510

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction.

PubMed

Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

2017-03-20

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

Feature and contrast enhancement of mammographic image based on multiscale analysis and morphology.

PubMed

Wu, Shibin; Yu, Shaode; Yang, Yuhan; Xie, Yaoqin

2013-01-01

A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII).

Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology

PubMed Central

Wu, Shibin; Xie, Yaoqin

2013-01-01

A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII). PMID:24416072

Clinical and imaging features in different inner border-zone infarct patterns.

PubMed

Wang, Yujie; Wang, Jian

2015-03-01

The clinical and imaging features of different inner border-zone infarct patterns, corona radiata (CR) and centrum semiovale (CSO), is not quiet clear. Both are mostly reported together in previous studies. We intended to observe their clinical and imaging features. We observed 83 patients-47 cases with CR infarct lesion pattern and 36 cases with CSO. The lesion patterns were determined by diffusion-weighted imaging. Basic, clinical and radiologic features were compared between the patients with CR and CSO infarct lesion patterns. There was no significant difference between CR and CSO infarct patterns in terms of risk factors. However, patients with CR infarct had a higher initial National Institutes of Health Stroke Scale (NIHSS) score at admission (5.2 ± 2.3) than with CSO (3.9 ± 2.0, p = 0.009). Early clinical deterioration (OR, 2.42; 95% CI, 1.12-5.21; p = 0.024) and middle cerebral artery (MCA) stenosis (OR, 10.31; 95% CI, 3.30-32.19; p < 0.0001) were independently associated with the CR infarct lesion pattern. Partial infarct lesion shape (OR, 5.95; 95% CI, 1.40-25.33; p = 0.016) and internal carotid artery (ICA) stenosis (OR, 5.28; 95% CI, 1.92-14.51; p = 0.001) were independently correlated with the CSO infarct lesion pattern. Although CR and CSO infarct patterns might share common etiology and mechanisms, their clinical and imaging features are different.

Atypical Teratoid Rhabdoid Tumor in a Teenager with Unusual Infiltration Into the Jugular Foramen.

PubMed

Udaka, Yoko T; Yoon, Janet M; Malicki, Denise M; Khanna, Paritosh C; Levy, Michael L; Crawford, John R

2015-12-01

Atypical teratoid rhabdoid tumor is a rare malignant neoplasm that represents 1%-2% of all pediatric central nervous system tumors. Immunohistochemistry plays an important role in establishing the diagnosis with a loss of INI-1 staining in tumor cells. In this case report, we describe a teenager with an unusual presentation and pattern of infiltration of the tumor. A 13-year-old boy presented with a history over several months of progressive nausea, weight loss, and hoarseness of voice associated with multiple lower cranial nerve palsies on neurologic examination. Magnetic resonance imaging revealed a large heterogeneously enhancing extra-axial neoplasm with extension and bony expansion of the jugular foramen. After near total resection, neuropathology demonstrated the absence of INI-1 expression consistent with a diagnosis of atypical teratoid rhabdoid tumor. This case highlights the diverse clinical presentation and infiltrative potential of atypical teratoid rhabdoid tumors, thus expanding the differential diagnosis of extra-axial tumors invading the jugular foramen. Copyright © 2015 Elsevier Inc. All rights reserved.

A naturally occurring cowpox virus with an ectromelia virus A-type inclusion protein gene displays atypical A-type inclusions.

PubMed

Okeke, Malachy Ifeanyi; Hansen, Hilde; Traavik, Terje

2012-01-01

Human orthopoxvirus (OPV) infections in Europe are usually caused by cowpox virus (CPXV). The genetic heterogeneity of CPXVs may in part be due to recombination with other OPV species. We describe the characterization of an atypical CPXV (CPXV-No-H2) isolated from a human patient in Norway. CPXV-No-H2 was characterized on the basis of A-type inclusion (ATI) phenotype as well as the DNA region containing the p4c and atip open reading frames. CPXV-No-H2 produced atypical V(+/) ATI, in which virions are on the surface of ATI but not within the ATI matrix. Phylogenetic analysis showed that the atip gene of CPXV-No-H2 clustered closely with that of ectromelia virus (ECTV) with a bootstrap support of 100% whereas its p4c gene is diverged compared to homologues in other OPV species. By recombination analysis we identified a putative crossover event at nucleotide 147, downstream the start of the atip gene. Our results suggest that CPXV-No-H2 originated from a recombination between CPXV and ECTV. Our findings are relevant to the evolution of OPVs. Copyright © 2011 Elsevier B.V. All rights reserved.

Hypertrophic Osteoarthropathy: Clinical and Imaging Features.

PubMed

Yap, Felix Y; Skalski, Matthew R; Patel, Dakshesh B; Schein, Aaron J; White, Eric A; Tomasian, Anderanik; Masih, Sulabha; Matcuk, George R

2017-01-01

Hypertrophic osteoarthropathy (HOA) is a medical condition characterized by abnormal proliferation of skin and periosteal tissues involving the extremities and characterized by three clinical features: digital clubbing (also termed Hippocratic fingers), periostosis of tubular bones, and synovial effusions. HOA can be a primary entity, known as pachydermoperiostosis, or can be secondary to extraskeletal conditions, with different prognoses and management implications for each. There is a high association between secondary HOA and malignancy, especially non-small cell lung cancer. In such cases, it can be considered a form of paraneoplastic syndrome. The most prevalent secondary causes of HOA are pulmonary in origin, which is why this condition was formerly referred to as hypertrophic pulmonary osteoarthropathy. HOA can also be associated with pleural, mediastinal, and cardiovascular causes, as well as extrathoracic conditions such as gastrointestinal tumors and infections, cirrhosis, and inflammatory bowel disease. Although the skeletal manifestations of HOA are most commonly detected with radiography, abnormalities can also be identified with other modalities such as computed tomography, magnetic resonance imaging, and bone scintigraphy. The authors summarize the pathogenesis, classification, causes, and symptoms and signs of HOA, including the genetics underlying the primary form (pachydermoperiostosis); describe key findings of HOA found at various imaging modalities, with examples of underlying causative conditions; and discuss features differentiating HOA from other causes of multifocal periostitis, such as thyroid acropachy, hypervitaminosis A, chronic venous insufficiency, voriconazole-induced periostitis, progressive diaphyseal dysplasia, and neoplastic causes such as lymphoma. © RSNA, 2016.

IDH mutation assessment of glioma using texture features of multimodal MR images

NASA Astrophysics Data System (ADS)

Zhang, Xi; Tian, Qiang; Wu, Yu-Xia; Xu, Xiao-Pan; Li, Bao-Juan; Liu, Yi-Xiong; Liu, Yang; Lu, Hong-Bing

2017-03-01

Purpose: To 1) find effective texture features from multimodal MRI that can distinguish IDH mutant and wild status, and 2) propose a radiomic strategy for preoperatively detecting IDH mutation patients with glioma. Materials and Methods: 152 patients with glioma were retrospectively included from the Cancer Genome Atlas. Corresponding T1-weighted image before- and post-contrast, T2-weighted image and fluid-attenuation inversion recovery image from the Cancer Imaging Archive were analyzed. Specific statistical tests were applied to analyze the different kind of baseline information of LrGG patients. Finally, 168 texture features were derived from multimodal MRI per patient. Then the support vector machine-based recursive feature elimination (SVM-RFE) and classification strategy was adopted to find the optimal feature subset and build the identification models for detecting the IDH mutation. Results: Among 152 patients, 92 and 60 were confirmed to be IDH-wild and mutant, respectively. Statistical analysis showed that the patients without IDH mutation was significant older than patients with IDH mutation (p<0.01), and the distribution of some histological subtypes was significant different between IDH wild and mutant groups (p<0.01). After SVM-RFE, 15 optimal features were determined for IDH mutation detection. The accuracy, sensitivity, specificity, and AUC after SVM-RFE and parameter optimization were 82.2%, 85.0%, 78.3%, and 0.841, respectively. Conclusion: This study presented a radiomic strategy for noninvasively discriminating IDH mutation of patients with glioma. It effectively incorporated kinds of texture features from multimodal MRI, and SVM-based classification strategy. Results suggested that features selected from SVM-RFE were more potential to identifying IDH mutation. The proposed radiomics strategy could facilitate the clinical decision making in patients with glioma.

Identification of atypical flight patterns

NASA Technical Reports Server (NTRS)

Statler, Irving C. (Inventor); Ferryman, Thomas A. (Inventor); Amidan, Brett G. (Inventor); Whitney, Paul D. (Inventor); White, Amanda M. (Inventor); Willse, Alan R. (Inventor); Cooley, Scott K. (Inventor); Jay, Joseph Griffith (Inventor); Lawrence, Robert E. (Inventor); Mosbrucker, Chris (Inventor)

2005-01-01

Method and system for analyzing aircraft data, including multiple selected flight parameters for a selected phase of a selected flight, and for determining when the selected phase of the selected flight is atypical, when compared with corresponding data for the same phase for other similar flights. A flight signature is computed using continuous-valued and discrete-valued flight parameters for the selected flight parameters and is optionally compared with a statistical distribution of other observed flight signatures, yielding atypicality scores for the same phase for other similar flights. A cluster analysis is optionally applied to the flight signatures to define an optimal collection of clusters. A level of atypicality for a selected flight is estimated, based upon an index associated with the cluster analysis.

Atypical Cutaneous Manifestations in Syphilis.

PubMed

Ivars Lleó, M; Clavo Escribano, P; Menéndez Prieto, B

2016-05-01

Although the diversity of the clinical manifestations of syphilis is well-known, atypical presentations can also occur. Such atypical presentations are associated with a high risk of transmission as a result of diagnostic confusion and treatment delays owing to the disease's ability to mimic other common skin diseases, deviate from classic clinical presentations, and adopt unique forms. Cases of atypical syphilis have been described most frequently in patients with concomitant human immunodeficiency virus (HIV) infection. Because the incidence of syphilis has been growing over recent years -particularly in patients with HIV co-infection- dermatologists need to be familiar with the less well-known clinical presentations of this venereal disease. Copyright © 2015 AEDV. Published by Elsevier España, S.L.U. All rights reserved.

High Resolution SAR Imaging Employing Geometric Features for Extracting Seismic Damage of Buildings

NASA Astrophysics Data System (ADS)

Cui, L. P.; Wang, X. P.; Dou, A. X.; Ding, X.

2018-04-01

Synthetic Aperture Radar (SAR) image is relatively easy to acquire but difficult for interpretation. This paper probes how to identify seismic damage of building using geometric features of SAR. The SAR imaging geometric features of buildings, such as the high intensity layover, bright line induced by double bounce backscattering and dark shadow is analysed, and show obvious differences texture features of homogeneity, similarity and entropy in combinatorial imaging geometric regions between the un-collapsed and collapsed buildings in airborne SAR images acquired in Yushu city damaged by 2010 Ms7.1 Yushu, Qinghai, China earthquake, which implicates a potential capability to discriminate collapsed and un-collapsed buildings from SAR image. Study also shows that the proportion of highlight (layover & bright line) area (HA) is related to the seismic damage degree, thus a SAR image damage index (SARDI), which related to the ratio of HA to the building occupation are of building in a street block (SA), is proposed. While HA is identified through feature extraction with high-pass and low-pass filtering of SAR image in frequency domain. A partial region with 58 natural street blocks in the Yushu City are selected as study area. Then according to the above method, HA is extracted, SARDI is then calculated and further classified into 3 classes. The results show effective through validation check with seismic damage classes interpreted artificially from post-earthquake airborne high resolution optical image, which shows total classification accuracy 89.3 %, Kappa coefficient 0.79 and identical to the practical seismic damage distribution. The results are also compared and discussed with the building damage identified from SAR image available by other authors.

Compact Representation of High-Dimensional Feature Vectors for Large-Scale Image Recognition and Retrieval.

PubMed

Zhang, Yu; Wu, Jianxin; Cai, Jianfei

2016-05-01

In large-scale visual recognition and image retrieval tasks, feature vectors, such as Fisher vector (FV) or the vector of locally aggregated descriptors (VLAD), have achieved state-of-the-art results. However, the combination of the large numbers of examples and high-dimensional vectors necessitates dimensionality reduction, in order to reduce its storage and CPU costs to a reasonable range. In spite of the popularity of various feature compression methods, this paper shows that the feature (dimension) selection is a better choice for high-dimensional FV/VLAD than the feature (dimension) compression methods, e.g., product quantization. We show that strong correlation among the feature dimensions in the FV and the VLAD may not exist, which renders feature selection a natural choice. We also show that, many dimensions in FV/VLAD are noise. Throwing them away using feature selection is better than compressing them and useful dimensions altogether using feature compression methods. To choose features, we propose an efficient importance sorting algorithm considering both the supervised and unsupervised cases, for visual recognition and image retrieval, respectively. Combining with the 1-bit quantization, feature selection has achieved both higher accuracy and less computational cost than feature compression methods, such as product quantization, on the FV and the VLAD image representations.