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Sample records for resonance image segmentation

  1. Magnetic resonance image segmentation using multifractal techniques

    NASA Astrophysics Data System (ADS)

    Yu, Yue-e.; Wang, Fang; Liu, Li-lin

    2015-11-01

    In order to delineate target region for magnetic resonance image (MRI) with diseases, the classical multifractal spectrum (MFS)-segmentation method and latest multifractal detrended fluctuation spectrum (MF-DFS)-based segmentation method are employed in our study. One of our main conclusions from experiments is that both of the two multifractal-based methods are workable for handling MRIs. The best result is obtained by MF-DFS-based method using Lh10 as local characteristic. The anti-noises experiments also suppot the conclusion. This interest finding shows that the features can be better represented by the strong fluctuations instead of the weak fluctuations for the MRIs. By comparing the multifractal nature between lesion and non-lesion area on the basis of the segmentation results, an interest finding is that the gray value's fluctuation in lesion area is much severer than that in non-lesion area.

  2. Adaptive fuzzy segmentation of magnetic resonance images.

    PubMed

    Pham, D L; Prince, J L

    1999-09-01

    An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, we fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, we also describe a new faster multigrid-based algorithm for its implementation. We show, using simulated MR data, that 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.

  3. Segmentation of neuroanatomy in magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Simmons, Andrew; Arridge, Simon R.; Barker, G. J.; Tofts, Paul S.

    1992-06-01

    Segmentation in neurological magnetic resonance imaging (MRI) is necessary for feature extraction, volume measurement and for the three-dimensional display of neuroanatomy. Automated and semi-automated methods offer considerable advantages over manual methods because of their lack of subjectivity, their data reduction capabilities, and the time savings they give. We have used dual echo multi-slice spin-echo data sets which take advantage of the intrinsically multispectral nature of MRI. As a pre-processing step, a rf non-uniformity correction is applied and if the data is noisy the images are smoothed using a non-isotropic blurring method. Edge-based processing is used to identify the skin (the major outer contour) and the eyes. Edge-focusing has been used to significantly simplify edge images and thus allow simple postprocessing to pick out the brain contour in each slice of the data set. Edge- focusing is a technique which locates significant edges using a high degree of smoothing at a coarse level and tracks these edges to a fine level where the edges can be determined with high positional accuracy. Both 2-D and 3-D edge-detection methods have been compared. Once isolated, the brain is further processed to identify CSF, and, depending upon the MR pulse sequence used, the brain itself may be sub-divided into gray matter and white matter using semi-automatic contrast enhancement and clustering methods.

  4. Unsupervised fuzzy segmentation of 3D magnetic resonance brain images

    NASA Astrophysics Data System (ADS)

    Velthuizen, Robert P.; Hall, Lawrence O.; Clarke, Laurence P.; Bensaid, Amine M.; Arrington, J. A.; Silbiger, Martin L.

    1993-07-01

    Unsupervised fuzzy methods are proposed for segmentation of 3D Magnetic Resonance images of the brain. Fuzzy c-means (FCM) has shown promising results for segmentation of single slices. FCM has been investigated for volume segmentations, both by combining results of single slices and by segmenting the full volume. Different strategies and initializations have been tried. In particular, two approaches have been used: (1) a method by which, iteratively, the furthest sample is split off to form a new cluster center, and (2) the traditional FCM in which the membership grade matrix is initialized in some way. Results have been compared with volume segmentations by k-means and with two supervised methods, k-nearest neighbors and region growing. Results of individual segmentations are presented as well as comparisons on the application of the different methods to a number of tumor patient data sets.

  5. A Scalable Framework For Segmenting Magnetic Resonance Images

    PubMed Central

    Hore, Prodip; Goldgof, Dmitry B.; Gu, Yuhua; Maudsley, Andrew A.; Darkazanli, Ammar

    2009-01-01

    A fast, accurate and fully automatic method of segmenting magnetic resonance images of the human brain is introduced. The approach scales well allowing fast segmentations of fine resolution images. The approach is based on modifications of the soft clustering algorithm, fuzzy c-means, that enable it to scale to large data sets. Two types of modifications to create incremental versions of fuzzy c-means are discussed. They are much faster when compared to fuzzy c-means for medium to extremely large data sets because they work on successive subsets of the data. They are comparable in quality to application of fuzzy c-means to all of the data. The clustering algorithms coupled with inhomogeneity correction and smoothing are used to create a framework for automatically segmenting magnetic resonance images of the human brain. The framework is applied to a set of normal human brain volumes acquired from different magnetic resonance scanners using different head coils, acquisition parameters and field strengths. Results are compared to those from two widely used magnetic resonance image segmentation programs, Statistical Parametric Mapping and the FMRIB Software Library (FSL). The results are comparable to FSL while providing significant speed-up and better scalability to larger volumes of data. PMID:20046893

  6. Segmentation and quantification of adipose tissue by magnetic resonance imaging

    PubMed Central

    Chen, Jun; Shen, Wei

    2016-01-01

    In this brief review, introductory concepts in animal and human adipose tissue segmentation using proton magnetic resonance imaging (MRI) and computed tomography are summarized in the context of obesity research. Adipose tissue segmentation and quantification using spin relaxation-based (e.g., T1-weighted, T2-weighted), relaxometry-based (e.g., T1-, T2-, T2*-mapping), chemical-shift selective, and chemical-shift encoded water–fat MRI pulse sequences are briefly discussed. The continuing interest to classify subcutaneous and visceral adipose tissue depots into smaller sub-depot compartments is mentioned. The use of a single slice, a stack of slices across a limited anatomical region, or a whole body protocol is considered. Common image post-processing steps and emerging atlas-based automated segmentation techniques are noted. Finally, the article identifies some directions of future research, including a discussion on the growing topic of brown adipose tissue and related segmentation considerations. PMID:26336839

  7. Automatic segmentation of the glenohumeral cartilages from magnetic resonance images.

    PubMed

    Neubert, A; Yang, Z; Engstrom, C; Xia, Y; Strudwick, M W; Chandra, S S; Fripp, J; Crozier, S

    2016-10-01

    Magnetic resonance (MR) imaging plays a key role in investigating early degenerative disorders and traumatic injuries of the glenohumeral cartilages. Subtle morphometric and biochemical changes of potential relevance to clinical diagnosis, treatment planning, and evaluation can be assessed from measurements derived from in vivo MR segmentation of the cartilages. However, segmentation of the glenohumeral cartilages, using approaches spanning manual to automated methods, is technically challenging, due to their thin, curved structure and overlapping intensities of surrounding tissues. Automatic segmentation of the glenohumeral cartilages from MR imaging is not at the same level compared to the weight-bearing knee and hip joint cartilages despite the potential applications with respect to clinical investigation of shoulder disorders. In this work, the authors present a fully automated segmentation method for the glenohumeral cartilages using MR images of healthy shoulders. The method involves automated segmentation of the humerus and scapula bones using 3D active shape models, the extraction of the expected bone-cartilage interface, and cartilage segmentation using a graph-based method. The cartilage segmentation uses localization, patient specific tissue estimation, and a model of the cartilage thickness variation. The accuracy of this method was experimentally validated using a leave-one-out scheme on a database of MR images acquired from 44 asymptomatic subjects with a true fast imaging with steady state precession sequence on a 3 T scanner (Siemens Trio) using a dedicated shoulder coil. The automated results were compared to manual segmentations from two experts (an experienced radiographer and an experienced musculoskeletal anatomist) using the Dice similarity coefficient (DSC) and mean absolute surface distance (MASD) metrics. Accurate and precise bone segmentations were achieved with mean DSC of 0.98 and 0.93 for the humeral head and glenoid fossa, respectively

  8. Novel technique in the segmentation of magnetic resonance image

    NASA Astrophysics Data System (ADS)

    Chan, Kwok-Leung

    1996-04-01

    In this investigation, automatic image segmentation is carried out on magnetic resonance image (MRI). A novel technique based on the maximum minimum measure is devised. The measure is improved by combining the smoothing and counting processes, and then normalizing the number of maximum and minimum positions over the region of interest (ROI). Two parameters (MM_H and MM_V) are generated and used for the segmentation. The technique is tested on some brain MRIs of a human male from the Visible Human Project of the National Library of Medicine, National Institutes of Health, USA. Preliminary results indicate that the maximum minimum measure can provide effective parameters for human tissue characterization and image segmentation with an added advantage of faster computation.

  9. Segmentation of the mouse hippocampal formation in magnetic resonance images.

    PubMed

    Richards, Kay; Watson, Charles; Buckley, Rachel F; Kurniawan, Nyoman D; Yang, Zhengyi; Keller, Marianne D; Beare, Richard; Bartlett, Perry F; Egan, Gary F; Galloway, Graham J; Paxinos, George; Petrou, Steven; Reutens, David C

    2011-10-01

    The hippocampal formation plays an important role in cognition, spatial navigation, learning, and memory. High resolution magnetic resonance (MR) imaging makes it possible to study in vivo changes in the hippocampus over time and is useful for comparing hippocampal volume and structure in wild type and mutant mice. Such comparisons demand a reliable way to segment the hippocampal formation. We have developed a method for the systematic segmentation of the hippocampal formation using the perfusion-fixed C57BL/6 mouse brain for application in longitudinal and comparative studies. Our aim was to develop a guide for segmenting over 40 structures in an adult mouse brain using 30 μm isotropic resolution images acquired with a 16.4 T MR imaging system and combined using super-resolution reconstruction.

  10. Probabilistic region growing method for improving magnetic resonance image segmentation

    NASA Astrophysics Data System (ADS)

    Zanaty, E. A.; Asaad, Ayman

    2013-12-01

    In this paper, we propose a new region growing algorithm called 'probabilistic region growing (PRG)' which could improve the magnetic resonance image (MRI) segmentation. The proposed approach includes a threshold based on estimating the probability of pixel intensities of a given image. This threshold uses a homogeneity criterion which is obtained automatically from characteristics of the regions. The homogeneity criterion will be calculated for each pixel as well as the probability of pixel value. The proposed PRG algorithm selects the pixels sequentially in a random walk starting at the seed point, and the homogeneity criterion is updated continuously. The proposed PRG algorithm is applied to the challenging applications: grey matter/white matter segmentation in MRI data sets. The experimental results compared with other segmentation techniques show that the proposed PRG method produces more accurate and stable results.

  11. Sequence-independent segmentation of magnetic resonance images.

    PubMed

    Fischl, Bruce; Salat, David H; van der Kouwe, André J W; Makris, Nikos; Ségonne, Florent; Quinn, Brian T; Dale, Anders M

    2004-01-01

    We present a set of techniques for embedding the physics of the imaging process that generates a class of magnetic resonance images (MRIs) into a segmentation or registration algorithm. This results in substantial invariance to acquisition parameters, as the effect of these parameters on the contrast properties of various brain structures is explicitly modeled in the segmentation. In addition, the integration of image acquisition with tissue classification allows the derivation of sequences that are optimal for segmentation purposes. Another benefit of these procedures is the generation of probabilistic models of the intrinsic tissue parameters that cause MR contrast (e.g., T1, proton density, T2*), allowing access to these physiologically relevant parameters that may change with disease or demographic, resulting in nonmorphometric alterations in MR images that are otherwise difficult to detect. Finally, we also present a high band width multiecho FLASH pulse sequence that results in high signal-to-noise ratio with minimal image distortion due to B0 effects. This sequence has the added benefit of allowing the explicit estimation of T2* and of reducing test-retest intensity variability.

  12. Segmentation of magnetic resonance image using fractal dimension

    NASA Astrophysics Data System (ADS)

    Yau, Joseph K. K.; Wong, Sau-hoi; Chan, Kwok-Leung

    1997-04-01

    In recent years, much research has been conducted in the three-dimensional visualization of medical image. This requires a good segmentation technique. Many early works use first-order and second-order statistics. First-order statistical parameters can be calculated quickly but their effectiveness is influenced by many factors such as illumination, contrast and random noise of the image. Second-order statistical parameters, such as spatial gray level co-occurrence matrices statistics, take longer time to compute but can extract the textural information. In this investigating, two different parameters, namely the entropy and the fractal dimension, are employed to perform segmentation of the magnetic resonance images of the head of a male cadaver. The entropy is calculated from the spatial gray level co-occurrence matrices. The fractal dimension is calculated by the reticular cell counting method. Several regions of the human head are chosen for analysis. They are the bone, gyrus and lobe. Results show that the parameters are able to segment different types of tissue. The entropy gives very good result but it requires very long computation time and large amount of memory. The performance of the fractal dimension is comparable with the entropy. It is simple to estimate and demands lesser memory space.

  13. Applications of magnetic resonance image segmentation in neurology

    NASA Astrophysics Data System (ADS)

    Heinonen, Tomi; Lahtinen, Antti J.; Dastidar, Prasun; Ryymin, Pertti; Laarne, Paeivi; Malmivuo, Jaakko; Laasonen, Erkki; Frey, Harry; Eskola, Hannu

    1999-05-01

    After the introduction of digital imagin devices in medicine computerized tissue recognition and classification have become important in research and clinical applications. Segmented data can be applied among numerous research fields including volumetric analysis of particular tissues and structures, construction of anatomical modes, 3D visualization, and multimodal visualization, hence making segmentation essential in modern image analysis. In this research project several PC based software were developed in order to segment medical images, to visualize raw and segmented images in 3D, and to produce EEG brain maps in which MR images and EEG signals were integrated. The software package was tested and validated in numerous clinical research projects in hospital environment.

  14. Image segmentation by EM-based adaptive pulse coupled neural networks in brain magnetic resonance imaging.

    PubMed

    Fu, J C; Chen, C C; Chai, J W; Wong, S T C; Li, I C

    2010-06-01

    We propose an automatic hybrid image segmentation model that integrates the statistical expectation maximization (EM) model and the spatial pulse coupled neural network (PCNN) for brain magnetic resonance imaging (MRI) segmentation. In addition, an adaptive mechanism is developed to fine tune the PCNN parameters. The EM model serves two functions: evaluation of the PCNN image segmentation and adaptive adjustment of the PCNN parameters for optimal segmentation. To evaluate the performance of the adaptive EM-PCNN, we use it to segment MR brain image into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). The performance of the adaptive EM-PCNN is compared with that of the non-adaptive EM-PCNN, EM, and Bias Corrected Fuzzy C-Means (BCFCM) algorithms. The result is four sets of boundaries for the GM and the brain parenchyma (GM+WM), the two regions of most interest in medical research and clinical applications. Each set of boundaries is compared with the golden standard to evaluate the segmentation performance. The adaptive EM-PCNN significantly outperforms the non-adaptive EM-PCNN, EM, and BCFCM algorithms in gray mater segmentation. In brain parenchyma segmentation, the adaptive EM-PCNN significantly outperforms the BCFCM only. However, the adaptive EM-PCNN is better than the non-adaptive EM-PCNN and EM on average. We conclude that of the three approaches, the adaptive EM-PCNN yields the best results for gray matter and brain parenchyma segmentation.

  15. Cerebral magnetic resonance image segmentation using data fusion

    SciTech Connect

    Rajapakse, J.C.; Giedd, J.N.; Krain, A.L.; Hamburger, S.D.; Rapoport, J.L.; DeCarli, C.

    1996-03-01

    A semiautomated method is described for segmenting dual echo MR head scans into gray and white matter and CSF. The method is applied to brain scans of 80 healthy children and adolescents. A probabilistic data fusion equation was used to combine simultaneously acquired T2-weighted and proton density head scans for tissue segmentation. The fusion equation optimizes the probability of a voxel being a particular tissue type, given the corresponding probabilities from both images. The algorithm accounts for the intensity inhomogeneities present in the images by fusion of local regions of the images. The method was validated using a phantom (agarose gel with iron oxide particles) and hand-segmented imager. Gray and white matter volumes for subjects aged 20-30 years were close to those previously published. White matter and CSF volume increased and gray matter volume decreased significantly across ages 4-18 years. White matter, gray matter, and CSF volumes were larger for males than for females. Males and females showed similar change of gray and white matter volumes with age. This simple, reliable, and valid method can be employed in clinical research for quantification of gray and white matter and CSF volumes in MR head scans. Increase in white matter volume may reflect ongoing axonal growth and myelination, and gray matter reductions may reflect synaptic pruning or cell death in the age span of 4-18 years. 41 refs., 5 figs., 3 tabs.

  16. Segmentation of magnetic resonance images to construct human head model for diffuse optical imaging

    NASA Astrophysics Data System (ADS)

    Kurihara, Kazuki; Kawaguchi, Hiroshi; Takahashi, Yosuke; Obata, Takayuki; Okada, Eiji

    2011-07-01

    The brain activation image obtained by diffuse optical tomography (DOT) is obtained by solving inverse problem using the spatial sensitivity profile (SSP). The SSP can be obtained from the analysis of the light propagation using threedimensional head models. The head model is based upon segmented magnetic resonance (MR) image and there are several types of software based on binarization for segmentation of MR head images. We segmented superficial tissues which effect the light propagation in human head from MR images acquired with FATSAT and FIESTA pulse sequences by using region growing algorithm and morphological operation to facilitate the construction of the individual head models for DOT. The pixel intensity distribution of these images has appropriate characteristics to extract the superficial tissues by using algorithm based on binarization. The result of extraction was compared with the extraction from T2-weighted image which is commonly used to extract superficial tissues. The result of extraction from FATSAT or FIESTA image agree well with ground truth determined by manual segmentation.

  17. Magnetic Resonance Imaging of the Newborn Brain: Automatic Segmentation of Brain Images into 50 Anatomical Regions

    PubMed Central

    Gousias, Ioannis S.; Hammers, Alexander; Counsell, Serena J.; Srinivasan, Latha; Rutherford, Mary A.; Heckemann, Rolf A.; Hajnal, Jo V.; Rueckert, Daniel; Edwards, A. David

    2013-01-01

    We studied methods for the automatic segmentation of neonatal and developing brain images into 50 anatomical regions, utilizing a new set of manually segmented magnetic resonance (MR) images from 5 term-born and 15 preterm infants imaged at term corrected age called ALBERTs. Two methods were compared: individual registrations with label propagation and fusion; and template based registration with propagation of a maximum probability neonatal ALBERT (MPNA). In both cases we evaluated the performance of different neonatal atlases and MPNA, and the approaches were compared with the manual segmentations by means of the Dice overlap coefficient. Dice values, averaged across regions, were 0.81±0.02 using label propagation and fusion for the preterm population, and 0.81±0.02 using the single registration of a MPNA for the term population. Segmentations of 36 further unsegmented target images of developing brains yielded visibly high-quality results. This registration approach allows the rapid construction of automatically labeled age-specific brain atlases for neonates and the developing brain. PMID:23565180

  18. Magnetic resonance imaging segmentation techniques using batch-type learning vector quantization algorithms.

    PubMed

    Yang, Miin-Shen; Lin, Karen Chia-Ren; Liu, Hsiu-Chih; Lirng, Jiing-Feng

    2007-02-01

    In this article, we propose batch-type learning vector quantization (LVQ) segmentation techniques for the magnetic resonance (MR) images. Magnetic resonance imaging (MRI) segmentation is an important technique to differentiate abnormal and normal tissues in MR image data. The proposed LVQ segmentation techniques are compared with the generalized Kohonen's competitive learning (GKCL) methods, which were proposed by Lin et al. [Magn Reson Imaging 21 (2003) 863-870]. Three MRI data sets of real cases are used in this article. The first case is from a 2-year-old girl who was diagnosed with retinoblastoma in her left eye. The second case is from a 55-year-old woman who developed complete left side oculomotor palsy immediately after a motor vehicle accident. The third case is from an 84-year-old man who was diagnosed with Alzheimer disease (AD). Our comparisons are based on sensitivity of algorithm parameters, the quality of MRI segmentation with the contrast-to-noise ratio and the accuracy of the region of interest tissue. Overall, the segmentation results from batch-type LVQ algorithms present good accuracy and quality of the segmentation images, and also flexibility of algorithm parameters in all the comparison consequences. The results support that the proposed batch-type LVQ algorithms are better than the previous GKCL algorithms. Specifically, the proposed fuzzy-soft LVQ algorithm works well in segmenting AD MRI data set to accurately measure the hippocampus volume in AD MR images.

  19. Sensitive segmentation of low-contrast multispectral images based on multiparameter space-resonance imaging method

    NASA Astrophysics Data System (ADS)

    Akhmetshin, Alexander M.; Akhmetshin, Lyudmila G.

    2001-10-01

    A new method of low contrast multispectral, hyperspectral and multiparameter images segmentation is outlined. The one has significant advantage in sensitivity and space resolving power of segmentation in comparison with known methods such as principal component transformation and fuzzy C-means clustering segmentation ones. New method is based on using of two important stages: 1) application virtual long-wave holographic transformation to each separate image of analyzed multispectral sequence (it is needed for increasing sensitivity of further analysis); 2) to each pixel of analyzed multispectral image is compare a virtual nonrecursive digital filter with complex coefficients. The one is characterized by its amplitude-frequency (AFC) and phase-frequency (PFC) characteristics. Information features used for visualization and segmentation are frequencies corresponded to maximum (resonance point) or minimum (antiresonance point) of AFC and group delay function calculated on base PFC. Information possibilities of new method are demonstrated on examples of multispectral remote sensing, various physical nature geophysical fields fusion and multiparameter MRI brain tumor hidden area influence detection.

  20. Semi-automatic segmentation of nonviable cardiac tissue using cine and delayed enhancement magnetic resonance images

    NASA Astrophysics Data System (ADS)

    O'Donnell, Thomas P.; Xu, Ning; Setser, Randolph M.; White, Richard D.

    2003-05-01

    Post myocardial infarction, the identification and assessment of non-viable (necrotic) tissues is necessary for effective development of intervention strategies and treatment plans. Delayed Enhancement Magnetic Resonance (DEMR) imaging is a technique whereby non-viable cardiac tissue appears with increased signal intensity. Radiologists typically acquire these images in conjunction with other functional modalities (e.g., MR Cine), and use domain knowledge and experience to isolate the non-viable tissues. In this paper, we present a technique for automatically segmenting these tissues given the delineation of myocardial borders in the DEMR and in the End-systolic and End-diastolic MR Cine images. Briefly, we obtain a set of segmentations furnished by an expert and employ an artificial intelligence technique, Support Vector Machines (SVMs), to "learn" the segmentations based on features culled from the images. Using those features we then allow the SVM to predict the segmentations the expert would provide on previously unseen images.

  1. A new prostate segmentation approach using multispectral magnetic resonance imaging and a statistical pattern classifier

    NASA Astrophysics Data System (ADS)

    Maan, Bianca; van der Heijden, Ferdi; Fütterer, Jurgen J.

    2012-02-01

    Prostate segmentation is essential for calculating prostate volume, creating patient-specific prostate anatomical models and image fusion. Automatic segmentation methods are preferable because manual segmentation is timeconsuming and highly subjective. Most of the currently available segmentation methods use a priori knowledge of the prostate shape. However, there is a large variation in prostate shape between patients. Our approach uses multispectral magnetic resonance imaging (MRI) data, containing T1, T2 and proton density (PD) weighted images and the distance from the voxel to the centroid of the prostate, together with statistical pattern classifiers. We investigated the performance of a parametric and a non-parametric classification approach by applying a Baysian-quadratic and a k-nearest-neighbor classifier respectively. An annotated data set is made by manual labeling of the image. Using this data set, the classifiers are trained and evaluated. sThe following results are obtained after three experiments. Firstly, using feature selection we showed that the average segmentation error rates are lowest when combining all three images and the distance with the k-nearest-neighbor classifier. Secondly, the confusion matrix showed that the k-nearest-neighbor classifier has the sensitivity. Finally, the prostate is segmented using both classifier. The segmentation boundaries approach the prostate boundaries for most slices. However, in some slices the segmentation result contained errors near the borders of the prostate. The current results showed that segmenting the prostate using multispectral MRI data combined with a statistical classifier is a promising method.

  2. Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review.

    PubMed

    Tohka, Jussi

    2014-11-28

    Quantitative analysis of magnetic resonance (MR) brain images are facilitated by the development of automated segmentation algorithms. A single image voxel may contain of several types of tissues due to the finite spatial resolution of the imaging device. This phenomenon, termed partial volume effect (PVE), complicates the segmentation process, and, due to the complexity of human brain anatomy, the PVE is an important factor for accurate brain structure quantification. Partial volume estimation refers to a generalized segmentation task where the amount of each tissue type within each voxel is solved. This review aims to provide a systematic, tutorial-like overview and categorization of methods for partial volume estimation in brain MRI. The review concentrates on the statistically based approaches for partial volume estimation and also explains differences to other, similar image segmentation approaches.

  3. Automatic graph-cut based segmentation of bones from knee magnetic resonance images for osteoarthritis research

    PubMed Central

    Prescott, Jeff W.; Gurcan, Metin N.

    2011-01-01

    In this paper, a new, fully automated, content-based system is proposed for knee bone segmentation from magnetic resonance images (MRI). The purpose of the bone segmentation is to support the discovery and characterization of imaging biomarkers for the incidence and progression of osteoarthritis, a debilitating joint disease, which affects a large portion of the aging population. The segmentation algorithm includes a novel content-based, two-pass disjoint block discovery mechanism, which is designed to support automation, segmentation initialization, and post processing. The block discovery is achieved by classifying the image content to bone and background blocks according to their similarity to the categories in the training data collected from typical bone structures. The classified blocks are then used to design an efficient graph-cut based segmentation algorithm. This algorithm requires constructing a graph using image pixel data followed by applying a maximum-flow algorithm which generates a minimum graph-cut that corresponds to an initial image segmentation. Content-based refinements and morphological operations are then applied to obtain the final segmentation. The proposed segmentation technique does not require any user interaction and can distinguish between bone and highly similar adjacent structures, such as fat tissues with high accuracy. The performance of the proposed system is evaluated by testing it on 376 MR images from the Osteoarthritis Initiative (OAI) database. This database included a selection of single images containing the femur and tibia from 200 subjects with varying levels of osteoarthritis severity. Additionally, a full three-dimensional segmentation of the bones from ten subjects with 14 slices each, and synthetic images with background having intensity and spatial characteristics similar to those of bone are used to assess the robustness and consistency of the developed algorithm. The results show an automatic bone detection rate of

  4. Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation

    PubMed Central

    Azmi, Reza; Pishgoo, Boshra; Norozi, Narges; Yeganeh, Samira

    2013-01-01

    Brain magnetic resonance images (MRIs) tissue segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unsupervised segmentation methods have no prior knowledge and lead to low level of performance. However, semi-supervised learning which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-supervised frame-work for segmenting of brain magnetic resonance imaging (MRI) tissues that it has been used results of several semi-supervised classifiers simultaneously. Selecting appropriate classifiers has a significant role in the performance of this frame-work. Hence, in this paper, we present two semi-supervised algorithms expectation filtering maximization and MCo_Training that are improved versions of semi-supervised methods expectation maximization and Co_Training and increase segmentation accuracy. Afterward, we use these improved classifiers together with graph-based semi-supervised classifier as components of the ensemble frame-work. Experimental results show that performance of segmentation in this approach is higher than both supervised methods and the individual semi-supervised classifiers. PMID:24098863

  5. Magnetic resonance imaging of water ascent in embolized xylem vessels of grapevine stem segments

    Treesearch

    Mingtao Wang; Melvin T. Tyree; Roderick E. Wasylishen

    2013-01-01

    Temporal and spatial information about water refilling of embolized xylem vessels and the rate of water ascent in these vessels is critical for understanding embolism repair in intact living vascular plants. High-resolution 1H magnetic resonance imaging (MRI) experiments have been performed on embolized grapevine stem segments while they were...

  6. Robust kernelized local information fuzzy C-means clustering for brain magnetic resonance image segmentation.

    PubMed

    Elazab, Ahmed; AbdulAzeem, Yousry M; Wu, Shiqian; Hu, Qingmao

    2016-03-17

    Brain tissue segmentation from magnetic resonance (MR) images is an importance task for clinical use. The segmentation process becomes more challenging in the presence of noise, grayscale inhomogeneity, and other image artifacts. In this paper, we propose a robust kernelized local information fuzzy C-means clustering algorithm (RKLIFCM). It incorporates local information into the segmentation process (both grayscale and spatial) for more homogeneous segmentation. In addition, the Gaussian radial basis kernel function is adopted as a distance metric to replace the standard Euclidean distance. The main advantages of the new algorithm are: efficient utilization of local grayscale and spatial information, robustness to noise, ability to preserve image details, free from any parameter initialization, and with high speed as it runs on image histogram. We compared the proposed algorithm with 7 soft clustering algorithms that run on both image histogram and image pixels to segment brain MR images. Experimental results demonstrate that the proposed RKLIFCM algorithm is able to overcome the influence of noise and achieve higher segmentation accuracy with low computational complexity.

  7. Vessel segmentation in 4D arterial spin labeling magnetic resonance angiography images of the brain

    NASA Astrophysics Data System (ADS)

    Phellan, Renzo; Lindner, Thomas; Falcão, Alexandre X.; Forkert, Nils D.

    2017-03-01

    4D arterial spin labeling magnetic resonance angiography (4D ASL MRA) is a non-invasive and safe modality for cerebrovascular imaging procedures. It uses the patient's magnetically labeled blood as intrinsic contrast agent, so that no external contrast media is required. It provides important 3D structure and blood flow information but a sufficient cerebrovascular segmentation is important since it can help clinicians to analyze and diagnose vascular diseases faster, and with higher confidence as compared to simple visual rating of raw ASL MRA images. This work presents a new method for automatic cerebrovascular segmentation in 4D ASL MRA images of the brain. In this process images are denoised, corresponding image label/control image pairs of the 4D ASL MRA sequences are subtracted, and temporal intensity averaging is used to generate a static representation of the vascular system. After that, sets of vessel and background seeds are extracted and provided as input for the image foresting transform algorithm to segment the vascular system. Four 4D ASL MRA datasets of the brain arteries of healthy subjects and corresponding time-of-flight (TOF) MRA images were available for this preliminary study. For evaluation of the segmentation results of the proposed method, the cerebrovascular system was automatically segmented in the high-resolution TOF MRA images using a validated algorithm and the segmentation results were registered to the 4D ASL datasets. Corresponding segmentation pairs were compared using the Dice similarity coefficient (DSC). On average, a DSC of 0.9025 was achieved, indicating that vessels can be extracted successfully from 4D ASL MRA datasets by the proposed segmentation method.

  8. The use of atlas registration and graph cuts for prostate segmentation in magnetic resonance images

    SciTech Connect

    Korsager, Anne Sofie Østergaard, Lasse Riis; Fortunati, Valerio; Lijn, Fedde van der; Niessen, Wiro; Walsum, Theo van; Carl, Jesper

    2015-04-15

    Purpose: An automatic method for 3D prostate segmentation in magnetic resonance (MR) images is presented for planning image-guided radiotherapy treatment of prostate cancer. Methods: A spatial prior based on intersubject atlas registration is combined with organ-specific intensity information in a graph cut segmentation framework. The segmentation is tested on 67 axial T{sub 2}-weighted MR images in a leave-one-out cross validation experiment and compared with both manual reference segmentations and with multiatlas-based segmentations using majority voting atlas fusion. The impact of atlas selection is investigated in both the traditional atlas-based segmentation and the new graph cut method that combines atlas and intensity information in order to improve the segmentation accuracy. Best results were achieved using the method that combines intensity information, shape information, and atlas selection in the graph cut framework. Results: A mean Dice similarity coefficient (DSC) of 0.88 and a mean surface distance (MSD) of 1.45 mm with respect to the manual delineation were achieved. Conclusions: This approaches the interobserver DSC of 0.90 and interobserver MSD 0f 1.15 mm and is comparable to other studies performing prostate segmentation in MR.

  9. Segmentation of magnetic resonance images of the thighs for a new National Institutes of Health initiative

    NASA Astrophysics Data System (ADS)

    Monzon, A.; Hemler, P. F.; Nalls, M.; Manini, T.; Clark, B. C.; Harris, T. B.; McAuliffe, M. J.

    2007-03-01

    This paper describes a new system for semi-automatically segmenting the background, subcutaneous fat, interstitial fat, muscle, bone, and bone marrow from magnetic resonance images (MRI's) of volunteers for a new osteoarthritis study. Our system first creates separate right and left thigh images from a single MR image containing both legs. The subcutaneous fat boundary is very difficult to detect in these images and is therefore interactively defined with a single boundary. The volume within the boundary is then automatically processed with a series of clustering and morphological operations designed to identify and classify the different tissue types required for this study. Once the tissues have been identified, the volume of each tissue is determined and a single, false colored, segmented image results. We quantitatively compare the segmentation in three different ways. In our first method we simply compare the tissue volumes of the resulting segmentations performed independently on both the left and right thigh. A second quantification method compares our results temporally with three image sets of the same volunteer made one month apart including a month of leg disuse. Our final quantification methodology compares the volumes of different tissues detected with our system to the results of a manual segmentation performed by a trained expert. The segmented image results of four different volunteers using images acquired at three different times suggests that the system described in this paper provides more consistent results than the manually segmented set. Furthermore, measurements of the left and right thigh and temporal results for both segmentation methods follow the anticipated trend of increasing fat and decreasing muscle over the period of disuse.

  10. Segmentation of tongue muscles from super-resolution magnetic resonance images.

    PubMed

    Ibragimov, Bulat; Prince, Jerry L; Murano, Emi Z; Woo, Jonghye; Stone, Maureen; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

    2015-02-01

    Imaging and quantification of tongue anatomy is helpful in surgical planning, post-operative rehabilitation of tongue cancer patients, and studying of how humans adapt and learn new strategies for breathing, swallowing and speaking to compensate for changes in function caused by disease, medical interventions or aging. In vivo acquisition of high-resolution three-dimensional (3D) magnetic resonance (MR) images with clearly visible tongue muscles is currently not feasible because of breathing and involuntary swallowing motions that occur over lengthy imaging times. However, recent advances in image reconstruction now allow the generation of super-resolution 3D MR images from sets of orthogonal images, acquired at a high in-plane resolution and combined using super-resolution techniques. This paper presents, to the best of our knowledge, the first attempt towards automatic tongue muscle segmentation from MR images. We devised a database of ten super-resolution 3D MR images, in which the genioglossus and inferior longitudinalis tongue muscles were manually segmented and annotated with landmarks. We demonstrate the feasibility of segmenting the muscles of interest automatically by applying the landmark-based game-theoretic framework (GTF), where a landmark detector based on Haar-like features and an optimal assignment-based shape representation were integrated. The obtained segmentation results were validated against an independent manual segmentation performed by a second observer, as well as against B-splines and demons atlasing approaches. The segmentation performance resulted in mean Dice coefficients of 85.3%, 81.8%, 78.8% and 75.8% for the second observer, GTF, B-splines atlasing and demons atlasing, respectively. The obtained level of segmentation accuracy indicates that computerized tongue muscle segmentation may be used in surgical planning and treatment outcome analysis of tongue cancer patients, and in studies of normal subjects and subjects with speech and

  11. Segmentation of Tongue Muscles from Super-Resolution Magnetic Resonance Images

    PubMed Central

    Ibragimov, Bulat; Prince, Jerry L.; Murano, Emi Z.; Woo, Jonghye; Stone, Maureen; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

    2014-01-01

    Imaging and quantification of tongue anatomy is helpful in surgical planning, post-operative rehabilitation of tongue cancer patients, and studying of how humans adapt and learn new strategies for breathing, swallowing and speaking to compensate for changes in function caused by disease, medical interventions or aging. In vivo acquisition of high-resolution three-dimensional (3D) magnetic resonance (MR) images with clearly visible tongue muscles is currently not feasible because of breathing and involuntary swallowing motions that occur over lengthy imaging times. However, recent advances in image reconstruction now allow the generation of super-resolution 3D MR images from sets of orthogonal images, acquired at a high in-plane resolution and combined using super-resolution techniques. This paper presents, to the best of our knowledge, the first attempt towards automatic tongue muscle segmentation from MR images. We devised a database of ten super-resolution 3D MR images, in which the genioglossus and inferior longitudinalis tongue muscles were manually segmented and annotated with landmarks. We demonstrate the feasibility of segmenting the muscles of interest automatically by applying the landmark-based game-theoretic framework (GTF), where a landmark detector based on Haar-like features and an optimal assignment-based shape representation were integrated. The obtained segmentation results were validated against an independent manual segmentation performed by a second observer, as well as against B-splines and demons atlasing approaches. The segmentation performance resulted in mean Dice coefficients of 85.3%, 81.8%, 78.8% and 75.8% for the second observer, GTF, B-splines atlasing and demons atlasing, respectively. The obtained level of segmentation accuracy indicates that computerized tongue muscle segmentation may be used in surgical planning and treatment outcome analysis of tongue cancer patients, and in studies of normal subjects and subjects with speech and

  12. Segmenting subregions of the human hippocampus on structural magnetic resonance image scans: An illustrated tutorial

    PubMed Central

    Dalton, Marshall A.; Zeidman, Peter; Barry, Daniel N.; Williams, Elaine; Maguire, Eleanor A.

    2017-01-01

    Background: The hippocampus plays a central role in cognition, and understanding the specific contributions of its subregions will likely be key to explaining its wide-ranging functions. However, delineating substructures within the human hippocampus in vivo from magnetic resonance image scans is fraught with difficulties. To our knowledge, the extant literature contains only brief descriptions of segmentation procedures used to delineate hippocampal subregions in magnetic resonance imaging/functional magnetic resonance imaging studies. Methods: Consequently, here we provide a clear, step-by-step and fully illustrated guide to segmenting hippocampal subregions along the entire length of the human hippocampus on 3T magnetic resonance images. Results: We give a detailed description of how to segment the hippocampus into the following six subregions: dentate gyrus/Cornu Ammonis 4, CA3/2, CA1, subiculum, pre/parasubiculum and the uncus. Importantly, this in-depth protocol incorporates the most recent cyto- and chemo-architectural evidence and includes a series of comprehensive figures which compare slices of histologically stained tissue with equivalent 3T images. Conclusion: As hippocampal subregion segmentation is an evolving field of research, we do not suggest this protocol is definitive or final. Rather, we present a fully explained and expedient method of manual segmentation which remains faithful to our current understanding of human hippocampal neuroanatomy. We hope that this ‘tutorial’-style guide, which can be followed by experts and non-experts alike, will be a practical resource for clinical and research scientists with an interest in the human hippocampus. PMID:28596993

  13. Three-dimensional brain magnetic resonance imaging segmentation via knowledge-driven decision theory.

    PubMed

    Verma, Nishant; Muralidhar, Gautam S; Bovik, Alan C; Cowperthwaite, Matthew C; Burnett, Mark G; Markey, Mia K

    2014-10-01

    Brain tissue segmentation on magnetic resonance (MR) imaging is a difficult task because of significant intensity overlap between the tissue classes. We present a new knowledge-driven decision theory (KDT) approach that incorporates prior information of the relative extents of intensity overlap between tissue class pairs for volumetric MR tissue segmentation. The proposed approach better handles intensity overlap between tissues without explicitly employing methods for removal of MR image corruptions (such as bias field). Adaptive tissue class priors are employed that combine probabilistic atlas maps with spatial contextual information obtained from Markov random fields to guide tissue segmentation. The energy function is minimized using a variational level-set-based framework, which has shown great promise for MR image analysis. We evaluate the proposed method on two well-established real MR datasets with expert ground-truth segmentations and compare our approach against existing segmentation methods. KDT has low-computational complexity and shows better segmentation performance than other segmentation methods evaluated using these MR datasets.

  14. Three-dimensional brain magnetic resonance imaging segmentation via knowledge-driven decision theory

    PubMed Central

    Verma, Nishant; Muralidhar, Gautam S.; Bovik, Alan C.; Cowperthwaite, Matthew C.; Burnett, Mark G.; Markey, Mia K.

    2014-01-01

    Abstract. Brain tissue segmentation on magnetic resonance (MR) imaging is a difficult task because of significant intensity overlap between the tissue classes. We present a new knowledge-driven decision theory (KDT) approach that incorporates prior information of the relative extents of intensity overlap between tissue class pairs for volumetric MR tissue segmentation. The proposed approach better handles intensity overlap between tissues without explicitly employing methods for removal of MR image corruptions (such as bias field). Adaptive tissue class priors are employed that combine probabilistic atlas maps with spatial contextual information obtained from Markov random fields to guide tissue segmentation. The energy function is minimized using a variational level-set-based framework, which has shown great promise for MR image analysis. We evaluate the proposed method on two well-established real MR datasets with expert ground-truth segmentations and compare our approach against existing segmentation methods. KDT has low-computational complexity and shows better segmentation performance than other segmentation methods evaluated using these MR datasets. PMID:26158060

  15. Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging.

    PubMed

    García-Lorenzo, Daniel; Francis, Simon; Narayanan, Sridar; Arnold, Douglas L; Collins, D Louis

    2013-01-01

    Magnetic resonance (MR) imaging is often used to characterize and quantify multiple sclerosis (MS) lesions in the brain and spinal cord. The number and volume of lesions have been used to evaluate MS disease burden, to track the progression of the disease and to evaluate the effect of new pharmaceuticals in clinical trials. Accurate identification of MS lesions in MR images is extremely difficult due to variability in lesion location, size and shape in addition to anatomical variability between subjects. Since manual segmentation requires expert knowledge, is time consuming and is subject to intra- and inter-expert variability, many methods have been proposed to automatically segment lesions. The objective of this study was to carry out a systematic review of the literature to evaluate the state of the art in automated multiple sclerosis lesion segmentation. From 1240 hits found initially with PubMed and Google scholar, our selection criteria identified 80 papers that described an automatic lesion segmentation procedure applied to MS. Only 47 of these included quantitative validation with at least one realistic image. In this paper, we describe the complexity of lesion segmentation, classify the automatic MS lesion segmentation methods found, and review the validation methods applied in each of the papers reviewed. Although many segmentation solutions have been proposed, including some with promising results using MRI data obtained on small groups of patients, no single method is widely employed due to performance issues related to the high variability of MS lesion appearance and differences in image acquisition. The challenge remains to provide segmentation techniques that work in all cases regardless of the type of MS, duration of the disease, or MRI protocol, and this within a comprehensive, standardized validation framework. MS lesion segmentation remains an open problem.

  16. Level set segmentation of brain magnetic resonance images based on local Gaussian distribution fitting energy.

    PubMed

    Wang, Li; Chen, Yunjie; Pan, Xiaohua; Hong, Xunning; Xia, Deshen

    2010-05-15

    This paper presents a variational level set approach in a multi-phase formulation to segmentation of brain magnetic resonance (MR) images with intensity inhomogeneity. In our model, the local image intensities are characterized by Gaussian distributions with different means and variances. We define a local Gaussian distribution fitting energy with level set functions and local means and variances as variables. The means and variances of local intensities are considered as spatially varying functions. Therefore, our method is able to deal with intensity inhomogeneity without inhomogeneity correction. Our method has been applied to 3T and 7T MR images with promising results.

  17. Validation of a protocol for manual segmentation of the thalamus on magnetic resonance imaging scans.

    PubMed

    Power, Brian D; Wilkes, Fiona A; Hunter-Dickson, Mitchell; van Westen, Danielle; Santillo, Alexander F; Walterfang, Mark; Nilsson, Christer; Velakoulis, Dennis; Looi, Jeffrey C L

    2015-04-30

    We present a validated protocol for manual segmentation of the thalamus on T1-weighted magnetic resonance imaging (MRI) scans using brain image analysis software. The MRI scans of five normal control subjects were randomly selected from a larger cohort recruited from Lund University Hospital and Landskrona Hospital, Sweden. MRIs were performed using a 3.0T Philips MR scanner, with an eight-channel head coil, and high resolution images were acquired using a T1-weighted turbo field echo (T1 TFE) pulse sequence, with resulting voxel size 1×1×1 mm3. Manual segmentation of the left and right thalami and volume measurement was performed on 28-30 contiguous coronal slices, using ANALYZE 11.0 software. Reliability of image analysis was performed by measuring intra-class correlations between initial segmentation and random repeated segmentation of the left and right thalami (in total 10 thalami for segmentation); inter-rater reliability was measured using volumes obtained by two other experienced tracers. Intra-class correlations for two independent raters were 0.95 and 0.98; inter-class correlations between the expert rater and two independent raters were 0.92 and 0.98. We anticipate that mapping thalamic morphology in various neuropsychiatric disorders may yield clinically useful disease-specific biomarkers. Crown Copyright © 2015. Published by Elsevier Ireland Ltd. All rights reserved.

  18. Automatic segmentation of left ventricle cavity from short-axis cardiac magnetic resonance images.

    PubMed

    Yang, Xulei; Song, Qing; Su, Yi

    2017-02-03

    In this paper, a computational framework is proposed to perform a fully automatic segmentation of the left ventricle (LV) cavity from short-axis cardiac magnetic resonance (CMR) images. In the initial phase, the region of interest (ROI) is automatically identified on the first image frame of the CMR slices. This is done by partitioning the image into different regions using a standard fuzzy c-means (FCM) clustering algorithm where the LV region is identified according to its intensity, size and circularity in the image. Next, LV segmentation is performed within the identified ROI by using a novel clustering method that utilizes an objective functional with a dissimilarity measure that incorporates a circular shape function. This circular shape-constrained FCM algorithm is able to differentiate pixels with similar intensity but are located in different regions (e.g. LV cavity and non-LV cavity), thus improving the accuracy of the segmentation even in the presence of papillary muscles. In the final step, the segmented LV cavity is propagated to the adjacent image frame to act as the ROI. The segmentation and ROI propagation are then iteratively executed until the segmentation has been performed for the whole cardiac sequence. Experiment results using the LV Segmentation Challenge validation datasets show that our proposed framework can achieve an average perpendicular distance (APD) shift of 2.23 ± 0.50 mm and the Dice metric (DM) index of 0.89 ± 0.03, which is comparable to the existing cutting edge methods. The added advantage over state of the art is that our approach is fully automatic, does not need manual initialization and does not require a prior trained model.

  19. Effects of sagittal endplate shape on lumbar segmental mobility as evaluated by kinetic magnetic resonance imaging.

    PubMed

    Li, Yawei; Lord, Elizabeth; Cohen, Yermie; Ruangchainikom, Monchai; Wang, Bing; Lv, Guohua; Wang, Jeffrey C

    2014-08-01

    Retrospective analysis using kinetic magnetic resonance imaging. To investigate relationships between vertebral endplate remodeling, Modic changes, disc degeneration, and lumbar segmental mobility. Previous studies have shown that disc degeneration and vertebral endplate Modic changes are associated with differences in spinal motion, however, the effects of vertebral endplate morphology on lumbar segmental motion have not been fully investigated. A total of 420 patients underwent kinetic magnetic resonance imaging of 2100 lumbar motion segments. Sagittal endplate shapes (concave, flat, irregular), Modic changes (types, 0-3), and disc degeneration (grade, I-V) were assessed along with translational and angular motion of vertebral segments in flexion, extension, and neutral positions. The most common findings were concave endplate shape (63.24%), type 2 Modic change (71.79%), and grade II disc degeneration (40.33%). Flat, irregular endplates were more common at L1-L2, L4-L5, and L5-S1 than L2-L3 and L3-L4. Types 1, 2, and 3 Modic changes increased in frequency according to endplate shape: concave less than flat less than irregular. Type 0 was observed to decrease with the change of endplate shape from flat to concave to irregular. Vertebral levels with irregular endplates had more disc generation than those with flat; levels with flat endplates had significantly more disc degeneration than those with concave. Translational motion of the lumbar segment was greatest at levels with irregular endplates and decreased at those with flat and then concaves endplates. Angular motion was least at levels with irregular endplates and increased at levels with flat, then concave endplates. The degree of pathogenic lumbar segmental motion is associated with remodeling of the sagittal endplate. Endplate remodeling may occur as an adaptation to restrain abnormal movement of the lumbar segment. N/A.

  20. Generalized method for partial volume estimation and tissue segmentation in cerebral magnetic resonance images

    PubMed Central

    Khademi, April; Venetsanopoulos, Anastasios; Moody, Alan R.

    2014-01-01

    Abstract. An artifact found in magnetic resonance images (MRI) called partial volume averaging (PVA) has received much attention since accurate segmentation of cerebral anatomy and pathology is impeded by this artifact. Traditional neurological segmentation techniques rely on Gaussian mixture models to handle noise and PVA, or high-dimensional feature sets that exploit redundancy in multispectral datasets. Unfortunately, model-based techniques may not be optimal for images with non-Gaussian noise distributions and/or pathology, and multispectral techniques model probabilities instead of the partial volume (PV) fraction. For robust segmentation, a PV fraction estimation approach is developed for cerebral MRI that does not depend on predetermined intensity distribution models or multispectral scans. Instead, the PV fraction is estimated directly from each image using an adaptively defined global edge map constructed by exploiting a relationship between edge content and PVA. The final PVA map is used to segment anatomy and pathology with subvoxel accuracy. Validation on simulated and real, pathology-free T1 MRI (Gaussian noise), as well as pathological fluid attenuation inversion recovery MRI (non-Gaussian noise), demonstrate that the PV fraction is accurately estimated and the resultant segmentation is robust. Comparison to model-based methods further highlight the benefits of the current approach. PMID:26158022

  1. On the use of coupled shape priors for segmentation of magnetic resonance images of the knee

    PubMed Central

    Pang, Jincheng; Driban, Jeffrey B.; McAlindon, Timothy E.; Tamez-Peña, José G.; Fripp, Jurgen; Miller, Eric L.

    2015-01-01

    Active contour techniques have been widely employed for medical image segmentation. Significant effort has been focused on the use of training data to build prior statistical models applicable specifically to problems where the objects of interest are embedded in cluttered background. Usually the training data consists of whole shapes of certain organs or structures obtained manually by clinical experts. The resulting prior models enforce segmentation accuracy uniformly over the entire structure or structures to be identified. In this paper, we consider a new coupled prior shape model which is demonstrated to provide high accuracy, specifically in the region of the interest where precision is most needed for the application of the segmentation of the femur and tibia in magnetic resonance (MR) images. Experimental results for the segmentation of MR images of human knees demonstrate that the combination of the new coupled prior shape and a directional edge force provides the improved segmentation performance. Moreover, the new approach allows for equivalent accurate identification of bone marrow lesions (BMLs), a promising biomarker related to osteoarthritis (OA), to the current state of the art but requires significantly less manual interaction. PMID:25014973

  2. Contour-based brain segmentation method for magnetic resonance imaging human head scans.

    PubMed

    Somasundaram, K; Kalavathi, P

    2013-01-01

    The high-resolution magnetic resonance brain images often contain some nonbrain tissues (ie, skin, fat, muscle, neck, eye balls, etc) compared with the functional images such as positron emission tomography, single-photon emission computed tomography, and functional magnetic resonance imaging (MRI) scans, which usually contain few nonbrain tissues. Automatic segmentation of brain tissues from MRI scans remains a challenging task due to the variation in shape and size, use of different pulse sequences, overlapping signal intensities and imaging artifacts. This article presents a contour-based automatic brain segmentation method to segment the brain regions from T1-, T2-, and proton density-weighted MRI of human head scans. The proposed method consists of 2 stages. In stage 1, the brain regions in the middle slice is extracted. Many of the existing methods failed to extract brain regions in the lower and upper slices of the brain volume, where the brain appears in more than 1 connected region. To overcome this problem, in the proposed method, a landmark circle is drawn at the center of the extracted brain region of a middle slice and is likely to pass through all the brain regions in the remaining lower and upper slices irrespective of whether the brain is composed of 1 or more connected components. In stage 2, the brain regions in the remaining slices are extracted with reference to the landmark circle obtained in stage 1. The proposed method is robust to the variability of brain anatomy, image orientation, and image type, and it extracts the brain regions accurately in T1-, T2-, and proton density-weighted normal and abnormal brain images. Experimental results by applying the proposed method on 100 volumes of brain images show that the proposed method exhibits best and consistent performance than by the popular existing methods brain extraction tool, brain surface extraction, watershed algorithm, hybrid watershed algorithm, and skull stripping using graph cuts.

  3. Adaptive segmentation of magnetic resonance images with intensity inhomogeneity using level set method.

    PubMed

    Liu, Lixiong; Zhang, Qi; Wu, Min; Li, Wu; Shang, Fei

    2013-05-01

    It is a big challenge to segment magnetic resonance (MR) images with intensity inhomogeneity. The widely used segmentation algorithms are region based, which mostly rely on the intensity homogeneity, and could bring inaccurate results. In this paper, we propose a novel region-based active contour model in a variational level set formulation. Based on the fact that intensities in a relatively small local region are separable, a local intensity clustering criterion function is defined. Then, the local function is integrated around the neighborhood center to formulate a global intensity criterion function, which defines the energy term to drive the evolution of the active contour locally. Simultaneously, an intensity fitting term that drives the motion of the active contour globally is added to the energy. In order to segment the image fast and accurately, we utilize a coefficient to make the segmentation adaptive. Finally, the energy is incorporated into a level set formulation with a level set regularization term, and the energy minimization is conducted by a level set evolution process. Experiments on synthetic and real MR images show the effectiveness of our method. Copyright © 2013 Elsevier Inc. All rights reserved.

  4. Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images.

    PubMed

    Chen, Yunjie; Zhan, Tianming; Zhang, Ji; Wang, Hongyuan

    2016-01-01

    We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms.

  5. Accuracy Validation of an Automated Method for Prostate Segmentation in Magnetic Resonance Imaging.

    PubMed

    Shahedi, Maysam; Cool, Derek W; Bauman, Glenn S; Bastian-Jordan, Matthew; Fenster, Aaron; Ward, Aaron D

    2017-03-24

    Three dimensional (3D) manual segmentation of the prostate on magnetic resonance imaging (MRI) is a laborious and time-consuming task that is subject to inter-observer variability. In this study, we developed a fully automatic segmentation algorithm for T2-weighted endorectal prostate MRI and evaluated its accuracy within different regions of interest using a set of complementary error metrics. Our dataset contained 42 T2-weighted endorectal MRI from prostate cancer patients. The prostate was manually segmented by one observer on all of the images and by two other observers on a subset of 10 images. The algorithm first coarsely localizes the prostate in the image using a template matching technique. Then, it defines the prostate surface using learned shape and appearance information from a set of training images. To evaluate the algorithm, we assessed the error metric values in the context of measured inter-observer variability and compared performance to that of our previously published semi-automatic approach. The automatic algorithm needed an average execution time of ∼60 s to segment the prostate in 3D. When compared to a single-observer reference standard, the automatic algorithm has an average mean absolute distance of 2.8 mm, Dice similarity coefficient of 82%, recall of 82%, precision of 84%, and volume difference of 0.5 cm(3) in the mid-gland. Concordant with other studies, accuracy was highest in the mid-gland and lower in the apex and base. Loss of accuracy with respect to the semi-automatic algorithm was less than the measured inter-observer variability in manual segmentation for the same task.

  6. Segmentation of the C57BL/6J mouse cerebellum in magnetic resonance images.

    PubMed

    Ullmann, Jeremy F P; Keller, Marianne D; Watson, Charles; Janke, Andrew L; Kurniawan, Nyoman D; Yang, Zhengyi; Richards, Kay; Paxinos, George; Egan, Gary F; Petrou, Steven; Bartlett, Perry; Galloway, Graham J; Reutens, David C

    2012-09-01

    The C57BL mouse is the centerpiece of efforts to use gene-targeting technology to understand cerebellar pathology, thus creating a need for a detailed magnetic resonance imaging (MRI) atlas of the cerebellum of this strain. In this study we present a methodology for systematic delineation of the vermal and hemispheric lobules of the C57BL/6J mouse cerebellum in magnetic resonance images. We have successfully delineated 38 cerebellar and cerebellar-related structures. The higher signal-to-noise ratio achieved by group averaging facilitated the identification of anatomical structures. In addition, we have calculated average region volumes and created probabilistic maps for each structure. The segmentation method and the probabilistic maps we have created will provide a foundation for future studies of cerebellar disorders using transgenic mouse models.

  7. Atlas-based segmentation of brainstem regions in neuromelanin-sensitive magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Puigvert, Marc; Castellanos, Gabriel; Uranga, Javier; Abad, Ricardo; Fernández-Seara, María. A.; Pastor, Pau; Pastor, María. A.; Muñoz-Barrutia, Arrate; Ortiz de Solórzano, Carlos

    2015-03-01

    We present a method for the automatic delineation of two neuromelanin rich brainstem structures -substantia nigra pars compacta (SN) and locus coeruleus (LC)- in neuromelanin sensitive magnetic resonance images of the brain. The segmentation method uses a dynamic multi-image reference atlas and a pre-registration atlas selection strategy. To create the atlas, a pool of 35 images of healthy subjects was pair-wise pre-registered and clustered in groups using an affinity propagation approach. Each group of the atlas is represented by a single exemplar image. Each new target image to be segmented is registered to the exemplars of each cluster. Then all the images of the highest performing clusters are enrolled into the final atlas, and the results of the registration with the target image are propagated using a majority voting approach. All registration processes used combined one two-stage affine and one elastic B-spline algorithm, to account for global positioning, region selection and local anatomic differences. In this paper, we present the algorithm, with emphasis in the atlas selection method and the registration scheme. We evaluate the performance of the atlas selection strategy using 35 healthy subjects and 5 Parkinson's disease patients. Then, we quantified the volume and contrast ratio of neuromelanin signal of these structures in 47 normal subjects and 40 Parkinson's disease patients to confirm that this method can detect neuromelanin-containing neurons loss in Parkinson's disease patients and could eventually be used for the early detection of SN and LC damage.

  8. Three-dimensional left ventricular segmentation from magnetic resonance imaging for patient-specific modelling purposes.

    PubMed

    Caiani, Enrico G; Colombo, Andrea; Pepi, Mauro; Piazzese, Concetta; Maffessanti, Francesco; Lang, Roberto M; Carminati, Maria Chiara

    2014-11-01

    To propose a nearly automated left ventricular (LV) three-dimensional (3D) surface segmentation procedure, based on active shape modelling (ASM) and built on a database of 3D echocardiographic (3DE) LV surfaces, for cardiac magnetic resonance (CMR) images, and to test its accuracy for LV volumes computation compared with 'gold standard' manual tracings and discs-summation method. The ASM was created based on segmented LV surfaces (4D LV analysis, Tomtec) from 3DE datasets of 205 patients. Then, it was applied to the cardiac magnetic resonance imaging short-axis (SAX) images stack of 12 consecutive patients. After proper realignment using two- and four-chambers CMR long-axis views both as reference and for initializing LV apex and base (six points in total), the ASM was iteratively and automatically updated to match the information of all the SAX planes contemporaneously, resulting in an endocardial LV 3D mesh from which volume was directly derived. The same CMR images were analysed by an experienced cardiologist to derive end-diastolic and end-systolic volumes. Linear correlation and Bland-Altman analyses were applied vs. the manual 'gold standard'. Active shape modelling results showed high correlations with manual values both for LV volumes (r(2) > 0.98) and ejection fraction (EF) (r(2) > 0.90), non-significant biases and narrow limits of agreement. The proposed method resulted in accurate detection of 3D LV endocardial surfaces, which lead to fast and reliable measurements of LV volumes and EF when compared with manual tracing of CMR SAX images. The segmented 3D mesh, including a realistic LV apex and base, could constitute a novel starting point for more realistic patient-specific finite element modelling. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2014. For permissions please email: journals.permissions@oup.com.

  9. Self-Trained Supervised Segmentation of Subcortical Brain Structures Using Multispectral Magnetic Resonance Images

    PubMed Central

    Larobina, Michele; Murino, Loredana; Cervo, Amedeo; Alfano, Bruno

    2015-01-01

    The aim of this paper is investigate the feasibility of automatically training supervised methods, such as k-nearest neighbor (kNN) and principal component discriminant analysis (PCDA), and to segment the four subcortical brain structures: caudate, thalamus, pallidum, and putamen. The adoption of supervised classification methods so far has been limited by the need to define a representative training dataset, operation that usually requires the intervention of an operator. In this work the selection of the training data was performed on the subject to be segmented in a fully automated manner by registering probabilistic atlases. Evaluation of automatically trained kNN and PCDA classifiers that combine voxel intensities and spatial coordinates was performed on 20 real datasets selected from two publicly available sources of multispectral magnetic resonance studies. The results demonstrate that atlas-guided training is an effective way to automatically define a representative and reliable training dataset, thus giving supervised methods the chance to successfully segment magnetic resonance brain images without the need for user interaction. PMID:26583131

  10. DATASET OF MAGNETIC RESONANCE IMAGES OF NONEPILEPTIC SUBJECTS AND TEMPORAL LOBE EPILEPSY PATIENTS FOR VALIDATION OF HIPPOCAMPAL SEGMENTATION TECHNIQUES

    PubMed Central

    Jafari-Khouzani, Kourosh; Elisevich, Kost V.; Patel, Suresh; Soltanian-Zadeh, Hamid

    2013-01-01

    Summary The hippocampus has become the focus of research in several neurodegenerative disorders. Automatic segmentation of this structure from magnetic resonance (MR) imaging scans of the brain facilitates this work. Segmentation techniques must be evaluated using a dataset of MR images with accurate hippocampal outlines generated manually. Manual segmentation is not a trivial task. Lack of a unique segmentation protocol and poor image quality are only two factors that have confounded the consistency required for comparative study. We have developed a publicly available dataset of T1-weighted (T1W) MR images of epileptic and nonepileptic subjects along with their hippocampal outlines to provide a means of evaluation of segmentation techniques. This dataset contains 50 T1W MR images, 40 epileptic and 10 nonepileptic. All images were manually segmented by a widely used protocol. Twenty five images were selected for training and were provided with hippocampal labels. Twenty five other images were provided without labels for testing algorithms. The users are allowed to evaluate their generated labels for the test images using 11 segmentation similarity metrics. Using this dataset, we evaluated two segmentation algorithms, Brain Parser and Classifier Fusion and Labeling (CFL), trained by the training set. For Brain Parser, an average Dice coefficient of 0.64 was obtained with the testing set. For CFL, this value was 0.75. Such findings indicate a need for further improvement of segmentation algorithms in order to enhance reliability. PMID:21286946

  11. Automatic Segmentation and Quantitative Analysis of the Articular Cartilages From Magnetic Resonance Images of the Knee

    PubMed Central

    Fripp, Jurgen; Crozier, Stuart; Warfield, Simon K.; Ourselin, Sébastien

    2010-01-01

    In this paper, we present a segmentation scheme that automatically and accurately segments all the cartilages from magnetic resonance (MR) images of nonpathological knees. Our scheme involves the automatic segmentation of the bones using a three-dimensional active shape model, the extraction of the expected bone-cartilage interface (BCI), and cartilage segmentation from the BCI using a deformable model that utilizes localization, patient specific tissue estimation and a model of the thickness variation. The accuracy of this scheme was experimentally validated using leave one out experiments on a database of fat suppressed spoiled gradient recall MR images. The scheme was compared to three state of the art approaches, tissue classification, a modified semi-automatic watershed algorithm and nonrigid registration (B-spline based free form deformation). Our scheme obtained an average Dice similarity coefficient (DSC) of (0.83, 0.83, 0.85) for the (patellar, tibial, femoral) cartilages, while (0.82, 0.81, 0.86) was obtained with a tissue classifier and (0.73, 0.79, 0.76) was obtained with nonrigid registration. The average DSC obtained for all the cartilages using a semi-automatic watershed algorithm (0.90) was slightly higher than our approach (0.89), however unlike this approach we segment each cartilage as a separate object. The effectiveness of our approach for quantitative analysis was evaluated using volume and thickness measures with a median volume difference error of (5.92, 4.65, 5.69) and absolute Laplacian thickness difference of (0.13, 0.24, 0.12) mm. PMID:19520633

  12. Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee.

    PubMed

    Fripp, Jurgen; Crozier, Stuart; Warfield, Simon K; Ourselin, Sébastien

    2010-01-01

    In this paper, we present a segmentation scheme that automatically and accurately segments all the cartilages from magnetic resonance (MR) images of nonpathological knees. Our scheme involves the automatic segmentation of the bones using a three-dimensional active shape model, the extraction of the expected bone-cartilage interface (BCI), and cartilage segmentation from the BCI using a deformable model that utilizes localization, patient specific tissue estimation and a model of the thickness variation. The accuracy of this scheme was experimentally validated using leave one out experiments on a database of fat suppressed spoiled gradient recall MR images. The scheme was compared to three state of the art approaches, tissue classification, a modified semi-automatic watershed algorithm and nonrigid registration (B-spline based free form deformation). Our scheme obtained an average Dice similarity coefficient (DSC) of (0.83, 0.83, 0.85) for the (patellar, tibial, femoral) cartilages, while (0.82, 0.81, 0.86) was obtained with a tissue classifier and (0.73, 0.79, 0.76) was obtained with nonrigid registration. The average DSC obtained for all the cartilages using a semi-automatic watershed algorithm (0.90) was slightly higher than our approach (0.89), however unlike this approach we segment each cartilage as a separate object. The effectiveness of our approach for quantitative analysis was evaluated using volume and thickness measures with a median volume difference error of (5.92, 4.65, 5.69) and absolute Laplacian thickness difference of (0.13, 0.24, 0.12) mm.

  13. Visual saliency-based active learning for prostate magnetic resonance imaging segmentation.

    PubMed

    Mahapatra, Dwarikanath; Buhmann, Joachim M

    2016-01-01

    We propose an active learning (AL) approach for prostate segmentation from magnetic resonance images. Our label query strategy is inspired from the principles of visual saliency that have similar considerations for choosing the most salient region. These similarities are encoded in a graph using classification maps and low-level features. Random walks are used to identify the most informative node, which is equivalent to the label query sample in AL. To reduce computation time, a volume of interest (VOI) is identified and all subsequent analysis, such as probability map generation using semisupervised random forest classifiers and label query, is restricted to this VOI. The negative log-likelihood of the probability maps serves as the penalty cost in a second-order Markov random field cost function, which is optimized using graph cuts for prostate segmentation. Experimental results on the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2012 prostate segmentation challenge show the superior performance of our approach to conventional methods using fully supervised learning.

  14. Automatic falx cerebri and tentorium cerebelli segmentation from magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Glaister, Jeffrey; Carass, Aaron; Pham, Dzung L.; Butman, John A.; Prince, Jerry L.

    2017-03-01

    The falx cerebri and tentorium cerebelli are dural structures found in the brain. Due to the roles both structures play in constraining brain motion, the falx and tentorium must be identified and included in finite element models of the head to accurately predict brain dynamics during injury events. To date there has been very little research work on automatically segmenting these two structures, which is understandable given that their 1) thin structure challenges the resolution limits of in vivo 3D imaging, and 2) contrast with respect to surrounding tissue is low in standard magnetic resonance imaging. An automatic segmentation algorithm to find the falx and tentorium which uses the results of a multi-atlas segmentation and cortical reconstruction algorithm is proposed. Gray matter labels are used to find the location of the falx and tentorium. The proposed algorithm is applied to five datasets with manual delineations. 3D visualizations of the final results are provided, and Hausdorff distance (HD) and mean surface distance (MSD) is calculated to quantify the accuracy of the proposed method. For the falx, the mean HD is 43.84 voxels and the mean MSD is 2.78 voxels, with the largest errors occurring at the frontal inferior falx boundary. For the tentorium, the mean HD is 14.50 voxels and mean MSD is 1.38 voxels.

  15. Focused shape models for hip joint segmentation in 3D magnetic resonance images.

    PubMed

    Chandra, Shekhar S; Xia, Ying; Engstrom, Craig; Crozier, Stuart; Schwarz, Raphael; Fripp, Jurgen

    2014-04-01

    Deformable models incorporating shape priors have proved to be a successful approach in segmenting anatomical regions and specific structures in medical images. This paper introduces weighted shape priors for deformable models in the context of 3D magnetic resonance (MR) image segmentation of the bony elements of the human hip joint. The fully automated approach allows the focusing of the shape model energy to a priori selected anatomical structures or regions of clinical interest by preferentially ordering the shape representation (or eigen-modes) within this type of model to the highly weighted areas. This focused shape model improves accuracy of the shape constraints in those regions compared to standard approaches. The proposed method achieved femoral head and acetabular bone segmentation mean absolute surface distance errors of 0.55±0.18mm and 0.75±0.20mm respectively in 35 3D unilateral MR datasets from 25 subjects acquired at 3T with different limited field of views for individual bony components of the hip joint.

  16. Automated segmentation of the corpus callosum in midsagittal brain magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Lee, Chulhee; Huh, Shin; Ketter, Terence A.; Unser, Michael A.

    2000-04-01

    We propose a new algorithm to find the corpus callosum automatically from midsagittal brain MR (magnetic resonance) images using the statistical characteristics and shape information of the corpus callosum. We first extract regions satisfying the statistical characteristics (gray level distributions) of the corpus callosum that have relatively high intensity values. Then we try to find a region matching the shape information of the corpus callosum. In order to match the shape information, we propose a new directed window region growing algorithm instead of using conventional contour matching. An innovative feature of the algorithm is that we adaptively relax the statistical requirement until we find a region matching the shape information. After the initial segmentation, a directed border path pruning algorithm is proposed in order to remove some undesired artifacts, especially on the top of the corpus callosum. The proposed algorithm was applied to over 120 images and provided promising results.

  17. Improving Brain Magnetic Resonance Image (MRI) Segmentation via a Novel Algorithm based on Genetic and Regional Growth

    PubMed Central

    A., Javadpour; A., Mohammadi

    2016-01-01

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

  18. Improving Brain Magnetic Resonance Image (MRI) Segmentation via a Novel Algorithm based on Genetic and Regional Growth.

    PubMed

    A, Javadpour; A, Mohammadi

    2016-06-01

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

  19. A new fuzzy C-means method for magnetic resonance image brain segmentation

    NASA Astrophysics Data System (ADS)

    Altameem, Torki; Zanaty, E. A.; Tolba, Amr

    2015-10-01

    In this paper, we introduce a new fuzzy c-means (FCM) method in order to improve the magnetic resonance images' (MRIs) segmentation. The proposed method combines the FCM and possiblistic c-means (PCM) functions using a weighted Gaussian function. The weighted Gaussian function is given to indicate the spatial influence of the neighbouring pixels on the central pixel. The parameters of weighting coefficients are automatically determined in the implementation using the Gaussian function for every pixel in the image. The proposed method is realised by modifying the objective function of the PCM algorithm to produce memberships and possibilities simultaneously, along with the usual point prototypes or cluster centres for each cluster. The membership values can be interpreted as degrees of possibility of the points belonging to the classes, that is, the compatibilities of the points with the class prototypes to overcome the coincident clusters problem of PCM. The efficiency of the proposed algorithm is demonstrated by extensive segmentation experiments using MRIs and comparison with other state-of-the-art algorithms. In the proposed method, the effect of noise is controlled by incorporating the possibility (typicality) function in addition to the membership function. Consideration of these constraints can greatly control the noise in the image as shown in our experiments.

  20. Unsupervised tissue segmentation from dynamic contrast-enhanced magnetic resonance imaging.

    PubMed

    Chiusano, Gabriele; Staglianò, Alessandra; Basso, Curzio; Verri, Alessandro

    2014-05-01

    Design, implement, and validate an unsupervised method for tissue segmentation from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). For each DCE-MRI acquisition, after a spatial registration phase, the time-varying intensity of each voxel is represented as a sparse linear combination of adaptive basis signals. Both the basis signals and the sparse coefficients are learned by minimizing a functional consisting of a data fidelity term and a sparsity inducing penalty. Tissue segmentation is then obtained by applying a standard clustering algorithm to the computed representation. Quantitative estimates on two real data sets are presented. In the first case, the overlap with expert annotation measured with the DICE metric is nearly 90% and thus 5% more accurate than state-of-the-art techniques. In the second case, assessment of the correlation between quantitative scores, obtained by the proposed method against imagery manually annotated by two experts, achieved a Pearson coefficient of 0.83 and 0.87, and a Spearman coefficient of 0.83 and 0.71, respectively. The sparse representation of DCE MRI signals obtained by means of adaptive dictionary learning techniques appears to be well-suited for unsupervised tissue segmentation and applicable to different clinical contexts with little effort. Copyright © 2014 Elsevier B.V. All rights reserved.

  1. Evaluation of multiatlas label fusion for in vivo magnetic resonance imaging orbital segmentation

    PubMed Central

    Panda, Swetasudha; Asman, Andrew J.; Khare, Shweta P.; Thompson, Lindsey; Mawn, Louise A.; Smith, Seth A.; Landman, Bennett A.

    2014-01-01

    Abstract. Multiatlas methods have been successful for brain segmentation, but their application to smaller anatomies remains relatively unexplored. We evaluate seven statistical and voting-based label fusion algorithms (and six additional variants) to segment the optic nerves, eye globes, and chiasm. For nonlocal simultaneous truth and performance level estimation (STAPLE), we evaluate different intensity similarity measures (including mean square difference, locally normalized cross-correlation, and a hybrid approach). Each algorithm is evaluated in terms of the Dice overlap and symmetric surface distance metrics. Finally, we evaluate refinement of label fusion results using a learning-based correction method for consistent bias correction and Markov random field regularization. The multiatlas labeling pipelines were evaluated on a cohort of 35 subjects including both healthy controls and patients. Across all three structures, nonlocal spatial STAPLE (NLSS) with a mixed weighting type provided the most consistent results; for the optic nerve NLSS resulted in a median Dice similarity coefficient of 0.81, mean surface distance of 0.41 mm, and Hausdorff distance 2.18 mm for the optic nerves. Joint label fusion resulted in slightly superior median performance for the optic nerves (0.82, 0.39 mm, and 2.15 mm), but slightly worse on the globes. The fully automated multiatlas labeling approach provides robust segmentations of orbital structures on magnetic resonance imaging even in patients for whom significant atrophy (optic nerve head drusen) or inflammation (multiple sclerosis) is present. PMID:25558466

  2. Identification of breast contour for nipple segmentation in breast magnetic resonance images

    SciTech Connect

    Gwo, Chih-Ying; Gwo, Allen; Wei, Chia-Hung; Huang, Pai Jung

    2014-02-15

    Purpose: The purpose of this study is to develop a method to simulate the breast contour and segment the nipple in breast magnetic resonance images. Methods: This study first identifies the chest wall and removes the chest part from the breast MR images. Subsequently, the cleavage and its motion artifacts are removed, distinguishing the separate breasts, where the edge points are sampled for curve fitting. Next, a region growing method is applied to find the potential nipple region. Finally, the potential nipple region above the simulated curve can be removed in order to retain the original smooth contour. Results: The simulation methods can achieve the least root mean square error (RMSE) for certain cases. The proposed YBnd and (Dmin+Dmax)/2 methods are significant due toP = 0.000. The breast contour curve detected by the two proposed methods is closer than that determined by the edge detection method. The (Dmin+Dmax)/2 method can achieve the lowest RMSE of 1.1029 on average, while the edge detection method results in the highest RMSE of 6.5655. This is only slighter better than the comparison methods, which implies that the performance of these methods depends upon the conditions of the cases themselves. Under this method, the maximal Dice coefficient is 0.881, and the centroid difference is 0.36 pixels. Conclusions: The contributions of this study are twofold. First, a method was proposed to identify and segment the nipple in breast MR images. Second, a curve-fitting method was used to simulate the breast contour, allowing the breast to retain its original smooth shape.

  3. Automatic human knee cartilage segmentation from 3D magnetic resonance images.

    PubMed

    Dodin, Pierre; Pelletier, Jean-Pierre; Martel-Pelletier, Johanne; Abram, François

    2010-11-01

    This study aimed at developing a new automatic segmentation algorithm for human knee cartilage volume quantification from magnetic resonance images (MRI). Imaging was performed using a 3T scanner and a knee coil, and the exam consisted of a DESS sequence which contrasts cartilage and soft tissues including the synovial fluid. The algorithm was developed on MRI 3D images in which the bone-cartilage interface for the femur and tibia was segmented by an independent segmentation process, giving a parametric surface of the interface. Firstly, the MR images are resampled in the neighborhood of the bone surface. Secondly, by using texture analysis techniques optimized by filtering, the cartilage is discriminated as a bright and homogeneous tissue. This process of excluding soft tissues enables the detection of the external boundary of the cartilage. Thirdly, a technology based on a Bayesian decision criterion enables the automatic separation of the cartilage and synovial fluid. Finally, the cartilage volume and changes in volume for an individual between visits was assessed using the developed technology. Validation included first, for nine knee osteoarthritis patients, a comparison of the cartilage volume and changes over time between the developed automatic system and a validated semi-automatic cartilage volume system, and second, for five knee osteoarthritis patients, a test-retest procedure. Data revealed excellent Pearson correlations and Dice Similarity Coefficients (DSC) for the global knee (r=0.96, p<0.0001, median DSC=0.84), for the femur (r=0.95, p<0.0001, median DSC=0.85) and the tibia (r=0.83, p<0.0001, median DSC=0.84). Very good similarity between the automatic and semi-automatic methods in regard to cartilage loss was also found for the global knee (r=0.76, p=0.016) as well as for the femur (r=0.79, p=0.011). The test-retest revealed an excellent measurement error of -0.3?1.6% for the global knee and 0.14?1.7% for the femur. In conclusion, the newly

  4. A multiresolution prostate representation for automatic segmentation in magnetic resonance images.

    PubMed

    Alvarez, Charlens; Martínez, Fabio; Romero, Eduardo

    2017-04-01

    Accurate prostate delineation is necessary in radiotherapy processes for concentrating the dose onto the prostate and reducing side effects in neighboring organs. Currently, manual delineation is performed over magnetic resonance imaging (MRI) taking advantage of its high soft tissue contrast property. Nevertheless, as human intervention is a consuming task with high intra- and interobserver variability rates, (semi)-automatic organ delineation tools have emerged to cope with these challenges, reducing the time spent for these tasks. This work presents a multiresolution representation that defines a novel metric and allows to segment a new prostate by combining a set of most similar prostates in a dataset. The proposed method starts by selecting the set of most similar prostates with respect to a new one using the proposed multiresolution representation. This representation characterizes the prostate through a set of salient points, extracted from a region of interest (ROI) that encloses the organ and refined using structural information, allowing to capture main relevant features of the organ boundary. Afterward, the new prostate is automatically segmented by combining the nonrigidly registered expert delineations associated to the previous selected similar prostates using a weighted patch-based strategy. Finally, the prostate contour is smoothed based on morphological operations. The proposed approach was evaluated with respect to the expert manual segmentation under a leave-one-out scheme using two public datasets, obtaining averaged Dice coefficients of 82% ± 0.07 and 83% ± 0.06, and demonstrating a competitive performance with respect to atlas-based state-of-the-art methods. The proposed multiresolution representation provides a feature space that follows a local salient point criteria and a global rule of the spatial configuration among these points to find out the most similar prostates. This strategy suggests an easy adaptation in the clinical

  5. Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based Fuzzy C-Means Clustering.

    PubMed

    Elazab, Ahmed; Wang, Changmiao; Jia, Fucang; Wu, Jianhuang; Li, Guanglin; Hu, Qingmao

    2015-01-01

    An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.

  6. Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based Fuzzy C-Means Clustering

    PubMed Central

    Wang, Changmiao; Jia, Fucang; Wu, Jianhuang; Li, Guanglin

    2015-01-01

    An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity. PMID:26793269

  7. Using magnetic resonance imaging to identify the lumbosacral segment in children.

    PubMed

    Milicić, Gordana; Krolo, Ivan; Vrdoljak, Javor; Marotti, Miljenko; Roić, Goran; Hat, Josip

    2006-03-01

    Identification of the lumbosacral (L-S) segment on magnetic resonance (MR) images is important for appropriate treatment of disease in the lumbosacral (L-S) area. In the study, data obtained from plain A-P radiographs of the L-S spine and sagittal MR imaging scans (sagittal T1- and T2-weighted sequences) of the L-S spine and sacrum with the coccygeal bone, are analyzed. Twenty-six children aged 10 to 14 years were examined for back pain. On the standard A-P radiographs of the L-S spine, a L-S transitional vertebra as classified according to the method of Castellvi et al. was found in 17 subjects. The problem arose as to whether this was lumbalisation or sacralisation, and how to determine which vertebra was L5 wich S1. On the sagittal MR imaging studies the same question applied. A need emerged for a simple method which would identify the L-S segment on the sagittal MR imaging studies of the L-S spine in children so that in case of a tumor, inflammation, spondilolystesis, or protrusion of a disc, the level in the L-S spine where the problem is localized can be accurately identified. To this objective we selected the method using detection of the S1 vertebra. This involved that, in addition to the sagittal MR imaging scans of the L-S spine, sagittal images of the sacrum and coccygeal bone be also obtained. on the T2-weighted sequence, the sacrum can be clearly distinquished from the coccygeal bone. By counting from the S5 up, the S1 vertebra can be accurately identified. Determination of the S1 vertebra enables detection of the L5 vertebra and, in turn, of all other lumbar vertebrae. In patients in whom a T2-weighted MR studies were done S1 could be precisely determined and so could the L5 vertebra. In this process, whether the patient had a transitional vertebra or whether there was lumbarisation or sacralisation was irrelevant.

  8. Computational simulation of breast compression based on segmented breast and fibroglandular tissues on magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Shih, Tzu-Ching; Chen, Jeon-Hor; Liu, Dongxu; Nie, Ke; Sun, Lizhi; Lin, Muqing; Chang, Daniel; Nalcioglu, Orhan; Su, Min-Ying

    2010-07-01

    This study presents a finite element-based computational model to simulate the three-dimensional deformation of a breast and fibroglandular tissues under compression. The simulation was based on 3D MR images of the breast, and craniocaudal and mediolateral oblique compression, as used in mammography, was applied. The geometry of the whole breast and the segmented fibroglandular tissues within the breast were reconstructed using triangular meshes by using the Avizo® 6.0 software package. Due to the large deformation in breast compression, a finite element model was used to simulate the nonlinear elastic tissue deformation under compression, using the MSC.Marc® software package. The model was tested in four cases. The results showed a higher displacement along the compression direction compared to the other two directions. The compressed breast thickness in these four cases at a compression ratio of 60% was in the range of 5-7 cm, which is a typical range of thickness in mammography. The projection of the fibroglandular tissue mesh at a compression ratio of 60% was compared to the corresponding mammograms of two women, and they demonstrated spatially matched distributions. However, since the compression was based on magnetic resonance imaging (MRI), which has much coarser spatial resolution than the in-plane resolution of mammography, this method is unlikely to generate a synthetic mammogram close to the clinical quality. Whether this model may be used to understand the technical factors that may impact the variations in breast density needs further investigation. Since this method can be applied to simulate compression of the breast at different views and different compression levels, another possible application is to provide a tool for comparing breast images acquired using different imaging modalities--such as MRI, mammography, whole breast ultrasound and molecular imaging--that are performed using different body positions and under different compression

  9. Automatic segmentation of white matter lesions on magnetic resonance images of the brain by using an outlier detection strategy.

    PubMed

    Wang, Rui; Li, Chao; Wang, Jie; Wei, Xiaoer; Li, Yuehua; Hui, Chun; Zhu, Yuemin; Zhang, Su

    2014-12-01

    White matter lesions (WMLs) are commonly observed on the magnetic resonance (MR) images of normal elderly in association with vascular risk factors, such as hypertension or stroke. An accurate WML detection provides significant information for disease tracking, therapy evaluation, and normal aging research. In this article, we present an unsupervised WML segmentation method that uses Gaussian mixture model to describe the intensity distribution of the normal brain tissues and detects the WMLs as outliers to the normal brain tissue model based on extreme value theory. The detection of WMLs is performed by comparing the probability distribution function of a one-sided normal distribution and a Gumbel distribution, which is a specific extreme value distribution. The performance of the automatic segmentation is validated on synthetic and clinical MR images with regard to different imaging sequences and lesion loads. Results indicate that the segmentation method has a favorable accuracy competitive with other state-of-the-art WML segmentation methods.

  10. Automatic Segmentation of Pelvic Structures From Magnetic Resonance Images for Prostate Cancer Radiotherapy

    SciTech Connect

    Pasquier, David . E-mail: d-pasquier@o-lambret.fr; Lacornerie, Thomas; Vermandel, Maximilien; Rousseau, Jean; Lartigau, Eric; Betrouni, Nacim

    2007-06-01

    Purpose: Target-volume and organ-at-risk delineation is a time-consuming task in radiotherapy planning. The development of automated segmentation tools remains problematic, because of pelvic organ shape variability. We evaluate a three-dimensional (3D), deformable-model approach and a seeded region-growing algorithm for automatic delineation of the prostate and organs-at-risk on magnetic resonance images. Methods and Materials: Manual and automatic delineation were compared in 24 patients using a sagittal T2-weighted (T2-w) turbo spin echo (TSE) sequence and an axial T1-weighted (T1-w) 3D fast-field echo (FFE) or TSE sequence. For automatic prostate delineation, an organ model-based method was used. Prostates without seminal vesicles were delineated as the clinical target volume (CTV). For automatic bladder and rectum delineation, a seeded region-growing method was used. Manual contouring was considered the reference method. The following parameters were measured: volume ratio (Vr) (automatic/manual), volume overlap (Vo) (ratio of the volume of intersection to the volume of union; optimal value = 1), and correctly delineated volume (Vc) (percent ratio of the volume of intersection to the manually defined volume; optimal value 100). Results: For the CTV, the Vr, Vo, and Vc were 1.13 ({+-}0.1 SD), 0.78 ({+-}0.05 SD), and 94.75 ({+-}3.3 SD), respectively. For the rectum, the Vr, Vo, and Vc were 0.97 ({+-}0.1 SD), 0.78 ({+-}0.06 SD), and 86.52 ({+-}5 SD), respectively. For the bladder, the Vr, Vo, and Vc were 0.95 ({+-}0.03 SD), 0.88 ({+-}0.03 SD), and 91.29 ({+-}3.1 SD), respectively. Conclusions: Our results show that the organ-model method is robust, and results in reproducible prostate segmentation with minor interactive corrections. For automatic bladder and rectum delineation, magnetic resonance imaging soft-tissue contrast enables the use of region-growing methods.

  11. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain.

    PubMed

    Hall, L O; Bensaid, A M; Clarke, L P; Velthuizen, R P; Silbiger, M S; Bezdek, J C

    1992-01-01

    Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms, and a supervised computational neural network. Initial clinical results are presented on normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. For a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed, with fuzz-c-means approaches being slightly preferred over feedforward cascade correlation results. Various facets of both approaches, such as supervised versus unsupervised learning, time complexity, and utility for the diagnostic process, are compared.

  12. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

    NASA Technical Reports Server (NTRS)

    Hall, Lawrence O.; Bensaid, Amine M.; Clarke, Laurence P.; Velthuizen, Robert P.; Silbiger, Martin S.; Bezdek, James C.

    1992-01-01

    Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms and a supervised computational neural network, a dynamic multilayered perception trained with the cascade correlation learning algorithm. Initial clinical results are presented on both normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. However, for a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed.

  13. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

    NASA Technical Reports Server (NTRS)

    Hall, Lawrence O.; Bensaid, Amine M.; Clarke, Laurence P.; Velthuizen, Robert P.; Silbiger, Martin S.; Bezdek, James C.

    1992-01-01

    Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms and a supervised computational neural network, a dynamic multilayered perception trained with the cascade correlation learning algorithm. Initial clinical results are presented on both normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. However, for a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed.

  14. Automatic segmentation of the bone and extraction of the bone cartilage interface from magnetic resonance images of the knee

    NASA Astrophysics Data System (ADS)

    Fripp, Jurgen; Crozier, Stuart; Warfield, Simon K.; Ourselin, Sébastien

    2007-03-01

    The accurate segmentation of the articular cartilages from magnetic resonance (MR) images of the knee is important for clinical studies and drug trials into conditions like osteoarthritis. Currently, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards obtaining automatic and accurate segmentations of the cartilages, namely an approach to automatically segment the bones and extract the bone-cartilage interfaces (BCI) in the knee. The segmentation is performed using three-dimensional active shape models, which are initialized using an affine registration to an atlas. The BCI are then extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. The accuracy and robustness of the approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images. The (femur, tibia, patella) bone segmentation had a median Dice similarity coefficient of (0.96, 0.96, 0.89) and an average point-to-surface error of 0.16 mm on the BCI. The extracted BCI had a median surface overlap of 0.94 with the real interface, demonstrating its usefulness for subsequent cartilage segmentation or quantitative analysis.

  15. Automatic segmentation of the bone and extraction of the bone-cartilage interface from magnetic resonance images of the knee.

    PubMed

    Fripp, Jurgen; Crozier, Stuart; Warfield, Simon K; Ourselin, Sébastien

    2007-03-21

    The accurate segmentation of the articular cartilages from magnetic resonance (MR) images of the knee is important for clinical studies and drug trials into conditions like osteoarthritis. Currently, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards obtaining automatic and accurate segmentations of the cartilages, namely an approach to automatically segment the bones and extract the bone-cartilage interfaces (BCI) in the knee. The segmentation is performed using three-dimensional active shape models, which are initialized using an affine registration to an atlas. The BCI are then extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. The accuracy and robustness of the approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images. The (femur, tibia, patella) bone segmentation had a median Dice similarity coefficient of (0.96, 0.96, 0.89) and an average point-to-surface error of 0.16 mm on the BCI. The extracted BCI had a median surface overlap of 0.94 with the real interface, demonstrating its usefulness for subsequent cartilage segmentation or quantitative analysis.

  16. Application of variable threshold intensity to segmentation for white matter hyperintensities in fluid attenuated inversion recovery magnetic resonance images.

    PubMed

    Yoo, Byung Il; Lee, Jung Jae; Han, Ji Won; Oh, San Yeo Wool; Lee, Eun Young; MacFall, James R; Payne, Martha E; Kim, Tae Hui; Kim, Jae Hyoung; Kim, Ki Woong

    2014-04-01

    White matter hyperintensities (WMHs) are regions of abnormally high intensity on T2-weighted or fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI). Accurate and reproducible automatic segmentation of WMHs is important since WMHs are often seen in the elderly and are associated with various geriatric and psychiatric disorders. We developed a fully automated monospectral segmentation method for WMHs using FLAIR MRIs. Through this method, we introduce an optimal threshold intensity (I O ) for segmenting WMHs, which varies with WMHs volume (V WMH), and we establish the I O -V WMH relationship. Our method showed accurate validations in volumetric and spatial agreements of automatically segmented WMHs compared with manually segmented WMHs for 32 confirmatory images. Bland-Altman values of volumetric agreement were 0.96 ± 8.311 ml (bias and 95 % confidence interval), and the similarity index of spatial agreement was 0.762 ± 0.127 (mean ± standard deviation). Furthermore, similar validation accuracies were obtained in the images acquired from different scanners. The proposed segmentation method uses only FLAIR MRIs, has the potential to be accurate with images obtained from different scanners, and can be implemented with a fully automated procedure. In our study, validation results were obtained with FLAIR MRIs from only two scanner types. The design of the method may allow its use in large multicenter studies with correct efficiency.

  17. Automated medical image segmentation techniques

    PubMed Central

    Sharma, Neeraj; Aggarwal, Lalit M.

    2010-01-01

    Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images. PMID:20177565

  18. Automated medical image segmentation techniques.

    PubMed

    Sharma, Neeraj; Aggarwal, Lalit M

    2010-01-01

    Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images.

  19. Automatic segmentation of articular cartilage in magnetic resonance images of the knee.

    PubMed

    Fripp, Jurgen; Crozier, Stuart; Warfield, Simon K; Ourselin, Sébastien

    2007-01-01

    To perform cartilage quantitative analysis requires the accurate segmentation of each individual cartilage. In this paper we present a model based scheme that can automatically and accurately segment each individual cartilage in healthy knees from a clinical MR sequence (fat suppressed spoiled gradient recall). This scheme consists of three stages; the automatic segmentation of the bones, the extraction of the bone-cartilage interfaces (BCI) and segmentation of the cartilages. The bone segmentation is performed using three-dimensional active shape models. The BCI is extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. A cartilage thickness model then provides constraints and regularizes the cartilage segmentation performed from the BCI. The accuracy and robustness of the approach was experimentally validated, with (patellar, tibial and femoral) cartilage segmentations having a median DSC of (0.870, 0.855, 0.870), performing significantly better than non-rigid registration (0.787, 0.814, 0.795). The total cartilage segmentation had an average DSC of (0.891), close to the (0.896) obtained using a semi-automatic watershed algorithm. The error in quantitative volume and thickness measures was (8.29, 4.94, 5.56)% and (0.19, 0.33, 0.10) mm respectively.

  20. A semiautomatic method for rapid segmentation of velocity-encoded myocardial magnetic resonance imaging data.

    PubMed

    Espe, Emil K S; Skårdal, Kristine; Aronsen, Jan Magnus; Zhang, Lili; Sjaastad, Ivar

    2017-09-01

    To develop a semiautomatic method for rapid segmentation of myocardial tissue phase mapping (TPM) data. Manual segmentation of the myocardium was performed at end-diastole and end-systole. The points in both user-defined masks were then automatically tracked over the entire cardiac cycle using temporal integration of the velocity field. Paths that failed to visit both masks at the expected times were excluded, after which masks for all time points were generated automatically from the accepted paths. Midventricular and basal phase contrast TPM slices from 12 rats were segmented using the proposed method and fully manual segmentation. The results were compared using Dice's metric and Bland-Altman analysis, and interobserver variability was assessed. The semiautomatic method reduced the average user input time from 21 min to 1 min per slice. The Dice metrics between the methods were 0.88 ± 0.03 (midventricular) and 0.83 ± 0.06 (basal), and Bland-Altman limits of agreement of peak systolic and diastolic regional velocities were: midventricular: 0.05 ± 0.65 cm/s, -0.02 ± 0.42 cm/s, and -0.03 ± 0.40 cm/s (radial, tangential, longitudinal); basal: -0.04 ± 0.73 cm/s, 0.03 ± 0.60 cm/s, and -0.04 ± 0.48 cm/s (radial, tangential, longitudinal). Interobserver variability following semiautomatic segmentation was lower than for manual segmentation. The proposed method reduced the segmentation time substantially and exhibited well-preserved data quality and excellent interobserver limits of agreement. Magn Reson Med 78:1199-1207, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

  1. Automated segmentation of the quadratus lumborum muscle from magnetic resonance images using a hybrid atlas based - geodesic active contour scheme.

    PubMed

    Jurcak, V; Fripp, J; Engstrom, C; Walker, D; Salvado, O; Ourselin, S; Crozier, S

    2008-01-01

    This study presents a novel method for the automatic segmentation of the quadratus lumborum (QL) muscle from axial magnetic resonance (MR) images using a hybrid scheme incorporating the use of non-rigid registration with probabilistic atlases (PAs) and geodesic active contours (GACs). The scheme was evaluated on an MR database of 7mm axial images of the lumbar spine from 20 subjects (fast bowlers and athletic controls). This scheme involved several steps, including (i) image pre-processing, (ii) generation of PAs for the QL, psoas (PS) and erector spinae+multifidus (ES+MT) muscles and (iii) segmentation, using 3D GACs initialized and constrained by the propagation of the PAs using non-rigid registration. Pre-processing of the images involved bias field correction based on local entropy minimization with a bicubic spline model and a reverse diffusion interpolation algorithm to increase the slice resolution to 0.98 x 0.98 x 1.75mm. The processed images were then registered (affine and non-rigid) and used to generate an average atlas. The PAs for the QL, PS and ES+MT were then generated by propagation of manual segmentations. These atlases were further analysed with specialised filtering to constrain the QL segmentation from adjacent non-muscle tissues (kidney, fat). This information was then used in 3D GACs to obtain the final segmentation of the QL. The automatic segmentation results were compared with the manual segmentations using the Dice similarity metric (DSC), with a median DSC for the right and left QL muscles of 0.78 (mean = 0.77, sd=0.07) and 0.75 (mean =0.74, sd=0.07), respectively.

  2. 3D segmentation of annulus fibrosus and nucleus pulposus from T2-weighted magnetic resonance images.

    PubMed

    Castro-Mateos, Isaac; Pozo, Jose M; Eltes, Peter E; Rio, Luis Del; Lazary, Aron; Frangi, Alejandro F

    2014-12-21

    Computational medicine aims at employing personalised computational models in diagnosis and treatment planning. The use of such models to help physicians in finding the best treatment for low back pain (LBP) is becoming popular. One of the challenges of creating such models is to derive patient-specific anatomical and tissue models of the lumbar intervertebral discs (IVDs), as a prior step. This article presents a segmentation scheme that obtains accurate results irrespective of the degree of IVD degeneration, including pathological discs with protrusion or herniation. The segmentation algorithm, employing a novel feature selector, iteratively deforms an initial shape, which is projected into a statistical shape model space at first and then, into a B-Spline space to improve accuracy.The method was tested on a MR dataset of 59 patients suffering from LBP. The images follow a standard T2-weighted protocol in coronal and sagittal acquisitions. These two image volumes were fused in order to overcome large inter-slice spacing. The agreement between expert-delineated structures, used here as gold-standard, and our automatic segmentation was evaluated using Dice Similarity Index and surface-to-surface distances, obtaining a mean error of 0.68 mm in the annulus segmentation and 1.88 mm in the nucleus, which are the best results with respect to the image resolution in the current literature.

  3. 3D segmentation of annulus fibrosus and nucleus pulposus from T2-weighted magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Castro-Mateos, Isaac; Pozo, Jose M.; Eltes, Peter E.; Del Rio, Luis; Lazary, Aron; Frangi, Alejandro F.

    2014-12-01

    Computational medicine aims at employing personalised computational models in diagnosis and treatment planning. The use of such models to help physicians in finding the best treatment for low back pain (LBP) is becoming popular. One of the challenges of creating such models is to derive patient-specific anatomical and tissue models of the lumbar intervertebral discs (IVDs), as a prior step. This article presents a segmentation scheme that obtains accurate results irrespective of the degree of IVD degeneration, including pathological discs with protrusion or herniation. The segmentation algorithm, employing a novel feature selector, iteratively deforms an initial shape, which is projected into a statistical shape model space at first and then, into a B-Spline space to improve accuracy. The method was tested on a MR dataset of 59 patients suffering from LBP. The images follow a standard T2-weighted protocol in coronal and sagittal acquisitions. These two image volumes were fused in order to overcome large inter-slice spacing. The agreement between expert-delineated structures, used here as gold-standard, and our automatic segmentation was evaluated using Dice Similarity Index and surface-to-surface distances, obtaining a mean error of 0.68 mm in the annulus segmentation and 1.88 mm in the nucleus, which are the best results with respect to the image resolution in the current literature.

  4. Diffusion Maps Clustering for Magnetic Resonance Q-Ball Imaging Segmentation

    PubMed Central

    Wassermann, Demian; Descoteaux, Maxime; Deriche, Rachid

    2008-01-01

    White matter fiber clustering aims to get insight about anatomical structures in order to generate atlases, perform clear visualizations, and compute statistics across subjects, all important and current neuroimaging problems. In this work, we present a diffusion maps clustering method applied to diffusion MRI in order to segment complex white matter fiber bundles. It is well known that diffusion tensor imaging (DTI) is restricted in complex fiber regions with crossings and this is why recent high-angular resolution diffusion imaging (HARDI) such as Q-Ball imaging (QBI) has been introduced to overcome these limitations. QBI reconstructs the diffusion orientation distribution function (ODF), a spherical function that has its maxima agreeing with the underlying fiber populations. In this paper, we use a spherical harmonic ODF representation as input to the diffusion maps clustering method. We first show the advantage of using diffusion maps clustering over classical methods such as N-Cuts and Laplacian eigenmaps. In particular, our ODF diffusion maps requires a smaller number of hypothesis from the input data, reduces the number of artifacts in the segmentation, and automatically exhibits the number of clusters segmenting the Q-Ball image by using an adaptive scale-space parameter. We also show that our ODF diffusion maps clustering can reproduce published results using the diffusion tensor (DT) clustering with N-Cuts on simple synthetic images without crossings. On more complex data with crossings, we show that our ODF-based method succeeds to separate fiber bundles and crossing regions whereas the DT-based methods generate artifacts and exhibit wrong number of clusters. Finally, we show results on a real-brain dataset where we segment well-known fiber bundles. PMID:18317506

  5. Spinal Cord Segmentation by One Dimensional Normalized Template Matching: A Novel, Quantitative Technique to Analyze Advanced Magnetic Resonance Imaging Data.

    PubMed

    Cadotte, Adam; Cadotte, David W; Livne, Micha; Cohen-Adad, Julien; Fleet, David; Mikulis, David; Fehlings, Michael G

    2015-01-01

    Spinal cord segmentation is a developing area of research intended to aid the processing and interpretation of advanced magnetic resonance imaging (MRI). For example, high resolution three-dimensional volumes can be segmented to provide a measurement of spinal cord atrophy. Spinal cord segmentation is difficult due to the variety of MRI contrasts and the variation in human anatomy. In this study we propose a new method of spinal cord segmentation based on one-dimensional template matching and provide several metrics that can be used to compare with other segmentation methods. A set of ground-truth data from 10 subjects was manually-segmented by two different raters. These ground truth data formed the basis of the segmentation algorithm. A user was required to manually initialize the spinal cord center-line on new images, taking less than one minute. Template matching was used to segment the new cord and a refined center line was calculated based on multiple centroids within the segmentation. Arc distances down the spinal cord and cross-sectional areas were calculated. Inter-rater validation was performed by comparing two manual raters (n = 10). Semi-automatic validation was performed by comparing the two manual raters to the semi-automatic method (n = 10). Comparing the semi-automatic method to one of the raters yielded a Dice coefficient of 0.91 +/- 0.02 for ten subjects, a mean distance between spinal cord center lines of 0.32 +/- 0.08 mm, and a Hausdorff distance of 1.82 +/- 0.33 mm. The absolute variation in cross-sectional area was comparable for the semi-automatic method versus manual segmentation when compared to inter-rater manual segmentation. The results demonstrate that this novel segmentation method performs as well as a manual rater for most segmentation metrics. It offers a new approach to study spinal cord disease and to quantitatively track changes within the spinal cord in an individual case and across cohorts of subjects.

  6. Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Yang, Zhengyi; Fripp, Jurgen; Chandra, Shekhar S.; Neubert, Aleš; Xia, Ying; Strudwick, Mark; Paproki, Anthony; Engstrom, Craig; Crozier, Stuart

    2015-02-01

    We present a statistical shape model approach for automated segmentation of the proximal humerus and scapula with subsequent bone-cartilage interface (BCI) extraction from 3D magnetic resonance (MR) images of the shoulder region. Manual and automated bone segmentations from shoulder MR examinations from 25 healthy subjects acquired using steady-state free precession sequences were compared with the Dice similarity coefficient (DSC). The mean DSC scores between the manual and automated segmentations of the humerus and scapula bone volumes surrounding the BCI region were 0.926  ±  0.050 and 0.837  ±  0.059, respectively. The mean DSC values obtained for BCI extraction were 0.806  ±  0.133 for the humerus and 0.795  ±  0.117 for the scapula. The current model-based approach successfully provided automated bone segmentation and BCI extraction from MR images of the shoulder. In future work, this framework appears to provide a promising avenue for automated segmentation and quantitative analysis of cartilage in the glenohumeral joint.

  7. Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images.

    PubMed

    Yang, Zhengyi; Fripp, Jurgen; Chandra, Shekhar S; Neubert, Aleš; Xia, Ying; Strudwick, Mark; Paproki, Anthony; Engstrom, Craig; Crozier, Stuart

    2015-02-21

    We present a statistical shape model approach for automated segmentation of the proximal humerus and scapula with subsequent bone-cartilage interface (BCI) extraction from 3D magnetic resonance (MR) images of the shoulder region. Manual and automated bone segmentations from shoulder MR examinations from 25 healthy subjects acquired using steady-state free precession sequences were compared with the Dice similarity coefficient (DSC). The mean DSC scores between the manual and automated segmentations of the humerus and scapula bone volumes surrounding the BCI region were 0.926  ±  0.050 and 0.837  ±  0.059, respectively. The mean DSC values obtained for BCI extraction were 0.806  ±  0.133 for the humerus and 0.795  ±  0.117 for the scapula. The current model-based approach successfully provided automated bone segmentation and BCI extraction from MR images of the shoulder. In future work, this framework appears to provide a promising avenue for automated segmentation and quantitative analysis of cartilage in the glenohumeral joint.

  8. Postediting prostate magnetic resonance imaging segmentation consistency and operator time using manual and computer-assisted segmentation: multiobserver study.

    PubMed

    Shahedi, Maysam; Cool, Derek W; Romagnoli, Cesare; Bauman, Glenn S; Bastian-Jordan, Matthew; Rodrigues, George; Ahmad, Belal; Lock, Michael; Fenster, Aaron; Ward, Aaron D

    2016-10-01

    Prostate segmentation on T2w MRI is important for several diagnostic and therapeutic procedures for prostate cancer. Manual segmentation is time-consuming, labor-intensive, and subject to high interobserver variability. This study investigated the suitability of computer-assisted segmentation algorithms for clinical translation, based on measurements of interoperator variability and measurements of the editing time required to yield clinically acceptable segmentations. A multioperator pilot study was performed under three pre- and postediting conditions: manual, semiautomatic, and automatic segmentation. We recorded the required editing time for each segmentation and measured the editing magnitude based on five different spatial metrics. We recorded average editing times of 213, 328, and 393 s for manual, semiautomatic, and automatic segmentation respectively, while an average fully manual segmentation time of 564 s was recorded. The reduced measured postediting interoperator variability of semiautomatic and automatic segmentations compared to the manual approach indicates the potential of computer-assisted segmentation for generating a clinically acceptable segmentation faster with higher consistency. The lack of strong correlation between editing time and the values of typically used error metrics ([Formula: see text]) implies that the necessary postsegmentation editing time needs to be measured directly in order to evaluate an algorithm's suitability for clinical translation.

  9. Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging

    PubMed Central

    Agner, Shannon C.; Xu, Jun; Madabhushi, Anant

    2013-01-01

    Purpose: Segmentation of breast lesions on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is the first step in lesion diagnosis in a computer-aided diagnosis framework. Because manual segmentation of such lesions is both time consuming and highly susceptible to human error and issues of reproducibility, an automated lesion segmentation method is highly desirable. Traditional automated image segmentation methods such as boundary-based active contour (AC) models require a strong gradient at the lesion boundary. Even when region-based terms are introduced to an AC model, grayscale image intensities often do not allow for clear definition of foreground and background region statistics. Thus, there is a need to find alternative image representations that might provide (1) strong gradients at the margin of the object of interest (OOI); and (2) larger separation between intensity distributions and region statistics for the foreground and background, which are necessary to halt evolution of the AC model upon reaching the border of the OOI. Methods: In this paper, the authors introduce a spectral embedding (SE) based AC (SEAC) for lesion segmentation on breast DCE-MRI. SE, a nonlinear dimensionality reduction scheme, is applied to the DCE time series in a voxelwise fashion to reduce several time point images to a single parametric image where every voxel is characterized by the three dominant eigenvectors. This parametric eigenvector image (PrEIm) representation allows for better capture of image region statistics and stronger gradients for use with a hybrid AC model, which is driven by both boundary and region information. They compare SEAC to ACs that employ fuzzy c-means (FCM) and principal component analysis (PCA) as alternative image representations. Segmentation performance was evaluated by boundary and region metrics as well as comparing lesion classification using morphological features from SEAC, PCA+AC, and FCM+AC. Results: On a cohort of 50

  10. Interactive 3D segmentation of the prostate in magnetic resonance images using shape and local appearance similarity analysis

    NASA Astrophysics Data System (ADS)

    Shahedi, Maysam; Fenster, Aaron; Cool, Derek W.; Romagnoli, Cesare; Ward, Aaron D.

    2013-03-01

    3D segmentation of the prostate in medical images is useful to prostate cancer diagnosis and therapy guidance, but is time-consuming to perform manually. Clinical translation of computer-assisted segmentation algorithms for this purpose requires a comprehensive and complementary set of evaluation metrics that are informative to the clinical end user. We have developed an interactive 3D prostate segmentation method for 1.5T and 3.0T T2-weighted magnetic resonance imaging (T2W MRI) acquired using an endorectal coil. We evaluated our method against manual segmentations of 36 3D images using complementary boundary-based (mean absolute distance; MAD), regional overlap (Dice similarity coefficient; DSC) and volume difference (ΔV) metrics. Our technique is based on inter-subject prostate shape and local boundary appearance similarity. In the training phase, we calculated a point distribution model (PDM) and a set of local mean intensity patches centered on the prostate border to capture shape and appearance variability. To segment an unseen image, we defined a set of rays - one corresponding to each of the mean intensity patches computed in training - emanating from the prostate centre. We used a radial-based search strategy and translated each mean intensity patch along its corresponding ray, selecting as a candidate the boundary point with the highest normalized cross correlation along each ray. These boundary points were then regularized using the PDM. For the whole gland, we measured a mean+/-std MAD of 2.5+/-0.7 mm, DSC of 80+/-4%, and ΔV of 1.1+/-8.8 cc. We also provided an anatomic breakdown of these metrics within the prostatic base, mid-gland, and apex.

  11. Segmentation of multidimensional magnetic resonance (MR) images using a fuzzy neural network

    NASA Astrophysics Data System (ADS)

    Ma, Jesse C.; Rodriguez, Jeffrey J.

    1994-09-01

    Methods of 3-D visualization of the brain based on fuzzy c-means (FCM) classified magnetic resonance (MR) images and a neural network trained on the FCM data are presented. A 3-D MR scan of a volunteer serves as the basis for the unsupervised classification techniques. The images were first classified into different tissue types by using FCM. The classified images were then reconstructed for 3-D display. Results show that individual tissue types can be discriminated during the 3-D rendering process. A neural network trained on the fuzzy classification data was also implemented. By using the cascade correlation algorithm during the network training, much of the tedious training work was avoided. The preliminary results from the neural network approach are quite encouraging.

  12. Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images.

    PubMed

    He, Renjie; Datta, Sushmita; Sajja, Balasrinivasa Rao; Narayana, Ponnada A

    2008-07-01

    An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, distance measure adopted in Gustafson-Kessel (G-K) algorithm is used instead of the Euclidean distance, to account for the non-spherical shape of the clusters in the feature space. These algorithms are quantitatively evaluated on MR brain images using the similarity measures.

  13. A novel semi-automatic segmentation method for volumetric assessment of the colon based on magnetic resonance imaging.

    PubMed

    Sandberg, Thomas Holm; Nilsson, Matias; Poulsen, Jakob Lykke; Gram, Mikkel; Frøkjær, Jens Brøndum; Østergaard, Lasse Riis; Drewes, Asbjørn Mohr

    2015-10-01

    To develop a novel semi-automatic segmentation method for quantification of the colon from magnetic resonance imaging (MRI). Fourteen abdominal T2-weighted and dual-echo Dixon-type water-only MRI scans were obtained from four healthy subjects. Regions of interest containing the colon were outlined manually on the T2-weighted images. Segmentation of the colon and feces was obtained using k-means clustering and image registration. Regional colonic and fecal volumes were obtained. Inter-observer agreement between two observers was assessed using the Dice similarity coefficient as measure of overlap. Colonic segmentations showed wide variation in volume and morphology between subjects. Colon volumes of the four healthy subjects for both observers were (median [interquartile range]) ascending colon 200 mL [169.5-260], transverse 200.5 mL [113.5-242.5], descending 148 mL [121.5-178.5], sigmoid-rectum 277 mL [192-345], and total 819 mL [687-898.5]. Overlap agreement for the total colon segmentation between the two observers was high with a Dice similarity coefficient of 0.91 [0.84-0.94]. The colon volume to feces volume ratio was on average 0.7. Regional colon volumes were comparable to previous findings using fully manual segmentation. The method showed good agreement between observers and may be used in future studies of gastrointestinal disorders to assess colon and fecal volume and colon morphology. Novel insight into morphology and quantitative assessment of the colon using this method may provide new biomarkers for constipation and abdominal pain compared to radiography which suffers from poor reliability.

  14. Protocol for volumetric segmentation of medial temporal structures using high-resolution 3-D magnetic resonance imaging.

    PubMed

    Bonilha, Leonardo; Kobayashi, Eliane; Cendes, Fernando; Min Li, Li

    2004-06-01

    Quantitative analysis of brain structures in normal subjects and in different neurological conditions can be carried out in vivo through magnetic resonance imaging (MRI) volumetric studies. The use of high-resolution MRI combined with image post-processing that allows simultaneous multiplanar view may facilitate volumetric segmentation of temporal lobe structures. We define a protocol for volumetric studies of medial temporal lobe structures using high-resolution MR images and we studied 30 healthy subjects (19 women; mean age, 33 years; age range, 21-55 years). Images underwent field non-homogeneity correction and linear stereotaxic transformation into a standard space. Structures of interest comprised temporopolar, entorhinal, perirhinal, parahippocampal cortices, hippocampus, and the amygdala. Segmentation was carried out with multiplanar assessment. There was no statistically significant left/right-sided asymmetry concerning any structure analyzed. Neither gender nor age influenced the volumes obtained. The coefficient of repeatability showed no significant difference of intra- and interobserver measurements. Imaging post-processing and simultaneous multiplanar view of high-resolution MRI facilitates volumetric assessment of the medial portion of the temporal lobe with strict adherence to anatomic landmarks. This protocol shows no significant inter- and intraobserver variations and thus is reliable for longitudinal studies.

  15. Robust Skull-Stripping Segmentation Based on Irrational Mask for Magnetic Resonance Brain Images.

    PubMed

    Moldovanu, Simona; Moraru, Luminița; Biswas, Anjan

    2015-12-01

    This paper proposes a new method for simple, efficient, and robust removal of the non-brain tissues in MR images based on an irrational mask for filtration within a binary morphological operation framework. The proposed skull-stripping segmentation is based on two irrational 3 × 3 and 5 × 5 masks, having the sum of its weights equal to the transcendental number π value provided by the Gregory-Leibniz infinite series. It allows maintaining a lower rate of useful pixel loss. The proposed method has been tested in two ways. First, it has been validated as a binary method by comparing and contrasting with Otsu's, Sauvola's, Niblack's, and Bernsen's binary methods. Secondly, its accuracy has been verified against three state-of-the-art skull-stripping methods: the graph cuts method, the method based on Chan-Vese active contour model, and the simplex mesh and histogram analysis skull stripping. The performance of the proposed method has been assessed using the Dice scores, overlap and extra fractions, and sensitivity and specificity as statistical methods. The gold standard has been provided by two neurologist experts. The proposed method has been tested and validated on 26 image series which contain 216 images from two publicly available databases: the Whole Brain Atlas and the Internet Brain Segmentation Repository that include a highly variable sample population (with reference to age, sex, healthy/diseased). The approach performs accurately on both standardized databases. The main advantage of the proposed method is its robustness and speed.

  16. Medical image segmentation by MDP model

    NASA Astrophysics Data System (ADS)

    Lu, Yisu; Chen, Wufan

    2011-11-01

    MDP (Dirichlet Process Mixtures) model is applied to segment medical images in this paper. Segmentation can been automatically done without initializing segmentation class numbers. The MDP model segmentation algorithm is used to segment natural images and MR (Magnetic Resonance) images in the paper. To demonstrate the accuracy of the MDP model segmentation algorithm, many compared experiments, such as EM (Expectation Maximization) image segmentation algorithm, K-means image segmentation algorithm and MRF (Markov Field) image segmentation algorithm, have been done to segment medical MR images. All the methods are also analyzed quantitatively by using DSC (Dice Similarity Coefficients). The experiments results show that DSC of MDP model segmentation algorithm of all slices exceed 90%, which show that the proposed method is robust and accurate.

  17. Magnetic Resonance Imaging of Myocardial Strain After Acute ST-Segment-Elevation Myocardial Infarction: A Systematic Review.

    PubMed

    Mangion, Kenneth; McComb, Christie; Auger, Daniel A; Epstein, Frederick H; Berry, Colin

    2017-08-01

    The purpose of this systematic review is to provide a clinically relevant, disease-based perspective on myocardial strain imaging in patients with acute myocardial infarction or stable ischemic heart disease. Cardiac magnetic resonance imaging uniquely integrates myocardial function with pathology. Therefore, this review focuses on strain imaging with cardiac magnetic resonance. We have specifically considered the relationships between left ventricular (LV) strain, infarct pathologies, and their associations with prognosis. A comprehensive literature review was conducted in accordance with the PRISMA guidelines. Publications were identified that (1) described the relationship between strain and infarct pathologies, (2) assessed the relationship between strain and subsequent LV outcomes, and (3) assessed the relationship between strain and health outcomes. In patients with acute myocardial infarction, circumferential strain predicts the recovery of LV systolic function in the longer term. The prognostic value of longitudinal strain is less certain. Strain differentiates between infarcted versus noninfarcted myocardium, even in patients with stable ischemic heart disease with preserved LV ejection fraction. Strain recovery is impaired in infarcted segments with intramyocardial hemorrhage or microvascular obstruction. There are practical limitations to measuring strain with cardiac magnetic resonance in the acute setting, and knowledge gaps, including the lack of data showing incremental value in clinical practice. Critically, studies of cardiac magnetic resonance strain imaging in patients with ischemic heart disease have been limited by sample size and design. Strain imaging has potential as a tool to assess for early or subclinical changes in LV function, and strain is now being included as a surrogate measure of outcome in therapeutic trials. © 2017 American Heart Association, Inc.

  18. Assessment of subconjunctival and intrascleral drug delivery to the posterior segment using dynamic contrast-enhanced magnetic resonance imaging.

    PubMed

    Kim, Stephanie H; Galbán, Craig J; Lutz, Robert J; Dedrick, Robert L; Csaky, Karl G; Lizak, Martin J; Wang, Nam Sun; Tansey, Ginger; Robinson, Michael R

    2007-02-01

    Sustained-release intravitreal drug implants for posterior segment diseases are associated with significant complications. As an alternative, subconjunctival infusions of drug to the episclera of the back of the eye have been performed, but results in clinical trials for macular diseases showed mixed To improve understanding of transscleral drug delivery to the posterior segment, the distribution and clearance of gadolinium-diethylene-triamino-penta-acetic acid (Gd-DTPA) infused in the subconjunctival or intrascleral space was investigated by means of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In anesthetized rabbits, catheters were placed anteriorly in the subconjunctival or intrascleral space and infused with Gd-DTPA at 1 and 10 muL/min. Distribution and clearance of Gd-DTPA were measured using DCE-MRI. Histologic examination was performed to assess ocular toxicity of the delivery system. results. Subconjunctival infusions failed to produce detectable levels of Gd-DTPA in the back of the eye. In contrast, intrascleral infusions expanded the suprachoroidal layer and delivered Gd-DTPA to the posterior segment. Suprachoroidal clearance of Gd-DTPA followed first-order kinetics with an average half-life of 5.4 and 11.8 minutes after intrascleral infusions at 1 and 10 muL/min, respectively. Histologic examination demonstrated expansion of the tissues in the suprachoroidal space that normalized after infusion termination. An intrascleral infusion was successful in transporting Gd-DTPA to the posterior segment from an anterior infusion site with limited anterior segment exposure. The suprachoroidal space appears to be an expandible conduit for drug transport to the posterior segment. Further studies are indicated to explore the feasibility of clinical applications.

  19. Automated segmentation and analysis of normal and osteoarthritic knee menisci from magnetic resonance images--data from the Osteoarthritis Initiative.

    PubMed

    Paproki, A; Engstrom, C; Chandra, S S; Neubert, A; Fripp, J; Crozier, S

    2014-09-01

    To validate an automatic scheme for the segmentation and quantitative analysis of the medial meniscus (MM) and lateral meniscus (LM) in magnetic resonance (MR) images of the knee. We analysed sagittal water-excited double-echo steady-state MR images of the knee from a subset of the Osteoarthritis Initiative (OAI) cohort. The MM and LM were automatically segmented in the MR images based on a deformable model approach. Quantitative parameters including volume, subluxation and tibial-coverage were automatically calculated for comparison (Wilcoxon tests) between knees with variable radiographic osteoarthritis (rOA), medial and lateral joint space narrowing (mJSN, lJSN) and pain. Automatic segmentations and estimated parameters were evaluated for accuracy using manual delineations of the menisci in 88 pathological knee MR examinations at baseline and 12 months time-points. The median (95% confidence-interval (CI)) Dice similarity index (DSI) (2 ∗|Auto ∩ Manual|/(|Auto|+|Manual|)∗ 100) between manual and automated segmentations for the MM and LM volumes were 78.3% (75.0-78.7), 83.9% (82.1-83.9) at baseline and 75.3% (72.8-76.9), 83.0% (81.6-83.5) at 12 months. Pearson coefficients between automatic and manual segmentation parameters ranged from r = 0.70 to r = 0.92. MM in rOA/mJSN knees had significantly greater subluxation and smaller tibial-coverage than no-rOA/no-mJSN knees. LM in rOA knees had significantly greater volumes and tibial-coverage than no-rOA knees. Our automated method successfully segmented the menisci in normal and osteoarthritic knee MR images and detected meaningful morphological differences with respect to rOA and joint space narrowing (JSN). Our approach will facilitate analyses of the menisci in prospective MR cohorts such as the OAI for investigations into pathophysiological changes occurring in early osteoarthritis (OA) development. Copyright © 2014 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights

  20. Comparative performance evaluation of automated segmentation methods of hippocampus from magnetic resonance images of temporal lobe epilepsy patients

    PubMed Central

    Hosseini, Mohammad-Parsa; Nazem-Zadeh, Mohammad-Reza; Pompili, Dario; Jafari-Khouzani, Kourosh; Elisevich, Kost; Soltanian-Zadeh, Hamid

    2016-01-01

    Purpose: Segmentation of the hippocampus from magnetic resonance (MR) images is a key task in the evaluation of mesial temporal lobe epilepsy (mTLE) patients. Several automated algorithms have been proposed although manual segmentation remains the benchmark. Choosing a reliable algorithm is problematic since structural definition pertaining to multiple edges, missing and fuzzy boundaries, and shape changes varies among mTLE subjects. Lack of statistical references and guidance for quantifying the reliability and reproducibility of automated techniques has further detracted from automated approaches. The purpose of this study was to develop a systematic and statistical approach using a large dataset for the evaluation of automated methods and establish a method that would achieve results better approximating those attained by manual tracing in the epileptogenic hippocampus. Methods: A template database of 195 (81 males, 114 females; age range 32–67 yr, mean 49.16 yr) MR images of mTLE patients was used in this study. Hippocampal segmentation was accomplished manually and by two well-known tools (FreeSurfer and hammer) and two previously published methods developed at their institution [Automatic brain structure segmentation (ABSS) and LocalInfo]. To establish which method was better performing for mTLE cases, several voxel-based, distance-based, and volume-based performance metrics were considered. Statistical validations of the results using automated techniques were compared with the results of benchmark manual segmentation. Extracted metrics were analyzed to find the method that provided a more similar result relative to the benchmark. Results: Among the four automated methods, ABSS generated the most accurate results. For this method, the Dice coefficient was 5.13%, 14.10%, and 16.67% higher, Hausdorff was 22.65%, 86.73%, and 69.58% lower, precision was 4.94%, −4.94%, and 12.35% higher, and the root mean square (RMS) was 19.05%, 61.90%, and 65.08% lower than

  1. Functional measurements based on feature tracking of cine magnetic resonance images identify left ventricular segments with myocardial scar

    PubMed Central

    2009-01-01

    Background The aim of the study was to perform a feature tracking analysis on cine magnetic resonance (MR) images to elucidate if functional measurements of the motion of the left ventricular wall may detect scar defined with gadolinium enhanced MR. Myocardial contraction can be measured in terms of the velocity, displacement and local deformation (strain) of a particular myocardial segment. Contraction of the myocardial wall will be reduced in the presence of scar and as a consequence of reduced myocardial blood flow. Methods Thirty patients (3 women and 27 men) were selected based on the presence or absence of extensive scar in the anteroseptal area of the left ventricle. The patients were investigated in stable clinical condition, 4-8 weeks post ST-elevation myocardial infarction treated with percutaneous coronary intervention. Seventeen had a scar area >75% in at least one anteroseptal segment (scar) and thirteen had scar area <1% (non-scar). Velocity, displacement and strain were calculated in the longitudinal direction, tangential to the endocardial outline, and in the radial direction, perpendicular to the tangent. Results In the scar patients, segments with scar showed lower functional measurements than remote segments. Radial measurements of velocity, displacement and strain performed better in terms of receiver-operator-characteristic curves (ROC) than the corresponding longitudinal measurements. The best area-under-curve was for radial strain, 0.89, where a cut-off value of 38.8% had 80% sensitivity and 86% specificity for the detection of a segment with scar area >50%. As a percentage of the mean, intraobserver variability was 16-14-26% for radial measurements of displacement-velocity-strain and corresponding interobserver variability was 13-12-18%. Conclusion Feature tracking analysis of cine-MR displays velocity, displacement and strain in the radial and longitudinal direction and may be used for the detection of transmural scar. The accuracy and

  2. Development and evaluation of an algorithm for the computer-assisted segmentation of the human hypothalamus on 7-Tesla magnetic resonance images.

    PubMed

    Schindler, Stephanie; Schönknecht, Peter; Schmidt, Laura; Anwander, Alfred; Strauß, Maria; Trampel, Robert; Bazin, Pierre-Louis; Möller, Harald E; Hegerl, Ulrich; Turner, Robert; Geyer, Stefan

    2013-01-01

    Post mortem studies have shown volume changes of the hypothalamus in psychiatric patients. With 7T magnetic resonance imaging this effect can now be investigated in vivo in detail. To benefit from the sub-millimeter resolution requires an improved segmentation procedure. The traditional anatomical landmarks of the hypothalamus were refined using 7T T1-weighted magnetic resonance images. A detailed segmentation algorithm (unilateral hypothalamus) was developed for colour-coded, histogram-matched images, and evaluated in a sample of 10 subjects. Test-retest and inter-rater reliabilities were estimated in terms of intraclass-correlation coefficients (ICC) and Dice's coefficient (DC). The computer-assisted segmentation algorithm ensured test-retest reliabilities of ICC≥.97 (DC≥96.8) and inter-rater reliabilities of ICC≥.94 (DC = 95.2). There were no significant volume differences between the segmentation runs, raters, and hemispheres. The estimated volumes of the hypothalamus lie within the range of previous histological and neuroimaging results. We present a computer-assisted algorithm for the manual segmentation of the human hypothalamus using T1-weighted 7T magnetic resonance imaging. Providing very high test-retest and inter-rater reliabilities, it outperforms former procedures established at 1.5T and 3T magnetic resonance images and thus can serve as a gold standard for future automated procedures.

  3. Development and Evaluation of an Algorithm for the Computer-Assisted Segmentation of the Human Hypothalamus on 7-Tesla Magnetic Resonance Images

    PubMed Central

    Schmidt, Laura; Anwander, Alfred; Strauß, Maria; Trampel, Robert; Bazin, Pierre-Louis; Möller, Harald E.; Hegerl, Ulrich; Turner, Robert; Geyer, Stefan

    2013-01-01

    Post mortem studies have shown volume changes of the hypothalamus in psychiatric patients. With 7T magnetic resonance imaging this effect can now be investigated in vivo in detail. To benefit from the sub-millimeter resolution requires an improved segmentation procedure. The traditional anatomical landmarks of the hypothalamus were refined using 7T T1-weighted magnetic resonance images. A detailed segmentation algorithm (unilateral hypothalamus) was developed for colour-coded, histogram-matched images, and evaluated in a sample of 10 subjects. Test-retest and inter-rater reliabilities were estimated in terms of intraclass-correlation coefficients (ICC) and Dice's coefficient (DC). The computer-assisted segmentation algorithm ensured test-retest reliabilities of ICC≥.97 (DC≥96.8) and inter-rater reliabilities of ICC≥.94 (DC = 95.2). There were no significant volume differences between the segmentation runs, raters, and hemispheres. The estimated volumes of the hypothalamus lie within the range of previous histological and neuroimaging results. We present a computer-assisted algorithm for the manual segmentation of the human hypothalamus using T1-weighted 7T magnetic resonance imaging. Providing very high test-retest and inter-rater reliabilities, it outperforms former procedures established at 1.5T and 3T magnetic resonance images and thus can serve as a gold standard for future automated procedures. PMID:23935821

  4. High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging.

    PubMed

    Winterburn, Julie; Pruessner, Jens C; Sofia, Chavez; Schira, Mark M; Lobaugh, Nancy J; Voineskos, Aristotle N; Chakravarty, M Mallar

    2015-11-10

    The human hippocampus has been broadly studied in the context of memory and normal brain function and its role in different neuropsychiatric disorders has been heavily studied. While many imaging studies treat the hippocampus as a single unitary neuroanatomical structure, it is, in fact, composed of several subfields that have a complex three-dimensional geometry. As such, it is known that these subfields perform specialized functions and are differentially affected through the course of different disease states. Magnetic resonance (MR) imaging can be used as a powerful tool to interrogate the morphology of the hippocampus and its subfields. Many groups use advanced imaging software and hardware (>3T) to image the subfields; however this type of technology may not be readily available in most research and clinical imaging centers. To address this need, this manuscript provides a detailed step-by-step protocol for segmenting the full anterior-posterior length of the hippocampus and its subfields: cornu ammonis (CA) 1, CA2/CA3, CA4/dentate gyrus (DG), strata radiatum/lacunosum/moleculare (SR/SL/SM), and subiculum. This protocol has been applied to five subjects (3F, 2M; age 29-57, avg. 37). Protocol reliability is assessed by resegmenting either the right or left hippocampus of each subject and computing the overlap using the Dice's kappa metric. Mean Dice's kappa (range) across the five subjects are: whole hippocampus, 0.91 (0.90-0.92); CA1, 0.78 (0.77-0.79); CA2/CA3, 0.64 (0.56-0.73); CA4/dentate gyrus, 0.83 (0.81-0.85); strata radiatum/lacunosum/moleculare, 0.71 (0.68-0.73); and subiculum 0.75 (0.72-0.78). The segmentation protocol presented here provides other laboratories with a reliable method to study the hippocampus and hippocampal subfields in vivo using commonly available MR tools.

  5. Comparison of magnetic resonance imaging findings in non-ST-segment elevation versus ST-segment elevation myocardial infarction patients undergoing early invasive intervention.

    PubMed

    Xu, Jianqiang; Song, Young Bin; Hahn, Joo-Yong; Chang, Sung-A; Lee, Sang-Chol; Choe, Yeon Hyeon; Choi, Seung-Hyuk; Choi, Jin-Ho; Lee, Sang Hoon; Oh, Jae K; Gwon, Hyeon-Cheol

    2012-08-01

    To define causes and pathological mechanisms underlying differences in clinical outcomes, we compared the findings of contrast-enhanced magnetic resonance imaging (CE-MRI) between ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI). In 168 patients undergoing early invasive intervention for STEMI (n = 113) and NSTEMI (n = 55), CE-MRI was performed a median of 6 days after the index event. Infarct size was measured on delayed-enhancement imaging, and area at risk (AAR) was quantified on T2-weighted images. The median infarct size was significantly smaller in the NSTEMI group than in the STEMI group (10.7% [5.6-18.1] vs. 19.2% [10.3-30.7], P < 0.001). Although there was a trend toward a greater myocardial salvage index ([AAR - infarct size] × 100/AAR) in the NSTEMI group compared to the STEMI group (48.2 [30.4-66.8] vs. 40.5 [24.8-53.5], P = 0.056), myocardial salvage index was similar between the groups in patients with anterior infarction (39.6 [20.0-54.9] vs. 35.5 [23.2-53.4], P = 0.96). The NSTEMI group also had a significantly lower extent of microvascular obstruction and a smaller number of segments with >75% of infarct transmurality relative to the STEMI group (0% [0-0.6] vs. 0.9% [0-2.3], P < 0.001 and 3.0 ± 2.3 vs. 4.6 ± 2.9, P = 0.001, respectively). Myocardial hemorrhage was detected less frequently in the NSTEMI group than the STEMI group (22.6% vs. 43.8%, P = 0.029). In the multivariate analysis, baseline Thrombolysis In Myocardial Infarction flow grade 3 and hemorrhagic infarction were closely associated with ST-segment elevation (OR 0.32, 95% CI 0.13-0.81, P = 0.017; OR 5.66, 95% CI 1.77-18.12, P = 0.003, respectively). In conclusion, in vivo pathophysiological differences revealed by CE-MRI assessment include more favorable infarct size, AAR, myocardial salvage and reperfusion injury in patients with NSTEMI compared to those with STEMI undergoing early invasive intervention.

  6. Fusing Markov random fields with anatomical knowledge and shape-based analysis to segment multiple sclerosis white matter lesions in magnetic resonance images of the brain

    NASA Astrophysics Data System (ADS)

    AlZubi, Stephan; Toennies, Klaus D.; Bodammer, N.; Hinrichs, Herman

    2002-05-01

    This paper proposes an image analysis system to segment multiple sclerosis lesions of magnetic resonance (MR) brain volumes consisting of 3 mm thick slices using three channels (images showing T1-, T2- and PD -weighted contrast). The method uses the statistical model of Markov Random Fields (MRF) both at low and high levels. The neighborhood system used in this MRF is defined in three types: (1) Voxel to voxel: a low-level heterogeneous neighborhood system is used to restore noisy images. (2) Voxel to segment: a fuzzy atlas, which indicates the probability distribution of each tissue type in the brain, is registered elastically with the MRF. It is used by the MRF as a-priori knowledge to correct miss-classified voxels. (3) Segment to segment: Remaining lesion candidates are processed by a feature based classifier that looks at unary and neighborhood information to eliminate more false positives. An expert's manual segmentation was compared with the algorithm.

  7. Accuracy and Reliability of Automated Gray Matter Segmentation Pathways on Real and Simulated Structural Magnetic Resonance Images of the Human Brain

    PubMed Central

    Eggert, Lucas D.; Sommer, Jens; Jansen, Andreas; Kircher, Tilo; Konrad, Carsten

    2012-01-01

    Automated gray matter segmentation of magnetic resonance imaging data is essential for morphometric analyses of the brain, particularly when large sample sizes are investigated. However, although detection of small structural brain differences may fundamentally depend on the method used, both accuracy and reliability of different automated segmentation algorithms have rarely been compared. Here, performance of the segmentation algorithms provided by SPM8, VBM8, FSL and FreeSurfer was quantified on simulated and real magnetic resonance imaging data. First, accuracy was assessed by comparing segmentations of twenty simulated and 18 real T1 images with corresponding ground truth images. Second, reliability was determined in ten T1 images from the same subject and in ten T1 images of different subjects scanned twice. Third, the impact of preprocessing steps on segmentation accuracy was investigated. VBM8 showed a very high accuracy and a very high reliability. FSL achieved the highest accuracy but demonstrated poor reliability and FreeSurfer showed the lowest accuracy, but high reliability. An universally valid recommendation on how to implement morphometric analyses is not warranted due to the vast number of scanning and analysis parameters. However, our analysis suggests that researchers can optimize their individual processing procedures with respect to final segmentation quality and exemplifies adequate performance criteria. PMID:23028771

  8. Accuracy and reliability of automated gray matter segmentation pathways on real and simulated structural magnetic resonance images of the human brain.

    PubMed

    Eggert, Lucas D; Sommer, Jens; Jansen, Andreas; Kircher, Tilo; Konrad, Carsten

    2012-01-01

    Automated gray matter segmentation of magnetic resonance imaging data is essential for morphometric analyses of the brain, particularly when large sample sizes are investigated. However, although detection of small structural brain differences may fundamentally depend on the method used, both accuracy and reliability of different automated segmentation algorithms have rarely been compared. Here, performance of the segmentation algorithms provided by SPM8, VBM8, FSL and FreeSurfer was quantified on simulated and real magnetic resonance imaging data. First, accuracy was assessed by comparing segmentations of twenty simulated and 18 real T1 images with corresponding ground truth images. Second, reliability was determined in ten T1 images from the same subject and in ten T1 images of different subjects scanned twice. Third, the impact of preprocessing steps on segmentation accuracy was investigated. VBM8 showed a very high accuracy and a very high reliability. FSL achieved the highest accuracy but demonstrated poor reliability and FreeSurfer showed the lowest accuracy, but high reliability. An universally valid recommendation on how to implement morphometric analyses is not warranted due to the vast number of scanning and analysis parameters. However, our analysis suggests that researchers can optimize their individual processing procedures with respect to final segmentation quality and exemplifies adequate performance criteria.

  9. Multiview active appearance models for simultaneous segmentation of cardiac 2- and 4-chamber long-axis magnetic resonance images.

    PubMed

    Uzümcü, Mehmet; van der Geest, Rob J; Sonka, Milan; Lamb, Hildo J; Reiber, Johan H C; Lelieveldt, Boudewijn P F

    2005-04-01

    Long-axis cardiac magnetic resonance (MR) views enable a rapid, online evaluation of cardiac function from only 2 views. In this article, we aimed to evaluate a model-based method for the simultaneous detection of 2- and 4-chamber endocardial and epicardial contours in end-diastolic and end-systolic phases of MR images. We introduce multiview Active Appearance Models for the automated segmentation of long-axis cardiac MR images of the left ventricle. Two modes of initialization were used to test the accuracy of the model with minimal user interaction and the best-obtainable accuracy with this model. The segmentation was initialized by annotating 2 points in the base and one in the apex. We tested the method's performance by comparing the point-to-curve errors, ejection fractions, and biplane area-length volumes calculated with the automatically detected contours to those calculated from contours that were annotated manually by experts. Leave-one-out experiments were performed with 2- and 4-chamber long axis MR images of 59 subjects in end-diastolic and end-systolic phases. When initializing in all 4 frames, 97% of the segmentations were successful, and the standard deviation in the volume-errors with respect to the average manually identified volume was 9.0% for the end-diastolic volumes and 15% for the end-systolic volumes. When the method was initialized in the end-systolic frames only, 92% of the segmentations were successful, and the standard deviation in the errors in the volumes with respect to the average manually identified volume was 13.3% for the end-diastolic volumes and 16.7% for the end-systolic volumes. Bland-Altman plots showed that the errors were distributed randomly around 0, and by using a paired t test comparing manual and computer-determined volumes, we were able to detect that the volume differences were not significant. Simultaneous detection of the endocardial and epicardial contours in 2- and 4-chamber views and end-diastolic and end

  10. 3D-FIESTA Magnetic Resonance Angiography Fusion Imaging of Distal Segment of Occluded Middle Cerebral Artery.

    PubMed

    Kuribara, Tomoyoshi; Haraguchi, Koichi; Ogane, Kazumi; Matsuura, Nobuki; Ito, Takeo

    2015-01-01

    Middle cerebral artery (MCA) occlusion was examined with basi-parallel anatomical scanning (BPAS) using three-dimensional fast imaging employing steady-state acquisition (3D-FIESTA), and 3D-FIESTA and magnetic resonance angiography (MRA) fusion images were created. We expected that an incidence of hemorrhagic complications due to vessel perforations would be decreased by obtaining vascular information beyond the occlusion and thus acute endovascular revascularization could be performed using such techniques. We performed revascularization for acute MCA occlusion for five patients who were admitted in our hospital from October 2012 to October 2014. Patients consisted of 1 man and 4 women with a mean age of 76.2 years (range: 59-86 years). Fusion images were created from three-dimensional time of flight (3D-TOF) MRA and 3D-FIESTA with phase cycling (3D-FIESTA-C). Then thrombectomy was performed in all the 5 patients. Merci retriever to 1 patient, Penumbra system to 1, urokinase infusion to 2, and Solitaire to 1 using such techniques. In all cases, a 3D-FIESTA-MRA fusion imaging could depict approximately clear vascular information to at least the M3 segment beyond the occlusion. And each acute revascularization was able to perform smoothly using these imaging techniques. In all cases, there was no symptomatic hemorrhagic complication. It showed that 3D-FIESTA MRA fusion imaging technique could obtain vascular information beyond the MCA occlusion. In this study, no symptomatic hemorrhagic complications were detected. It could imply that such techniques were useful not only to improve treatment efficiency but also to reduce the risk of development of hemorrhagic complications caused by vessel perforations in acute revascularization.

  11. 3D-FIESTA Magnetic Resonance Angiography Fusion Imaging of Distal Segment of Occluded Middle Cerebral Artery

    PubMed Central

    KURIBARA, Tomoyoshi; HARAGUCHI, Koichi; OGANE, Kazumi; MATSUURA, Nobuki; ITO, Takeo

    2015-01-01

    Middle cerebral artery (MCA) occlusion was examined with basi-parallel anatomical scanning (BPAS) using three-dimensional fast imaging employing steady-state acquisition (3D-FIESTA), and 3D-FIESTA and magnetic resonance angiography (MRA) fusion images were created. We expected that an incidence of hemorrhagic complications due to vessel perforations would be decreased by obtaining vascular information beyond the occlusion and thus acute endovascular revascularization could be performed using such techniques. We performed revascularization for acute MCA occlusion for five patients who were admitted in our hospital from October 2012 to October 2014. Patients consisted of 1 man and 4 women with a mean age of 76.2 years (range: 59–86 years). Fusion images were created from three-dimensional time of flight (3D-TOF) MRA and 3D-FIESTA with phase cycling (3D-FIESTA-C). Then thrombectomy was performed in all the 5 patients. Merci retriever to 1 patient, Penumbra system to 1, urokinase infusion to 2, and Solitaire to 1 using such techniques. In all cases, a 3D-FIESTA-MRA fusion imaging could depict approximately clear vascular information to at least the M3 segment beyond the occlusion. And each acute revascularization was able to perform smoothly using these imaging techniques. In all cases, there was no symptomatic hemorrhagic complication. It showed that 3D-FIESTA MRA fusion imaging technique could obtain vascular information beyond the MCA occlusion. In this study, no symptomatic hemorrhagic complications were detected. It could imply that such techniques were useful not only to improve treatment efficiency but also to reduce the risk of development of hemorrhagic complications caused by vessel perforations in acute revascularization. PMID:26369877

  12. Color image segmentation

    NASA Astrophysics Data System (ADS)

    McCrae, Kimberley A.; Ruck, Dennis W.; Rogers, Steven K.; Oxley, Mark E.

    1994-03-01

    The most difficult stage of automated target recognition is segmentation. Current segmentation problems include faces and tactical targets; previous efforts to segment these objects have used intensity and motion cues. This paper develops a color preprocessing scheme to be used with the other segmentation techniques. A neural network is trained to identify the color of a desired object, eliminating all but that color from the scene. Gabor correlations and 2D wavelet transformations will be performed on stationary images; and 3D wavelet transforms on multispectral data will incorporate color and motion detection into the machine visual system. The paper will demonstrate that color and motion cues can enhance a computer segmentation system. Results from segmenting faces both from the AFIT data base and from video taped television are presented; results from tactical targets such as tanks and airplanes are also given. Color preprocessing is shown to greatly improve the segmentation in most cases.

  13. Image segmentation using fuzzy LVQ clustering networks

    NASA Technical Reports Server (NTRS)

    Tsao, Eric Chen-Kuo; Bezdek, James C.; Pal, Nikhil R.

    1992-01-01

    In this note we formulate image segmentation as a clustering problem. Feature vectors extracted from a raw image are clustered into subregions, thereby segmenting the image. A fuzzy generalization of a Kohonen learning vector quantization (LVQ) which integrates the Fuzzy c-Means (FCM) model with the learning rate and updating strategies of the LVQ is used for this task. This network, which segments images in an unsupervised manner, is thus related to the FCM optimization problem. Numerical examples on photographic and magnetic resonance images are given to illustrate this approach to image segmentation.

  14. Understanding how axial loads on the spine influence segmental biomechanics for idiopathic scoliosis patients: A magnetic resonance imaging study.

    PubMed

    Little, J P; Pearcy, M J; Izatt, M T; Boom, K; Labrom, R D; Askin, G N; Adam, C J

    2016-02-01

    Segmental biomechanics of the scoliotic spine are important since the overall spinal deformity is comprised of the cumulative coronal and axial rotations of individual joints. This study investigates the coronal plane segmental biomechanics for adolescent idiopathic scoliosis patients in response to physiologically relevant axial compression. Individual spinal joint compliance in the coronal plane was measured for a series of 15 idiopathic scoliosis patients using axially loaded magnetic resonance imaging. Each patient was first imaged in the supine position with no axial load, and then again following application of an axial compressive load. Coronal plane disc wedge angles in the unloaded and loaded configurations were measured. Joint moments exerted by the axial compressive load were used to derive estimates of individual joint compliance. The mean standing major Cobb angle for this patient series was 46°. Mean intra-observer measurement error for endplate inclination was 1.6°. Following loading, initially highly wedged discs demonstrated a smaller change in wedge angle, than less wedged discs for certain spinal levels (+2,+1,-2 relative to the apex, (p<0.05)). Highly wedged discs were observed near the apex of the curve, which corresponded to lower joint compliance in the apical region. While individual patients exhibit substantial variability in disc wedge angles and joint compliance, overall there is a pattern of increased disc wedging near the curve apex, and reduced joint compliance in this region. Approaches such as this can provide valuable biomechanical data on in vivo spinal biomechanics of the scoliotic spine, for analysis of deformity progression and surgical planning. Copyright © 2015 Elsevier Ltd. All rights reserved.

  15. Image segmentation survey

    NASA Technical Reports Server (NTRS)

    Haralick, R. M.

    1982-01-01

    The methodologies and capabilities of image segmentation techniques are reviewed. Single linkage schemes, hybrid linkage schemes, centroid linkage schemes, histogram mode seeking, spatial clustering, and split and merge schemes are addressed.

  16. Scorpion image segmentation system

    NASA Astrophysics Data System (ADS)

    Joseph, E.; Aibinu, A. M.; Sadiq, B. A.; Bello Salau, H.; Salami, M. J. E.

    2013-12-01

    Death as a result of scorpion sting has been a major public health problem in developing countries. Despite the high rate of death as a result of scorpion sting, little report exists in literature of intelligent device and system for automatic detection of scorpion. This paper proposed a digital image processing approach based on the floresencing characteristics of Scorpion under Ultra-violet (UV) light for automatic detection and identification of scorpion. The acquired UV-based images undergo pre-processing to equalize uneven illumination and colour space channel separation. The extracted channels are then segmented into two non-overlapping classes. It has been observed that simple thresholding of the green channel of the acquired RGB UV-based image is sufficient for segmenting Scorpion from other background components in the acquired image. Two approaches to image segmentation have also been proposed in this work, namely, the simple average segmentation technique and K-means image segmentation. The proposed algorithm has been tested on over 40 UV scorpion images obtained from different part of the world and results obtained show an average accuracy of 97.7% in correctly classifying the pixel into two non-overlapping clusters. The proposed 1system will eliminate the problem associated with some of the existing manual approaches presently in use for scorpion detection.

  17. MRI (Magnetic Resonance Imaging)

    MedlinePlus

    ... Procedures Medical Imaging MRI (Magnetic Resonance Imaging) MRI (Magnetic Resonance Imaging) Share Tweet Linkedin Pin it More sharing options Linkedin Pin it Email Print Magnetic Resonance Imaging (MRI) is a medical imaging procedure for making ...

  18. 2D segmentation of intervertebral discs and its degree of degeneration from T2-weighted magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Castro-Mateos, Isaac; Pozo, José Maria; Lazary, Aron; Frangi, Alejandro F.

    2014-03-01

    Low back pain (LBP) is a disorder suffered by a large population around the world. A key factor causing this illness is Intervertebral Disc (IVD) degeneration, whose early diagnosis could help in preventing this widespread condition. Clinicians base their diagnosis on visual inspection of 2D slices of Magnetic Resonance (MR) images, which is subject to large interobserver variability. In this work, an automatic classification method is presented, which provides the Pfirrmann degree of degeneration from a mid-sagittal MR slice. The proposed method utilizes Active Contour Models, with a new geometrical energy, to achieve an initial segmentation, which is further improved using fuzzy C-means. Then, IVDs are classified according to their degree of degeneration. This classification is attained by employing Adaboost on five specific features: the mean and the variance of the probability map of the nucleus using two different approaches and the eccentricity of the fitting ellipse to the contour of the IVD. The classification method was evaluated using a cohort of 150 intervertebral discs assessed by three experts, resulting in a mean specificity (93%) and sensitivity (83%) similar to the one provided by every expert with respect to the most voted value. The segmentation accuracy was evaluated using the Dice Similarity Index (DSI) and Root Mean Square Error (RMSE) of the point-to-contour distance. The mean DSI ± 2 standard deviation was 91:7% ±5:6%, the mean RMSE was 0:82mm and the 95 percentile was 1:36mm. These results were found accurate when compared to the state-of-the-art.

  19. Adjacent segmental degeneration following Wallis interspinous stabilization implantation: Biomechanical explanations and the value of magnetic resonance imaging.

    PubMed

    Zhou, Zhiguo; Xiong, Wei; Li, Li; Li, Feng

    2017-06-01

    Adjacent segmental degeneration (ASD) is a major issue after pedicular fixation. This study examined the degeneration of the adjacent levels due to the insertion of the Wallis interspinous stabilization system compared with discectomy, using magnetic resonance imaging (MRI).Thirty-eight patients diagnosed with lumbar degeneration disorders at L4-L5 were reviewed: 19 patients underwent discectomy and Wallis system implantation (group A), and 19 patients underwent discectomy (group B). The Visual Analog Scale (VAS) and Oswestry Disability Index (ODI) were assessed preoperatively and postoperatively. ASD was evaluated by MRI.There was no difference in the preoperative ODI scores between the 2 groups (non-normal distribution, median, 50 (40, 50) vs 50 (50, 50), P = .331), but the postoperative ODI scores were different (non-normal distribution, median, 0 (0, 32) vs 20 (20, 30), P < .005). Similar results were observed for VAS. In group A, ASD occurred in 4 patients (21.1%) in the disc and 8 (42.1%) in the facet joint at L3/4, and in 4 (21.1%) in the disc and 5 (26.3%) in the facet joint at L5/S1. In Group B, ASD occurred in 3 patients (15.8%) in the disc at L3/4, and in 4 (21.1%) in the disc at L5/S1. In general, there was no difference between the 2 groups (P > .05), except at L3/4 (P = .015).ASD of the facet joint in the cranial segment occurred after Wallis system implantation, suggesting that the Wallis system cannot prevent ASD of the facet joint, but could have some other benefits for the discs.

  20. Multispectral image segmentation of breast pathology

    NASA Astrophysics Data System (ADS)

    Hornak, Joseph P.; Blaakman, Andre; Rubens, Deborah; Totterman, Saara

    1991-06-01

    The signal intensity in a magnetic resonance image is not only a function of imaging parameters but also of several intrinsic tissue properties. Therefore, unlike other medical imaging modalities, magnetic resonance imaging (MRI) allows the imaging scientist to locate pathology using multispectral image segmentation. Multispectral image segmentation works best when orthogonal spectral regions are employed. In MRI, possible spectral regions are spin density (rho) , spin-lattice relaxation time T1, spin-spin relaxation time T2, and texture for each nucleus type and chemical shift. This study examines the ability of multispectral image segmentation to locate breast pathology using the total hydrogen T1, T2, and (rho) . The preliminary results indicate that our technique can locate cysts and fibroadenoma breast lesions with a minimum number of false-positives and false-negatives. Results, T1, T2, and (rho) algorithms, and segmentation techniques are presented.

  1. Rapid Semi-Automatic Segmentation of the Spinal Cord from Magnetic Resonance Images: Application in Multiple Sclerosis

    PubMed Central

    Horsfield, Mark A.; Sala, Stefania; Neema, Mohit; Absinta, Martina; Bakshi, Anshika; Sormani, Maria Pia; Rocca, Maria A.; Bakshi, Rohit; Filippi, Massimo

    2010-01-01

    A new semi-automatic method for segmenting the spinal cord from MR images is presented. The method is based on an active surface (AS) model of the cord surface, with intrinsic smoothness constraints. The model is initialized by the user marking the approximate cord center-line on a few representative slices, and the compact surface parametrization results in a rapid segmentation, taking on the order of one minute. Using 3-D acquired T1-weighted images of the cervical spine from human controls and patients with multiple sclerosis, the intra- and inter-observer reproducibilities were evaluated, and compared favorably with an existing cord segmentation method. While the AS method overestimated the cord area by approximately 14% compared to manual outlining, correlations between cord cross-sectional area and clinical disability scores confirmed the relevance of the new method in measuring cord atrophy in multiple sclerosis. Segmentation of the cord from 2-D multi-slice T2-weighted images is also demonstrated over the cervical and thoracic region. Since the cord center-line is an intrinsic parameter extracted as part of the segmentation process, the image can be resampled such that the center-line forms one coordinate axis of a new image, allowing simple visualization of the cord structure and pathology; this could find wider application in standard radiological practice. PMID:20060481

  2. Segmentation of knee cartilage by using a hierarchical active shape model based on multi-resolution transforms in magnetic resonance images

    NASA Astrophysics Data System (ADS)

    León, Madeleine; Escalante-Ramirez, Boris

    2013-11-01

    Knee osteoarthritis (OA) is characterized by the morphological degeneration of cartilage. Efficient segmentation of cartilage is important for cartilage damage diagnosis and to support therapeutic responses. We present a method for knee cartilage segmentation in magnetic resonance images (MRI). Our method incorporates the Hermite Transform to obtain a hierarchical decomposition of contours which describe knee cartilage shapes. Then, we compute a statistical model of the contour of interest from a set of training images. Thereby, our Hierarchical Active Shape Model (HASM) captures a large range of shape variability even from a small group of training samples, improving segmentation accuracy. The method was trained with a training set of 16- MRI of knee and tested with leave-one-out method.

  3. A hybrid technique for medical image segmentation.

    PubMed

    Nyma, Alamgir; Kang, Myeongsu; Kwon, Yung-Keun; Kim, Cheol-Hong; Kim, Jong-Myon

    2012-01-01

    Medical image segmentation is an essential and challenging aspect in computer-aided diagnosis and also in pattern recognition research. This paper proposes a hybrid method for magnetic resonance (MR) image segmentation. We first remove impulsive noise inherent in MR images by utilizing a vector median filter. Subsequently, Otsu thresholding is used as an initial coarse segmentation method that finds the homogeneous regions of the input image. Finally, an enhanced suppressed fuzzy c-means is used to partition brain MR images into multiple segments, which employs an optimal suppression factor for the perfect clustering in the given data set. To evaluate the robustness of the proposed approach in noisy environment, we add different types of noise and different amount of noise to T1-weighted brain MR images. Experimental results show that the proposed algorithm outperforms other FCM based algorithms in terms of segmentation accuracy for both noise-free and noise-inserted MR images.

  4. Two and three-dimensional segmentation of hyperpolarized 3He magnetic resonance imaging of pulmonary gas distribution

    NASA Astrophysics Data System (ADS)

    Heydarian, Mohammadreza; Kirby, Miranda; Wheatley, Andrew; Fenster, Aaron; Parraga, Grace

    2012-03-01

    A semi-automated method for generating hyperpolarized helium-3 (3He) measurements of individual slice (2D) or whole lung (3D) gas distribution was developed. 3He MRI functional images were segmented using two-dimensional (2D) and three-dimensional (3D) hierarchical K-means clustering of the 3He MRI signal and in addition a seeded region-growing algorithm was employed for segmentation of the 1H MRI thoracic cavity volume. 3He MRI pulmonary function measurements were generated following two-dimensional landmark-based non-rigid registration of the 3He and 1H pulmonary images. We applied this method to MRI of healthy subjects and subjects with chronic obstructive lung disease (COPD). The results of hierarchical K-means 2D and 3D segmentation were compared to an expert observer's manual segmentation results using linear regression, Pearson correlations and the Dice similarity coefficient. 2D hierarchical K-means segmentation of ventilation volume (VV) and ventilation defect volume (VDV) was strongly and significantly correlated with manual measurements (VV: r=0.98, p<.0001 VDV: r=0.97, p<.0001) and mean Dice coefficients were greater than 92% for all subjects. 3D hierarchical K-means segmentation of VV and VDV was also strongly and significantly correlated with manual measurements (VV: r=0.98, p<.0001 VDV: r=0.64, p<.0001) and the mean Dice coefficients were greater than 91% for all subjects. Both 2D and 3D semi-automated segmentation of 3He MRI gas distribution provides a way to generate novel pulmonary function measurements.

  5. Cooperative processes in image segmentation

    NASA Technical Reports Server (NTRS)

    Davis, L. S.

    1982-01-01

    Research into the role of cooperative, or relaxation, processes in image segmentation is surveyed. Cooperative processes can be employed at several levels of the segmentation process as a preprocessing enhancement step, during supervised or unsupervised pixel classification and, finally, for the interpretation of image segments based on segment properties and relations.

  6. Cooperative processes in image segmentation

    NASA Technical Reports Server (NTRS)

    Davis, L. S.

    1982-01-01

    Research into the role of cooperative, or relaxation, processes in image segmentation is surveyed. Cooperative processes can be employed at several levels of the segmentation process as a preprocessing enhancement step, during supervised or unsupervised pixel classification and, finally, for the interpretation of image segments based on segment properties and relations.

  7. Lower Lumbar Segmental Arteries Can Intersect Over the Intervertebral Disc in the Oblique Lateral Interbody Fusion Approach With a Risk for Arterial Injury: Radiological Analysis of Lumbar Segmental Arteries by Using Magnetic Resonance Imaging.

    PubMed

    Orita, Sumihisa; Inage, Kazuhide; Sainoh, Takeshi; Fujimoto, Kazuki; Sato, Jun; Shiga, Yasuhiro; Kanamoto, Hirohito; Abe, Koki; Yamauchi, Kazuyo; Aoki, Yasuchika; Nakamura, Junichi; Matsuura, Yusuke; Suzuki, Takane; Kubota, Go; Eguchi, Yawara; Terakado, Atsushi; Takahashi, Kazuhisa; Ohtori, Seiji

    2017-02-01

    A retrospective radiological study on vascular anatomy. The aim of this study was to evaluate the anatomical and radiological features of lumbar segmental arteries with respect to the surgical field of the oblique lateral interbody fusion (OLIF) approach by using magnetic resonance imaging (MRI). OLIF surgery restores disc height and enables indirect decompression of narrowed spinal canals through an oblique lateral approach to the spine, by using a specially designed retractor. In a minimal surgical field, injuring segmental arteries can cause massive hemorrhage. We reviewed 272 lumbar MRIs. In the sagittal images, the intersection of one-third of the anterior and median lines of the intervertebral disc (IVD) was considered the center of the virtually installed OLIF retractor. The cephalad/caudal distances from the center and branch angles of segmental arteries to the longitudinal axes of the aorta were measured to determine whether the segmental arteries run into the surgical area. Statistical significance was set at P < 0.05. The branch angles of segmental arteries were significantly acute (≤90°) in L1-L3 arteries and significantly blunt (>90°) in L4 and L5 arteries. The average distance to the center of the caudal adjacent IVD was significantly larger, and there were generally low possibilities for the existence of segmental arteries below half of the vertebral height, where the surgeons can install fixation pins with ease and safety. Among the lumbar segmental arteries, L5 showed specific characteristics with significant deviation, a four times (4.1% vs. L1-L3 segmental arteries) higher adjacency rate, and a two-fifth (38.6% vs. 100%) lower existence rate. Segmental arteries can be involved in the surgical field of OLIF especially in the lower lumbar spine level of L4 and L5 arteries, which can directly run across IVDs. L5 segmental arteries can also be iliolumbar arteries that have an abnormal trajectory by nature. 4.

  8. Bone image segmentation.

    PubMed

    Liu, Z Q; Liew, H L; Clement, J G; Thomas, C D

    1999-05-01

    Characteristics of microscopic structures in bone cross sections carry essential clues in age determination in forensic science and in the study of age-related bone developments and bone diseases. Analysis of bone cross sections represents a major area of research in bone biology. However, traditional approaches in bone biology have relied primarily on manual processes with very limited number of bone samples. As a consequence, it is difficult to reach reliable and consistent conclusions. In this paper we present an image processing system that uses microstructural and relational knowledge present in the bone cross section for bone image segmentation. This system automates the bone image analysis process and is able to produce reliable results based on quantitative measurements from a large number of bone images. As a result, using large databases of bone images to study the correlation between bone structural features and age-related bone developments becomes feasible.

  9. Neural Network-Based Learning Kernel for Automatic Segmentation of Multiple Sclerosis Lesions on Magnetic Resonance Images

    PubMed Central

    Khastavaneh, H.; Ebrahimpour-Komleh, H.

    2017-01-01

    Background: Multiple Sclerosis (MS) is a degenerative disease of central nervous system. MS patients have some dead tissues in their brains called MS lesions. MRI is an imaging technique sensitive to soft tissues such as brain that shows MS lesions as hyper-intense or hypo-intense signals. Since manual segmentation of these lesions is a laborious and time consuming task, automatic segmentation is a need. Materials and Methods: In order to segment MS lesions, a method based on learning kernels has been proposed. The proposed method has three main steps namely; pre-processing, sub-region extraction and segmentation. The segmentation is performed by a kernel. This kernel is trained using a modified version of a special type of Artificial Neural Networks (ANN) called Massive Training ANN (MTANN). The kernel incorporates surrounding pixel information as features for classification of middle pixel of kernel. The materials of this study include a part of MICCAI 2008 MS lesion segmentation grand challenge data-set. Results: Both qualitative and quantitative results show promising results. Similarity index of 70 percent in some cases is considered convincing. These results are obtained from information of only one MRI channel rather than multi-channel MRIs. Conclusion: This study shows the potential of surrounding pixel information to be incorporated in segmentation by learning kernels. The performance of proposed method will be improved using a special pre-processing pipeline and also a post-processing step for reducing false positives/negatives. An important advantage of proposed model is that it uses just FLAIR MRI that reduces computational time and brings comfort to patients. PMID:28580337

  10. D-Dimer Levels Predict Myocardial Injury in ST-Segment Elevation Myocardial Infarction: A Cardiac Magnetic Resonance Imaging Study

    PubMed Central

    Song, Young Bin; Lima, Joao A. C.; Guallar, Eliseo; Choe, Yeon Hyeon; Hwang, Jin Kyung; Kim, Eun Kyoung; Yang, Jeong Hoon; Hahn, Joo-Yong; Choi, Seung-Hyuk; Lee, Sang-Chol; Lee, Sang Hoon; Gwon, Hyeon-Cheol

    2016-01-01

    Objectives Elevated D-dimer levels on admission predict prognosis in patients undergoing primary percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI), but the association of D-dimer levels with structural markers of myocardial injury in these patients is unknown. Methods We performed cardiac magnetic resonance (CMR) imaging in 208 patients treated with primary PCI for STEMI. CMR was performed a median of 3 days after the index procedure. Of the 208 patients studied, 75 patients had D-dimer levels above the normal range on admission (>0.5 μg/mL; high D-dimer group) while 133 had normal levels (≤0.5 μg/mL; low D-dimer group). The primary outcome was myocardial infarct size assessed by CMR. Secondary outcomes included area at risk (AAR), microvascular obstruction (MVO) area, and myocardial salvage index (MSI). Results In CMR analysis, myocardial infarct size was larger in the high D-dimer group than in the low D-dimer group (22.3% [16.2–30.5] versus 18.8% [10.7–26.7]; p = 0.02). Compared to the low D-dimer group, the high D-dimer group also had a larger AAR (38.1% [31.7–46.9] versus 35.8% [24.2–45.3]; p = 0.04) and a smaller MSI (37.7 [28.2–46.9] versus 47.1 [33.2–57.0]; p = 0.01). In multivariate analysis, high D-dimer levels were significantly associated with larger myocardial infarct (OR 2.59; 95% CI 1.37–4.87; p<0.01) and lower MSI (OR 2.62; 95% CI 1.44–4.78; p<0.01). Conclusions In STEMI patients undergoing primary PCI, high D-dimer levels on admission were associated with a larger myocardial infarct size, a greater extent of AAR, and lower MSI, as assessed by CMR data. Elevated initial D-dimer level may be a marker of advanced myocardial injury in patients treated with primary PCI for STEMI. PMID:27513758

  11. Semi-automatic segmentation and modeling of the cervical spinal cord for volume quantification in multiple sclerosis patients from magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Sonkova, Pavlina; Evangelou, Iordanis E.; Gallo, Antonio; Cantor, Fredric K.; Ohayon, Joan; McFarland, Henry F.; Bagnato, Francesca

    2008-03-01

    Spinal cord (SC) tissue loss is known to occur in some patients with multiple sclerosis (MS), resulting in SC atrophy. Currently, no measurement tools exist to determine the magnitude of SC atrophy from Magnetic Resonance Images (MRI). We have developed and implemented a novel semi-automatic method for quantifying the cervical SC volume (CSCV) from Magnetic Resonance Images (MRI) based on level sets. The image dataset consisted of SC MRI exams obtained at 1.5 Tesla from 12 MS patients (10 relapsing-remitting and 2 secondary progressive) and 12 age- and gender-matched healthy volunteers (HVs). 3D high resolution image data were acquired using an IR-FSPGR sequence acquired in the sagittal plane. The mid-sagittal slice (MSS) was automatically located based on the entropy calculation for each of the consecutive sagittal slices. The image data were then pre-processed by 3D anisotropic diffusion filtering for noise reduction and edge enhancement before segmentation with a level set formulation which did not require re-initialization. The developed method was tested against manual segmentation (considered ground truth) and intra-observer and inter-observer variability were evaluated.

  12. Validation and Development of a New Automatic Algorithm for Time-Resolved Segmentation of the Left Ventricle in Magnetic Resonance Imaging.

    PubMed

    Tufvesson, Jane; Hedström, Erik; Steding-Ehrenborg, Katarina; Carlsson, Marcus; Arheden, Håkan; Heiberg, Einar

    2015-01-01

    Manual delineation of the left ventricle is clinical standard for quantification of cardiovascular magnetic resonance images despite being time consuming and observer dependent. Previous automatic methods generally do not account for one major contributor to stroke volume, the long-axis motion. Therefore, the aim of this study was to develop and validate an automatic algorithm for time-resolved segmentation covering the whole left ventricle, including basal slices affected by long-axis motion. Ninety subjects imaged with a cine balanced steady state free precession sequence were included in the study (training set n = 40, test set n = 50). Manual delineation was reference standard and second observer analysis was performed in a subset (n = 25). The automatic algorithm uses deformable model with expectation-maximization, followed by automatic removal of papillary muscles and detection of the outflow tract. The mean differences between automatic segmentation and manual delineation were EDV -11 mL, ESV 1 mL, EF -3%, and LVM 4 g in the test set. The automatic LV segmentation algorithm reached accuracy comparable to interobserver for manual delineation, thereby bringing automatic segmentation one step closer to clinical routine. The algorithm and all images with manual delineations are available for benchmarking.

  13. Magnetic resonance image segmentation using semi-automated software for quantification of knee articular cartilage—initial evaluation of a technique for paired scans

    PubMed Central

    Brem, M. H.; Lang, P. K.; Neumann, G.; Schlechtweg, P. M.; Schneider, E.; Jackson, R.; Yu, J.; Eaton, C. B.; Hennig, F. F.; Yoshioka, H.; Pappas, G.; Duryea, J.

    2010-01-01

    Purpose Software-based image analysis is important for studies of cartilage changes in knee osteoarthritis (OA). This study describes an evaluation of a semi-automated cartilage segmentation software tool capable of quantifying paired images for potential use in longitudinal studies of knee OA. We describe the methodology behind the analysis and demonstrate its use by determination of test–retest analysis precision of duplicate knee magnetic resonance imaging (MRI) data sets. Methods Test–retest knee MR images of 12 subjects with a range of knee health were evaluated from the Osteoarthritis Initiative (OAI) pilot MR study. Each subject was removed from the magnet between the two scans. The 3D DESS (sagittal, 0.456 mm×0.365 mm, 0.7 mm slice thickness, TR 16.5 ms, TE 4.7 ms) images were obtained on a 3-T Siemens Trio MR system with a USA Instruments quadrature transmit–receive extremity coil. Segmentation of one 3D-image series was first performed and then the corresponding retest series was segmented by viewing both image series concurrently in two adjacent windows. After manual registration of the series, the first segmentation cartilage outline served as an initial estimate for the second segmentation. We evaluated morphometric measures of the bone and cartilage surface area (tAB and AC), cartilage volume (VC), and mean thickness (ThC.me) for medial/lateral tibia (MT/LT), total femur (F) and patella (P). Test–retest reproducibility was assessed using the root-mean square coefficient of variation (RMS CV%). Results For the paired analyses, RMS CV % ranged from 0.9% to 1.2% for VC, from 0.3% to 0.7% for AC, from 0.6% to 2.7% for tAB and 0.8% to 1.5% for ThC.me. Conclusion Paired image analysis improved the measurement precision of cartilage segmentation. Our results are in agreement with other publications supporting the use of paired analysis for longitudinal studies of knee OA. PMID:19252907

  14. Magnetic resonance image segmentation using semi-automated software for quantification of knee articular cartilage---initial evaluation of a technique for paired scans.

    PubMed

    Brem, M H; Lang, P K; Neumann, G; Schlechtweg, P M; Schneider, E; Jackson, R; Yu, J; Eaton, C B; Hennig, F F; Yoshioka, H; Pappas, G; Duryea, J

    2009-05-01

    Software-based image analysis is important for studies of cartilage changes in knee osteoarthritis (OA). This study describes an evaluation of a semi-automated cartilage segmentation software tool capable of quantifying paired images for potential use in longitudinal studies of knee OA. We describe the methodology behind the analysis and demonstrate its use by determination of test-retest analysis precision of duplicate knee magnetic resonance imaging (MRI) data sets. Test-retest knee MR images of 12 subjects with a range of knee health were evaluated from the Osteoarthritis Initiative (OAI) pilot MR study. Each subject was removed from the magnet between the two scans. The 3D DESS (sagittal, 0.456 mm x 0.365 mm, 0.7 mm slice thickness, TR 16.5 ms, TE 4.7 ms) images were obtained on a 3-T Siemens Trio MR system with a USA Instruments quadrature transmit-receive extremity coil. Segmentation of one 3D-image series was first performed and then the corresponding retest series was segmented by viewing both image series concurrently in two adjacent windows. After manual registration of the series, the first segmentation cartilage outline served as an initial estimate for the second segmentation. We evaluated morphometric measures of the bone and cartilage surface area (tAB and AC), cartilage volume (VC), and mean thickness (ThC.me) for medial/lateral tibia (MT/LT), total femur (F) and patella (P). Test-retest reproducibility was assessed using the root-mean square coefficient of variation (RMS CV%). For the paired analyses, RMS CV % ranged from 0.9% to 1.2% for VC, from 0.3% to 0.7% for AC, from 0.6% to 2.7% for tAB and 0.8% to 1.5% for ThC.me. Paired image analysis improved the measurement precision of cartilage segmentation. Our results are in agreement with other publications supporting the use of paired analysis for longitudinal studies of knee OA.

  15. Segmentation of stereo terrain images

    NASA Astrophysics Data System (ADS)

    George, Debra A.; Privitera, Claudio M.; Blackmon, Theodore T.; Zbinden, Eric; Stark, Lawrence W.

    2000-06-01

    We have studied four approaches to segmentation of images: three automatic ones using image processing algorithms and a fourth approach, human manual segmentation. We were motivated toward helping with an important NASA Mars rover mission task -- replacing laborious manual path planning with automatic navigation of the rover on the Mars terrain. The goal of the automatic segmentations was to identify an obstacle map on the Mars terrain to enable automatic path planning for the rover. The automatic segmentation was first explored with two different segmentation methods: one based on pixel luminance, and the other based on pixel altitude generated through stereo image processing. The third automatic segmentation was achieved by combining these two types of image segmentation. Human manual segmentation of Martian terrain images was used for evaluating the effectiveness of the combined automatic segmentation as well as for determining how different humans segment the same images. Comparisons between two different segmentations, manual or automatic, were measured using a similarity metric, SAB. Based on this metric, the combined automatic segmentation did fairly well in agreeing with the manual segmentation. This was a demonstration of a positive step towards automatically creating the accurate obstacle maps necessary for automatic path planning and rover navigation.

  16. Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A Novel Statistical Shape Model for Treatment Planning of Retinoblastoma

    SciTech Connect

    Ciller, Carlos; De Zanet, Sandro I.; Rüegsegger, Michael B.; Pica, Alessia; Sznitman, Raphael; Thiran, Jean-Philippe; Maeder, Philippe; Munier, Francis L.; Kowal, Jens H.; and others

    2015-07-15

    Purpose: Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of retinoblastoma in infants, where it serves as a source of information, complementary to the fundus or ultrasonographic imaging. Here we present a framework to fully automatically segment the eye anatomy for MRI based on 3D active shape models (ASM), and we validate the results and present a proof of concept to automatically segment pathological eyes. Methods and Materials: Manual and automatic segmentation were performed in 24 images of healthy children's eyes (3.29 ± 2.15 years of age). Imaging was performed using a 3-T MRI scanner. The ASM consists of the lens, the vitreous humor, the sclera, and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens, and the optic nerve, and then aligning the model and fitting it to the patient. We validated our segmentation method by using a leave-one-out cross-validation. The segmentation results were evaluated by measuring the overlap, using the Dice similarity coefficient (DSC) and the mean distance error. Results: We obtained a DSC of 94.90 ± 2.12% for the sclera and the cornea, 94.72 ± 1.89% for the vitreous humor, and 85.16 ± 4.91% for the lens. The mean distance error was 0.26 ± 0.09 mm. The entire process took 14 seconds on average per eye. Conclusion: We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor, and the lens, using MRI. We additionally present a proof of concept for fully automatically segmenting eye pathology. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor.

  17. Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A Novel Statistical Shape Model for Treatment Planning of Retinoblastoma.

    PubMed

    Ciller, Carlos; De Zanet, Sandro I; Rüegsegger, Michael B; Pica, Alessia; Sznitman, Raphael; Thiran, Jean-Philippe; Maeder, Philippe; Munier, Francis L; Kowal, Jens H; Cuadra, Meritxell Bach

    2015-07-15

    Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of retinoblastoma in infants, where it serves as a source of information, complementary to the fundus or ultrasonographic imaging. Here we present a framework to fully automatically segment the eye anatomy for MRI based on 3D active shape models (ASM), and we validate the results and present a proof of concept to automatically segment pathological eyes. Manual and automatic segmentation were performed in 24 images of healthy children's eyes (3.29 ± 2.15 years of age). Imaging was performed using a 3-T MRI scanner. The ASM consists of the lens, the vitreous humor, the sclera, and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens, and the optic nerve, and then aligning the model and fitting it to the patient. We validated our segmentation method by using a leave-one-out cross-validation. The segmentation results were evaluated by measuring the overlap, using the Dice similarity coefficient (DSC) and the mean distance error. We obtained a DSC of 94.90 ± 2.12% for the sclera and the cornea, 94.72 ± 1.89% for the vitreous humor, and 85.16 ± 4.91% for the lens. The mean distance error was 0.26 ± 0.09 mm. The entire process took 14 seconds on average per eye. We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor, and the lens, using MRI. We additionally present a proof of concept for fully automatically segmenting eye pathology. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Lumbo-pelvic joint protection against antigravity forces: motor control and segmental stiffness assessed with magnetic resonance imaging.

    PubMed

    Richardson, C A; Hides, J A; Wilson, S; Stanton, W; Snijders, C J

    2004-07-01

    The antigravity muscles of the lumbo-pelvic region, especially transversus abdominis (TrA), are important for the protection and support of the weightbearing joints. Measures of TrA function (the response to the postural cue of drawing in the abdominal wall) have been developed and quantified using magnetic resonance imaging (MRI). Cross-sections through the trunk allowed muscle contraction as well as the large fascial attachments of the TrA to be visualized. The cross sectional area (CSA) of the deep musculo-fascial system was measured at rest and in the contracted state, using static images as well as a cine sequence. In this developmental study, MRI measures were undertaken on a small sample of low back pain (LBP) and non LBP subjects. Results demonstrated that, in non LBP subjects, the draw in action produced a symmetrical deep musculo-fascial "corset" which encircles the abdomen. This study demonstrated a difference in this "corset" measure between subjects with and without LBP. These measures may also prove useful to quantify the effect of unloading in bedrest and microgravity exposure.

  19. Effects of segmental traction therapy on lumbar disc herniation in patients with acute low back pain measured by magnetic resonance imaging: A single arm clinical trial.

    PubMed

    Karimi, Noureddin; Akbarov, Parvin; Rahnama, Leila

    2017-01-01

    Low Back Pain (LBP) is considered as one of the most frequent disorders, which about 80% of adults experience in their lives. Lumbar disc herniation (LDH) is a cause for acute LBP. Among conservative treatments, traction is frequently used by clinicians to manage LBP resulting from LDH. However, there is still a lack of consensus about its efficacy. The purpose of this study was to evaluate the effects of segmental traction therapy on lumbar discs herniation, pain, lumbar range of motion (ROM), and back extensor muscles endurance in patients with acute LBP induced by LDH. Fifteen patients with acute LBP diagnosed by LDH participated in the present study. Participants undertook 15 sessions of segmental traction therapy along with conventional physiotherapy, 5 times a week for 3 weeks. Lumbar herniated mass size was measured before and after the treatment protocol using magnetic resonance imaging. Furthermore, pain, lumbar ROM and back muscle endurance were evaluated before and after the procedure using clinical outcome measures. Following the treatment protocol, herniated mass size and patients' pain were reduced significantly. In addition, lumbar flexion ROM showed a significant improvement. However, no significant change was observed for back extensor muscle endurance after the treatment procedure. The result of the present study showed segmental traction therapy might play an important role in the treatment of acute LBP stimulated by LDH.

  20. Spectroscopic magnetic resonance imaging of the brain: voxel localisation and tissue segmentation in the follow up of brain tumour.

    PubMed

    Poloni, Guy; Bastianello, S; Vultaggio, Angela; Pozzi, S; Maccabelli, Gloria; Germani, Giancarlo; Chiarati, Patrizia; Pichiecchio, Anna

    2008-01-01

    The field of application of magnetic resonance spectroscopy (MRS) in biomedical research is expanding all the time and providing opportunities to investigate tissue metabolism and function. The data derived can be integrated with the information on tissue structure gained from conventional and non-conventional magnetic resonance imaging (MRI) techniques. Clinical MRS is also strongly expected to play an important role as a diagnostic tool. Essential for the future success of MRS as a clinical and research tool in biomedical sciences, both in vivo and in vitro, is the development of an accurate, biochemically relevant and physically consistent and reliable data analysis standard. Stable and well established analysis algorithms, in both the time and the frequency domain, are already available, as is free commercial software for implementing them. In this study, we propose an automatic algorithm that takes into account anatomical localisation, relative concentrations of white matter, grey matter, cerebrospinal fluid and signal abnormalities and inter-scan patient movement. The endpoint is the collection of a series of covariates that could be implemented in a multivariate analysis of covariance (MANCOVA) of the MRS data, as a tool for dealing with differences that may be ascribed to the anatomical variability of the subjects, to inaccuracies in the localisation of the voxel or slab, or to movement, rather than to the pathology under investigation. The aim was to develop an analysis procedure that can be consistently and reliably applied in the follow up of brain tumour. In this study, we demonstrate that the inclusion of such variables in the data analysis of quantitative MRS is fundamentally important (especially in view of the reduced accuracy typical of MRS measures compared to other MRI techniques), reducing the occurrence of false positives.

  1. Dose-Volume Differences for Computed Tomography and Magnetic Resonance Imaging Segmentation and Planning for Proton Prostate Cancer Therapy

    SciTech Connect

    Yeung, Anamaria R.; Vargas, Carlos E. Falchook, Aaron; Louis, Debbie C.; Olivier, Kenneth; Keole, Sameer; Yeung, Daniel; Mendenhall, Nancy P.; Li Zuofeng

    2008-12-01

    Purpose: To determine the influence of magnetic-resonance-imaging (MRI)-vs. computed-tomography (CT)-based prostate and normal structure delineation on the dose to the target and organs at risk during proton therapy. Methods and Materials: Fourteen patients were simulated in the supine position using both CT and T2 MRI. The prostate, rectum, and bladder were delineated on both imaging modalities. The planning target volume (PTV) was generated from the delineated prostates with a 5-mm axial and 8-mm superior and inferior margin. Two plans were generated and analyzed for each patient: an MRI plan based on the MRI-delineated PTV, and a CT plan based on the CT-delineated PTV. Doses of 78 Gy equivalents (GE) were prescribed to the PTV. Results: Doses to normal structures were lower when MRI was used to delineate the rectum and bladder compared with CT: bladder V50 was 15.3% lower (p = 0.04), and rectum V50 was 23.9% lower (p = 0.003). Poor agreement on the definition of the prostate apex was seen between CT and MRI (p = 0.007). The CT-defined prostate apex was within 2 mm of the apex on MRI only 35.7% of the time. Coverage of the MRI-delineated PTV was significantly decreased with the CT-based plan: the minimum dose to the PTV was reduced by 43% (p < 0.001), and the PTV V99% was reduced by 11% (p < 0.001). Conclusions: Using MRI to delineate the prostate results in more accurate target definition and a smaller target volume compared with CT, allowing for improved target coverage and decreased doses to critical normal structures.

  2. Medical image segmentation using 3D MRI data

    NASA Astrophysics Data System (ADS)

    Voronin, V.; Marchuk, V.; Semenishchev, E.; Cen, Yigang; Agaian, S.

    2017-05-01

    Precise segmentation of three-dimensional (3D) magnetic resonance imaging (MRI) image can be a very useful computer aided diagnosis (CAD) tool in clinical routines. Accurate automatic extraction a 3D component from images obtained by magnetic resonance imaging (MRI) is a challenging segmentation problem due to the small size objects of interest (e.g., blood vessels, bones) in each 2D MRA slice and complex surrounding anatomical structures. Our objective is to develop a specific segmentation scheme for accurately extracting parts of bones from MRI images. In this paper, we use a segmentation algorithm to extract the parts of bones from Magnetic Resonance Imaging (MRI) data sets based on modified active contour method. As a result, the proposed method demonstrates good accuracy in a comparison between the existing segmentation approaches on real MRI data.

  3. Neural network for image segmentation

    NASA Astrophysics Data System (ADS)

    Skourikhine, Alexei N.; Prasad, Lakshman; Schlei, Bernd R.

    2000-10-01

    Image analysis is an important requirement of many artificial intelligence systems. Though great effort has been devoted to inventing efficient algorithms for image analysis, there is still much work to be done. It is natural to turn to mammalian vision systems for guidance because they are the best known performers of visual tasks. The pulse- coupled neural network (PCNN) model of the cat visual cortex has proven to have interesting properties for image processing. This article describes the PCNN application to the processing of images of heterogeneous materials; specifically PCNN is applied to image denoising and image segmentation. Our results show that PCNNs do well at segmentation if we perform image smoothing prior to segmentation. We use PCNN for obth smoothing and segmentation. Combining smoothing and segmentation enable us to eliminate PCNN sensitivity to the setting of the various PCNN parameters whose optimal selection can be difficult and can vary even for the same problem. This approach makes image processing based on PCNN more automatic in our application and also results in better segmentation.

  4. Segmenting Images for a Better Diagnosis

    NASA Technical Reports Server (NTRS)

    2004-01-01

    NASA's Hierarchical Segmentation (HSEG) software has been adapted by Bartron Medical Imaging, LLC, for use in segmentation feature extraction, pattern recognition, and classification of medical images. Bartron acquired licenses from NASA Goddard Space Flight Center for application of the HSEG concept to medical imaging, from the California Institute of Technology/Jet Propulsion Laboratory to incorporate pattern-matching software, and from Kennedy Space Center for data-mining and edge-detection programs. The Med-Seg[TM] united developed by Bartron provides improved diagnoses for a wide range of medical images, including computed tomography scans, positron emission tomography scans, magnetic resonance imaging, ultrasound, digitized Z-ray, digitized mammography, dental X-ray, soft tissue analysis, and moving object analysis. It also can be used in analysis of soft-tissue slides. Bartron's future plans include the application of HSEG technology to drug development. NASA is advancing it's HSEG software to learn more about the Earth's magnetosphere.

  5. Magnetic resonance segmentation with the bubble wave algorithm

    NASA Astrophysics Data System (ADS)

    Cline, Harvey E.; Ludke, Siegwalt

    2003-05-01

    A new bubble wave algorithm provides automatic segmentation of three-dimensional magnetic resonance images of both the peripheral vasculature and the brain. Simple connectivity algorithms are not reliable in these medical applications because there are unwanted connections through background noise. The bubble wave algorithm restricts connectivity using curvature by testing spherical regions on a propagating active contour to eliminate noise bridges. After the user places seeds in both the selected regions and in the regions that are not desired, the method provides the critical threshold for segmentation using binary search. Today, peripheral vascular disease is diagnosed using magnetic resonance imaging with a timed contrast bolus. A new blood pool contrast agent MS-325 (Epix Medical) binds to albumen in the blood and provides high-resolution three-dimensional images of both arteries and veins. The bubble wave algorithm provides a means to automatically suppress the veins that obscure the arteries in magnetic resonance angiography. Monitoring brain atrophy is needed for trials of drugs that retard the progression of dementia. The brain volume is measured by placing seeds in both the brain and scalp to find the critical threshold that prevents connections between the brain volume and the scalp. Examples from both three-dimensional magnetic resonance brain and contrast enhanced vascular images were segmented with minimal user intervention.

  6. Transfer learning improves supervised image segmentation across imaging protocols.

    PubMed

    van Opbroek, Annegreet; Ikram, M Arfan; Vernooij, Meike W; de Bruijne, Marleen

    2015-05-01

    The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two magnetic resonance imaging brain-segmentation tasks with multi-site data: white matter, gray matter, and cerebrospinal fluid segmentation; and white-matter-/MS-lesion segmentation. The experiments showed that when there is only a small amount of representative training data available, transfer learning can greatly outperform common supervised-learning approaches, minimizing classification errors by up to 60%.

  7. Image Information Mining Utilizing Hierarchical Segmentation

    NASA Technical Reports Server (NTRS)

    Tilton, James C.; Marchisio, Giovanni; Koperski, Krzysztof; Datcu, Mihai

    2002-01-01

    The Hierarchical Segmentation (HSEG) algorithm is an approach for producing high quality, hierarchically related image segmentations. The VisiMine image information mining system utilizes clustering and segmentation algorithms for reducing visual information in multispectral images to a manageable size. The project discussed herein seeks to enhance the VisiMine system through incorporating hierarchical segmentations from HSEG into the VisiMine system.

  8. Distribution Metrics and Image Segmentation

    PubMed Central

    Georgiou, Tryphon; Michailovich, Oleg; Rathi, Yogesh; Malcolm, James; Tannenbaum, Allen

    2007-01-01

    The purpose of this paper is to describe certain alternative metrics for quantifying distances between distributions, and to explain their use and relevance in visual tracking. Besides the theoretical interest, such metrics may be used to design filters for image segmentation, that is for solving the key visual task of separating an object from the background in an image. The segmenting curve is represented as the zero level set of a signed distance function. Most existing methods in the geometric active contour framework perform segmentation by maximizing the separation of intensity moments between the interior and the exterior of an evolving contour. Here one can use the given distributional metric to determine a flow which minimizes changes in the distribution inside and outside the curve. PMID:18769529

  9. Functional Magnetic Resonance Imaging

    ERIC Educational Resources Information Center

    Voos, Avery; Pelphrey, Kevin

    2013-01-01

    Functional magnetic resonance imaging (fMRI), with its excellent spatial resolution and ability to visualize networks of neuroanatomical structures involved in complex information processing, has become the dominant technique for the study of brain function and its development. The accessibility of in-vivo pediatric brain-imaging techniques…

  10. Functional Magnetic Resonance Imaging

    ERIC Educational Resources Information Center

    Voos, Avery; Pelphrey, Kevin

    2013-01-01

    Functional magnetic resonance imaging (fMRI), with its excellent spatial resolution and ability to visualize networks of neuroanatomical structures involved in complex information processing, has become the dominant technique for the study of brain function and its development. The accessibility of in-vivo pediatric brain-imaging techniques…

  11. Magnetic resonance imaging

    SciTech Connect

    Stark, D.D.; Bradley, W.G. Jr.

    1988-01-01

    The authors present a review of magnetic resonance imaging. Many topics are explored from instrumentation, spectroscopy, blood flow and sodium imaging to detailed clinical applications such as the differential diagnosis of multiple sclerosis or adrenal adenoma. The emphasis throughout is on descriptions of normal multiplanar anatomy and pathology as displayed by MRI.

  12. Colony image acquisition and segmentation

    NASA Astrophysics Data System (ADS)

    Wang, W. X.

    2007-12-01

    For counting of both colonies and plaques, there is a large number of applications including food, dairy, beverages, hygiene, environmental monitoring, water, toxicology, sterility testing, AMES testing, pharmaceuticals, paints, sterile fluids and fungal contamination. Recently, many researchers and developers have made efforts for this kind of systems. By investigation, some existing systems have some problems. The main problems are image acquisition and image segmentation. In order to acquire colony images with good quality, an illumination box was constructed as: the box includes front lightning and back lightning, which can be selected by users based on properties of colony dishes. With the illumination box, lightning can be uniform; colony dish can be put in the same place every time, which make image processing easy. The developed colony image segmentation algorithm consists of the sub-algorithms: (1) image classification; (2) image processing; and (3) colony delineation. The colony delineation algorithm main contain: the procedures based on grey level similarity, on boundary tracing, on shape information and colony excluding. In addition, a number of algorithms are developed for colony analysis. The system has been tested and satisfactory.

  13. XRA image segmentation using regression

    NASA Astrophysics Data System (ADS)

    Jin, Jesse S.

    1996-04-01

    Segmentation is an important step in image analysis. Thresholding is one of the most important approaches. There are several difficulties in segmentation, such as automatic selecting threshold, dealing with intensity distortion and noise removal. We have developed an adaptive segmentation scheme by applying the Central Limit Theorem in regression. A Gaussian regression is used to separate the distribution of background from foreground in a single peak histogram. The separation will help to automatically determine the threshold. A small 3 by 3 widow is applied and the modal of the local histogram is used to overcome noise. Thresholding is based on local weighting, where regression is used again for parameter estimation. A connectivity test is applied to the final results to remove impulse noise. We have applied the algorithm to x-ray angiogram images to extract brain arteries. The algorithm works well for single peak distribution where there is no valley in the histogram. The regression provides a method to apply knowledge in clustering. Extending regression for multiple-level segmentation needs further investigation.

  14. A semi-automated measuring system of brain diffusion and perfusion magnetic resonance imaging abnormalities in patients with multiple sclerosis based on the integration of coregistration and tissue segmentation procedures.

    PubMed

    Revenaz, Alfredo; Ruggeri, Massimiliano; Laganà, Marcella; Bergsland, Niels; Groppo, Elisabetta; Rovaris, Marco; Fainardi, Enrico

    2016-01-14

    Diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) abnormalities in patients with multiple sclerosis (MS) are currently measured by a complex combination of separate procedures. Therefore, the purpose of this study was to provide a reliable method for reducing analysis complexity and obtaining reproducible results. We implemented a semi-automated measuring system in which different well-known software components for magnetic resonance imaging (MRI) analysis are integrated to obtain reliable measurements of DWI and PWI disturbances in MS. We generated the Diffusion/Perfusion Project (DPP) Suite, in which a series of external software programs are managed and harmonically and hierarchically incorporated by in-house developed Matlab software to perform the following processes: 1) image pre-processing, including imaging data anonymization and conversion from DICOM to Nifti format; 2) co-registration of 2D and 3D non-enhanced and Gd-enhanced T1-weighted images in fluid-attenuated inversion recovery (FLAIR) space; 3) lesion segmentation and classification, in which FLAIR lesions are at first segmented and then categorized according to their presumed evolution; 4) co-registration of segmented FLAIR lesion in T1 space to obtain the FLAIR lesion mask in the T1 space; 5) normal appearing tissue segmentation, in which T1 lesion mask is used to segment basal ganglia/thalami, normal appearing grey matter (NAGM) and normal appearing white matter (NAWM); 6) DWI and PWI map generation; 7) co-registration of basal ganglia/thalami, NAGM, NAWM, DWI and PWI maps in previously segmented FLAIR space; 8) data analysis. All these steps are automatic, except for lesion segmentation and classification. We developed a promising method to limit misclassifications and user errors, providing clinical researchers with a practical and reproducible tool to measure DWI and PWI changes in MS.

  15. Spectral clustering algorithms for ultrasound image segmentation.

    PubMed

    Archip, Neculai; Rohling, Robert; Cooperberg, Peter; Tahmasebpour, Hamid; Warfield, Simon K

    2005-01-01

    Image segmentation algorithms derived from spectral clustering analysis rely on the eigenvectors of the Laplacian of a weighted graph obtained from the image. The NCut criterion was previously used for image segmentation in supervised manner. We derive a new strategy for unsupervised image segmentation. This article describes an initial investigation to determine the suitability of such segmentation techniques for ultrasound images. The extension of the NCut technique to the unsupervised clustering is first described. The novel segmentation algorithm is then performed on simulated ultrasound images. Tests are also performed on abdominal and fetal images with the segmentation results compared to manual segmentation. Comparisons with the classical NCut algorithm are also presented. Finally, segmentation results on other types of medical images are shown.

  16. Turbo segmentation of textured images.

    PubMed

    Lehmann, Frederic

    2011-01-01

    We consider the problem of semi-supervised segmentation of textured images. Existing model-based approaches model the intensity field of textured images as a Gauss-Markov random field to take into account the local spatial dependencies between the pixels. Classical Bayesian segmentation consists of also modeling the label field as a Markov random field to ensure that neighboring pixels correspond to the same texture class with high probability. Well-known relaxation techniques are available which find the optimal label field with respect to the maximum a posteriori or the maximum posterior mode criterion. But, these techniques are usually computationally intensive because they require a large number of iterations to converge. In this paper, we propose a new Bayesian framework by modeling two-dimensional textured images as the concatenation of two one-dimensional hidden Markov autoregressive models for the lines and the columns, respectively. A segmentation algorithm, which is similar to turbo decoding in the context of error-correcting codes, is obtained based on a factor graph approach. The proposed method estimates the unknown parameters using the Expectation-Maximization algorithm.

  17. Segmentation algorithms for ear image data towards biomechanical studies.

    PubMed

    Ferreira, Ana; Gentil, Fernanda; Tavares, João Manuel R S

    2014-01-01

    In recent years, the segmentation, i.e. the identification, of ear structures in video-otoscopy, computerised tomography (CT) and magnetic resonance (MR) image data, has gained significant importance in the medical imaging area, particularly those in CT and MR imaging. Segmentation is the fundamental step of any automated technique for supporting the medical diagnosis and, in particular, in biomechanics studies, for building realistic geometric models of ear structures. In this paper, a review of the algorithms used in ear segmentation is presented. The review includes an introduction to the usually biomechanical modelling approaches and also to the common imaging modalities. Afterwards, several segmentation algorithms for ear image data are described, and their specificities and difficulties as well as their advantages and disadvantages are identified and analysed using experimental examples. Finally, the conclusions are presented as well as a discussion about possible trends for future research concerning the ear segmentation.

  18. Metric Learning to Enhance Hyperspectral Image Segmentation

    NASA Technical Reports Server (NTRS)

    Thompson, David R.; Castano, Rebecca; Bue, Brian; Gilmore, Martha S.

    2013-01-01

    Unsupervised hyperspectral image segmentation can reveal spatial trends that show the physical structure of the scene to an analyst. They highlight borders and reveal areas of homogeneity and change. Segmentations are independently helpful for object recognition, and assist with automated production of symbolic maps. Additionally, a good segmentation can dramatically reduce the number of effective spectra in an image, enabling analyses that would otherwise be computationally prohibitive. Specifically, using an over-segmentation of the image instead of individual pixels can reduce noise and potentially improve the results of statistical post-analysis. In this innovation, a metric learning approach is presented to improve the performance of unsupervised hyperspectral image segmentation. The prototype demonstrations attempt a superpixel segmentation in which the image is conservatively over-segmented; that is, the single surface features may be split into multiple segments, but each individual segment, or superpixel, is ensured to have homogenous mineralogy.

  19. Quantifying the Area at Risk in Reperfused ST-Segment-Elevation Myocardial Infarction Patients Using Hybrid Cardiac Positron Emission Tomography-Magnetic Resonance Imaging.

    PubMed

    Bulluck, Heerajnarain; White, Steven K; Fröhlich, Georg M; Casson, Steven G; O'Meara, Celia; Newton, Ayla; Nicholas, Jennifer; Weale, Peter; Wan, Simon M Y; Sirker, Alex; Moon, James C; Yellon, Derek M; Groves, Ashley; Menezes, Leon; Hausenloy, Derek J

    2016-03-01

    Hybrid positron emission tomography and magnetic resonance allows the advantages of magnetic resonance in tissue characterizing the myocardium to be combined with the unique metabolic insights of positron emission tomography. We hypothesized that the area of reduced myocardial glucose uptake would closely match the area at risk delineated by T2 mapping in ST-segment-elevation myocardial infarction patients. Hybrid positron emission tomography and magnetic resonance using (18)F-fluorodeoxyglucose (FDG) for glucose uptake was performed in 21 ST-segment-elevation myocardial infarction patients at a median of 5 days. Follow-up scans were performed in a subset of patients 12 months later. The area of reduced FDG uptake was significantly larger than the infarct size quantified by late gadolinium enhancement (37.2±11.6% versus 22.3±11.7%; P<0.001) and closely matched the area at risk by T2 mapping (37.2±11.6% versus 36.3±12.2%; P=0.10, R=0.98, bias 0.9±4.4%). On the follow-up scans, the area of reduced FDG uptake was significantly smaller in size when compared with the acute scans (19.5 [6.3%-31.8%] versus 44.0 [21.3%-55.3%]; P=0.002) and closely correlated with the areas of late gadolinium enhancement (R 0.98) with a small bias of 2.0±5.6%. An FDG uptake of ≥45% on the acute scans could predict viable myocardium on the follow-up scan. Both transmural extent of late gadolinium enhancement and FDG uptake on the acute scan performed equally well to predict segmental wall motion recovery. Hybrid positron emission tomography and magnetic resonance in the reperfused ST-segment-elevation myocardial infarction patients showed reduced myocardial glucose uptake within the area at risk and closely matched the area at risk delineated by T2 mapping. FDG uptake, as well as transmural extent of late gadolinium enhancement, acutely can identify viable myocardial segments. © 2016 American Heart Association, Inc.

  20. Multiple images segmentation based on saliency map

    NASA Astrophysics Data System (ADS)

    Ning, XiaoLan; Xu, Cheng; Li, SiQi; Li, ShiYing; Li, ZhiQi

    2017-06-01

    Aiming at discovering and segmenting out common objects from multiple images, co-segmentation is a effective method. It is more accurate to make full use of the relationships between images in segmenting than only single image. The first step is to deal with single image with employing hierarchical segmentation to get a Contour Map, saliency detection to obtain the saliency map and object detection to find the possible common part. Then, constructing a digraph with the multiple local regions, and dealing with the digraph. When a digraph is constructed, the corresponding between adjacent two images is influential to the co-segmentation results. This paper develops a method to sort the images to co-segment. Also, we test the method on ICOSEG and MSRC datasets, and compare it with four proposed method. And the results show that it is efficient in co-segmentation with higher precision than many existing and conventional co-segmentation methods.

  1. Chronic ischemic mitral regurgitation and papillary muscle infarction detected by late gadolinium-enhanced cardiac magnetic resonance imaging in patients with ST-segment elevation myocardial infarction.

    PubMed

    Bouma, Wobbe; Willemsen, Hendrik M; Lexis, Chris P H; Prakken, Niek H; Lipsic, Erik; van Veldhuisen, Dirk J; Mariani, Massimo A; van der Harst, Pim; van der Horst, Iwan C C

    2016-12-01

    Both papillary muscle infarction (PMI) and chronic ischemic mitral regurgitation (CIMR) are associated with reduced survival after myocardial infarction. The influence of PMI on CIMR and factors influencing both entities are incompletely understood. We sought to determine the influence of PMI on CIMR after primary percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI) and to define independent predictors of PMI and CIMR. Between January 2011 and May 2013, 263 patients (mean age 57.8 ± 11.5 years) underwent late gadolinium-enhanced cardiac magnetic resonance imaging and transthoracic echocardiography 4 months after PCI for STEMI. Infarct size, PMI, and mitral valve and left ventricular geometric and functional parameters were assessed. Univariate and multivariate analyses were performed to identify predictors of PMI and CIMR (≥grade 2+). PMI was present in 61 patients (23 %) and CIMR was present in 86 patients (33 %). In patients with PMI, 52 % had CIMR, and in patients without PMI, 27 % had CIMR (P < 0.001). In multivariate analyses, infarct size [odds ratio (OR) 1.09 (95 % confidence interval 1.04-1.13), P < 0.001], inferior MI [OR 4.64 (1.04-20.62), P = 0.044], and circumflex infarct-related artery [OR 8.21 (3.80-17.74), P < 0.001] were independent predictors of PMI. Age [OR 1.08 (1.04-1.11), P < 0.001], infarct size [OR 1.09 (1.03-1.16), P = 0.003], tethering height [OR 19.30 (3.28-113.61), P = 0.001], and interpapillary muscle distance [OR 3.32 (1.31-8.42), P = 0.011] were independent predictors of CIMR. The risk of PMI is mainly associated with inferior infarction and infarction in the circumflex coronary artery. Although the prevalence of CIMR is almost doubled in the presence of PMI, PMI is not an independent predictor of CIMR. Tethering height and interpapillary muscle distance are the strongest independent predictors of CIMR.

  2. Automatic segmentation of cartilage in high-field magnetic resonance images of the knee joint with an improved voxel-classification-driven region-growing algorithm using vicinity-correlated subsampling.

    PubMed

    Öztürk, Ceyda Nur; Albayrak, Songül

    2016-05-01

    Anatomical structures that can deteriorate over time, such as cartilage, can be successfully delineated with voxel-classification approaches in magnetic resonance (MR) images. However, segmentation via voxel-classification is a computationally demanding process for high-field MR images with high spatial resolutions. In this study, the whole femoral, tibial, and patellar cartilage compartments in the knee joint were automatically segmented in high-field MR images obtained from Osteoarthritis Initiative using a voxel-classification-driven region-growing algorithm with sample-expand method. Computational complexity of the classification was alleviated via subsampling of the background voxels in the training MR images and selecting a small subset of significant features by taking into consideration systems with limited memory and processing power. Although subsampling of the voxels may lead to a loss of generality of the training models and a decrease in segmentation accuracies, effective subsampling strategies can overcome these problems. Therefore, different subsampling techniques, which involve uniform, Gaussian, vicinity-correlated (VC) sparse, and VC dense subsampling, were used to generate four training models. The segmentation system was experimented using 10 training and 23 testing MR images, and the effects of different training models on segmentation accuracies were investigated. Experimental results showed that the highest mean Dice similarity coefficient (DSC) values for all compartments were obtained when the training models of VC sparse subsampling technique were used. Mean DSC values optimized with this technique were 82.6%, 83.1%, and 72.6% for femoral, tibial, and patellar cartilage compartments, respectively, when mean sensitivities were 79.9%, 84.0%, and 71.5%, and mean specificities were 99.8%, 99.9%, and 99.9%.

  3. Stepped Impedance Resonators for High Field Magnetic Resonance Imaging

    PubMed Central

    Akgun, Can E.; DelaBarre, Lance; Yoo, Hyoungsuk; Sohn, Sung-Min; Snyder, Carl J.; Adriany, Gregor; Ugurbil, Kamil; Gopinath, Anand; Vaughan, J. Thomas

    2014-01-01

    Multi-element volume radio-frequency (RF) coils are an integral aspect of the growing field of high field magnetic resonance imaging (MRI). In these systems, a popular volume coil of choice has become the transverse electromagnetic (TEM) multi-element transceiver coil consisting of microstrip resonators. In this paper, to further advance this design approach, a new microstrip resonator strategy in which the transmission line is segmented into alternating impedance sections referred to as stepped impedance resonators (SIRs) is investigated. Single element simulation results in free space and in a phantom at 7 tesla (298 MHz) demonstrate the rationale and feasibility of the SIR design strategy. Simulation and image results at 7 tesla in a phantom and human head illustrate the improvements in transmit magnetic field, as well as, RF efficiency (transmit magnetic field versus SAR) when two different SIR designs are incorporated in 8-element volume coil configurations and compared to a volume coil consisting of microstrip elements. PMID:23508243

  4. Stepped impedance resonators for high-field magnetic resonance imaging.

    PubMed

    Akgun, Can E; DelaBarre, Lance; Yoo, Hyoungsuk; Sohn, Sung-Min; Snyder, Carl J; Adriany, Gregor; Ugurbil, Kamil; Gopinath, Anand; Vaughan, J Thomas

    2014-02-01

    Multi-element volume radio-frequency (RF) coils are an integral aspect of the growing field of high-field magnetic resonance imaging. In these systems, a popular volume coil of choice has become the transverse electromagnetic (TEM) transceiver coil consisting of microstrip resonators. In this paper, to further advance this design approach, a new microstrip resonator strategy in which the transmission line is segmented into alternating impedance sections, referred to as stepped impedance resonators (SIRs), is investigated. Single-element simulation results in free space and in a phantom at 7 T (298 MHz) demonstrate the rationale and feasibility of the SIR design strategy. Simulation and image results at 7 T in a phantom and human head illustrate the improvements in a transmit magnetic field, as well as RF efficiency (transmit magnetic field versus specific absorption rate) when two different SIR designs are incorporated in 8-element volume coil configurations and compared to a volume coil consisting of microstrip elements.

  5. Liver segmentation in color images (Conference Presentation)

    NASA Astrophysics Data System (ADS)

    Ma, Burton; Kingham, T. Peter; Miga, Michael I.; Jarnagin, William R.; Simpson, Amber L.

    2017-03-01

    We describe the use of a deep learning method for semantic segmentation of the liver from color images. Our intent is to eventually embed a semantic segmentation method into a stereo-vision based navigation system for open liver surgery. Semantic segmentation of the stereo images will allow us to reconstruct a point cloud containing the liver surfaces and excluding all other non-liver structures. We trained a deep learning algorithm using 136 images and 272 augmented images computed by rotating the original images. We tested the trained algorithm on 27 images that were not used for training purposes. The method achieves an 88% median pixel labeling accuracy over the test images.

  6. Hierarchical image segmentation for learning object priors

    SciTech Connect

    Prasad, Lakshman; Yang, Xingwei; Latecki, Longin J; Li, Nan

    2010-11-10

    The proposed segmentation approach naturally combines experience based and image based information. The experience based information is obtained by training a classifier for each object class. For a given test image, the result of each classifier is represented as a probability map. The final segmentation is obtained with a hierarchial image segmentation algorithm that considers both the probability maps and the image features such as color and edge strength. We also utilize image region hierarchy to obtain not only local but also semi-global features as input to the classifiers. Moreover, to get robust probability maps, we take into account the region context information by averaging the probability maps over different levels of the hierarchical segmentation algorithm. The obtained segmentation results are superior to the state-of-the-art supervised image segmentation algorithms.

  7. Segmentation of brain magnetic resonance images based on multi-atlas likelihood fusion: testing using data with a broad range of anatomical and photometric profiles

    PubMed Central

    Tang, Xiaoying; Crocetti, Deana; Kutten, Kwame; Ceritoglu, Can; Albert, Marilyn S.; Mori, Susumu; Mostofsky, Stewart H.; Miller, Michael I.

    2015-01-01

    We propose a hierarchical pipeline for skull-stripping and segmentation of anatomical structures of interest from T1-weighted images of the human brain. The pipeline is constructed based on a two-level Bayesian parameter estimation algorithm called multi-atlas likelihood fusion (MALF). In MALF, estimation of the parameter of interest is performed via maximum a posteriori estimation using the expectation-maximization (EM) algorithm. The likelihoods of multiple atlases are fused in the E-step while the optimal estimator, a single maximizer of the fused likelihoods, is then obtained in the M-step. There are two stages in the proposed pipeline; first the input T1-weighted image is automatically skull-stripped via a fast MALF, then internal brain structures of interest are automatically extracted using a regular MALF. We assess the performance of each of the two modules in the pipeline based on two sets of images with markedly different anatomical and photometric contrasts; 3T MPRAGE scans of pediatric subjects with developmental disorders vs. 1.5T SPGR scans of elderly subjects with dementia. Evaluation is performed quantitatively using the Dice overlap as well as qualitatively via visual inspections. As a result, we demonstrate subject-level differences in the performance of the proposed pipeline, which may be accounted for by age, diagnosis, or the imaging parameters (particularly the field strength). For the subcortical and ventricular structures of the two datasets, the hierarchical pipeline is capable of producing automated segmentations with Dice overlaps ranging from 0.8 to 0.964 when compared with the gold standard. Comparisons with other representative segmentation algorithms are presented, relative to which the proposed hierarchical pipeline demonstrates comparative or superior accuracy. PMID:25784852

  8. Image Segmentation Using Hierarchical Merge Tree.

    PubMed

    Liu, Ting; Seyedhosseini, Mojtaba; Tasdizen, Tolga

    2016-07-18

    This paper investigates one of the most fundamental computer vision problems: image segmentation. We propose a supervised hierarchical approach to object-independent image segmentation. Starting with over-segmenting superpixels, we use a tree structure to represent the hierarchy of region merging, by which we reduce the problem of segmenting image regions to finding a set of label assignment to tree nodes. We formulate the tree structure as a constrained conditional model to associate region merging with likelihoods predicted using an ensemble boundary classifier. Final segmentations can then be inferred by finding globally optimal solutions to the model efficiently. We also present an iterative training and testing algorithm that generates various tree structures and combines them to emphasize accurate boundaries by segmentation accumulation. Experiment results and comparisons with other recent methods on six public data sets demonstrate that our approach achieves state-of-the-art region accuracy and is competitive in image segmentation without semantic priors.

  9. Core Recursive Hierarchical Image Segmentation

    NASA Technical Reports Server (NTRS)

    Tilton, James

    2011-01-01

    The Recursive Hierarchical Image Segmentation (RHSEG) software has been repackaged to provide a version of the RHSEG software that is not subject to patent restrictions and that can be released to the general public through NASA GSFC's Open Source release process. Like the Core HSEG Software Package, this Core RHSEG Software Package also includes a visualization program called HSEGViewer along with a utility program HSEGReader. It also includes an additional utility program called HSEGExtract. The unique feature of the Core RHSEG package is that it is a repackaging of the RHSEG technology designed to specifically avoid the inclusion of the certain software technology. Unlike the Core HSEG package, it includes the recursive portions of the technology, but does not include processing window artifact elimination technology.

  10. Segmentation in dermatological hyperspectral images: dedicated methods.

    PubMed

    Koprowski, Robert; Olczyk, Paweł

    2016-08-17

    Segmentation of hyperspectral medical images is one of many image segmentation methods which require profiling. This profiling involves either the adjustment of existing, known image segmentation methods or a proposal of new dedicated methods of hyperspectral image segmentation. Taking into consideration the size of analysed data, the time of analysis is of major importance. Therefore, the authors proposed three new dedicated methods of hyperspectral image segmentation with special reference to the time of analysis. The segmentation methods presented in this paper were tested and profiled to the images acquired from different hyperspectral cameras including SOC710 Hyperspectral Imaging System, Specim sCMOS-50-V10E. Correct functioning of the method was tested for over 10,000 2D images constituting the sequence of over 700 registrations of the areas of the left and right hand and the forearm. As a result, three new methods of hyperspectral image segmentation have been proposed: fast analysis of emissivity curves (SKE), 3D segmentation (S3D) and hierarchical segmentation (SH). They have the following features: are fully automatic; allow for implementation of fast segmentation methods; are profiled to hyperspectral image segmentation; use emissivity curves in the model form, can be applied in any type of objects not necessarily biological ones, are faster (SKE-2.3 ms, S3D-1949 ms, SH-844 ms for the computer with Intel(®) Core i7 4960X CPU 3.6 GHz) and more accurate (SKE-accuracy 79 %, S3D-90 %, SH-92 %) in comparison with typical methods known from the literature. Profiling and/or proposing new methods of hyperspectral image segmentation is an indispensable element of developing software. This ensures speed, repeatability and low sensitivity of the algorithm to changing parameters.

  11. Intelligent multi-spectral IR image segmentation

    NASA Astrophysics Data System (ADS)

    Lu, Thomas; Luong, Andrew; Heim, Stephen; Patel, Maharshi; Chen, Kang; Chao, Tien-Hsin; Chow, Edward; Torres, Gilbert

    2017-05-01

    This article presents a neural network based multi-spectral image segmentation method. A neural network is trained on the selected features of both the objects and background in the longwave (LW) Infrared (IR) images. Multiple iterations of training are performed until the accuracy of the segmentation reaches satisfactory level. The segmentation boundary of the LW image is used to segment the midwave (MW) and shortwave (SW) IR images. A second neural network detects the local discontinuities and refines the accuracy of the local boundaries. This article compares the neural network based segmentation method to the Wavelet-threshold and Grab-Cut methods. Test results have shown increased accuracy and robustness of this segmentation scheme for multi-spectral IR images.

  12. Livewire based single still image segmentation

    NASA Astrophysics Data System (ADS)

    Zhang, Jun; Yang, Rong; Liu, Xiaomao; Yue, Hao; Zhu, Hao; Tian, Dandan; Chen, Shu; Li, Yiquan; Tian, Jinwen

    2011-11-01

    In the application of the video contactless measurement, the quality of the image taken from underwater is not very well. It is well known that automatic image segmental method cannot provide acceptable segmentation result with low quality single still image. Snake algorithm can provide better result in this case with the aiding of human. However, sometimes the segmental result of Snake may far from the initial segmental contour drawn by user. Livewire algorithm can keep the location of the seed points that user selected nailed from the beginning to the end. But the contour may have burrs when the image's noise is quite high and the contrast is low. In this paper, we modified the cost function of Livewire algorithm and proposed a new segmentation method that can be used for single still image segmentation with high noise and low contrast.

  13. Colour application on mammography image segmentation

    NASA Astrophysics Data System (ADS)

    Embong, R.; Aziz, N. M. Nik Ab.; Karim, A. H. Abd; Ibrahim, M. R.

    2017-09-01

    The segmentation process is one of the most important steps in image processing and computer vision since it is vital in the initial stage of image analysis. Segmentation of medical images involves complex structures and it requires precise segmentation result which is necessary for clinical diagnosis such as the detection of tumour, oedema, and necrotic tissues. Since mammography images are grayscale, researchers are looking at the effect of colour in the segmentation process of medical images. Colour is known to play a significant role in the perception of object boundaries in non-medical colour images. Processing colour images require handling more data, hence providing a richer description of objects in the scene. Colour images contain ten percent (10%) additional edge information as compared to their grayscale counterparts. Nevertheless, edge detection in colour image is more challenging than grayscale image as colour space is considered as a vector space. In this study, we implemented red, green, yellow, and blue colour maps to grayscale mammography images with the purpose of testing the effect of colours on the segmentation of abnormality regions in the mammography images. We applied the segmentation process using the Fuzzy C-means algorithm and evaluated the percentage of average relative error of area for each colour type. The results showed that all segmentation with the colour map can be done successfully even for blurred and noisy images. Also the size of the area of the abnormality region is reduced when compare to the segmentation area without the colour map. The green colour map segmentation produced the smallest percentage of average relative error (10.009%) while yellow colour map segmentation gave the largest percentage of relative error (11.367%).

  14. Cardiovascular Magnetic Resonance Imaging

    NASA Astrophysics Data System (ADS)

    Pelc, Norbert

    2000-03-01

    Cardiovascular diseases are a major source of morbidity and mortality in the United States. Early detection of disease can often be used to improved outcomes, either through direct interventions (e.g. surgical corrections) or by causing the patient to modify his or her behavior (e.g. smoking cessation or dietary changes). Ideally, the detection process should be noninvasive (i.e. it should not be associated with significant risk). Magnetic Resonance Imaging (MRI) refers to the formation of images by localizing NMR signals, typically from protons in the body. As in other applications of NMR, a homogeneous static magnetic field ( ~0.5 to 4 T) is used to create ``longitudinal" magnetization. A magnetic field rotating at the Larmor frequency (proportional to the static field) excites spins, converting longitudinal magnetization to ``transverse" magnetization and generating a signal. Localization is performed using pulsed gradients in the static field. MRI can produce images of 2-D slices, 3-D volumes, time-resolved images of pseudo-periodic phenomena such as heart function, and even real-time imaging. It is also possible to acquire spatially localized NMR spectra. MRI has a number of advantages, but perhaps the most fundamental is the richness of the contrast mechanisms. Tissues can be differentiated by differences in proton density, NMR properties, and even flow or motion. We also have the ability to introduce substances that alter NMR signals. These contrast agents can be used to enhance vascular structures and measure perfusion. Cardiovascular MRI allows the reliable diagnosis of important conditions. It is possible to image the blood vessel tree, quantitate flow and perfusion, and image cardiac contraction. Fundamentally, the power of MRI as a diagnostic tool stems from the richness of the contrast mechanisms and the flexibility in control of imaging parameters.

  15. Iris image segmentation based on phase congruency

    NASA Astrophysics Data System (ADS)

    Gao, Chao; Jiang, Da-Qin; Guo, Yong-Cai

    2006-09-01

    Iris image segmentation is very important for an iris recognition system. There are always iris noises as eyelash, eyelid, reflection and pupil in iris images. The paper proposes a virtual method of segmentation. By locating and normalizing iris images with Gabor filter, we can acquire information of image texture in a series of frequencies and orientations. Iris noise regions are determined based on phase congruency by a group of Gabor filters whose kernels are suitable for edge detection. These regions are filled according to the characteristics of iris noise. The experimental results show that the proposed method can segment iris images effectively.

  16. Breast ultrasound image segmentation: a survey.

    PubMed

    Huang, Qinghua; Luo, Yaozhong; Zhang, Qiangzhi

    2017-03-01

    Breast cancer is the most common form of cancer among women worldwide. Ultrasound imaging is one of the most frequently used diagnostic tools to detect and classify abnormalities of the breast. Recently, computer-aided diagnosis (CAD) systems using ultrasound images have been developed to help radiologists to increase diagnosis accuracy. However, accurate ultrasound image segmentation remains a challenging problem due to various ultrasound artifacts. In this paper, we investigate approaches developed for breast ultrasound (BUS) image segmentation. In this paper, we reviewed the literature on the segmentation of BUS images according to the techniques adopted, especially over the past 10 years. By dividing into seven classes (i.e., thresholding-based, clustering-based, watershed-based, graph-based, active contour model, Markov random field and neural network), we have introduced corresponding techniques and representative papers accordingly. We have summarized and compared many techniques on BUS image segmentation and found that all these techniques have their own pros and cons. However, BUS image segmentation is still an open and challenging problem due to various ultrasound artifacts introduced in the process of imaging, including high speckle noise, low contrast, blurry boundaries, low signal-to-noise ratio and intensity inhomogeneity CONCLUSIONS: To the best of our knowledge, this is the first comprehensive review of the approaches developed for segmentation of BUS images. With most techniques involved, this paper will be useful and helpful for researchers working on segmentation of ultrasound images, and for BUS CAD system developers.

  17. Metric Learning for Hyperspectral Image Segmentation

    NASA Technical Reports Server (NTRS)

    Bue, Brian D.; Thompson, David R.; Gilmore, Martha S.; Castano, Rebecca

    2011-01-01

    We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use tasks-agnostic measures of similarity. Here we learn task-specific similarity measures from training data, improving segment fidelity to classes of interest. Multiclass Linear Discriminate Analysis produces a linear transform that optimally separates a labeled set of training classes. The defines a distance metric that generalized to a new scenes, enabling graph-based segmentation that emphasizes key spectral features. We describe tests based on data from the Compact Reconnaissance Imaging Spectrometer (CRISM) in which learned metrics improve segment homogeneity with respect to mineralogical classes.

  18. Iterative Vessel Segmentation of Fundus Images.

    PubMed

    Roychowdhury, Sohini; Koozekanani, Dara D; Parhi, Keshab K

    2015-07-01

    This paper presents a novel unsupervised iterative blood vessel segmentation algorithm using fundus images. First, a vessel enhanced image is generated by tophat reconstruction of the negative green plane image. An initial estimate of the segmented vasculature is extracted by global thresholding the vessel enhanced image. Next, new vessel pixels are identified iteratively by adaptive thresholding of the residual image generated by masking out the existing segmented vessel estimate from the vessel enhanced image. The new vessel pixels are, then, region grown into the existing vessel, thereby resulting in an iterative enhancement of the segmented vessel structure. As the iterations progress, the number of false edge pixels identified as new vessel pixels increases compared to the number of actual vessel pixels. A key contribution of this paper is a novel stopping criterion that terminates the iterative process leading to higher vessel segmentation accuracy. This iterative algorithm is robust to the rate of new vessel pixel addition since it achieves 93.2-95.35% vessel segmentation accuracy with 0.9577-0.9638 area under ROC curve (AUC) on abnormal retinal images from the STARE dataset. The proposed algorithm is computationally efficient and consistent in vessel segmentation performance for retinal images with variations due to pathology, uneven illumination, pigmentation, and fields of view since it achieves a vessel segmentation accuracy of about 95% in an average time of 2.45, 3.95, and 8 s on images from three public datasets DRIVE, STARE, and CHASE_DB1, respectively. Additionally, the proposed algorithm has more than 90% segmentation accuracy for segmenting peripapillary blood vessels in the images from the DRIVE and CHASE_DB1 datasets.

  19. Segmentation by surface-to-image registration

    NASA Astrophysics Data System (ADS)

    Xie, Zhiyong; Tamez-Pena, Jose; Gieseg, Michael; Liachenko, Serguei; Dhamija, Shantanu; Chiao, Ping

    2006-03-01

    This paper presents a new image segmentation algorithm using surface-to-image registration. The algorithm employs multi-level transformations and multi-resolution image representations to progressively register atlas surfaces (modeling anatomical structures) to subject images based on weighted external forces in which weights and forces are determined by gradients and local intensity profiles obtained from images. The algorithm is designed to prevent atlas surfaces converging to unintended strong edges or leaking out of structures of interest through weak edges where the image contrast is low. Segmentation of bone structures on MR images of rat knees analyzed in this manner performs comparably to technical experts using a semi-automatic tool.

  20. Partially orthogonal resonators for magnetic resonance imaging

    NASA Astrophysics Data System (ADS)

    Chacon-Caldera, Jorge; Malzacher, Matthias; Schad, Lothar R.

    2017-02-01

    Resonators for signal reception in magnetic resonance are traditionally planar to restrict coil material and avoid coil losses. Here, we present a novel concept to model resonators partially in a plane with maximum sensitivity to the magnetic resonance signal and partially in an orthogonal plane with reduced signal sensitivity. Thus, properties of individual elements in coil arrays can be modified to optimize physical planar space and increase the sensitivity of the overall array. A particular case of the concept is implemented to decrease H-field destructive interferences in planar concentric in-phase arrays. An increase in signal to noise ratio of approximately 20% was achieved with two resonators placed over approximately the same planar area compared to common approaches at a target depth of 10 cm at 3 Tesla. Improved parallel imaging performance of this configuration is also demonstrated. The concept can be further used to increase coil density.

  1. Partially orthogonal resonators for magnetic resonance imaging

    PubMed Central

    Chacon-Caldera, Jorge; Malzacher, Matthias; Schad, Lothar R.

    2017-01-01

    Resonators for signal reception in magnetic resonance are traditionally planar to restrict coil material and avoid coil losses. Here, we present a novel concept to model resonators partially in a plane with maximum sensitivity to the magnetic resonance signal and partially in an orthogonal plane with reduced signal sensitivity. Thus, properties of individual elements in coil arrays can be modified to optimize physical planar space and increase the sensitivity of the overall array. A particular case of the concept is implemented to decrease H-field destructive interferences in planar concentric in-phase arrays. An increase in signal to noise ratio of approximately 20% was achieved with two resonators placed over approximately the same planar area compared to common approaches at a target depth of 10 cm at 3 Tesla. Improved parallel imaging performance of this configuration is also demonstrated. The concept can be further used to increase coil density. PMID:28186135

  2. Partially orthogonal resonators for magnetic resonance imaging.

    PubMed

    Chacon-Caldera, Jorge; Malzacher, Matthias; Schad, Lothar R

    2017-02-10

    Resonators for signal reception in magnetic resonance are traditionally planar to restrict coil material and avoid coil losses. Here, we present a novel concept to model resonators partially in a plane with maximum sensitivity to the magnetic resonance signal and partially in an orthogonal plane with reduced signal sensitivity. Thus, properties of individual elements in coil arrays can be modified to optimize physical planar space and increase the sensitivity of the overall array. A particular case of the concept is implemented to decrease H-field destructive interferences in planar concentric in-phase arrays. An increase in signal to noise ratio of approximately 20% was achieved with two resonators placed over approximately the same planar area compared to common approaches at a target depth of 10 cm at 3 Tesla. Improved parallel imaging performance of this configuration is also demonstrated. The concept can be further used to increase coil density.

  3. Adaptive textural segmentation of medical images

    NASA Astrophysics Data System (ADS)

    Kuklinski, Walter S.; Frost, Gordon S.; MacLaughlin, Thomas

    1992-06-01

    A number of important problems in medical imaging can be described as segmentation problems. Previous fractal-based image segmentation algorithms have used either the local fractal dimension alone or the local fractal dimension and the corresponding image intensity as features for subsequent pattern recognition algorithms. An image segmentation algorithm that utilized the local fractal dimension, image intensity, and the correlation coefficient of the local fractal dimension regression analysis computation, to produce a three-dimension feature space that was partitioned to identify specific pixels of dental radiographs as being either bone, teeth, or a boundary between bone and teeth also has been reported. In this work we formulated the segmentation process as a configurational optimization problem and discuss the application of simulated annealing optimization methods to the solution of this specific optimization problem. The configurational optimization method allows information about both, the degree of correspondence between a candidate segment and an assumed textural model, and morphological information about the candidate segment to be used in the segmentation process. To apply this configurational optimization technique with a fractal textural model however, requires the estimation of the fractal dimension of an irregularly shaped candidate segment. The potential utility of a discrete Gerchberg-Papoulis bandlimited extrapolation algorithm to the estimation of the fractal dimension of an irregularly shaped candidate segment is also discussed.

  4. Comparison of automated and manual segmentation of hippocampus MR images

    NASA Astrophysics Data System (ADS)

    Haller, John W.; Christensen, Gary E.; Miller, Michael I.; Joshi, Sarang C.; Gado, Mokhtar; Csernansky, John G.; Vannier, Michael W.

    1995-05-01

    The precision and accuracy of area estimates from magnetic resonance (MR) brain images and using manual and automated segmentation methods are determined. Areas of the human hippocampus were measured to compare a new automatic method of segmentation with regions of interest drawn by an expert. MR images of nine normal subjects and nine schizophrenic patients were acquired with a 1.5-T unit (Siemens Medical Systems, Inc., Iselin, New Jersey). From each individual MPRAGE 3D volume image a single comparable 2-D slice (matrix equals 256 X 256) was chosen which corresponds to the same coronal slice of the hippocampus. The hippocampus was first manually segmented, then segmented using high dimensional transformations of a digital brain atlas to individual brain MR images. The repeatability of a trained rater was assessed by comparing two measurements from each individual subject. Variability was also compared within and between subject groups of schizophrenics and normal subjects. Finally, the precision and accuracy of automated segmentation of hippocampal areas were determined by comparing automated measurements to manual segmentation measurements made by the trained rater on MR and brain slice images. The results demonstrate the high repeatability of area measurement from MR images of the human hippocampus. Automated segmentation using high dimensional transformations from a digital brain atlas provides repeatability superior to that of manual segmentation. Furthermore, the validity of automated measurements was demonstrated by a high correlation with manual segmentation measurements made by a trained rater. Quantitative morphometry of brain substructures (e.g. hippocampus) is feasible by use of a high dimensional transformation of a digital brain atlas to an individual MR image. This method automates the search for neuromorphological correlates of schizophrenia by a new mathematically robust method with unprecedented sensitivity to small local and regional differences.

  5. A Unified Framework for Brain Segmentation in MR Images

    PubMed Central

    Yazdani, S.; Yusof, R.; Karimian, A.; Riazi, A. H.; Bennamoun, M.

    2015-01-01

    Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets. PMID:26089978

  6. Improving image segmentation by learning region affinities

    SciTech Connect

    Prasad, Lakshman; Yang, Xingwei; Latecki, Longin J

    2010-11-03

    We utilize the context information of other regions in hierarchical image segmentation to learn new regions affinities. It is well known that a single choice of quantization of an image space is highly unlikely to be a common optimal quantization level for all categories. Each level of quantization has its own benefits. Therefore, we utilize the hierarchical information among different quantizations as well as spatial proximity of their regions. The proposed affinity learning takes into account higher order relations among image regions, both local and long range relations, making it robust to instabilities and errors of the original, pairwise region affinities. Once the learnt affinities are obtained, we use a standard image segmentation algorithm to get the final segmentation. Moreover, the learnt affinities can be naturally unutilized in interactive segmentation. Experimental results on Berkeley Segmentation Dataset and MSRC Object Recognition Dataset are comparable and in some aspects better than the state-of-art methods.

  7. Regression Segmentation for M³ Spinal Images.

    PubMed

    Wang, Zhijie; Zhen, Xiantong; Tay, KengYeow; Osman, Said; Romano, Walter; Li, Shuo

    2015-08-01

    Clinical routine often requires to analyze spinal images of multiple anatomic structures in multiple anatomic planes from multiple imaging modalities (M(3)). Unfortunately, existing methods for segmenting spinal images are still limited to one specific structure, in one specific plane or from one specific modality (S(3)). In this paper, we propose a novel approach, Regression Segmentation, that is for the first time able to segment M(3) spinal images in one single unified framework. This approach formulates the segmentation task innovatively as a boundary regression problem: modeling a highly nonlinear mapping function from substantially diverse M(3) images directly to desired object boundaries. Leveraging the advancement of sparse kernel machines, regression segmentation is fulfilled by a multi-dimensional support vector regressor (MSVR) which operates in an implicit, high dimensional feature space where M(3) diversity and specificity can be systematically categorized, extracted, and handled. The proposed regression segmentation approach was thoroughly tested on images from 113 clinical subjects including both disc and vertebral structures, in both sagittal and axial planes, and from both MRI and CT modalities. The overall result reaches a high dice similarity index (DSI) 0.912 and a low boundary distance (BD) 0.928 mm. With our unified and expendable framework, an efficient clinical tool for M(3) spinal image segmentation can be easily achieved, and will substantially benefit the diagnosis and treatment of spinal diseases.

  8. From MIP image to MRA segmentation using fuzzy set theory.

    PubMed

    Vermandel, Maximilien; Betrouni, Nacim; Taschner, Christian; Vasseur, Christian; Rousseau, Jean

    2007-04-01

    The aim of this paper is to describe a semi-automatic method of segmentation in magnetic resonance angiography (MRA). This method, based on fuzzy set theory, uses the information (gray levels) contained in the maximum intensity projection (MIP) image to segment the 3D vascular structure from slices. Tests have been carried out on vascular phantom and on clinical MRA images. This 3D segmentation method has proved to be satisfactory for the detection of vascular structures even for very complex shapes. Finally, this MIP-based approach is semi-automatic and produces a robust segmentation thanks to the contrast-to-noise ratio and to the slice profile which are taken into account to determine the membership of a voxel to the vascular structure.

  9. An efficient neural network based method for medical image segmentation.

    PubMed

    Torbati, Nima; Ayatollahi, Ahmad; Kermani, Ali

    2014-01-01

    The aim of this research is to propose a new neural network based method for medical image segmentation. Firstly, a modified self-organizing map (SOM) network, named moving average SOM (MA-SOM), is utilized to segment medical images. After the initial segmentation stage, a merging process is designed to connect the objects of a joint cluster together. A two-dimensional (2D) discrete wavelet transform (DWT) is used to build the input feature space of the network. The experimental results show that MA-SOM is robust to noise and it determines the input image pattern properly. The segmentation results of breast ultrasound images (BUS) demonstrate that there is a significant correlation between the tumor region selected by a physician and the tumor region segmented by our proposed method. In addition, the proposed method segments X-ray computerized tomography (CT) and magnetic resonance (MR) head images much better than the incremental supervised neural network (ISNN) and SOM-based methods.

  10. Image segmentation on a quantum computer

    NASA Astrophysics Data System (ADS)

    Caraiman, Simona; Manta, Vasile I.

    2015-05-01

    In this paper, we address the field of quantum information processing and analyze the prospects of applying quantum computation concepts to image processing tasks. Specifically, we discuss the development of a quantum version for the image segmentation operation. This is an important technique that comes up in many image processing applications. We consider the threshold-based segmentation and show that a quantum circuit to achieve this operation can be built using a quantum oracle that implements the thresholding function. We discuss the circuit implementation of the oracle operator and provide examples of segmenting synthetic and real images. The main advantage of the quantum version for image segmentation over the classical approach is its speedup and is provided by the special properties of quantum information processing: superposition of states and inherent parallelism.

  11. Image Segmentation Using Affine Wavelets

    DTIC Science & Technology

    1991-12-12

    2-4 2.7 Artificial Neural Network Radial Basis Function Classifiers . . . 2-5 2.7.1 Localized Receptive Fields...used a radial 1-3 basis function artificial neural network, which accomplishes the segmentation based on the multiresolution features. 1.7 Objectives...as the features? "* Can the Radial Basis Function (RBF) artificial neural network be trained to au- tonomously segment SAR and FLIR imagery using the

  12. SAR Image Segmentation Using Morphological Attribute Profiles

    NASA Astrophysics Data System (ADS)

    Boldt, M.; Thiele, A.; Schulz, K.; Hinz, S.

    2014-08-01

    In the last years, the spatial resolution of remote sensing sensors and imagery has continuously improved. Focusing on spaceborne Synthetic Aperture Radar (SAR) sensors, the satellites of the current generation (TerraSAR-X, COSMO-SykMed) are able to acquire images with sub-meter resolution. Indeed, high resolution imagery is visually much better interpretable, but most of the established pixel-based analysis methods have become more or less impracticable since, in high resolution images, self-sufficient objects (vehicle, building) are represented by a large number of pixels. Methods dealing with Object-Based Image Analysis (OBIA) provide help. Objects (segments) are groupings of pixels resulting from image segmentation algorithms based on homogeneity criteria. The image set is represented by image segments, which allows the development of rule-based analysis schemes. For example, segments can be described or categorized by their local neighborhood in a context-based manner. In this paper, a novel method for the segmentation of high resolution SAR images is presented. It is based on the calculation of morphological differential attribute profiles (DAP) which are analyzed pixel-wise in a region growing procedure. The method distinguishes between heterogeneous and homogeneous image content and delivers a precise segmentation result.

  13. Contour detection and hierarchical image segmentation.

    PubMed

    Arbeláez, Pablo; Maire, Michael; Fowlkes, Charless; Malik, Jitendra

    2011-05-01

    This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization framework based on spectral clustering. Our segmentation algorithm consists of generic machinery for transforming the output of any contour detector into a hierarchical region tree. In this manner, we reduce the problem of image segmentation to that of contour detection. Extensive experimental evaluation demonstrates that both our contour detection and segmentation methods significantly outperform competing algorithms. The automatically generated hierarchical segmentations can be interactively refined by user-specified annotations. Computation at multiple image resolutions provides a means of coupling our system to recognition applications.

  14. Identification of High-Risk Patients with Non-ST Segment Elevation Myocardial Infarction using Strain Doppler Echocardiography: Correlation with Cardiac Magnetic Resonance Imaging

    PubMed Central

    Loutfi, Mohamed; Ashour, Sanaa; El-Sharkawy, Eman; El-Fawal, Sara; El-Touny, Karim

    2016-01-01

    Assessment of left ventricular (LV) function is important for decision-making and risk stratification in patients with acute coronary syndrome. Many patients with non-ST segment elevation myocardial infarction (NSTEMI) have substantial infarction, but these patients often do not reveal clinical signs of instability, and they rarely fulfill criteria for acute revascularization therapy. AIM This study evaluated the potential of strain Doppler echocardiography analysis for the assessment of LV infarct size when compared with standard two-dimensional echo and cardiac magnetic resonance (CMR) data. METHODS Thirty patients with NSTEMI were examined using echocardiography after hospitalization for 1.8 ± 1.1 days for the assessment of left ventricular ejection fraction, wall motion score index (WMSI), and LV global longitudinal strain (GLS). Infarct size was assessed using delayed enhancement CMR 6.97 ± 3.2 days after admission as a percentage of total myocardial volume. RESULTS GLS was performed in 30 patients, and 82.9% of the LV segments were accepted for GLS analysis. Comparisons between patients with a complete set of GLS and standard echo, GLS and CMR were performed. The linear relationship demonstrated moderately strong and significant associations between GLS and ejection fraction (EF) as determined using standard echo (r = 0.452, P = 0.012), WMSI (r = 0.462, P = 0.010), and the gold standard CMR-determined EF (r = 0.57, P < 0.001). Receiver operating characteristic curves were used to analyze the ability of GLS to evaluate infarct size. GLS was the best predictor of infarct size in a multivariate linear regression analysis (β = 1.51, P = 0.027). WMSI >1.125 and a GLS cutoff value of −11.29% identified patients with substantial infarction (≥12% of total myocardial volume measured using CMR) with accuracies of 76.7% and 80%, respectively. However, GLS remained the only independent predictor in a multivariate logistic regression analysis to identify an infarct

  15. Chromosome image segmentation on PAL parallel image processor

    NASA Astrophysics Data System (ADS)

    Shi, Hongchi; Gader, Paul D.; Li, Hongzheng

    1997-09-01

    Chromosome image segmentation is an important step toward automatic karyotyping that involves visualization and interpretation of chromosomes. In this paper, we analyze the characteristics of chromosome images that can be effectively used for segmenting chromosomes and can be efficiently extracted on the Lockheed-Martin PAL parallel image processor. We design and implement a parallel algorithm that uses local features to split touching chromosomes.

  16. Convergent Coarseness Regulation for Segmented Images

    SciTech Connect

    Paglieroni, D W

    2004-05-27

    In segmentation of remotely sensed images, the number of pixel classes and their spectral representations are often unknown a priori. Even with prior knowledge, pixels with spectral components from multiple classes lead to classification errors and undesired small region artifacts. Coarseness regulation for segmented images is proposed as an efficient novel technique for handling these problems. Beginning with an over-segmented image, perceptually similar connected regions are iteratively merged using a method reminiscent of region growing, except the primitives are regions, not pixels. Interactive coarseness regulation is achieved by specifying the area {alpha} of the largest region eligible for merging. A region with area less than {alpha} is merged with the most spectrally similar connected region, unless the regions are perceived as spectrally dissimilar. In convergent coarseness regulation, which requires no user interaction, {alpha} is specified as the total number of pixels in the image, and the coarseness regulation output converges to a steady-state segmentation that remains unchanged as {alpha} is further increased. By applying convergent coarseness regulation to AVIRIS, IKONOS and DigitalGlobe images, and quantitatively comparing computer-generated segmentations to segmentations generated manually by a human analyst, it was found that the quality of the input segmentations was consistently and dramatically improved.

  17. Automatic bone segmentation and alignment from MR knee images

    NASA Astrophysics Data System (ADS)

    Shan, Liang; Zach, Christopher; Styner, Martin; Charles, Cecil; Niethammer, Marc

    2010-03-01

    Automatic image analysis of magnetic resonance (MR) images of the knee is simplified by bringing the knee into a reference position. While the knee is typically put into a reference position during image acquisition, this alignment will generally not be perfect. To correct for imperfections, we propose a two-step process of bone segmentation followed by elastic tissue deformation. The approach makes use of a fully-automatic segmentation of femur and tibia from T1 and T2* images. The segmentation algorithm is based on a continuous convex optimization problem, incorporating regional, and shape information. The regional terms are included from a probabilistic viewpoint, which readily allows the inclusion of shape information. Segmentation of the outer boundary of the cortical bone is encouraged by adding simple appearance-based information to the optimization problem. The resulting segmentation without the shape alignment step is globally optimal. Standard registration is problematic for knee alignment due to the distinct physical properties of the tissues constituting the knee (bone, muscle, etc.). We therefore develop an alternative alignment approach based on a simple elastic deformation model combined with strict enforcement of similarity transforms for femur and tibia based on the obtained segmentations.

  18. Medical image segmentation using genetic algorithms.

    PubMed

    Maulik, Ujjwal

    2009-03-01

    Genetic algorithms (GAs) have been found to be effective in the domain of medical image segmentation, since the problem can often be mapped to one of search in a complex and multimodal landscape. The challenges in medical image segmentation arise due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. The resulting search space is therefore often noisy with a multitude of local optima. Not only does the genetic algorithmic framework prove to be effective in coming out of local optima, it also brings considerable flexibility into the segmentation procedure. In this paper, an attempt has been made to review the major applications of GAs to the domain of medical image segmentation.

  19. Calculus of variations and image segmentation.

    PubMed

    Carriero, Michele; Leaci, Antonio; Tomarelli, Franco

    2003-01-01

    A survey of free discontinuity problems related to image segmentation is given. The main properties and open problems about Mumford and Shah and Blake and Zisserman functionals are shown together with an extensive bibliography about recent mathematical developments.

  20. A segmentation algorithm for noisy images

    SciTech Connect

    Xu, Y.; Olman, V.; Uberbacher, E.C.

    1996-12-31

    This paper presents a 2-D image segmentation algorithm and addresses issues related to its performance on noisy images. The algorithm segments an image by first constructing a minimum spanning tree representation of the image and then partitioning the spanning tree into sub-trees representing different homogeneous regions. The spanning tree is partitioned in such a way that the sum of gray-level variations over all partitioned subtrees is minimized under the constraints that each subtree has at least a specified number of pixels and two adjacent subtrees have significantly different ``average`` gray-levels. Two types of noise, transmission errors and Gaussian additive noise. are considered and their effects on the segmentation algorithm are studied. Evaluation results have shown that the segmentation algorithm is robust in the presence of these two types of noise.

  1. A watershed approach for improving medical image segmentation.

    PubMed

    Zanaty, E A; Afifi, Ashraf

    2013-01-01

    In this paper, a novel watershed approach based on seed region growing and image entropy is presented which could improve the medical image segmentation. The proposed algorithm enables the prior information of seed region growing and image entropy in its calculation. The algorithm starts by partitioning the image into several levels of intensity using watershed multi-degree immersion process. The levels of intensity are the input to a computationally efficient seed region segmentation process which produces the initial partitioning of the image regions. These regions are fed to entropy procedure to carry out a suitable merging which produces the final segmentation. The latter process uses a region-based similarity representation of the image regions to decide whether regions can be merged. The region is isolated from the level and the residual pixels are uploaded to the next level and so on, we recall this process as multi-level process and the watershed is called multi-level watershed. The proposed algorithm is applied to challenging applications: grey matter-white matter segmentation in magnetic resonance images (MRIs). The established methods and the proposed approach are experimented by these applications to a variety of simulating immersion, multi-degree, multi-level seed region growing and multi-level seed region growing with entropy. It is shown that the proposed method achieves more accurate results for medical image oversegmentation.

  2. Segmentation of color images using genetic algorithm with image histogram

    NASA Astrophysics Data System (ADS)

    Sneha Latha, P.; Kumar, Pawan; Kahu, Samruddhi; Bhurchandi, Kishor M.

    2015-02-01

    This paper proposes a family of color image segmentation algorithms using genetic approach and color similarity threshold in terns of Just noticeable difference. Instead of segmenting and then optimizing, the proposed technique directly uses GA for optimized segmentation of color images. Application of GA on larger size color images is computationally heavy so they are applied on 4D-color image histogram table. The performance of the proposed algorithms is benchmarked on BSD dataset with color histogram based segmentation and Fuzzy C-means Algorithm using Probabilistic Rand Index (PRI). The proposed algorithms yield better analytical and visual results.

  3. A summary of image segmentation techniques

    NASA Technical Reports Server (NTRS)

    Spirkovska, Lilly

    1993-01-01

    Machine vision systems are often considered to be composed of two subsystems: low-level vision and high-level vision. Low level vision consists primarily of image processing operations performed on the input image to produce another image with more favorable characteristics. These operations may yield images with reduced noise or cause certain features of the image to be emphasized (such as edges). High-level vision includes object recognition and, at the highest level, scene interpretation. The bridge between these two subsystems is the segmentation system. Through segmentation, the enhanced input image is mapped into a description involving regions with common features which can be used by the higher level vision tasks. There is no theory on image segmentation. Instead, image segmentation techniques are basically ad hoc and differ mostly in the way they emphasize one or more of the desired properties of an ideal segmenter and in the way they balance and compromise one desired property against another. These techniques can be categorized in a number of different groups including local vs. global, parallel vs. sequential, contextual vs. noncontextual, interactive vs. automatic. In this paper, we categorize the schemes into three main groups: pixel-based, edge-based, and region-based. Pixel-based segmentation schemes classify pixels based solely on their gray levels. Edge-based schemes first detect local discontinuities (edges) and then use that information to separate the image into regions. Finally, region-based schemes start with a seed pixel (or group of pixels) and then grow or split the seed until the original image is composed of only homogeneous regions. Because there are a number of survey papers available, we will not discuss all segmentation schemes. Rather than a survey, we take the approach of a detailed overview. We focus only on the more common approaches in order to give the reader a flavor for the variety of techniques available yet present enough

  4. Magnetic resonance brain tissue segmentation based on sparse representations

    NASA Astrophysics Data System (ADS)

    Rueda, Andrea

    2015-12-01

    Segmentation or delineation of specific organs and structures in medical images is an important task in the clinical diagnosis and treatment, since it allows to characterize pathologies through imaging measures (biomarkers). In brain imaging, segmentation of main tissues or specific structures is challenging, due to the anatomic variability and complexity, and the presence of image artifacts (noise, intensity inhomogeneities, partial volume effect). In this paper, an automatic segmentation strategy is proposed, based on sparse representations and coupled dictionaries. Image intensity patterns are singly related to tissue labels at the level of small patches, gathering this information in coupled intensity/segmentation dictionaries. This dictionaries are used within a sparse representation framework to find the projection of a new intensity image onto the intensity dictionary, and the same projection can be used with the segmentation dictionary to estimate the corresponding segmentation. Preliminary results obtained with two publicly available datasets suggest that the proposal is capable of estimating adequate segmentations for gray matter (GM) and white matter (WM) tissues, with an average overlapping of 0:79 for GM and 0:71 for WM (with respect to original segmentations).

  5. Growing Region Segmentation Software (GRES) for quantitative magnetic resonance imaging of multiple sclerosis: intra- and inter-observer agreement variability: a comparison with manual contouring method.

    PubMed

    Parodi, Roberto C; Sardanelli, Francesco; Renzetti, Paolo; Rosso, Elisabetta; Losacco, Caterina; Ferrari, Alessandra; Levrero, Fabrizio; Pilot, Alberto; Inglese, Matilde; Mancardi, Giovanni L

    2002-04-01

    Lesion area measurement in multiple sclerosis (MS) is one of the key points in evaluating the natural history and in monitoring the efficacy of treatments. This study was performed to check the intra- and inter-observer agreement variability of a locally developed Growing Region Segmentation Software (GRES), comparing them to those obtained using manual contouring (MC). From routine 1.5-T MRI study of clinically definite multiple sclerosis patients, 36 lesions seen on proton-density-weighted images (PDWI) and 36 enhancing lesion on Gd-DTPA-BMA-enhanced T1-weighted images (Gd-T1WI) were randomly chosen and were evaluated by three observers. The mean range of lesion size was 9.9-536.0 mm(2) on PDWI and 3.6-57.2 mm(2) on Gd-T1WI. The median intra- and inter-observer agreement were, respectively, 97.1 and 90.0% using GRES on PDWI, 81.0 and 70.0% using MC on PDWI, 88.8 and 80.0% using GRES on Gd-T1WI, and 85.8 and 70.0% using MC on Gd-T1WI. The intra- and inter-observer agreements were significantly greater for GRES compared with MC ( P<0.0001 and P=0.0023, respectively) for PDWI, while no difference was found between GRES an MC for Gd-T1WI. The intra-observer variability for GRES was significantly lower on both PDWI ( P=0.0001) and Gd-T1WI ( P=0.0067), whereas for MC the same result was found only for PDWI ( P=0.0147). These data indicate that GRES reduces both the intra- and the inter-observer variability in assessing the area of MS lesions on PDWI and may prove useful in multicentre studies.

  6. Boundary overlap for medical image segmentation evaluation

    NASA Astrophysics Data System (ADS)

    Yeghiazaryan, Varduhi; Voiculescu, Irina

    2017-03-01

    All medical image segmentation algorithms need to be validated and compared, and yet no evaluation framework is widely accepted within the imaging community. Collections of segmentation results often need to be compared and ranked by their effectiveness. Evaluation measures which are popular in the literature are based on region overlap or boundary distance. None of these are consistent in the way they rank segmentation results: they tend to be sensitive to one or another type of segmentation error (size, location, shape) but no single measure covers all error types. We introduce a new family of measures, with hybrid characteristics. These measures quantify similarity/difference of segmented regions by considering their overlap around the region boundaries. This family is more sensitive than other measures in the literature to combinations of segmentation error types. We compare measure performance on collections of segmentation results sourced from carefully compiled 2D synthetic data, and also on 3D medical image volumes. We show that our new measure: (1) penalises errors successfully, especially those around region boundaries; (2) gives a low similarity score when existing measures disagree, thus avoiding overly inflated scores; and (3) scores segmentation results over a wider range of values. We consider a representative measure from this family and the effect of its only free parameter on error sensitivity, typical value range, and running time.

  7. Functional magnetic resonance imaging.

    PubMed

    Buchbinder, Bradley R

    2016-01-01

    Functional magnetic resonance imaging (fMRI) maps the spatiotemporal distribution of neural activity in the brain under varying cognitive conditions. Since its inception in 1991, blood oxygen level-dependent (BOLD) fMRI has rapidly become a vital methodology in basic and applied neuroscience research. In the clinical realm, it has become an established tool for presurgical functional brain mapping. This chapter has three principal aims. First, we review key physiologic, biophysical, and methodologic principles that underlie BOLD fMRI, regardless of its particular area of application. These principles inform a nuanced interpretation of the BOLD fMRI signal, along with its neurophysiologic significance and pitfalls. Second, we illustrate the clinical application of task-based fMRI to presurgical motor, language, and memory mapping in patients with lesions near eloquent brain areas. Integration of BOLD fMRI and diffusion tensor white-matter tractography provides a road map for presurgical planning and intraoperative navigation that helps to maximize the extent of lesion resection while minimizing the risk of postoperative neurologic deficits. Finally, we highlight several basic principles of resting-state fMRI and its emerging translational clinical applications. Resting-state fMRI represents an important paradigm shift, focusing attention on functional connectivity within intrinsic cognitive networks.

  8. An unsupervised strategy for biomedical image segmentation.

    PubMed

    Rodríguez, Roberto; Hernández, Rubén

    2010-01-01

    Many segmentation techniques have been published, and some of them have been widely used in different application problems. Most of these segmentation techniques have been motivated by specific application purposes. Unsupervised methods, which do not assume any prior scene knowledge can be learned to help the segmentation process, and are obviously more challenging than the supervised ones. In this paper, we present an unsupervised strategy for biomedical image segmentation using an algorithm based on recursively applying mean shift filtering, where entropy is used as a stopping criterion. This strategy is proven with many real images, and a comparison is carried out with manual segmentation. With the proposed strategy, errors less than 20% for false positives and 0% for false negatives are obtained.

  9. An unsupervised strategy for biomedical image segmentation

    PubMed Central

    Rodríguez, Roberto; Hernández, Rubén

    2010-01-01

    Many segmentation techniques have been published, and some of them have been widely used in different application problems. Most of these segmentation techniques have been motivated by specific application purposes. Unsupervised methods, which do not assume any prior scene knowledge can be learned to help the segmentation process, and are obviously more challenging than the supervised ones. In this paper, we present an unsupervised strategy for biomedical image segmentation using an algorithm based on recursively applying mean shift filtering, where entropy is used as a stopping criterion. This strategy is proven with many real images, and a comparison is carried out with manual segmentation. With the proposed strategy, errors less than 20% for false positives and 0% for false negatives are obtained. PMID:21918628

  10. Active segmentation of 3D axonal images.

    PubMed

    Muralidhar, Gautam S; Gopinath, Ajay; Bovik, Alan C; Ben-Yakar, Adela

    2012-01-01

    We present an active contour framework for segmenting neuronal axons on 3D confocal microscopy data. Our work is motivated by the need to conduct high throughput experiments involving microfluidic devices and femtosecond lasers to study the genetic mechanisms behind nerve regeneration and repair. While most of the applications for active contours have focused on segmenting closed regions in 2D medical and natural images, there haven't been many applications that have focused on segmenting open-ended curvilinear structures in 2D or higher dimensions. The active contour framework we present here ties together a well known 2D active contour model [5] along with the physics of projection imaging geometry to yield a segmented axon in 3D. Qualitative results illustrate the promise of our approach for segmenting neruonal axons on 3D confocal microscopy data.

  11. Review methods for image segmentation from computed tomography images

    SciTech Connect

    Mamat, Nurwahidah; Rahman, Wan Eny Zarina Wan Abdul; Soh, Shaharuddin Cik; Mahmud, Rozi

    2014-12-04

    Image segmentation is a challenging process in order to get the accuracy of segmentation, automation and robustness especially in medical images. There exist many segmentation methods that can be implemented to medical images but not all methods are suitable. For the medical purposes, the aims of image segmentation are to study the anatomical structure, identify the region of interest, measure tissue volume to measure growth of tumor and help in treatment planning prior to radiation therapy. In this paper, we present a review method for segmentation purposes using Computed Tomography (CT) images. CT images has their own characteristics that affect the ability to visualize anatomic structures and pathologic features such as blurring of the image and visual noise. The details about the methods, the goodness and the problem incurred in the methods will be defined and explained. It is necessary to know the suitable segmentation method in order to get accurate segmentation. This paper can be a guide to researcher to choose the suitable segmentation method especially in segmenting the images from CT scan.

  12. Semantic Image Segmentation with Contextual Hierarchical Models

    PubMed Central

    Seyedhosseini, Mojtaba; Tasdizen, Tolga

    2016-01-01

    Semantic segmentation is the problem of assigning an object label to each pixel. It unifies the image segmentation and object recognition problems. The importance of using contextual information in semantic segmentation frameworks has been widely realized in the field. We propose a contextual framework, called contextual hierarchical model (CHM), which learns contextual information in a hierarchical framework for semantic segmentation. At each level of the hierarchy, a classifier is trained based on downsampled input images and outputs of previous levels. Our model then incorporates the resulting multi-resolution contextual information into a classifier to segment the input image at original resolution. This training strategy allows for optimization of a joint posterior probability at multiple resolutions through the hierarchy. Contextual hierarchical model is purely based on the input image patches and does not make use of any fragments or shape examples. Hence, it is applicable to a variety of problems such as object segmentation and edge detection. We demonstrate that CHM performs at par with state-of-the-art on Stanford background and Weizmann horse datasets. It also outperforms state-of-the-art edge detection methods on NYU depth dataset and achieves state-of-the-art on Berkeley segmentation dataset (BSDS 500). PMID:26336116

  13. Homeomorphic Brain Image Segmentation with Topological and Statistical Atlases

    PubMed Central

    Bazin, Pierre-Louis; Pham, Dzung L.

    2008-01-01

    Atlas-based segmentation techniques are often employed to encode anatomical information for the delineation of multiple structures in magnetic resonance images of the brain. One of the primary challenges of these approaches is to efficiently model qualitative and quantitative anatomical knowledge without introducing a strong bias toward certain anatomical preferences when segmenting new images. This paper explores the use of topological information as a prior and proposes a segmentation framework based on both topological and statistical atlases of brain anatomy. Topology can be used to describe continuity of structures, as well as the relationships between structures, and is often a critical component in cortical surface reconstruction and deformation-based morphometry. Our method guarantees strict topological equivalence between the segmented image and the atlas, and relies only weakly on a statistical atlas of shape. Tissue classification and fast marching methods are used to provide a powerful and flexible framework to handle multiple image contrasts, high levels of noise, gain field inhomogeneities, and variable anatomies. The segmentation algorithm has been validated on simulated and real brain image data and made freely available to researchers. Our experiments demonstrate the accuracy and robustness of the method and the limited influence of the statistical atlas. PMID:18640069

  14. Interactive medical image segmentation using PDE control of active contours.

    PubMed

    Karasev, Peter; Kolesov, Ivan; Fritscher, Karl; Vela, Patricio; Mitchell, Phillip; Tannenbaum, Allen

    2013-11-01

    Segmentation of injured or unusual anatomic structures in medical imagery is a problem that has continued to elude fully automated solutions. In this paper, the goal of easy-to-use and consistent interactive segmentation is transformed into a control synthesis problem. A nominal level set partial differential equation (PDE) is assumed to be given; this open-loop system achieves correct segmentation under ideal conditions, but does not agree with a human expert's ideal boundary for real image data. Perturbing the state and dynamics of a level set PDE via the accumulated user input and an observer-like system leads to desirable closed-loop behavior. The input structure is designed such that a user can stabilize the boundary in some desired state without needing to understand any mathematical parameters. Effectiveness of the technique is illustrated with applications to the challenging segmentations of a patellar tendon in magnetic resonance and a shattered femur in computed tomography.

  15. Hierarchical morphological segmentation for image sequence coding.

    PubMed

    Salembier, P; Pardas, M

    1994-01-01

    This paper deals with a hierarchical morphological segmentation algorithm for image sequence coding. Mathematical morphology is very attractive for this purpose because it efficiently deals with geometrical features such as size, shape, contrast, or connectivity that can be considered as segmentation-oriented features. The algorithm follows a top-down procedure. It first takes into account the global information and produces a coarse segmentation, that is, with a small number of regions. Then, the segmentation quality is improved by introducing regions corresponding to more local information. The algorithm, considering sequences as being functions on a 3-D space, directly segments 3-D regions. A 3-D approach is used to get a segmentation that is stable in time and to directly solve the region correspondence problem. Each segmentation stage relies on four basic steps: simplification, marker extraction, decision, and quality estimation. The simplification removes information from the sequence to make it easier to segment. Morphological filters based on partial reconstruction are proven to be very efficient for this purpose, especially in the case of sequences. The marker extraction identifies the presence of homogeneous 3-D regions. It is based on constrained flat region labeling and morphological contrast extraction. The goal of the decision is to precisely locate the contours of regions detected by the marker extraction. This decision is performed by a modified watershed algorithm. Finally, the quality estimation concentrates on the coding residue, all the information about the 3-D regions that have not been properly segmented and therefore coded. The procedure allows the introduction of the texture and contour coding schemes within the segmentation algorithm. The coding residue is transmitted to the next segmentation stage to improve the segmentation and coding quality. Finally, segmentation and coding examples are presented to show the validity and interest of

  16. Medical image segmentation on GPUs--a comprehensive review.

    PubMed

    Smistad, Erik; Falch, Thomas L; Bozorgi, Mohammadmehdi; Elster, Anne C; Lindseth, Frank

    2015-02-01

    Segmentation of anatomical structures, from modalities like computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound, is a key enabling technology for medical applications such as diagnostics, planning and guidance. More efficient implementations are necessary, as most segmentation methods are computationally expensive, and the amount of medical imaging data is growing. The increased programmability of graphic processing units (GPUs) in recent years have enabled their use in several areas. GPUs can solve large data parallel problems at a higher speed than the traditional CPU, while being more affordable and energy efficient than distributed systems. Furthermore, using a GPU enables concurrent visualization and interactive segmentation, where the user can help the algorithm to achieve a satisfactory result. This review investigates the use of GPUs to accelerate medical image segmentation methods. A set of criteria for efficient use of GPUs are defined and each segmentation method is rated accordingly. In addition, references to relevant GPU implementations and insight into GPU optimization are provided and discussed. The review concludes that most segmentation methods may benefit from GPU processing due to the methods' data parallel structure and high thread count. However, factors such as synchronization, branch divergence and memory usage can limit the speedup. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

  17. Self imaging in segmented waveguide arrays

    NASA Astrophysics Data System (ADS)

    Heinrich, Matthias; Szameit, Alexander; Dreisow, Felix; Pertsch, Thomas; Nolte, Stefan; Tünnermann, Andreas; Suran, Eric; Louradour, Frédéric; Bathélémy, Alain; Longhi, Stefano

    2009-02-01

    Self-imaging in integrated optical devices is interesting for many applications including image transmission, optical collimation and even reshaping of ultrashort laser pulses. However, in general this relies on boundary-free light propagation, since interaction with boundaries results in a considerable distortion of the self-imaging effect. This problem can be overcome in waveguide arrays by segmentation of particular lattice sites, yielding phase shifts which result in image reconstruction in one- as well as two-dimensional configurations. Here, we demonstrate the first experimental realization of this concept. For the fabrication of the segmented waveguide arrays we used the femtosecond laser direct-writing technique. The total length of the arrays is 50mm with a waveguide spacing of 16 μm and 20μm in the one- and two-dimensional case, respectively. The length of the segmented area was 2.6mm, while the segmentation period was chosen to be 16 μm. This results in a complete inversion of the global phase of the travelling field inside the array, so that the evolution dynamics are reversed and the input field is imaged onto the sample output facet. Accordingly, segmented integrated optical devices provide a new and attractive opportunity for image transmission in finite systems.

  18. Automatic segmentation of cerebral MR images using artificial neural networks

    SciTech Connect

    Alirezaie, J.; Jernigan, M.E.; Nahmias, C.

    1996-12-31

    In this paper we present an unsupervised clustering technique for multispectral segmentation of magnetic resonance (MR) images of the human brain. Our scheme utilizes the Self Organizing Feature Map (SOFM) artificial neural network for feature mapping and generates a set of codebook vectors. By extending the network with an additional layer the map will be classified and each tissue class will be labelled. An algorithm has been developed for extracting the cerebrum from the head scan prior to the segmentation. Extracting the cerebrum is performed by stripping away the skull pixels from the T2 image. Three tissue types of the brain: white matter, gray matter and cerebral spinal fluid (CSF) are segmented accurately. To compare the results with other conventional approaches we applied the c-means algorithm to the problem.

  19. Segmented wave analysis of surface plasmon resonance on curved surface

    NASA Astrophysics Data System (ADS)

    Lee, Hyunwoong; Kim, Donghyun

    2017-07-01

    Surface plasmon resonance (SPR) has been heavily used as biosensors and studied dominantly on a flat surface. Recently, flexible sensor platforms have emerged, for example, as wearable devices. Here, we report investigation of SPR characteristics on a curved film structure. A rigorous 3D computational model requires extremely heavy calculation time and resources. Therefore, we adopted segmentation analysis in which curved surface is divided into an array of flat segments. Such analysis allows fast and efficient calculation. The results indicate that increased curvature produces broader SPR due to wider momentum-matching. The segmentation analysis is expected to play a critical role for diverse optical elements on curved surface.

  20. Image segmentation using neural tree networks

    NASA Astrophysics Data System (ADS)

    Samaddar, Sumitro; Mammone, Richard J.

    1993-06-01

    We present a technique for Image Segmentation using Neural Tree Networks (NTN). We also modify the NTN architecture to let is solve multi-class classification problems with only binary fan-out. We have used a realistic case study of segmenting the pole, coil and painted coil regions of light bulb filaments (LBF). The input to the network is a set of maximum, minimum and average of intensities in radial slices of a circular window around a pixel, taken from a front-lit and a back-lit image of an LBF. Training is done with a composite image drawn from images of many LBFs. Each node of the NTN is a multi-layer perceptron and has one output for each segment class. These outputs are treated as probabilities to compute a confidence value for the segmentation of that pixel. Segmentation results with high confidence values are deemed to be correct and not processed further, while those with moderate and low confidence values are deemed to be outliers by this node and passed down the tree to children nodes. These tend to be pixels in boundary of different regions. The results are favorably compared with a traditional segmentation technique applied to the LBF test case.

  1. Neural tree network method for image segmentation

    NASA Astrophysics Data System (ADS)

    Samaddar, Sumitro; Mammone, Richard J.

    1994-02-01

    We present an extension of the neural tree network (NTN) architecture to let it solve multi- class classification problems with only binary fan-out. We then demonstrate it's effectiveness by applying it in a method for image segmentation. Each node of the NTN is a multi-layer perceptron and has one output for each segment class. These outputs are treated as probabilities to compute a confidence value for the segmentation of that pixel. Segmentation results with high confidence values are deemed to be correct and not processed further, while those with moderate and low confidence values are deemed to be outliers by this node and passed down the tree to children nodes. These tend to be pixels in boundary of different regions. We have used a realistic case study of segmenting the pole, coil and painted coil regions of light bulb filaments (LBF). The input to the network is a set of maximum, minimum and average of intensities in radial slices of a circular window around a pixel, taken from a front-lit and a back-lit image of an LBF. Training is done with a composite image drawn from images of many LBFs. The results are favorably compared with a traditional segmentation technique applied to the LBF test case.

  2. Intuitionistic fuzzy segmentation of medical images.

    PubMed

    Chaira, Tamalika

    2010-06-01

    This paper proposes a novel and probably the first method, using Attanassov intuitionistic fuzzy set theory to segment blood vessels and also the blood cells in pathological images. This type of segmentation is very important in detecting different types of human diseases, e.g., an increase in the number of vessels may lead to cancer in prostates, mammary, etc. The medical images are not properly illuminated, and segmentation in that case becomes very difficult. A novel image segmentation approach using intuitionistic fuzzy set theory and a new membership function is proposed using restricted equivalence function from automorphisms, for finding the membership values of the pixels of the image. An intuitionistic fuzzy image is constructed using Sugeno type intuitionistic fuzzy generator. Local thresholding is applied to threshold medical images. The results showed a much better performance on poor contrast medical images, where almost all the blood vessels and blood cells are visible properly. There are several fuzzy and intuitionistic fuzzy thresholding methods, but these methods are not related to the medical images. To make a comparison with the proposed method with other thresholding methods, the method is compared with six nonfuzzy, fuzzy, and intuitionistic fuzzy methods.

  3. CONSTRAINED SPECTRAL CLUSTERING FOR IMAGE SEGMENTATION

    PubMed Central

    Sourati, Jamshid; Brooks, Dana H.; Dy, Jennifer G.; Erdogmus, Deniz

    2013-01-01

    Constrained spectral clustering with affinity propagation in its original form is not practical for large scale problems like image segmentation. In this paper we employ novelty selection sub-sampling strategy, besides using efficient numerical eigen-decomposition methods to make this algorithm work efficiently for images. In addition, entropy-based active learning is also employed to select the queries posed to the user more wisely in an interactive image segmentation framework. We evaluate the algorithm on general and medical images to show that the segmentation results will improve using constrained clustering even if one works with a subset of pixels. Furthermore, this happens more efficiently when pixels to be labeled are selected actively. PMID:24466500

  4. Simplified labeling process for medical image segmentation.

    PubMed

    Gao, Mingchen; Huang, Junzhou; Huang, Xiaolei; Zhang, Shaoting; Metaxas, Dimitris N

    2012-01-01

    Image segmentation plays a crucial role in many medical imaging applications by automatically locating the regions of interest. Typically supervised learning based segmentation methods require a large set of accurately labeled training data. However, thel labeling process is tedious, time consuming and sometimes not necessary. We propose a robust logistic regression algorithm to handle label outliers such that doctors do not need to waste time on precisely labeling images for training set. To validate its effectiveness and efficiency, we conduct carefully designed experiments on cervigram image segmentation while there exist label outliers. Experimental results show that the proposed robust logistic regression algorithms achieve superior performance compared to previous methods, which validates the benefits of the proposed algorithms.

  5. Magnetic Resonance Imaging (MRI) Safety

    MedlinePlus

    ... Resources Professions Site Index A-Z Magnetic Resonance Imaging (MRI) Safety What is MRI and how does ... the area being scanned include: Metallic spinal rod Plates, pins, screws, or metal mesh used to repair ...

  6. Advances in breast imaging: magnetic resonance imaging.

    PubMed

    Bartella, Lia; Morris, Elizabeth A

    2006-01-01

    Magnetic resonance imaging (MRI) of the breast is rapidly becoming incorporated into clinical practice. Indications for breast MRI include staging of known breast cancer, monitoring response to chemotherapy, assessing recurrence, problem solving, and high-risk screening. Magnetic resonance spectroscopy is a promising technique that may decrease the number of benign biopsies generated by breast MRI in the clinical setting.

  7. Optical image segmentation using wavelet filtering techniques

    NASA Astrophysics Data System (ADS)

    Veronin, Christopher P.

    1990-12-01

    This research effort successfully implemented an automatic, optically based image segmentation scheme for locating potential targets in a cluttered FLIR image. Such a design is critical to achieve real-time segmentation and classification for machine vision applications. The segmentation scheme used in this research was based on texture discrimination and employs orientation specific, bandpass spatial filters as its main component. The orientation specific, bandpass spatial filters designed during this research include symmetrically located circular apertures implemented on heavy, black aluminum foil; cosine and sine Gabor filters implemented with detour-phase computer generated holography photoreduced onto glass slides; and symmetrically located circular apertures implemented on a liquid crystal television (LCTV) for real-time filter selection. The most successful design was the circular aperture pairs implemented on the aluminum foil. Segmentation was illustrated for simple and complex texture slides, glass template slides, and static and real-time FLIR imagery displayed on an LCTV.

  8. Hippocampus and amygdala volumes from magnetic resonance images in children: Assessing accuracy of FreeSurfer and FSL against manual segmentation.

    PubMed

    Schoemaker, Dorothee; Buss, Claudia; Head, Kevin; Sandman, Curt A; Davis, Elysia P; Chakravarty, M Mallar; Gauthier, Serge; Pruessner, Jens C

    2016-04-01

    The volumetric quantification of brain structures is of great interest in pediatric populations because it allows the investigation of different factors influencing neurodevelopment. FreeSurfer and FSL both provide frequently used packages for automatic segmentation of brain structures. In this study, we examined the accuracy and consistency of those two automated protocols relative to manual segmentation, commonly considered as the "gold standard" technique, for estimating hippocampus and amygdala volumes in a sample of preadolescent children aged between 6 to 11 years. The volumes obtained with FreeSurfer and FSL-FIRST were evaluated and compared with manual segmentations with respect to volume difference, spatial agreement and between- and within-method correlations. Results highlighted a tendency for both automated techniques to overestimate hippocampus and amygdala volumes, in comparison to manual segmentation. This was more pronounced when using FreeSurfer than FSL-FIRST and, for both techniques, the overestimation was more marked for the amygdala than the hippocampus. Pearson correlations support moderate associations between manual tracing and FreeSurfer for hippocampus (right r=0.69, p<0.001; left r=0.77, p<0.001) and amygdala (right r=0.61, p<0.001; left r=0.67, p<0.001) volumes. Correlation coefficients between manual segmentation and FSL-FIRST were statistically significant (right hippocampus r=0.59, p<0.001; left hippocampus r=0.51, p<0.001; right amygdala r=0.35, p<0.001; left amygdala r=0.31, p<0.001) but were significantly weaker, for all investigated structures. When computing intraclass correlation coefficients between manual tracing and automatic segmentation, all comparisons, except for left hippocampus volume estimated with FreeSurfer, failed to reach 0.70. When looking at each method separately, correlations between left and right hemispheric volumes showed strong associations between bilateral hippocampus and bilateral amygdala volumes when

  9. A new distribution metric for image segmentation

    NASA Astrophysics Data System (ADS)

    Sandhu, Romeil; Georgiou, Tryphon; Tannenbaum, Allen

    2008-03-01

    In this paper, we present a new distribution metric for image segmentation that arises as a result in prediction theory. Forming a natural geodesic, our metric quantifies "distance" for two density functionals as the standard deviation of the difference between logarithms of those distributions. Using level set methods, we incorporate an energy model based on the metric into the Geometric Active Contour framework. Moreover, we briefly provide a theoretical comparison between the popular Fisher Information metric, from which the Bhattacharyya distance originates, with the newly proposed similarity metric. In doing so, we demonstrate that segmentation results are directly impacted by the type of metric used. Specifically, we qualitatively compare the Bhattacharyya distance and our algorithm on the Kaposi Sarcoma, a pathology that infects the skin. We also demonstrate the algorithm on several challenging medical images, which further ensure the viability of the metric in the context of image segmentation.

  10. OCT image segmentation of the prostate nerves

    NASA Astrophysics Data System (ADS)

    Chitchian, Shahab; Weldon, Thomas P.; Fried, Nathaniel M.

    2009-08-01

    The cavernous nerves course along the surface of the prostate and are responsible for erectile function. Improvements in identification, imaging, and visualization of the cavernous nerves during prostate cancer surgery may improve nerve preservation and postoperative sexual potency. In this study, 2-D OCT images of the rat prostate were segmented to differentiate the cavernous nerves from the prostate gland. Three image features were employed: Gabor filter, Daubechies wavelet, and Laws filter. The features were segmented using a nearestneighbor classifier. N-ary morphological post-processing was used to remove small voids. The cavernous nerves were differentiated from the prostate gland with a segmentation error rate of only 0.058 +/- 0.019.

  11. Video-based noncooperative iris image segmentation.

    PubMed

    Du, Yingzi; Arslanturk, Emrah; Zhou, Zhi; Belcher, Craig

    2011-02-01

    In this paper, we propose a video-based noncooperative iris image segmentation scheme that incorporates a quality filter to quickly eliminate images without an eye, employs a coarse-to-fine segmentation scheme to improve the overall efficiency, uses a direct least squares fitting of ellipses method to model the deformed pupil and limbic boundaries, and develops a window gradient-based method to remove noise in the iris region. A remote iris acquisition system is set up to collect noncooperative iris video images. An objective method is used to quantitatively evaluate the accuracy of the segmentation results. The experimental results demonstrate the effectiveness of this method. The proposed method would make noncooperative iris recognition or iris surveillance possible.

  12. Hierarchical Segmentation Enhances Diagnostic Imaging

    NASA Technical Reports Server (NTRS)

    2007-01-01

    Bartron Medical Imaging LLC (BMI), of New Haven, Connecticut, gained a nonexclusive license from Goddard Space Flight Center to use the RHSEG software in medical imaging. To manage image data, BMI then licensed two pattern-matching software programs from NASA's Jet Propulsion Laboratory that were used in image analysis and three data-mining and edge-detection programs from Kennedy Space Center. More recently, BMI made NASA history by being the first company to partner with the Space Agency through a Cooperative Research and Development Agreement to develop a 3-D version of RHSEG. With U.S. Food and Drug Administration clearance, BMI will sell its Med-Seg imaging system with the 2-D version of the RHSEG software to analyze medical imagery from CAT and PET scans, MRI, ultrasound, digitized X-rays, digitized mammographies, dental X-rays, soft tissue analyses, moving object analyses, and soft-tissue slides such as Pap smears for the diagnoses and management of diseases. Extending the software's capabilities to three dimensions will eventually enable production of pixel-level views of a tumor or lesion, early identification of plaque build-up in arteries, and identification of density levels of microcalcification in mammographies.

  13. A Level Set Approach to Image Segmentation With Intensity Inhomogeneity.

    PubMed

    Zhang, Kaihua; Zhang, Lei; Lam, Kin-Man; Zhang, David

    2016-02-01

    It is often a difficult task to accurately segment images with intensity inhomogeneity, because most of representative algorithms are region-based that depend on intensity homogeneity of the interested object. In this paper, we present a novel level set method for image segmentation in the presence of intensity inhomogeneity. The inhomogeneous objects are modeled as Gaussian distributions of different means and variances in which a sliding window is used to map the original image into another domain, where the intensity distribution of each object is still Gaussian but better separated. The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying a bias field with the original signal within the window. A maximum likelihood energy functional is then defined on the whole image region, which combines the bias field, the level set function, and the piecewise constant function approximating the true image signal. The proposed level set method can be directly applied to simultaneous segmentation and bias correction for 3 and 7T magnetic resonance images. Extensive evaluation on synthetic and real-images demonstrate the superiority of the proposed method over other representative algorithms.

  14. Performance evaluation of image segmentation algorithms on microscopic image data.

    PubMed

    Beneš, Miroslav; Zitová, Barbara

    2015-01-01

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

  15. Optically detected magnetic resonance imaging

    SciTech Connect

    Blank, Aharon; Shapiro, Guy; Fischer, Ran; London, Paz; Gershoni, David

    2015-01-19

    Optically detected magnetic resonance provides ultrasensitive means to detect and image a small number of electron and nuclear spins, down to the single spin level with nanoscale resolution. Despite the significant recent progress in this field, it has never been combined with the power of pulsed magnetic resonance imaging techniques. Here, we demonstrate how these two methodologies can be integrated using short pulsed magnetic field gradients to spatially encode the sample. This result in what we denote as an 'optically detected magnetic resonance imaging' technique. It offers the advantage that the image is acquired in parallel from all parts of the sample, with well-defined three-dimensional point-spread function, and without any loss of spectroscopic information. In addition, this approach may be used in the future for parallel but yet spatially selective efficient addressing and manipulation of the spins in the sample. Such capabilities are of fundamental importance in the field of quantum spin-based devices and sensors.

  16. Parallel fuzzy connected image segmentation on GPU

    PubMed Central

    Zhuge, Ying; Cao, Yong; Udupa, Jayaram K.; Miller, Robert W.

    2011-01-01

    Purpose: Image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm implementation on NVIDIA’s compute unified device Architecture (cuda) platform for segmenting medical image data sets. Methods: In the FC algorithm, there are two major computational tasks: (i) computing the fuzzy affinity relations and (ii) computing the fuzzy connectedness relations. These two tasks are implemented as cuda kernels and executed on GPU. A dramatic improvement in speed for both tasks is achieved as a result. Results: Our experiments based on three data sets of small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 24.4x, 18.1x, and 10.3x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over the implementation of the algorithm on CPU, and takes 0.25, 0.72, and 15.04 s, correspondingly, for the three data sets. Conclusions: The authors developed a parallel algorithm of the widely used fuzzy connected image segmentation method on the NVIDIA GPUs, which are far more cost- and speed-effective than both cluster of workstations and multiprocessing systems. A near-interactive speed of segmentation has been achieved, even for the large data set. PMID:21859037

  17. Advances in Magnetic Resonance Imaging

    NASA Astrophysics Data System (ADS)

    Price, R. R.

    1996-05-01

    Nuclear Magnetic Resonance (NMR) Imaging, now more commonly referred to as Magnetic Resonance Imaging (MRI), developed into an important clinical modality between the years of 1978 and 1985. In 1945 it was demonstrated independently by Bloch(F. Bloch, The Principle of Nuclear Induction, Nobel Lectures in Physics: 1946-1962 New York, Elsevier Science Publishing Co., Inc. 1964.) and Purcell(E.M. Purcell, Research in Nuclear Magnetism, Nobel Lectures in Physics: 1946-1962, New York. Elsevier Science Publishing Co., Inc. 1964.) that magnetic nuclei in a sample when placed in a static magnetic field exhibit a characteristic resonance frequency which is proportional to the field strength and unique to nuclei of the same type and same environment. The net magnetization of the sample when irradiated by an RF wave at the resonance frequency could thus be manipulated to produce an induced "NMR signal" in a conducting loop placed near the sample. In the early 1970's, methods were developed whereby the NMR signal could be spatially encoded in both frequency and phase by means of superimposed linear magnetic field gradients to produce NMR images. NMR image contrast is a function of nuclear concentration and magnetic relaxation times (T1 and T2). MRI became the first medical imaging modality to provide both high resolution and high contrast images of soft tissue. Current clinical MRI systems produce images of the distribution of ^1H nuclei (primarily water) within the body. Other biologically important nuclei (^13C, ^23N, ^31P), as well as the imaging of hyperpolarized inert gases (^3He, ^129Xe) are under investigation. Recent developments in ^1H-MRI have included chemical shift imaging (hydrogen containing metabolites), blood flow imaging (MR angiography), ultra high-speed imaging (Echo Planar), and imaging of brain function based upon magnetic susceptibility differences resulting from blood oxygenation changes during brain activity.

  18. Active Mask Segmentation of Fluorescence Microscope Images

    PubMed Central

    Srinivasa, Gowri; Fickus, Matthew C.; Guo, Yusong; Linstedt, Adam D.; Kovačević, Jelena

    2009-01-01

    We propose a new active mask algorithm for the segmentation of fluorescence microscope images of punctate patterns. It combines the (a) flexibility offered by active-contour methods, (b) speed offered by multiresolution methods, (c) smoothing offered by multiscale methods, and (d) statistical modeling offered by region-growing methods into a fast and accurate segmentation tool. The framework moves from the idea of the “contour” to that of “inside and outside”, or, masks, allowing for easy multidimensional segmentation. It adapts to the topology of the image through the use of multiple masks. The algorithm is almost invariant under initialization, allowing for random initialization, and uses a few easily tunable parameters. Experiments show that the active mask algorithm matches the ground truth well, and outperforms the algorithm widely used in fluorescence microscopy, seeded watershed, both qualitatively as well as quantitatively. PMID:19380268

  19. Color Image Segmentation in a Quaternion Framework

    PubMed Central

    Subakan, Özlem N.; Vemuri, Baba C.

    2010-01-01

    In this paper, we present a feature/detail preserving color image segmentation framework using Hamiltonian quaternions. First, we introduce a novel Quaternionic Gabor Filter (QGF) which can combine the color channels and the orientations in the image plane. Using the QGFs, we extract the local orientation information in the color images. Second, in order to model this derived orientation information, we propose a continuous mixture of appropriate hypercomplex exponential basis functions. We derive a closed form solution for this continuous mixture model. This analytic solution is in the form of a spatially varying kernel which, when convolved with the signed distance function of an evolving contour (placed in the color image), yields a detail preserving segmentation. PMID:21243101

  20. Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.

    PubMed

    Pereira, Sergio; Pinto, Adriano; Alves, Victor; Silva, Carlos A

    2016-05-01

    Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 ×3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0.88, 0.83, 0.77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0.78, 0.65, and 0.75 for the complete, core, and enhancing regions, respectively.

  1. Brain Tumor Segmentation using Convolutional Neural Networks in MRI Images.

    PubMed

    Pereira, Sergio; Pinto, Adriano; Alves, Victor; Silva, Carlos A

    2016-03-04

    Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 33 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0:88, 0:83, 0:77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0:78, 0:65, and 0:75 for the complete, core, and enhancing regions, respectively.

  2. A Survey of Digital Image Segmentation Algorithms

    DTIC Science & Technology

    1995-01-01

    features. Thresholding techniques arc also useful in segmenting such binary images as printed documents, line drawings, and multispectral and x-ray...algorithms, pixel labeling and run-length connectivity analysis, arc discussed in the following sections. Therefore, in exammmg g(x, y), pixels that are...edge linking, graph searching, curve fitting, Hough transform, and others arc applicablc to image segmematio~. Difficulties with boundary-based methods

  3. Segmentation and Classification of Burn Color Images

    DTIC Science & Technology

    2001-10-25

    SEGMENTATION AND CLASSIFICATION OF BURN COLOR IMAGES Begoña Acha1, Carmen Serrano1, Laura Roa2 1Área de Teoría de la Señal y Comunicaciones ...subjectivity and high experience requirement in visual inspection. In this way, research about the relationship between depth and superficial...in unallowable economical cost. In spite of that, there is hardly bibliography about burn depth determination by visual image analysis and

  4. Quantitative color analysis for capillaroscopy image segmentation.

    PubMed

    Goffredo, Michela; Schmid, Maurizio; Conforto, Silvia; Amorosi, Beatrice; D'Alessio, Tommaso; Palma, Claudio

    2012-06-01

    This communication introduces a novel approach for quantitatively evaluating the role of color space decomposition in digital nailfold capillaroscopy analysis. It is clinically recognized that any alterations of the capillary pattern, at the periungual skin region, are directly related to dermatologic and rheumatic diseases. The proposed algorithm for the segmentation of digital capillaroscopy images is optimized with respect to the choice of the color space and the contrast variation. Since the color space is a critical factor for segmenting low-contrast images, an exhaustive comparison between different color channels is conducted and a novel color channel combination is presented. Results from images of 15 healthy subjects are compared with annotated data, i.e. selected images approved by clinicians. By comparison, a set of figures of merit, which highlights the algorithm capability to correctly segment capillaries, their shape and their number, is extracted. Experimental tests depict that the optimized procedure for capillaries segmentation, based on a novel color channel combination, presents values of average accuracy higher than 0.8, and extracts capillaries whose shape and granularity are acceptable. The obtained results are particularly encouraging for future developments on the classification of capillary patterns with respect to dermatologic and rheumatic diseases.

  5. From Acoustic Segmentation to Language Processing: Evidence from Optical Imaging

    PubMed Central

    Obrig, Hellmuth; Rossi, Sonja; Telkemeyer, Silke; Wartenburger, Isabell

    2010-01-01

    During language acquisition in infancy and when learning a foreign language, the segmentation of the auditory stream into words and phrases is a complex process. Intuitively, learners use “anchors” to segment the acoustic speech stream into meaningful units like words and phrases. Regularities on a segmental (e.g., phonological) or suprasegmental (e.g., prosodic) level can provide such anchors. Regarding the neuronal processing of these two kinds of linguistic cues a left-hemispheric dominance for segmental and a right-hemispheric bias for suprasegmental information has been reported in adults. Though lateralization is common in a number of higher cognitive functions, its prominence in language may also be a key to understanding the rapid emergence of the language network in infants and the ease at which we master our language in adulthood. One question here is whether the hemispheric lateralization is driven by linguistic input per se or whether non-linguistic, especially acoustic factors, “guide” the lateralization process. Methodologically, functional magnetic resonance imaging provides unsurpassed anatomical detail for such an enquiry. However, instrumental noise, experimental constraints and interference with EEG assessment limit its applicability, pointedly in infants and also when investigating the link between auditory and linguistic processing. Optical methods have the potential to fill this gap. Here we review a number of recent studies using optical imaging to investigate hemispheric differences during segmentation and basic auditory feature analysis in language development. PMID:20725516

  6. Magnetic Resonance Imaging (MRI)

    MedlinePlus

    ... an image. Repeated exposure can be harmful.An MRI scan takes longer to perform (30 to 60 minutes, ... a treatment plan.Depending on your symptoms, an MRI will scan a specific portion of your body to diagnose: ...

  7. Neutron Resonance Spin Determination Using Multi-Segmented Detector DANCE

    SciTech Connect

    Baramsai, B.; Mitchell, G. E.; Chyzh, A.; Dashdorj, D.; Walker, C.; Agvaanluvsan, U.; Becvar, F.; Krticka, M.; Bredeweg, T. A.; Couture, A.; Haight, R. C.; Jandel, M.; Keksis, A. L.; O'Donnell, J. M.; Rundberg, R. S.; Ullmann, J. L.; Vieira, D. J.; Wouters, J. M.

    2011-06-01

    A sensitive method to determine the spin of neutron resonances is introduced based on the statistical pattern recognition technique. The new method was used to assign the spins of s-wave resonances in {sup 155}Gd. The experimental neutron capture data for these nuclei were measured with the DANCE (Detector for Advanced Neutron Capture Experiment) calorimeter at the Los Alamos Neutron Science Center. The highly segmented calorimeter provided detailed multiplicity distributions of the capture {gamma}-rays. Using this information, the spins of the neutron capture resonances were determined. With these new spin assignments, level spacings are determined separately for s-wave resonances with J{sup {pi}} = 1{sup -} and 2{sup -}.

  8. Left Ventricle: Fully Automated Segmentation Based on Spatiotemporal Continuity and Myocardium Information in Cine Cardiac Magnetic Resonance Imaging (LV-FAST)

    PubMed Central

    Wang, Lijia; Pei, Mengchao; Codella, Noel C. F.; Kochar, Minisha; Weinsaft, Jonathan W.; Li, Jianqi; Prince, Martin R.

    2015-01-01

    CMR quantification of LV chamber volumes typically and manually defines the basal-most LV, which adds processing time and user-dependence. This study developed an LV segmentation method that is fully automated based on the spatiotemporal continuity of the LV (LV-FAST). An iteratively decreasing threshold region growing approach was used first from the midventricle to the apex, until the LV area and shape discontinued, and then from midventricle to the base, until less than 50% of the myocardium circumference was observable. Region growth was constrained by LV spatiotemporal continuity to improve robustness of apical and basal segmentations. The LV-FAST method was compared with manual tracing on cardiac cine MRI data of 45 consecutive patients. Of the 45 patients, LV-FAST and manual selection identified the same apical slices at both ED and ES and the same basal slices at both ED and ES in 38, 38, 38, and 41 cases, respectively, and their measurements agreed within −1.6 ± 8.7 mL, −1.4 ± 7.8 mL, and 1.0 ± 5.8% for EDV, ESV, and EF, respectively. LV-FAST allowed LV volume-time course quantitatively measured within 3 seconds on a standard desktop computer, which is fast and accurate for processing the cine volumetric cardiac MRI data, and enables LV filling course quantification over the cardiac cycle. PMID:25738153

  9. Mammographic images segmentation using texture descriptors.

    PubMed

    Mascaro, Angelica A; Mello, Carlos A B; Santos, Wellington P; Cavalcanti, George D C

    2009-01-01

    Tissue classification in mammography can help the diagnosis of breast cancer by separating healthy tissue from lesions. We present herein the use of three texture descriptors for breast tissue segmentation purposes: the Sum Histogram, the Gray Level Co-Occurrence Matrix (GLCM) and the Local Binary Pattern (LBP). A modification of the LBP is also proposed for a better distinction of the tissues. In order to segment the image into its tissues, these descriptors are compared using a fidelity index and two clustering algorithms: k-Means and SOM (Self-Organizing Maps).

  10. Hepatic lesions segmentation in ultrasound nonlinear imaging

    NASA Astrophysics Data System (ADS)

    Kissi, Adelaide A.; Cormier, Stephane; Pourcelot, Leandre; Tranquart, Francois

    2005-04-01

    Doppler has been used for many years for cardiovascular exploration in order to visualize the vessels walls and anatomical or functional diseases. The use of ultrasound contrast agents makes it possible to improve ultrasonic information. Nonlinear ultrasound imaging highlights the detection of these agents within an organ and hence is a powerful technique to image perfusion of an organ in real-time. The visualization of flow and perfusion provides important information for the diagnosis of various diseases as well as for the detection of tumors. However, the images are buried in noise, the speckle, inherent in the image formation. Furthermore at portal phase, there is often an absence of clear contrast between lesions and surrounding tissues because the organ is filled with agents. In this context, we propose a new method of automatic liver lesions segmentation in nonlinear imaging sequences for the quantification of perfusion. Our method of segmentation is divided into two stages. Initially, we developed an anisotropic diffusion step which raised the structural characteristics to eliminate the speckle. Then, a fuzzy competitive clustering process allowed us to delineate liver lesions. This method has been used to detect focal hepatic lesions (metastasis, nodular hyperplasia, adenoma). Compared to medical expert"s report obtained on 15 varied lesions, the automatic segmentation allows us to identify and delineate focal liver lesions during the portal phase which high accuracy. Our results show that this method improves markedly the recognition of focal hepatic lesions and opens the way for future precise quantification of contrast enhancement.

  11. Image Segmentation With Cage Active Contours.

    PubMed

    Garrido, Lluís; Guerrieri, Marité; Igual, Laura

    2015-12-01

    In this paper, we present a framework for image segmentation based on parametrized active contours. The evolving contour is parametrized according to a reduced set of control points that form a closed polygon and have a clear visual interpretation. The parametrization, called mean value coordinates, stems from the techniques used in computer graphics to animate virtual models. Our framework allows to easily formulate region-based energies to segment an image. In particular, we present three different local region-based energy terms: 1) the mean model; 2) the Gaussian model; 3) and the histogram model. We show the behavior of our method on synthetic and real images and compare the performance with state-of-the-art level set methods.

  12. Cavity- and waveguide-resonators in electron paramagnetic resonance, nuclear magnetic resonance, and magnetic resonance imaging.

    PubMed

    Webb, Andrew

    2014-11-01

    Cavity resonators are widely used in electron paramagnetic resonance, very high field magnetic resonance microimaging and also in high field human imaging. The basic principles and designs of different forms of cavity resonators including rectangular, cylindrical, re-entrant, cavity magnetrons, toroidal cavities and dielectric resonators are reviewed. Applications in EPR and MRI are summarized, and finally the topic of traveling wave MRI using the magnet bore as a waveguide is discussed.

  13. Magnetic resonance imaging of optic nerve

    PubMed Central

    Gala, Foram

    2015-01-01

    Optic nerves are the second pair of cranial nerves and are unique as they represent an extension of the central nervous system. Apart from clinical and ophthalmoscopic evaluation, imaging, especially magnetic resonance imaging (MRI), plays an important role in the complete evaluation of optic nerve and the entire visual pathway. In this pictorial essay, the authors describe segmental anatomy of the optic nerve and review the imaging findings of various conditions affecting the optic nerves. MRI allows excellent depiction of the intricate anatomy of optic nerves due to its excellent soft tissue contrast without exposure to ionizing radiation, better delineation of the entire visual pathway, and accurate evaluation of associated intracranial pathologies. PMID:26752822

  14. Pediatric Body Magnetic Resonance Imaging.

    PubMed

    Kandasamy, Devasenathipathy; Goyal, Ankur; Sharma, Raju; Gupta, Arun Kumar

    2016-09-01

    Magnetic resonance imaging (MRI) is a radiation-free imaging modality with excellent contrast resolution and multiplanar capabilities. Since ionizing radiation is an important concern in the pediatric population, MRI serves as a useful alternative to computed tomography (CT) and also provides additional clues to diagnosis, not discernible on other investigations. Magnetic resonance cholangiopancreatography (MRCP), urography, angiography, enterography, dynamic multiphasic imaging and diffusion-weighted imaging provide wealth of information. The main limitations include, long scan time, need for sedation/anesthesia, cost and lack of widespread availability. With the emergence of newer sequences and variety of contrast agents, MRI has become a robust modality and may serve as a one-stop shop for both anatomical and functional information.

  15. Image quality, compression and segmentation in medicine.

    PubMed

    Morgan, Pam; Frankish, Clive

    2002-12-01

    This review considers image quality in the context of the evolving technology of image compression, and the effects image compression has on perceived quality. The concepts of lossless, perceptually lossless, and diagnostically lossless but lossy compression are described, as well as the possibility of segmented images, combining lossy compression with perceptually lossless regions of interest. The different requirements for diagnostic and training images are also discussed. The lack of established methods for image quality evaluation is highlighted and available methods discussed in the light of the information that may be inferred from them. Confounding variables are also identified. Areas requiring further research are illustrated, including differences in perceptual quality requirements for different image modalities, image regions, diagnostic subtleties, and tasks. It is argued that existing tools for measuring image quality need to be refined and new methods developed. The ultimate aim should be the development of standards for image quality evaluation which take into consideration both the task requirements of the images and the acceptability of the images to the users.

  16. Coronary magnetic resonance imaging.

    PubMed

    Manning, Warren J; Nezafat, Reza; Appelbaum, Evan; Danias, Peter G; Hauser, Thomas H; Yeon, Susan B

    2007-02-01

    This article highlights the technical challenges and general imaging strategies for coronary MRI. This is followed by a review of the clinical results for the assessment of anomalous CAD, coronary artery aneurysms, native vessel integrity, and coronary artery bypass graft disease using the more commonly applied MRI methods. It concludes with a brief discussion of the advantages/disadvantages and clinical results comparing coronary MRI with multidetector CT (MDCT) coronary angiography.

  17. Low field magnetic resonance imaging

    DOEpatents

    Pines, Alexander; Sakellariou, Dimitrios; Meriles, Carlos A.; Trabesinger, Andreas H.

    2010-07-13

    A method and system of magnetic resonance imaging does not need a large homogenous field to truncate a gradient field. Spatial information is encoded into the spin magnetization by allowing the magnetization to evolve in a non-truncated gradient field and inducing a set of 180 degree rotations prior to signal acquisition.

  18. An Expert Vision System for Medical Image Segmentation

    NASA Astrophysics Data System (ADS)

    Chen, Shiuh-Yung J.; Lin, Wei-Chung; Chen, Chin-Tu

    1989-05-01

    In this paper, an expert vision system is proposed which integrates knowledge from diverse sources for tomographic image segmentation. The system miinicks the reasoning process of an expert to divide a tomographic brain image into semantically meaningful entities. These entities can then be related to the fundamental biomedical processes, both in health and in disease, that are of interest or of importance to health care research. The images under study include those acquired from x-ray CT (Computed Tomography), MRI (Magnetic Resonance Imaging), and PET (Positron Emission Tomography). Given a set of three (correlated) images acquired from these three different modalities at the same slicing level and angle of a human brain, the proposed system performs image segmentation based on (1) knowledge about the characteristics of the three different sensors, (2) knowledge about the anatomic structures of human brains, (3) knowledge about brain diseases, and (4) knowledge about image processing and analysis tools. Since the problem domain is characterized by incomplete and uncertain information, the blackboard architecture which is an opportunistic reasoning model is adopted as the framework of the proposed system.

  19. Use of Model-Segmentation Criteria in Clustering and Segmentation of Time Series and Digital Images.

    DTIC Science & Technology

    1983-05-05

    DANS LAS REPARTITION? ET LA SEGMENTATION DES SERIES TEMPORELLES ET DES IMAGES NUMtRICALES Cet article traite le d~veloppement et l’utilisation des...multidimensionnelles et du no-bre des classes des segments dans la segmentation des series temporelles et des imaqes numericales. Les criteres comme ceux de Akaike...NATIONAL BURCAU OF STAND)AROS 1963 A USE OF MODEL-SEGMENTATION CRITERIA IN CLUSTERING AND SEGMENTATION OF TIME SERIES AND DIGITAL IMAGES by STANLEY L

  20. Unsupervised Cardiac Image Segmentation via Multiswarm Active Contours with a Shape Prior

    PubMed Central

    Cruz-Aceves, I.; Avina-Cervantes, J. G.; Lopez-Hernandez, J. M.; Garcia-Hernandez, M. G.; Ibarra-Manzano, M. A.

    2013-01-01

    This paper presents a new unsupervised image segmentation method based on particle swarm optimization and scaled active contours with shape prior. The proposed method uses particle swarm optimization over a polar coordinate system to perform the segmentation task, increasing the searching capability on medical images with respect to different interactive segmentation techniques. This method is used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, where the shape prior is acquired by cardiologists, and it is utilized as the initial active contour. Moreover, to assess the performance of the cardiac medical image segmentations obtained by the proposed method and by the interactive techniques regarding the regions delineated by experts, a set of validation metrics has been adopted. The experimental results are promising and suggest that the proposed method is capable of segmenting human heart and ventricular areas accurately, which can significantly help cardiologists in clinical decision support. PMID:24198850

  1. Segmental Isotopic Labeling of Proteins for Nuclear Magnetic Resonance

    PubMed Central

    Dongsheng, Liu; Xu, Rong; Cowburn, David

    2009-01-01

    Nuclear Magnetic Resonance (NMR) spectroscopy has emerged as one of the principle techniques of structural biology. It is not only a powerful method for elucidating the 3D structures under near physiological conditions, but also a convenient method for studying protein-ligand interactions and protein dynamics. A major drawback of macromolecular NMR is its size limitation caused by slower tumbling rates and greater complexity of the spectra as size increases. Segmental isotopic labeling allows specific segment(s) within a protein to be selectively examined by NMR thus significantly reducing the spectral complexity for large proteins and allowing a variety of solution-based NMR strategies to be applied. Two related approaches are generally used in the segmental isotopic labeling of proteins: expressed protein ligation and protein trans-splicing. Here we describe the methodology and recent application of expressed protein ligation and protein trans-splicing for NMR structural studies of proteins and protein complexes. We also describe the protocol used in our lab for the segmental isotopic labeling of a 50 kDa protein Csk (C-terminal Src Kinase) using expressed protein ligation methods. PMID:19632474

  2. Automatic segmentation of mammogram and tomosynthesis images

    NASA Astrophysics Data System (ADS)

    Sargent, Dusty; Park, Sun Young

    2016-03-01

    Breast cancer is a one of the most common forms of cancer in terms of new cases and deaths both in the United States and worldwide. However, the survival rate with breast cancer is high if it is detected and treated before it spreads to other parts of the body. The most common screening methods for breast cancer are mammography and digital tomosynthesis, which involve acquiring X-ray images of the breasts that are interpreted by radiologists. The work described in this paper is aimed at optimizing the presentation of mammography and tomosynthesis images to the radiologist, thereby improving the early detection rate of breast cancer and the resulting patient outcomes. Breast cancer tissue has greater density than normal breast tissue, and appears as dense white image regions that are asymmetrical between the breasts. These irregularities are easily seen if the breast images are aligned and viewed side-by-side. However, since the breasts are imaged separately during mammography, the images may be poorly centered and aligned relative to each other, and may not properly focus on the tissue area. Similarly, although a full three dimensional reconstruction can be created from digital tomosynthesis images, the same centering and alignment issues can occur for digital tomosynthesis. Thus, a preprocessing algorithm that aligns the breasts for easy side-by-side comparison has the potential to greatly increase the speed and accuracy of mammogram reading. Likewise, the same preprocessing can improve the results of automatic tissue classification algorithms for mammography. In this paper, we present an automated segmentation algorithm for mammogram and tomosynthesis images that aims to improve the speed and accuracy of breast cancer screening by mitigating the above mentioned problems. Our algorithm uses information in the DICOM header to facilitate preprocessing, and incorporates anatomical region segmentation and contour analysis, along with a hidden Markov model (HMM) for

  3. Image segmentation via motion vector estimates

    NASA Astrophysics Data System (ADS)

    Abdel-Malek, Aiman A.; Hasekioglu, Orkun; Bloomer, John J.

    1990-07-01

    In the visual world moving edges in the periphery represent vital pieces of information that directs the human foveation mechanism to selectively gather information around these specific locations. This computationally efficient approach of allocating resources at key locations has inspired computer visionists to develop new target detection and hacking algorithms based on motion detection in image sequences. In this study we implemented a recursive algorithm for estimating motion vector fields for each pixel in a sequence of Digital Subtraction Angiography (DSA) images. Velocity information is used to segment the image and perform linear quadratic and acceleration-based frame interpolation to produce an apparent frame rate increase. Our results demonstrate the feasibility of low-rate digital fluoroscopy hence less exposure risks while preserving image quality. Furthermore the technique can be useful in the medical Picture Archival and Communication Systems (PACS) where image data can be compressed by storing and transmiting only the motion fields associated with the moving pixels. 1.

  4. Image segmentation with a unified graphical model.

    PubMed

    Zhang, Lei; Ji, Qiang

    2010-08-01

    We propose a unified graphical model that can represent both the causal and noncausal relationships among random variables and apply it to the image segmentation problem. Specifically, we first propose to employ Conditional Random Field (CRF) to model the spatial relationships among image superpixel regions and their measurements. We then introduce a multilayer Bayesian Network (BN) to model the causal dependencies that naturally exist among different image entities, including image regions, edges, and vertices. The CRF model and the BN model are then systematically and seamlessly combined through the theories of Factor Graph to form a unified probabilistic graphical model that captures the complex relationships among different image entities. Using the unified graphical model, image segmentation can be performed through a principled probabilistic inference. Experimental results on the Weizmann horse data set, on the VOC2006 cow data set, and on the MSRC2 multiclass data set demonstrate that our approach achieves favorable results compared to state-of-the-art approaches as well as those that use either the BN model or CRF model alone.

  5. An interactive medical image segmentation framework using iterative refinement.

    PubMed

    Kalshetti, Pratik; Bundele, Manas; Rahangdale, Parag; Jangra, Dinesh; Chattopadhyay, Chiranjoy; Harit, Gaurav; Elhence, Abhay

    2017-02-13

    Segmentation is often performed on medical images for identifying diseases in clinical evaluation. Hence it has become one of the major research areas. Conventional image segmentation techniques are unable to provide satisfactory segmentation results for medical images as they contain irregularities. They need to be pre-processed before segmentation. In order to obtain the most suitable method for medical image segmentation, we propose MIST (Medical Image Segmentation Tool), a two stage algorithm. The first stage automatically generates a binary marker image of the region of interest using mathematical morphology. This marker serves as the mask image for the second stage which uses GrabCut to yield an efficient segmented result. The obtained result can be further refined by user interaction, which can be done using the proposed Graphical User Interface (GUI). Experimental results show that the proposed method is accurate and provides satisfactory segmentation results with minimum user interaction on medical as well as natural images.

  6. Image Segmentation, Registration, Compression, and Matching

    NASA Technical Reports Server (NTRS)

    Yadegar, Jacob; Wei, Hai; Yadegar, Joseph; Ray, Nilanjan; Zabuawala, Sakina

    2011-01-01

    A novel computational framework was developed of a 2D affine invariant matching exploiting a parameter space. Named as affine invariant parameter space (AIPS), the technique can be applied to many image-processing and computer-vision problems, including image registration, template matching, and object tracking from image sequence. The AIPS is formed by the parameters in an affine combination of a set of feature points in the image plane. In cases where the entire image can be assumed to have undergone a single affine transformation, the new AIPS match metric and matching framework becomes very effective (compared with the state-of-the-art methods at the time of this reporting). No knowledge about scaling or any other transformation parameters need to be known a priori to apply the AIPS framework. An automated suite of software tools has been created to provide accurate image segmentation (for data cleaning) and high-quality 2D image and 3D surface registration (for fusing multi-resolution terrain, image, and map data). These tools are capable of supporting existing GIS toolkits already in the marketplace, and will also be usable in a stand-alone fashion. The toolkit applies novel algorithmic approaches for image segmentation, feature extraction, and registration of 2D imagery and 3D surface data, which supports first-pass, batched, fully automatic feature extraction (for segmentation), and registration. A hierarchical and adaptive approach is taken for achieving automatic feature extraction, segmentation, and registration. Surface registration is the process of aligning two (or more) data sets to a common coordinate system, during which the transformation between their different coordinate systems is determined. Also developed here are a novel, volumetric surface modeling and compression technique that provide both quality-guaranteed mesh surface approximations and compaction of the model sizes by efficiently coding the geometry and connectivity

  7. Image Segmentation for Improvised Explosive Devices

    DTIC Science & Technology

    2012-12-01

    zj, αj) measuring the similarity between adjacent pixels zi and zj . The function V (zi, αi, zj, αj) results 22 in large values when similar pixels... value distribution. The second and the third rows show the histogram for the object and the background after they were segmented...Images B and C show the directly assembled gray- value histograms for the background (B) and the object (C) . . . . . . . . . . . . . . . . 22 Figure

  8. AUTOMATIC MULTI-ATLAS-BASED CARTILAGE SEGMENTATION FROM KNEE MR IMAGES.

    PubMed

    Shan, Liang; Charles, Cecil; Niethammer, Marc

    2012-12-31

    In this paper, we propose a multi-atlas-based method to automatically segment the femoral and tibial cartilage from T1 weighted magnetic resonance (MR) knee images. The segmentation result is a joint decision of the spatial priors from a multi-atlas registration and the local likelihoods within a Bayesian framework. The cartilage likelihoods are obtained from a probabilistic k nearest neighbor classification. Validation results on 18 knee MR images against the manual expert segmentations from a dataset acquired for osteoarthritis research show good performance for the segmentation of femoral and tibial cartilage (mean Dice similarity coefficient of 75.2% and 81.7% respectively).

  9. AUTOMATIC MULTI-ATLAS-BASED CARTILAGE SEGMENTATION FROM KNEE MR IMAGES

    PubMed Central

    Shan, Liang; Charles, Cecil; Niethammer, Marc

    2012-01-01

    In this paper, we propose a multi-atlas-based method to automatically segment the femoral and tibial cartilage from T1 weighted magnetic resonance (MR) knee images. The segmentation result is a joint decision of the spatial priors from a multi-atlas registration and the local likelihoods within a Bayesian framework. The cartilage likelihoods are obtained from a probabilistic k nearest neighbor classification. Validation results on 18 knee MR images against the manual expert segmentations from a dataset acquired for osteoarthritis research show good performance for the segmentation of femoral and tibial cartilage (mean Dice similarity coefficient of 75.2% and 81.7% respectively). PMID:24443678

  10. Cerebral microbleed segmentation from susceptibility weighted images

    NASA Astrophysics Data System (ADS)

    Roy, Snehashis; Jog, Amod; Magrath, Elizabeth; Butman, John A.; Pham, Dzung L.

    2015-03-01

    Cerebral microbleeds (CMB) are a common marker of traumatic brain injury. Accurate detection and quantification of the CMBs are important for better understanding the progression and prognosis of the injury. Previous microbleed detection methods have suffered from a high rate of false positives, which is time consuming to manually correct. In this paper, we propose a fully automatic, example-based method to segment CMBs from susceptibility-weighted (SWI) scans, where examples from an already segmented template SWI image are used to detect CMBs in a new image. First, multiple radial symmetry transforms (RST) are performed on the template SWI to detect small ellipsoidal structures, which serve as potential microbleed candidates. Then 3D patches from the SWI and its RSTs are combined to form a feature vector at each voxel of the image. A random forest regression is trained using the feature vectors, where the dependent variable is the binary segmentation voxel of the template. Once the regression is learnt, it is applied to a new SWI scan, whose feature vectors contain patches from SWI and its RSTs. Experiments on 26 subjects with mild to severe brain injury show a CMB detection sensitivity of 85:7%, specificity 99:5%, and a false positive to true positive ratio of 1:73, which is competitive with published methods while providing a significant reduction in computation time.

  11. Image segmentation applied to atherosclerotic lesion

    NASA Astrophysics Data System (ADS)

    Morales, R. Rodríguez; Martínez, T. E. Alarcón; Cuello, L. Sánchez; Fernández-Britto, J. E.; Taylor, Charles

    2000-10-01

    The results obtained using two techniques: a supervised method and other unsupervised for image segmentation of atherosclerotic lesions of the thoracic aorta, are presented. Segmentation was used both with and without pre-processing. In this paper, the advantages of pre-processing prior to are shown for discriminating among the different atherosclerotic lesions (fatty streaks, fibrous plaque, complicated plaques and calcified plaques) and identifying them. The results using a supervised method were poor when searching vector consisted of two components, the mean and the variance. This digital image processing was done in order to use the automated atherometric system. This methodology has been considered to be suitable for the characterization of the atherosclerotic lesions in any artery and its organ-related damage in any vascular sector or group of patients. Final results were compared with manual segmentation realized by an expert, where difference errors less than 3% were observed. It is demonstrated by extensive experimentation, using real image data, that proposed strategy is fast and robust in the environment of a personal computer.

  12. Level set method coupled with Energy Image features for brain MR image segmentation.

    PubMed

    Punga, Mirela Visan; Gaurav, Rahul; Moraru, Luminita

    2014-06-01

    Up until now, the noise and intensity inhomogeneity are considered one of the major drawbacks in the field of brain magnetic resonance (MR) image segmentation. This paper introduces the energy image feature approach for intensity inhomogeneity correction. Our approach of segmentation takes the advantage of image features and preserves the advantages of the level set methods in region-based active contours framework. The energy image feature represents a new image obtained from the original image when the pixels' values are replaced by local energy values computed in the 3×3 mask size. The performance and utility of the energy image features were tested and compared through two different variants of level set methods: one as the encompassed local and global intensity fitting method and the other as the selective binary and Gaussian filtering regularized level set method. The reported results demonstrate the flexibility of the energy image feature to adapt to level set segmentation framework and to perform the challenging task of brain lesion segmentation in a rather robust way.

  13. Fuzzy rule-based image segmentation in dynamic MR images of the liver

    NASA Astrophysics Data System (ADS)

    Kobashi, Syoji; Hata, Yutaka; Tokimoto, Yasuhiro; Ishikawa, Makato

    2000-06-01

    This paper presents a fuzzy rule-based region growing method for segmenting two-dimensional (2-D) and three-dimensional (3- D) magnetic resonance (MR) images. The method is an extension of the conventional region growing method. The proposed method evaluates the growing criteria by using fuzzy inference techniques. The use of the fuzzy if-then rules is appropriate for describing the knowledge of the legions on the MR images. To evaluate the performance of the proposed method, it was applied to artificially generated images. In comparison with the conventional method, the proposed method shows high robustness for noisy images. The method then applied for segmenting the dynamic MR images of the liver. The dynamic MR imaging has been used for diagnosis of hepatocellular carcinoma (HCC), portal hypertension, and so on. Segmenting the liver, portal vein (PV), and inferior vena cava (IVC) can give useful description for the diagnosis, and is a basis work of a pres-surgery planning system and a virtual endoscope. To apply the proposed method, fuzzy if-then rules are derived from the time-density curve of ROIs. In the experimental results, the 2-D reconstructed and 3-D rendered images of the segmented liver, PV, and IVC are shown. The evaluation by a physician shows that the generated images are comparable to the hepatic anatomy, and they would be useful to understanding, diagnosis, and pre-surgery planning.

  14. Interventional Cardiovascular Magnetic Resonance Imaging

    PubMed Central

    Saikus, Christina E.; Lederman, Robert J.

    2010-01-01

    Cardiovascular magnetic resonance (CMR) combines excellent soft-tissue contrast, multiplanar views, and dynamic imaging of cardiac function without ionizing radiation exposure. Interventional cardiovascular magnetic resonance (iCMR) leverages these features to enhance conventional interventional procedures or to enable novel ones. Although still awaiting clinical deployment, this young field has tremendous potential. We survey promising clinical applications for iCMR. Next, we discuss the technologies that allow CMR-guided interventions and, finally, what still needs to be done to bring them to the clinic. PMID:19909937

  15. On a nonlocal model of image segmentation

    NASA Astrophysics Data System (ADS)

    Gajewski, Herbert; Gärtner, Klaus

    2005-07-01

    We understand an image as binary grey ‘alloy’ of a black and a white component and use a nonlocal phase separation model to describe image segmentation. The model consists in a degenerate nonlinear parabolic equation with a nonlocal drift term additionally to the familiar Perona-Malik model. We formulate conditions for the model parameters to guarantee global existence of a unique solution that tends exponentially in time to a unique steady state. This steady state is solution of a nonlocal nonlinear elliptic boundary value problem and allows a variational characterization. Numerical examples demonstrate the properties of the model.

  16. Application of an enhanced fuzzy algorithm for MR brain tumor image segmentation

    NASA Astrophysics Data System (ADS)

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

    2010-02-01

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

  17. 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.

  18. Perceived visual speed constrained by image segmentation

    NASA Technical Reports Server (NTRS)

    Verghese, P.; Stone, L. S.

    1996-01-01

    Little is known about how or where the visual system parses the visual scene into objects or surfaces. However, it is generally assumed that the segmentation and grouping of pieces of the image into discrete entities is due to 'later' processing stages, after the 'early' processing of the visual image by local mechanisms selective for attributes such as colour, orientation, depth, and motion. Speed perception is also thought to be mediated by early mechanisms tuned for speed. Here we show that manipulating the way in which an image is parsed changes the way in which local speed information is processed. Manipulations that cause multiple stimuli to appear as parts of a single patch degrade speed discrimination, whereas manipulations that perceptually divide a single large stimulus into parts improve discrimination. These results indicate that processes as early as speed perception may be constrained by the parsing of the visual image into discrete entities.

  19. Magnetic Resonance Imaging (MRI): Brain (For Parents)

    MedlinePlus

    ... to 2-Year-Old Magnetic Resonance Imaging (MRI): Brain KidsHealth > For Parents > Magnetic Resonance Imaging (MRI): Brain ... child may be given headphones to listen to music or earplugs to block the noise, and will ...

  20. Magnetic Resonance Imaging of Electrolysis.

    PubMed Central

    Meir, Arie; Hjouj, Mohammad; Rubinsky, Liel; Rubinsky, Boris

    2015-01-01

    This study explores the hypothesis that Magnetic Resonance Imaging (MRI) can image the process of electrolysis by detecting pH fronts. The study has relevance to real time control of cell ablation with electrolysis. To investigate the hypothesis we compare the following MR imaging sequences: T1 weighted, T2 weighted and Proton Density (PD), with optical images acquired using pH-sensitive dyes embedded in a physiological saline agar solution phantom treated with electrolysis and discrete measurements with a pH microprobe. We further demonstrate the biological relevance of our work using a bacterial E. Coli model, grown on the phantom. The results demonstrate the ability of MRI to image electrolysis produced pH changes in a physiological saline phantom and show that these changes correlate with cell death in the E. Coli model grown on the phantom. The results are promising and invite further experimental research. PMID:25659942

  1. Magnetic Resonance Imaging of Electrolysis.

    NASA Astrophysics Data System (ADS)

    Meir, Arie; Hjouj, Mohammad; Rubinsky, Liel; Rubinsky, Boris

    2015-02-01

    This study explores the hypothesis that Magnetic Resonance Imaging (MRI) can image the process of electrolysis by detecting pH fronts. The study has relevance to real time control of cell ablation with electrolysis. To investigate the hypothesis we compare the following MR imaging sequences: T1 weighted, T2 weighted and Proton Density (PD), with optical images acquired using pH-sensitive dyes embedded in a physiological saline agar solution phantom treated with electrolysis and discrete measurements with a pH microprobe. We further demonstrate the biological relevance of our work using a bacterial E. Coli model, grown on the phantom. The results demonstrate the ability of MRI to image electrolysis produced pH changes in a physiological saline phantom and show that these changes correlate with cell death in the E. Coli model grown on the phantom. The results are promising and invite further experimental research.

  2. Magnetic Resonance Imaging and Magnetic Resonance Spectroscopy in Dementias

    PubMed Central

    Hsu, Yuan-Yu; Du, An-Tao; Schuff, Norbert; Weiner, Michael W.

    2007-01-01

    This article reviews recent studies of magnetic resonance imaging and magnetic resonance spectroscopy in dementia, including Alzheimer's disease, frontotemporal dementia, dementia with Lewy bodies, idiopathic Parkinson's disease, Huntington's disease, and vascular dementia. Magnetic resonance imaging and magnetic resonance spectroscopy can detect structural alteration and biochemical abnormalities in the brain of demented subjects and may help in the differential diagnosis and early detection of affected individuals, monitoring disease progression, and evaluation of therapeutic effect. PMID:11563438

  3. Automatic Atlas-based Three-label Cartilage Segmentation from MR Knee Images

    PubMed Central

    Shan, Liang; Charles, Cecil; Niethammer, Marc

    2013-01-01

    This paper proposes a method to build a bone-cartilage atlas of the knee and to use it to automatically segment femoral and tibial cartilage from T1 weighted magnetic resonance (MR) images. Anisotropic spatial regularization is incorporated into a three-label segmentation framework to improve segmentation results for the thin cartilage layers. We jointly use the atlas information and the output of a probabilistic k nearest neighbor classifier within the segmentation method. The resulting cartilage segmentation method is fully automatic. Validation results on 18 knee MR images against manual expert segmentations from a dataset acquired for osteoarthritis research show good performance for the segmentation of femoral and tibial cartilage (mean Dice similarity coefficient of 78.2% and 82.6% respectively). PMID:23685704

  4. Automatic Atlas-based Three-label Cartilage Segmentation from MR Knee Images.

    PubMed

    Shan, Liang; Charles, Cecil; Niethammer, Marc

    2012-01-01

    This paper proposes a method to build a bone-cartilage atlas of the knee and to use it to automatically segment femoral and tibial cartilage from T1 weighted magnetic resonance (MR) images. Anisotropic spatial regularization is incorporated into a three-label segmentation framework to improve segmentation results for the thin cartilage layers. We jointly use the atlas information and the output of a probabilistic k nearest neighbor classifier within the segmentation method. The resulting cartilage segmentation method is fully automatic. Validation results on 18 knee MR images against manual expert segmentations from a dataset acquired for osteoarthritis research show good performance for the segmentation of femoral and tibial cartilage (mean Dice similarity coefficient of 78.2% and 82.6% respectively).

  5. Optimal retinal cyst segmentation from OCT images

    NASA Astrophysics Data System (ADS)

    Oguz, Ipek; Zhang, Li; Abramoff, Michael D.; Sonka, Milan

    2016-03-01

    Accurate and reproducible segmentation of cysts and fluid-filled regions from retinal OCT images is an important step allowing quantification of the disease status, longitudinal disease progression, and response to therapy in wet-pathology retinal diseases. However, segmentation of fluid-filled regions from OCT images is a challenging task due to their inhomogeneous appearance, the unpredictability of their number, size and location, as well as the intensity profile similarity between such regions and certain healthy tissue types. While machine learning techniques can be beneficial for this task, they require large training datasets and are often over-fitted to the appearance models of specific scanner vendors. We propose a knowledge-based approach that leverages a carefully designed cost function and graph-based segmentation techniques to provide a vendor-independent solution to this problem. We illustrate the results of this approach on two publicly available datasets with a variety of scanner vendors and retinal disease status. Compared to a previous machine-learning based approach, the volume similarity error was dramatically reduced from 81:3+/-56:4% to 22:2+/-21:3% (paired t-test, p << 0:001).

  6. Embedded Implementation of VHR Satellite Image Segmentation.

    PubMed

    Li, Chao; Balla-Arabé, Souleymane; Ginhac, Dominique; Yang, Fan

    2016-05-27

    Processing and analysis of Very High Resolution (VHR) satellite images provide a mass of crucial information, which can be used for urban planning, security issues or environmental monitoring. However, they are computationally expensive and, thus, time consuming, while some of the applications, such as natural disaster monitoring and prevention, require high efficiency performance. Fortunately, parallel computing techniques and embedded systems have made great progress in recent years, and a series of massively parallel image processing devices, such as digital signal processors or Field Programmable Gate Arrays (FPGAs), have been made available to engineers at a very convenient price and demonstrate significant advantages in terms of running-cost, embeddability, power consumption flexibility, etc. In this work, we designed a texture region segmentation method for very high resolution satellite images by using the level set algorithm and the multi-kernel theory in a high-abstraction C environment and realize its register-transfer level implementation with the help of a new proposed high-level synthesis-based design flow. The evaluation experiments demonstrate that the proposed design can produce high quality image segmentation with a significant running-cost advantage.

  7. Embedded Implementation of VHR Satellite Image Segmentation

    PubMed Central

    Li, Chao; Balla-Arabé, Souleymane; Ginhac, Dominique; Yang, Fan

    2016-01-01

    Processing and analysis of Very High Resolution (VHR) satellite images provide a mass of crucial information, which can be used for urban planning, security issues or environmental monitoring. However, they are computationally expensive and, thus, time consuming, while some of the applications, such as natural disaster monitoring and prevention, require high efficiency performance. Fortunately, parallel computing techniques and embedded systems have made great progress in recent years, and a series of massively parallel image processing devices, such as digital signal processors or Field Programmable Gate Arrays (FPGAs), have been made available to engineers at a very convenient price and demonstrate significant advantages in terms of running-cost, embeddability, power consumption flexibility, etc. In this work, we designed a texture region segmentation method for very high resolution satellite images by using the level set algorithm and the multi-kernel theory in a high-abstraction C environment and realize its register-transfer level implementation with the help of a new proposed high-level synthesis-based design flow. The evaluation experiments demonstrate that the proposed design can produce high quality image segmentation with a significant running-cost advantage. PMID:27240370

  8. Unsupervised texture image segmentation by improved neural network ART2

    NASA Technical Reports Server (NTRS)

    Wang, Zhiling; Labini, G. Sylos; Mugnuolo, R.; Desario, Marco

    1994-01-01

    We here propose a segmentation algorithm of texture image for a computer vision system on a space robot. An improved adaptive resonance theory (ART2) for analog input patterns is adapted to classify the image based on a set of texture image features extracted by a fast spatial gray level dependence method (SGLDM). The nonlinear thresholding functions in input layer of the neural network have been constructed by two parts: firstly, to reduce the effects of image noises on the features, a set of sigmoid functions is chosen depending on the types of the feature; secondly, to enhance the contrast of the features, we adopt fuzzy mapping functions. The cluster number in output layer can be increased by an autogrowing mechanism constantly when a new pattern happens. Experimental results and original or segmented pictures are shown, including the comparison between this approach and K-means algorithm. The system written in C language is performed on a SUN-4/330 sparc-station with an image board IT-150 and a CCD camera.

  9. Basics of magnetic resonance imaging

    SciTech Connect

    Oldendorf, W.; Oldendorf, W. Jr.

    1988-01-01

    Beginning with the behavior of a compass needle in a magnetic field, this text uses analogies from everyday experience to explain the phenomenon of nuclear magnetic resonance and how it is used for imaging. Using a minimum of scientific abbreviations and symbols, the basics of tissue visualization and characterization are presented. A description of the various types of magnets and scanners is followed by the practical advantages and limitations of MRI relative to x-ray CT scanning.

  10. Fast fetal magnetic resonance imaging.

    PubMed

    Sandrasegaran, Kumaresan; Lall, Chandana; Aisen, Alex A; Rajesh, Arumugam; Cohen, Mervyn D

    2005-01-01

    Fetal magnetic resonance imaging (MRI) can be used as a problem-solving tool when ultrasonic findings are equivocal. The role of fetal MRI has increased as obstetricians become aware of its potential and in utero therapy for anomalies becomes increasingly sophisticated. In this pictorial essay, we present a wide range of anomalies diagnosed or confirmed using MRI and discuss findings that help in the differential diagnosis.

  11. Nerves on magnetic resonance imaging.

    PubMed Central

    Collins, J. D.; Shaver, M. L.; Batra, P.; Brown, K.

    1989-01-01

    Nerves are often visualized on magnetic resonance imaging (MRI) studies of the soft tissues on the chest and shoulder girdle. To learn the reasons for the contrast between the nerves and adjacent tissues, the authors obtained a fresh specimen containing part of the brachial plexus nerves from the left axilla and compared MRI with x-ray projections and photomicrographs of histologic sections. The results suggest that the high signals from the nerves stand out in contrast to the low signals from their rich vascular supply. Images Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6A Figure 6B Figure 7 PMID:2733051

  12. Segmentation of polycystic kidneys from MR images

    NASA Astrophysics Data System (ADS)

    Racimora, Dimitri; Vivier, Pierre-Hugues; Chandarana, Hersh; Rusinek, Henry

    2010-03-01

    Polycystic kidney disease (PKD) is a disorder characterized by the growth of numerous fluid filled cysts in the kidneys. Measuring cystic kidney volume is thus crucial to monitoring the evolution of the disease. While T2-weighted MRI delineates the organ, automatic segmentation is very difficult due to highly variable shape and image contrast. The interactive stereology methods used currently involve a compromise between segmentation accuracy and time. We have investigated semi-automated methods: active contours and a sub-voxel morphology based algorithm. Coronal T2- weighted images of 17 patients were acquired in four breath-holds using the HASTE sequence on a 1.5 Tesla MRI unit. The segmentation results were compared to ground truth kidney masks obtained as a consensus of experts. Automatic active contour algorithm yielded an average 22% +/- 8.6% volume error. A recently developed method (Bridge Burner) based on thresholding and constrained morphology failed to separate PKD from the spleen, yielding 37.4% +/- 8.7% volume error. Manual post-editing reduced the volume error to 3.2% +/- 0.8% for active contours and 3.2% +/- 0.6% for Bridge Burner. The total time (automated algorithm plus editing) was 15 min +/- 5 min for active contours and 19 min +/- 11 min for Bridge Burner. The average volume errors for stereology method were 5.9%, 6.2%, 5.4% for mesh size 6.6, 11, 16.5 mm. The average processing times were 17, 7, 4 min. These results show that nearly two-fold improvement in PKD segmentation accuracy over stereology technique can be achieved with a combination of active contours and postediting.

  13. Imaging segmentation along the Cascadia subduction zone

    NASA Astrophysics Data System (ADS)

    Allen, R. M.; Hawley, W. B.; Martin-Short, R.

    2015-12-01

    As we learn more about the Cascadia subduction zone, there is clear evidence for segmentation in the character of the many physical processes along its 1000 km length. There is segmentation in the arc magmas, in the seismicity, episodic tremor and slip, crustal structure and mantle structure all the way down to ~400 km depth. What is striking is the fact that the segment boundaries for these processes at depths of a few kilometers to hundreds of kilometers align. We must determine if this is coincidence, or if not, what the causative process is. The seismic deployments of the Cascadia Initiative onshore and offshore allow us to image the structure of the subduction zone, including the incoming Juan de Fuca plate, with unprecedented resolution. We use data from three one-year deployments of 70 ocean bottom seismometers across the Juan de Fuca plate, along with hundreds of onshore stations from the Pacific Northwest Seismic Network, the Berkeley Digital Seismic Network, the Earthscope Transportable Array, and smaller temporary seismic deployments. Our 3D tomographic models show significant variation in the structure of the subducting slab along its length. It extends deepest in the south (the Gorda section) where the plate is youngest, and shallows to the north across southern Oregon. There is a gap in the slab beneath northern Oregon, which appears to correlate with the geochemistry of the arc magmas. The slab is then visible again beneath Washington. We also constrain mantle flow paths using shear-wave splitting measurements at the offshore and onshore seismic stations. Beneath the Juan de Fuca plate the flow is sub-parallel to the motion of the plate. However, beneath the Gorda section of the Juan de Fuca place the flow is sub-parallel to the motion of the Pacific plate, not the Juan de Fuca plate. We are thus beginning to image a complex mantle flow pattern that may also play a role in the observed segmentation.

  14. Image segmentation for stone-size inspection

    NASA Astrophysics Data System (ADS)

    Hsu, Jui-Pin; Fuh, Chiou-Shann

    1995-04-01

    Object size inspection is an important task and has various applications in computer vision, for example, automatic control stone-breaking machines. In this paper, an algorithm is proposed for image segmentation on size inspection of almost round stones with strong textures or almost no textures. We use one camera and multiple light sources at difference positions to take one image when each of the light sources is on. Then we compute the image differences and threshold them to extract edges. We will explain, step by step, picture taking, edge extraction, noise removal, and edge gap filling. Experimental results will be presented. Through various experiments, we find our algorithm robust on various stones and under noise.

  15. Multidimensionally encoded magnetic resonance imaging.

    PubMed

    Lin, Fa-Hsuan

    2013-07-01

    Magnetic resonance imaging (MRI) typically achieves spatial encoding by measuring the projection of a q-dimensional object over q-dimensional spatial bases created by linear spatial encoding magnetic fields (SEMs). Recently, imaging strategies using nonlinear SEMs have demonstrated potential advantages for reconstructing images with higher spatiotemporal resolution and reducing peripheral nerve stimulation. In practice, nonlinear SEMs and linear SEMs can be used jointly to further improve the image reconstruction performance. Here, we propose the multidimensionally encoded (MDE) MRI to map a q-dimensional object onto a p-dimensional encoding space where p > q. MDE MRI is a theoretical framework linking imaging strategies using linear and nonlinear SEMs. Using a system of eight surface SEM coils with an eight-channel radiofrequency coil array, we demonstrate the five-dimensional MDE MRI for a two-dimensional object as a further generalization of PatLoc imaging and O-space imaging. We also present a method of optimizing spatial bases in MDE MRI. Results show that MDE MRI with a higher dimensional encoding space can reconstruct images more efficiently and with a smaller reconstruction error when the k-space sampling distribution and the number of samples are controlled. Copyright © 2012 Wiley Periodicals, Inc.

  16. Underwater color image segmentation method via RGB channel fusion

    NASA Astrophysics Data System (ADS)

    Xuan, Li; Mingjun, Zhang

    2017-02-01

    Aiming at the problem of low segmentation accuracy and high computation time by applying existing segmentation methods for underwater color images, this paper has proposed an underwater color image segmentation method via RGB color channel fusion. Based on thresholding segmentation methods to conduct fast segmentation, the proposed method relies on dynamic estimation of the optimal weights for RGB channel fusion to obtain the grayscale image with high foreground-background contrast and reaches high segmentation accuracy. To verify the segmentation accuracy of the proposed method, the authors have conducted various underwater comparative experiments. The experimental results demonstrate that the proposed method is robust to illumination, and it is superior to existing methods in terms of both segmentation accuracy and computation time. Moreover, a segmentation technique is proposed for image sequences for real-time autonomous underwater vehicle operations.

  17. Automatic segmentation of MR images of the developing newborn brain.

    PubMed

    Prastawa, Marcel; Gilmore, John H; Lin, Weili; Gerig, Guido

    2005-10-01

    This paper describes an automatic tissue segmentation method for newborn brains from magnetic resonance images (MRI). The analysis and study of newborn brain MRI is of great interest due to its potential for studying early growth patterns and morphological changes in neurodevelopmental disorders. Automatic segmentation of newborn MRI is a challenging task mainly due to the low intensity contrast and the growth process of the white matter tissue. Newborn white matter tissue undergoes a rapid myelination process, where the nerves are covered in myelin sheathes. It is necessary to identify the white matter tissue as myelinated or non-myelinated regions. The degree of myelination is a fractional voxel property that represents regional changes of white matter as a function of age. Our method makes use of a registered probabilistic brain atlas. The method first uses robust graph clustering and parameter estimation to find the initial intensity distributions. The distribution estimates are then used together with the spatial priors to perform bias correction. Finally, the method refines the segmentation using training sample pruning and non-parametric kernel density estimation. Our results demonstrate that the method is able to segment the brain tissue and identify myelinated and non-myelinated white matter regions.

  18. A Semi-automated Approach to Improve the Efficiency of Medical Imaging Segmentation for Haptic Rendering.

    PubMed

    Banerjee, Pat; Hu, Mengqi; Kannan, Rahul; Krishnaswamy, Srinivasan

    2017-08-01

    The Sensimmer platform represents our ongoing research on simultaneous haptics and graphics rendering of 3D models. For simulation of medical and surgical procedures using Sensimmer, 3D models must be obtained from medical imaging data, such as magnetic resonance imaging (MRI) or computed tomography (CT). Image segmentation techniques are used to determine the anatomies of interest from the images. 3D models are obtained from segmentation and their triangle reduction is required for graphics and haptics rendering. This paper focuses on creating 3D models by automating the segmentation of CT images based on the pixel contrast for integrating the interface between Sensimmer and medical imaging devices, using the volumetric approach, Hough transform method, and manual centering method. Hence, automating the process has reduced the segmentation time by 56.35% while maintaining the same accuracy of the output at ±2 voxels.

  19. Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation.

    PubMed

    Chiu, Stephanie J; Li, Xiao T; Nicholas, Peter; Toth, Cynthia A; Izatt, Joseph A; Farsiu, Sina

    2010-08-30

    Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming. Results show that this method accurately segments eight retinal layer boundaries in normal adult eyes more closely to an expert grader as compared to a second expert grader.

  20. Robust vessel segmentation in fundus images.

    PubMed

    Budai, A; Bock, R; Maier, A; Hornegger, J; Michelson, G

    2013-01-01

    One of the most common modalities to examine the human eye is the eye-fundus photograph. The evaluation of fundus photographs is carried out by medical experts during time-consuming visual inspection. Our aim is to accelerate this process using computer aided diagnosis. As a first step, it is necessary to segment structures in the images for tissue differentiation. As the eye is the only organ, where the vasculature can be imaged in an in vivo and noninterventional way without using expensive scanners, the vessel tree is one of the most interesting and important structures to analyze. The quality and resolution of fundus images are rapidly increasing. Thus, segmentation methods need to be adapted to the new challenges of high resolutions. In this paper, we present a method to reduce calculation time, achieve high accuracy, and increase sensitivity compared to the original Frangi method. This method contains approaches to avoid potential problems like specular reflexes of thick vessels. The proposed method is evaluated using the STARE and DRIVE databases and we propose a new high resolution fundus database to compare it to the state-of-the-art algorithms. The results show an average accuracy above 94% and low computational needs. This outperforms state-of-the-art methods.

  1. Physiological basis and image processing in functional magnetic resonance imaging: Neuronal and motor activity in brain

    PubMed Central

    Sharma, Rakesh; Sharma, Avdhesh

    2004-01-01

    Functional magnetic resonance imaging (fMRI) is recently developing as imaging modality used for mapping hemodynamics of neuronal and motor event related tissue blood oxygen level dependence (BOLD) in terms of brain activation. Image processing is performed by segmentation and registration methods. Segmentation algorithms provide brain surface-based analysis, automated anatomical labeling of cortical fields in magnetic resonance data sets based on oxygen metabolic state. Registration algorithms provide geometric features using two or more imaging modalities to assure clinically useful neuronal and motor information of brain activation. This review article summarizes the physiological basis of fMRI signal, its origin, contrast enhancement, physical factors, anatomical labeling by segmentation, registration approaches with examples of visual and motor activity in brain. Latest developments are reviewed for clinical applications of fMRI along with other different neurophysiological and imaging modalities. PMID:15125779

  2. Association Between Early Q Waves and Reperfusion Success in Patients With ST-Segment-Elevation Myocardial Infarction Treated With Primary Percutaneous Coronary Intervention: A Cardiac Magnetic Resonance Imaging Study.

    PubMed

    Topal, Divan Gabriel; Lønborg, Jacob; Ahtarovski, Kiril Aleksov; Nepper-Christensen, Lars; Helqvist, Steffen; Holmvang, Lene; Pedersen, Frants; Clemmensen, Peter; Saünamaki, Kari; Jørgensen, Erik; Kyhl, Kasper; Ghotbi, Ali; Schoos, Mikkel Malby; Göransson, Christoffer; Bertelsen, Litten; Høfsten, Dan; Køber, Lars; Kelbæk, Henning; Vejlstrup, Niels; Engstrøm, Thomas

    2017-03-01

    Pathological early Q waves (QW) are associated with adverse outcomes in patients with ST-segment-elevation myocardial infarction (STEMI). Primary percutaneous coronary intervention (PCI) may therefore be less beneficial in patients with QW than in patients without QW. Myocardial salvage index and microvascular obstruction (MVO) are markers for reperfusion success. Thus, to clarify the benefit from primary PCI in STEMI patients with QW, we examined the association between baseline QW and myocardial salvage index and MVO in STEMI patients treated with primary PCI. The ECG was assessed before primary PCI for the presence of QW (early) in 515 STEMI patients. The patients underwent a cardiac magnetic resonance imaging scan at day 1 (interquartile range [IQR], 1-1) and again at day 92 (IQR, 89-96). Early QW was observed in 108 (21%) patients and was related to smaller final myocardial salvage index (0.59 [IQR, 0.39-0.69] versus 0.65 [IQR, 0.46-0.84]; P<0.001) and larger MVO (1.4 [IQR, 0.0-5.4] versus 0.0 [IQR, 0.0-2.4]; P<0.001) compared with non-QW. QW remained associated with both final myocardial salvage index (β=-0.12; P=0.03) and MVO (β=0.18; P=0.001) after adjusting for potential confounders. Patients presenting with their first STEMI and early QW in the ECG had smaller myocardial salvage index and more extensive MVO than non-QW despite treatment within 12 hours after symptom onset. However, final myocardial salvage index in patients with QW was substantial, and patients with QW still benefit from primary PCI. URL: http://www.clinicaltrials.gov. Unique identifier: NCT01435408. © 2017 American Heart Association, Inc.

  3. Magnetic resonance imaging in medicine

    NASA Astrophysics Data System (ADS)

    Keevil, Stephen F.

    2001-11-01

    Over the past twenty years, magnetic resonance imaging (MRI) has become one of the most important imaging modalities available to clinical medicine. It offers great technical flexibility, and is free of the hazards associated with ionizing radiation. In addition to its role as a routine imaging technique with a growing range of clinical applications, the pace of development in MRI methodology remains high, and new ideas with significant potential emerge on a regular basis. MRI is a prime example of the spin-off benefits of basic science, and is an area of medicine in which physical science continues to play a major role, both in supporting clinical applications and in developing new techniques. This article presents a brief history of MRI and an overview of the underlying physics, followed by a short survey of current and emerging clinical applications.

  4. a Minimum Spanning Tree Based Method for Uav Image Segmentation

    NASA Astrophysics Data System (ADS)

    Wang, Ping; Wei, Zheng; Cui, Weihong; Lin, Zhiyong

    2016-06-01

    This paper proposes a Minimum Span Tree (MST) based image segmentation method for UAV images in coastal area. An edge weight based optimal criterion (merging predicate) is defined, which based on statistical learning theory (SLT). And we used a scale control parameter to control the segmentation scale. Experiments based on the high resolution UAV images in coastal area show that the proposed merging predicate can keep the integrity of the objects and prevent results from over segmentation. The segmentation results proves its efficiency in segmenting the rich texture images with good boundary of objects.

  5. Efficient Optimal Multi-level Thresholding for Biofilm Image Segmentation

    NASA Astrophysics Data System (ADS)

    Rojas, Darío; Rueda, Luis; Urrutia, Homero; Ngom, Alioune

    A microbial biofilm is structured mainly by a protective sticky matrix of extracellular polymeric substances. The appreciation of such structures is useful for the microbiologist and can be subjective to the observer. Thus, quantifying the underlying images in useful parameters by means of an objective image segmentation process helps substantially to reduce errors in quantification. This paper proposes an approach to segmentation of biofilm images using optimal multilevel thresholding and indices of clustering validity. A comparison of automatically segmented images with manual segmentation is done through different thresholding criteria, and clustering validity indices are used to find the correct number of thresholds, obtaining results similar to the segmentation done by an expert.

  6. Fusion Segmentation Method Based on Fuzzy Theory for Color Images

    NASA Astrophysics Data System (ADS)

    Zhao, J.; Huang, G.; Zhang, J.

    2017-09-01

    The image segmentation method based on two-dimensional histogram segments the image according to the thresholds of the intensity of the target pixel and the average intensity of its neighborhood. This method is essentially a hard-decision method. Due to the uncertainties when labeling the pixels around the threshold, the hard-decision method can easily get the wrong segmentation result. Therefore, a fusion segmentation method based on fuzzy theory is proposed in this paper. We use membership function to model the uncertainties on each color channel of the color image. Then, we segment the color image according to the fuzzy reasoning. The experiment results show that our proposed method can get better segmentation results both on the natural scene images and optical remote sensing images compared with the traditional thresholding method. The fusion method in this paper can provide new ideas for the information extraction of optical remote sensing images and polarization SAR images.

  7. Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data

    NASA Technical Reports Server (NTRS)

    Tilton, James C.; Lawrence, William T.

    2005-01-01

    NASA's Goddard Space Flight Center has developed a fast and effective method for generating image segmentation hierarchies. These segmentation hierarchies organize image data in a manner that makes their information content more accessible for analysis. Image segmentation enables analysis through the examination of image regions rather than individual image pixels. In addition, the segmentation hierarchy provides additional analysis clues through the tracing of the behavior of image region characteristics at several levels of segmentation detail. The potential for extracting the information content from imagery data based on segmentation hierarchies has not been fully explored for the benefit of the Earth and space science communities. This paper explores the potential of exploiting these segmentation hierarchies for the analysis of multi-date data sets, and for the particular application of change monitoring.

  8. Towards Automatic Image Segmentation Using Optimised Region Growing Technique

    NASA Astrophysics Data System (ADS)

    Alazab, Mamoun; Islam, Mofakharul; Venkatraman, Sitalakshmi

    Image analysis is being adopted extensively in many applications such as digital forensics, medical treatment, industrial inspection, etc. primarily for diagnostic purposes. Hence, there is a growing interest among researches in developing new segmentation techniques to aid the diagnosis process. Manual segmentation of images is labour intensive, extremely time consuming and prone to human errors and hence an automated real-time technique is warranted in such applications. There is no universally applicable automated segmentation technique that will work for all images as the image segmentation is quite complex and unique depending upon the domain application. Hence, to fill the gap, this paper presents an efficient segmentation algorithm that can segment a digital image of interest into a more meaningful arrangement of regions and objects. Our algorithm combines region growing approach with optimised elimination of false boundaries to arrive at more meaningful segments automatically. We demonstrate this using X-ray teeth images that were taken for real-life dental diagnosis.

  9. Semisupervised synthetic aperture radar image segmentation with multilayer superpixels

    NASA Astrophysics Data System (ADS)

    Wang, Can; Su, Weimin; Gu, Hong; Gong, Dachen

    2015-01-01

    Image segmentation plays a significant role in synthetic aperture radar (SAR) image processing. However, SAR image segmentation is challenging due to speckle. We propose a semisupervised bipartite graph method for segmentation of an SAR image. First, the multilayer over-segmentation of the SAR image, referred to as superpixels, is computed using existing segmentation algorithms. Second, an unbalanced bipartite graph is constructed in which the correlation between pixels is replaced by the texture similarity between superpixels, to reduce the dimension of the edge matrix. To also improve efficiency, we define a new method, called the combination of the Manhattan distance and symmetric Kullback-Leibler divergence, to measure texture similarity. Third, by the Moore-Penrose inverse matrix and semisupervised learning, we construct an across-affinity matrix. A quantitative evaluation using SAR images shows that the new algorithm produces significantly high-quality segmentations as compared with state-of-the-art segmentation algorithms.

  10. Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

    NASA Astrophysics Data System (ADS)

    Zheng, Qiang; Li, Honglun; Fan, Baode; Wu, Shuanhu; Xu, Jindong

    2017-09-01

    Active contour model (ACM) has been one of the most widely utilized methods in magnetic resonance (MR) brain image segmentation because of its ability of capturing topology changes. However, most of the existing ACMs only consider single-slice information in MR brain image data, i.e., the information used in ACMs based segmentation method is extracted only from one slice of MR brain image, which cannot take full advantage of the adjacent slice images' information, and cannot satisfy the local segmentation of MR brain images. In this paper, a novel ACM is proposed to solve the problem discussed above, which is based on multi-variate local Gaussian distribution and combines the adjacent slice images' information in MR brain image data to satisfy segmentation. The segmentation is finally achieved through maximizing the likelihood estimation. Experiments demonstrate the advantages of the proposed ACM over the single-slice ACM in local segmentation of MR brain image series.

  11. Breast mass segmentation on dynamic contrast-enhanced magnetic resonance scans using the level set method

    NASA Astrophysics Data System (ADS)

    Shi, Jiazheng; Sahiner, Berkman; Chan, Heang-Ping; Paramagul, Chintana; Hadjiiski, Lubomir M.; Helvie, Mark; Wu, Yi-Ta; Ge, Jun; Zhang, Yiheng; Zhou, Chuan; Wei, Jun

    2008-03-01

    The goal of this study was to develop an automated method to segment breast masses on dynamic contrast-enhanced (DCE) magnetic resonance (MR) scans that were performed to monitor breast cancer response to neoadjuvant chemotherapy. A radiologist experienced in interpreting breast MR scans defined the mass using a cuboid volume of interest (VOI). Our method then used the K-means clustering algorithm followed by morphological operations for initial mass segmentation on the VOI. The initial segmentation was then refined by a three-dimensional level set (LS) method. The velocity field of the LS method was formulated in terms of the mean curvature which guaranteed the smoothness of the surface and the Sobel edge information which attracted the zero LS to the desired mass margin. We also designed a method to reduce segmentation leak by adapting a region growing technique. Our method was evaluated on twenty DCE-MR scans of ten patients who underwent neoadjuvant chemotherapy. Each patient had pre- and post-chemotherapy DCE-MR scans on a 1.5 Tesla magnet. Computer segmentation was applied to coronal T1-weighted images. The in-plane pixel size ranged from 0.546 to 0.703 mm and the slice thickness ranged from 2.5 to 4.0 mm. The flip angle was 15 degrees, repetition time ranged from 5.98 to 6.7 ms, and echo time ranged from 1.2 to 1.3 ms. The computer segmentation results were compared to the radiologist's manual segmentation in terms of the overlap measure defined as the ratio of the intersection of the computer and the radiologist's segmentations to the radiologist's segmentation. Pre- and post-chemotherapy masses had overlap measures of 0.81+/-0.11 (mean+/-s.d.) and 0.70+/-0.21, respectively.

  12. Soft-tissues Image Processing: Comparison of Traditional Segmentation Methods with 2D active Contour Methods

    NASA Astrophysics Data System (ADS)

    Mikulka, J.; Gescheidtova, E.; Bartusek, K.

    2012-01-01

    The paper deals with modern methods of image processing, especially image segmentation, classification and evaluation of parameters. It focuses primarily on processing medical images of soft tissues obtained by magnetic resonance tomography (MR). It is easy to describe edges of the sought objects using segmented images. The edges found can be useful for further processing of monitored object such as calculating the perimeter, surface and volume evaluation or even three-dimensional shape reconstruction. The proposed solutions can be used for the classification of healthy/unhealthy tissues in MR or other imaging. Application examples of the proposed segmentation methods are shown. Research in the area of image segmentation focuses on methods based on solving partial differential equations. This is a modern method for image processing, often called the active contour method. It is of great advantage in the segmentation of real images degraded by noise with fuzzy edges and transitions between objects. In the paper, results of the segmentation of medical images by the active contour method are compared with results of the segmentation by other existing methods. Experimental applications which demonstrate the very good properties of the active contour method are given.

  13. Efficient segmentation of skin epidermis in whole slide histopathological images.

    PubMed

    Xu, Hongming; Mandal, Mrinal

    2015-01-01

    Segmentation of epidermis areas is an important step towards automatic analysis of skin histopathological images. This paper presents a robust technique for epidermis segmentation in whole slide skin histopathological images. The proposed technique first performs a coarse epidermis segmentation using global thresholding and shape analysis. The epidermis thickness is then estimated by a series of line segments perpendicular to the main axis of the initially segmented epidermis mask. If the segmented epidermis mask has a thickness greater than a predefined threshold, the segmentation is suspected to be inaccurate. A second pass of fine segmentation using k-means algorithm is then carried out over these coarsely segmented result to enhance the performance. Experimental results on 64 different skin histopathological images show that the proposed technique provides a superior performance compared to the existing techniques.

  14. Automatic segmentation for brain MR images via a convex optimized segmentation and bias field correction coupled model.

    PubMed

    Chen, Yunjie; Zhao, Bo; Zhang, Jianwei; Zheng, Yuhui

    2014-09-01

    Accurate segmentation of magnetic resonance (MR) images remains challenging mainly due to the intensity inhomogeneity, which is also commonly known as bias field. Recently active contour models with geometric information constraint have been applied, however, most of them deal with the bias field by using a necessary pre-processing step before segmentation of MR data. This paper presents a novel automatic variational method, which can segment brain MR images meanwhile correcting the bias field when segmenting images with high intensity inhomogeneities. We first define a function for clustering the image pixels in a smaller neighborhood. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. In order to reduce the effect of the noise, the local intensity variations are described by the Gaussian distributions with different means and variances. Then, the objective functions are integrated over the entire domain. In order to obtain the global optimal and make the results independent of the initialization of the algorithm, we reconstructed the energy function to be convex and calculated it by using the Split Bregman theory. A salient advantage of our method is that its result is independent of initialization, which allows robust and fully automated application. Our method is able to estimate the bias of quite general profiles, even in 7T MR images. Moreover, our model can also distinguish regions with similar intensity distribution with different variances. The proposed method has been rigorously validated with images acquired on variety of imaging modalities with promising results.

  15. A Review on Segmentation of Positron Emission Tomography Images

    PubMed Central

    Foster, Brent; Bagci, Ulas; Mansoor, Awais; Xu, Ziyue; Mollura, Daniel J.

    2014-01-01

    Positron Emission Tomography (PET), a non-invasive functional imaging method at the molecular level, images the distribution of biologically targeted radiotracers with high sensitivity. PET imaging provides detailed quantitative information about many diseases and is often used to evaluate inflammation, infection, and cancer by detecting emitted photons from a radiotracer localized to abnormal cells. In order to differentiate abnormal tissue from surrounding areas in PET images, image segmentation methods play a vital role; therefore, accurate image segmentation is often necessary for proper disease detection, diagnosis, treatment planning, and follow-ups. In this review paper, we present state-of-the-art PET image segmentation methods, as well as the recent advances in image segmentation techniques. In order to make this manuscript self-contained, we also briefly explain the fundamentals of PET imaging, the challenges of diagnostic PET image analysis, and the effects of these challenges on the segmentation results. PMID:24845019

  16. Interactive natural image segmentation via spline regression.

    PubMed

    Xiang, Shiming; Nie, Feiping; Zhang, Chunxia; Zhang, Changshui

    2009-07-01

    This paper presents an interactive algorithm for segmentation of natural images. The task is formulated as a problem of spline regression, in which the spline is derived in Sobolev space and has a form of a combination of linear and Green's functions. Besides its nonlinear representation capability, one advantage of this spline in usage is that, once it has been constructed, no parameters need to be tuned to data. We define this spline on the user specified foreground and background pixels, and solve its parameters (the combination coefficients of functions) from a group of linear equations. To speed up spline construction, K-means clustering algorithm is employed to cluster the user specified pixels. By taking the cluster centers as representatives, this spline can be easily constructed. The foreground object is finally cut out from its background via spline interpolation. The computational complexity of the proposed algorithm is linear in the number of the pixels to be segmented. Experiments on diverse natural images, with comparison to existing algorithms, illustrate the validity of our method.

  17. Image enhancement and segmentation using weighted morphological connected slope filters

    NASA Astrophysics Data System (ADS)

    Mendiola-Santibañez, Jorge D.; Terol-Villalobos, Iván R.

    2013-04-01

    The morphological connected slope filters (MCSFs) are studied as gray level transformations, and two contributions are made on these operators with the purpose of modifying the gradient criterion performance. The proposals consist of: (a) the introduction of three weighting functions and (b) the application of a displacement parameter. The displacement parameter will permit the image segmentation in a certain intensity interval and the contrast improvement at the same time. This characteristic is an important difference among the MCSFs introduced previously, together with the other transformations defined in the current literature utilized uniquely to enhance contrast. Also, an application example of the weighted morphological slope filters is provided. In such an example, white matter is separated from brain magnetic resonance images T1.

  18. Microscopy image segmentation tool: robust image data analysis.

    PubMed

    Valmianski, Ilya; Monton, Carlos; Schuller, Ivan K

    2014-03-01

    We present a software package called Microscopy Image Segmentation Tool (MIST). MIST is designed for analysis of microscopy images which contain large collections of small regions of interest (ROIs). Originally developed for analysis of porous anodic alumina scanning electron images, MIST capabilities have been expanded to allow use in a large variety of problems including analysis of biological tissue, inorganic and organic film grain structure, as well as nano- and meso-scopic structures. MIST provides a robust segmentation algorithm for the ROIs, includes many useful analysis capabilities, and is highly flexible allowing incorporation of specialized user developed analysis. We describe the unique advantages MIST has over existing analysis software. In addition, we present a number of diverse applications to scanning electron microscopy, atomic force microscopy, magnetic force microscopy, scanning tunneling microscopy, and fluorescent confocal laser scanning microscopy.

  19. Microscopy image segmentation tool: Robust image data analysis

    SciTech Connect

    Valmianski, Ilya Monton, Carlos; Schuller, Ivan K.

    2014-03-15

    We present a software package called Microscopy Image Segmentation Tool (MIST). MIST is designed for analysis of microscopy images which contain large collections of small regions of interest (ROIs). Originally developed for analysis of porous anodic alumina scanning electron images, MIST capabilities have been expanded to allow use in a large variety of problems including analysis of biological tissue, inorganic and organic film grain structure, as well as nano- and meso-scopic structures. MIST provides a robust segmentation algorithm for the ROIs, includes many useful analysis capabilities, and is highly flexible allowing incorporation of specialized user developed analysis. We describe the unique advantages MIST has over existing analysis software. In addition, we present a number of diverse applications to scanning electron microscopy, atomic force microscopy, magnetic force microscopy, scanning tunneling microscopy, and fluorescent confocal laser scanning microscopy.

  20. Accurate brain volumetry with diffusion-weighted spin-echo single-shot echo-planar-imaging and dual-clustering segmentation: comparison with volumetry-validated quantitative magnetic resonance imaging.

    PubMed

    Watanabe, Memi; Sakai, Osamu; Norbash, Alexander M; Jara, Hernán

    2010-03-01

    Although the diffusion-weighted spin-echo single-shot echo-planar-imaging (DW-SE-sshEPI) is an established quantitative MRI (qMRI) pulse sequence, it is known to be vulnerable to geometric distortions. The purpose of this work is to study is to study brain volumetry accuracy with DW-SE-sshEPI relative previously published volumetry-validated qMRI pulse sequence, specifically the mixed turbo spin-echo (mixed-TSE) pulse sequence, which is robust to magnetic field inhomogeneities and affords much higher spatial resolution. Twenty-eight subjects were imaged with both DW-SE-sshEPI and mixed-TSE pulse sequences. For each subject, the intracranial structures were segmented using a single-qMRI-channel dual-clustering algorithm (for DW-SE-sshEPI) and a three-qMRI-channel dual-clustering algorithm (for mixed-TSE). The respective intracranial volumes were calculated with both data sets and then compared. The intracranial volumes derived from DW-SE-sshEPI and mixed-TSE data sets are highly and linearly correlated (R2 = 0.9353) with a slope of 0.9911, with one distorted DW-SE-sshEPI data set demonstrating remarkable volume underestimation. Excluding this outlier resulted in improved linear correlation (R2 = 0.9681) with a slope of 1.0003. Brain volumetry with DW-SE-sshEPI at 1.5 T data sets can be very accurate for most patients for whom the major magnetic field inhomogeneities result from typical tissue interfaces (e.g., air-tissue or bone-tissue) and typical dental fillings, but not from larger metallic implants.

  1. Dynamic magnetic resonance imaging of the pharynx during deglutition.

    PubMed

    Amin, Milan R; Achlatis, Stratos; Lazarus, Cathy L; Branski, Ryan C; Storey, Pippa; Praminik, Bidyut; Fang, Yixin; Sodickson, Daniel K

    2013-03-01

    We utilized dynamic magnetic resonance imaging to visualize the pharynx and upper esophageal segment in normal, healthy subjects. A 3-T scanner with a 4-channel head coil and a dual-channel neck coil was used to obtain high-speed magnetic resonance images of subjects who were swallowing liquids and pudding. Ninety sequential images were acquired with a temporal resolution of 113 ms. Imaging was performed in axial planes at the levels of the oropharynx and the pharyngoesophageal segment. The images were then analyzed for variables related to alterations in the area of the pharynx and pharyngoesophageal segment during swallowing, as well as temporal measures related to these structures. All subjects tolerated the study protocol without complaint. Changes in the area of the pharyngeal wall lumen and temporal measurements were consistent within and between subjects. The inter-rater and intra-rater reliabilities for the measurement tool were excellent. Dynamic magnetic resonance imaging of the swallow sequence is both feasible and reliable and may eventually complement currently used diagnostic methods, as it adds substantive information.

  2. An improved FCM medical image segmentation algorithm based on MMTD.

    PubMed

    Zhou, Ningning; Yang, Tingting; Zhang, Shaobai

    2014-01-01

    Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) is one of the popular clustering algorithms for medical image segmentation. But FCM is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. It establishes the medium similarity measure based on the measure of medium truth degree (MMTD) and uses the correlation of the pixel and its neighbors to define the medium membership function. An improved FCM medical image segmentation algorithm based on MMTD which takes some spatial features into account is proposed in this paper. The experimental results show that the proposed algorithm is more antinoise than the standard FCM, with more certainty and less fuzziness. This will lead to its practicable and effective applications in medical image segmentation.

  3. Automatic three-dimensional segmentation of MR images applied to the rat uterus

    NASA Astrophysics Data System (ADS)

    Akselrod-Ballin, Ayelet; Eyal, Erez; Galun, Meirav; Furman-Haran, Edna; Gomori, John M.; Basri, Ronen; Degani, Hadassa; Brandt, Achi

    2006-03-01

    We introduce an automatic 3D multiscale automatic segmentation algorithm for delineating specific organs in Magnetic Resonance images (MRI). The algorithm can process several modalities simultaneously, and handle both isotropic and anisotropic data in only linear time complexity. It produces a hierarchical decomposition of MRI scans. During this segmentation process a rich set of features describing the segments in terms of intensity, shape and location are calculated, reflecting the formation of the hierarchical decomposition. We show that this method can delineate the entire uterus of the rat abdomen in 3D MR images utilizing a combination of scanning protocols that jointly achieve high contrast between the uterus and other abdominal organs and between inner structures of the rat uterus. Both single and multi-channel automatic segmentation demonstrate high correlation to a manual segmentation. While the focus here is on the rat uterus, the general approach can be applied to recognition in 2D, 3D and multi-channel medical images.

  4. A Clustering Algorithm for Liver Lesion Segmentation of Diffusion-Weighted MR Images

    PubMed Central

    Jha, Abhinav K.; Rodríguez, Jeffrey J.; Stephen, Renu M.; Stopeck, Alison T.

    2010-01-01

    In diffusion-weighted magnetic resonance imaging, accurate segmentation of liver lesions in the diffusion-weighted images is required for computation of the apparent diffusion coefficient (ADC) of the lesion, the parameter that serves as an indicator of lesion response to therapy. However, the segmentation problem is challenging due to low SNR, fuzzy boundaries and speckle and motion artifacts. We propose a clustering algorithm that incorporates spatial information and a geometric constraint to solve this issue. We show that our algorithm provides improved accuracy compared to existing segmentation algorithms. PMID:21151837

  5. Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

    PubMed Central

    Attique, Muhammad; Gilanie, Ghulam; Hafeez-Ullah; Mehmood, Malik S.; Naweed, Muhammad S.; Ikram, Masroor; Kamran, Javed A.; Vitkin, Alex

    2012-01-01

    Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described. PMID:22479421

  6. Image Segmentation Based on Chaos Immune Clone Selection Algorithm

    NASA Astrophysics Data System (ADS)

    Cheng, Junna; Ji, Guangrong; Feng, Chen

    Image segmentation is a fundamental step in image processing. Otsu's threshold method is a widely used method for image segmentation. In this paper, a novel image segmentation method based on chaos immune clone selection algorithm (CICSA) and Otus's threshold method is presented. By introducing the chaos optimization algorithm into the parallel and distributed search mechanism of immune clone selection algorithm, CICSA takes advantage of global and local search ability. The experimental results demonstrate that the performance of CICSA on application of image segmentation has the characteristic of stability and efficiency.

  7. Image segmentation using trainable fuzzy set classifiers

    NASA Astrophysics Data System (ADS)

    Schalkoff, Robert J.; Carver, Albrecht E.; Gurbuz, Sabri

    1999-07-01

    A general image analysis and segmentation method using fuzzy set classification and learning is described. The method uses a learned fuzzy representation of pixel region characteristics, based upon the conjunction and disjunction of extracted and derived fuzzy color and texture features. Both positive and negative exemplars of some visually apparent characteristic which forms the basis of the inspection, input by a human operator, are used together with a clustering algorithm to construct positive similarity membership functions and negative similarity membership functions. Using these composite fuzzified images, P and N, are produced using fuzzy union. Classification is accomplished via image defuzzification, whereby linguistic meaning is assigned to each pixel in the fuzzy set using a fuzzy inference operation. The technique permits: (1) strict color and texture discrimination, (2) machine learning of color and texture characteristics of regions, (3) and judicious labeling of each pixel based upon leaned fuzzy representation and fuzzy classification. This approach appears ideal for applications involving visual inspection and allows the development of image-based inspection systems which may be trained and used by relatively unskilled workers. We show three different examples involving the visual inspection of mixed waste drums, lumber and woven fabric.

  8. Image Segmentation for Connectomics Using Machine Learning

    SciTech Connect

    Tasdizen, Tolga; Seyedhosseini, Mojtaba; Liu, TIng; Jones, Cory; Jurrus, Elizabeth R.

    2014-12-01

    Reconstruction of neural circuits at the microscopic scale of individual neurons and synapses, also known as connectomics, is an important challenge for neuroscience. While an important motivation of connectomics is providing anatomical ground truth for neural circuit models, the ability to decipher neural wiring maps at the individual cell level is also important in studies of many neurodegenerative diseases. Reconstruction of a neural circuit at the individual neuron level requires the use of electron microscopy images due to their extremely high resolution. Computational challenges include pixel-by-pixel annotation of these images into classes such as cell membrane, mitochondria and synaptic vesicles and the segmentation of individual neurons. State-of-the-art image analysis solutions are still far from the accuracy and robustness of human vision and biologists are still limited to studying small neural circuits using mostly manual analysis. In this chapter, we describe our image analysis pipeline that makes use of novel supervised machine learning techniques to tackle this problem.

  9. Gynecologic masses: value of magnetic resonance imaging.

    PubMed

    Hricak, H; Lacey, C; Schriock, E; Fisher, M R; Amparo, E; Dooms, G; Jaffe, R

    1985-09-01

    Forty-two women with gynecologic abnormalities were studied with the use of magnetic resonance imaging. Magnetic resonance imaging correctly assessed the origin of the pelvic mass in all patients. In the evaluation of leiomyoma, magnetic resonance imaging accurately depicted the number, size, and location of the lesion. In the evaluation of endometrial carcinoma, magnetic resonance imaging depicted the location of the lesion, the presence of cervical extension, and the depth of myometrial penetration in the majority of the cases. In the analysis of adnexal cysts, magnetic resonance imaging was sensitive in localizing the lesion and was able to distinguish serous from hemorrhagic fluid. This preliminary report indicates that magnetic resonance imaging may become a valuable imaging modality in the diagnosis of gynecologic abnormalities.

  10. Multi-atlas segmentation with particle-based group-wise image registration

    NASA Astrophysics Data System (ADS)

    Lee, Joohwi; Lyu, Ilwoo; Styner, Martin

    2014-03-01

    We propose a novel multi-atlas segmentation method that employs a group-wise image registration method for the brain segmentation on rodent magnetic resonance (MR) images. The core element of the proposed segmentation is the use of a particle-guided image registration method that extends the concept of particle correspondence into the volumetric image domain. The registration method performs a group-wise image registration that simultaneously registers a set of images toward the space defined by the average of particles. The particle-guided image registration method is robust with low signal-to-noise ratio images as well as differing sizes and shapes observed in the developing rodent brain. Also, the use of an implicit common reference frame can prevent potential bias induced by the use of a single template in the segmentation process. We show that the use of a particle guided-image registration method can be naturally extended to a novel multi-atlas segmentation method and improves the registration method to explicitly use the provided template labels as an additional constraint. In the experiment, we show that our segmentation algorithm provides more accuracy with multi-atlas label fusion and stability against pair-wise image registration. The comparison with previous group-wise registration method is provided as well.

  11. Multi-atlas segmentation with particle-based group-wise image registration

    PubMed Central

    Lee, Joohwi; Lyu, Ilwoo; Styner, Martin

    2014-01-01

    We propose a novel multi-atlas segmentation method that employs a group-wise image registration method for the brain segmentation on rodent magnetic resonance (MR) images. The core element of the proposed segmentation is the use of a particle-guided image registration method that extends the concept of particle correspondence into the volumetric image domain. The registration method performs a group-wise image registration that simultaneously registers a set of images toward the space defined by the average of particles. The particle-guided image registration method is robust with low signal-to-noise ratio images as well as differing sizes and shapes observed in the developing rodent brain. Also, the use of an implicit common reference frame can prevent potential bias induced by the use of a single template in the segmentation process. We show that the use of a particle guided-image registration method can be naturally extended to a novel multi-atlas segmentation method and improves the registration method to explicitly use the provided template labels as an additional constraint. In the experiment, we show that our segmentation algorithm provides more accuracy with multi-atlas label fusion and stability against pair-wise image registration. The comparison with previous group-wise registration method is provided as well. PMID:25075158

  12. 3D segmentation of masses in DCE-MRI images using FCM and adaptive MRF

    NASA Astrophysics Data System (ADS)

    Zhang, Chengjie; Li, Lihua

    2014-03-01

    Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is a sensitive imaging modality for the detection of breast cancer. Automated segmentation of breast lesions in DCE-MRI images is challenging due to inherent signal-to-noise ratios and high inter-patient variability. A novel 3D segmentation method based on FCM and MRF is proposed in this study. In this method, a MRI image is segmented by spatial FCM, firstly. And then MRF segmentation is conducted to refine the result. We combined with the 3D information of lesion in the MRF segmentation process by using segmentation result of contiguous slices to constraint the slice segmentation. At the same time, a membership matrix of FCM segmentation result is used for adaptive adjustment of Markov parameters in MRF segmentation process. The proposed method was applied for lesion segmentation on 145 breast DCE-MRI examinations (86 malignant and 59 benign cases). An evaluation of segmentation was taken using the traditional overlap rate method between the segmented region and hand-drawing ground truth. The average overlap rates for benign and malignant lesions are 0.764 and 0.755 respectively. Then we extracted five features based on the segmentation region, and used an artificial neural network (ANN) to classify between malignant and benign cases. The ANN had a classification performance measured by the area under the ROC curve of AUC=0.73. The positive and negative predictive values were 0.86 and 0.58, respectively. The results demonstrate the proposed method not only achieves a better segmentation performance in accuracy also has a reasonable classification performance.

  13. CALM: cascading system with leaking detection mechanism for medical image segmentation

    NASA Astrophysics Data System (ADS)

    Liu, Jiang; Lim, Joo Hwee; Li, Huiqi

    2008-03-01

    Medical image segmentation is a challenging process due to possible image over-segmentation and under-segmentation (leaking). The CALM medical image segmentation system is constructed with an innovative scheme that cascades threshold level-set and region-growing segmentation algorithms using Union and Intersection set operators. These set operators help to balance the over-segmentation rate and under-segmentation rate of the system respectively. While adjusting the curvature scalar parameter in the threshold level-set algorithm, we observe that the abrupt change in the size of the segmented areas coincides with the occurrences of possible leaking. Instead of randomly choose a value or use the system default curvature scalar values, this observation prompts us to use the following formula in CALM to automatically decide the optimal curvature values γ to prevent the occurrence of leaking : δ2S/δγ2 >= M, where S is the size of the segmented area and M is a large positive number. Motivated for potential applications in organ transplant and analysis, the CALM system is tested on the segmentation of the kidney regions from the Magnetic Resonance images taken from the National University Hospital of Singapore. Due to the nature of MR imaging, low-contrast, weak edges and overlapping regions of adjacent organs at kidney boundaries are frequently seen in the datasets and hence kidney segmentation is prone to leaking. The kidney segmentation accuracy rate achieved by CALM is 22% better compared with those achieved by the component algorithms or the system without leaking detection mechanism. CALM is easy-to-implement and can be applied to many applications besides kidney segmentation.

  14. An Interactive Image Segmentation Method in Hand Gesture Recognition

    PubMed Central

    Chen, Disi; Li, Gongfa; Sun, Ying; Kong, Jianyi; Jiang, Guozhang; Tang, Heng; Ju, Zhaojie; Yu, Hui; Liu, Honghai

    2017-01-01

    In order to improve the recognition rate of hand gestures a new interactive image segmentation method for hand gesture recognition is presented, and popular methods, e.g., Graph cut, Random walker, Interactive image segmentation using geodesic star convexity, are studied in this article. The Gaussian Mixture Model was employed for image modelling and the iteration of Expectation Maximum algorithm learns the parameters of Gaussian Mixture Model. We apply a Gibbs random field to the image segmentation and minimize the Gibbs Energy using Min-cut theorem to find the optimal segmentation. The segmentation result of our method is tested on an image dataset and compared with other methods by estimating the region accuracy and boundary accuracy. Finally five kinds of hand gestures in different backgrounds are tested on our experimental platform, and the sparse representation algorithm is used, proving that the segmentation of hand gesture images helps to improve the recognition accuracy. PMID:28134818

  15. An Interactive Image Segmentation Method in Hand Gesture Recognition.

    PubMed

    Chen, Disi; Li, Gongfa; Sun, Ying; Kong, Jianyi; Jiang, Guozhang; Tang, Heng; Ju, Zhaojie; Yu, Hui; Liu, Honghai

    2017-01-27

    In order to improve the recognition rate of hand gestures a new interactive image segmentation method for hand gesture recognition is presented, and popular methods, e.g., Graph cut, Random walker, Interactive image segmentation using geodesic star convexity, are studied in this article. The Gaussian Mixture Model was employed for image modelling and the iteration of Expectation Maximum algorithm learns the parameters of Gaussian Mixture Model. We apply a Gibbs random field to the image segmentation and minimize the Gibbs Energy using Min-cut theorem to find the optimal segmentation. The segmentation result of our method is tested on an image dataset and compared with other methods by estimating the region accuracy and boundary accuracy. Finally five kinds of hand gestures in different backgrounds are tested on our experimental platform, and the sparse representation algorithm is used, proving that the segmentation of hand gesture images helps to improve the recognition accuracy.

  16. An entropy-based objective evaluation method for image segmentation

    NASA Astrophysics Data System (ADS)

    Zhang, Hui; Fritts, Jason E.; Goldman, Sally A.

    2003-12-01

    Accurate image segmentation is important for many image, video and computer vision applications. Over the last few decades, many image segmentation methods have been proposed. However, the results of these segmentation methods are usually evaluated only visually, qualitatively, or indirectly by the effectiveness of the segmentation on the subsequent processing steps. Such methods are either subjective or tied to particular applications. They do not judge the performance of a segmentation method objectively, and cannot be used as a means to compare the performance of different segmentation techniques. A few quantitative evaluation methods have been proposed, but these early methods have been based entirely on empirical analysis and have no theoretical grounding. In this paper, we propose a novel objective segmentation evaluation method based on information theory. The new method uses entropy as the basis for measuring the uniformity of pixel characteristics (luminance is used in this paper) within a segmentation region. The evaluation method provides a relative quality score that can be used to compare different segmentations of the same image. This method can be used to compare both various parameterizations of one particular segmentation method as well as fundamentally different segmentation techniques. The results from this preliminary study indicate that the proposed evaluation method is superior to the prior quantitative segmentation evaluation techniques, and identify areas for future research in objective segmentation evaluation.

  17. Segmentation of range images using morphological operations: review and examples

    NASA Astrophysics Data System (ADS)

    Gee, Linda A.; Abidi, Mongi A.

    1995-10-01

    Image segmentation involves calculating the position of object boundaries. For scene analysis, the intent is to differentiate objects from clutter by means of preprocessing. The object of this paper is to examine and discuss two morphological techniques for preprocessing and segmenting range images. A Morphological Watershed Algorithm has been studied in detail for segmenting range images. This algorithm uses a unique approach for defining the boundaries of objects from a morphological gradient. Several sets of range images are used as input to the algorithm to demonstrate the flexibility of the watershed technique and the experimental results support this approach as an effective method for segmenting range images. Morphological image operators present another means for segmenting range images. In particular, the results from implementing gray-scale morphological techniques indicate that these operators are useful for segmentation. This is made possible by converting a range image of a scene to a gray-scale image representation. The result represents the umbra of the surface of the objects within the scene. By applying morphological operations to the gray values of the image, the operations are applied to the umbra. Each pixel represents a point of the object's umbra, thereby yielding scene segmentation. The techniques that are discussed are found to be useful for preprocessing and segmenting range images which are direct extensions to object recognition, scene analysis, and image understanding.

  18. Segmentation of thermographic images of hands using a genetic algorithm

    NASA Astrophysics Data System (ADS)

    Ghosh, Payel; Mitchell, Melanie; Gold, Judith

    2010-01-01

    This paper presents a new technique for segmenting thermographic images using a genetic algorithm (GA). The individuals of the GA also known as chromosomes consist of a sequence of parameters of a level set function. Each chromosome represents a unique segmenting contour. An initial population of segmenting contours is generated based on the learned variation of the level set parameters from training images. Each segmenting contour (an individual) is evaluated for its fitness based on the texture of the region it encloses. The fittest individuals are allowed to propagate to future generations of the GA run using selection, crossover and mutation. The dataset consists of thermographic images of hands of patients suffering from upper extremity musculo-skeletal disorders (UEMSD). Thermographic images are acquired to study the skin temperature as a surrogate for the amount of blood flow in the hands of these patients. Since entire hands are not visible on these images, segmentation of the outline of the hands on these images is typically performed by a human. In this paper several different methods have been tried for segmenting thermographic images: Gabor-wavelet-based texture segmentation method, the level set method of segmentation and our GA which we termed LSGA because it combines level sets with genetic algorithms. The results show a comparative evaluation of the segmentation performed by all the methods. We conclude that LSGA successfully segments entire hands on images in which hands are only partially visible.

  19. Anatomy packing with hierarchical segments: an algorithm for segmentation of pulmonary nodules in CT images.

    PubMed

    Tsou, Chi-Hsuan; Lor, Kuo-Lung; Chang, Yeun-Chung; Chen, Chung-Ming

    2015-05-14

    This paper proposes a semantic segmentation algorithm that provides the spatial distribution patterns of pulmonary ground-glass nodules with solid portions in computed tomography (CT) images. The proposed segmentation algorithm, anatomy packing with hierarchical segments (APHS), performs pulmonary nodule segmentation and quantification in CT images. In particular, the APHS algorithm consists of two essential processes: hierarchical segmentation tree construction and anatomy packing. It constructs the hierarchical segmentation tree based on region attributes and local contour cues along the region boundaries. Each node of the tree corresponds to the soft boundary associated with a family of nested segmentations through different scales applied by a hierarchical segmentation operator that is used to decompose the image in a structurally coherent manner. The anatomy packing process detects and localizes individual object instances by optimizing a hierarchical conditional random field model. Ninety-two histopathologically confirmed pulmonary nodules were used to evaluate the performance of the proposed APHS algorithm. Further, a comparative study was conducted with two conventional multi-label image segmentation algorithms based on four assessment metrics: the modified Williams index, percentage statistic, overlapping ratio, and difference ratio. Under the same framework, the proposed APHS algorithm was applied to two clinical applications: multi-label segmentation of nodules with a solid portion and surrounding tissues and pulmonary nodule segmentation. The results obtained indicate that the APHS-generated boundaries are comparable to manual delineations with a modified Williams index of 1.013. Further, the resulting segmentation of the APHS algorithm is also better than that achieved by two conventional multi-label image segmentation algorithms. The proposed two-level hierarchical segmentation algorithm effectively labelled the pulmonary nodule and its surrounding

  20. Automatic bone segmentation in knee MR images using a coarse-to-fine strategy

    NASA Astrophysics Data System (ADS)

    Park, Sang Hyun; Lee, Soochahn; Yun, Il Dong; Lee, Sang Uk

    2012-02-01

    Segmentation of bone and cartilage from a three dimensional knee magnetic resonance (MR) image is a crucial element in monitoring and understanding of development and progress of osteoarthritis. Until now, various segmentation methods have been proposed to separate the bone from other tissues, but it still remains challenging problem due to different modality of MR images, low contrast between bone and tissues, and shape irregularity. In this paper, we present a new fully-automatic segmentation method of bone compartments using relevant bone atlases from a training set. To find the relevant bone atlases and obtain the segmentation, a coarse-to-fine strategy is proposed. In the coarse step, the best atlas among the training set and an initial segmentation are simultaneously detected using branch and bound tree search. Since the best atlas in the coarse step is not accurately aligned, all atlases from the training set are aligned to the initial segmentation, and the best aligned atlas is selected in the middle step. Finally, in the fine step, segmentation is conducted as adaptively integrating shape of the best aligned atlas and appearance prior based on characteristics of local regions. For experiment, femur and tibia bones of forty test MR images are segmented by the proposed method using sixty training MR images. Experimental results show that a performance of the segmentation and the registration becomes better as going near the fine step, and the proposed method obtain the comparable performance with the state-of-the-art methods.

  1. [Presurgical functional magnetic resonance imaging].

    PubMed

    Stippich, C

    2010-02-01

    Functional magnetic resonance imaging (fMRI) is an important and novel neuroimaging modality for patients with brain tumors. By non-invasive measurement, localization and lateralization of brain activiation, most importantly of motor and speech function, fMRI facilitates the selection of the most appropriate and sparing treatment and function-preserving surgery. Prerequisites for the diagnostic use of fMRI are the application of dedicated clinical imaging protocols and standardization of the respective imaging procedures. The combination with diffusion tensor imaging (DTI) also enables tracking and visualization of important fiber bundles such as the pyramidal tract and the arcuate fascicle. These multimodal MR data can be implemented in computer systems for functional neuronavigation or radiation treatment. The practicability, accuracy and reliability of presurgical fMRI have been validated by large numbers of published data. However, fMRI cannot be considered as a fully established modality of diagnostic neuroimaging due to the lack of guidelines of the responsible medical associations as well as the lack of medical certification of important hardware and software components. This article reviews the current research in the field and provides practical information relevant for presurgical fMRI.

  2. Near field imaging with resonant cavity lens.

    PubMed

    Li, Guixin; Li, Jensen; Tam, H L; Chan, C T; Cheah, K W

    2010-02-01

    We showed that a Ag-SiO(2)-Ag Fabry-Pérot cavity can be used in near-field imaging based on omnidirectional resonance tunneling. The omnidirectional resonance was experimentally demonstrated in the Ag-SiO(2)-Ag resonant cavity working at a wavelength of 365 nm. The resonant cavity lens with high transmittance and high image fidelity was fabricated using standard photolithography method. Grating source with 190 nm line resolution was imaged through the resonant cavity lens with a total thickness of 128 nm.

  3. Segmentation of x-ray images using Probabilistic Relaxation Labeling

    SciTech Connect

    Thai, T.Q.

    1991-01-01

    Segmentation is a process of separating objects of interest from their background or from other objects in an image. Without a suitable segmentation scheme, it is very difficult to detect contraband in X-rays images. In this paper, a Probabilistic Relaxation Labeling (PRL) segmentation scheme is presented and compared with other segmentation methods. PRL segmentation is an interative algorithm that labels each pixel in an image by cooperative use of two information sources: the pixel probability and the degree of certainty of its probability supported by the neighboring pixels. The practical implementation and results of the PRL segmentation on X-ray baggage images are also discussed and compared with other segmentation methods. 13 refs., 12 figs.

  4. Multiple Active Contours Guided by Differential Evolution for Medical Image Segmentation

    PubMed Central

    Cruz-Aceves, I.; Avina-Cervantes, J. G.; Lopez-Hernandez, J. M.; Rostro-Gonzalez, H.; Garcia-Capulin, C. H.; Torres-Cisneros, M.; Guzman-Cabrera, R.

    2013-01-01

    This paper presents a new image segmentation method based on multiple active contours guided by differential evolution, called MACDE. The segmentation method uses differential evolution over a polar coordinate system to increase the exploration and exploitation capabilities regarding the classical active contour model. To evaluate the performance of the proposed method, a set of synthetic images with complex objects, Gaussian noise, and deep concavities is introduced. Subsequently, MACDE is applied on datasets of sequential computed tomography and magnetic resonance images which contain the human heart and the human left ventricle, respectively. Finally, to obtain a quantitative and qualitative evaluation of the medical image segmentations compared to regions outlined by experts, a set of distance and similarity metrics has been adopted. According to the experimental results, MACDE outperforms the classical active contour model and the interactive Tseng method in terms of efficiency and robustness for obtaining the optimal control points and attains a high accuracy segmentation. PMID:23983809

  5. Nerve Fascicles and Epineurium Volume Segmentation of Peripheral Nerve Using Magnetic Resonance Micro-neurography.

    PubMed

    Felisaz, Paolo Florent; Balducci, Francesco; Gitto, Salvatore; Carne, Irene; Montagna, Stefano; De Icco, Roberto; Pichiecchio, Anna; Baldi, Maurizia; Calliada, Fabrizio; Bastianello, Stefano

    2016-08-01

    The aims of this study were to propose a semiautomated technique to segment and measure the volume of different nerve components of the tibial nerve, such as the nerve fascicles and the epineurium, based on magnetic resonance microneurography and a segmentation tool derived from brain imaging; and to assess the reliability of this method by measuring interobserver and intraobserver agreement. The tibial nerve of 20 healthy volunteers (age range = 23-69; mean = 47; standard deviation = 15) was investigated at the ankle level. High-resolution images were obtained through tailored microneurographic sequences, covering 28 mm of nerve length. Two operators manually segmented the nerve using the in-phase image. This region of interest was used to mask the nerve in the water image, and two-class segmentation was performed to measure the fascicular volume, epineurial volume, nerve volume, and fascicular to nerve volume ratio (FNR). Interobserver and intraobserver agreements were calculated. The nerve structure was clearly visualized with distinction of the fascicles and the epineurium. Segmentation provided absolute volumes for nerve volume, fascicular volume, and epineurial volume. The mean FNR resulted in 0.69 with a standard deviation of 0.04 and appeared to be not correlated with age and sex. Interobserver and intraobserver agreements were excellent with alpha values >0.9 for each parameter investigated, with measurements free of systematic errors at the Bland-Altman analysis. We concluded that the method is reproducible and the parameter FNR is a novel feature that may help in the diagnosis of neuropathies detecting changes in volume of the fascicles or the epineurium. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

  6. Off-resonance frequency filtered magnetic resonance imaging.

    PubMed

    Medic, Jure; Tomazic, Saso

    2010-05-01

    One of the main problems with rapid magnetic resonance imaging (MRI) techniques is the artifacts that result from off-resonance effects. The proposed off-resonance frequency filtered MRI (OFF-MRI) method focuses on the elimination of off-resonance components from the image of the observed object. To maintain imaging speed and simultaneously achieve good frequency selectivity, MRI is divided into two steps: signal acquisition and post-processing. After the preliminary phase in which we determine imaging parameters, MRI takes place; the signal from the same object is successively acquired M times. As a result, we obtain M partial signals in k-space, from which we form the image of the observed object in the post-processing phase, after signal acquisition has been completed. This paper demonstrates that with proper selection of acquisition parameters and weighting coefficients in the post-processing phase, OFF-MRI is equivalent to filtering the signal by finite impulse response filter of length M. It is shown that with M successive acquisitions M-1 off-resonance components can be eliminated (filtered-out) from images, and therefore, only two acquisitions are needed to eliminate one off-resonance components. On the other hand, with OFF-MRI, it is also possible to form the image of an arbitrary off-resonance component by eliminating all other off-resonance components, including the on-resonance component. The proposed OFF-MRI method is suitable for MRI where rapid acquisition is required and elimination of off-resonance components can improve reliability of measurements. 2010 Elsevier Inc. All rights reserved.

  7. Magnetic resonance imaging of the temporomandibular joint.

    PubMed

    Hayt, M W; Abrahams, J J; Blair, J

    2000-04-01

    The spectrum of disease that affects the temporomandibular joint (TMJ) can be varied. To differentiate among the diseases that cause pain and dysfunction, an intimate knowledge of the anatomy, physiology, and pathology of this region is necessary. Due to the joint's complex anatomy and relationship to the skin, it has been difficult to image in the past. Magnetic resonance imaging is ideally suited for visualizing TMJ because of its superb contrast resolution when imaging soft tissues. Magnetic resonance imaging allows simultaneous bilateral visualization of both joints. The ability to noninvasively resolve anatomic detail can be performed easily and quickly using magnetic resonance imaging. The development of magnetic resonance imaging has greatly aided the diagnosis of TMJ disorders. An understanding of TMJ anatomy and pathogenesis of TMJ pain is crucial for interpretation of magnetic resonance imaging and subsequent treatment.

  8. Magnetic resonance imaging: Principles and applications

    SciTech Connect

    Kean, D.; Smith, M.

    1986-01-01

    This text covers the physics underlying magnetic resonance (MR) imaging; pulse sequences; image production; equipment; aspects of clinical imaging; and the imaging of the head and neck, thorax, abdomen and pelvis, and musculoskeletal system; and MR imaging. The book provides about 150 examples of MR images that give an overview of the pathologic conditions imaged. There is a discussion of the physics of MR imaging and also on the spin echo.

  9. A wrapper-based approach to image segmentation and classification.

    PubMed

    Farmer, Michael E; Jain, Anil K

    2005-12-01

    The traditional processing flow of segmentation followed by classification in computer vision assumes that the segmentation is able to successfully extract the object of interest from the background image. It is extremely difficult to obtain a reliable segmentation without any prior knowledge about the object that is being extracted from the scene. This is further complicated by the lack of any clearly defined metrics for evaluating the quality of segmentation or for comparing segmentation algorithms. We propose a method of segmentation that addresses both of these issues, by using the object classification subsystem as an integral part of the segmentation. This will provide contextual information regarding the objects to be segmented, as well as allow us to use the probability of correct classification as a metric to determine the quality of the segmentation. We view traditional segmentation as a filter operating on the image that is independent of the classifier, much like the filter methods for feature selection. We propose a new paradigm for segmentation and classification that follows the wrapper methods of feature selection. Our method wraps the segmentation and classification together, and uses the classification accuracy as the metric to determine the best segmentation. By using shape as the classification feature, we are able to develop a segmentation algorithm that relaxes the requirement that the object of interest to be segmented must be homogeneous in some low-level image parameter, such as texture, color, or grayscale. This represents an improvement over other segmentation methods that have used classification information only to modify the segmenter parameters, since these algorithms still require an underlying homogeneity in some parameter space. Rather than considering our method as, yet, another segmentation algorithm, we propose that our wrapper method can be considered as an image segmentation framework, within which existing image segmentation

  10. Inference With Collaborative Model for Interactive Tumor Segmentation in Medical Image Sequences.

    PubMed

    Lin, Liang; Yang, Wei; Li, Chenglong; Tang, Jin; Cao, Xiaochun

    2015-10-29

    Segmenting organisms or tumors from medical data (e.g., computed tomography volumetric images, ultrasound, or magnetic resonance imaging images/image sequences) is one of the fundamental tasks in medical image analysis and diagnosis, and has received long-term attentions. This paper studies a novel computational framework of interactive segmentation for extracting liver tumors from image sequences, and it is suitable for different types of medical data. The main contributions are twofold. First, we propose a collaborative model to jointly formulate the tumor segmentation from two aspects: 1) region partition and 2) boundary presence. The two terms are complementary but simultaneously competing: the former extracts the tumor based on its appearance/texture information, while the latter searches for the palpable tumor boundary. Moreover, in order to adapt the data variations, we allow the model to be discriminatively trained based on both the seed pixels traced by the Lucas-Kanade algorithm and the scribbles placed by the user. Second, we present an effective inference algorithm that iterates to: 1) solve tumor segmentation using the augmented Lagrangian method and 2) propagate the segmentation across the image sequence by searching for distinctive matches between images. We keep the collaborative model updated during the inference in order to well capture the tumor variations over time. We have verified our system for segmenting liver tumors from a number of clinical data, and have achieved very promising results. The software developed with this paper can be found at http://vision.sysu.edu.cn/projects/med-interactive-seg/.

  11. Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation

    PubMed Central

    Maji, Pradipta; Roy, Shaswati

    2015-01-01

    Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices. PMID:25848961

  12. TIN based image segmentation for man-made feature extraction

    NASA Astrophysics Data System (ADS)

    Jiang, Wanshou; Xie, Junfeng

    2005-10-01

    Traditionally, the splitting and merging algorithm of image segmentation is based on quad tree data structure, which is not convenient to express the topography of regions, the line segments and other information. A new framework is discussed in this paper. It is "TIN based image segmentation and grouping", in which edge information and region information are integrated directly. Firstly, the constrained triangle mesh is constructed with edge segments extracted by EDISON or other algorithm. And then, region growing based on triangles is processed to generate a coarse segmentation. At last, the regions are combined further with perceptual organization rule.

  13. Functional Magnetic Resonance Imaging Methods

    PubMed Central

    Chen, Jingyuan E.; Glover, Gary H.

    2015-01-01

    Since its inception in 1992, Functional Magnetic Resonance Imaging (fMRI) has become an indispensible tool for studying cognition in both the healthy and dysfunctional brain. FMRI monitors changes in the oxygenation of brain tissue resulting from altered metabolism consequent to a task-based evoked neural response or from spontaneous fluctuations in neural activity in the absence of conscious mentation (the “resting state”). Task-based studies have revealed neural correlates of a large number of important cognitive processes, while fMRI studies performed in the resting state have demonstrated brain-wide networks that result from brain regions with synchronized, apparently spontaneous activity. In this article, we review the methods used to acquire and analyze fMRI signals. PMID:26248581

  14. Markov random field method for dynamic PET image segmentation

    NASA Astrophysics Data System (ADS)

    Lin, Kang-Ping; Lou, Shyhliang A.; Yu, Chin-Lung; Chung, Being-Tau; Wu, Liang-Chi; Liu, Ren-Shyan

    1998-06-01

    In this paper, the Markov random field (MRF) clustering method for highly noisy medical image segmentation is presented. In MRF method, the image to be segmented is analyzed in a probabilistic way that establishes image model by a posteriori probability density function with Bayes' theorem, with relation between pixel positions as well as gray-levels involved. The adaptive threshold parameter is determined in the iterative clustering process to achieve global optimal segmentation. The presented method and other segmentation methods in use are tested on simulation images of different noise levels, and the numerical comparison result is presented. It also is applied on the highly noisy positron emission tomography images, in that the diagnostic hypoxia fraction is automatically calculated. The experimental results are acceptable, and show that the presented method is suitable and robust for noisy image segmentation.

  15. Multimodal Correlative Preclinical Whole Body Imaging and Segmentation

    PubMed Central

    Akselrod-Ballin, Ayelet; Dafni, Hagit; Addadi, Yoseph; Biton, Inbal; Avni, Reut; Brenner, Yafit; Neeman, Michal

    2016-01-01

    Segmentation of anatomical structures and particularly abdominal organs is a fundamental problem for quantitative image analysis in preclinical research. This paper presents a novel approach for whole body segmentation of small animals in a multimodal setting of MR, CT and optical imaging. The algorithm integrates multiple imaging sequences into a machine learning framework, which generates supervoxels by an efficient hierarchical agglomerative strategy and utilizes multiple SVM-kNN classifiers each constrained by a heatmap prior region to compose the segmentation. We demonstrate results showing segmentation of mice images into several structures including the heart, lungs, liver, kidneys, stomach, vena cava, bladder, tumor, and skeleton structures. Experimental validation on a large set of mice and organs, indicated that our system outperforms alternative state of the art approaches. The system proposed can be generalized to various tissues and imaging modalities to produce automatic atlas-free segmentation, thereby enabling a wide range of applications in preclinical studies of small animal imaging. PMID:27325178

  16. Pocket atlas of cranial magnetic resonance imaging

    SciTech Connect

    Haughton, V.M.; Daniels, D.L.

    1986-01-01

    This atlas illustrates normal cerebral anatomy in magnetic resonance images. From their studies in cerebral anatomy utilizing cryomicrotome and other techniques, the authors selected more than 100 high-resolution images that represent the most clinically useful scans.

  17. Segmentation and learning in the quantitative analysis of microscopy images

    NASA Astrophysics Data System (ADS)

    Ruggiero, Christy; Ross, Amy; Porter, Reid

    2015-02-01

    In material science and bio-medical domains the quantity and quality of microscopy images is rapidly increasing and there is a great need to automatically detect, delineate and quantify particles, grains, cells, neurons and other functional "objects" within these images. These are challenging problems for image processing because of the variability in object appearance that inevitably arises in real world image acquisition and analysis. One of the most promising (and practical) ways to address these challenges is interactive image segmentation. These algorithms are designed to incorporate input from a human operator to tailor the segmentation method to the image at hand. Interactive image segmentation is now a key tool in a wide range of applications in microscopy and elsewhere. Historically, interactive image segmentation algorithms have tailored segmentation on an image-by-image basis, and information derived from operator input is not transferred between images. But recently there has been increasing interest to use machine learning in segmentation to provide interactive tools that accumulate and learn from the operator input over longer periods of time. These new learning algorithms reduce the need for operator input over time, and can potentially provide a more dynamic balance between customization and automation for different applications. This paper reviews the state of the art in this area, provides a unified view of these algorithms, and compares the segmentation performance of various design choices.

  18. Automated image segmentation using support vector machines

    NASA Astrophysics Data System (ADS)

    Powell, Stephanie; Magnotta, Vincent A.; Andreasen, Nancy C.

    2007-03-01

    Neurodegenerative and neurodevelopmental diseases demonstrate problems associated with brain maturation and aging. Automated methods to delineate brain structures of interest are required to analyze large amounts of imaging data like that being collected in several on going multi-center studies. We have previously reported on using artificial neural networks (ANN) to define subcortical brain structures including the thalamus (0.88), caudate (0.85) and the putamen (0.81). In this work, apriori probability information was generated using Thirion's demons registration algorithm. The input vector consisted of apriori probability, spherical coordinates, and an iris of surrounding signal intensity values. We have applied the support vector machine (SVM) machine learning algorithm to automatically segment subcortical and cerebellar regions using the same input vector information. SVM architecture was derived from the ANN framework. Training was completed using a radial-basis function kernel with gamma equal to 5.5. Training was performed using 15,000 vectors collected from 15 training images in approximately 10 minutes. The resulting support vectors were applied to delineate 10 images not part of the training set. Relative overlap calculated for the subcortical structures was 0.87 for the thalamus, 0.84 for the caudate, 0.84 for the putamen, and 0.72 for the hippocampus. Relative overlap for the cerebellar lobes ranged from 0.76 to 0.86. The reliability of the SVM based algorithm was similar to the inter-rater reliability between manual raters and can be achieved without rater intervention.

  19. Joint Lung CT Image Segmentation: A Hierarchical Bayesian Approach

    PubMed Central

    Cheng, Wenjun; Ma, Luyao; Yang, Tiejun; Liang, Jiali

    2016-01-01

    Accurate lung CT image segmentation is of great clinical value, especially when it comes to delineate pathological regions including lung tumor. In this paper, we present a novel framework that jointly segments multiple lung computed tomography (CT) images via hierarchical Dirichlet process (HDP). In specifics, based on the assumption that lung CT images from different patients share similar image structure (organ sets and relative positioning), we derive a mathematical model to segment them simultaneously so that shared information across patients could be utilized to regularize each individual segmentation. Moreover, compared to many conventional models, the algorithm requires little manual involvement due to the nonparametric nature of Dirichlet process (DP). We validated proposed model upon clinical data consisting of healthy and abnormal (lung cancer) patients. We demonstrate that, because of the joint segmentation fashion, more accurate and consistent segmentations could be obtained. PMID:27611188

  20. Segmentation of knee injury swelling on infrared images

    NASA Astrophysics Data System (ADS)

    Puentes, John; Langet, Hélène; Herry, Christophe; Frize, Monique

    2011-03-01

    Interpretation of medical infrared images is complex due to thermal noise, absence of texture, and small temperature differences in pathological zones. Acute inflammatory response is a characteristic symptom of some knee injuries like anterior cruciate ligament sprains, muscle or tendons strains, and meniscus tear. Whereas artificial coloring of the original grey level images may allow to visually assess the extent inflammation in the area, their automated segmentation remains a challenging problem. This paper presents a hybrid segmentation algorithm to evaluate the extent of inflammation after knee injury, in terms of temperature variations and surface shape. It is based on the intersection of rapid color segmentation and homogeneous region segmentation, to which a Laplacian of a Gaussian filter is applied. While rapid color segmentation enables to properly detect the observed core of swollen area, homogeneous region segmentation identifies possible inflammation zones, combining homogeneous grey level and hue area segmentation. The hybrid segmentation algorithm compares the potential inflammation regions partially detected by each method to identify overlapping areas. Noise filtering and edge segmentation are then applied to common zones in order to segment the swelling surfaces of the injury. Experimental results on images of a patient with anterior cruciate ligament sprain show the improved performance of the hybrid algorithm with respect to its separated components. The main contribution of this work is a meaningful automatic segmentation of abnormal skin temperature variations on infrared thermography images of knee injury swelling.

  1. Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation

    PubMed Central

    Chiu, Stephanie J.; Li, Xiao T.; Nicholas, Peter; Toth, Cynthia A.; Izatt, Joseph A.; Farsiu, Sina

    2010-01-01

    Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming. Results show that this method accurately segments eight retinal layer boundaries in normal adult eyes more closely to an expert grader as compared to a second expert grader. PMID:20940837

  2. Segmental approach to imaging of congenital heart disease.

    PubMed

    Lapierre, Chantale; Déry, Julie; Guérin, Ronald; Viremouneix, Loïc; Dubois, Josée; Garel, Laurent

    2010-03-01

    The segmental approach, which is widely used in the imaging work-up of congenital heart disease, consists of a three-step evaluation of the cardiac anatomy. In step 1, the visceroatrial situs is determined. Visceroatrial situs refers to the position of the atria in relation to the nearby anatomy (including the stomach, liver, spleen, and bronchi). Three different anatomic configurations may be observed: situs solitus (normal), situs inversus (inverted), or situs ambiguus (ambiguous). In step 2, the left- or rightward orientation of the ventricular loop is evaluated, and the positions of the ventricles are identified on the basis of their internal morphologic features. In step 3, the position of the great vessels is determined first, and any abnormalities are noted. Abnormalities in the origin of the great vessels, or conotruncal anomalies, are predominantly of three types: D-transposition (dextrotransposition), L-transposition (levotransposition), and D-malposition with double outlet right ventricle. Next, the relationships between the atria and ventricles and the ventricles and great vessels are determined at two levels: atrioventricular (concordant, discordant, ambiguous, double inlet, absence of right or left connection) and ventriculoarterial (concordant, discordant, double outlet). Last, a search is performed for any associated abnormalities of the cardiac chambers, septa, outflow tract, and great vessels. By executing these steps sequentially during image review, the radiologist can achieve a more accurate interpretation. Multiplanar reconstructions of cross-sectional image data obtained with computed tomography or magnetic resonance imaging are particularly useful for evaluating congenital heart disease.

  3. Image manifold revealing for breast lesion segmentation in DCE-MRI.

    PubMed

    Hu, Liang; Cheng, Zhaoning; Wang, Manning; Song, Zhijian

    2015-01-01

    Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely used for breast lesion differentiation. Manual segmentation in DCE-MRI is difficult and open to viewer interpretation. In this paper, an automatic segmentation method based on image manifold revealing was introduced to overcome the problems of the currently used method. First, high dimensional datasets were constructed from a dynamic image series. Next, an embedded image manifold was revealed in the feature image by nonlinear dimensionality reduction technique. In the last stage, k-means clustering was performed to obtain final segmentation results. The proposed method was applied in actual clinical cases and compared with the gold standard. Statistical analysis showed that the proposed method achieved an acceptable accuracy, sensitivity, and specificity rates.

  4. Magnetic resonance imaging in patients with unstable angina: comparison with acute myocardial infarction and normals

    SciTech Connect

    Ahmad, M.; Johnson, R.F. Jr.; Fawcett, H.D.; Schreiber, M.H.

    1988-09-01

    The role of magnetic resonance imaging in characterizing normal, ischemic and infarcted segments of myocardium was examined in 8 patients with unstable angina, 11 patients with acute myocardial infarction, and 7 patients with stable angina. Eleven normal volunteers were imaged for comparison. Myocardial segments in short axis magnetic resonance images were classified as normal or abnormal on the basis of perfusion changes observed in thallium-201 images in 22 patients and according to the electrocariographic localization of infarction in 4 patients. T2 relaxation time was measured in 57 myocardial segments with abnormal perfusion (24 with reversible and 33 with irreversible perfusion changes) and in 25 normally perfused segments. T2 measurements in normally perfused segments of patients with acute myocardial infarction, unstable angina and stable angina were within normal range derived from T2 measurements in 48 myocardial segments of 11 normal volunteers (42 +/- 10 ms). T2 in abnormal myocardial segments of patients with stable angina also was not significantly different from normal. T2 of abnormal segments in patients with unstable angina (64 +/- 14 in reversibly ischemic and 67 +/- 21 in the irreversibly ischemic segments) was prolonged when compared to normal (p less than 0.0001) and was not significantly different from T2 in abnormal segments of patients with acute myocardial infarction (62 +/- 18 for reversibly and 66 +/- 11 for irreversibly ischemic segments). The data indicate that T2 prolongation is not specific for acute myocardial infarction and may be observed in abnormally perfused segments of patients with unstable angina.

  5. Segmented surface coil resonator for in vivo EPR applications at 1.1 GHz

    NASA Astrophysics Data System (ADS)

    Petryakov, Sergey; Samouilov, Alexandre; Chzhan-Roytenberg, Michael; Kesselring, Eric; Sun, Ziqi; Zweier, Jay L.

    2009-05-01

    A four-loop segmented surface coil resonator (SSCR) with electronic frequency and coupling adjustments was constructed with 18 mm aperture and loading capability suitable for in vivo Electron Paramagnetic Resonance (EPR) spectroscopy and imaging applications at L-band. Increased sample volume and loading capability were achieved by employing a multi-loop three-dimensional surface coil structure. Symmetrical design of the resonator with coupling to each loop resulted in high homogeneity of RF magnetic field. Parallel loops were coupled to the feeder cable via balancing circuitry containing varactor diodes for electronic coupling and tuning over a wide range of loading conditions. Manually adjusted high Q trimmer capacitors were used for initial tuning with subsequent tuning electronically controlled using varactor diodes. This design provides transparency and homogeneity of magnetic field modulation in the sample volume, while matching components are shielded to minimize interference with modulation and ambient RF fields. It can accommodate lossy samples up to 90% of its aperture with high homogeneity of RF and modulation magnetic fields and can function as a surface loop or a slice volume resonator. Along with an outer coaxial NMR surface coil, the SSCR enabled EPR/NMR co-imaging of paramagnetic probes in living rats to a depth of 20 mm.

  6. Magnetic resonance image guided brachytherapy.

    PubMed

    Tanderup, Kari; Viswanathan, Akila N; Kirisits, Christian; Frank, Steven J

    2014-07-01

    The application of magnetic resonance image (MRI)-guided brachytherapy has demonstrated significant growth during the past 2 decades. Clinical improvements in cervix cancer outcomes have been linked to the application of repeated MRI for identification of residual tumor volumes during radiotherapy. This has changed clinical practice in the direction of individualized dose administration, and resulted in mounting evidence of improved clinical outcome regarding local control, overall survival as well as morbidity. MRI-guided prostate high-dose-rate and low-dose-rate brachytherapies have improved the accuracy of target and organs-at-risk delineation, and the potential exists for improved dose prescription and reporting for the prostate gland and organs at risk. Furthermore, MRI-guided prostate brachytherapy has significant potential to identify prostate subvolumes and dominant lesions to allow for dose administration reflecting the differential risk of recurrence. MRI-guided brachytherapy involves advanced imaging, target concepts, and dose planning. The key issue for safe dissemination and implementation of high-quality MRI-guided brachytherapy is establishment of qualified multidisciplinary teams and strategies for training and education.

  7. Adaptive image segmentation applied to plant reproduction by tissue culture

    NASA Astrophysics Data System (ADS)

    Vazquez Rueda, Martin G.; Hahn, Federico; Zapata, Jose L.

    1997-04-01

    This paper presents that experimental results obtained on indoor tissue culture using the adaptive image segmentation system. The performance of the adaptive technique is contrasted with different non-adaptive techniques commonly used in the computer vision field to demonstrate the improvement provided by the adaptive image segmentation system.

  8. A fast and efficient segmentation scheme for cell microscopic image.

    PubMed

    Lebrun, G; Charrier, C; Lezoray, O; Meurie, C; Cardot, H

    2007-04-27

    Microscopic cellular image segmentation schemes must be efficient for reliable analysis and fast to process huge quantity of images. Recent studies have focused on improving segmentation quality. Several segmentation schemes have good quality but processing time is too expensive to deal with a great number of images per day. For segmentation schemes based on pixel classification, the classifier design is crucial since it is the one which requires most of the processing time necessary to segment an image. The main contribution of this work is focused on how to reduce the complexity of decision functions produced by support vector machines (SVM) while preserving recognition rate. Vector quantization is used in order to reduce the inherent redundancy present in huge pixel databases (i.e. images with expert pixel segmentation). Hybrid color space design is also used in order to improve data set size reduction rate and recognition rate. A new decision function quality criterion is defined to select good trade-off between recognition rate and processing time of pixel decision function. The first results of this study show that fast and efficient pixel classification with SVM is possible. Moreover posterior class pixel probability estimation is easy to compute with Platt method. Then a new segmentation scheme using probabilistic pixel classification has been developed. This one has several free parameters and an automatic selection must dealt with, but criteria for evaluate segmentation quality are not well adapted for cell segmentation, especially when comparison with expert pixel segmentation must be achieved. Another important contribution in this paper is the definition of a new quality criterion for evaluation of cell segmentation. The results presented here show that the selection of free parameters of the segmentation scheme by optimisation of the new quality cell segmentation criterion produces efficient cell segmentation.

  9. Magnetic resonance imaging of radiation optic neuropathy

    SciTech Connect

    Zimmerman, C.F.; Schatz, N.J.; Glaser, J.S. )

    1990-10-15

    Three patients with delayed radiation optic neuropathy after radiation therapy for parasellar neoplasms underwent magnetic resonance imaging. The affected optic nerves and chiasms showed enlargement and focal gadopentetate dimeglumine enhancement. The magnetic resonance imaging technique effectively detected and defined anterior visual pathway changes of radionecrosis and excluded the clinical possibility of visual loss because of tumor recurrence.

  10. Functional Magnetic Resonance Imaging and Pediatric Anxiety

    ERIC Educational Resources Information Center

    Pine, Daniel S.; Guyer, Amanda E.; Leibenluft, Ellen; Peterson, Bradley S.; Gerber, Andrew

    2008-01-01

    The use of functional magnetic resonance imaging in investigating pediatric anxiety disorders is studied. Functional magnetic resonance imaging can be utilized in demonstrating parallels between the neural architecture of difference in anxiety of humans and the neural architecture of attention-orienting behavior in nonhuman primates or rodents.…

  11. Functional Magnetic Resonance Imaging and Pediatric Anxiety

    ERIC Educational Resources Information Center

    Pine, Daniel S.; Guyer, Amanda E.; Leibenluft, Ellen; Peterson, Bradley S.; Gerber, Andrew

    2008-01-01

    The use of functional magnetic resonance imaging in investigating pediatric anxiety disorders is studied. Functional magnetic resonance imaging can be utilized in demonstrating parallels between the neural architecture of difference in anxiety of humans and the neural architecture of attention-orienting behavior in nonhuman primates or rodents.…

  12. An analysis of methods for the selection of atlases for use in medical image segmentation

    NASA Astrophysics Data System (ADS)

    Prescott, Jeffrey W.; Best, Thomas M.; Haq, Furqan; Jackson, Rebecca; Gurcan, Metin

    2010-03-01

    The use of atlases has been shown to be a robust method for segmentation of medical images. In this paper we explore different methods of selection of atlases for the segmentation of the quadriceps muscles in magnetic resonance (MR) images, although the results are pertinent for a wide range of applications. The experiments were performed using 103 images from the Osteoarthritis Initiative (OAI). The images were randomly split into a training set consisting of 50 images and a testing set of 53 images. Three different atlas selection methods were systematically compared. First, a set of readers was assigned the task of selecting atlases from a training population of images, which were selected to be representative subgroups of the total population. Second, the same readers were instructed to select atlases from a subset of the training data which was stratified based on population modes. Finally, every image in the training set was employed as an atlas, with no input from the readers, and the atlas which had the best initial registration, judged by an appropriate registration metric, was used in the final segmentation procedure. The segmentation results were quantified using the Zijdenbos similarity index (ZSI). The results show that over all readers the agreement of the segmentation algorithm decreased from 0.76 to 0.74 when using population modes to assist in atlas selection. The use of every image in the training set as an atlas outperformed both manual atlas selection methods, achieving a ZSI of 0.82.

  13. Intelligent segmentation of industrial radiographic images using neural networks

    NASA Astrophysics Data System (ADS)

    Lawson, Shaun W.; Parker, Graham A.

    1994-10-01

    An application of machine vision, incorporating neural networks, which aims to fully automate real-time radiographic inspection in welding process is described. The current methodology adopted comprises two distinct stages - the segmentation of the weld from the background content of the radiographic image, and the segmentation of suspect defect areas inside the weld region itself. In the first stage, a back propagation neural network has been employed to adaptively and accurately segment the weld region from a given image. The training of the network is achieved with a single image showing a typical weld in the run which is to be inspected, coupled with a very simple schematic weld 'template'. The second processing stage utilizes a further backpropagation network which is trained on a test set of image data previously segmented by a conventional adaptive threshold method. It is shown that the two techniques can be combined to fully segment radiographic weld images.

  14. GPU accelerated fuzzy connected image segmentation by using CUDA.

    PubMed

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

    2009-01-01

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

  15. Analysis of image thresholding segmentation algorithms based on swarm intelligence

    NASA Astrophysics Data System (ADS)

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

    2013-03-01

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

  16. Cellular image segmentation using n-agent cooperative game theory

    NASA Astrophysics Data System (ADS)

    Dimock, Ian B.; Wan, Justin W. L.

    2016-03-01

    Image segmentation is an important problem in computer vision and has significant applications in the segmentation of cellular images. Many different imaging techniques exist and produce a variety of image properties which pose difficulties to image segmentation routines. Bright-field images are particularly challenging because of the non-uniform shape of the cells, the low contrast between cells and background, and imaging artifacts such as halos and broken edges. Classical segmentation techniques often produce poor results on these challenging images. Previous attempts at bright-field imaging are often limited in scope to the images that they segment. In this paper, we introduce a new algorithm for automatically segmenting cellular images. The algorithm incorporates two game theoretic models which allow each pixel to act as an independent agent with the goal of selecting their best labelling strategy. In the non-cooperative model, the pixels choose strategies greedily based only on local information. In the cooperative model, the pixels can form coalitions, which select labelling strategies that benefit the entire group. Combining these two models produces a method which allows the pixels to balance both local and global information when selecting their label. With the addition of k-means and active contour techniques for initialization and post-processing purposes, we achieve a robust segmentation routine. The algorithm is applied to several cell image datasets including bright-field images, fluorescent images and simulated images. Experiments show that the algorithm produces good segmentation results across the variety of datasets which differ in cell density, cell shape, contrast, and noise levels.

  17. Dental x-ray image segmentation

    NASA Astrophysics Data System (ADS)

    Said, Eyad; Fahmy, Gamal F.; Nassar, Diaa; Ammar, Hany

    2004-08-01

    Law enforcement agencies have been exploiting biometric identifiers for decades as key tools in forensic identification. With the evolution in information technology and the huge volume of cases that need to be investigated by forensic specialists, it has become important to automate forensic identification systems. While, ante mortem (AM) identification, that is identification prior to death, is usually possible through comparison of many biometric identifiers, postmortem (PM) identification, that is identification after death, is impossible using behavioral biometrics (e.g. speech, gait). Moreover, under severe circumstances, such as those encountered in mass disasters (e.g. airplane crashers) or if identification is being attempted more than a couple of weeks postmortem, under such circumstances, most physiological biometrics may not be employed for identification, because of the decay of soft tissues of the body to unidentifiable states. Therefore, a postmortem biometric identifier has to resist the early decay that affects body tissues. Because of their survivability and diversity, the best candidates for postmortem biometric identification are the dental features. In this paper we present an over view about an automated dental identification system for Missing and Unidentified Persons. This dental identification system can be used by both law enforcement and security agencies in both forensic and biometric identification. We will also present techniques for dental segmentation of X-ray images. These techniques address the problem of identifying each individual tooth and how the contours of each tooth are extracted.

  18. Automatic Magnetic Resonance Spinal Cord Segmentation with Topology Constraints for Variable Fields of View

    PubMed Central

    Chen, Min; Carass, Aaron; Oh, Jiwon; Nair, Govind; Pham, Dzung L.; Reich, Daniel S.; Prince, Jerry L.

    2013-01-01

    Spinal cord segmentation is an important step in the analysis of neurological diseases such as multiple sclerosis. Several studies have shown correlations between disease progression and metrics relating to spinal cord atrophy and shape changes. Current practices primarily involve segmenting the spinal cord manually or semi-automatically, which can be inconsistent and time-consuming for large datasets. An automatic method that segments the spinal cord and cerebrospinal fluid from magnetic resonance images is presented. The method uses a deformable atlas and topology constraints to produce results that are robust to noise and artifacts. The method is designed to be easily extended to new data with different modalities, resolutions, and fields of view. Validation was performed on two distinct datasets. The first consists of magnetization transfer-prepared T2*-weighted gradient-echo MRI centered only on the cervical vertebrae (C1-C5). The second consists of T1-weighted MRI that cover both the cervical and portions of the thoracic vertebrae (C1-T4). Results were found to be highly accurate in comparison to manual segmentations. A pilot study was carried out to demonstrate the potential utility of this new method for research and clinical studies of multiple sclerosis. PMID:23927903

  19. Automatic magnetic resonance spinal cord segmentation with topology constraints for variable fields of view.

    PubMed

    Chen, Min; Carass, Aaron; Oh, Jiwon; Nair, Govind; Pham, Dzung L; Reich, Daniel S; Prince, Jerry L

    2013-12-01

    Spinal cord segmentation is an important step in the analysis of neurological diseases such as multiple sclerosis. Several studies have shown correlations between disease progression and metrics relating to spinal cord atrophy and shape changes. Current practices primarily involve segmenting the spinal cord manually or semi-automatically, which can be inconsistent and time-consuming for large datasets. An automatic method that segments the spinal cord and cerebrospinal fluid from magnetic resonance images is presented. The method uses a deformable atlas and topology constraints to produce results that are robust to noise and artifacts. The method is designed to be easily extended to new data with different modalities, resolutions, and fields of view. Validation was performed on two distinct datasets. The first consists of magnetization transfer-prepared T2*-weighted gradient-echo MRI centered only on the cervical vertebrae (C1-C5). The second consists of T1-weighted MRI that covers both the cervical and portions of the thoracic vertebrae (C1-T4). Results were found to be highly accurate in comparison to manual segmentations. A pilot study was carried out to demonstrate the potential utility of this new method for research and clinical studies of multiple sclerosis.

  20. Example-based segmentation for breast mass images

    NASA Astrophysics Data System (ADS)

    Huang, Qingying; Xu, Songhua; Luo, Xiaonan

    2013-03-01

    A new example-based mass segmentation algorithm is proposed for breast mass images. The training examples used in the new algorithm are prepared by three medical imaging professionals who manually outlined mass contours of 45 sample breast mass images. These manually segmented mass images are then partitioned into small regular grid cells, which are used as reference samples by the algorithm. Each time when the algorithm is applied to segment a previously unseen breast mass image, it first detects grid cell regions in the image that likely overlap with the underlying mass region. Upon identifying such candidate regions, the algorithm then locates the exact mass contour through an example based segmentation procedure where the algorithm retrieves, transfers, and re-applies the human expert knowledge regarding mass segmentation as encoded in the reference samples. The key advantage of our approach lies in its adaptability in tailoring to the skills and preferences of multiple experts through simply switching to a different corpus of human segmentation samples. To explore the effectiveness of the new approach, we comparatively evaluated the accuracy of the algorithm for mass segmentation against segmentation results both manually produced by several medical imaging professionals and automatically by a state-of-the-art level set based method. The comparison results demonstrate that the new algorithm achieves a higher accuracy than the level set based peer method with statistical significance.2

  1. Segmentation of stochastic images with a stochastic random walker method.

    PubMed

    Pätz, Torben; Preusser, Tobias

    2012-05-01

    We present an extension of the random walker segmentation to images with uncertain gray values. Such gray-value uncertainty may result from noise or other imaging artifacts or more general from measurement errors in the image acquisition process. The purpose is to quantify the influence of the gray-value uncertainty onto the result when using random walker segmentation. In random walker segmentation, a weighted graph is built from the image, where the edge weights depend on the image gradient between the pixels. For given seed regions, the probability is evaluated for a random walk on this graph starting at a pixel to end in one of the seed regions. Here, we extend this method to images with uncertain gray values. To this end, we consider the pixel values to be random variables (RVs), thus introducing the notion of stochastic images. We end up with stochastic weights for the graph in random walker segmentation and a stochastic partial differential equation (PDE) that has to be solved. We discretize the RVs and the stochastic PDE by the method of generalized polynomial chaos, combining the recent developments in numerical methods for the discretization of stochastic PDEs and an interactive segmentation algorithm. The resulting algorithm allows for the detection of regions where the segmentation result is highly influenced by the uncertain pixel values. Thus, it gives a reliability estimate for the resulting segmentation, and it furthermore allows determining the probability density function of the segmented object volume.

  2. Robust image modeling technique with a bioluminescence image segmentation application

    NASA Astrophysics Data System (ADS)

    Zhong, Jianghong; Wang, Ruiping; Tian, Jie

    2009-02-01

    A robust pattern classifier algorithm for the variable symmetric plane model, where the driving noise is a mixture of a Gaussian and an outlier process, is developed. The veracity and high-speed performance of the pattern recognition algorithm is proved. Bioluminescence tomography (BLT) has recently gained wide acceptance in the field of in vivo small animal molecular imaging. So that it is very important for BLT to how to acquire the highprecision region of interest in a bioluminescence image (BLI) in order to decrease loss of the customers because of inaccuracy in quantitative analysis. An algorithm in the mode is developed to improve operation speed, which estimates parameters and original image intensity simultaneously from the noise corrupted image derived from the BLT optical hardware system. The focus pixel value is obtained from the symmetric plane according to a more realistic assumption for the noise sequence in the restored image. The size of neighborhood is adaptive and small. What's more, the classifier function is base on the statistic features. If the qualifications for the classifier are satisfied, the focus pixel intensity is setup as the largest value in the neighborhood.Otherwise, it will be zeros.Finally,pseudo-color is added up to the result of the bioluminescence segmented image. The whole process has been implemented in our 2D BLT optical system platform and the model is proved.

  3. A Latent Source Model for Patch-Based Image Segmentation.

    PubMed

    Chen, George H; Shah, Devavrat; Golland, Polina

    2015-10-01

    Despite the popularity and empirical success of patch-based nearest-neighbor and weighted majority voting approaches to medical image segmentation, there has been no theoretical development on when, why, and how well these nonparametric methods work. We bridge this gap by providing a theoretical performance guarantee for nearest-neighbor and weighted majority voting segmentation under a new probabilistic model for patch-based image segmentation. Our analysis relies on a new local property for how similar nearby patches are, and fuses existing lines of work on modeling natural imagery patches and theory for nonparametric classification. We use the model to derive a new patch-based segmentation algorithm that iterates between inferring local label patches and merging these local segmentations to produce a globally consistent image segmentation. Many existing patch-based algorithms arise as special cases of the new algorithm.

  4. Anterior segment imaging in glaucoma: An updated review

    PubMed Central

    Maslin, Jessica S; Barkana, Yaniv; Dorairaj, Syril K

    2015-01-01

    Anterior segment imaging allows for an objective method of visualizing the anterior segment angle. Two of the most commonly used devices for anterior segment imaging include the anterior segment optical coherence tomography (AS-OCT) and the ultrasound biomicroscopy (UBM). AS-OCT technology has several types, including time-domain, swept-source, and spectral-domain-based configurations. We performed a literature search on PubMed for articles containing the text “anterior segment OCT,” “ultrasound biomicroscopy,” and “anterior segment imaging” since 2004, with some pertinent references before 2004 included for completeness. This review compares the advantages and disadvantages of AS-OCT and UBM, and summarizes the most recent literature regarding the importance of these devices in glaucoma diagnosis and management. These devices not only aid in visualization of the angle, but also have important postsurgical applications in bleb and tube imaging. PMID:26576519

  5. Robust model for segmenting images with/without intensity inhomogeneities.

    PubMed

    Li, Changyang; Wang, Xiuying; Eberl, Stefan; Fulham, Michael; Feng, David Dagan

    2013-08-01

    Intensity inhomogeneities and different types/levels of image noise are the two major obstacles to accurate image segmentation by region-based level set models. To provide a more general solution to these challenges, we propose a novel segmentation model that considers global and local image statistics to eliminate the influence of image noise and to compensate for intensity inhomogeneities. In our model, the global energy derived from a Gaussian model estimates the intensity distribution of the target object and background; the local energy derived from the mutual influences of neighboring pixels can eliminate the impact of image noise and intensity inhomogeneities. The robustness of our method is validated on segmenting synthetic images with/without intensity inhomogeneities, and with different types/levels of noise, including Gaussian noise, speckle noise, and salt and pepper noise, as well as images from different medical imaging modalities. Quantitative experimental comparisons demonstrate that our method is more robust and more accurate in segmenting the images with intensity inhomogeneities than the local binary fitting technique and its more recent systematic model. Our technique also outperformed the region-based Chan–Vese model when dealing with images without intensity inhomogeneities and produce better segmentation results than the graph-based algorithms including graph-cuts and random walker when segmenting noisy images.

  6. Correlation of Magnetic Resonance Imaging Tumor Volume with Histopathology

    PubMed Central

    Turkbey, Baris; Mani, Haresh; Aras, Omer; Rastinehad, Ardeshir R.; Shah, Vijay; Bernardo, Marcelino; Pohida, Thomas; Daar, Dagane; Benjamin, Compton; McKinney, Yolanda L.; Linehan, W. Marston; Wood, Bradford J.; Merino, Maria J.; Choyke, Peter L.; Pinto, Peter A.

    2017-01-01

    Purpose The biology of prostate cancer may be influenced by the index lesion. The definition of index lesion volume is important for appropriate decision making, especially for image guided focal treatment. We determined the accuracy of magnetic resonance imaging for determining index tumor volume compared with volumes derived from histopathology. Materials and Methods We evaluated 135 patients (mean age 59.3 years) with a mean prostate specific antigen of 6.74 ng/dl who underwent multiparametric 3T endorectal coil magnetic resonance imaging of the prostate and subsequent radical prostatectomy. Index tumor volume was determined prospectively and independently by magnetic resonance imaging and histopathology. The ellipsoid formula was applied to determine histopathology tumor volume, whereas manual tumor segmentation was used to determine magnetic resonance tumor volume. Histopathology tumor volume was correlated with age and prostate specific antigen whereas magnetic resonance tumor volume involved Pearson correlation and linear regression methods. In addition, the predictive power of magnetic resonance tumor volume, prostate specific antigen and age for estimating histopathology tumor volume (greater than 0.5 cm3) was assessed by ROC analysis. The same analysis was also conducted for the 1.15 shrinkage factor corrected histopathology data set. Results There was a positive correlation between histopathology tumor volume and magnetic resonance tumor volume (Pearson coefficient 0.633, p <0.0001), but a weak correlation between prostate specific antigen and histopathology tumor volume (Pearson coefficient 0.237, p=0.003). On linear regression analysis histopathology tumor volume and magnetic resonance tumor volume were correlated (r2=0.401, p <0.00001). On ROC analysis AUC values for magnetic resonance tumor volume, prostate specific antigen and age in estimating tumors larger than 0.5 cm3 at histopathology were 0.949 (p <0.0000001), 0.685 (p=0.001) and 0.627 (p=0

  7. Correlation of magnetic resonance imaging tumor volume with histopathology.

    PubMed

    Turkbey, Baris; Mani, Haresh; Aras, Omer; Rastinehad, Ardeshir R; Shah, Vijay; Bernardo, Marcelino; Pohida, Thomas; Daar, Dagane; Benjamin, Compton; McKinney, Yolanda L; Linehan, W Marston; Wood, Bradford J; Merino, Maria J; Choyke, Peter L; Pinto, Peter A

    2012-10-01

    The biology of prostate cancer may be influenced by the index lesion. The definition of index lesion volume is important for appropriate decision making, especially for image guided focal treatment. We determined the accuracy of magnetic resonance imaging for determining index tumor volume compared with volumes derived from histopathology. We evaluated 135 patients (mean age 59.3 years) with a mean prostate specific antigen of 6.74 ng/dl who underwent multiparametric 3T endorectal coil magnetic resonance imaging of the prostate and subsequent radical prostatectomy. Index tumor volume was determined prospectively and independently by magnetic resonance imaging and histopathology. The ellipsoid formula was applied to determine histopathology tumor volume, whereas manual tumor segmentation was used to determine magnetic resonance tumor volume. Histopathology tumor volume was correlated with age and prostate specific antigen whereas magnetic resonance tumor volume involved Pearson correlation and linear regression methods. In addition, the predictive power of magnetic resonance tumor volume, prostate specific antigen and age for estimating histopathology tumor volume (greater than 0.5 cm(3)) was assessed by ROC analysis. The same analysis was also conducted for the 1.15 shrinkage factor corrected histopathology data set. There was a positive correlation between histopathology tumor volume and magnetic resonance tumor volume (Pearson coefficient 0.633, p <0.0001), but a weak correlation between prostate specific antigen and histopathology tumor volume (Pearson coefficient 0.237, p = 0.003). On linear regression analysis histopathology tumor volume and magnetic resonance tumor volume were correlated (r(2) = 0.401, p <0.00001). On ROC analysis AUC values for magnetic resonance tumor volume, prostate specific antigen and age in estimating tumors larger than 0.5 cm(3) at histopathology were 0.949 (p <0.0000001), 0.685 (p = 0.001) and 0.627 (p = 0.02), respectively. Similar

  8. A generative model for image segmentation based on label fusion.

    PubMed

    Sabuncu, Mert R; Yeo, B T Thomas; Van Leemput, Koen; Fischl, Bruce; Golland, Polina

    2010-10-01

    We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject. Such label fusion methods have been shown to yield accurate segmentation, since the use of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures. To the best of our knowledge, this manuscript presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multiatlas segmentation algorithms are interpreted as special cases of our framework. We conduct two sets of experiments to validate the proposed methods. In the first set of experiments, we use 39 brain MRI scans-with manually segmented white matter, cerebral cortex, ventricles and subcortical structures-to compare different label fusion algorithms and the widely-used FreeSurfer whole-brain segmentation tool. Our results indicate that the proposed framework yields more accurate segmentation than FreeSurfer and previous label fusion algorithms. In a second experiment, we use brain MRI scans of 282 subjects to demonstrate that the proposed segmentation tool is sufficiently sensitive to robustly detect hippocampal volume changes in a study of aging and Alzheimer's Disease.

  9. Sequential Registration-Based Segmentation of the Prostate Gland in MR Image Volumes.

    PubMed

    Khalvati, Farzad; Salmanpour, Aryan; Rahnamayan, Shahryar; Haider, Masoom A; Tizhoosh, H R

    2016-04-01

    Accurate and fast segmentation and volume estimation of the prostate gland in magnetic resonance (MR) images are necessary steps in the diagnosis, treatment, and monitoring of prostate cancer. This paper presents an algorithm for the prostate gland volume estimation based on the semi-automated segmentation of individual slices in T2-weighted MR image sequences. The proposed sequential registration-based segmentation (SRS) algorithm, which was inspired by the clinical workflow during medical image contouring, relies on inter-slice image registration and user interaction/correction to segment the prostate gland without the use of an anatomical atlas. It automatically generates contours for each slice using a registration algorithm, provided that the user edits and approves the marking in some previous slices. We conducted comprehensive experiments to measure the performance of the proposed algorithm using three registration methods (i.e., rigid, affine, and nonrigid). Five radiation oncologists participated in the study where they contoured the prostate MR (T2-weighted) images of 15 patients both manually and using the SRS algorithm. Compared to the manual segmentation, on average, the SRS algorithm reduced the contouring time by 62% (a speedup factor of 2.64×) while maintaining the segmentation accuracy at the same level as the intra-user agreement level (i.e., Dice similarity coefficient of 91 versus 90%). The proposed algorithm exploits the inter-slice similarity of volumetric MR image series to achieve highly accurate results while significantly reducing the contouring time.

  10. A Coupled Global Registration and Segmentation Framework With Application to Magnetic Resonance Prostate Imagery

    PubMed Central

    Sandhu, Romeil; Fichtinger, Gabor; Tannenbaum, Allen Robert

    2010-01-01

    Extracting the prostate from magnetic resonance (MR) imagery is a challenging and important task for medical image analysis and surgical planning. We present in this work a unified shape-based framework to extract the prostate from MR prostate imagery. In many cases, shape-based segmentation is a two-part problem. First, one must properly align a set of training shapes such that any variation in shape is not due to pose. Then segmentation can be performed under the constraint of the learnt shape. However, the general registration task of prostate shapes becomes increasingly difficult due to the large variations in pose and shape in the training sets, and is not readily handled through existing techniques. Thus, the contributions of this paper are twofold. We first explicitly address the registration problem by representing the shapes of a training set as point clouds. In doing so, we are able to exploit the more global aspects of registration via a certain particle filtering based scheme. In addition, once the shapes have been registered, a cost functional is designed to incorporate both the local image statistics as well as the learnt shape prior. We provide experimental results, which include several challenging clinical data sets, to highlight the algorithm’s capability of robustly handling supine/prone prostate registration and the overall segmentation task. PMID:20529727

  11. Improved document image segmentation algorithm using multiresolution morphology

    NASA Astrophysics Data System (ADS)

    Bukhari, Syed Saqib; Shafait, Faisal; Breuel, Thomas M.

    2011-01-01

    Page segmentation into text and non-text elements is an essential preprocessing step before optical character recognition (OCR) operation. In case of poor segmentation, an OCR classification engine produces garbage characters due to the presence of non-text elements. This paper describes modifications to the text/non-text segmentation algorithm presented by Bloomberg,1 which is also available in his open-source Leptonica library.2The modifications result in significant improvements and achieved better segmentation accuracy than the original algorithm for UW-III, UNLV, ICDAR 2009 page segmentation competition test images and circuit diagram datasets.

  12. On the Performance of Stochastic Model-Based Image Segmentation

    NASA Astrophysics Data System (ADS)

    Lei, Tianhu; Sewchand, Wilfred

    1989-11-01

    A new stochastic model-based image segmentation technique for X-ray CT image has been developed and has been extended to the more general nondiffraction CT images which include MRI, SPELT, and certain type of ultrasound images [1,2]. The nondiffraction CT image is modeled by a Finite Normal Mixture. The technique utilizes the information theoretic criterion to detect the number of the region images, uses the Expectation-Maximization algorithm to estimate the parameters of the image, and uses the Bayesian classifier to segment the observed image. How does this technique over/under-estimate the number of the region images? What is the probability of errors in the segmentation of this technique? This paper addresses these two problems and is a continuation of [1,2].

  13. Exterior Decay of Wood-Plastic Composite Boards: Characterization and Magnetic Resonance Imaging

    Treesearch

    Rebecca Ibach; Grace Sun; Marek Gnatowski; Jessie Glaeser; Mathew Leung; John Haight

    2016-01-01

    Magnetic resonance imaging (MRI) was used to evaluate free water content and distribution in wood-plastic composite (WPC) materials decayed during exterior exposure near Hilo, Hawaii. Two segments of the same board blend were selected from 6 commercial decking boards that had fungal fruiting bodies. One of the two board segments was exposed in sun, the other in shadow...

  14. Automatic segmentation and classification of outdoor images using neural networks.

    PubMed

    Campbell, N W; Thomas, B T; Troscianko, T

    1997-02-01

    The paper describes how neural networks may be used to segment and label objects in images. A self-organising feature map is used for the segmentation phase, and we quantify the quality of the segmentations produced as well as the contribution made by colour and texture features. A multi-layer perception is trained to label the regions produced by the segmentation process. It is shown that 91.1% of the image area is correctly classified into one of eleven categories which include cars, houses, fences, roads, vegetation and sky.

  15. Fast spectral color image segmentation based on filtering and clustering

    NASA Astrophysics Data System (ADS)

    Xing, Min; Li, Hongyu; Jia, Jinyuan; Parkkinen, Jussi

    2009-10-01

    This paper proposes a fast approach to spectral image segmentation. In the algorithm, two popular techniques are extended and applied to spectral color images: the mean-shift filtering and the kernel-based clustering. We claim that segmentation should be completed under illuminant F11 rather than directly using the original spectral reflectance, because such illumination can reduce data variability and expedite the following filtering. The modes obtained in the mean-shift filtering represent the local features of spectral images, and will be applied to segmentation in place of pixels. Since the modes are generally small in number, the eigendecomposition of kernel matrices, the crucial step in the kernelbased clustering, becomes much easier. The combination of these two techniques can efficiently enhance the performance of segmentation. Experiments show that the proposed segmentation method is feasible and very promising for spectral color images.

  16. Image segmentation on adaptive edge-preserving smoothing

    NASA Astrophysics Data System (ADS)

    He, Kun; Wang, Dan; Zheng, Xiuqing

    2016-09-01

    Nowadays, typical active contour models are widely applied in image segmentation. However, they perform badly on real images with inhomogeneous subregions. In order to overcome the drawback, this paper proposes an edge-preserving smoothing image segmentation algorithm. At first, this paper analyzes the edge-preserving smoothing conditions for image segmentation and constructs an edge-preserving smoothing model inspired by total variation. The proposed model has the ability to smooth inhomogeneous subregions and preserve edges. Then, a kind of clustering algorithm, which reasonably trades off edge-preserving and subregion-smoothing according to the local information, is employed to learn the edge-preserving parameter adaptively. At last, according to the confidence level of segmentation subregions, this paper constructs a smoothing convergence condition to avoid oversmoothing. Experiments indicate that the proposed algorithm has superior performance in precision, recall, and F-measure compared with other segmentation algorithms, and it is insensitive to noise and inhomogeneous-regions.

  17. Analyzing training information from random forests for improved image segmentation.

    PubMed

    Mahapatra, Dwarikanath

    2014-04-01

    Labeled training data are used for challenging medical image segmentation problems to learn different characteristics of the relevant domain. In this paper, we examine random forest (RF) classifiers, their learned knowledge during training and ways to exploit it for improved image segmentation. Apart from learning discriminative features, RFs also quantify their importance in classification. Feature importance is used to design a feature selection strategy critical for high segmentation and classification accuracy, and also to design a smoothness cost in a second-order MRF framework for graph cut segmentation. The cost function combines the contribution of different image features like intensity, texture, and curvature information. Experimental results on medical images show that this strategy leads to better segmentation accuracy than conventional graph cut algorithms that use only intensity information in the smoothness cost.

  18. Comparison of Model-Based Segmentation Algorithms for Color Images.

    DTIC Science & Technology

    1987-03-01

    image. Hunt and Kubler [Ref. 3] found that for image restoration, Karhunen-Loive transformation followed by single channel image processing worked...Algorithm for Segmentation of Multichannel Images. M.S.Thesis, Naval Postgraduate School, Monterey, CaliFornia, December 1993. 3. Hunt, B.R., Kubler 0

  19. Segmenting images analytically in shape space

    NASA Astrophysics Data System (ADS)

    Rathi, Yogesh; Dambreville, Samuel; Niethammer, Marc; Malcolm, James; Levitt, James; Shenton, Martha E.; Tannenbaum, Allen

    2008-03-01

    This paper presents a novel analytic technique to perform shape-driven segmentation. In our approach, shapes are represented using binary maps, and linear PCA is utilized to provide shape priors for segmentation. Intensity based probability distributions are then employed to convert a given test volume into a binary map representation, and a novel energy functional is proposed whose minimum can be analytically computed to obtain the desired segmentation in the shape space. We compare the proposed method with the log-likelihood based energy to elucidate some key differences. Our algorithm is applied to the segmentation of brain caudate nucleus and hippocampus from MRI data, which is of interest in the study of schizophrenia and Alzheimer's disease. Our validation (we compute the Hausdorff distance and the DICE coefficient between the automatic segmentation and ground-truth) shows that the proposed algorithm is very fast, requires no initialization and outperforms the log-likelihood based energy.

  20. A Segmentation Framework of Pulmonary Nodules in Lung CT Images.

    PubMed

    Mukhopadhyay, Sudipta

    2016-02-01

    Accurate segmentation of pulmonary nodules is a prerequisite for acceptable performance of computer-aided detection (CAD) system designed for diagnosis of lung cancer from lung CT images. Accurate segmentation helps to improve the quality of machine level features which could improve the performance of the CAD system. The well-circumscribed solid nodules can be segmented using thresholding, but segmentation becomes difficult for part-solid, non-solid, and solid nodules attached with pleura or vessels. We proposed a segmentation framework for all types of pulmonary nodules based on internal texture (solid/part-solid and non-solid) and external attachment (juxta-pleural and juxta-vascular). In the proposed framework, first pulmonary nodules are categorized into solid/part-solid and non-solid category by analyzing intensity distribution in the core of the nodule. Two separate segmentation methods are developed for solid/part-solid and non-solid nodules, respectively. After determining the category of nodule, the particular algorithm is set to remove attached pleural surface and vessels from the nodule body. The result of segmentation is evaluated in terms of four contour-based metrics and six region-based metrics for 891 pulmonary nodules from Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) public database. The experimental result shows that the proposed segmentation framework is reliable for segmentation of various types of pulmonary nodules with improved accuracy compared to existing segmentation methods.

  1. Real-time planar segmentation of depth images: from three-dimensional edges to segmented planes

    NASA Astrophysics Data System (ADS)

    Javan Hemmat, Hani; Bondarev, Egor; de With, Peter H. N.

    2015-09-01

    Real-time execution of processing algorithms for handling depth images in a three-dimensional (3-D) data framework is a major challenge. More specifically, considering depth images as point clouds and performing planar segmentation requires heavy computation, because available planar segmentation algorithms are mostly based on surface normals and/or curvatures, and, consequently, do not provide real-time performance. Aiming at the reconstruction of indoor environments, the spaces mainly consist of planar surfaces, so that a possible 3-D application would strongly benefit from a real-time algorithm. We introduce a real-time planar segmentation method for depth images avoiding any surface normal calculation. First, we detect 3-D edges in a depth image and generate line segments between the identified edges. Second, we fuse all the points on each pair of intersecting line segments into a plane candidate. Third and finally, we implement a validation phase to select planes from the candidates. Furthermore, various enhancements are applied to improve the segmentation quality. The GPU implementation of the proposed algorithm segments depth images into planes at the rate of 58 fps. Our pipeline-interleaving technique increases this rate up to 100 fps. With this throughput rate improvement, the application benefit of our algorithm may be further exploited in terms of quality and enhancing the localization.

  2. Automated Tumor Volumetry Using Computer-Aided Image Segmentation

    PubMed Central

    Bilello, Michel; Sadaghiani, Mohammed Salehi; Akbari, Hamed; Atthiah, Mark A.; Ali, Zarina S.; Da, Xiao; Zhan, Yiqang; O'Rourke, Donald; Grady, Sean M.; Davatzikos, Christos

    2015-01-01

    Rationale and Objectives Accurate segmentation of brain tumors, and quantification of tumor volume, is important for diagnosis, monitoring, and planning therapeutic intervention. Manual segmentation is not widely used because of time constraints. Previous efforts have mainly produced methods that are tailored to a particular type of tumor or acquisition protocol and have mostly failed to produce a method that functions on different tumor types and is robust to changes in scanning parameters, resolution, and image quality, thereby limiting their clinical value. Herein, we present a semiautomatic method for tumor segmentation that is fast, accurate, and robust to a wide variation in image quality and resolution. Materials and Methods A semiautomatic segmentation method based on the geodesic distance transform was developed and validated by using it to segment 54 brain tumors. Glioblastomas, meningiomas, and brain metastases were segmented. Qualitative validation was based on physician ratings provided by three clinical experts. Quantitative validation was based on comparing semiautomatic and manual segmentations. Results Tumor segmentations obtained using manual and automatic methods were compared quantitatively using the Dice measure of overlap. Subjective evaluation was performed by having human experts rate the computerized segmentations on a 0–5 rating scale where 5 indicated perfect segmentation. Conclusions The proposed method addresses a significant, unmet need in the field of neuro-oncology. Specifically, this method enables clinicians to obtain accurate and reproducible tumor volumes without the need for manual segmentation. PMID:25770633

  3. Automated tumor volumetry using computer-aided image segmentation.

    PubMed

    Gaonkar, Bilwaj; Macyszyn, Luke; Bilello, Michel; Sadaghiani, Mohammed Salehi; Akbari, Hamed; Atthiah, Mark A; Ali, Zarina S; Da, Xiao; Zhan, Yiqang; O'Rourke, Donald; Grady, Sean M; Davatzikos, Christos

    2015-05-01

    Accurate segmentation of brain tumors, and quantification of tumor volume, is important for diagnosis, monitoring, and planning therapeutic intervention. Manual segmentation is not widely used because of time constraints. Previous efforts have mainly produced methods that are tailored to a particular type of tumor or acquisition protocol and have mostly failed to produce a method that functions on different tumor types and is robust to changes in scanning parameters, resolution, and image quality, thereby limiting their clinical value. Herein, we present a semiautomatic method for tumor segmentation that is fast, accurate, and robust to a wide variation in image quality and resolution. A semiautomatic segmentation method based on the geodesic distance transform was developed and validated by using it to segment 54 brain tumors. Glioblastomas, meningiomas, and brain metastases were segmented. Qualitative validation was based on physician ratings provided by three clinical experts. Quantitative validation was based on comparing semiautomatic and manual segmentations. Tumor segmentations obtained using manual and automatic methods were compared quantitatively using the Dice measure of overlap. Subjective evaluation was performed by having human experts rate the computerized segmentations on a 0-5 rating scale where 5 indicated perfect segmentation. The proposed method addresses a significant, unmet need in the field of neuro-oncology. Specifically, this method enables clinicians to obtain accurate and reproducible tumor volumes without the need for manual segmentation. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

  4. Live minimal path for interactive segmentation of medical images

    NASA Astrophysics Data System (ADS)

    Chartrand, Gabriel; Tang, An; Chav, Ramnada; Cresson, Thierry; Chantrel, Steeve; De Guise, Jacques A.

    2015-03-01

    Medical image segmentation is nowadays required for medical device development and in a growing number of clinical and research applications. Since dedicated automatic segmentation methods are not always available, generic and efficient interactive tools can alleviate the burden of manual segmentation. In this paper we propose an interactive segmentation tool based on image warping and minimal path segmentation that is efficient for a wide variety of segmentation tasks. While the user roughly delineates the desired organs boundary, a narrow band along the cursors path is straightened, providing an ideal subspace for feature aligned filtering and minimal path algorithm. Once the segmentation is performed on the narrow band, the path is warped back onto the original image, precisely delineating the desired structure. This tool was found to have a highly intuitive dynamic behavior. It is especially efficient against misleading edges and required only coarse interaction from the user to achieve good precision. The proposed segmentation method was tested for 10 difficult liver segmentations on CT and MRI images, and the resulting 2D overlap Dice coefficient was 99% on average..

  5. Magnetic Resonance Imaging (MRI): Lumbar Spine (For Parents)

    MedlinePlus

    ... If You Have Questions en español Resonancia magnética: columna lumbar What It Is Magnetic resonance imaging (MRI) ... MORE ON THIS TOPIC Magnetic Resonance Imaging (MRI): Cervical Spine Lumbar Puncture (Spinal Tap) Magnetic Resonance Imaging ( ...

  6. Cavity resonator coil for high field magnetic resonance imaging.

    PubMed

    Solis, S E; Tomasi, D; Rodriguez, A O

    2007-01-01

    A variant coil of the high frequency cavity resonator coil was experimentally developed according to the theoretical frame proposed by Mansfield in 1990. This coil design is similar to the popular birdcage coil but it has the advantage that it can be easily built following the physical principles of the cavity resonators [1]. The equivalent circuit approach was used to compute the resonant frequency of this coil design, and compared the results with those frequency values obtained with theory. A transceiver coil composed of 4 cavities with a rod length of 4.5 cm, and a resonant frequency of 170.29 MHz was built. Phantom images were then acquired to test its viability using standard imaging sequences. The theory facilitates its development for high frequency MRI applications of animal models.

  7. Automated Segmentation of Kidneys from MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease

    PubMed Central

    Kim, Youngwoo; Ge, Yinghui; Tao, Cheng; Zhu, Jianbing; Chapman, Arlene B.; Torres, Vicente E.; Yu, Alan S.L.; Mrug, Michal; Bennett, William M.; Flessner, Michael F.; Landsittel, Doug P.

    2016-01-01

    Background and objectives Our study developed a fully automated method for segmentation and volumetric measurements of kidneys from magnetic resonance images in patients with autosomal dominant polycystic kidney disease and assessed the performance of the automated method with the reference manual segmentation method. Design, setting, participants, & measurements Study patients were selected from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease. At the enrollment of the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease Study in 2000, patients with autosomal dominant polycystic kidney disease were between 15 and 46 years of age with relatively preserved GFRs. Our fully automated segmentation method was on the basis of a spatial prior probability map of the location of kidneys in abdominal magnetic resonance images and regional mapping with total variation regularization and propagated shape constraints that were formulated into a level set framework. T2–weighted magnetic resonance image sets of 120 kidneys were selected from 60 patients with autosomal dominant polycystic kidney disease and divided into the training and test datasets. The performance of the automated method in reference to the manual method was assessed by means of two metrics: Dice similarity coefficient and intraclass correlation coefficient of segmented kidney volume. The training and test sets were swapped for crossvalidation and reanalyzed. Results Successful segmentation of kidneys was performed with the automated method in all test patients. The segmented kidney volumes ranged from 177.2 to 2634 ml (mean, 885.4±569.7 ml). The mean Dice similarity coefficient ±SD between the automated and manual methods was 0.88±0.08. The mean correlation coefficient between the two segmentation methods for the segmented volume measurements was 0.97 (P<0.001 for each crossvalidation set). The results from the crossvalidation sets were highly comparable

  8. Automated Segmentation of Kidneys from MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease.

    PubMed

    Kim, Youngwoo; Ge, Yinghui; Tao, Cheng; Zhu, Jianbing; Chapman, Arlene B; Torres, Vicente E; Yu, Alan S L; Mrug, Michal; Bennett, William M; Flessner, Michael F; Landsittel, Doug P; Bae, Kyongtae T

    2016-04-07

    Our study developed a fully automated method for segmentation and volumetric measurements of kidneys from magnetic resonance images in patients with autosomal dominant polycystic kidney disease and assessed the performance of the automated method with the reference manual segmentation method. Study patients were selected from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease. At the enrollment of the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease Study in 2000, patients with autosomal dominant polycystic kidney disease were between 15 and 46 years of age with relatively preserved GFRs. Our fully automated segmentation method was on the basis of a spatial prior probability map of the location of kidneys in abdominal magnetic resonance images and regional mapping with total variation regularization and propagated shape constraints that were formulated into a level set framework. T2-weighted magnetic resonance image sets of 120 kidneys were selected from 60 patients with autosomal dominant polycystic kidney disease and divided into the training and test datasets. The performance of the automated method in reference to the manual method was assessed by means of two metrics: Dice similarity coefficient and intraclass correlation coefficient of segmented kidney volume. The training and test sets were swapped for crossvalidation and reanalyzed. Successful segmentation of kidneys was performed with the automated method in all test patients. The segmented kidney volumes ranged from 177.2 to 2634 ml (mean, 885.4±569.7 ml). The mean Dice similarity coefficient ±SD between the automated and manual methods was 0.88±0.08. The mean correlation coefficient between the two segmentation methods for the segmented volume measurements was 0.97 (P<0.001 for each crossvalidation set). The results from the crossvalidation sets were highly comparable. We have developed a fully automated method for segmentation of kidneys from abdominal

  9. Automatic brain segmentation and validation: image-based versus atlas-based deformable models

    NASA Astrophysics Data System (ADS)

    Aboutanos, Georges B.; Dawant, Benoit M.

    1997-04-01

    Due to the complexity of the brain surface, there is at present no segmentation method that proves to work automatically and consistently on any 3-D magnetic resonance (MR) images of the head. There is a definite lack of validation studies related to automatic brain extraction. In this work we present an image-base automatic method for brain segmentation and use its results as an input to a deformable model method which we call image-based deformable model. Combining image-based methods with a deformable model can lead to a robust segmentation method without requiring registration of the image volumes into a standardized space, the automation of which remains challenging for pathological cases. We validate our segmentation results on 3-D MP-RAGE (magnetization-prepared rapid gradient-echo) volumes for the image model prior- and post-deformation and compare it to an atlas model prior- and post-deformation. Our validation is based on volume measurement comparison to manually segmented data. Our analysis shows that the improvement afforded by the deformable model methods are statistically significant, however there are no significant differences between the image-based and atlas-based deformable model methods.

  10. Graph run-length matrices for histopathological image segmentation.

    PubMed

    Tosun, Akif Burak; Gunduz-Demir, Cigdem

    2011-03-01

    The histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual interpretation of pathologists. To alleviate this problem, it is very important to develop computational quantitative tools, for which image segmentation constitutes the core step. In this paper, we introduce an effective and robust algorithm for the segmentation of histopathological tissue images. This algorithm incorporates the background knowledge of the tissue organization into segmentation. For this purpose, it quantifies spatial relations of cytological tissue components by constructing a graph and uses this graph to define new texture features for image segmentation. This new texture definition makes use of the idea of gray-level run-length matrices. However, it considers the runs of cytological components on a graph to form a matrix, instead of considering the runs of pixel intensities. Working with colon tissue images, our experiments demonstrate that the texture features extracted from "graph run-length matrices" lead to high segmentation accuracies, also providing a reasonable number of segmented regions. Compared with four other segmentation algorithms, the results show that the proposed algorithm is more effective in histopathological image segmentation.

  11. Imaging agents for in vivo magnetic resonance and scintigraphic imaging

    DOEpatents

    Engelstad, B.L.; Raymond, K.N.; Huberty, J.P.; White, D.L.

    1991-04-23

    Methods are provided for in vivo magnetic resonance imaging and/or scintigraphic imaging of a subject using chelated transition metal and lanthanide metal complexes. Novel ligands for these complexes are provided. No Drawings

  12. Imaging agents for in vivo magnetic resonance and scintigraphic imaging

    DOEpatents

    Engelstad, Barry L.; Raymond, Kenneth N.; Huberty, John P.; White, David L.

    1991-01-01

    Methods are provided for in vivo magnetic resonance imaging and/or scintigraphic imaging of a subject using chelated transition metal and lanthanide metal complexes. Novel ligands for these complexes are provided.

  13. Gaussian Mixture Model and Rjmcmc Based RS Image Segmentation

    NASA Astrophysics Data System (ADS)

    Shi, X.; Zhao, Q. H.

    2017-09-01

    For the image segmentation method based on Gaussian Mixture Model (GMM), there are some problems: 1) The number of component was usually a fixed number, i.e., fixed class and 2) GMM is sensitive to image noise. This paper proposed a RS image segmentation method that combining GMM with reversible jump Markov Chain Monte Carlo (RJMCMC). In proposed algorithm, GMM was designed to model the distribution of pixel intensity in RS image. Assume that the number of component was a random variable. Respectively build the prior distribution of each parameter. In order to improve noise resistance, used Gibbs function to model the prior distribution of GMM weight coefficient. According to Bayes' theorem, build posterior distribution. RJMCMC was used to simulate the posterior distribution and estimate its parameters. Finally, an optimal segmentation is obtained on RS image. Experimental results show that the proposed algorithm can converge to the optimal number of class and get an ideal segmentation results.

  14. Unsupervised texture image segmentation using multilayer data condensation spectral clustering

    NASA Astrophysics Data System (ADS)

    Liu, Hanqiang; Jiao, Licheng; Zhao, Feng

    2010-07-01

    A novel unsupervised texture image segmentation using a multilayer data condensation spectral clustering algorithm is presented. First, the texture features of each image pixel are extracted by the stationary wavelet transform and a multilayer data condensation method is performed on this texture features data set to obtain a condensation subset. Second, the spectral clustering algorithm based on the manifold similarity measure is used to cluster the condensation subset. Finally, according to the clustering result of the condensation subset, the nearest-neighbor method is adopted to obtain the original image-segmentation result. In the experiments, we apply our method to solve the texture and synthetic aperture radar image segmentation and take self-tuning k-nearest-neighbor spectral clustering and Nyström methods for baseline comparisons. The experimental results show that the proposed method is more robust and effective for texture image segmentation.

  15. Objective measurements to evaluate glottal space segmentation from laryngeal images.

    PubMed

    Gutiérrez-Arriola, J M; Osma-Ruiz, V; Sáenz-Lechón, N; Godino-Llorente, J I; Fraile, R; Arias-Londoño, J D

    2012-01-01

    Objective evaluation of the results of medical image segmentation is a known problem. Applied to the task of automatically detecting the glottal area from laryngeal images, this paper proposes a new objective measurement to evaluate the quality of a segmentation algorithm by comparing with the results given by a human expert. The new figure of merit is called Area Index, and its effectiveness is compared with one of the most used figures of merit found in the literature: the Pratt Index. Results over 110 laryngeal images presented high correlations between both indexes, demonstrating that the proposed measure is comparable to the Pratt Index and it is a good indicator of the segmentation quality.

  16. Integrating Non-Semantic Knowledge into Image Segmentation Processes.

    DTIC Science & Technology

    1984-03-01

    D-A149 571 INTEGRATING NON-SEMANTIC KNOWLEDGE INTO IMAGE 1/2 SEGMENTATION PROCESSES(U) MRSSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION S... IMAGE SEGMENTATION PROCESSES Ralf R. Kohler COINS Technical Report 84-04 SJAN 1 7 1985) This work was supported in part by the Office of Naval Rearch...RR07048-16. DITPI~rN STTM!4 j~pwvq jx public 7le" Dwtnutlfl nlmited . .. Teatn Non-SanatIC Knowledge into Image Segmentation Proces A Dissertation

  17. Magnetic resonance imaging of diabetic foot complications

    PubMed Central

    Low, Keynes TA; Peh, Wilfred CG

    2015-01-01

    This pictorial review aims to illustrate the various manifestations of the diabetic foot on magnetic resonance (MR) imaging. The utility of MR imaging and its imaging features in the diagnosis of pedal osteomyelitis are illustrated. There is often difficulty encountered in distinguishing osteomyelitis from neuroarthropathy, both clinically and on imaging. By providing an accurate diagnosis based on imaging, the radiologist plays a significant role in the management of patients with complications of diabetic foot. PMID:25640096

  18. Image segmentation on adaptive sub-region smoothing

    NASA Astrophysics Data System (ADS)

    Gao, Junruo; Liu, Xin; He, Kun

    2017-01-01

    To improve the performance of the active contour segmentation on real images, a new segmentation method is proposed. In this model, we construct a function about Gaussian variance according to sub-regions intensity. Further, to avoid the curve vanishing, we design the convergence condition based on the confidence level of segmentation sub-regions. Experimental results show that the proposed method is less sensitive to noise and can suppress inhomogeneous intensity regions efficiently.

  19. Image segmentation by iterative parallel region growing with application to data compression and image analysis

    NASA Technical Reports Server (NTRS)

    Tilton, James C.

    1988-01-01

    Image segmentation can be a key step in data compression and image analysis. However, the segmentation results produced by most previous approaches to region growing are suspect because they depend on the order in which portions of the image are processed. An iterative parallel segmentation algorithm avoids this problem by performing globally best merges first. Such a segmentation approach, and two implementations of the approach on NASA's Massively Parallel Processor (MPP) are described. Application of the segmentation approach to data compression and image analysis is then described, and results of such application are given for a LANDSAT Thematic Mapper image.

  20. An improved level set method for vertebra CT image segmentation

    PubMed Central

    2013-01-01

    Background Clinical diagnosis and therapy for the lumbar disc herniation requires accurate vertebra segmentation. The complex anatomical structure and the degenerative deformations of the vertebrae makes its segmentation challenging. Methods An improved level set method, namely edge- and region-based level set method (ERBLS), is proposed for vertebra CT images segmentation. By considering the gradient information and local region characteristics of images, the proposed model can efficiently segment images with intensity inhomogeneity and blurry or discontinuous boundaries. To reduce the dependency on manual initialization in many active contour models and for an automatic segmentation, a simple initialization method for the level set function is built, which utilizes the Otsu threshold. In addition, the need of the costly re-initialization procedure is completely eliminated. Results Experimental results on both synthetic and real images demonstrated that the proposed ERBLS model is very robust and efficient. Compared with the well-known local binary fitting (LBF) model, our method is much more computationally efficient and much less sensitive to the initial contour. The proposed method has also applied to 56 patient data sets and produced very promising results. Conclusions An improved level set method suitable for vertebra CT images segmentation is proposed. It has the flexibility of segmenting the vertebra CT images with blurry or discontinuous edges, internal inhomogeneity and no need of re-initialization. PMID:23714300

  1. A Bayesian Approach for Image Segmentation with Shape Priors

    SciTech Connect

    Chang, Hang; Yang, Qing; Parvin, Bahram

    2008-06-20

    Color and texture have been widely used in image segmentation; however, their performance is often hindered by scene ambiguities, overlapping objects, or missingparts. In this paper, we propose an interactive image segmentation approach with shape prior models within a Bayesian framework. Interactive features, through mouse strokes, reduce ambiguities, and the incorporation of shape priors enhances quality of the segmentation where color and/or texture are not solely adequate. The novelties of our approach are in (i) formulating the segmentation problem in a well-de?ned Bayesian framework with multiple shape priors, (ii) ef?ciently estimating parameters of the Bayesian model, and (iii) multi-object segmentation through user-speci?ed priors. We demonstrate the effectiveness of our method on a set of natural and synthetic images.

  2. Interactive medical image segmentation using snake and multiscale curve editing.

    PubMed

    Zhou, Wu; Xie, Yaoqin

    2013-01-01

    Image segmentation is typically applied to locate objects and boundaries, and it is an essential process that supports medical diagnosis, surgical planning, and treatments in medical applications. Generally, this process is done by clinicians manually, which may be accurate but tedious and very time consuming. To facilitate the process, numerous interactive segmentation methods have been proposed that allow the user to intervene in the process of segmentation by incorporating prior knowledge, validating results and correcting errors. The accurate segmentation results can potentially be obtained by such user-interactive process. In this work, we propose a novel framework of interactive medical image segmentation for clinical applications, which combines digital curves and the active contour model to obtain promising results. It allows clinicians to quickly revise or improve contours by simple mouse actions. Meanwhile, the snake model becomes feasible and practical in clinical applications. Experimental results demonstrate the effectiveness of the proposed method for medical images in clinical applications.

  3. 3D cerebral MR image segmentation using multiple-classifier system.

    PubMed

    Amiri, Saba; Movahedi, Mohammad Mehdi; Kazemi, Kamran; Parsaei, Hossein

    2017-03-01

    The three soft brain tissues white matter (WM), gray matter (GM), and cerebral spinal fluid (CSF) identified in a magnetic resonance (MR) image via image segmentation techniques can aid in structural and functional brain analysis, brain's anatomical structures measurement and visualization, neurodegenerative disorders diagnosis, and surgical planning and image-guided interventions, but only if obtained segmentation results are correct. This paper presents a multiple-classifier-based system for automatic brain tissue segmentation from cerebral MR images. The developed system categorizes each voxel of a given MR image as GM, WM, and CSF. The algorithm consists of preprocessing, feature extraction, and supervised classification steps. In the first step, intensity non-uniformity in a given MR image is corrected and then non-brain tissues such as skull, eyeballs, and skin are removed from the image. For each voxel, statistical features and non-statistical features were computed and used a feature vector representing the voxel. Three multilayer perceptron (MLP) neural networks trained using three different datasets were used as the base classifiers of the multiple-classifier system. The output of the base classifiers was fused using majority voting scheme. Evaluation of the proposed system was performed using Brainweb simulated MR images with different noise and intensity non-uniformity and internet brain segmentation repository (IBSR) real MR images. The quantitative assessment of the proposed method using Dice, Jaccard, and conformity coefficient metrics demonstrates improvement (around 5 % for CSF) in terms of accuracy as compared to single MLP classifier and the existing methods and tools such FSL-FAST and SPM. As accurately segmenting a MR image is of paramount importance for successfully promoting the clinical application of MR image segmentation techniques, the improvement obtained by using multiple-classifier-based system is encouraging.

  4. Design and validation of Segment - freely available software for cardiovascular image analysis

    PubMed Central

    2010-01-01

    Background Commercially available software for cardiovascular image analysis often has limited functionality and frequently lacks the careful validation that is required for clinical studies. We have already implemented a cardiovascular image analysis software package and released it as freeware for the research community. However, it was distributed as a stand-alone application and other researchers could not extend it by writing their own custom image analysis algorithms. We believe that the work required to make a clinically applicable prototype can be reduced by making the software extensible, so that researchers can develop their own modules or improvements. Such an initiative might then serve as a bridge between image analysis research and cardiovascular research. The aim of this article is therefore to present the design and validation of a cardiovascular image analysis software package (Segment) and to announce its release in a source code format. Results Segment can be used for image analysis in magnetic resonance imaging (MRI), computed tomography (CT), single photon emission computed tomography (SPECT) and positron emission tomography (PET). Some of its main features include loading of DICOM images from all major scanner vendors, simultaneous display of multiple image stacks and plane intersections, automated segmentation of the left ventricle, quantification of MRI flow, tools for manual and general object segmentation, quantitative regional wall motion analysis, myocardial viability analysis and image fusion tools. Here we present an overview of the validation results and validation procedures for the functionality of the software. We describe a technique to ensure continued accuracy and validity of the software by implementing and using a test script that tests the functionality of the software and validates the output. The software has been made freely available for research purposes in a source code format on the project home page http://segment

  5. Cytological image segmentation based on iterative generalized Hough transform

    NASA Astrophysics Data System (ADS)

    Liu, Zhuofu; Liu, Meimei; Sui, Lihua; Cheng, Li

    2006-09-01

    Automatic exact segmentation of medical images is very important, since applications need to extract precisely the interesting organic features in the human body. An important example is cell detection in cytological and histological images for the diagnosis of breast cancer. In this paper, we propose a time- and memory-efficient algorithm, called Iterative Generalized Hough Transform (IGHT) for automated cytological image segmentation. In addition to lowering memory requirement, the proposed algorithm reduces the excessive time with image scaling. Instead of being applied to a full-sized image, the IGHT scales down to half-sized and quarter-sized images. The proposed algorithm efficiently exploits both region and edge information. The results show that it is a reliable method for segmenting nuclei in cytological images and for extracting components of interest, which is a key step for diagnosing breast cancer and predicting the course of the disease.

  6. Contrast-enhanced magnetic resonance imaging of hypoperfused myocardium.

    PubMed

    Schaefer, S; Lange, R A; Gutekunst, D P; Parkey, R W; Willerson, J T; Peshock, R M

    1991-06-01

    Contrast-enhanced magnetic resonance (MR) imaging can define myocardial perfusion defects due to acute coronary occlusion. However, since most clinically important diagnostic examinations involve coronary arteries with subtotal stenoses, we investigated the ability of MR imaging with a manganese contrast agent to detect perfusion abnormalities in a canine model of partial coronary artery stenosis. The contrast agent was administered after the creation of a partial coronary artery stenosis with the addition of the coronary vasodilator dipyridamole in six of 12 animals. The hearts were imaged ex situ using gradient reversal and spin-echo sequences, and images were analyzed to determine differences in signal intensity between hypoperfused and normally perfused myocardium. Comparison of MR images with regional blood flow and thallium-201 measurements showed good concordance of hypoperfused segments in those animals given dipyridamole, with 75% of the abnormal segments correctly identified. In those animals not given dipyridamole, 48% of segments were correctly identified. Thus, ex vivo MR imaging with a paramagnetic contrast enhancement can be used to detect acute regional myocardial perfusion abnormalities due to severe partial coronary artery stenoses.

  7. A translational registration system for LANDSAT image segments

    NASA Technical Reports Server (NTRS)

    Parada, N. D. J. (Principal Investigator); Erthal, G. J.; Velasco, F. R. D.; Mascarenhas, N. D. D.

    1983-01-01

    The use of satellite images obtained from various dates is essential for crop forecast systems. In order to make possible a multitemporal analysis, it is necessary that images belonging to each acquisition have pixel-wise correspondence. A system developed to obtain, register and record image segments from LANDSAT images in computer compatible tapes is described. The translational registration of the segments is performed by correlating image edges in different acquisitions. The system was constructed for the Burroughs B6800 computer in ALGOL language.

  8. Gap-free segmentation of vascular networks with automatic image processing pipeline.

    PubMed

    Hsu, Chih-Yang; Ghaffari, Mahsa; Alaraj, Ali; Flannery, Michael; Zhou, Xiaohong Joe; Linninger, Andreas

    2017-03-01

    Current image processing techniques capture large vessels reliably but often fail to preserve connectivity in bifurcations and small vessels. Imaging artifacts and noise can create gaps and discontinuity of intensity that hinders segmentation of vascular trees. However, topological analysis of vascular trees require proper connectivity without gaps, loops or dangling segments. Proper tree connectivity is also important for high quality rendering of surface meshes for scientific visualization or 3D printing. We present a fully automated vessel enhancement pipeline with automated parameter settings for vessel enhancement of tree-like structures from customary imaging sources, including 3D rotational angiography, magnetic resonance angiography, magnetic resonance venography, and computed tomography angiography. The output of the filter pipeline is a vessel-enhanced image which is ideal for generating anatomical consistent network representations of the cerebral angioarchitecture for further topological or statistical analysis. The filter pipeline combined with computational modeling can potentially improve computer-aided diagnosis of cerebrovascular diseases by delivering biometrics and anatomy of the vasculature. It may serve as the first step in fully automatic epidemiological analysis of large clinical datasets. The automatic analysis would enable rigorous statistical comparison of biometrics in subject-specific vascular trees. The robust and accurate image segmentation using a validated filter pipeline would also eliminate operator dependency that has been observed in manual segmentation. Moreover, manual segmentation is time prohibitive given that vascular trees have more than thousands of segments and bifurcations so that interactive segmentation consumes excessive human resources. Subject-specific trees are a first step toward patient-specific hemodynamic simulations for assessing treatment outcomes. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Three-dimensional segmentation of bone structures in CT images

    NASA Astrophysics Data System (ADS)

    Boehm, Guenther; Knoll, Christian J.; Grau Colomer, Vincente; Alcaniz-Raya, Mariano L.; Albalat, Salvador E.

    1999-05-01

    This work is concerned with the implementation of a fully 3D-consistent, automatic segmentation of bone structures in CT images. The morphological watersheds algorithm has been chosen as the base of the low-level segmentation. The over- segmentation, a phenomenon normally involved with this transformation, has been sorted out successfully by inserting modifying modules that act already within the algorithm. When dealing with a maxillofacial image, this approach also includes the possibility to provide two different divisions of the image: a fine-grained tessellation geared to the following high-level segmentation and a more coarse-grained one for the segmentation of the teeth. In the knowledge-based high-level segmentation, probabilistic considerations make use of specific properties of the 3D low-level regions to find the most probable tissue for each region. Low-level regions that cannot be classified with the necessary certainty are passed to a second stage, where--embedded in their respective environment--they are compared with structural patterns deduced from anatomical knowledge. The tooth segmentation takes the coarse-grained tessellation as its starting point. The few regions making up each tooth are grouped to 3D envelopes--one envelope per tooth. Matched filtering detects the bases of these envelopes. After a refinement they are fitted into the fine- grained, high-level segmented image.

  10. A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT.

    PubMed

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

    2016-03-08

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

  11. Coronary computed tomography and magnetic resonance imaging.

    PubMed

    Kantor, Birgit; Nagel, Eike; Schoenhagen, Paul; Barkhausen, Jörg; Gerber, Thomas C

    2009-04-01

    Cardiac computed tomography and magnetic resonance are relatively new imaging modalities that can exceed the ability of established imaging modalities to detect present pathology or predict patient outcomes. Coronary calcium scoring may be useful in asymptomatic patients at intermediate risk. Computed tomographic coronary angiography is a first-line indication to evaluate congenitally abnormal coronary arteries and, along with stress magnetic resonance myocardial perfusion imaging, is useful in symptomatic patients with nondiagnostic conventional stress tests. Cardiac magnetic resonance is indicated for visualizing cardiac structure and function, and delayed enhancement magnetic resonance is a first-line indication for assessing myocardial viability. Imaging plaque and molecular mechanisms related to plaque rupture holds great promise for the presymptomatic detection of patients at risk for coronary events but is not yet suitable for routine clinical use.

  12. Magnetic resonance imaging of the cryptorchid testis.

    PubMed

    Landa, H M; Gylys-Morin, V; Mattrey, R F; Krous, H F; Kaplan, G W; Packer, M G

    1987-01-01

    Magnetic resonance imaging was used to evaluate seven patients with undescended testes. In six patients the presence or absence of testicular tissue was predicted correctly prior to surgery. Spermatic cord structures, if present, were accurately visualized in all patients.

  13. Coronary Computed Tomography and Magnetic Resonance Imaging

    PubMed Central

    Kantor, Birgit; Nagel, Eike; Schoenhagen, Paul; Barkhausen, Jörg; Gerber, Thomas C.

    2009-01-01

    Cardiac computed tomography and magnetic resonance are relatively new imaging modalities that can exceed the ability of established imaging modalities to detect present pathology or predict patient outcomes. Coronary calcium scoring may be useful in asymptomatic patients at intermediate risk. Computed tomographic coronary angiography is a first-line indication to evaluate congenitally abnormal coronary arteries and, along with stress magnetic resonance myocardial perfusion imaging, is useful in symptomatic patients with nondiagnostic conventional stress tests. Cardiac magnetic resonance is indicated for visualizing cardiac structure and function, and delayed enhancement magnetic resonance is a first-line indication for assessing myocardial viability. Imaging plaque and molecular mechanisms related to plaque rupture holds great promise for the presymptomatic detection of patients at risk for coronary events but is not yet suitable for routine clinical use. PMID:19269527

  14. A hybrid framework for 3D medical image segmentation.

    PubMed

    Chen, Ting; Metaxas, Dimitris

    2005-12-01

    In this paper we propose a novel hybrid 3D segmentation framework which combines Gibbs models, marching cubes and deformable models. In the framework, first we construct a new Gibbs model whose energy function is defined on a high order clique system. The new model includes both region and boundary information during segmentation. Next we improve the original marching cubes method to construct 3D meshes from Gibbs models' output. The 3D mesh serves as the initial geometry of the deformable model. Then we deform the deformable model using external image forces so that the model converges to the object surface. We run the Gibbs model and the deformable model recursively by updating the Gibbs model's parameters using the region and boundary information in the deformable model segmentation result. In our approach, the hybrid combination of region-based methods and boundary-based methods results in improved segmentations of complex structures. The benefit of the methodology is that it produces high quality segmentations of 3D structures using little prior information and minimal user intervention. The modules in this segmentation methodology are developed within the context of the Insight ToolKit (ITK). We present experimental segmentation results of brain tumors and evaluate our method by comparing experimental results with expert manual segmentations. The evaluation results show that the methodology achieves high quality segmentation results with computational efficiency. We also present segmentation results of other clinical objects to illustrate the strength of the methodology as a generic segmentation framework.

  15. Automatic segmentation of MR images using self-organizing feature mapping and neural networks

    NASA Astrophysics Data System (ADS)

    Alirezaie, Javad; Jernigan, M. Ed; Nahmias, Claude

    1997-04-01

    In this paper we present an unsupervised clustering technique for multispectral segmentation of magnetic resonance (MR) images of the human brain. Our scheme utilizes the self-organizing feature map (SOFM) artificial neural network (ANN) for feature mapping and generates a set of codebook vectors for each tissue class. Features are selected from three image spectra: T1, T2 and proton density (PD) weighted images. An algorithm has been developed for isolating the cerebrum from the head scan prior to the segmentation. To classify the map, we extend the network by adding an associative layer. Three tissue types of the brain: white matter, gray matter and cerebral spinal fluid (CSF) are segmented accurately. Any unclassified tissues were remained as unknown tissue class.

  16. Mammographic images segmentation based on chaotic map clustering algorithm

    PubMed Central

    2014-01-01

    Background This work investigates the applicability of a novel clustering approach to the segmentation of mammographic digital images. The chaotic map clustering algorithm is used to group together similar subsets of image pixels resulting in a medically meaningful partition of the mammography. Methods The image is divided into pixels subsets characterized by a set of conveniently chosen features and each of the corresponding points in the feature space is associated to a map. A mutual coupling strength between the maps depending on the associated distance between feature space points is subsequently introduced. On the system of maps, the simulated evolution through chaotic dynamics leads to its natural partitioning, which corresponds to a particular segmentation scheme of the initial mammographic image. Results The system provides a high recognition rate for small mass lesions (about 94% correctly segmented inside the breast) and the reproduction of the shape of regions with denser micro-calcifications in about 2/3 of the cases, while being less effective on identification of larger mass lesions. Conclusions We can summarize our analysis by asserting that due to the particularities of the mammographic images, the chaotic map clustering algorithm should not be used as the sole method of segmentation. It is rather the joint use of this method along with other segmentation techniques that could be successfully used for increasing the segmentation performance and for providing extra information for the subsequent analysis stages such as the classification of the segmented ROI. PMID:24666766

  17. Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding

    NASA Astrophysics Data System (ADS)

    Khan, Z. Faizal; Kannan, A.

    2014-04-01

    The performance of assessment in medical image segmentation is highly correlated with the extraction of anatomic structures from them, and the major task is how to separate the regions of interests from the background and soft tissues successfully. This paper proposes a fuzzy logic based bitplane method to automatically segment the background of images and to locate the region of interest of medical images. This segmentation algorithm consists of three steps, namely identification, rule firing, and inference. In the first step, we begin by identifying the bitplanes that represent the lungs clearly. For this purpose, the intensity value of a pixel is separated into bitplanes. In the second step, the triple signum function assigns an optimum threshold based on the grayscale values for the anatomical structure present in the medical images. Fuzzy rules are formed based on the available bitplanes to form the membership table and are stored in a knowledge base. Finally, rules are fired to assign final segmentation values through the inference process. The proposed new metrics are used to measure the accuracy of the segmentation method. From the analysis, it is observed that the proposed metrics are more suitable for the estimation of segmentation accuracy. The results obtained from this work show that the proposed method performs segmentation effectively for the different classes of medical images.

  18. Mammographic images segmentation based on chaotic map clustering algorithm.

    PubMed

    Iacomi, Marius; Cascio, Donato; Fauci, Francesco; Raso, Giuseppe

    2014-03-25

    This work investigates the applicability of a novel clustering approach to the segmentation of mammographic digital images. The chaotic map clustering algorithm is used to group together similar subsets of image pixels resulting in a medically meaningful partition of the mammography. The image is divided into pixels subsets characterized by a set of conveniently chosen features and each of the corresponding points in the feature space is associated to a map. A mutual coupling strength between the maps depending on the associated distance between feature space points is subsequently introduced. On the system of maps, the simulated evolution through chaotic dynamics leads to its natural partitioning, which corresponds to a particular segmentation scheme of the initial mammographic image. The system provides a high recognition rate for small mass lesions (about 94% correctly segmented inside the breast) and the reproduction of the shape of regions with denser micro-calcifications in about 2/3 of the cases, while being less effective on identification of larger mass lesions. We can summarize our analysis by asserting that due to the particularities of the mammographic images, the chaotic map clustering algorithm should not be used as the sole method of segmentation. It is rather the joint use of this method along with other segmentation techniques that could be successfully used for increasing the segmentation performance and for providing extra information for the subsequent analysis stages such as the classification of the segmented ROI.

  19. Renal compartment segmentation in DCE-MRI images.

    PubMed

    Yang, Xin; Le Minh, Hung; Tim Cheng, Kwang-Ting; Sung, Kyung Hyun; Liu, Wenyu

    2016-08-01

    Renal compartment segmentation from Dynamic Contrast-Enhanced MRI (DCE-MRI) images is an important task for functional kidney evaluation. Despite advancement in segmentation methods, most of them focus on segmenting an entire kidney on CT images, there still lacks effective and automatic solutions for accurate segmentation of internal renal structures (i.e. cortex, medulla and renal pelvis) from DCE-MRI images. In this paper, we introduce a method for renal compartment segmentation which can robustly achieve high segmentation accuracy for a wide range of DCE-MRI data, and meanwhile requires little manual operations and parameter settings. The proposed method consists of five main steps. First, we pre-process the image time series to reduce the motion artifacts caused by the movement of the patients during the scans and enhance the kidney regions. Second, the kidney is segmented as a whole based on the concept of Maximally Stable Temporal Volume (MSTV). The proposed MSTV detects anatomical structures that are homogeneous in the spatial domain and stable in terms of temporal dynamics. MSTV-based kidney segmentation is robust to noises and does not require a training phase. It can well adapt to kidney shape variations caused by renal dysfunction. Third, voxels in the segmented kidney are described by principal components (PCs) to remove temporal redundancy and noises. And then k-means clustering of PCs is applied to separate voxels into multiple clusters. Fourth, the clusters are automatically labeled as cortex, medulla and pelvis based on voxels' geometric locations and intensity distribution. Finally, an iterative refinement method is introduced to further remove noises in each segmented compartment. Experiments on 14 real clinical kidney datasets and 12 synthetic dataset demonstrate that results produced by our method match very well with those segmented manually and the performance of our method is superior to the other five existing methods.

  20. Atlas-based method for segmentation of cerebral vascular trees from phase-contrast magnetic resonance angiography

    NASA Astrophysics Data System (ADS)

    Passat, Nicolas; Ronse, Christian; Baruthio, Joseph; Armspach, Jean-Paul; Maillot, Claude; Jahn, Christine

    2004-05-01

    Phase-contrast magnetic resonance angiography (PC-MRA) can produce phase images which are 3-dimensional pictures of vascular structures. However, it also provides magnitude images, containing anatomical - but no vascular - data. Classically, algorithms dedicated to PC-MRA segmentation detect the cerebral vascular tree by only working on phase images. We propose here a new approach for segmentation of cerebral blood vessels in PC-MRA using both types of images. This approach is based on the hypothesis that a magnitude image contains anatomical information useful for vascular structures detection. That information can then be transposed from a normal case to any patient image by image registration. An atlas of the whole head has been developed in order to store such anatomical knowledge. It divides a magnitude image into several "vascular areas", each one having specific vessel properties. The atlas can be applied on any magnitude image of an entire or nearly entire head by deformable matching, thus helping to segment blood vessels from the associated phase image. The segmentation method used afterwards is composed of a topology-conserving region growing algorithm using adaptative threshold values depending on the current region of the atlas. This algorithm builds the arterial and venous trees by iteratively adding voxels which are selected according to their greyscale value and the variation of values in their neighborhood. The topology conservation is guaranteed by only selecting simple points during the growing process. The method has been performed on 15 PC-MRA's of the brain. The results have been validated using MIP and 3D surface rendering visualization; a comparison to other results obtained without an atlas proves that atlas-based methods are an effective way to optimize vascular segmentation strategies.

  1. Neural cell image segmentation method based on support vector machine

    NASA Astrophysics Data System (ADS)

    Niu, Shiwei; Ren, Kan

    2015-10-01

    In the analysis of neural cell images gained by optical microscope, accurate and rapid segmentation is the foundation of nerve cell detection system. In this paper, a modified image segmentation method based on Support Vector Machine (SVM) is proposed to reduce the adverse impact caused by low contrast ratio between objects and background, adherent and clustered cells' interference etc. Firstly, Morphological Filtering and OTSU Method are applied to preprocess images for extracting the neural cells roughly. Secondly, the Stellate Vector, Circularity and Histogram of Oriented Gradient (HOG) features are computed to train SVM model. Finally, the incremental learning SVM classifier is used to classify the preprocessed images, and the initial recognition areas identified by the SVM classifier are added to the library as the positive samples for training SVM model. Experiment results show that the proposed algorithm can achieve much better segmented results than the classic segmentation algorithms.

  2. Spectral segmentation of polygonized images with normalized cuts

    SciTech Connect

    Matsekh, Anna; Skurikhin, Alexei; Rosten, Edward

    2009-01-01

    We analyze numerical behavior of the eigenvectors corresponding to the lowest eigenvalues of the generalized graph Laplacians arising in the Normalized Cuts formulations of the image segmentation problem on coarse polygonal grids.

  3. Detection of atherosclerosis via magnetic resonance imaging

    NASA Astrophysics Data System (ADS)

    Alexander, Andrew L.; Pytlewski, Victor T.; Brown, Michael F.; Gmitro, Arthur F.

    1992-08-01

    Magnetic resonance imaging (MRI) of atherosclerotic lipids using a stimulated-echo diffusion- weighted (STED) sequence is demonstrated. The STED sequence exploits the large difference in diffusion between lipid (primarily cholesteryl ester) and water. The optimization of the STED sequence is discussed. The results of lipid imaging are corroborated with nuclear magnetic resonance (NMR) spectroscopy. This technique is non-invasive, and therefore, it is potentially useful in following the progression of the disease in animal models and in humans.

  4. 3D ultrasound image segmentation using multiple incomplete feature sets

    NASA Astrophysics Data System (ADS)

    Fan, Liexiang; Herrington, David M.; Santago, Peter, II

    1999-05-01

    We use three features, the intensity, texture and motion to obtain robust results for segmentation of intracoronary ultrasound images. Using a parameterized equation to describe the lumen-plaque and media-adventitia boundaries, we formulate the segmentation as a parameter estimation through a cost functional based on the posterior probability, which can handle the incompleteness of the features in ultrasound images by employing outlier detection.

  5. A new method of cardiographic image segmentation based on grammar

    NASA Astrophysics Data System (ADS)

    Hamdi, Salah; Ben Abdallah, Asma; Bedoui, Mohamed H.; Alimi, Adel M.

    2011-10-01

    The measurement of the most common ultrasound parameters, such as aortic area, mitral area and left ventricle (LV) volume, requires the delineation of the organ in order to estimate the area. In terms of medical image processing this translates into the need to segment the image and define the contours as accurately as possible. The aim of this work is to segment an image and make an automated area estimation based on grammar. The entity "language" will be projected to the entity "image" to perform structural analysis and parsing of the image. We will show how the idea of segmentation and grammar-based area estimation is applied to real problems of cardio-graphic image processing.

  6. Apparatus for investigating resonance with application to magnetic resonance imaging

    NASA Astrophysics Data System (ADS)

    Murphy, Sytil; Jones, Dyan L.; Gross, Josh; Zollman, Dean

    2015-11-01

    Resonance is typically studied in the context of either a pendulum or a mass on a spring. We have developed an apparatus that enables beginning students to investigate resonant behavior of changing magnetic fields, in addition to the properties of the magnetic field due to a wire and the superposition of magnetic fields. In this resonant system, a compass oscillates at a frequency determined by the compass's physical properties and an external magnetic field. While the analysis is mathematically similar to that of the pendulum, this apparatus has an advantage that the magnetic field is easily controlled, while it is difficult to control the strength of gravity. This apparatus has been incorporated into a teaching module on magnetic resonance imaging.

  7. An enhanced fast scanning algorithm for image segmentation

    NASA Astrophysics Data System (ADS)

    Ismael, Ahmed Naser; Yusof, Yuhanis binti

    2015-12-01

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

  8. Local and global evaluation for remote sensing image segmentation

    NASA Astrophysics Data System (ADS)

    Su, Tengfei; Zhang, Shengwei

    2017-08-01

    In object-based image analysis, how to produce accurate segmentation is usually a very important issue that needs to be solved before image classification or target recognition. The study for segmentation evaluation method is key to solving this issue. Almost all of the existent evaluation strategies only focus on the global performance assessment. However, these methods are ineffective for the situation that two segmentation results with very similar overall performance have very different local error distributions. To overcome this problem, this paper presents an approach that can both locally and globally quantify segmentation incorrectness. In doing so, region-overlapping metrics are utilized to quantify each reference geo-object's over and under-segmentation error. These quantified error values are used to produce segmentation error maps which have effective illustrative power to delineate local segmentation error patterns. The error values for all of the reference geo-objects are aggregated through using area-weighted summation, so that global indicators can be derived. An experiment using two scenes of very different high resolution images showed that the global evaluation part of the proposed approach was almost as effective as other two global evaluation methods, and the local part was a useful complement to comparing different segmentation results.

  9. Image segmentation and 3D visualization for MRI mammography

    NASA Astrophysics Data System (ADS)

    Li, Lihua; Chu, Yong; Salem, Angela F.; Clark, Robert A.

    2002-05-01

    MRI mammography has a number of advantages, including the tomographic, and therefore three-dimensional (3-D) nature, of the images. It allows the application of MRI mammography to breasts with dense tissue, post operative scarring, and silicon implants. However, due to the vast quantity of images and subtlety of difference in MR sequence, there is a need for reliable computer diagnosis to reduce the radiologist's workload. The purpose of this work was to develop automatic breast/tissue segmentation and visualization algorithms to aid physicians in detecting and observing abnormalities in breast. Two segmentation algorithms were developed: one for breast segmentation, the other for glandular tissue segmentation. In breast segmentation, the MRI image is first segmented using an adaptive growing clustering method. Two tracing algorithms were then developed to refine the breast air and chest wall boundaries of breast. The glandular tissue segmentation was performed using an adaptive thresholding method, in which the threshold value was spatially adaptive using a sliding window. The 3D visualization of the segmented 2D slices of MRI mammography was implemented under IDL environment. The breast and glandular tissue rendering, slicing and animation were displayed.

  10. Medical image segmentation using level set and watershed transform

    NASA Astrophysics Data System (ADS)

    Zhu, Fuping; Tian, Jie

    2003-07-01

    One of the most popular level set algorithms is the so-called fast marching method. In this paper, a medical image segmentation algorithm is proposed based on the combination of fast marching method and watershed transformation. First, the original image is smoothed using nonlinear diffusion filter, then the smoothed image is over-segmented by the watershed algorithm. Last, the image is segmented automatically using the modified fast marching method. Due to introducing over-segmentation, the arrival time the seeded point to the boundary of region should be calculated. For other pixels inside the region of the seeded point, the arrival time is not calculated because of the region homogeneity. So the algorithm"s speed improves greatly. Moreover, the speed function is redefined based on the statistical similarity degree of the nearby regions. We also extend our algorithm to 3D circumstance and segment medical image series. Experiments show that the algorithm can fast and accurately obtain segmentation results of medical images.

  11. Image segmentation based on kernel PCA and shape prior

    NASA Astrophysics Data System (ADS)

    Wan, Xiaoping; Boukerroui, Djamal; Cocquerez, Jean-Pierre

    2011-06-01

    The introduction of shape priori in the segmentation model ameliorates effectively the poor segmentation result due to the using of the image information alone to segment the image including noise, occlusion, or missing parts. But the presentation of shape via Principal Component Analysis (PCA) brings on the limitation of the similarity between the objet and the prior shape. In this paper, we proposed using Kernel PCA (KPCA) to capture the shape information - the variability. KPCA can present better shape prior knowledge. The model based on KPCA allows segmenting the object with nonlinear transformation or a quite difference with the priori shape. Moreover, since the shape model is incorporated into the deformable model, our segmentation model includes the image term and the shape term to balance the influence of the global image information and the shape prior knowledge in proceed of segmentation. Our model and the model based on PCA both are applied to synthetic images and CT medical images. The comparative results show that KPCA can more accurately identify the object with large deformation or from the noised seriously background.

  12. Watershed Merge Tree Classification for Electron Microscopy Image Segmentation

    SciTech Connect

    Liu, TIng; Jurrus, Elizabeth R.; Seyedhosseini, Mojtaba; Ellisman, Mark; Tasdizen, Tolga

    2012-11-11

    Automated segmentation of electron microscopy (EM) images is a challenging problem. In this paper, we present a novel method that utilizes a hierarchical structure and boundary classification for 2D neuron segmentation. With a membrane detection probability map, a watershed merge tree is built for the representation of hierarchical region merging from the watershed algorithm. A boundary classifier is learned with non-local image features to predict each potential merge in the tree, upon which merge decisions are made with consistency constraints in the sense of optimization to acquire the final segmentation. Independent of classifiers and decision strategies, our approach proposes a general framework for efficient hierarchical segmentation with statistical learning. We demonstrate that our method leads to a substantial improvement in segmentation accuracy.

  13. Overlapping image segmentation for context-dependent anomaly detection

    NASA Astrophysics Data System (ADS)

    Theiler, James; Prasad, Lakshman

    2011-06-01

    The challenge of finding small targets in big images lies in the characterization of the background clutter. The more homogeneous the background, the more distinguishable a typical target will be from its background. One way to homogenize the background is to segment the image into distinct regions, each of which is individually homogeneous, and then to treat each region separately. In this paper we will report on experiments in which the target is unspecified (it is an anomaly), and various segmentation strategies are employed, including an adaptive hierarchical tree-based scheme. We find that segmentations that employ overlap achieve better performance in the low false alarm rate regime.

  14. Biomedical image segmentation using geometric deformable models and metaheuristics.

    PubMed

    Mesejo, Pablo; Valsecchi, Andrea; Marrakchi-Kacem, Linda; Cagnoni, Stefano; Damas, Sergio

    2015-07-01

    This paper describes a hybrid level set approach for medical image segmentation. This new geometric deformable model combines region- and edge-based information with the prior shape knowledge introduced using deformable registration. Our proposal consists of two phases: training and test. The former implies the learning of the level set parameters by means of a Genetic Algorithm, while the latter is the proper segmentation, where another metaheuristic, in this case Scatter Search, derives the shape prior. In an experimental comparison, this approach has shown a better performance than a number of state-of-the-art methods when segmenting anatomical structures from different biomedical image modalities.

  15. Multi-object segmentation framework using deformable models for medical imaging analysis.

    PubMed

    Namías, Rafael; D'Amato, Juan Pablo; Del Fresno, Mariana; Vénere, Marcelo; Pirró, Nicola; Bellemare, Marc-Emmanuel

    2016-08-01

    Segmenting structures of interest in medical images is an important step in different tasks such as visualization, quantitative analysis, simulation, and image-guided surgery, among several other clinical applications. Numerous segmentation methods have been developed in the past three decades for extraction of anatomical or functional structures on medical imaging. Deformable models, which include the active contour models or snakes, are among the most popular methods for image segmentation combining several desirable features such as inherent connectivity and smoothness. Even though different approaches have been proposed and significant work has been dedicated to the improvement of such algorithms, there are still challenging research directions as the simultaneous extraction of multiple objects and the integration of individual techniques. This paper presents a novel open-source framework called deformable model array (DMA) for the segmentation of multiple and complex structures of interest in different imaging modalities. While most active contour algorithms can extract one region at a time, DMA allows integrating several deformable models to deal with multiple segmentation scenarios. Moreover, it is possible to consider any existing explicit deformable model formulation and even to incorporate new active contour methods, allowing to select a suitable combination in different conditions. The framework also introduces a control module that coordinates the cooperative evolution of the snakes and is able to solve interaction issues toward the segmentation goal. Thus, DMA can implement complex object and multi-object segmentations in both 2D and 3D using the contextual information derived from the model interaction. These are important features for several medical image analysis tasks in which different but related objects need to be simultaneously extracted. Experimental results on both computed tomography and magnetic resonance imaging show that the proposed

  16. Segmentation and image navigation in digitized spine x rays

    NASA Astrophysics Data System (ADS)

    Long, L. Rodney; Thoma, George R.

    2000-06-01

    The National Library of Medicine has archived a collection of 17,000 digitized x-rays of the cervical and lumbar spines. Extensive health information has been collected on the subjects of these x-rays, but no information has been derived from the image contents themselves. We are researching algorithms to segment anatomy in these images and to derive from the segmented data measurements useful for indexing this image set for characteristics important to researchers in rheumatology, bone morphometry, and related areas. Active Shape Modeling is currently being investigated for use in location and boundary definition for the vertebrae in these images.

  17. Image Segmentation Using Parametric Contours With Free Endpoints.

    PubMed

    Benninghoff, Heike; Garcke, Harald

    2016-04-01

    In this paper, we introduce a novel approach for active contours with free endpoints. A scheme for image segmentation is presented based on a discrete version of the Mumford-Shah functional where the contours can be both closed and open curves. Additional to a flow of the curves in normal direction, evolution laws for the tangential flow of the endpoints are derived. Using a parametric approach to describe the evolving contours together with an edge-preserving denoising, we obtain a fast method for image segmentation and restoration. The analytical and numerical schemes are presented followed by numerical experiments with artificial test images and with a real medical image.

  18. Image Segmentation Using Parametric Contours With Free Endpoints

    NASA Astrophysics Data System (ADS)

    Benninghoff, Heike; Garcke, Harald

    2016-04-01

    In this paper, we introduce a novel approach for active contours with free endpoints. A scheme is presented for image segmentation and restoration based on a discrete version of the Mumford-Shah functional where the contours can be both closed and open curves. Additional to a flow of the curves in normal direction, evolution laws for the tangential flow of the endpoints are derived. Using a parametric approach to describe the evolving contours together with an edge-preserving denoising, we obtain a fast method for image segmentation and restoration. The analytical and numerical schemes are presented followed by numerical experiments with artificial test images and with a real medical image.

  19. Segmentation of wooden members of ancient architecture from range image

    NASA Astrophysics Data System (ADS)

    Zhang, Ruiju; Wang, Yanmin; Li, Deren; Zhao, Jun; Song, Daixue

    2006-10-01

    Segmentation wooden member from range images is the basis of 3d reconstruction of wooden member and whole architecture because it provides reliable point clouds for modeling. This paper presents a segmentation strategy to extract point clouds of wooden member of ancient architecture from range image. Hybrid approach combining edge and region-based techniques is adopted in the paper to ensure a reliable and robust segmentation. First, range image is triangulated according to the implied topological relationships between point clouds. Second, filtering is processed by combination the two smoothing methods of λ|μ and Laplacian. Third, feature points are detected by local surface differential geometry properties, and feature edges are extracted according to a selective mechanism, so an initial, rough segmentation is provided based on edge information. And then, the initial edge-based segmentation is enhanced by region-based segmentation method. How to estimate the differential geometry properties robustly, and how to detect feature points and feature edges and so on are studied in the paper. Range images acquired from Forbidden City of China are used to test the segmentation strategy, and results prove its efficiency and robustness.

  20. Characterization of acute myocardial infarction by magnetic resonance imaging.

    PubMed

    Johnston, D L; Wendt, R E; Mulvagh, S L; Rubin, H

    1992-05-15

    The T2-weighted spin-echo technique is currently the most frequently used magnetic resonance imaging (MRI) method to visualize acute myocardial infarction. However, image quality is often degraded by ghost artifacts from blood flow, and respiratory and cardiac contractile motion. To enhance the usefulness of this technique for detailed characterization of infarction, a velocity-compensated spin-echo pulse sequence was tested by imaging a flow phantom, 6 normal subjects and 17 patients with acute myocardial infarction. After preliminary studies were performed in 7 patients to determine optimal imaging parameters, a standardized imaging protocol was used in the next 10. The location of myocardial infarction identified by the electrocardiogram and coronary anatomy was correctly identified in 10 of 10 patients. Distribution of the injury within the left ventricle was clearly visualized, and showed that patients often had a mixture of transmural and nontransmural injury. Heterogenous distribution of signal intensity within the infarction suggested the presence of hemorrhage. Papillary muscle involvement was readily apparent. Signal intensity of the infarction (brightest segment) was increased by 89 +/- 31% compared with the mean of the remote segments. The myocardial/skeletal muscle ratio was significantly (p less than 0.001) increased for the infarction segments compared with that for remote myocardium, allowing quantitative analysis of segmental signal intensity. The MRI wall motion study obtained as part of this protocol demonstrated wall thickening in 58% of the infarction segments and in 6 of 10 patients. This finding suggested the presence of reversibly injured myocardium. In conclusion, the results demonstrate the potential of MRI for detailed tissue characterization after acute myocardial infarction.

  1. Magnetic resonance imaging of the fetal brain.

    PubMed

    Tee, L Mf; Kan, E Yl; Cheung, J Cy; Leung, W C

    2016-06-01

    This review covers the recent literature on fetal brain magnetic resonance imaging, with emphasis on techniques, advances, common indications, and safety. We conducted a search of MEDLINE for articles published after 2010. The search terms used were "(fetal OR foetal OR fetus OR foetus) AND (MR OR MRI OR [magnetic resonance]) AND (brain OR cerebral)". Consensus statements from major authorities were also included. As a result, 44 relevant articles were included and formed the basis of this review. One major challenge is fetal motion that is largely overcome by ultra-fast sequences. Currently, single-shot fast spin-echo T2-weighted imaging remains the mainstay for motion resistance and anatomical delineation. Recently, a snap-shot inversion recovery sequence has enabled robust T1-weighted images to be obtained, which is previously a challenge for standard gradient-echo acquisitions. Fetal diffusion-weighted imaging, diffusion tensor imaging, and magnetic resonance spectroscopy are also being developed. With multiplanar capabilities, superior contrast resolution and field of view, magnetic resonance imaging does not have the limitations of sonography, and can provide additional important information. Common indications include ventriculomegaly, callosum and posterior fossa abnormalities, and twin complications. There are safety concerns about magnetic resonance-induced heating and acoustic damage but current literature showed no conclusive evidence of deleterious fetal effects. The American College of Radiology guideline states that pregnant patients can be accepted to undergo magnetic resonance imaging at any stage of pregnancy if risk-benefit ratio to patients warrants that the study be performed. Magnetic resonance imaging of the fetal brain is a safe and powerful adjunct to sonography in prenatal diagnosis. It can provide additional information that aids clinical management, prognostication, and counselling.

  2. White matter lesion segmentation using machine learning and weakly labeled MR images

    NASA Astrophysics Data System (ADS)

    Xie, Yuchen; Tao, Xiaodong

    2011-03-01

    We propose a fast, learning-based algorithm for segmenting white matter (WM) lesions for magnetic resonance (MR) brain images. The inputs to the algorithm are T1, T2, and FLAIR images. Unlike most of the previously reported learning-based algorithms, which treat expert labeled lesion map as ground truth in the training step, the proposed algorithm only requires the user to provide a few regions of interest (ROI's) containing lesions. An unsupervised clustering algorithm is applied to segment these ROI's into areas. Based on the assumption that lesion voxels have higher intensity on FLAIR image, areas corresponding to lesions are identified and their probability distributions in T1, T2, and FLAIR images are computed. The lesion segmentation in 3D is done by using the probability distributions to generate a confidence map of lesion and applying a graph based segmentation algorithm to label lesion voxels. The initial lesion label is used to further refine the probability distribution estimation for the final lesion segmentation. The advantages of the proposed algorithm are: 1. By using the weak labels, we reduced the dependency of the segmentation performance on the expert discrimination of lesion voxels in the training samples; 2. The training can be done using labels generated by users with only general knowledge of brain anatomy and image characteristics of WM lesion, instead of these carefully labeled by experienced radiologists; 3. The algorithm is fast enough to make interactive segmentation possible. We test the algorithm on nine ACCORD-MIND MRI datasets. Experimental results show that our algorithm agrees well with expert labels and outperforms a support vector machine based WM lesion segmentation algorithm.

  3. Image segmentation using common techniques and illumination applied to tissue culture

    NASA Astrophysics Data System (ADS)

    Vazquez Rueda, Martin G.; Hahn, Federico

    1998-03-01

    This paper present the comparation and performance on no adaptive image segmentation techniques using illumination and adaptive image segmentation techniques. Results obtained on indoor plant reproduction by tissue culture, show the improve in time process, simplify the image segmentation process, experimental results are presented using common techniques in image processing and illumination, contrasted with adaptive image segmentation.

  4. Active contours driven by local and global fitted image models for image segmentation robust to intensity inhomogeneity

    PubMed Central

    Garcia, Miguel Angel; Puig, Domenec

    2017-01-01

    This paper presents a region-based active contour method for the segmentation of intensity inhomogeneous images using an energy functional based on local and global fitted images. A square image fitted model is defined by using both local and global fitted differences. Moreover, local and global signed pressure force functions are introduced in the solution of the energy functional to stabilize the gradient descent flow. In the final gradient descent solution, the local fitted term helps extract regions with intensity inhomogeneity, whereas the global fitted term targets homogeneous regions. A Gaussian kernel is applied to regularize the contour at each step, which not only smoothes it but also avoids the computationally expensive re-initialization. Intensity inhomogeneous images contain undesired smooth intensity variations (bias field) that alter the results of intensity-based segmentation methods. The bias field is approximated with a Gaussian distribution and the bias of intensity inhomogeneous regions is corrected by dividing the original image by the approximated bias field. In this paper, a two-phase model is first derived and then extended to a four-phase model to segment brain magnetic resonance (MR) images into the desired regions of interest. Experimental results with both synthetic and real brain MR images are used for a quantitative and qualitative comparison with state-of-the-art active contour methods to show the advantages of the proposed segmentation technique in practical terms. PMID:28376124

  5. A Multiple Object Geometric Deformable Model for Image Segmentation

    PubMed Central

    Bogovic, John A.; Prince, Jerry L.; Bazin, Pierre-Louis

    2012-01-01

    Deformable models are widely used for image segmentation, most commonly to find single objects within an image. Although several methods have been proposed to segment multiple objects using deformable models, substantial limitations in their utility remain. This paper presents a multiple object segmentation method using a novel and efficient object representation for both two and three dimensions. The new framework guarantees object relationships and topology, prevents overlaps and gaps, enables boundary-specific speeds, and has a computationally efficient evolution scheme that is largely independent of the number of objects. Maintaining object relationships and straightforward use of object-specific and boundary-specific smoothing and advection forces enables the segmentation of objects with multiple compartments, a critical capability in the parcellation of organs in medical imaging. Comparing the new framework with previous approaches shows its superior performance and scalability. PMID:23316110

  6. Robust Cell Segmentation for Histological Images of Glioblastoma

    PubMed Central

    Kong, Jun; Zhang, Pengyue; Liang, Yanhui; Teodoro, G