WorldWideScience

Sample records for hierarchical image segmentation

  1. Hierarchical image segmentation for learning object priors

    Energy Technology Data Exchange (ETDEWEB)

    Prasad, Lakshman [Los Alamos National Laboratory; Yang, Xingwei [TEMPLE UNIV.; Latecki, Longin J [TEMPLE UNIV.; Li, Nan [TEMPLE UNIV.

    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.

  2. Image Segmentation Using Hierarchical Merge Tree

    Science.gov (United States)

    Liu, Ting; Seyedhosseini, Mojtaba; Tasdizen, Tolga

    2016-10-01

    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 very recent methods on six public data sets demonstrate that our approach achieves the state-of-the-art region accuracy and is very competitive in image segmentation without semantic priors.

  3. Semantic Image Segmentation with Contextual Hierarchical Models.

    Science.gov (United States)

    Seyedhosseini, Mojtaba; Tasdizen, Tolga

    2016-05-01

    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).

  4. Hierarchical Non-linear Image Registration Integrating Deformable Segmentation

    Institute of Scientific and Technical Information of China (English)

    RAN Xin; QI Fei-hu

    2005-01-01

    A hierarchical non-linear method for image registration was presented, which integrates image segmentation and registration under a variational framework. An improved deformable model is used to simultaneously segment and register feature from multiple images. The objects in the image pair are segmented by evolving a single contour and meanwhile the parameters of affine registration transformation are found out. After that, a contour-constrained elastic registration is applied to register the images correctly. The experimental results indicate that the proposed approach is effective to segment and register medical images.

  5. Image Segmentation by Hierarchical Spatial and Color Spaces Clustering

    Institute of Scientific and Technical Information of China (English)

    YU Wei

    2005-01-01

    Image segmentation, as a basic building block for many high-level image analysis problems, has attracted many research attentions over years. Existing approaches, however, are mainly focusing on the clustering analysis in the single channel information, i.e., either in color or spatial space, which may lead to unsatisfactory segmentation performance. Considering the spatial and color spaces jointly, this paper proposes a new hierarchical image segmentation algorithm, which alternately clusters the image regions in color and spatial spaces in a fine to coarse manner. Without losing the perceptual consistence, the proposed algorithm achieves the segmentation result using only very few number of colors according to user specification.

  6. Low-Level Hierarchical Multiscale Segmentation Statistics of Natural Images.

    Science.gov (United States)

    Akbas, Emre; Ahuja, Narendra

    2014-09-01

    This paper is aimed at obtaining the statistics as a probabilistic model pertaining to the geometric, topological and photometric structure of natural images. The image structure is represented by its segmentation graph derived from the low-level hierarchical multiscale image segmentation. We first estimate the statistics of a number of segmentation graph properties from a large number of images. Our estimates confirm some findings reported in the past work, as well as provide some new ones. We then obtain a Markov random field based model of the segmentation graph which subsumes the observed statistics. To demonstrate the value of the model and the statistics, we show how its use as a prior impacts three applications: image classification, semantic image segmentation and object detection.

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

    Science.gov (United States)

    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

  8. Multiscale stochastic hierarchical image segmentation by spectral clustering

    Institute of Scientific and Technical Information of China (English)

    LI XiaoBin; TIAN Zheng

    2007-01-01

    This paper proposes a sampling based hierarchical approach for solving the computational demands of the spectral clustering methods when applied to the problem of image segmentation. The authors first define the distance between a pixel and a cluster, and then derive a new theorem to estimate the number of samples needed for clustering. Finally, by introducing a scale parameter into the similarity function, a novel spectral clustering based image segmentation method has been developed. An important characteristic of the approach is that in the course of image segmentation one needs not only to tune the scale parameter to merge the small size clusters or split the large size clusters but also take samples from the data set at the different scales. The multiscale and stochastic nature makes it feasible to apply the method to very large grouping problem. In addition, it also makes the segmentation compute in time that is linear in the size of the image. The experimental results on various synthetic and real world images show the effectiveness of the approach.

  9. Hierarchical stochastic image grammars for classification and segmentation.

    Science.gov (United States)

    Wang, Wiley; Pollak, Ilya; Wong, Tak-Shing; Bouman, Charles A; Harper, Mary P; Siskind, Jeffrey M

    2006-10-01

    We develop a new class of hierarchical stochastic image models called spatial random trees (SRTs) which admit polynomial-complexity exact inference algorithms. Our framework of multitree dictionaries is the starting point for this construction. SRTs are stochastic hidden tree models whose leaves are associated with image data. The states at the tree nodes are random variables, and, in addition, the structure of the tree is random and is generated by a probabilistic grammar. We describe an efficient recursive algorithm for obtaining the maximum a posteriori estimate of both the tree structure and the tree states given an image. We also develop an efficient procedure for performing one iteration of the expectation-maximization algorithm and use it to estimate the model parameters from a set of training images. We address other inference problems arising in applications such as maximization of posterior marginals and hypothesis testing. Our models and algorithms are illustrated through several image classification and segmentation experiments, ranging from the segmentation of synthetic images to the classification of natural photographs and the segmentation of scanned documents. In each case, we show that our method substantially improves accuracy over a variety of existing methods.

  10. Hierarchical segmentation-assisted multimodal registration for MR brain images.

    Science.gov (United States)

    Lu, Huanxiang; Beisteiner, Roland; Nolte, Lutz-Peter; Reyes, Mauricio

    2013-04-01

    Information theory-based metric such as mutual information (MI) is widely used as similarity measurement for multimodal registration. Nevertheless, this metric may lead to matching ambiguity for non-rigid registration. Moreover, maximization of MI alone does not necessarily produce an optimal solution. In this paper, we propose a segmentation-assisted similarity metric based on point-wise mutual information (PMI). This similarity metric, termed SPMI, enhances the registration accuracy by considering tissue classification probabilities as prior information, which is generated from an expectation maximization (EM) algorithm. Diffeomorphic demons is then adopted as the registration model and is optimized in a hierarchical framework (H-SPMI) based on different levels of anatomical structure as prior knowledge. The proposed method is evaluated using Brainweb synthetic data and clinical fMRI images. Both qualitative and quantitative assessment were performed as well as a sensitivity analysis to the segmentation error. Compared to the pure intensity-based approaches which only maximize mutual information, we show that the proposed algorithm provides significantly better accuracy on both synthetic and clinical data.

  11. Image segmentation by connectivity preserving relinking in hierarchical graph structures

    NARCIS (Netherlands)

    Nacken, P.F.M.

    1995-01-01

    The method of image segmentation by pyramid relinking is extended to the formalism of hierarchies of region adjacency graphs. This approach has a number of advantages: (1) resulting regions are connected; (2) the method is adaptive, and therefore artifacts caused by a regular grid are avoided; and (

  12. Hierarchical Segmentation of Falsely Touching Characters from Camera Captured Degraded Document Images

    Directory of Open Access Journals (Sweden)

    Satadal Saha

    2011-07-01

    Full Text Available An innovative hierarchical image segmentation scheme is reported in this research communication. Unlike static/ spatially divided sub-images, the current innovation concentrates on object level hierarchy for segmentation of gray scale or color images into constituent component/ sub-parts. As for example, a gray scale document image may be segmented (binarized in case of two-level segmentation into connected foreground components (text/ graphics and background component by hierarchically applying a gray level threshold selection algorithm in the object-space. In any hierarchy, constituent objects are identified as connected foreground pixels, as classified by the gray scale threshold selection algorithm. To preserve the global information, thresholds for each object in any hierarchy are estimated as a weighted aggregate of the current and previous thresholds relevant to the object. The developed technique may be customized as a general purpose hierarchical information clustering algorithm in the domain of pattern analysis, data mining, bioinformatics etc.

  13. Nucleus segmentation in histology images with hierarchical multilevel thresholding

    Science.gov (United States)

    Ahmady Phoulady, Hady; Goldgof, Dmitry B.; Hall, Lawrence O.; Mouton, Peter R.

    2016-03-01

    Automatic segmentation of histological images is an important step for increasing throughput while maintaining high accuracy, avoiding variation from subjective bias, and reducing the costs for diagnosing human illnesses such as cancer and Alzheimer's disease. In this paper, we present a novel method for unsupervised segmentation of cell nuclei in stained histology tissue. Following an initial preprocessing step involving color deconvolution and image reconstruction, the segmentation step consists of multilevel thresholding and a series of morphological operations. The only parameter required for the method is the minimum region size, which is set according to the resolution of the image. Hence, the proposed method requires no training sets or parameter learning. Because the algorithm requires no assumptions or a priori information with regard to cell morphology, the automatic approach is generalizable across a wide range of tissues. Evaluation across a dataset consisting of diverse tissues, including breast, liver, gastric mucosa and bone marrow, shows superior performance over four other recent methods on the same dataset in terms of F-measure with precision and recall of 0.929 and 0.886, respectively.

  14. Hierarchical Image Segmentation of Remotely Sensed Data using Massively Parallel GNU-LINUX Software

    Science.gov (United States)

    Tilton, James C.

    2003-01-01

    A hierarchical set of image segmentations is a set of several image segmentations of the same image at different levels of detail in which the segmentations at coarser levels of detail can be produced from simple merges of regions at finer levels of detail. In [1], Tilton, et a1 describes an approach for producing hierarchical segmentations (called HSEG) and gave a progress report on exploiting these hierarchical segmentations for image information mining. The HSEG algorithm is a hybrid of region growing and constrained spectral clustering that produces a hierarchical set of image segmentations based on detected convergence points. In the main, HSEG employs the hierarchical stepwise optimization (HSWO) approach to region growing, which was described as early as 1989 by Beaulieu and Goldberg. The HSWO approach seeks to produce segmentations that are more optimized than those produced by more classic approaches to region growing (e.g. Horowitz and T. Pavlidis, [3]). In addition, HSEG optionally interjects between HSWO region growing iterations, merges between spatially non-adjacent regions (i.e., spectrally based merging or clustering) constrained by a threshold derived from the previous HSWO region growing iteration. While the addition of constrained spectral clustering improves the utility of the segmentation results, especially for larger images, it also significantly increases HSEG s computational requirements. To counteract this, a computationally efficient recursive, divide-and-conquer, implementation of HSEG (RHSEG) was devised, which includes special code to avoid processing artifacts caused by RHSEG s recursive subdivision of the image data. The recursive nature of RHSEG makes for a straightforward parallel implementation. This paper describes the HSEG algorithm, its recursive formulation (referred to as RHSEG), and the implementation of RHSEG using massively parallel GNU-LINUX software. Results with Landsat TM data are included comparing RHSEG with classic

  15. Recursive Hierarchical Image Segmentation by Region Growing and Constrained Spectral Clustering

    Science.gov (United States)

    Tilton, James C.

    2002-01-01

    This paper describes an algorithm for hierarchical image segmentation (referred to as HSEG) and its recursive formulation (referred to as RHSEG). The HSEG algorithm is a hybrid of region growing and constrained spectral clustering that produces a hierarchical set of image segmentations based on detected convergence points. In the main, HSEG employs the hierarchical stepwise optimization (HS WO) approach to region growing, which seeks to produce segmentations that are more optimized than those produced by more classic approaches to region growing. In addition, HSEG optionally interjects between HSWO region growing iterations merges between spatially non-adjacent regions (i.e., spectrally based merging or clustering) constrained by a threshold derived from the previous HSWO region growing iteration. While the addition of constrained spectral clustering improves the segmentation results, especially for larger images, it also significantly increases HSEG's computational requirements. To counteract this, a computationally efficient recursive, divide-and-conquer, implementation of HSEG (RHSEG) has been devised and is described herein. Included in this description is special code that is required to avoid processing artifacts caused by RHSEG s recursive subdivision of the image data. Implementations for single processor and for multiple processor computer systems are described. Results with Landsat TM data are included comparing HSEG with classic region growing. Finally, an application to image information mining and knowledge discovery is discussed.

  16. Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration

    Science.gov (United States)

    Sun, Kaioqiong; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.

    2014-03-01

    This paper proposes a thoracic anatomy segmentation method based on hierarchical recognition and delineation guided by a built fuzzy model. Labeled binary samples for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The gray intensity distributions of the corresponding regions of the organ in the original image are recorded in the model. The hierarchical relation and mean location relation between different organs are also captured in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connected delineation method is then used to obtain the final segmentation result of organs with seed points provided by recognition. The hierarchical structure and location relation integrated in the model provide the initial parameters for registration and make the recognition efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both non-sparse and sparse organs. The results on real images are presented and shown to be better than a recently reported fuzzy model-based anatomy recognition strategy.

  17. Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images

    Science.gov (United States)

    Alshehhi, Rasha; Marpu, Prashanth Reddy

    2017-04-01

    Extraction of road networks in urban areas from remotely sensed imagery plays an important role in many urban applications (e.g. road navigation, geometric correction of urban remote sensing images, updating geographic information systems, etc.). It is normally difficult to accurately differentiate road from its background due to the complex geometry of the buildings and the acquisition geometry of the sensor. In this paper, we present a new method for extracting roads from high-resolution imagery based on hierarchical graph-based image segmentation. The proposed method consists of: 1. Extracting features (e.g., using Gabor and morphological filtering) to enhance the contrast between road and non-road pixels, 2. Graph-based segmentation consisting of (i) Constructing a graph representation of the image based on initial segmentation and (ii) Hierarchical merging and splitting of image segments based on color and shape features, and 3. Post-processing to remove irregularities in the extracted road segments. Experiments are conducted on three challenging datasets of high-resolution images to demonstrate the proposed method and compare with other similar approaches. The results demonstrate the validity and superior performance of the proposed method for road extraction in urban areas.

  18. Multiple Object Retrieval in Image Databases Using Hierarchical Segmentation Tree

    Science.gov (United States)

    Chen, Wei-Bang

    2012-01-01

    The purpose of this research is to develop a new visual information analysis, representation, and retrieval framework for automatic discovery of salient objects of user's interest in large-scale image databases. In particular, this dissertation describes a content-based image retrieval framework which supports multiple-object retrieval. The…

  19. Multiple Object Retrieval in Image Databases Using Hierarchical Segmentation Tree

    Science.gov (United States)

    Chen, Wei-Bang

    2012-01-01

    The purpose of this research is to develop a new visual information analysis, representation, and retrieval framework for automatic discovery of salient objects of user's interest in large-scale image databases. In particular, this dissertation describes a content-based image retrieval framework which supports multiple-object retrieval. The…

  20. Automatic and hierarchical segmentation of the human skeleton in CT images

    Science.gov (United States)

    Fu, Yabo; Liu, Shi; Li, H. Harold; Yang, Deshan

    2017-04-01

    Accurate segmentation of each bone of the human skeleton is useful in many medical disciplines. The results of bone segmentation could facilitate bone disease diagnosis and post-treatment assessment, and support planning and image guidance for many treatment modalities including surgery and radiation therapy. As a medium level medical image processing task, accurate bone segmentation can facilitate automatic internal organ segmentation by providing stable structural reference for inter- or intra-patient registration and internal organ localization. Even though bones in CT images can be visually observed with minimal difficulty due to the high image contrast between the bony structures and surrounding soft tissues, automatic and precise segmentation of individual bones is still challenging due to the many limitations of the CT images. The common limitations include low signal-to-noise ratio, insufficient spatial resolution, and indistinguishable image intensity between spongy bones and soft tissues. In this study, a novel and automatic method is proposed to segment all the major individual bones of the human skeleton above the upper legs in CT images based on an articulated skeleton atlas. The reported method is capable of automatically segmenting 62 major bones, including 24 vertebrae and 24 ribs, by traversing a hierarchical anatomical tree and by using both rigid and deformable image registration. The degrees of freedom of femora and humeri are modeled to support patients in different body and limb postures. The segmentation results are evaluated using the Dice coefficient and point-to-surface error (PSE) against manual segmentation results as the ground-truth. The results suggest that the reported method can automatically segment and label the human skeleton into detailed individual bones with high accuracy. The overall average Dice coefficient is 0.90. The average PSEs are 0.41 mm for the mandible, 0.62 mm for cervical vertebrae, 0.92 mm for thoracic

  1. Automatic and hierarchical segmentation of the human skeleton in CT images.

    Science.gov (United States)

    Fu, Yabo; Liu, Shi; Li, Hui Harold; Yang, Deshan

    2017-02-14

    Accurate segmentation of each bone in human skeleton is useful in many medical disciplines. Results of bone segmentation could facilitate bone disease diagnosis and post-treatment assessment, and support planning and image guidance for many treatment modalities including surgery and radiation therapy. As a medium level medical image processing task, accurate bone segmentation can facilitate automatic internal organ segmentation by providing stable structural reference for inter- or intra-patient registration and internal organ localization. Even though bones in CT images can be visually observed with minimal difficulties due to high image contrast between bony structures and surrounding soft tissues, automatic and precise segmentation of individual bones is still challenging due to many limitations in the CT images. The common limitations include low signal-to-noise ratio, insufficient spatial resolution, and indistinguishable image intensity between spongy bones and soft tissues. In this study, a novel and automatic method is proposed to segment all major individual bones of human skeleton above the upper legs in the CT images based on an articulated skeleton atlas. The reported method is capable of automatically segmenting 62 major bones, including 24 vertebrae and 24 ribs, by traversing a hierarchical anatomical tree and by using both rigid and deformable image registration. Degrees of freedom of femora and humeri are modeled to support patients in different body and limb postures. Segmentation results are evaluated using Dice coefficient and point-to-surface error (PSE) against manual segmentation results as ground truth. The results suggest that the reported method can automatically segment and label human skeleton into detailed individual bones with high accuracy. The overall average Dice coefficient is 0.90. The average PSEs are 0.41 mm for mandible, 0.62 mm for cervical vertebrae, 0.92 mm for thoracic vertebrae, and 1.45 mm for pelvis bones.

  2. Interactive Hierarchical-Flow Segmentation of Scar Tissue From Late-Enhancement Cardiac MR Images.

    Science.gov (United States)

    Rajchl, Martin; Yuan, Jing; White, James A; Ukwatta, Eranga; Stirrat, John; Nambakhsh, Cyrus M S; Li, Feng P; Peters, Terry M

    2014-01-01

    We propose a novel multi-region image segmentation approach to extract myocardial scar tissue from 3-D whole-heart cardiac late-enhancement magnetic resonance images in an interactive manner. For this purpose, we developed a graphical user interface to initialize a fast max-flow-based segmentation algorithm and segment scar accurately with progressive interaction. We propose a partially-ordered Potts (POP) model to multi-region segmentation to properly encode the known spatial consistency of cardiac regions. Its generalization introduces a custom label/region order constraint to Potts model to multi-region segmentation. The combinatorial optimization problem associated with the proposed POP model is solved by means of convex relaxation, for which a novel multi-level continuous max-flow formulation, i.e., the hierarchical continuous max-flow (HMF) model, is proposed and studied. We demonstrate that the proposed HMF model is dual or equivalent to the convex relaxed POP model and introduces a new and efficient hierarchical continuous max-flow based algorithm by modern convex optimization theory. In practice, the introduced hierarchical continuous max-flow based algorithm can be implemented on the parallel GPU to achieve significant acceleration in numerics. Experiments are performed in 50 whole heart 3-D LE datasets, 35 with left-ventricular and 15 with right-ventricular scar. The experimental results are compared to full-width-at-half-maximum and Signal-threshold to reference-mean methods using manual expert myocardial segmentations and operator variabilities and the effect of user interaction are assessed. The results indicate a substantial reduction in image processing time with robust accuracy for detection of myocardial scar. This is achieved without the need for additional region constraints and using a single optimization procedure, substantially reducing the potential for error.

  3. Simultaneous hierarchical segmentation and vectorization of satellite images through combined data sampling and anisotropic triangulation

    Energy Technology Data Exchange (ETDEWEB)

    Grazzini, Jacopo [Los Alamos National Laboratory; Prasad, Lakshman [Los Alamos National Laboratory; Dillard, Scott [PNNL

    2010-10-21

    The automatic detection, recognition , and segmentation of object classes in remote sensed images is of crucial importance for scene interpretation and understanding. However, it is a difficult task because of the high variability of satellite data. Indeed, the observed scenes usually exhibit a high degree of complexity, where complexity refers to the large variety of pictorial representations of objects with the same semantic meaning and also to the extensive amount of available det.ails. Therefore, there is still a strong demand for robust techniques for automatic information extraction and interpretation of satellite images. In parallel, there is a growing interest in techniques that can extract vector features directly from such imagery. In this paper, we investigate the problem of automatic hierarchical segmentation and vectorization of multispectral satellite images. We propose a new algorithm composed of the following steps: (i) a non-uniform sampling scheme extracting most salient pixels in the image, (ii) an anisotropic triangulation constrained by the sampled pixels taking into account both strength and directionality of local structures present in the image, (iii) a polygonal grouping scheme merging, through techniques based on perceptual information , the obtained segments to a smaller quantity of superior vectorial objects. Besides its computational efficiency, this approach provides a meaningful polygonal representation for subsequent image analysis and/or interpretation.

  4. Teaching a machine to see: unsupervised image segmentation and categorisation using growing neural gas and hierarchical clustering

    CERN Document Server

    Hocking, Alex; Davey, Neil; Sun, Yi

    2015-01-01

    We present a novel unsupervised learning approach to automatically segment and label images in astronomical surveys. Automation of this procedure will be essential as next-generation surveys enter the petabyte scale: data volumes will exceed the capability of even large crowd-sourced analyses. We demonstrate how a growing neural gas (GNG) can be used to encode the feature space of imaging data. When coupled with a technique called hierarchical clustering, imaging data can be automatically segmented and labelled by organising nodes in the GNG. The key distinction of unsupervised learning is that these labels need not be known prior to training, rather they are determined by the algorithm itself. Importantly, after training a network can be be presented with images it has never 'seen' before and provide consistent categorisation of features. As a proof-of-concept we demonstrate application on data from the Hubble Space Telescope Frontier Fields: images of clusters of galaxies containing a mixture of galaxy type...

  5. Discrete Topology Based Hierarchical Segmentation for Efficient Object-Based Image Analyis: Application to Object Detection in High Resolution Satellite Images

    Science.gov (United States)

    Syed, A. H.; Saber, E.; Messinger, D.

    2013-05-01

    With rapid developments in satellite and sensor technologies, there has been a dramatic increase in the availability of high resolution (HR) remotely sensed images. Hence, the ability to collect images remotely is expected to far exceed our capacity to analyse these images manually. Consequently, techniques that can handle large volumes of data are urgently needed. In many of today's multiscale techniques the underlying representation of objects is still pixel-based, i.e. object entities are still described/accessed via pixelbased descriptors, thereby creating a bottleneck when processing large volumes of data. Also, these techniques do not yet leverage the topological and contextual information present in the image. We propose a framework for Discrete Topology based hierarchical segmentation, addressing both the algorithms and data structures that will be required. The framework consists of three components: 1) Conversion to dart-based representation, 2) Size-Constrained-Region Merging to generate multiple segmentations, and 3) Update of two sparse arrays SIGMA and LAMBDA which together encode the topology of each region in the hierarchy. The results of our representation are demonstrated both on a synthetic and a real high resolution images. Application of this representation to objectdetection is also discussed.

  6. SAR Imagery Segmentation by Statistical Region Growing and Hierarchical Merging

    Energy Technology Data Exchange (ETDEWEB)

    Ushizima, Daniela Mayumi; Carvalho, E.A.; Medeiros, F.N.S.; Martins, C.I.O.; Marques, R.C.P.; Oliveira, I.N.S.

    2010-05-22

    This paper presents an approach to accomplish synthetic aperture radar (SAR) image segmentation, which are corrupted by speckle noise. Some ordinary segmentation techniques may require speckle filtering previously. Our approach performs radar image segmentation using the original noisy pixels as input data, eliminating preprocessing steps, an advantage over most of the current methods. The algorithm comprises a statistical region growing procedure combined with hierarchical region merging to extract regions of interest from SAR images. The region growing step over-segments the input image to enable region aggregation by employing a combination of the Kolmogorov-Smirnov (KS) test with a hierarchical stepwise optimization (HSWO) algorithm for the process coordination. We have tested and assessed the proposed technique on artificially speckled image and real SAR data containing different types of targets.

  7. Statistical Images Segmentation

    Directory of Open Access Journals (Sweden)

    Corina Curilă

    2008-05-01

    Full Text Available This paper deals with fuzzy statistical imagesegmentation. We introduce a new hierarchicalMarkovian fuzzy hidden field model, which extends to thefuzzy case the classical Pérez and Heitz hard model. Twofuzzy statistical segmentation methods related with themodel proposed are defined in this paper and we show viasimulations that they are competitive with, in some casesthan, the classical Maximum Posterior Mode (MPMbased methods. Furthermore, they are faster, which willshould facilitate extensions to more than two hard classesin future work. In addition, the model proposed isapplicable to the multiscale segmentation andmultiresolution images fusion problems.

  8. GEODESIC RECONSTRUCTION, SADDLE ZONES & HIERARCHICAL SEGMENTATION

    Directory of Open Access Journals (Sweden)

    Serge Beucher

    2011-05-01

    Full Text Available The morphological reconstruction based on geodesic operators, is a powerful tool in mathematical morphology. The general definition of this reconstruction supposes the use of a marker function f which is not necessarily related to the function g to be built. However, this paper deals with operations where the marker function is defined from given characteristic regions of the initial function f, as it is the case, for instance, for the extrema (maxima or minima but also for the saddle zones. Firstly, we show that the intuitive definition of a saddle zone is not easy to handle, especially when digitised images are involved. However, some of these saddle zones (regional ones also called overflow zones can be defined, this definition providing a simple algorithm to extract them. The second part of the paper is devoted to the use of these overflow zones as markers in image reconstruction. This reconstruction provides a new function which exhibits a new hierarchy of extrema. This hierarchy is equivalent to the hierarchy produced by the so-called waterfall algorithm. We explain why the waterfall algorithm can be achieved by performing a watershed transform of the function reconstructed by its initial watershed lines. Finally, some examples of use of this hierarchical segmentation are described.

  9. Segmentation Algorithm for Oil Spill SAR Images Based on Hierarchical Agglomerative Clustering%基于HAC的溢油SAR图像分割算法

    Institute of Scientific and Technical Information of China (English)

    苏腾飞; 孟俊敏; 张晰

    2013-01-01

    图像分割是SAR溢油检测中的关键步骤,但由于SAR影像中存在斑点噪声,使得一般的图像分割算法难以收到理想的效果,严重影响溢油检测的精度.发展一种基于凝聚层次聚类(Hierarchical Agglomerative Clustering,HAC)的溢油SAR图像分割算法.该算法利用多尺度分割的思想,能够有效保持SAR影像中溢油斑块的形状特征,并能减少细碎斑块的产生.利用2010年墨西哥湾的Envisat ASAR影像开展了溢油SAR图像分割实验,并将该算法和Canny边缘检测、OTSU阈值分割、FCM分割、水平集分割等方法进行了对比.结果显示,HAC方法可以有效减少细碎斑块的产生,有助于提高SAR溢油检测的精度.%Image segmentation is a crucial stage in the SAR oil spill detection.However,the common image segmentation algorithms can hardly achieve satisfactory results due to speckle noise in the SAR images,thus affecting seriously the accuracy of oil spill detection.For this reason,an image segmentation algorithm which is based on HAC (Hierarchical Agglomerative Clustering) is developed for the oil spill SAR images.This method takes advantage of multi-resolution segmentation to maintain effectively the shape property of oil spill patches,and can reduce the formation of small patches.By using Envisat ASAR images of the Gulf of Mexico obtained in 2010,an experiment of SAR oil spill image segmentation has been conducted.Comparing with other approaches such as Canny,OTSU,FCM and Levelset,the results show that HAC can effectively reduce the producing of small patches,which is helpful to improve the accuracy of SAR oil spill detection.

  10. Hierarchical Subtopic Segmentation of Web Document

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The paper proposes a novel method for subtopics segmentation of Web document. An effective retrieval results may be obtained by using subtopics segmentation. The proposed method can segment hierarchically subtopics and identify the boundary of each subtopic. Based on the term frequency matrix, the method measures the similarity between adjacent blocks, such as paragraphs, passages. In the real-world sample experiment, the macro-averaged precision and recall reach 73.4% and 82.5%, and the micro-averaged precision and recall reach 72.9% and 83.1%. Moreover, this method is equally efficient to other Asian languages such as Japanese and Korean, as well as other western languages.

  11. Hierarchical temporal video segmentation and content characterization

    Science.gov (United States)

    Gunsel, Bilge; Fu, Yue; Tekalp, A. Murat

    1997-10-01

    This paper addresses the segmentation of a video sequence into shots, specification of edit effects and subsequent characterization of shots in terms of color and motion content. The proposed scheme uses DC images extracted from MPEG compressed video and performs an unsupervised clustering for the extraction of camera shots. The specification of edit effects, such as fade-in/out and dissolve is based on the analysis of distribution of mean value for the luminance components. This step is followed by the representation of visual content of temporal segments in terms of key frames selected by similarity analysis of mean color histograms. For characterization of the similar temporal segments, motion and color characteristics are classified into different categories using a set of different features derived from motion vectors of triangular meshes and mean histograms of video shots.

  12. Parallel Implementation of the Recursive Approximation of an Unsupervised Hierarchical Segmentation Algorithm. Chapter 5

    Science.gov (United States)

    Tilton, James C.; Plaza, Antonio J. (Editor); Chang, Chein-I. (Editor)

    2008-01-01

    The hierarchical image segmentation algorithm (referred to as HSEG) is a hybrid of hierarchical step-wise optimization (HSWO) and constrained spectral clustering that produces a hierarchical set of image segmentations. HSWO is an iterative approach to region grooving segmentation in which the optimal image segmentation is found at N(sub R) regions, given a segmentation at N(sub R+1) regions. HSEG's addition of constrained spectral clustering makes it a computationally intensive algorithm, for all but, the smallest of images. To counteract this, a computationally efficient recursive approximation of HSEG (called RHSEG) has been devised. Further improvements in processing speed are obtained through a parallel implementation of RHSEG. This chapter describes this parallel implementation and demonstrates its computational efficiency on a Landsat Thematic Mapper test scene.

  13. Improving image segmentation by learning region affinities

    Energy Technology Data Exchange (ETDEWEB)

    Prasad, Lakshman [Los Alamos National Laboratory; Yang, Xingwei [TEMPLE UNIV.; Latecki, Longin J [TEMPLE UNIV.

    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.

  14. Automatic Hierarchical Color Image Classification

    Directory of Open Access Journals (Sweden)

    Jing Huang

    2003-02-01

    Full Text Available Organizing images into semantic categories can be extremely useful for content-based image retrieval and image annotation. Grouping images into semantic classes is a difficult problem, however. Image classification attempts to solve this hard problem by using low-level image features. In this paper, we propose a method for hierarchical classification of images via supervised learning. This scheme relies on using a good low-level feature and subsequently performing feature-space reconfiguration using singular value decomposition to reduce noise and dimensionality. We use the training data to obtain a hierarchical classification tree that can be used to categorize new images. Our experimental results suggest that this scheme not only performs better than standard nearest-neighbor techniques, but also has both storage and computational advantages.

  15. Skin Images Segmentation

    Directory of Open Access Journals (Sweden)

    Ali E. Zaart

    2010-01-01

    Full Text Available Problem statement: Image segmentation is a fundamental step in many applications of image processing. Skin cancer has been the most common of all new cancers detected each year. At early stage detection of skin cancer, simple and economic treatment can cure it mostly. An accurate segmentation of skin images can help the diagnosis to define well the region of the cancer. The principal approach of segmentation is based on thresholding (classification that is lied to the problem of the thresholds estimation. Approach: The objective of this study is to develop a method to segment the skin images based on a mixture of Beta distributions. We assume that the data in skin images can be modeled by a mixture of Beta distributions. We used an unsupervised learning technique with Beta distribution to estimate the statistical parameters of the data in skin image and then estimate the thresholds for segmentation. Results: The proposed method of skin images segmentation was implemented and tested on different skin images. We obtained very good results in comparing with the same techniques with Gamma distribution. Conclusion: The experiment showed that the proposed method obtained very good results but it requires more testing on different types of skin images.

  16. Dictionary Based Image Segmentation

    DEFF Research Database (Denmark)

    Dahl, Anders Bjorholm; Dahl, Vedrana Andersen

    2015-01-01

    We propose a method for weakly supervised segmentation of natural images, which may contain both textured or non-textured regions. Our texture representation is based on a dictionary of image patches. To divide an image into separated regions with similar texture we use an implicit level sets...

  17. Scorpion image segmentation system

    Science.gov (United States)

    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.

  18. Microscopic Halftone Image Segmentation

    Institute of Scientific and Technical Information of China (English)

    WANG Yong-gang; YANG Jie; DING Yong-sheng

    2004-01-01

    Microscopic halftone image recognition and analysis can provide quantitative evidence for printing quality control and fault diagnosis of printing devices, while halftone image segmentation is one of the significant steps during the procedure. Automatic segmentation on microscopic dots by the aid of the Fuzzy C-Means (FCM) method that takes account of the fuzziness of halftone image and utilizes its color information adequately is realized. Then some examples show the technique effective and simple with better performance of noise immunity than some usual methods. In addition, the segmentation results obtained by the FCM in different color spaces are compared, which indicates that the method using the FCM in the f1f2f3 color space is superior to the rest.

  19. Dermoscopic Image Segmentation using Machine Learning Algorithm

    Directory of Open Access Journals (Sweden)

    L. P. Suresh

    2011-01-01

    Full Text Available Problem statement: Malignant melanoma is the most frequent type of skin cancer. Its incidence has been rapidly increasing over the last few decades. Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. Approach: This study explains the task of segmenting skin lesions in Dermoscopy images based on intelligent systems such as Fuzzy and Neural Networks clustering techniques for the early diagnosis of Malignant Melanoma. The various intelligent system based clustering techniques used are Fuzzy C Means Algorithm (FCM, Possibilistic C Means Algorithm (PCM, Hierarchical C Means Algorithm (HCM; C-mean based Fuzzy Hopfield Neural Network, Adaline Neural Network and Regression Neural Network. Results: The segmented images are compared with the ground truth image using various parameters such as False Positive Error (FPE, False Negative Error (FNE Coefficient of similarity, spatial overlap and their performance is evaluated. Conclusion: The experimental results show that the Hierarchical C Means algorithm( Fuzzy provides better segmentation than other (Fuzzy C Means, Possibilistic C Means, Adaline Neural Network, FHNN and GRNN clustering algorithms. Thus Hierarchical C Means approach can handle uncertainties that exist in the data efficiently and useful for the lesion segmentation in a computer aided diagnosis system to assist the clinical diagnosis of dermatologists.

  20. Image meshing via hierarchical optimization

    Institute of Scientific and Technical Information of China (English)

    Hao XIE; Ruo-feng TONG‡

    2016-01-01

    Vector graphic, as a kind of geometric representation of raster images, has many advantages, e.g., defi nition independence and editing facility. A popular way to convert raster images into vector graphics is image meshing, the aim of which is to fi nd a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it difficult to fi nd a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to fi ner ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images.

  1. Image meshing via hierarchical optimization*

    Institute of Scientific and Technical Information of China (English)

    Hao XIE; Ruo-feng TONGS

    2016-01-01

    Vector graphic, as a kind of geometric representation of raster images, has many advantages, e.g., definition independence and editing facility. A popular way to convert raster images into vector graphics is image meshing, the aim of which is to find a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it difficult to find a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to finer ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images.

  2. Automated medical image segmentation techniques

    Directory of Open Access Journals (Sweden)

    Sharma Neeraj

    2010-01-01

    Full Text Available 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.

  3. Segmenting Images for a Better Diagnosis

    Science.gov (United States)

    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.

  4. Spontaneous and Hierarchical Segmentation of Non-functional Events

    DEFF Research Database (Denmark)

    Nielbo, Kristoffer Laigaard

    2012-01-01

    The dissertation, Spontaneous and Hierarchical Segmentation of Non-functional Events (SHSNE henceforth), explores and tests human perception of so-called non-functional events (i.e., events or action sequences that lack a necessary link between sub-actions and sequence goal), which typically...... ritual behavior. Part 1 concludes with five primary theoretical hypotheses: I) non-functional events will increase the human event segmentation rate; II) transitions between events will increase the cognitive prediction error signal independent of event type, but this signal will be chronically high......, consisting of four experiments in total, and four computer simulations. The first set of experiments shows that the event segmentation rate increases for human participants that observe non-functional events compared to functional events. Furthermore, it appears that context information does not have...

  5. Multiple Segmentation of Image Stacks

    DEFF Research Database (Denmark)

    Smets, Jonathan; Jaeger, Manfred

    2014-01-01

    We propose a method for the simultaneous construction of multiple image segmentations by combining a recently proposed “convolution of mixtures of Gaussians” model with a multi-layer hidden Markov random field structure. The resulting method constructs for a single image several, alternative...... segmentations that capture different structural elements of the image. We also apply the method to collections of images with identical pixel dimensions, which we call image stacks. Here it turns out that the method is able to both identify groups of similar images in the stack, and to provide segmentations...

  6. 基于多空间多层次谱聚类的非监督SAR图像分割算法%Segmentation method for SAR images based on unsupervised spectral clustering of multi-hierarchical region

    Institute of Scientific and Technical Information of China (English)

    田玲; 邓旌波; 廖紫纤; 石博; 何楚

    2013-01-01

    提出了一种基于多层区域谱聚类的非监督SAR图像分割算法(multi-space and multi-hierarchical region based spectral clustering,MSMHSC).该算法首先在特征与几何空间求距离,快速获得初始过分割区域,然后在过分割区域的谱空间上进行聚类,最终实现非监督的SAR图像分割.该方法计算复杂度小,无须训练样本,使用层次化思想使其能更充分地利用SAR图像各类先验与似然信息.在MSTAR真实SAR数据集上的实验验证了该算法的快速性和有效性.%This paper proposed a method based on the hierarchical clustering concept.First,it over-segmented the source image into many small regions.And then,it conducted a spectral clustering algorithm on those regions.The algorithm was tested on the MSTAR SAR data set,and was proved to be fast and efficient.

  7. Hierarchical Segmentation Using Tree-Based Shape Spaces.

    Science.gov (United States)

    Xu, Yongchao; Carlinet, Edwin; Geraud, Thierry; Najman, Laurent

    2017-03-01

    Current trends in image segmentation are to compute a hierarchy of image segmentations from fine to coarse. A classical approach to obtain a single meaningful image partition from a given hierarchy is to cut it in an optimal way, following the seminal approach of the scale-set theory. While interesting in many cases, the resulting segmentation, being a non-horizontal cut, is limited by the structure of the hierarchy. In this paper, we propose a novel approach that acts by transforming an input hierarchy into a new saliency map. It relies on the notion of shape space: a graph representation of a set of regions extracted from the image. Each region is characterized with an attribute describing it. We weigh the boundaries of a subset of meaningful regions (local minima) in the shape space by extinction values based on the attribute. This extinction-based saliency map represents a new hierarchy of segmentations highlighting regions having some specific characteristics. Each threshold of this map represents a segmentation which is generally different from any cut of the original hierarchy. This new approach thus enlarges the set of possible partition results that can be extracted from a given hierarchy. Qualitative and quantitative illustrations demonstrate the usefulness of the proposed method.

  8. Neural network for image segmentation

    Science.gov (United States)

    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.

  9. The Analysis of Image Segmentation Hierarchies with a Graph-based Knowledge Discovery System

    Science.gov (United States)

    Tilton, James C.; Cooke, diane J.; Ketkar, Nikhil; Aksoy, Selim

    2008-01-01

    Currently available pixel-based analysis techniques do not effectively extract the information content from the increasingly available high spatial resolution remotely sensed imagery data. A general consensus is that object-based image analysis (OBIA) is required to effectively analyze this type of data. OBIA is usually a two-stage process; image segmentation followed by an analysis of the segmented objects. We are exploring an approach to OBIA in which hierarchical image segmentations provided by the Recursive Hierarchical Segmentation (RHSEG) software developed at NASA GSFC are analyzed by the Subdue graph-based knowledge discovery system developed by a team at Washington State University. In this paper we discuss out initial approach to representing the RHSEG-produced hierarchical image segmentations in a graphical form understandable by Subdue, and provide results on real and simulated data. We also discuss planned improvements designed to more effectively and completely convey the hierarchical segmentation information to Subdue and to improve processing efficiency.

  10. A Hierarchical Building Segmentation in Digital Surface Models for 3D Reconstruction

    Directory of Open Access Journals (Sweden)

    Yiming Yan

    2017-01-01

    Full Text Available In this study, a hierarchical method for segmenting buildings in a digital surface model (DSM, which is used in a novel framework for 3D reconstruction, is proposed. Most 3D reconstructions of buildings are model-based. However, the limitations of these methods are overreliance on completeness of the offline-constructed models of buildings, and the completeness is not easily guaranteed since in modern cities buildings can be of a variety of types. Therefore, a model-free framework using high precision DSM and texture-images buildings was introduced. There are two key problems with this framework. The first one is how to accurately extract the buildings from the DSM. Most segmentation methods are limited by either the terrain factors or the difficult choice of parameter-settings. A level-set method are employed to roughly find the building regions in the DSM, and then a recently proposed ‘occlusions of random textures model’ are used to enhance the local segmentation of the buildings. The second problem is how to generate the facades of buildings. Synergizing with the corresponding texture-images, we propose a roof-contour guided interpolation of building facades. The 3D reconstruction results achieved by airborne-like images and satellites are compared. Experiments show that the segmentation method has good performance, and 3D reconstruction is easily performed by our framework, and better visualization results can be obtained by airborne-like images, which can be further replaced by UAV images.

  11. High-throughput biomarker segmentation on ovarian cancer tissue microarrays via hierarchical normalized cuts.

    Science.gov (United States)

    Janowczyk, Andrew; Chandran, Sharat; Singh, Rajendra; Sasaroli, Dimitra; Coukos, George; Feldman, Michael D; Madabhushi, Anant

    2012-05-01

    We present a system for accurately quantifying the presence and extent of stain on account of a vascular biomarker on tissue microarrays. We demonstrate our flexible, robust, accurate, and high-throughput minimally supervised segmentation algorithm, termed hierarchical normalized cuts (HNCuts) for the specific problem of quantifying extent of vascular staining on ovarian cancer tissue microarrays. The high-throughput aspect of HNCut is driven by the use of a hierarchically represented data structure that allows us to merge two powerful image segmentation algorithms-a frequency weighted mean shift and the normalized cuts algorithm. HNCuts rapidly traverses a hierarchical pyramid, generated from the input image at various color resolutions, enabling the rapid analysis of large images (e.g., a 1500 × 1500 sized image under 6 s on a standard 2.8-GHz desktop PC). HNCut is easily generalizable to other problem domains and only requires specification of a few representative pixels (swatch) from the object of interest in order to segment the target class. Across ten runs, the HNCut algorithm was found to have average true positive, false positive, and false negative rates (on a per pixel basis) of 82%, 34%, and 18%, in terms of overlap, when evaluated with respect to a pathologist annotated ground truth of the target region of interest. By comparison, a popular supervised classifier (probabilistic boosting trees) was only able to marginally improve on the true positive and false negative rates (84% and 14%) at the expense of a higher false positive rate (73%), with an additional computation time of 62% compared to HNCut. We also compared our scheme against a k-means clustering approach, which both the HNCut and PBT schemes were able to outperform. Our success in accurately quantifying the extent of vascular stain on ovarian cancer TMAs suggests that HNCut could be a very powerful tool in digital pathology and bioinformatics applications where it could be used to

  12. Research Progress on Image Segmentation

    Science.gov (United States)

    1981-06-01

    AUTHOR. HANON A.R. AND RISEMAM E- 𔃻. TITLE G SE MENTATION OF NATURA’ CNIE PAGE 6 REFERENCES FOR< IMAGE...PUBLICAIION-DAIA : 1976 KEY-WORDS : IMAGE SEGMENTATION AU’ihOR I KRUGER K. P., THOMPSON W. B, AND TURNER A. F, TITLE COMPUTER DIAGNOSIS OF TNEUMOCONIOSIS

  13. An Active Contour for Range Image Segmentation

    OpenAIRE

    Khaldi Amine; Merouani Hayet Farida

    2012-01-01

    In this paper a new classification of range image segmentation method is proposed according to the criterion of homogeneity which obeys the segmentation, then, a deformable model-type active contour “Snake” is applied to segment range images.

  14. Image Segmentation Based on Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    XU Hai-xiang; ZHU Guang-xi; TIAN Jin-wen; ZHANG Xiang; PENG Fu-yuan

    2005-01-01

    Image segmentation is a necessary step in image analysis. Support vector machine (SVM) approach is proposed to segment images and its segmentation performance is evaluated.Experimental results show that: the effects of kernel function and model parameters on the segmentation performance are significant; SVM approach is less sensitive to noise in image segmentation; The segmentation performance of SVM approach is better than that of back-propagation multi-layer perceptron (BP-MLP) approach and fuzzy c-means (FCM) approach.

  15. Intensity-based hierarchical clustering in CT-scans: application to interactive segmentation in cardiology

    Science.gov (United States)

    Hadida, Jonathan; Desrosiers, Christian; Duong, Luc

    2011-03-01

    The segmentation of anatomical structures in Computed Tomography Angiography (CTA) is a pre-operative task useful in image guided surgery. Even though very robust and precise methods have been developed to help achieving a reliable segmentation (level sets, active contours, etc), it remains very time consuming both in terms of manual interactions and in terms of computation time. The goal of this study is to present a fast method to find coarse anatomical structures in CTA with few parameters, based on hierarchical clustering. The algorithm is organized as follows: first, a fast non-parametric histogram clustering method is proposed to compute a piecewise constant mask. A second step then indexes all the space-connected regions in the piecewise constant mask. Finally, a hierarchical clustering is achieved to build a graph representing the connections between the various regions in the piecewise constant mask. This step builds up a structural knowledge about the image. Several interactive features for segmentation are presented, for instance association or disassociation of anatomical structures. A comparison with the Mean-Shift algorithm is presented.

  16. Segmentation in dermatological hyperspectral images: dedicated methods

    OpenAIRE

    Koprowski, Robert; Olczyk, Paweł

    2016-01-01

    Background 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...

  17. Overlapping image segmentation for context-dependent anomaly detection

    Science.gov (United States)

    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.

  18. Medical image segmentation by MDP model

    Science.gov (United States)

    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.

  19. Hierarchical partial matching and segmentation of interacting cells.

    Science.gov (United States)

    Wu, Zheng; Gurari, Danna; Wong, Joyce Y; Betke, Margrit

    2012-01-01

    We propose a method that automatically tracks and segments living cells in phase-contrast image sequences, especially for cells that deform and interact with each other or clutter. We formulate the problem as a many-to-one elastic partial matching problem between closed curves. We introduce Double Cyclic Dynamic Time Warping for the scenario where a collision event yields a single boundary that encloses multiple touching cells and that needs to be cut into separate cell boundaries. The resulting individual boundaries may consist of segments to be connected to produce closed curves that match well with the individual cell boundaries before the collision event. We show how to convert this partial-curve matching problem into a shortest path problem that we then solve efficiently by reusing the computed shortest path tree. We also use our shortest path algorithm to fill the gaps between the segments of the target curves. Quantitative results demonstrate the benefit of our method by showing maintained accurate recognition of individual cell boundaries across 8068 images containing multiple cell interactions.

  20. A hierarchical classification scheme of psoriasis images

    DEFF Research Database (Denmark)

    Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær

    2003-01-01

    the normal skin in the second stage. These tools are the Expectation-Maximization Algorithm, the quadratic discrimination function and a classification window of optimal size. Extrapolation of classification parameters of a given image to other images of the set is evaluated by means of Cohen's Kappa......A two-stage hierarchical classification scheme of psoriasis lesion images is proposed. These images are basically composed of three classes: normal skin, lesion and background. The scheme combines conventional tools to separate the skin from the background in the first stage, and the lesion from...

  1. Segmentation of Color Images Based on Different Segmentation Techniques

    Directory of Open Access Journals (Sweden)

    Purnashti Bhosale

    2013-03-01

    Full Text Available In this paper, we propose an Color image segmentation algorithm based on different segmentation techniques. We recognize the background objects such as the sky, ground, and trees etc based on the color and texture information using various methods of segmentation. The study of segmentation techniques by using different threshold methods such as global and local techniques and they are compared with one another so as to choose the best technique for threshold segmentation. Further segmentation is done by using clustering method and Graph cut method to improve the results of segmentation.

  2. Image Segmentation, Registration, Compression, and Matching

    Science.gov (United States)

    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

  3. Hierarchical manifold learning for regional image analysis.

    Science.gov (United States)

    Bhatia, Kanwal K; Rao, Anil; Price, Anthony N; Wolz, Robin; Hajnal, Joseph V; Rueckert, Daniel

    2014-02-01

    We present a novel method of hierarchical manifold learning which aims to automatically discover regional properties of image datasets. While traditional manifold learning methods have become widely used for dimensionality reduction in medical imaging, they suffer from only being able to consider whole images as single data points. We extend conventional techniques by additionally examining local variations, in order to produce spatially-varying manifold embeddings that characterize a given dataset. This involves constructing manifolds in a hierarchy of image patches of increasing granularity, while ensuring consistency between hierarchy levels. We demonstrate the utility of our method in two very different settings: 1) to learn the regional correlations in motion within a sequence of time-resolved MR images of the thoracic cavity; 2) to find discriminative regions of 3-D brain MR images associated with neurodegenerative disease.

  4. Image segmentation by hierarchial agglomeration of polygons using ecological statistics

    Science.gov (United States)

    Prasad, Lakshman; Swaminarayan, Sriram

    2013-04-23

    A method for rapid hierarchical image segmentation based on perceptually driven contour completion and scene statistics is disclosed. The method begins with an initial fine-scale segmentation of an image, such as obtained by perceptual completion of partial contours into polygonal regions using region-contour correspondences established by Delaunay triangulation of edge pixels as implemented in VISTA. The resulting polygons are analyzed with respect to their size and color/intensity distributions and the structural properties of their boundaries. Statistical estimates of granularity of size, similarity of color, texture, and saliency of intervening boundaries are computed and formulated into logical (Boolean) predicates. The combined satisfiability of these Boolean predicates by a pair of adjacent polygons at a given segmentation level qualifies them for merging into a larger polygon representing a coarser, larger-scale feature of the pixel image and collectively obtains the next level of polygonal segments in a hierarchy of fine-to-coarse segmentations. The iterative application of this process precipitates textured regions as polygons with highly convolved boundaries and helps distinguish them from objects which typically have more regular boundaries. The method yields a multiscale decomposition of an image into constituent features that enjoy a hierarchical relationship with features at finer and coarser scales. This provides a traversable graph structure from which feature content and context in terms of other features can be derived, aiding in automated image understanding tasks. The method disclosed is highly efficient and can be used to decompose and analyze large images.

  5. XRA image segmentation using regression

    Science.gov (United States)

    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.

  6. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

    DEFF Research Database (Denmark)

    Menze, Bjoern H.; Jakab, Andras; Bauer, Stefan

    2015-01-01

    In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low......- and high-grade glioma patients – manually annotated by up to four raters – and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74...... a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing...

  7. An Active Contour for Range Image Segmentation

    Directory of Open Access Journals (Sweden)

    Khaldi Amine

    2012-06-01

    Full Text Available In this paper a new classification of range image segmentation method is proposed according to the criterion of homogeneity which obeys the segmentation, then, a deformable model-type active contour “Snake” is applied to segment range images.

  8. Image Segmentation by Using Threshold Techniques

    CERN Document Server

    Al-amri, Salem Saleh; D., Khamitkar S

    2010-01-01

    This paper attempts to undertake the study of segmentation image techniques by using five threshold methods as Mean method, P-tile method, Histogram Dependent Technique (HDT), Edge Maximization Technique (EMT) and visual Technique and they are compared with one another so as to choose the best technique for threshold segmentation techniques image. These techniques applied on three satellite images to choose base guesses for threshold segmentation image.

  9. Hierarchical organization of segmentation in non-functional action sequences

    DEFF Research Database (Denmark)

    Nielbo, Kristoffer Laigaard; Schjødt, Uffe; Sørensen, Jesper

    2013-01-01

    Both folk and scientific taxonomies of behavior distinguish between instrumental and ritual behavior. Recent studies indicate that behaviors dominated by ritual features tend to increase cognitive load by focusing attentional and working memory resources on low-level perceptual details and psycho......-physics. In contrast to the general consensus in anthropology and the study of religion, one study did not find any modulation effect of expectations (e.g., cultural information or priors) on cognitive load. It has, therefore, been suggested that the increase reflects a perceptual mechanism that drives categorization...... of ritual behavior. The present study investigated how an increase in cognitive load elicited by ritual behavior can influence hierarchically-related representations of actions and if expectation can modulate such hierarchical action representations. The study found that hierarchical alignment during...

  10. Liver segmentation in color images (Conference Presentation)

    Science.gov (United States)

    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.

  11. Convolutional Neural Networks for SAR Image Segmentation

    DEFF Research Database (Denmark)

    Malmgren-Hansen, David; Nobel-Jørgensen, Morten

    2015-01-01

    Segmentation of Synthetic Aperture Radar (SAR) images has several uses, but it is a difficult task due to a number of properties related to SAR images. In this article we show how Convolutional Neural Networks (CNNs) can easily be trained for SAR image segmentation with good results. Besides...

  12. Distance measures for image segmentation evaluation

    OpenAIRE

    Monteiro, Fernando C.; Campilho, Aurélio C.

    2012-01-01

    In this paper we present a study of evaluation measures that enable the quantification of the quality of an image segmentation result. Despite significant advances in image segmentation techniques, evaluation of these techniques thus far has been largely subjective. Typically, the effectiveness of a new algorithm is demonstrated only by the presentation of a few segmented images and is otherwise left to subjective evaluation by the reader. Such an evaluation criterion can be useful for differ...

  13. Unsupervised Performance Evaluation of Image Segmentation

    Directory of Open Access Journals (Sweden)

    Chabrier Sebastien

    2006-01-01

    Full Text Available We present in this paper a study of unsupervised evaluation criteria that enable the quantification of the quality of an image segmentation result. These evaluation criteria compute some statistics for each region or class in a segmentation result. Such an evaluation criterion can be useful for different applications: the comparison of segmentation results, the automatic choice of the best fitted parameters of a segmentation method for a given image, or the definition of new segmentation methods by optimization. We first present the state of art of unsupervised evaluation, and then, we compare six unsupervised evaluation criteria. For this comparative study, we use a database composed of 8400 synthetic gray-level images segmented in four different ways. Vinet's measure (correct classification rate is used as an objective criterion to compare the behavior of the different criteria. Finally, we present the experimental results on the segmentation evaluation of a few gray-level natural images.

  14. Different Image Segmentation Techniques for Dental Image Extraction

    Directory of Open Access Journals (Sweden)

    R. Bala Subramanyam

    2014-07-01

    Full Text Available Image segmentation is the process of partitioning a digital image into multiple segments and often used to locate objects and boundaries (lines, curves etc.. In this paper, we have proposed image segmentation techniques: Region based, Texture based, Edge based. These techniques have been implemented on dental radiographs and gained good results compare to conventional technique known as Thresholding based technique. The quantitative results show the superiority of the image segmentation technique over three proposed techniques and conventional technique.

  15. Hierarchical imaging of the human knee

    Science.gov (United States)

    Schulz, Georg; Götz, Christian; Deyhle, Hans; Müller-Gerbl, Magdalena; Zanette, Irene; Zdora, Marie-Christine; Khimchenko, Anna; Thalmann, Peter; Rack, Alexander; Müller, Bert

    2016-10-01

    Among the clinically relevant imaging techniques, computed tomography (CT) reaches the best spatial resolution. Sub-millimeter voxel sizes are regularly obtained. For investigations on true micrometer level lab-based μCT has become gold standard. The aim of the present study is the hierarchical investigation of a human knee post mortem using hard X-ray μCT. After the visualization of the entire knee using a clinical CT with a spatial resolution on the sub-millimeter range, a hierarchical imaging study was performed using a laboratory μCT system nanotom m. Due to the size of the whole knee the pixel length could not be reduced below 65 μm. These first two data sets were directly compared after a rigid registration using a cross-correlation algorithm. The μCT data set allowed an investigation of the trabecular structures of the bones. The further reduction of the pixel length down to 25 μm could be achieved by removing the skin and soft tissues and measuring the tibia and the femur separately. True micrometer resolution could be achieved after extracting cylinders of several millimeters diameters from the two bones. The high resolution scans revealed the mineralized cartilage zone including the tide mark line as well as individual calcified chondrocytes. The visualization of soft tissues including cartilage, was arranged by X-ray grating interferometry (XGI) at ESRF and Diamond Light Source. Whereas the high-energy measurements at ESRF allowed the simultaneous visualization of soft and hard tissues, the low-energy results from Diamond Light Source made individual chondrocytes within the cartilage visual.

  16. Multiscale MRF-based Texture Segmentation of SAR Image

    Institute of Scientific and Technical Information of China (English)

    XUXin; LIDeren; SUNHong

    2004-01-01

    We propose a multiscale Bayesian segmentation algorithm for SAR image in this paper. A hierarchical two-level Markov random field (MRF) is applied to represent both texture and region label over the wavelet lattice. The high level uses an isotropic Multi-level logistic (MLL) random field to characterize the blob-like region formation process at each scale and the interscale dependencies over the corresponding multiresolution region. At lower level a novel Causal Gaussian autoregressive (CGAR) process is proposed to describe the fill-in of multiresolution region. Once the multiscale double MRFs model is established, in term of Sequential maximum a posteriori (SMAP), model parameter estimate and region segmentation are performed alternately from coarse to fine scale. Our segmentation method is tested on both synthetic and ERS-1 SAR images.

  17. Plugin procedure in segmentation and application to hyperspectral image segmentation

    CERN Document Server

    Girard, R

    2010-01-01

    In this article we give our contribution to the problem of segmentation with plug-in procedures. We give general sufficient conditions under which plug in procedure are efficient. We also give an algorithm that satisfy these conditions. We give an application of the used algorithm to hyperspectral images segmentation. Hyperspectral images are images that have both spatial and spectral coherence with thousands of spectral bands on each pixel. In the proposed procedure we combine a reduction dimension technique and a spatial regularisation technique. This regularisation is based on the mixlet modelisation of Kolaczyck and Al.

  18. Colour application on mammography image segmentation

    Science.gov (United States)

    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%).

  19. Hierarchization and segmentation of informal care markets in Slovenia.

    Science.gov (United States)

    Hrženjak, Majda

    2012-01-01

    The article is the result of qualitative research of informal care markets in Slovenia in the field of childcare, elder care, and cleaning. The author assesses Slovenia's position in the “global care chain” and finds that “local care chains” prevail in the field of childcare and elder care, while a co-occurrence of female gender, “other” ethnicity, and poverty is typical in the field of household cleaning. The main emphasis of the article is on the analysis of hierarchization of the informal market of care work according to following two criteria: social reputation of individual type of care work and citizenship status of care workers.

  20. Iris image segmentation based on phase congruency

    Science.gov (United States)

    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.

  1. Iris image Segmentation Based on Phase Congruency

    Institute of Scientific and Technical Information of China (English)

    GAO Chao; JIANG Da-qin; Guo Yong-cai

    2006-01-01

    @@ 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.

  2. Probabilistic segmentation of remotely sensed images.

    NARCIS (Netherlands)

    Gorte, B.

    1998-01-01

    For information extraction from image data to create or update geographic information systems, objects are identified and labeled using an integration of segmentation and classification. This yields geometric and thematic information, respectively.Bayesian image classifiers calculate class posterior

  3. Multispectral image segmentation of breast pathology

    Science.gov (United States)

    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.

  4. A modified hierarchical graph cut based video segmentation approach for high frame rate video

    Science.gov (United States)

    Hu, Xuezhang; Chakravarty, Sumit; She, Qi; Wang, Boyu

    2013-03-01

    Video object segmentation entails selecting and extracting objects of interest from a video sequence. Video Segmentation of Objects (VSO) is a critical task which has many applications, such as video edit, video decomposition and object recognition. The core of VSO system consists of two major problems of computer vision, namely object segmentation and object tracking. These two difficulties need to be solved in tandem in an efficient manner to handle variations in shape deformation, appearance alteration and background clutter. Along with segmentation efficiency computational expense is also a critical parameter for algorithm development. Most existing methods utilize advanced tracking algorithms such as mean shift and particle filter, applied together with object segmentation schemes like Level sets or graph methods. As video is a spatiotemporal data, it gives an extensive opportunity to focus on the regions of high spatiotemporal variation. We propose a new algorithm to concentrate on the high variations of the video data and use modified hierarchical processing to capture the spatiotemporal variation. The novelty of the research presented here is to utilize a fast object tracking algorithm conjoined with graph cut based segmentation in a hierarchical framework. This involves modifying both the object tracking algorithm and the graph cut segmentation algorithm to work in an optimized method in a local spatial region while also ensuring all relevant motion has been accounted for. Using an initial estimate of object and a hierarchical pyramid framework the proposed algorithm tracks and segments the object of interest in subsequent frames. Due to the modified hierarchal framework we can perform local processing of the video thereby enabling the proposed algorithm to target specific regions of the video where high spatiotemporal variations occur. Experiments performed with high frame rate video data shows the viability of the proposed approach.

  5. A New Framework for Interactive Images Segmentation

    Directory of Open Access Journals (Sweden)

    MUHAMMAD ASHRAF

    2017-07-01

    Full Text Available Image segmentation has become a widely studied research problem in image processing. There exist different graph based solutions for interactive image segmentation but the domain of image segmentation still needs persistent improvements. The segmentation quality of existing techniques generally depends on the manual input provided in beginning, therefore, these algorithms may not produce quality segmentation with initial seed labels provided by a novice user. In this work we investigated the use of cellular automata in image segmentation and proposed a new algorithm that follows a cellular automaton in label propagation. It incorporates both the pixels? local and global information in the segmentation process. We introduced the novel global constraints in automata evolution rules; hence proposed scheme of automata evolution is more effective than the automata based earlier evolution schemes. Global constraints are also effective in deceasing the sensitivity towards small changes made in manual input; therefore proposed approach is less dependent on label seed marks. It can produce the quality segmentation with modest user efforts. Segmentation results indicate that the proposed algorithm performs better than the earlier segmentation techniques.

  6. Cluster Ensemble-based Image Segmentation

    OpenAIRE

    Xiaoru Wang; Junping Du; Shuzhe Wu; Xu Li; Fu Li

    2013-01-01

    Image segmentation is the foundation of computer vision applications. In this paper, we propose a new cluster ensemble-based image segmentation algorithm, which overcomes several problems of traditional methods. We make two main contributions in this paper. First, we introduce the cluster ensemble concept to fuse the segmentation results from different types of visual features effectively, which can deliver a better final result and achieve a much more stable performance for broad categories ...

  7. Image segmentation with a finite element method

    DEFF Research Database (Denmark)

    Bourdin, Blaise

    1999-01-01

    The Mumford-Shah functional for image segmentation is an original approach of the image segmentation problem, based on a minimal energy criterion. Its minimization can be seen as a free discontinuity problem and is based on \\Gamma-convergence and bounded variation functions theories.Some new regu...

  8. Image Series Segmentation and Improved MC Algorithm

    Institute of Scientific and Technical Information of China (English)

    WAN Wei-bing; SHI Peng-fei

    2008-01-01

    A semiautomatic segmentation method based on active contour is proposed for computed tomog-raphy (CT) image series. First, to get initial contour, one image slice was segmented exactly by C-V method based on Mumford-Shah model. Next, the computer will segment the nearby slice automatically using the snake model one by one. During segmenting of image slices, former slice boundary, as next slice initial con-tour, may cross over next slice real boundary and never return to right position. To avoid contour skipping over, the distance variance between two slices is evaluated by an threshold, which decides whether to initiate again. Moreover, a new improved marching cubes (MC) algorithm based on 2D images series segmentation boundary is given for 3D image reconstruction. Compared with the standard method, the proposed algorithm reduces detecting time and needs less storing memory. The effectiveness and capabilities of the algorithm were illustrated by experimental results.

  9. Metric Learning for Hyperspectral Image Segmentation

    Science.gov (United States)

    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.

  10. Unsupervised Color-texture Image Segmentation

    Institute of Scientific and Technical Information of China (English)

    YU Sheng-yang; ZHANG Fan; WANG Yong-gang; YANG Jie

    2008-01-01

    The measure J in J value segmentation (JSEG) falls to represent the discontinuity of color, which degrades the robustness and discrimination of JSEG. An improved approach for JSEG algorithm was proposed for unsupervised color-texture image segmentation. The texture and photometric invariant edge information were combined, which results in a discriminative measure for color-texture homogeneity. Based on the image whose pixel values are values of the new measure, region growing-merging algorithm used in JSEG was then employed to segment the image. Finally, experiments on a variety of real color images demonstrate performance improvement due to the proposed method.

  11. Adaptive textural segmentation of medical images

    Science.gov (United States)

    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.

  12. Fractal image perception provides novel insights into hierarchical cognition.

    Science.gov (United States)

    Martins, M J; Fischmeister, F P; Puig-Waldmüller, E; Oh, J; Geissler, A; Robinson, S; Fitch, W T; Beisteiner, R

    2014-08-01

    Hierarchical structures play a central role in many aspects of human cognition, prominently including both language and music. In this study we addressed hierarchy in the visual domain, using a novel paradigm based on fractal images. Fractals are self-similar patterns generated by repeating the same simple rule at multiple hierarchical levels. Our hypothesis was that the brain uses different resources for processing hierarchies depending on whether it applies a "fractal" or a "non-fractal" cognitive strategy. We analyzed the neural circuits activated by these complex hierarchical patterns in an event-related fMRI study of 40 healthy subjects. Brain activation was compared across three different tasks: a similarity task, and two hierarchical tasks in which subjects were asked to recognize the repetition of a rule operating transformations either within an existing hierarchical level, or generating new hierarchical levels. Similar hierarchical images were generated by both rules and target images were identical. We found that when processing visual hierarchies, engagement in both hierarchical tasks activated the visual dorsal stream (occipito-parietal cortex, intraparietal sulcus and dorsolateral prefrontal cortex). In addition, the level-generating task specifically activated circuits related to the integration of spatial and categorical information, and with the integration of items in contexts (posterior cingulate cortex, retrosplenial cortex, and medial, ventral and anterior regions of temporal cortex). These findings provide interesting new clues about the cognitive mechanisms involved in the generation of new hierarchical levels as required for fractals.

  13. An efficient algorithm for color image segmentation

    Directory of Open Access Journals (Sweden)

    Shikha Yadav

    2016-09-01

    Full Text Available In field of image processing, image segmentation plays an important role that focus on splitting the whole image into segments. Representation of an image so that it can be more easily analysed and involves more information is an important segmentation goal. The process of partitioning an image can be usually realized by Region based, Boundary based or edge based method. In this work a hybrid approach is followed that combines improved bee colony optimization and Tabu search for color image segmentation. The results produced from this hybrid approach are compared with non-sorted particle swarm optimization, non-sorted genetic algorithm and improved bee colony optimization. Results show that the Hybrid algorithm has better or somewhat similar performance as compared to other algorithms that are based on population. The algorithm is successfully implemented on MATLAB.

  14. Anterior Segment Imaging in Combat Ocular Trauma

    Directory of Open Access Journals (Sweden)

    Denise S. Ryan

    2013-01-01

    Full Text Available Purpose. To evaluate the use of ocular imaging to enhance management and diagnosis of war-related anterior segment ocular injuries. Methods. This study was a prospective observational case series from an ongoing IRB-approved combat ocular trauma tracking study. Subjects with anterior segment ocular injury were imaged, when possible, using anterior segment optical coherence tomography (AS-OCT, confocal microscopy (CM, and slit lamp biomicroscopy. Results. Images captured from participants with combat ocular trauma on different systems provided comprehensive and alternate views of anterior segment injury to investigators. Conclusion. In combat-related trauma of the anterior segment, adjunct image acquisition enhances slit lamp examination and enables real time In vivo observation of the cornea facilitating injury characterization, progression, and management.

  15. Probabilistic multiscale image segmentation by the hyperstack

    NARCIS (Netherlands)

    Vincken, Koenraad Lucas

    2001-01-01

    In the medical community, images from different modalities have found their way to a variety of medical disciplines. Multidimensional images have become indispensable in clinical diagnosis, therapy planning and evaluation. Image segmentation -- dividing an image into meaningful objects -- is a s

  16. SAR Image Segmentation using Vector Quantization Technique on Entropy Images

    CERN Document Server

    Kekre, H B; Sarode, Tanuja K

    2010-01-01

    The development and application of various remote sensing platforms result in the production of huge amounts of satellite image data. Therefore, there is an increasing need for effective querying and browsing in these image databases. In order to take advantage and make good use of satellite images data, we must be able to extract meaningful information from the imagery. Hence we proposed a new algorithm for SAR image segmentation. In this paper we propose segmentation using vector quantization technique on entropy image. Initially, we obtain entropy image and in second step we use Kekre's Fast Codebook Generation (KFCG) algorithm for segmentation of the entropy image. Thereafter, a codebook of size 128 was generated for the Entropy image. These code vectors were further clustered in 8 clusters using same KFCG algorithm and converted into 8 images. These 8 images were displayed as a result. This approach does not lead to over segmentation or under segmentation. We compared these results with well known Gray L...

  17. BW Trained HMM based Aerial Image Segmentation

    OpenAIRE

    R Rajasree; J. Nalini; S C Ramesh

    2011-01-01

    Image segmentation is an essential preprocessing tread in a complicated and composite image dealing out algorithm. In segmenting arial image the expenditure of misclassification could depend on the factual group of pupils. In this paper, aggravated by modern advances in contraption erudition conjecture, I introduce a modus operandi to make light of the misclassification expenditure with class-dependent expenditure. The procedure assumes the hidden Markov model (HMM) which has been popularly u...

  18. CONSTRAINED SPECTRAL CLUSTERING FOR IMAGE SEGMENTATION

    OpenAIRE

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

    2012-01-01

    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 ...

  19. Regression Segmentation for M³ Spinal Images.

    Science.gov (United States)

    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.

  20. Image segmentation based on competitive learning

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jing; LIU Qun; Baikunth Nath

    2004-01-01

    Image segment is a primary step in image analysis of unexploded ordnance (UXO) detection by ground p enetrating radar (GPR) sensor which is accompanied with a lot of noises and other elements that affect the recognition of real target size. In this paper we bring forward a new theory, that is, we look the weight sets as target vector sets which is the new cues in semi-automatic segmentation to form the final image segmentation. The experiment results show that the measure size of target with our method is much smaller than the size with other methods and close to the real size of target.

  1. Segmentation-based CT image compression

    Science.gov (United States)

    Thammineni, Arunoday; Mukhopadhyay, Sudipta; Kamath, Vidya

    2004-04-01

    The existing image compression standards like JPEG and JPEG 2000, compress the whole image as a single frame. This makes the system simple but inefficient. The problem is acute for applications where lossless compression is mandatory viz. medical image compression. If the spatial characteristics of the image are considered, it can give rise to a more efficient coding scheme. For example, CT reconstructed images have uniform background outside the field of view (FOV). Even the portion within the FOV can be divided as anatomically relevant and irrelevant parts. They have distinctly different statistics. Hence coding them separately will result in more efficient compression. Segmentation is done based on thresholding and shape information is stored using 8-connected differential chain code. Simple 1-D DPCM is used as the prediction scheme. The experiments show that the 1st order entropies of images fall by more than 11% when each segment is coded separately. For simplicity and speed of decoding Huffman code is chosen for entropy coding. Segment based coding will have an overhead of one table per segment but the overhead is minimal. Lossless compression of image based on segmentation resulted in reduction of bit rate by 7%-9% compared to lossless compression of whole image as a single frame by the same prediction coder. Segmentation based scheme also has the advantage of natural ROI based progressive decoding. If it is allowed to delete the diagnostically irrelevant portions, the bit budget can go down as much as 40%. This concept can be extended to other modalities.

  2. Scale selection for supervised image segmentation

    DEFF Research Database (Denmark)

    Li, Yan; Tax, David M J; Loog, Marco

    2012-01-01

    Finding the right scales for feature extraction is crucial for supervised image segmentation based on pixel classification. There are many scale selection methods in the literature; among them the one proposed by Lindeberg is widely used for image structures such as blobs, edges and ridges. Those...... schemes are usually unsupervised, as they do not take into account the actual segmentation problem at hand. In this paper, we consider the problem of selecting scales, which aims at an optimal discrimination between user-defined classes in the segmentation. We show the deficiency of the classical...... our approach back to Lindeberg's original proposal. In the experiments, the max rule is applied to artificial and real-world image segmentation tasks, which is shown to choose the right scales for different problems and lead to better segmentation results. © 2012 Elsevier B.V....

  3. Image Segmentation Analysis for NASA Earth Science Applications

    Science.gov (United States)

    Tilton, James C.

    2010-01-01

    NASA collects large volumes of imagery data from satellite-based Earth remote sensing sensors. Nearly all of the computerized image analysis of this data is performed pixel-by-pixel, in which an algorithm is applied directly to individual image pixels. While this analysis approach is satisfactory in many cases, it is usually not fully effective in extracting the full information content from the high spatial resolution image data that s now becoming increasingly available from these sensors. The field of object-based image analysis (OBIA) has arisen in recent years to address the need to move beyond pixel-based analysis. The Recursive Hierarchical Segmentation (RHSEG) software developed by the author is being used to facilitate moving from pixel-based image analysis to OBIA. The key unique aspect of RHSEG is that it tightly intertwines region growing segmentation, which produces spatially connected region objects, with region object classification, which groups sets of region objects together into region classes. No other practical, operational image segmentation approach has this tight integration of region growing object finding with region classification This integration is made possible by the recursive, divide-and-conquer implementation utilized by RHSEG, in which the input image data is recursively subdivided until the image data sections are small enough to successfully mitigat the combinatorial explosion caused by the need to compute the dissimilarity between each pair of image pixels. RHSEG's tight integration of region growing object finding and region classification is what enables the high spatial fidelity of the image segmentations produced by RHSEG. This presentation will provide an overview of the RHSEG algorithm and describe how it is currently being used to support OBIA or Earth Science applications such as snow/ice mapping and finding archaeological sites from remotely sensed data.

  4. FISICO: Fast Image SegmentatIon COrrection.

    Directory of Open Access Journals (Sweden)

    Waldo Valenzuela

    Full Text Available In clinical diagnosis, medical image segmentation plays a key role in the analysis of pathological regions. Despite advances in automatic and semi-automatic segmentation techniques, time-effective correction tools are commonly needed to improve segmentation results. Therefore, these tools must provide faster corrections with a lower number of interactions, and a user-independent solution to reduce the time frame between image acquisition and diagnosis.We present a new interactive method for correcting image segmentations. Our method provides 3D shape corrections through 2D interactions. This approach enables an intuitive and natural corrections of 3D segmentation results. The developed method has been implemented into a software tool and has been evaluated for the task of lumbar muscle and knee joint segmentations from MR images.Experimental results show that full segmentation corrections could be performed within an average correction time of 5.5±3.3 minutes and an average of 56.5±33.1 user interactions, while maintaining the quality of the final segmentation result within an average Dice coefficient of 0.92±0.02 for both anatomies. In addition, for users with different levels of expertise, our method yields a correction time and number of interaction decrease from 38±19.2 minutes to 6.4±4.3 minutes, and 339±157.1 to 67.7±39.6 interactions, respectively.

  5. Visual Knowledge Representation and Intelligent Image Segmentation

    Institute of Scientific and Technical Information of China (English)

    1992-01-01

    Automatic medical image analysis shows that image segmentation is a crucial task for any practical AI system in this field.On the basis of evaluation of the existing segmentation methods,a new image segmentation method is presented.To seek the perfct solution to knowledge representation in low level machine vision,a new knowledge representation approach--“Notebbok”approach is proposed and the processing of visual knowledge is discussed at all levels.To integrate the computer vision theory with Gestalt psychology and knowledge engineering,a new integrated method for intelligent image segmentation of sonargraphs- “Generalized-pattern guided segmentation”is proposed.With the methods and techniques mentioned above,the medical diagnosis expert system for sonargraphs can be built The work on the preliminary experiments is also introduced.

  6. Cluster Ensemble-based Image Segmentation

    Directory of Open Access Journals (Sweden)

    Xiaoru Wang

    2013-07-01

    Full Text Available Image segmentation is the foundation of computer vision applications. In this paper, we propose a new\tcluster ensemble-based image\tsegmentation algorithm, which overcomes several problems of traditional methods. We make two main contributions in this paper. First, we introduce the cluster ensemble concept to fuse the segmentation results from different types of visual features effectively, which can deliver a better final result and achieve a much more stable performance for broad categories of images. Second, we exploit the PageRank idea from Internet applications and apply it to the image segmentation task. This can improve the final segmentation results by combining the spatial information of the image and the semantic similarity of regions. Our experiments on four public image databases validate the superiority of our algorithm over conventional single type of feature or multiple types of features-based algorithms, since our algorithm can fuse multiple types of features effectively for better segmentation results. Moreover, our method is also proved to be very competitive in comparison with other state-of-the-art segmentation algorithms.

  7. Unsupervised segmentation of MRI knees using image partition forests

    Science.gov (United States)

    Marčan, Marija; Voiculescu, Irina

    2016-03-01

    Nowadays many people are affected by arthritis, a condition of the joints with limited prevention measures, but with various options of treatment the most radical of which is surgical. In order for surgery to be successful, it can make use of careful analysis of patient-based models generated from medical images, usually by manual segmentation. In this work we show how to automate the segmentation of a crucial and complex joint -- the knee. To achieve this goal we rely on our novel way of representing a 3D voxel volume as a hierarchical structure of partitions which we have named Image Partition Forest (IPF). The IPF contains several partition layers of increasing coarseness, with partitions nested across layers in the form of adjacency graphs. On the basis of a set of properties (size, mean intensity, coordinates) of each node in the IPF we classify nodes into different features. Values indicating whether or not any particular node belongs to the femur or tibia are assigned through node filtering and node-based region growing. So far we have evaluated our method on 15 MRI knee images. Our unsupervised segmentation compared against a hand-segmented gold standard has achieved an average Dice similarity coefficient of 0.95 for femur and 0.93 for tibia, and an average symmetric surface distance of 0.98 mm for femur and 0.73 mm for tibia. The paper also discusses ways to introduce stricter morphological and spatial conditioning in the bone labelling process.

  8. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).

    Science.gov (United States)

    Menze, Bjoern H; Jakab, Andras; Bauer, Stefan; Kalpathy-Cramer, Jayashree; Farahani, Keyvan; Kirby, Justin; Burren, Yuliya; Porz, Nicole; Slotboom, Johannes; Wiest, Roland; Lanczi, Levente; Gerstner, Elizabeth; Weber, Marc-André; Arbel, Tal; Avants, Brian B; Ayache, Nicholas; Buendia, Patricia; Collins, D Louis; Cordier, Nicolas; Corso, Jason J; Criminisi, Antonio; Das, Tilak; Delingette, Hervé; Demiralp, Çağatay; Durst, Christopher R; Dojat, Michel; Doyle, Senan; Festa, Joana; Forbes, Florence; Geremia, Ezequiel; Glocker, Ben; Golland, Polina; Guo, Xiaotao; Hamamci, Andac; Iftekharuddin, Khan M; Jena, Raj; John, Nigel M; Konukoglu, Ender; Lashkari, Danial; Mariz, José Antonió; Meier, Raphael; Pereira, Sérgio; Precup, Doina; Price, Stephen J; Raviv, Tammy Riklin; Reza, Syed M S; Ryan, Michael; Sarikaya, Duygu; Schwartz, Lawrence; Shin, Hoo-Chang; Shotton, Jamie; Silva, Carlos A; Sousa, Nuno; Subbanna, Nagesh K; Szekely, Gabor; Taylor, Thomas J; Thomas, Owen M; Tustison, Nicholas J; Unal, Gozde; Vasseur, Flor; Wintermark, Max; Ye, Dong Hye; Zhao, Liang; Zhao, Binsheng; Zikic, Darko; Prastawa, Marcel; Reyes, Mauricio; Van Leemput, Koen

    2015-10-01

    In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.

  9. Optimal atlas construction through hierarchical image registration

    Science.gov (United States)

    Grevera, George J.; Udupa, Jayaram K.; Odhner, Dewey; Torigian, Drew A.

    2016-03-01

    Atlases (digital or otherwise) are common in medicine. However, there is no standard framework for creating them from medical images. One traditional approach is to pick a representative subject and then proceed to label structures/regions of interest in this image. Another is to create a "mean" or average subject. Atlases may also contain more than a single representative (e.g., the Visible Human contains both a male and a female data set). Other criteria besides gender may be used as well, and the atlas may contain many examples for a given criterion. In this work, we propose that atlases be created in an optimal manner using a well-established graph theoretic approach using a min spanning tree (or more generally, a collection of them). The resulting atlases may contain many examples for a given criterion. In fact, our framework allows for the addition of new subjects to the atlas to allow it to evolve over time. Furthermore, one can apply segmentation methods to the graph (e.g., graph-cut, fuzzy connectedness, or cluster analysis) which allow it to be separated into "sub-atlases" as it evolves. We demonstrate our method by applying it to 50 3D CT data sets of the chest region, and by comparing it to a number of traditional methods using measures such as Mean Squared Difference, Mattes Mutual Information, and Correlation, and for rigid registration. Our results demonstrate that optimal atlases can be constructed in this manner and outperform other methods of construction using freely available software.

  10. Review methods for image segmentation from computed tomography images

    Energy Technology Data Exchange (ETDEWEB)

    Mamat, Nurwahidah; Rahman, Wan Eny Zarina Wan Abdul; Soh, Shaharuddin Cik [Faculty of Science Computer and Mathematics, Universiti Teknologi Mara Malaysia, 40450 Shah Alam Selangor (Malaysia); Mahmud, Rozi [Faculty of Medicine and Health Sciences, Universiti Putra Malaysia 43400 Serdang Selangor (Malaysia)

    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.

  11. Automated CT segmentation of diseased hip using hierarchical and conditional statistical shape models.

    Science.gov (United States)

    Yokota, Futoshi; Okada, Toshiyuki; Takao, Masaki; Sugano, Nobuhiko; Tada, Yukio; Tomiyama, Noriyuki; Sato, Yoshinobu

    2013-01-01

    Segmentation of the femur and pelvis is a prerequisite for patient-specific planning and simulation for hip surgery. Accurate boundary determination of the femoral head and acetabulum is the primary challenge in diseased hip joints because of deformed shapes and extreme narrowness of the joint space. To overcome this difficulty, we investigated a multi-stage method in which the hierarchical hip statistical shape model (SSM) is initially utilized to complete segmentation of the pelvis and distal femur, and then the conditional femoral head SSM is used under the condition that the regions segmented during the previous stage are known. CT data from 100 diseased patients categorized on the basis of their disease type and severity, which included 200 hemi-hips, were used to validate the method, which delivered significantly increased segmentation accuracy for the femoral head.

  12. On image segmentation using information theoretic criteria

    CERN Document Server

    Aue, Alexander; 10.1214/11-AOS925

    2012-01-01

    Image segmentation is a long-studied and important problem in image processing. Different solutions have been proposed, many of which follow the information theoretic paradigm. While these information theoretic segmentation methods often produce excellent empirical results, their theoretical properties are still largely unknown. The main goal of this paper is to conduct a rigorous theoretical study into the statistical consistency properties of such methods. To be more specific, this paper investigates if these methods can accurately recover the true number of segments together with their true boundaries in the image as the number of pixels tends to infinity. Our theoretical results show that both the Bayesian information criterion (BIC) and the minimum description length (MDL) principle can be applied to derive statistically consistent segmentation methods, while the same is not true for the Akaike information criterion (AIC). Numerical experiments were conducted to illustrate and support our theoretical fin...

  13. Natural color image segmentation using integrated mechanism

    Institute of Scientific and Technical Information of China (English)

    Jie Xu (徐杰); Pengfei Shi (施鹏飞)

    2003-01-01

    A new method for natural color image segmentation using integrated mechanism is proposed in this paper.Edges are first detected in term of the high phase congruency in the gray-level image. K-mean cluster is used to label long edge lines based on the global color information to estimate roughly the distribution of objects in the image, while short ones are merged based on their positions and local color differences to eliminate the negative affection caused by texture or other trivial features in image. Region growing technique is employed to achieve final segmentation results. The proposed method unifies edges, whole and local color distributions, as well as spatial information to solve the natural image segmentation problem.The feasibility and effectiveness of this method have been demonstrated by various experiments.

  14. DISR: Dental Image Segmentation and Retrieval.

    Science.gov (United States)

    Pilevar, Abdol Hamid

    2012-01-01

    In this paper, we propose novel algorithms for retrieving dental images from databases by their contents. Based on special information of dental images, for better content-based dental image retrieval and representation, the image attributes are used. We propose Dental Image Segmentation and Retrieval (DISR), a content-based image retrieval method that is robust to translation and scaling of the objects in the images. A novel model is used to calculate the features of the image. We implemented the dentition plaster casts and proposed a special technique for segmenting teeth in our dental study models. For testing the efficiency of the presented algorithm, a software system is developed and 60 dental study models are used. The models are covering different kinds of malocclusions. Our experiments show that 95% of the extracted results are accurate and the presented algorithm is efficient.

  15. An attribute-based image segmentation method

    Directory of Open Access Journals (Sweden)

    M.C. de Andrade

    1999-07-01

    Full Text Available This work addresses a new image segmentation method founded on Digital Topology and Mathematical Morphology grounds. The ABA (attribute based absorptions transform can be viewed as a region-growing method by flooding simulation working at the scale of the main structures of the image. In this method, the gray level image is treated as a relief flooded from all its local minima, which are progressively detected and merged as the flooding takes place. Each local minimum is exclusively associated to one catchment basin (CB. The CBs merging process is guided by their geometric parameters as depth, area and/or volume. This solution enables the direct segmentation of the original image without the need of a preprocessing step or the explicit marker extraction step, often required by other flooding simulation methods. Some examples of image segmentation, employing the ABA transform, are illustrated for uranium oxide samples. It is shown that the ABA transform presents very good segmentation results even in presence of noisy images. Moreover, it's use is often easier and faster when compared to similar image segmentation methods.

  16. Deep convolutional networks for pancreas segmentation in CT imaging

    Science.gov (United States)

    Roth, Holger R.; Farag, Amal; Lu, Le; Turkbey, Evrim B.; Summers, Ronald M.

    2015-03-01

    Automatic organ segmentation is an important prerequisite for many computer-aided diagnosis systems. The high anatomical variability of organs in the abdomen, such as the pancreas, prevents many segmentation methods from achieving high accuracies when compared to state-of-the-art segmentation of organs like the liver, heart or kidneys. Recently, the availability of large annotated training sets and the accessibility of affordable parallel computing resources via GPUs have made it feasible for "deep learning" methods such as convolutional networks (ConvNets) to succeed in image classification tasks. These methods have the advantage that used classification features are trained directly from the imaging data. We present a fully-automated bottom-up method for pancreas segmentation in computed tomography (CT) images of the abdomen. The method is based on hierarchical coarse-to-fine classification of local image regions (superpixels). Superpixels are extracted from the abdominal region using Simple Linear Iterative Clustering (SLIC). An initial probability response map is generated, using patch-level confidences and a two-level cascade of random forest classifiers, from which superpixel regions with probabilities larger 0.5 are retained. These retained superpixels serve as a highly sensitive initial input of the pancreas and its surroundings to a ConvNet that samples a bounding box around each superpixel at different scales (and random non-rigid deformations at training time) in order to assign a more distinct probability of each superpixel region being pancreas or not. We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing). Using ConvNets we achieve maximum Dice scores of an average 68% +/- 10% (range, 43-80%) in testing. This shows promise for accurate pancreas segmentation, using a deep learning approach and compares favorably to state-of-the-art methods.

  17. Monitoring Post Disturbance Forest Regeneration with Hierarchical Object-Based Image Analysis

    Directory of Open Access Journals (Sweden)

    L. Monika Moskal

    2013-10-01

    Full Text Available The main goal of this exploratory project was to quantify seedling density in post fire regeneration sites, with the following objectives: to evaluate the application of second order image texture (SOIT in image segmentation, and to apply the object-based image analysis (OBIA approach to develop a hierarchical classification. With the utilization of image texture we successfully developed a methodology to classify hyperspatial (high-spatial imagery to fine detail level of tree crowns, shadows and understory, while still allowing discrimination between density classes and mature forest versus burn classes. At the most detailed hierarchical Level I classification accuracies reached 78.8%, a Level II stand density classification produced accuracies of 89.1% and the same accuracy was achieved by the coarse general classification at Level III. Our interpretation of these results suggests hyperspatial imagery can be applied to post-fire forest density and regeneration mapping.

  18. Page Layout Analysis of the Document Image Based on the Region Classification in a Decision Hierarchical Structure

    Directory of Open Access Journals (Sweden)

    Hossein Pourghassem

    2010-10-01

    Full Text Available The conversion of document image to its electronic version is a very important problem in the saving, searching and retrieval application in the official automation system. For this purpose, analysis of the document image is necessary. In this paper, a hierarchical classification structure based on a two-stage segmentation algorithm is proposed. In this structure, image is segmented using the proposed two-stage segmentation algorithm. Then, the type of the image regions such as document and non-document image is determined using multiple classifiers in the hierarchical classification structure. The proposed segmentation algorithm uses two algorithms based on wavelet transform and thresholding. Texture features such as correlation, homogeneity and entropy that extracted from co-occurrenc matrix and also two new features based on wavelet transform are used to classifiy and lable the regions of the image. The hierarchical classifier is consisted of two Multilayer Perceptron (MLP classifiers and a Support Vector Machine (SVM classifier. The proposed algorithm is evaluated on a database consisting of document and non-document images that provides from Internet. The experimental results show the efficiency of the proposed approach in the region segmentation and classification. The proposed algorithm provides accuracy rate of 97.5% on classification of the regions.

  19. Self imaging in segmented waveguide arrays

    Science.gov (United States)

    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.

  20. MRI Brain Image Segmentation based on Thresholding

    Directory of Open Access Journals (Sweden)

    G. Evelin Sujji, Y.V.S. Lakshmi, G. Wiselin Jiji

    2013-03-01

    Full Text Available Medical Image processing is one of the mostchallenging topics in research field. The mainobjective of image segmentation is to extract variousfeatures of the image that are used foranalysing,interpretation and understanding of images.Medical Resonance Image plays a major role inMedical diagnostics. Image processing in MRI ofbrain is highlyessential due to accurate detection ofthe type of brain abnormality which can reduce thechance of fatal result. This paper outlines anefficient image segmentation technique that candistinguish the pathological tissues such asedemaandtumourfrom thenormal tissues such as WhiteMatter(WM,GreyMatter(GM, andCerebrospinal Fluid(CSF. Thresholding is simplerand most commonly used techniques in imagesegmentation. This technique can be used to detectthe contour of thetumourin brain.

  1. Image segmentation using neural tree networks

    Science.gov (United States)

    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.

  2. Neural tree network method for image segmentation

    Science.gov (United States)

    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.

  3. Segmentation of elongated structures in medical images

    NARCIS (Netherlands)

    Staal, Jozef Johannes

    2004-01-01

    The research described in this thesis concerns the automatic detection, recognition and segmentation of elongated structures in medical images. For this purpose techniques have been developed to detect subdimensional pointsets (e.g. ridges, edges) in images of arbitrary dimension. These pointsets ar

  4. Intuitionistic fuzzy segmentation of medical images.

    Science.gov (United States)

    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.

  5. Unsupervised image segmentation with neural networks

    NARCIS (Netherlands)

    Gieling, Th.H.; Janssen, H.J.J.; Vries, de H.C.; Loef, P.

    2001-01-01

    The segmentation of colour images (RGB), distinguishing clusters of image points, representing for example background, leaves and flowers, is performed in a multi-dimensional environment. Considering a two dimensional environment, clusters can be divided by lines. In a three dimensional environment

  6. CONSTRAINED SPECTRAL CLUSTERING FOR IMAGE SEGMENTATION

    Science.gov (United States)

    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

  7. A hybrid hierarchical approach for brain tissue segmentation by combining brain atlas and least square support vector machine.

    Science.gov (United States)

    Kasiri, Keyvan; Kazemi, Kamran; Dehghani, Mohammad Javad; Helfroush, Mohammad Sadegh

    2013-10-01

    In this paper, we present a new semi-automatic brain tissue segmentation method based on a hybrid hierarchical approach that combines a brain atlas as a priori information and a least-square support vector machine (LS-SVM). The method consists of three steps. In the first two steps, the skull is removed and the cerebrospinal fluid (CSF) is extracted. These two steps are performed using the toolbox FMRIB's automated segmentation tool integrated in the FSL software (FSL-FAST) developed in Oxford Centre for functional MRI of the brain (FMRIB). Then, in the third step, the LS-SVM is used to segment grey matter (GM) and white matter (WM). The training samples for LS-SVM are selected from the registered brain atlas. The voxel intensities and spatial positions are selected as the two feature groups for training and test. SVM as a powerful discriminator is able to handle nonlinear classification problems; however, it cannot provide posterior probability. Thus, we use a sigmoid function to map the SVM output into probabilities. The proposed method is used to segment CSF, GM and WM from the simulated magnetic resonance imaging (MRI) using Brainweb MRI simulator and real data provided by Internet Brain Segmentation Repository. The semi-automatically segmented brain tissues were evaluated by comparing to the corresponding ground truth. The Dice and Jaccard similarity coefficients, sensitivity and specificity were calculated for the quantitative validation of the results. The quantitative results show that the proposed method segments brain tissues accurately with respect to corresponding ground truth.

  8. Intravascular Ultrasound Image Segmentation Using Morphological Snakes

    Directory of Open Access Journals (Sweden)

    Hamdi Mohamed Ali

    2012-06-01

    Full Text Available From the first use of the technics of intravascular ultrasound (IVUS as an imaging technique for the coronary artery system at the 70th century until now , the segmentation of the arterial wall boundaries still an important problem . Much research has been done to give better segmentation result for better diagnostics , evaluation and therapy planning. In this paper we present a new segmentation technics based on Morphological Snakes which developed by Luis Álvarez used for the first time for IVUS segmentation. It is a simple , fast and stable approach of snakes evolution algorithm. Results are presented and discussed in order to demonstrate the effectiveness of this approach in IVUS segmentation.

  9. Image Segmentation Using Weak Shape Priors

    CERN Document Server

    Xu, Robert Sheng; Salama, Magdy

    2010-01-01

    The problem of image segmentation is known to become particularly challenging in the case of partial occlusion of the object(s) of interest, background clutter, and the presence of strong noise. To overcome this problem, the present paper introduces a novel approach segmentation through the use of "weak" shape priors. Specifically, in the proposed method, an segmenting active contour is constrained to converge to a configuration at which its geometric parameters attain their empirical probability densities closely matching the corresponding model densities that are learned based on training samples. It is shown through numerical experiments that the proposed shape modeling can be regarded as "weak" in the sense that it minimally influences the segmentation, which is allowed to be dominated by data-related forces. On the other hand, the priors provide sufficient constraints to regularize the convergence of segmentation, while requiring substantially smaller training sets to yield less biased results as compare...

  10. Evaluation for Uncertain Image Classification and Segmentation

    CERN Document Server

    Martin, Arnaud; Arnold-Bos, Andreas

    2008-01-01

    Each year, numerous segmentation and classification algorithms are invented or reused to solve problems where machine vision is needed. Generally, the efficiency of these algorithms is compared against the results given by one or many human experts. However, in many situations, the location of the real boundaries of the objects as well as their classes are not known with certainty by the human experts. Furthermore, only one aspect of the segmentation and classification problem is generally evaluated. In this paper we present a new evaluation method for classification and segmentation of image, where we take into account both the classification and segmentation results as well as the level of certainty given by the experts. As a concrete example of our method, we evaluate an automatic seabed characterization algorithm based on sonar images.

  11. Optical image segmentation using wavelet filtering techniques

    Science.gov (United States)

    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.

  12. Hierarchical Bayesian sparse image reconstruction with application to MRFM

    CERN Document Server

    Dobigeon, Nicolas; Tourneret, Jean-Yves

    2008-01-01

    This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g. by maximizing the estimated posterior distribution. In our fully Bayesian approach the posteriors of all the parameters are available. Thus our algorithm provides more information than other previously proposed sparse reconstr...

  13. BW Trained HMM based Aerial Image Segmentation

    Directory of Open Access Journals (Sweden)

    R Rajasree

    2011-03-01

    Full Text Available Image segmentation is an essential preprocessing tread in a complicated and composite image dealing out algorithm. In segmenting arial image the expenditure of misclassification could depend on the factual group of pupils. In this paper, aggravated by modern advances in contraption erudition conjecture, I introduce a modus operandi to make light of the misclassification expenditure with class-dependent expenditure. The procedure assumes the hidden Markov model (HMM which has been popularly used for image segmentation in recent years. We represent all feasible HMM based segmenters (or classifiers as a set of points in the beneficiary operating characteristic (ROC space. optimizing HMM parameters is still an important and challenging work in automatic image segmentation research area. Usually the Baum-Welch (B-W Algorithm is used to calculate the HMM model parameters. However, the B-W algorithm uses an initial random guess of the parameters, therefore after convergence the output tends to be close to this initial value of the algorithm, which is not necessarily the global optimum of the model parameters. In this project, a Adaptive Baum-Welch (GA-BW is proposed.

  14. Segmentation and Classification of Burn Color Images

    Science.gov (United States)

    2007-11-02

    2Grupo de Ingeniería Biomédica. Escuela Superior de Ingenieros. Universidad de Sevilla. Spain. e-mail: bacha@viento.us.es, cserrano@viento.us.es...Abstract-The aim of the algorithm described in this paper is to separate burned skin from normal skin in burn color images and to classify them...Segmentation Results To perform the segmentation, a previous characterization of the hue and saturation component histograms for both normal and burnt skin

  15. A new method for image segmentation based on Fuzzy C-means algorithm on pixonal images formed by bilateral filtering

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Sharifzadeh, Sara

    2013-01-01

    In this paper, a new pixon-based method is presented for image segmentation. In the proposed algorithm, bilateral filtering is used as a kernel function to form a pixonal image. Using this filter reduces the noise and smoothes the image slightly. By using this pixon-based method, the image over s...... the hierarchical clustering method (Fuzzy C-means algorithm). The experimental results show that the proposed pixon-based approach has a reduced computational load and a better accuracy compared to the other existing pixon-based image segmentation techniques.......In this paper, a new pixon-based method is presented for image segmentation. In the proposed algorithm, bilateral filtering is used as a kernel function to form a pixonal image. Using this filter reduces the noise and smoothes the image slightly. By using this pixon-based method, the image over...

  16. Video-based noncooperative iris image segmentation.

    Science.gov (United States)

    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.

  17. MEDICAL IMAGE SEGMENTATION FOR ANATOMICAL KNOWLEDGE EXTRACTION

    Directory of Open Access Journals (Sweden)

    Ms Maya Eapen

    2014-01-01

    Full Text Available Computed Tomography-Angiography (CTA images of the abdomen, followed by precise segmentation and subsequent computation of shape based features of liver play an important role in hepatic surgery, patient/donor diagnosis during liver transplantation and at various treatment stages. Nevertheless, the issues like intensity similarity and Partial Volume Effect (PVE between the neighboring organs; left the task of liver segmentation critical. The accurate segmentation of liver helps the surgeons to perfectly classify the patients based on their liver anatomy which in turn helps them in the treatment decision phase. In this study, we propose an effective Advanced Region Growing (ARG algorithm for segmentation of liver from CTA images. The performance of the proposed technique was tested with several CTA images acquired across a wide range of patients. The proposed ARG algorithm identifies the liver regions on the images based on the statistical features (intensity distribution and orientation value. The proposed technique addressed the aforementioned issues and been evaluated both quantitatively and qualitatively. For quantitative analysis proposed method was compared with manual segmentation (gold standard. The method was also compared with standard region growing.

  18. Remote Sensing Image Segmentation with Probabilistic Neural Networks

    Institute of Scientific and Technical Information of China (English)

    LIU Gang

    2005-01-01

    This paper focuses on the image segmentation with probabilistic neural networks (PNNs). Back propagation neural networks (BpNNs) and multi perceptron neural networks (MLPs) are also considered in this study. Especially, this paper investigates the implementation of PNNs in image segmentation and optimal processing of image segmentation with a PNN. The comparison between image segmentations with PNNs and with other neural networks is given. The experimental results show that PNNs can be successfully applied to image segmentation for good results.

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

    Science.gov (United States)

    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.

  20. Neural network segmentation of magnetic resonance images

    Science.gov (United States)

    Frederick, Blaise

    1990-07-01

    Neural networks are well adapted to the task of grouping input patterns into subsets which share some similarity. Moreover once trained they can generalize their classification rules to classify new data sets. Sets of pixel intensities from magnetic resonance (MR) images provide a natural input to a neural network by varying imaging parameters MR images can reflect various independent physical parameters of tissues in their pixel intensities. A neural net can then be trained to classify physically similar tissue types based on sets of pixel intensities resulting from different imaging studies on the same subject. A neural network classifier for image segmentation was implemented on a Sun 4/60 and was tested on the task of classifying tissues of canine head MR images. Four images of a transaxial slice with different imaging sequences were taken as input to the network (three spin-echo images and an inversion recovery image). The training set consisted of 691 representative samples of gray matter white matter cerebrospinal fluid bone and muscle preclassified by a neuroscientist. The network was trained using a fast backpropagation algorithm to derive the decision criteria to classify any location in the image by its pixel intensities and the image was subsequently segmented by the classifier. The classifier''s performance was evaluated as a function of network size number of network layers and length of training. A single layer neural network performed quite well at

  1. EFFICIENT IMAGE SEGMENTATION FOR SEMANTIC OBJECT GENERATION

    Institute of Scientific and Technical Information of China (English)

    Chen Xiaotang; Yu Yinglin

    2002-01-01

    This letter presents an efficient and simple image segmentation method for semantic object spatial segmentation. First, the image is filtered using contour-preserving filters. Then it is quasi-flat labeled. The small regions near the contour are classified as uncertain regions and are eliminated by region growing and merging. Further region merging is used to reduce the region number. The simulation results show its efficiency and simplicity. It can preserve the semantic object shape while emphasize on the perceptual complex part of the object. So it conforms to the human visual perception very well.

  2. EFFICIENT IMAGE SEGMENTATION FOR SEMANTIC OBJECT GENERATION

    Institute of Scientific and Technical Information of China (English)

    ChenXiaotang; YuYinglin

    2002-01-01

    This letter presents an efficient and simple image segmentation method for semantic object spatial segmentation.First,the image is filtered using contour-preserving filters.Then it is quasi-flat labeled.The small regions near the contour are classified as uncertain regions and are eliminated by region growing and merging.Further region merging is used to reduce the region number.The simulation results show its efficiency and simplicity,It can preserve the semantic object shape while emphasize on the perceptual complex part of the object.So it conforms to the humann visual perception very well.

  3. A Bintree Energy Approach for Colour Image Segmentation Using Adaptive Channel Selection

    Institute of Scientific and Technical Information of China (English)

    TU Sheng-xian; ZHANG Su; CHEN Ya-zhu; XIAO Chang-yan; ZHANG Lei

    2008-01-01

    A new hierarchical approach called bintree energy segmentation was presented for color image seg-mentation. The image features are extracted by adaptive clustering on multi-channel data at each level and used as the criteria to dynamically select the best chromatic channel, where the segmentation is carried out. In this approach, an extended direct energy computation method based on the Chan-Vese model was proposed to segment the selected channel, and the segmentation outputs are then fused with other channels into new images,from which a new channel with better features is selected for the second round segmentation. This procedure is repeated until the preset condition is met. Finally, a binary segmentation tree is formed, in which each leaf represents a class of objects with a distinctive color. To facilitate the data organization, image background is employed in segmentation and channels fusion. The bintree energy segmentation exploits color information involved in all channels data and tries to optimize the global segmentation result by choosing the "best" chan-nel for segmentation at each level. The experiments show that the method is effective in speed, accuracy and flexibility.

  4. Parallel fuzzy connected image segmentation on GPU

    Science.gov (United States)

    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

  5. Automatic segmentation of bladder in CT images

    Institute of Scientific and Technical Information of China (English)

    Feng SHI; Jie YANG; Yue-min ZHU

    2009-01-01

    Segmentation of the bladder in computerized tomography (CT) images is an important step in radiation therapy planning of prostate cancer. We present a new segmentation scheme to automatically delineate the bladder contour in CT images with three major steps. First, we use the mean shift algorithm to obtain a clustered image containing the rough contour of the bladder, which is then extracted in the second step by applying a region-growing algorithm with the initial seed point selected from a line-by-line scanning process. The third step is to refine the bladder contour more accurately using the rolling-ball algorithm. These steps are then extended to segment the bladder volume in a slice-by-slice manner. The obtained results were compared to manual segmentation by radiation oncologists. The average values of sensitivity, specificity, positive predictive value, negative predictive value, and Hausdorff distance are 86.5%, 96.3%, 90.5%, 96.5%, and 2.8 pixels, respectively. The results show that the bladder can be accurately segmented.

  6. Text line Segmentation of Curved Document Images

    Directory of Open Access Journals (Sweden)

    Anusree.M

    2014-05-01

    Full Text Available Document image analysis has been widely used in historical and heritage studies, education and digital library. Document image analytical techniques are mainly used for improving the human readability and the OCR quality of the document. During the digitization, camera captured images contain warped document due perspective and geometric distortions. The main difficulty is text line detection in the document. Many algorithms had been proposed to address the problem of printed document text line detection, but they failed to extract text lines in curved document. This paper describes a segmentation technique that detects the curled text line in camera captured document images.

  7. Field Sampling from a Segmented Image

    CSIR Research Space (South Africa)

    Debba, Pravesh

    2008-06-01

    Full Text Available Image Debba, Stein, van der Meer, Carranza, Lucieer Objective Study Site Methods The ICM Algorithm Sampling Per Category Sample Size Per Category Fitness Function Per Category Simulated Annealing Per Category Results Experiment Case... Study Conclusions Field Sampling from a Segmented Image P. Debba1 A. Stein2 F.D. van der Meer2 E.J.M. Carranza2 A. Lucieer3 1The Council for Scientific and Industrial Research (CSIR), Logistics and Quantitative Methods, CSIR Built Environment, P...

  8. Quantitative color analysis for capillaroscopy image segmentation.

    Science.gov (United States)

    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.

  9. An algorithm of image segmentation for overlapping grain image

    Institute of Scientific and Technical Information of China (English)

    WANG Zhi; JIN Guang; SUN Xiao-wei

    2005-01-01

    Aiming at measurement of granularity size of nonmetal grain, an algorithm of image segmentation and parameter calculation for microscopic overlapping grain image was studied. This algorithm presents some new attributes of graph sequence from discrete attribute of graph,and consequently achieves the geometrical characteristics from input graph, and the new graph sequence in favor of image segmentation is recombined. The conception that image edge denoted with "twin-point" is put forward, base on geometrical characters of point, image edge is transformed into serial edge, and on recombined serial image edge, based on direction vector definition of line and some additional restricted conditions, the segmentation twin-points are searched with, thus image segmentation is accomplished. Serial image edge is transformed into twin-point pattern, to realize calculation of area and granularity size of nonmetal grain. The inkling and uncertainty on selection of structure element which base on mathematical morphology are avoided in this algorithm, and image segmentation and parameter calculation are realized without changing grain's self statistical characters.

  10. Medical image segmentation using improved FCM

    Institute of Scientific and Technical Information of China (English)

    ZHANG XiaoFeng; ZHANG CaiMing; TANG WenJing; WEI ZhenWen

    2012-01-01

    Image segmentation is one of the most important problems in medical image processing,and the existence of partial volume effect and other phenomena makes the problem much more complex. Fuzzy Cmeans,as an effective tool to deal with PVE,however,is faced with great challenges in efficiency.Aiming at this,this paper proposes one improved FCM algorithm based on the histogram of the given image,which will be denoted as HisFCM and divided into two phases.The first phase will retrieve several intervals on which to compute cluster centroids,and the second one will perform image segmentation based on improved FCM algorithm.Compared with FCM and other improved algorithms,HisFCM is of much higher efficiency with satisfying results.Experiments on medical images show that HisFCM can achieve good segmentation results in less than 0.1 second,and can satisfy real-time requirements of medical image processing.

  11. Wavelet based hierarchical coding scheme for radar image compression

    Science.gov (United States)

    Sheng, Wen; Jiao, Xiaoli; He, Jifeng

    2007-12-01

    This paper presents a wavelet based hierarchical coding scheme for radar image compression. Radar signal is firstly quantized to digital signal, and reorganized as raster-scanned image according to radar's repeated period frequency. After reorganization, the reformed image is decomposed to image blocks with different frequency band by 2-D wavelet transformation, each block is quantized and coded by the Huffman coding scheme. A demonstrating system is developed, showing that under the requirement of real time processing, the compression ratio can be very high, while with no significant loss of target signal in restored radar image.

  12. Polynomial-time solutions to image segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Asano, Tetsuo [Osaka Electro-Communication Univ., Neyagawa (Japan); Chen, D.Z. [Notre Dame, South Bend, IN (United States); Katoh, Naoki [Kobe Univ. of Commerce (Japan)

    1996-12-31

    Separating an object in an image from its background is a central problem (called segmentation) in pattern recognition and computer vision. In this paper, we study the complexity of the segmentation problem, assuming that the object forms a connected region in an intensity image. We show that the optimization problem of separating a connected region in an n-pixel grid is NP-hard under the interclass variance, a criterion that is used in discriminant analysis. More importantly, we consider the basic case in which the object is separated by two x-monotone curves (i.e., the object itself is x-monotone), and present polynomial-time algorithms for computing exact and approximate optimal segmentation. Our main algorithm for exact optimal segmentation by two x-monotone curves runs in O(n{sup 2}) time; this algorithm is based on several techniques such as a parametric optimization formulation, a hand-probing algorithm for the convex hull of an unknown point set, and dynamic programming using fast matrix searching. Our efficient approximation scheme obtains an {epsilon}-approximate solution in O({epsilon}{sup -1} n log L) time, where {epsilon} is any fixed constant with 1 > {epsilon} > 0, and L is the total sum of the absolute values of brightness levels of the image.

  13. Mammographic images segmentation using texture descriptors.

    Science.gov (United States)

    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).

  14. Segmentation Toolbox for Tomographic Image Data

    DEFF Research Database (Denmark)

    Einarsdottir, Hildur

    , techniques to automatically analyze such data becomes ever more important. Most segmentation methods for large datasets, such as CT images, deal with simple thresholding techniques, where intensity values cut offs are predetermined and hard coded. For data where the intensity difference is not sufficient...... to automatically determine parameters of the different classes present in the data, and edge weighted smoothing of the final segmentation based on Markov Random Fields (MRF). The toolbox is developed for Matlab users and requires only minimal background knowledge of Matlab....

  15. Segmentation of Medical Image using Clustering and Watershed Algorithms

    OpenAIRE

    M. C.J. Christ; R.M.S Parvathi

    2011-01-01

    Problem statement: Segmentation plays an important role in medical imaging. Segmentation of an image is the division or separation of the image into dissimilar regions of similar attribute. In this study we proposed a methodology that integrates clustering algorithm and marker controlled watershed segmentation algorithm for medical image segmentation. The use of the conservative watershed algorithm for medical image analysis is pervasive because of its advantages, such as always being able to...

  16. A comparative study on medical image segmentation methods

    OpenAIRE

    Praylin Selva Blessy SELVARAJ ASSLEY; Helen Sulochana CHELLAKKON

    2014-01-01

    Image segmentation plays an important role in medical images. It has been a relevant research area in computer vision and image analysis. Many segmentation algorithms have been proposed for medical images. This paper makes a review on segmentation methods for medical images. In this survey, segmentation methods are divided into five categories: region based, boundary based, model based, hybrid based and atlas based. The five different categories with their principle ideas, advantages and disa...

  17. Image quality, compression and segmentation in medicine.

    Science.gov (United States)

    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.

  18. A Survey on Image Segmentation Techniques Used In Leukemia Detection

    Directory of Open Access Journals (Sweden)

    Mashiat Fatma

    2014-04-01

    Full Text Available Image segmentation commonly known as partitioning of an image is one of the intrinsic parts of any image processing technique. In this image processing step, the digital image of choice is segregated into sets of pixels on the basis of some predefined and preselected measures or standards. There have been presented many algorithms for segmenting a digital image. This paper presents a general review of algorithms that have been presented for the purpose of image segmentation.

  19. Automatic segmentation of mammogram and tomosynthesis images

    Science.gov (United States)

    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

  20. Image segmentation with a unified graphical model.

    Science.gov (United States)

    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.

  1. Transfer learning improves supervised image segmentation across imaging protocols

    DEFF Research Database (Denmark)

    van Opbroek, Annegreet; Ikram, M. Arfan; Vernooij, Meike W.;

    2015-01-01

    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......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...... 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...

  2. Fat segmentation on chest CT images via fuzzy models

    Science.gov (United States)

    Tong, Yubing; Udupa, Jayaram K.; Wu, Caiyun; Pednekar, Gargi; Subramanian, Janani Rajan; Lederer, David J.; Christie, Jason; Torigian, Drew A.

    2016-03-01

    Quantification of fat throughout the body is vital for the study of many diseases. In the thorax, it is important for lung transplant candidates since obesity and being underweight are contraindications to lung transplantation given their associations with increased mortality. Common approaches for thoracic fat segmentation are all interactive in nature, requiring significant manual effort to draw the interfaces between fat and muscle with low efficiency and questionable repeatability. The goal of this paper is to explore a practical way for the segmentation of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) components of chest fat based on a recently developed body-wide automatic anatomy recognition (AAR) methodology. The AAR approach involves 3 main steps: building a fuzzy anatomy model of the body region involving all its major representative objects, recognizing objects in any given test image, and delineating the objects. We made several modifications to these steps to develop an effective solution to delineate SAT/VAT components of fat. Two new objects representing interfaces of SAT and VAT regions with other tissues, SatIn and VatIn are defined, rather than using directly the SAT and VAT components as objects for constructing the models. A hierarchical arrangement of these new and other reference objects is built to facilitate their recognition in the hierarchical order. Subsequently, accurate delineations of the SAT/VAT components are derived from these objects. Unenhanced CT images from 40 lung transplant candidates were utilized in experimentally evaluating this new strategy. Mean object location error achieved was about 2 voxels and delineation error in terms of false positive and false negative volume fractions were, respectively, 0.07 and 0.1 for SAT and 0.04 and 0.2 for VAT.

  3. Heat Equation to 3D Image Segmentation

    Directory of Open Access Journals (Sweden)

    Nikolay Sirakov

    2006-04-01

    Full Text Available This paper presents a new approach, capable of 3D image segmentation and objects' surface reconstruction. The main advantages of the method are: large capture range; quick segmentation of a 3D scene/image to regions; multiple 3D objects reconstruction. The method uses centripetal force and penalty function to segment the entire 3D scene/image to regions containing a single 3D object. Each region is inscribed in a convex, smooth closed surface, which defines a centripetal force. Then the surface is evolved by the geometric heat differential equation toward the force's direction. The penalty function is defined to stop evolvement of those surface patches, whose normal vectors encountered object's surface. On the base of the theoretical model Forward Difference Algorithm was developed and coded by Mathematica. Stability convergence condition, truncation error and calculation complexity of the algorithm are determined. The obtained results, advantages and disadvantages of the method are discussed at the end of this paper.

  4. Assessing the scale of tumor heterogeneity by complete hierarchical segmentation of MRI

    Science.gov (United States)

    Gensheimer, Michael F.; Hawkins, Douglas S.; Ermoian, Ralph P.; Trister, Andrew D.

    2015-02-01

    In many cancers, intratumoral heterogeneity has been found in histology, genetic variation and vascular structure. We developed an algorithm to interrogate different scales of heterogeneity using clinical imaging. We hypothesize that heterogeneity of perfusion at coarse scale may correlate with treatment resistance and propensity for disease recurrence. The algorithm recursively segments the tumor image into increasingly smaller regions. Each dividing line is chosen so as to maximize signal intensity difference between the two regions. This process continues until the tumor has been divided into single voxels, resulting in segments at multiple scales. For each scale, heterogeneity is measured by comparing each segmented region to the adjacent region and calculating the difference in signal intensity histograms. Using digital phantom images, we showed that the algorithm is robust to image artifacts and various tumor shapes. We then measured the primary tumor scales of contrast enhancement heterogeneity in MRI of 18 rhabdomyosarcoma patients. Using Cox proportional hazards regression, we explored the influence of heterogeneity parameters on relapse-free survival. Coarser scale of maximum signal intensity heterogeneity was prognostic of shorter survival (p = 0.05). By contrast, two fractal parameters and three Haralick texture features were not prognostic. In summary, our algorithm produces a biologically motivated segmentation of tumor regions and reports the amount of heterogeneity at various distance scales. If validated on a larger dataset, this prognostic imaging biomarker could be useful to identify patients at higher risk for recurrence and candidates for alternative treatment.

  5. A comparative study on medical image segmentation methods

    Directory of Open Access Journals (Sweden)

    Praylin Selva Blessy SELVARAJ ASSLEY

    2014-03-01

    Full Text Available Image segmentation plays an important role in medical images. It has been a relevant research area in computer vision and image analysis. Many segmentation algorithms have been proposed for medical images. This paper makes a review on segmentation methods for medical images. In this survey, segmentation methods are divided into five categories: region based, boundary based, model based, hybrid based and atlas based. The five different categories with their principle ideas, advantages and disadvantages in segmenting different medical images are discussed.

  6. An Improved Image Segmentation Based on Mean Shift Algorithm

    Institute of Scientific and Technical Information of China (English)

    CHENHanfeng; QIFeihu

    2003-01-01

    Gray image segmentation is to segment an image into some homogeneous regions and only one gray level is defined for each region as the result. These grayl evels are called major gray levels. Mean shift algorithm(MSA) has shown its efficiency in image segmentation. An improved gray image segmentation method based on MSAis proposed in this paper since usual image segmentation methods based on MSA often fail in segmenting imageswith weak edges. Corrupted block and its J-value are defined firstly in the proposed method. Then, J-matrix gotten from corrupted blocks are proposed to measure whether weak edges appear in the image. According to the J-matrix, major gray levels gotten with usual segmen-tation methods based on MSA are augmented and corre-sponding allocation windows are modified to detect weak edges. Experimental results demonstrate the effectiveness of the proposed method in gray image segmentation.

  7. Automatic Vessel Segmentation on Retinal Images

    Institute of Scientific and Technical Information of China (English)

    Chun-Yuan Yu; Chia-Jen Chang; Yen-Ju Yao; Shyr-Shen Yu

    2014-01-01

    Several features of retinal vessels can be used to monitor the progression of diseases. Changes in vascular structures, for example, vessel caliber, branching angle, and tortuosity, are portents of many diseases such as diabetic retinopathy and arterial hyper-tension. This paper proposes an automatic retinal vessel segmentation method based on morphological closing and multi-scale line detection. First, an illumination correction is performed on the green band retinal image. Next, the morphological closing and subtraction processing are applied to obtain the crude retinal vessel image. Then, the multi-scale line detection is used to fine the vessel image. Finally, the binary vasculature is extracted by the Otsu algorithm. In this paper, for improving the drawbacks of multi-scale line detection, only the line detectors at 4 scales are used. The experimental results show that the accuracy is 0.939 for DRIVE (digital retinal images for vessel extraction) retinal database, which is much better than other methods.

  8. Image segmentation using association rule features.

    Science.gov (United States)

    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.

  9. Hierarchical clustering techniques for image database organization and summarization

    Science.gov (United States)

    Vellaikal, Asha; Kuo, C.-C. Jay

    1998-10-01

    This paper investigates clustering techniques as a method of organizing image databases to support popular visual management functions such as searching, browsing and navigation. Different types of hierarchical agglomerative clustering techniques are studied as a method of organizing features space as well as summarizing image groups by the selection of a few appropriate representatives. Retrieval performance using both single and multiple level hierarchies are experimented with and the algorithms show an interesting relationship between the top k correct retrievals and the number of comparisons required. Some arguments are given to support the use of such cluster-based techniques for managing distributed image databases.

  10. Advanced numerical methods for image denoising and segmentation

    OpenAIRE

    Liu, Xiaoyang

    2013-01-01

    Image denoising is one of the most major steps in current image processing. It is a pre-processing step which aims to remove certain unknown, random noise from an image and obtain an image free of noise for further image processing, such as image segmentation. Image segmentation, as another branch of image processing, plays a significant role in connecting low-level image processing and high-level image processing. Its goal is to segment an image into different parts and extract meaningful in...

  11. Optimal retinal cyst segmentation from OCT images

    Science.gov (United States)

    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).

  12. Embedded Implementation of VHR Satellite Image Segmentation.

    Science.gov (United States)

    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.

  13. Text segmentation in degraded historical document images

    Directory of Open Access Journals (Sweden)

    A.S. Kavitha

    2016-07-01

    Full Text Available Text segmentation from degraded Historical Indus script images helps Optical Character Recognizer (OCR to achieve good recognition rates for Hindus scripts; however, it is challenging due to complex background in such images. In this paper, we present a new method for segmenting text and non-text in Indus documents based on the fact that text components are less cursive compared to non-text ones. To achieve this, we propose a new combination of Sobel and Laplacian for enhancing degraded low contrast pixels. Then the proposed method generates skeletons for text components in enhanced images to reduce computational burdens, which in turn helps in studying component structures efficiently. We propose to study the cursiveness of components based on branch information to remove false text components. The proposed method introduces the nearest neighbor criterion for grouping components in the same line, which results in clusters. Furthermore, the proposed method classifies these clusters into text and non-text cluster based on characteristics of text components. We evaluate the proposed method on a large dataset containing varieties of images. The results are compared with the existing methods to show that the proposed method is effective in terms of recall and precision.

  14. Architecture of the parallel hierarchical network for fast image recognition

    Science.gov (United States)

    Timchenko, Leonid; Wójcik, Waldemar; Kokriatskaia, Natalia; Kutaev, Yuriy; Ivasyuk, Igor; Kotyra, Andrzej; Smailova, Saule

    2016-09-01

    Multistage integration of visual information in the brain allows humans to respond quickly to most significant stimuli while maintaining their ability to recognize small details in the image. Implementation of this principle in technical systems can lead to more efficient processing procedures. The multistage approach to image processing includes main types of cortical multistage convergence. The input images are mapped into a flexible hierarchy that reflects complexity of image data. Procedures of the temporal image decomposition and hierarchy formation are described in mathematical expressions. The multistage system highlights spatial regularities, which are passed through a number of transformational levels to generate a coded representation of the image that encapsulates a structure on different hierarchical levels in the image. At each processing stage a single output result is computed to allow a quick response of the system. The result is presented as an activity pattern, which can be compared with previously computed patterns on the basis of the closest match. With regard to the forecasting method, its idea lies in the following. In the results synchronization block, network-processed data arrive to the database where a sample of most correlated data is drawn using service parameters of the parallel-hierarchical network.

  15. A wrapper-based strategy to jointly combine remote sensing image segmentation and object detection

    Science.gov (United States)

    Wang, Jun; Xie, Baoguo; Zhang, Meng; Yin, Wenjun; Du, Hui; Tang, Yujia

    2017-04-01

    Geospatial Object-Based Image Analysis (GEOBIA) has emerged as a new paradigm for remote sensing image classification and interpretation over the last two decades. However, due to the different sensor spatial characteristics, image timing, viewing geometry, etc., the object-based assumption was challenged across a whole scene when dealing with those targets with highly heterogeneous internal characteristics. In this paper we go one step further and address the problem of object detection using a wrapper-based strategy to jointly combine image segmentation and object detection. It offers an insight into full exploitation of the available remotely sensed data. Object detection and image interpretation not only rely on information from raw images, but also depend on knowledge of the target even knowledge from the outside world beyond the image. Regarding this issue, we used high-level object constraint knowledge to refine the image segmentation, going beyond bottom-up models and considering higher order cliques behind the image contexts. As segmentation helps recognition and recognized or delineated objects constrain segmentation, meanwhile, the "correct" segmentation is somehow a compromise between Top-Down & Bottom-Up segmentation. The framework of the proposed approach suggested that the high resolution satellite image was preprocessed, and then the wrapper-based strategy that jointly combined image segmentation and object detection was conducted to represent and interpreted the total scene. In fact, if image objects are badly constructed, objects cannot be accurately identified and recognized. The key idea of this study is to adopt the wrapper-based strategy for remote sensing image analysis, which enables hierarchically representation and recognition of remote sensing images. This kind of representation is determined by the physical properties of the geospatial objects and implemented based on the multi-scale scheme of real world geospatial objects. The method

  16. [A medical image semantic modeling based on hierarchical Bayesian networks].

    Science.gov (United States)

    Lin, Chunyi; Ma, Lihong; Yin, Junxun; Chen, Jianyu

    2009-04-01

    A semantic modeling approach for medical image semantic retrieval based on hierarchical Bayesian networks was proposed, in allusion to characters of medical images. It used GMM (Gaussian mixture models) to map low-level image features into object semantics with probabilities, then it captured high-level semantics through fusing these object semantics using a Bayesian network, so that it built a multi-layer medical image semantic model, aiming to enable automatic image annotation and semantic retrieval by using various keywords at different semantic levels. As for the validity of this method, we have built a multi-level semantic model from a small set of astrocytoma MRI (magnetic resonance imaging) samples, in order to extract semantics of astrocytoma in malignant degree. Experiment results show that this is a superior approach.

  17. Co-adaptability solution to conflict events in construction projects by segmented hierarchical algorithm

    Institute of Scientific and Technical Information of China (English)

    HOU XueLiang; LU Mei

    2008-01-01

    In order to seek the co-adaptability solution to conflict events in construction en-gineering projects,a new method referred to as segmented hierarchical algorithm is proposed in this paper by means of comparing co-adaptability evolution process of conflict events to the stackelberg model.By this new algorithm,local solutions to the first-order transformation of co-adaptability for conflict events can be ob-tained,based upon which,a global solution to the second-order transformation of co-adaptability for conflict events can also be decided by judging satisfaction de-gree of local solutions.The research results show that this algorithm can be used not only for obtaining co-adaptability solution to conflict events efficiently,but also for other general decision-making problems with multi-layers and multi-subsidi-aries in project management field.

  18. Co-adaptability solution to conflict events in construction projects by segmented hierarchical algorithm

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    In order to seek the co-adaptability solution to conflict events in construction engineering projects, a new method referred to as segmented hierarchical algorithm is proposed in this paper by means of comparing co-adaptability evolution process of conflict events to the stackelberg model. By this new algorithm, local solutions to the first-order transformation of co-adaptability for conflict events can be obtained, based upon which, a global solution to the second-order transformation of co-adaptability for conflict events can also be decided by judging satisfaction degree of local solutions. The research results show that this algorithm can be used not only for obtaining co-adaptability solution to conflict events efficiently, but also for other general decision-making problems with multi-layers and multi-subsidi-aries in project management field.

  19. Multiphase Image Segmentation Using the Deformable Simplicial Complex Method

    DEFF Research Database (Denmark)

    Dahl, Vedrana Andersen; Christiansen, Asger Nyman; Bærentzen, Jakob Andreas

    2014-01-01

    The deformable simplicial complex method is a generic method for tracking deformable interfaces. It provides explicit interface representation, topological adaptivity, and multiphase support. As such, the deformable simplicial complex method can readily be used for representing active contours...... in image segmentation based on deformable models. We show the benefits of using the deformable simplicial complex method for image segmentation by segmenting an image into a known number of segments characterized by distinct mean pixel intensities....

  20. Histological image segmentation using fast mean shift clustering method

    OpenAIRE

    Wu, Geming; Zhao, Xinyan; Luo, Shuqian; Shi, Hongli

    2015-01-01

    Background Colour image segmentation is fundamental and critical for quantitative histological image analysis. The complexity of the microstructure and the approach to make histological images results in variable staining and illumination variations. And ultra-high resolution of histological images makes it is hard for image segmentation methods to achieve high-quality segmentation results and low computation cost at the same time. Methods Mean Shift clustering approach is employed for histol...

  1. Unsupervised Multiresolution Image Segmentation Integrating Color and Texture

    Institute of Scientific and Technical Information of China (English)

    XINGQiang; YUANBaozong; TANGXiaofang

    2004-01-01

    Unsupervised segmentation of images is highly useful in various applications including contentbased image retrieval. A novel multiresolution image segmentation algorithm, designed to separate a focused object of interest from background automatically, is described in this paper. According to the principle of human vision system, our algorithm first searches the salient block representing object in global image domain. Then all image blocks are clustered using the feature of color moments and texture in salient block. At last the algorithm classifies the image blocks belonging to object class in high resolution. Experiment shows that our algorithm achieves better segmentation results at higher speed compared with the traditional image segmentation approach using global optimization.

  2. Segmentation of moving images by the human visual system.

    Science.gov (United States)

    Chantelau, K

    1997-08-01

    New segments appearing in an image sequence or spontaneously accelerated segments are band limited by the visual system due to a nonperfect tracking of these segments by eye movements. In spite of this band limitation and acceleration of segments, a coarse segmentation (initial segmentation phase) can be performed by the visual system. This is interesting for the development of purely automatic segmentation algorithms for multimedia applications. In this paper the segmentation of the visual system is modelled and used in an automatic coarse initial segmentation. A suitable model for motion processing based on a spectral representation is presented and applied to the segmentation of synthetic and real image sequences with band limited and accelerated moving foreground and background segments.

  3. Underwater color image segmentation method via RGB channel fusion

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  5. A class-adaptive spatially variant mixture model for image segmentation.

    Science.gov (United States)

    Nikou, Christophoros; Galatsanos, Nikolaos P; Likas, Aristidis C

    2007-04-01

    We propose a new approach for image segmentation based on a hierarchical and spatially variant mixture model. According to this model, the pixel labels are random variables and a smoothness prior is imposed on them. The main novelty of this work is a new family of smoothness priors for the label probabilities in spatially variant mixture models. These Gauss-Markov random field-based priors allow all their parameters to be estimated in closed form via the maximum a posteriori (MAP) estimation using the expectation-maximization methodology. Thus, it is possible to introduce priors with multiple parameters that adapt to different aspects of the data. Numerical experiments are presented where the proposed MAP algorithms were tested in various image segmentation scenarios. These experiments demonstrate that the proposed segmentation scheme compares favorably to both standard and previous spatially constrained mixture model-based segmentation.

  6. A Semantic Connected Coherence Scheme for Efficient Image Segmentation

    Directory of Open Access Journals (Sweden)

    S.Pannirselvam

    2012-06-01

    Full Text Available Image processing is a comprehensively research topic with an elongated history. Segmenting an image is the most challenging and difficult task in image processing and analysis. The principal intricacy met in image segmentation is the ability of techniques to discover semantic objects efficiently from an image without any prior knowledge. One recent work presented connected coherence tree algorithm (CCTA for image segmentation (with no prior knowledge which discovered regions of semantic coherence based on neighbor coherence segmentation criteria. It deployed an adaptive spatial scale and a suitable intensity-difference scale to extract several sets of coherent neighboring pixels and maximize the probability of single image content and minimize complex backgrounds. However CCTA segmented images either consists of small, lengthy and slender objects or rigorously ruined by noise, irregular lighting, occlusion, poor illumination, and shadow.In this paper, we present a Cluster based Semantic Coherent Tree (CBSCT scheme for image segmentation. CBSCT’s initial work is on the semantic connected coherence criteria for the image segregation. Semantic coherent regions are clustered based on Bayesian nearest neighbor search of neighborhood pixels. The segmentation regions are extracted from the images based on the cluster object purity obtained through semantic coherent regions. The clustered image regions are post processed with non linear noise filters. Performance metrics used in the evaluation of CBSCT are semantic coherent pixel size, number of cluster objects, and purity levels of the cluster, segmented coherent region intensity threshold, and quality of segmented images in terms of image clarity with PSNR.

  7. Temporal segmentation of video objects for hierarchical object-based motion description.

    Science.gov (United States)

    Fu, Yue; Ekin, Ahmet; Tekalp, A Murat; Mehrotra, Rajiv

    2002-01-01

    This paper describes a hierarchical approach for object-based motion description of video in terms of object motions and object-to-object interactions. We present a temporal hierarchy for object motion description, which consists of low-level elementary motion units (EMU) and high-level action units (AU). Likewise, object-to-object interactions are decomposed into a hierarchy of low-level elementary reaction units (ERU) and high-level interaction units (IU). We then propose an algorithm for temporal segmentation of video objects into EMUs, whose dominant motion can be described by a single representative parametric model. The algorithm also computes a representative (dominant) affine model for each EMU. We also provide algorithms for identification of ERUs and for classification of the type of ERUs. Experimental results demonstrate that segmenting the life-span of video objects into EMUS and ERUs facilitates the generation of high-level visual summaries for fast browsing and navigation. At present, the formation of high-level action and interaction units is done interactively. We also provide a set of query-by-example results for low-level EMU retrieval from a database based on similarity of the representative dominant affine models.

  8. Hierarchical model-based interferometric synthetic aperture radar image registration

    Science.gov (United States)

    Wang, Yang; Huang, Haifeng; Dong, Zhen; Wu, Manqing

    2014-01-01

    With the rapid development of spaceborne interferometric synthetic aperture radar technology, classical image registration methods are incompetent for high-efficiency and high-accuracy masses of real data processing. Based on this fact, we propose a new method. This method consists of two steps: coarse registration that is realized by cross-correlation algorithm and fine registration that is realized by hierarchical model-based algorithm. Hierarchical model-based algorithm is a high-efficiency optimization algorithm. The key features of this algorithm are a global model that constrains the overall structure of the motion estimated, a local model that is used in the estimation process, and a coarse-to-fine refinement strategy. Experimental results from different kinds of simulated and real data have confirmed that the proposed method is very fast and has high accuracy. Comparing with a conventional cross-correlation method, the proposed method provides markedly improved performance.

  9. SAR Image Segmentation Based On Hybrid PSOGSA Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Amandeep Kaur

    2014-09-01

    Full Text Available Image segmentation is useful in many applications. It can identify the regions of interest in a scene or annotate the data. It categorizes the existing segmentation algorithm into region-based segmentation, data clustering, and edge-base segmentation. Region-based segmentation includes the seeded and unseeded region growing algorithms, the JSEG, and the fast scanning algorithm. Due to the presence of speckle noise, segmentation of Synthetic Aperture Radar (SAR images is still a challenging problem. We proposed a fast SAR image segmentation method based on Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA. In this method, threshold estimation is regarded as a search procedure that examinations for an appropriate value in a continuous grayscale interval. Hence, PSO-GSA algorithm is familiarized to search for the optimal threshold. Experimental results indicate that our method is superior to GA based, AFS based and ABC based methods in terms of segmentation accuracy, segmentation time, and Thresholding.

  10. Variation-based approach to image segmentation Variation-based approach to image segmentation

    Institute of Scientific and Technical Information of China (English)

    张永平; 郑南宁; 赵荣椿

    2001-01-01

    A new approach to image segmentation is presented using a variation framework. Regarding the edge points as interpolating points and minimizing an energy functional to interpolate a smooth threshold surface it carries out the image segmentation. In order to preserve the edge information of the original image in the threshold surface, without unduly sharping the edge of the image, a non-convex energy functional is adopted. A relaxation algorithm with the property of global convergence, for solving the optimization problem, is proposed by introducing a binary energy. As a result the non-convex optimization problem is transformed into a series of convex optimization problems, and the problem of slow convergence or nonconvergence is solved. The presented method is also tested experimentally. Finally the method of determining the parameters in optimizing is also explored.   

  11. An Improved FCM Medical Image Segmentation Algorithm Based on MMTD

    Directory of Open Access Journals (Sweden)

    Ningning Zhou

    2014-01-01

    Full Text Available 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.

  12. Statistical multiscale image segmentation via Alpha-stable modeling

    OpenAIRE

    Wan, Tao; Canagarajah, CN; Achim, AM

    2007-01-01

    This paper presents a new statistical image segmentation algorithm, in which the texture features are modeled by symmetric alpha-stable (SalphaS) distributions. These features are efficiently combined with the dominant color feature to perform automatic segmentation. First, the image is roughly segmented into textured and nontextured regions using the dual-tree complex wavelet transform (DT-CWT) with the sub-band coefficients modeled as SalphaS random variables. A mul-tiscale segmentation is ...

  13. An Efficient Approach for Tree Digital Image Segmentation

    Institute of Scientific and Technical Information of China (English)

    Cheng Lei; Song Tieying

    2004-01-01

    This paper proposes an improved method to segment tree image based on color and texture feature and amends the segmented result by mathematical morphology. The crown and trunk of one tree have been successfully segmented and the experimental result is deemed effective. The authors conclude that building a standard data base for a range of species, featuring color and texture is a necessary condition and constitutes the essential groundwork for tree image segmentation in order to insure its quality.

  14. Fingerprint Image Enhancement:Segmentation to Thinning

    Directory of Open Access Journals (Sweden)

    Iwasokun Gabriel Babatunde

    2012-01-01

    Full Text Available Fingerprint has remained a very vital index for human recognition. In the field of security, series of Automatic Fingerprint Identification Systems (AFIS have been developed. One of the indices for evaluating the contributions of these systems to the enforcement of security is the degree with which they appropriately verify or identify input fingerprints. This degree is generally determined by the quality of the fingerprint images and the efficiency of the algorithm. In this paper, some of the sub-models of an existing mathematical algorithm for the fingerprint image enhancement were modified to obtain new and improved versions. The new versions consist of different mathematical models for fingerprint image segmentation, normalization, ridge orientation estimation, ridge frequency estimation, Gabor filtering, binarization and thinning. The implementation was carried out in an environment characterized by Window Vista Home Basic operating system as platform and Matrix Laboratory (MatLab as frontend engine. Synthetic images as well as real fingerprints obtained from the FVC2004 fingerprint database DB3 set A were used to test the adequacy of the modified sub-models and the resulting algorithm. The results show that the modified sub-models perform well with significant improvement over the original versions. The results also show the necessity of each level of the enhancement.

  15. Image Segmentation for Connectomics Using Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    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.

  16. [An adaptive threshloding segmentation method for urinary sediment image].

    Science.gov (United States)

    Li, Yongming; Zeng, Xiaoping; Qin, Jian; Han, Liang

    2009-02-01

    In this paper is proposed a new method to solve the segmentation of the complicated defocusing urinary sediment image. The main points of the method are: (1) using wavelet transforms and morphology to erase the effect of defocusing and realize the first segmentation, (2) using adaptive threshold processing in accordance to the subimages after wavelet processing, and (3) using 'peel off' algorithm to deal with the overlapped cells' segmentations. The experimental results showed that this method was not affected by the defocusing, and it made good use of many kinds of characteristics of the images. So this new mehtod can get very precise segmentation; it is effective for defocusing urinary sediment image segmentation.

  17. INTER-GROUP IMAGE REGISTRATION BY HIERARCHICAL GRAPH SHRINKAGE.

    Science.gov (United States)

    Ying, Shihui; Wu, Guorong; Liao, Shu; Shen, Dinggang

    2013-12-31

    In this paper, we propose a novel inter-group image registration method to register different groups of images (e.g., young and elderly brains) simultaneously. Specifically, we use a hierarchical two-level graph to model the distribution of entire images on the manifold, with intra-graph representing the image distribution in each group and the inter-graph describing the relationship between two groups. Then the procedure of inter-group registration is formulated as a dynamic evolution of graph shrinkage. The advantage of our method is that the topology of entire image distribution is explored to guide the image registration. In this way, each image coordinates with its neighboring images on the manifold to deform towards the population center, by following the deformation pathway simultaneously optimized within the graph. Our proposed method has been also compared with other state-of-the-art inter-group registration methods, where our method achieves better registration results in terms of registration accuracy and robustness.

  18. Hierarchical discriminant manifold learning for dimensionality reduction and image classification

    Science.gov (United States)

    Chen, Weihai; Zhao, Changchen; Ding, Kai; Wu, Xingming; Chen, Peter C. Y.

    2015-09-01

    In the field of image classification, it has been a trend that in order to deliver a reliable classification performance, the feature extraction model becomes increasingly more complicated, leading to a high dimensionality of image representations. This, in turn, demands greater computation resources for image classification. Thus, it is desirable to apply dimensionality reduction (DR) methods for image classification. It is necessary to apply DR methods to relieve the computational burden as well as to improve the classification accuracy. However, traditional DR methods are not compatible with modern feature extraction methods. A framework that combines manifold learning based DR and feature extraction in a deeper way for image classification is proposed. A multiscale cell representation is extracted from the spatial pyramid to satisfy the locality constraints for a manifold learning method. A spectral weighted mean filtering is proposed to eliminate noise in the feature space. A hierarchical discriminant manifold learning is proposed which incorporates both category label and image scale information to guide the DR process. Finally, the image representation is generated by concatenating dimensionality reduced cell representations from the same image. Extensive experiments are conducted to test the proposed algorithm on both scene and object recognition datasets in comparison with several well-established and state-of-the-art methods with respect to classification precision and computational time. The results verify the effectiveness of incorporating manifold learning in the feature extraction procedure and imply that the multiscale cell representations may be distributed on a manifold.

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

    Science.gov (United States)

    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.

  20. SVM for density estimation and application to medical image segmentation

    Institute of Scientific and Technical Information of China (English)

    ZHANG Zhao; ZHANG Su; ZHANG Chen-xi; CHEN Ya-zhu

    2006-01-01

    A method of medical image segmentation based on support vector machine (SVM) for density estimation is presented. We used this estimator to construct a prior model of the image intensity and curvature profile of the structure from training images. When segmenting a novel image similar to the training images, the technique of narrow level set method is used. The higher dimensional surface evolution metric is defined by the prior model instead of by energy minimization function. This method offers several advantages. First, SVM for density estimation is consistent and its solution is sparse. Second, compared to the traditional level set methods, this method incorporates shape information on the object to be segmented into the segmentation process.Segmentation results are demonstrated on synthetic images, MR images and ultrasonic images.

  1. A Survey Paper on Fuzzy Image Segmentation Techniques

    Directory of Open Access Journals (Sweden)

    Ms. R. Saranya Pon Selvi

    2014-03-01

    Full Text Available The image segmentation plays an important role in the day-to-day life. The new technologies are emerging in the field of Image processing, especially in the domain of segmentation.Segmentation is considered as one of the main steps in image processing. It divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. This paper presents a brief outline on some of the most commonly used segmentation techniques like thresholding, Region based, Model based, Edge detection..etc. mentioning its advantages as well as the drawbacks. Some of the techniques are suitable for noisy images.

  2. A CT Image Segmentation Algorithm Based on Level Set Method

    Institute of Scientific and Technical Information of China (English)

    QU Jing-yi; SHI Hao-shan

    2006-01-01

    Level Set methods are robust and efficient numerical tools for resolving curve evolution in image segmentation. This paper proposes a new image segmentation algorithm based on Mumford-Shah module. The method is used to CT images and the experiment results demonstrate its efficiency and veracity.

  3. Application of reinforcement learning for segmentation of transrectal ultrasound images

    Directory of Open Access Journals (Sweden)

    Tizhoosh Hamid R

    2008-04-01

    Full Text Available Abstract Background Among different medical image modalities, ultrasound imaging has a very widespread clinical use. But, due to some factors, such as poor image contrast, noise and missing or diffuse boundaries, the ultrasound images are inherently difficult to segment. An important application is estimation of the location and volume of the prostate in transrectal ultrasound (TRUS images. For this purpose, manual segmentation is a tedious and time consuming procedure. Methods We introduce a new method for the segmentation of the prostate in transrectal ultrasound images, using a reinforcement learning scheme. This algorithm is used to find the appropriate local values for sub-images and to extract the prostate. It contains an offline stage, where the reinforcement learning agent uses some images and manually segmented versions of these images to learn from. The reinforcement agent is provided with reward/punishment, determined objectively to explore/exploit the solution space. After this stage, the agent has acquired knowledge stored in the Q-matrix. The agent can then use this knowledge for new input images to extract a coarse version of the prostate. Results We have carried out experiments to segment TRUS images. The results demonstrate the potential of this approach in the field of medical image segmentation. Conclusion By using the proposed method, we can find the appropriate local values and segment the prostate. This approach can be used for segmentation tasks containing one object of interest. To improve this prototype, more investigations are needed.

  4. SAR image target segmentation based on entropy maximization and morphology

    Institute of Scientific and Technical Information of China (English)

    柏正尧; 刘洲峰; 何佩琨

    2004-01-01

    Entropy maximization thresholding is a simple, effective image segmentation method. The relation between the histogram entropy and the gray level of an image is analyzed. An approach, which speeds the computation of optimal threshold based on entropy maximization, is proposed. The suggested method has been applied to the synthetic aperture radar (SAR) image targets segmentation. Mathematical morphology works well in reducing the residual noise.

  5. Image segmentation using an improved differential algorithm

    Science.gov (United States)

    Gao, Hao; Shi, Yujiao; Wu, Dongmei

    2014-10-01

    Among all the existing segmentation techniques, the thresholding technique is one of the most popular due to its simplicity, robustness, and accuracy (e.g. the maximum entropy method, Otsu's method, and K-means clustering). However, the computation time of these algorithms grows exponentially with the number of thresholds due to their exhaustive searching strategy. As a population-based optimization algorithm, differential algorithm (DE) uses a population of potential solutions and decision-making processes. It has shown considerable success in solving complex optimization problems within a reasonable time limit. Thus, applying this method into segmentation algorithm should be a good choice during to its fast computational ability. In this paper, we first propose a new differential algorithm with a balance strategy, which seeks a balance between the exploration of new regions and the exploitation of the already sampled regions. Then, we apply the new DE into the traditional Otsu's method to shorten the computation time. Experimental results of the new algorithm on a variety of images show that, compared with the EA-based thresholding methods, the proposed DE algorithm gets more effective and efficient results. It also shortens the computation time of the traditional Otsu method.

  6. Graph Laplacian for spectral clustering and seeded image segmentation

    OpenAIRE

    Wallace Correa de Oliveira Casaca

    2014-01-01

    Image segmentation is an essential tool to enhance the ability of computer systems to efficiently perform elementary cognitive tasks such as detection, recognition and tracking. In this thesis we concentrate on the investigation of two fundamental topics in the context of image segmentation: spectral clustering and seeded image segmentation. We introduce two new algorithms for those topics that, in summary, rely on Laplacian-based operators, spectral graph theory, and minimization of energy f...

  7. Advanced techniques in medical image segmentation of the liver

    OpenAIRE

    López Mir, Fernando

    2016-01-01

    [EN] Image segmentation is, along with multimodal and monomodal registration, the operation with the greatest applicability in medical image processing. There are many operations and filters, as much as applications and cases, where the segmentation of an organic tissue is the first step. The case of liver segmentation in radiological images is, after the brain, that on which the highest number of scientific publications can be found. This is due, on the one hand, to the need to continue inno...

  8. Segmentation of neuroanatomy in magnetic resonance images

    Science.gov (United States)

    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.

  9. Two-stage image segmentation based on edge and region information

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    A two-stage method for image segmentation based on edge and region information is proposed. Different deformation schemes are used at two stages for segmenting the object correctly in image plane. At the first stage, the contour of the model is divided into several segments hierarchically that deform respectively using affine transformation. After the contour is deformed to the approximate boundary of object, a fine match mechanism using statistical information of local region to redefine the external energy of the model is used to make the contour fit the object's boundary exactly. The algorithm is effective, as the hierarchical segmental deformation makes use of the globe and local information of the image, the affine transformation keeps the consistency of the model, and the reformative approaches of computing the internal energy and external energy are proposed to reduce the algorithm complexity. The adaptive method of defining the search area at the second stage makes the model converge quickly. The experimental results indicate that the proposed model is effective and robust to local minima and able to search for concave objects.

  10. Applications of magnetic resonance image segmentation in neurology

    Science.gov (United States)

    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.

  11. A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation

    Science.gov (United States)

    Jung, Jinha; Pasolli, Edoardo; Prasad, Saurabh; Tilton, James C.; Crawford, Melba M.

    2014-01-01

    Acquiring current, accurate land-use information is critical for monitoring and understanding the impact of anthropogenic activities on natural environments.Remote sensing technologies are of increasing importance because of their capability to acquire information for large areas in a timely manner, enabling decision makers to be more effective in complex environments. Although optical imagery has demonstrated to be successful for land cover classification, active sensors, such as light detection and ranging (LiDAR), have distinct capabilities that can be exploited to improve classification results. However, utilization of LiDAR data for land cover classification has not been fully exploited. Moreover, spatial-spectral classification has recently gained significant attention since classification accuracy can be improved by extracting additional information from the neighboring pixels. Although spatial information has been widely used for spectral data, less attention has been given to LiDARdata. In this work, a new framework for land cover classification using discrete return LiDAR data is proposed. Pseudo-waveforms are generated from the LiDAR data and processed by hierarchical segmentation. Spatial featuresare extracted in a region-based way using a new unsupervised strategy for multiple pruning of the segmentation hierarchy. The proposed framework is validated experimentally on a real dataset acquired in an urban area. Better classification results are exhibited by the proposed framework compared to the cases in which basic LiDAR products such as digital surface model and intensity image are used. Moreover, the proposed region-based feature extraction strategy results in improved classification accuracies in comparison with a more traditional window-based approach.

  12. SEGMENTATION AND QUALITY ANALYSIS OF LONG RANGE CAPTURED IRIS IMAGE

    Directory of Open Access Journals (Sweden)

    Anand Deshpande

    2016-05-01

    Full Text Available The iris segmentation plays a major role in an iris recognition system to increase the performance of the system. This paper proposes a novel method for segmentation of iris images to extract the iris part of long range captured eye image and an approach to select best iris frame from the iris polar image sequences by analyzing the quality of iris polar images. The quality of iris image is determined by the frequency components present in the iris polar images. The experiments are carried out on CASIA-long range captured iris image sequences. The proposed segmentation method is compared with Hough transform based segmentation and it has been determined that the proposed method gives higher accuracy for segmentation than Hough transform.

  13. A novel stepwise thresholding for fuzzy image segmentation

    Institute of Scientific and Technical Information of China (English)

    HE Xiao-hai; LUO Dai-sheng; WU Xiao-qiang; JIANG Li; TENG Qi-zhi; Tao De-yuan

    2001-01-01

    A novel stepwise thresholding method for fuzzy image segmentation is proposed. Unlike the published iterative or recursive thresholding mehtods, this method segments regions into sub-regions iteratively by increasing threshold value in a stepwise manner, based on a preset intensity homogeneity criteria. The method is particularly suited to segmentation of the laser scanning confocal microscopy (LSCM) images, computerised tomography (CT) images, magnetic resonance (MR) images, fingerprint images, etc. The method has been tested on some typical fuzzy image data sets. In this paper, the novel stepwise thresholding is first addressed. Next a new method of region labelling for region extraction is introduced.Then the design of intensity homogeneity segmentation criteria is presented. Some examples of the experiment results of fuzzy image segmentation by the method are given at the end.

  14. A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine.

    Science.gov (United States)

    Mastmeyer, André; Engelke, Klaus; Fuchs, Christina; Kalender, Willi A

    2006-08-01

    We have developed a new hierarchical 3D technique to segment the vertebral bodies in order to measure bone mineral density (BMD) with high trueness and precision in volumetric CT datasets. The hierarchical approach starts with a coarse separation of the individual vertebrae, applies a variety of techniques to segment the vertebral bodies with increasing detail and ends with the definition of an anatomic coordinate system for each vertebral body, relative to which up to 41 trabecular and cortical volumes of interest are positioned. In a pre-segmentation step constraints consisting of Boolean combinations of simple geometric shapes are determined that enclose each individual vertebral body. Bound by these constraints viscous deformable models are used to segment the main shape of the vertebral bodies. Volume growing and morphological operations then capture the fine details of the bone-soft tissue interface. In the volumes of interest bone mineral density and content are determined. In addition, in the segmented vertebral bodies geometric parameters such as volume or the length of the main axes of inertia can be measured. Intra- and inter-operator precision errors of the segmentation procedure were analyzed using existing clinical patient datasets. Results for segmented volume, BMD, and coordinate system position were below 2.0%, 0.6%, and 0.7%, respectively. Trueness was analyzed using phantom scans. The bias of the segmented volume was below 4%; for BMD it was below 1.5%. The long-term goal of this work is improved fracture prediction and patient monitoring in the field of osteoporosis. A true 3D segmentation also enables an accurate measurement of geometrical parameters that may augment the clinical value of a pure BMD analysis.

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

    Science.gov (United States)

    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

  16. A Comparison of X-Ray Image Segmentation Techniques

    Directory of Open Access Journals (Sweden)

    STOLOJESCU-CRISAN, C.

    2013-08-01

    Full Text Available Image segmentation operation has a great importance in most medical imaging applications, by extracting anatomical structures from medical images. There are many image segmentation techniques available in the literature, each of them having advantages and disadvantages. The extraction of bone contours from X-ray images has received a considerable amount of attention in the literature recently, because they represent a vital step in the computer analysis of this kind of images. The aim of X-ray segmentation is to subdivide the image in various portions, so that it can help doctors during the study of the bone structure, for the detection of fractures in bones, or for planning the treatment before surgery. The goal of this paper is to review the most important image segmentation methods starting from a data base composed by real X-ray images. We will discuss the principle and the mathematical model for each method, highlighting the strengths and weaknesses.

  17. [Segmentation Method for Liver Organ Based on Image Sequence Context].

    Science.gov (United States)

    Zhang, Meiyun; Fang, Bin; Wang, Yi; Zhong, Nanchang

    2015-10-01

    In view of the problems of more artificial interventions and segmentation defects in existing two-dimensional segmentation methods and abnormal liver segmentation errors in three-dimensional segmentation methods, this paper presents a semi-automatic liver organ segmentation method based on the image sequence context. The method takes advantage of the existing similarity between the image sequence contexts of the prior knowledge of liver organs, and combines region growing and level set method to carry out semi-automatic segmentation of livers, along with the aid of a small amount of manual intervention to deal with liver mutation situations. The experiment results showed that the liver segmentation algorithm presented in this paper had a high precision, and a good segmentation effect on livers which have greater variability, and can meet clinical application demands quite well.

  18. Fuzzy entropy image segmentation based on particle Swarm optimization

    Institute of Scientific and Technical Information of China (English)

    Linyi Li; Deren Li

    2008-01-01

    Partide swaFnl optimization is a stochastic global optimization algorithm that is based on swarm intelligence.Because of its excellent performance,particle swarm optimization is introduced into fuzzy entropy image segmentation to select the optimal fuzzy parameter combination and fuzzy threshold adaptively.In this study,the particles in the swarm are constructed and the swarm search strategy is proposed to meet the needs of the segmentation application.Then fuzzy entropy image segmentation based on particle swarm opti-mization is implemented and the proposed method obtains satisfactory results in the segmentation experiments.Compared with the exhaustive search method,particle swarm optimization can give the salne optimal fuzzy parameter combination and fuzzy threshold while needing less search time in the segmentation experiments and also has good search stability in the repeated experiments.Therefore,fuzzy entropy image segmentation based on particle swarm optimization is an efficient and promising segmentation method.

  19. General Purpose Segmentation for Microorganisms in Microscopy Images

    DEFF Research Database (Denmark)

    Jensen, Sebastian H. Nesgaard; Moeslund, Thomas B.; Rankl, Christian

    2014-01-01

    In this paper, we propose an approach for achieving generalized segmentation of microorganisms in mi- croscopy images. It employs a pixel-wise classification strategy based on local features. Multilayer percep- trons are utilized for classification of the local features and is trained for each...... specific segmentation problem using supervised learning. This approach was tested on five different segmentation problems in bright field, differential interference contrast, fluorescence and laser confocal scanning microscopy. In all instance good results were achieved with the segmentation quality...

  20. Medical image segmentation using 3D MRI data

    Science.gov (United States)

    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.

  1. Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams.

    Science.gov (United States)

    Adams, Roy J; Saleheen, Nazir; Thomaz, Edison; Parate, Abhinav; Kumar, Santosh; Marlin, Benjamin M

    2016-06-01

    The field of mobile health (mHealth) has the potential to yield new insights into health and behavior through the analysis of continuously recorded data from wearable health and activity sensors. In this paper, we present a hierarchical span-based conditional random field model for the key problem of jointly detecting discrete events in such sensor data streams and segmenting these events into high-level activity sessions. Our model includes higher-order cardinality factors and inter-event duration factors to capture domain-specific structure in the label space. We show that our model supports exact MAP inference in quadratic time via dynamic programming, which we leverage to perform learning in the structured support vector machine framework. We apply the model to the problems of smoking and eating detection using four real data sets. Our results show statistically significant improvements in segmentation performance relative to a hierarchical pairwise CRF.

  2. An Automatic Cognitive Graph-Based Segmentation for Detection of Blood Vessels in Retinal Images

    Directory of Open Access Journals (Sweden)

    Rasha Al Shehhi

    2016-01-01

    Full Text Available This paper presents a hierarchical graph-based segmentation for blood vessel detection in digital retinal images. This segmentation employs some of perceptual Gestalt principles: similarity, closure, continuity, and proximity to merge segments into coherent connected vessel-like patterns. The integration of Gestalt principles is based on object-based features (e.g., color and black top-hat (BTH morphology and context and graph-analysis algorithms (e.g., Dijkstra path. The segmentation framework consists of two main steps: preprocessing and multiscale graph-based segmentation. Preprocessing is to enhance lighting condition, due to low illumination contrast, and to construct necessary features to enhance vessel structure due to sensitivity of vessel patterns to multiscale/multiorientation structure. Graph-based segmentation is to decrease computational processing required for region of interest into most semantic objects. The segmentation was evaluated on three publicly available datasets. Experimental results show that preprocessing stage achieves better results compared to state-of-the-art enhancement methods. The performance of the proposed graph-based segmentation is found to be consistent and comparable to other existing methods, with improved capability of detecting small/thin vessels.

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

    Science.gov (United States)

    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.

  4. FULLY AUTOMATIC FRAMEWORK FOR SEGMENTATION OF BRAIN MRI IMAGE

    Institute of Scientific and Technical Information of China (English)

    Lin Pan; Zheng Chongxun; Yang Yong; Gu Jianwen

    2005-01-01

    Objective To propose an automatic framework for segmentation of brain image in this paper. Methods The brain MRI image segmentation framework consists of three-step segmentation procedures. First, Non-brain structures removal by level set method. Then, the non-uniformity correction method is based on computing estimates of tissue intensity variation. Finally, it uses a statistical model based on Markov random filed for MRI brain image segmentation. The brain tissue can be classified into cerebrospinal fluid, white matter and gray matter. Results To evaluate the proposed our method, we performed two sets of experiments, one on simulated MR and another on real MR brain data. Conclusion The efficacy of the brain MRI image segmentation framework has been demonstrated by the extensive experiments. In the future, we are also planning on a large-scale clinical evaluation of this segmentation framework.

  5. Segmentation of knee injury swelling on infrared images

    Science.gov (United States)

    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.

  6. Image mosaic method based on SIFT features of line segment.

    Science.gov (United States)

    Zhu, Jun; Ren, Mingwu

    2014-01-01

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

  7. Precise acquisition and unsupervised segmentation of multi-spectral images

    DEFF Research Database (Denmark)

    Gomez, David Delgado; Clemmensen, Line Katrine Harder; Ersbøll, Bjarne Kjær

    2007-01-01

    In this work, an integrated imaging system to obtain accurate and reproducible multi-spectral images and a novel multi-spectral image segmentation algorithm are proposed. The system collects up to 20 different spectral bands within a range that vary from 395 nm to 970 nm. The system is designed...... to acquire geometrically and chromatically corrected images in homogeneous and diffuse illumination, so images can be compared over time. The proposed segmentation algorithm combines the information provided by all the spectral bands to segment the different regions of interest. Three experiments...

  8. Deformable meshes for medical image segmentation accurate automatic segmentation of anatomical structures

    CERN Document Server

    Kainmueller, Dagmar

    2014-01-01

    ? Segmentation of anatomical structures in medical image data is an essential task in clinical practice. Dagmar Kainmueller introduces methods for accurate fully automatic segmentation of anatomical structures in 3D medical image data. The author's core methodological contribution is a novel deformation model that overcomes limitations of state-of-the-art Deformable Surface approaches, hence allowing for accurate segmentation of tip- and ridge-shaped features of anatomical structures. As for practical contributions, she proposes application-specific segmentation pipelines for a range of anatom

  9. Image Segmentation and Denoising Based on Shrira-Pesenson Equation

    Science.gov (United States)

    Pesenson, M.; Moshir, M.; Makovoz, D.; Frayer, D.; Henderson, D.

    2005-12-01

    We propose a nonlinear partial differential equation to control the trade-off between smoothing and segmentation of images. Its solutions approximate discontinuities, thus leading to detection of sharp boundaries in images. The performance of the approach is evaluated by applying it to images obtained by the Multiband Imaging Photometer for Spitzer (MIPS), 70 micron imaging band.

  10. Optimization of Segmentation Quality of Integrated Circuit Images

    Directory of Open Access Journals (Sweden)

    Gintautas Mušketas

    2012-04-01

    Full Text Available The paper presents investigation into the application of genetic algorithms for the segmentation of the active regions of integrated circuit images. This article is dedicated to a theoretical examination of the applied methods (morphological dilation, erosion, hit-and-miss, threshold and describes genetic algorithms, image segmentation as optimization problem. The genetic optimization of the predefined filter sequence parameters is carried out. Improvement to segmentation accuracy using a non optimized filter sequence makes 6%.Artcile in Lithuanian

  11. A bio-inspired software for segmenting digital images.

    OpenAIRE

    Díaz Pernil, Daniel; Molina Abril, Helena; Real Jurado, Pedro; Gutiérrez Naranjo, Miguel Ángel

    2010-01-01

    Segmentation in computer vision refers to the process of partitioning a digital image into multiple segments (sets of pixels). It has several features which make it suitable for techniques inspired by nature. It can be parallelized, locally solved and the input data can be easily encoded by bio-inspired representations. In this paper, we present a new software for performing a segmentation of 2D digital images based on Membrane Computing techniques.

  12. A new level set model for cell image segmentation

    Science.gov (United States)

    Ma, Jing-Feng; Hou, Kai; Bao, Shang-Lian; Chen, Chun

    2011-02-01

    In this paper we first determine three phases of cell images: background, cytoplasm and nucleolus according to the general physical characteristics of cell images, and then develop a variational model, based on these characteristics, to segment nucleolus and cytoplasm from their relatively complicated backgrounds. In the meantime, the preprocessing obtained information of cell images using the OTSU algorithm is used to initialize the level set function in the model, which can speed up the segmentation and present satisfactory results in cell image processing.

  13. A new level set model for cell image segmentation

    Institute of Scientific and Technical Information of China (English)

    Ma Jing-Feng; Hou Kai; Bao Shang-Lian; Chen Chun

    2011-01-01

    In this paper we first determine three phases of cell images: background, cytoplasm and nucleolus according to the general physical characteristics of cell images, and then develop a variational model, based on these characteristics, to segment nucleolus and cytoplasm from their relatively complicated backgrounds. In the meantime, the preprocessing obtained information of cell images using the OTSU algorithm is used to initialize the level set function in the model, which can speed up the segmentation and present satisfactory results in cell image processing.

  14. Adaptive image segmentation applied to plant reproduction by tissue culture

    Science.gov (United States)

    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.

  15. Color Image Segmentation via Improved K-Means Algorithm

    Directory of Open Access Journals (Sweden)

    Ajay Kumar

    2016-03-01

    Full Text Available Data clustering techniques are often used to segment the real world images. Unsupervised image segmentation algorithms that are based on the clustering suffer from random initialization. There is a need for efficient and effective image segmentation algorithm, which can be used in the computer vision, object recognition, image recognition, or compression. To address these problems, the authors present a density-based initialization scheme to segment the color images. In the kernel density based clustering technique, the data sample is mapped to a high-dimensional space for the effective data classification. The Gaussian kernel is used for the density estimation and for the mapping of sample image into a high- dimensional color space. The proposed initialization scheme for the k-means clustering algorithm can homogenously segment an image into the regions of interest with the capability of avoiding the dead centre and the trapped centre by local minima phenomena. The performance of the experimental result indicates that the proposed approach is more effective, compared to the other existing clustering-based image segmentation algorithms. In the proposed approach, the Berkeley image database has been used for the comparison analysis with the recent clustering-based image segmentation algorithms like k-means++, k-medoids and k-mode.

  16. A fast and efficient segmentation scheme for cell microscopic image.

    Science.gov (United States)

    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.

  17. Two-level hierarchical feature learning for image classification

    Institute of Scientific and Technical Information of China (English)

    Guang-hui SONG; Xiao-gang JIN; Gen-lang CHEN; Yan NIE

    2016-01-01

    In some image classifi cation tasks, similarities among different categories are different and the samples are usually misclassifi ed as highly similar categories. To distinguish highly similar categories, more specifi c features are required so that the classifi er can improve the classifi cation performance. In this paper, we propose a novel two-level hierarchical feature learning framework based on the deep convolutional neural network (CNN), which is simple and effective. First, the deep feature extractors of different levels are trained using the transfer learning method that fi ne-tunes the pre-trained deep CNN model toward the new target dataset. Second, the general feature extracted from all the categories and the specifi c feature extracted from highly similar categories are fused into a feature vector. Then the fi nal feature representation is fed into a linear classifi er. Finally, experiments using the Caltech-256, Oxford Flower-102, and Tasmania Coral Point Count (CPC) datasets demonstrate that the expression ability of the deep features resulting from two-level hierarchical feature learning is powerful. Our proposed method effectively increases the classifi cation accuracy in comparison with fl at multiple classifi cation methods.

  18. Content Based Image Retrieval using Hierarchical and K-Means Clustering Techniques

    Directory of Open Access Journals (Sweden)

    V.S.V.S. Murthy

    2010-03-01

    Full Text Available In this paper we present an image retrieval system that takes an image as the input query and retrieves images based on image content. Content Based Image Retrieval is an approach for retrieving semantically-relevant images from an image database based on automatically-derived image features. The unique aspect of the system is the utilization of hierarchical and k-means clustering techniques. The proposed procedure consists of two stages. First, here we are going to filter most of the images in the hierarchical clustering and then apply the clustered images to KMeans, so that we can get better favored image results.

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

    Science.gov (United States)

    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.

  20. Segmentation of Natural Images by Texture and Boundary Compression

    CERN Document Server

    Mobahi, Hossein; Yang, Allen Y; Sastry, Shankar S; Ma, Yi

    2010-01-01

    We present a novel algorithm for segmentation of natural images that harnesses the principle of minimum description length (MDL). Our method is based on observations that a homogeneously textured region of a natural image can be well modeled by a Gaussian distribution and the region boundary can be effectively coded by an adaptive chain code. The optimal segmentation of an image is the one that gives the shortest coding length for encoding all textures and boundaries in the image, and is obtained via an agglomerative clustering process applied to a hierarchy of decreasing window sizes as multi-scale texture features. The optimal segmentation also provides an accurate estimate of the overall coding length and hence the true entropy of the image. We test our algorithm on the publicly available Berkeley Segmentation Dataset. It achieves state-of-the-art segmentation results compared to other existing methods.

  1. GPU accelerated fuzzy connected image segmentation by using CUDA.

    Science.gov (United States)

    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.

  2. Cellular image segmentation using n-agent cooperative game theory

    Science.gov (United States)

    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.

  3. Fingerprint Image Segmentation Using Haar Wavelet and Self Organizing Map

    Directory of Open Access Journals (Sweden)

    Sri Suwarno

    2013-10-01

    Full Text Available Fingerprint image segmentation is one of the important preprocessing steps in Automatic Fingerprint Identification Systems (AFIS. Segmentation separates image background from image foreground, removing unnecessary information from the image. This paper proposes a new fingerprint segmentation method using Haar wavelet and Kohonen’s Self Organizing Map (SOM. Fingerprint image was decomposed using 2D Haar wavelet in two levels. To generate features vectors, the decomposed image was divided into nonoverlapping blocks of 2x2 pixels and converted into four elements vectors. These vectors were then fed into SOM network that grouped them into foreground and background clusters. Finally, blocks in the background area were removed based on indexes of blocks in the background cluster. From the research that has been carried out, we conclude that the proposed method is effective to segment background from fingerprint images.

  4. Flotation bubble image segmentation based on seed region boundary growing

    Institute of Scientific and Technical Information of China (English)

    Zhang Guoying; Zhu Hong; Xu Ning

    2011-01-01

    Segmenting blurred and conglutinated bubbles in a flotation image is done using a new segmentation method based on Seed Region and Boundary Growing (SRBG). Bright pixels located on bubble tops were extracted as the seed regions. Seed boundaries are divided into four curves: left-top, right-top, rightbottom, and left-bottom. Bubbles are segmented from the seed boundary by moving these curves to the bubble boundaries along the corresponding directions. The SRBG method can remove noisy areas and it avoids over- and under-segmentation problems. Each bubble is segmented separately rather than segmenting the entire flotation image. The segmentation results from the SRBG method are more accurate than those from the Watershed algorithm.

  5. Graph Based Segmentation in Content Based Image Retrieval

    Directory of Open Access Journals (Sweden)

    P. S. Suhasini

    2008-01-01

    Full Text Available Problem statement: Traditional image retrieval systems are content based image retrieval systems which rely on low-level features for indexing and retrieval of images. CBIR systems fail to meet user expectations because of the gap between the low level features used by such systems and the high level perception of images by humans. To meet the requirement as a preprocessing step Graph based segmentation is used in Content Based Image Retrieval (CBIR. Approach: Graph based segmentation is has the ability to preserve detail in low-variability image regions while ignoring detail in high-variability regions. After segmentation the features are extracted for the segmented images, texture features using wavelet transform and color features using histogram model and the segmented query image features are compared with the features of segmented data base images. The similarity measure used for texture features is Euclidean distance measure and for color features Quadratic distance approach. Results: The experimental results demonstrate about 12% improvement in the performance for color feature with segmentation. Conclusions/Recommendations: Along with this improvement Neural network learning can be embedded in this system to reduce the semantic gap.

  6. Segmentation of stochastic images with a stochastic random walker method.

    Science.gov (United States)

    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.

  7. A Latent Source Model for Patch-Based Image Segmentation.

    Science.gov (United States)

    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.

  8. A Novel Multiresolution Fuzzy Segmentation Method on MR Image

    Institute of Scientific and Technical Information of China (English)

    ZHANG HongMei(张红梅); BIAN ZhengZhong(卞正中); YUAN ZeJian(袁泽剑); YE Min(叶敏); JI Feng(冀峰)

    2003-01-01

    Multiresolution-based magnetic resonance (MR) image segmentation has attractedattention for its ability to capture rich information across scales compared with the conventionalsegmentation methods. In this paper, a new scale-space-based segmentation model is presented,where both the intra-scale and inter-scale properties are considered and formulated as two fuzzyenergy functions. Meanwhile, a control parameter is introduced to adjust the contribution of thesimilarity character across scales and the clustering character within the scale. By minimizing thecombined inter/intra energy function, the multiresolution fuzzy segmentation algorithm is derived.Then the coarse to fine leading segmentation is performed automatically and iteratively on a set ofmultiresolution images. The validity of the proposed algorithm is demonstrated by the test imageand pathological MR images. Experiments show that by this approach the segmentation results,especially in the tumor area delineation, are more precise than those of the conventional fuzzy segmentation methods.

  9. Image segmentation and selective smoothing by using Mumford-Shah model.

    Science.gov (United States)

    Gao, Song; Bui, Tien D

    2005-10-01

    Recently, Chan and Vese developed an active contour model for image segmentation and smoothing by using piecewise constant and smooth representation of an image. Tsai et al. also independently developed a segmentation and smoothing method similar to the Chan and Vese piecewise smooth approach. These models are active contours based on the Mumford-Shah variational approach and the level-set method. In this paper, we develop a new hierarchical method which has many advantages compared to the Chan and Vese multiphase active contour models. First, unlike previous works, the curve evolution partial differential equations (PDEs) for different level-set functions are decoupled. Each curve evolution PDE is the equation of motion of just one level-set function, and different level-set equations of motion are solved in a hierarchy. This decoupling of the motion equations of the level-set functions speeds up the segmentation process significantly. Second, because of the coupling of the curve evolution equations associated with different level-set functions, the initialization of the level sets in Chan and Vese's method is difficult to handle. In fact, different initial conditions may produce completely different results. The hierarchical method proposed in this paper can avoid the problem due to the choice of initial conditions. Third, in this paper, we use the diffusion equation for denoising. This method, therefore, can deal with very noisy images. In general, our method is fast, flexible, not sensitive to the choice of initial conditions, and produces very good results.

  10. Automatic segmentation of HeLa cell images

    CERN Document Server

    Urban, Jan

    2011-01-01

    In this work, the possibilities for segmentation of cells from their background and each other in digital image were tested, combined and improoved. Lot of images with young, adult and mixture cells were able to prove the quality of described algorithms. Proper segmentation is one of the main task of image analysis and steps order differ from work to work, depending on input images. Reply for biologicaly given question was looking for in this work, including filtration, details emphasizing, segmentation and sphericity computing. Order of algorithms and way to searching for them was also described. Some questions and ideas for further work were mentioned in the conclusion part.

  11. AUTOMATIC MULTILEVEL IMAGE SEGMENTATION BASED ON FUZZY REASONING

    Directory of Open Access Journals (Sweden)

    Liang Tang

    2011-05-01

    Full Text Available An automatic multilevel image segmentation method based on sup-star fuzzy reasoning (SSFR is presented. Using the well-known sup-star fuzzy reasoning technique, the proposed algorithm combines the global statistical information implied in the histogram with the local information represented by the fuzzy sets of gray-levels, and aggregates all the gray-levels into several classes characterized by the local maximum values of the histogram. The presented method has the merits of determining the number of the segmentation classes automatically, and avoiding to calculating thresholds of segmentation. Emulating and real image segmentation experiments demonstrate that the SSFR is effective.

  12. Object Recognition Algorithm Utilizing Graph Cuts Based Image Segmentation

    Directory of Open Access Journals (Sweden)

    Zhaofeng Li

    2014-02-01

    Full Text Available This paper concentrates on designing an object recognition algorithm utilizing image segmentation. The main innovations of this paper lie in that we convert the image segmentation problem into graph cut problem, and then the graph cut results can be obtained by calculating the probability of intensity for a given pixel which is belonged to the object and the background intensity. After the graph cut process, the pixels in a same component are similar, and the pixels in different components are dissimilar. To detect the objects in the test image, the visual similarity between the segments of the testing images and the object types deduced from the training images is estimated. Finally, a series of experiments are conducted to make performance evaluation. Experimental results illustrate that compared with existing methods, the proposed scheme can effectively detect the salient objects. Particularly, we testify that, in our scheme, the precision of object recognition is proportional to image segmentation accuracy

  13. Variational segmentation problems using prior knowledge in imaging and vision

    DEFF Research Database (Denmark)

    Fundana, Ketut

    -manifold of pose-invariant planar contours into both the Chan-Vese model and its convex formulation to segment an object of interest in a sequence of images. We apply the models to track the viewpoint onto 3D rigid object. The prior-based object segmentation models encounter the problem of shape alignment, where......This dissertation addresses variational formulation of segmentation problems using prior knowledge. Variational models are among the most successful approaches for solving many Computer Vision and Image Processing problems. The models aim at finding the solution to a given energy functional defined...... to describe a Computer Vision task through energy minimization. Image segmentation, as an ill-posed problem, is still a major challenge in Computer Vision. Due to the presence of noise, clutter and occlusion, the use of image information alone often gives poor segmentation results. To overcome this problem...

  14. Multi-resolution image segmentation based on Gaussian mixture model

    Institute of Scientific and Technical Information of China (English)

    Tang Yinggan; Liu Dong; Guan Xinping

    2006-01-01

    Mixture model based image segmentation method, which assumes that image pixels are independent and do not consider the position relationship between pixels, is not robust to noise and usually leads to misclassification. A new segmentation method, called multi-resolution Gaussian mixture model method, is proposed. First, an image pyramid is constructed and son-father link relationship is built between each level of pyramid. Then the mixture model segmentation method is applied to the top level. The segmentation result on the top level is passed top-down to the bottom level according to the son-father link relationship between levels. The proposed method considers not only local but also global information of image, it overcomes the effect of noise and can obtain better segmentation result. Experimental result demonstrates its effectiveness.

  15. Fast spectral color image segmentation based on filtering and clustering

    Science.gov (United States)

    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. Multiscale Segmentation of Polarimetric SAR Image Based on Srm Superpixels

    Science.gov (United States)

    Lang, F.; Yang, J.; Wu, L.; Li, D.

    2016-06-01

    Multi-scale segmentation of remote sensing image is more systematic and more convenient for the object-oriented image analysis compared to single-scale segmentation. However, the existing pixel-based polarimetric SAR (PolSAR) image multi-scale segmentation algorithms are usually inefficient and impractical. In this paper, we proposed a superpixel-based binary partition tree (BPT) segmentation algorithm by combining the generalized statistical region merging (GSRM) algorithm and the BPT algorithm. First, superpixels are obtained by setting a maximum region number threshold to GSRM. Then, the region merging process of the BPT algorithm is implemented based on superpixels but not pixels. The proposed algorithm inherits the advantages of both GSRM and BPT. The operation efficiency is obviously improved compared to the pixel-based BPT segmentation. Experiments using the Lband ESAR image over the Oberpfaffenhofen test site proved the effectiveness of the proposed method.

  17. Image segmentation on adaptive edge-preserving smoothing

    Science.gov (United States)

    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.

  18. Comparison of Model-Based Segmentation Algorithms for Color Images.

    Science.gov (United States)

    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. Real-time planar segmentation of depth images: from three-dimensional edges to segmented planes

    Science.gov (United States)

    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.

  20. IMAGE SEGMENTATION BASED ON MARKOV RANDOM FIELD AND WATERSHED TECHNIQUES

    Institute of Scientific and Technical Information of China (English)

    纳瑟; 刘重庆

    2002-01-01

    This paper presented a method that incorporates Markov Random Field(MRF), watershed segmentation and merging techniques for performing image segmentation and edge detection tasks. MRF is used to obtain an initial estimate of x regions in the image under process where in MRF model, gray level x, at pixel location i, in an image X, depends on the gray levels of neighboring pixels. The process needs an initial segmented result. An initial segmentation is got based on K-means clustering technique and the minimum distance, then the region process in modeled by MRF to obtain an image contains different intensity regions. Starting from this we calculate the gradient values of that image and then employ a watershed technique. When using MRF method it obtains an image that has different intensity regions and has all the edge and region information, then it improves the segmentation result by superimpose closed and an accurate boundary of each region using watershed algorithm. After all pixels of the segmented regions have been processed, a map of primitive region with edges is generated. Finally, a merge process based on averaged mean values is employed. The final segmentation and edge detection result is one closed boundary per actual region in the image.

  1. Graph run-length matrices for histopathological image segmentation.

    Science.gov (United States)

    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.

  2. Patch-based image segmentation of satellite imagery using minimum spanning tree construction

    Energy Technology Data Exchange (ETDEWEB)

    Skurikhin, Alexei N [Los Alamos National Laboratory

    2010-01-01

    We present a method for hierarchical image segmentation and feature extraction. This method builds upon the combination of the detection of image spectral discontinuities using Canny edge detection and the image Laplacian, followed by the construction of a hierarchy of segmented images of successively reduced levels of details. These images are represented as sets of polygonized pixel patches (polygons) attributed with spectral and structural characteristics. This hierarchy forms the basis for object-oriented image analysis. To build fine level-of-detail representation of the original image, seed partitions (polygons) are built upon a triangular mesh composed of irregular sized triangles, whose spatial arrangement is adapted to the image content. This is achieved by building the triangular mesh on the top of the detected spectral discontinuities that form a network of constraints for the Delaunay triangulation. A polygonized image is represented as a spatial network in the form of a graph with vertices which correspond to the polygonal partitions and graph edges reflecting pairwise partitions relations. Image graph partitioning is based on the iterative graph oontraction using Boruvka's Minimum Spanning Tree algorithm. An important characteristic of the approach is that the agglomeration of partitions is constrained by the detected spectral discontinuities; thus the shapes of agglomerated partitions are more likely to correspond to the outlines of real-world objects.

  3. FCM Clustering Algorithms for Segmentation of Brain MR Images

    Directory of Open Access Journals (Sweden)

    Yogita K. Dubey

    2016-01-01

    Full Text Available The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF, Gray Matter (GM, and White Matter (WM, has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzy c-means (FCM clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.

  4. Unsupervised texture image segmentation using multilayer data condensation spectral clustering

    Science.gov (United States)

    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.

  5. Gaussian Mixture Model and Rjmcmc Based RS Image Segmentation

    Science.gov (United States)

    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.

  6. Hierarchical image feature extraction by an irregular pyramid of polygonal partitions

    Energy Technology Data Exchange (ETDEWEB)

    Skurikhin, Alexei N [Los Alamos National Laboratory

    2008-01-01

    We present an algorithmic framework for hierarchical image segmentation and feature extraction. We build a successive fine-to-coarse hierarchy of irregular polygonal partitions of the original image. This multiscale hierarchy forms the basis for object-oriented image analysis. The framework incorporates the Gestalt principles of visual perception, such as proximity and closure, and exploits spectral and textural similarities of polygonal partitions, while iteratively grouping them until dissimilarity criteria are exceeded. Seed polygons are built upon a triangular mesh composed of irregular sized triangles, whose spatial arrangement is adapted to the image content. This is achieved by building the triangular mesh on the top of detected spectral discontinuities (such as edges), which form a network of constraints for the Delaunay triangulation. The image is then represented as a spatial network in the form of a graph with vertices corresponding to the polygonal partitions and edges reflecting their relations. The iterative agglomeration of partitions into object-oriented segments is formulated as Minimum Spanning Tree (MST) construction. An important characteristic of the approach is that the agglomeration of polygonal partitions is constrained by the detected edges; thus the shapes of agglomerated partitions are more likely to correspond to the outlines of real-world objects. The constructed partitions and their spatial relations are characterized using spectral, textural and structural features based on proximity graphs. The framework allows searching for object-oriented features of interest across multiple levels of details of the built hierarchy and can be generalized to the multi-criteria MST to account for multiple criteria important for an application.

  7. The Watershed Algorithm for Image Segmentation

    Institute of Scientific and Technical Information of China (English)

    OU Yan; LIN Nan

    2007-01-01

    This article introduced the watershed algorithm for the segmentation, illustrated the segmation process by implementing this algorithm. By comparing with another three related algorithm, this article revealed both the advantages and drawbacks of the watershed algorithm.

  8. Multilevel segmentation of intracranial aneurysms in CT angiography images

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Yan [Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California 94122 and University of Lyon, CREATIS, CNRS UMR 5220, INSERM U1206, UCB Lyon1, INSA Lyon, Lyon 69100 (France); Zhang, Yue, E-mail: y.zhang525@gmail.com [Veterans Affairs Medical Center, San Francisco, California 94121 and University of Lyon, CREATIS, CNRS UMR 5220, INSERM U1206, UCB Lyon1, INSA Lyon, Lyon 69100 (France); Navarro, Laurent [Ecole Nationale Superieure des Mines de Saint-Etienne, Saint-Etienne 42015 (France); Eker, Omer Faruk [CHU Montpellier, Neuroradiologie, Montpellier 34000 (France); Corredor Jerez, Ricardo A. [Ecole Polytechnique Federale de Lausanne, Lausanne 1015 (Switzerland); Chen, Yu; Zhu, Yuemin; Courbebaisse, Guy [University of Lyon, CREATIS, CNRS UMR 5220, INSERM U1206, UCB Lyon1, INSA Lyon, Lyon 69100 (France)

    2016-04-15

    Purpose: Segmentation of aneurysms plays an important role in interventional planning. Yet, the segmentation of both the lumen and the thrombus of an intracranial aneurysm in computed tomography angiography (CTA) remains a challenge. This paper proposes a multilevel segmentation methodology for efficiently segmenting intracranial aneurysms in CTA images. Methods: The proposed methodology first uses the lattice Boltzmann method (LBM) to extract the lumen part directly from the original image. Then, the LBM is applied again on an intermediate image whose lumen part is filled by the mean gray-level value outside the lumen, to yield an image region containing part of the aneurysm boundary. After that, an expanding disk is introduced to estimate the complete contour of the aneurysm. Finally, the contour detected is used as the initial contour of the level set with ellipse to refine the aneurysm. Results: The results obtained on 11 patients from different hospitals showed that the proposed segmentation was comparable with manual segmentation, and that quantitatively, the average segmentation matching factor (SMF) reached 86.99%, demonstrating good segmentation accuracy. Chan–Vese method, Sen’s model, and Luca’s model were used to compare the proposed method and their average SMF values were 39.98%, 40.76%, and 77.11%, respectively. Conclusions: The authors have presented a multilevel segmentation method based on the LBM and level set with ellipse for accurate segmentation of intracranial aneurysms. Compared to three existing methods, for all eleven patients, the proposed method can successfully segment the lumen with the highest SMF values for nine patients and second highest SMF values for the two. It also segments the entire aneurysm with the highest SMF values for ten patients and second highest SMF value for the one. This makes it potential for clinical assessment of the volume and aspect ratio of the intracranial aneurysms.

  9. COMPARISON OF DIFFERENT SEGMENTATION ALGORITHMS FOR DERMOSCOPIC IMAGES

    Directory of Open Access Journals (Sweden)

    A.A. Haseena Thasneem

    2015-05-01

    Full Text Available This paper compares different algorithms for the segmentation of skin lesions in dermoscopic images. The basic segmentation algorithms compared are Thresholding techniques (Global and Adaptive, Region based techniques (K-means, Fuzzy C means, Expectation Maximization and Statistical Region Merging, Contour models (Active Contour Model and Chan - Vese Model and Spectral Clustering. Accuracy, sensitivity, specificity, Border error, Hammoude distance, Hausdorff distance, MSE, PSNR and elapsed time metrices were used to evaluate various segmentation techniques.

  10. Unsupervised Neural Techniques Applied to MR Brain Image Segmentation

    Directory of Open Access Journals (Sweden)

    A. Ortiz

    2012-01-01

    Full Text Available The primary goal of brain image segmentation is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI segmentation is especially interesting, since accurate segmentation in white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders such as dementia, schizophrenia or Alzheimer’s disease (AD. Then, image segmentation results in a very interesting tool for neuroanatomical analyses. In this paper we show three alternatives to MR brain image segmentation algorithms, with the Self-Organizing Map (SOM as the core of the algorithms. The procedures devised do not use any a priori knowledge about voxel class assignment, and results in fully-unsupervised methods for MRI segmentation, making it possible to automatically discover different tissue classes. Our algorithm has been tested using the images from the Internet Brain Image Repository (IBSR outperforming existing methods, providing values for the average overlap metric of 0.7 for the white and grey matter and 0.45 for the cerebrospinal fluid. Furthermore, it also provides good results for high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain.

  11. A Bayesian Approach for Image Segmentation with Shape Priors

    Energy Technology Data Exchange (ETDEWEB)

    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.

  12. CT image segmentation using FEM with optimized boundary condition.

    Directory of Open Access Journals (Sweden)

    Hiroyuki Hishida

    Full Text Available The authors propose a CT image segmentation method using structural analysis that is useful for objects with structural dynamic characteristics. Motivation of our research is from the area of genetic activity. In order to reveal the roles of genes, it is necessary to create mutant mice and measure differences among them by scanning their skeletons with an X-ray CT scanner. The CT image needs to be manually segmented into pieces of the bones. It is a very time consuming to manually segment many mutant mouse models in order to reveal the roles of genes. It is desirable to make this segmentation procedure automatic. Although numerous papers in the past have proposed segmentation techniques, no general segmentation method for skeletons of living creatures has been established. Against this background, the authors propose a segmentation method based on the concept of destruction analogy. To realize this concept, structural analysis is performed using the finite element method (FEM, as structurally weak areas can be expected to break under conditions of stress. The contribution of the method is its novelty, as no studies have so far used structural analysis for image segmentation. The method's implementation involves three steps. First, finite elements are created directly from the pixels of a CT image, and then candidates are also selected in areas where segmentation is thought to be appropriate. The second step involves destruction analogy to find a single candidate with high strain chosen as the segmentation target. The boundary conditions for FEM are also set automatically. Then, destruction analogy is implemented by replacing pixels with high strain as background ones, and this process is iterated until object is decomposed into two parts. Here, CT image segmentation is demonstrated using various types of CT imagery.

  13. A comparative study of Image Region-Based Segmentation Algorithms

    Directory of Open Access Journals (Sweden)

    Lahouaoui LALAOUI

    2013-07-01

    Full Text Available Image segmentation has recently become an essential step in image processing as it mainly conditions the interpretation which is done afterwards. It is still difficult to justify the accuracy of a segmentation algorithm, regardless of the nature of the treated image. In this paper we perform an objective comparison of region-based segmentation techniques such as supervised and unsupervised deterministic classification, non-parametric and parametric probabilistic classification. Eight methods among the well-known and used in the scientific community have been selected and compared. The Martin’s(GCE, LCE, probabilistic Rand Index (RI, Variation of Information (VI and Boundary Displacement Error (BDE criteria are used to evaluate the performance of these algorithms on Magnetic Resonance (MR brain images, synthetic MR image, and synthetic images. MR brain image are composed of the gray matter (GM, white matter (WM and cerebrospinal fluid (CSF and others, and the synthetic MR image composed of the same for real image and the plus edema, and the tumor. Results show that segmentation is an image dependent process and that some of the evaluated methods are well suited for a better segmentation.

  14. Remote Sensing Image Registration with Line Segments and Their Intersections

    Directory of Open Access Journals (Sweden)

    Chengjin Lyu

    2017-05-01

    Full Text Available Image registration is a basic but essential step for remote sensing image processing, and finding stable features in multitemporal images is one of the most considerable challenges in the field. The main shape contours of artificial objects (e.g., roads, buildings, farmlands, and airports can be generally described as a group of line segments, which are stable features, even in images with evident background changes (e.g., images taken before and after a disaster. In this study, a registration method that uses line segments and their intersections is proposed for multitemporal remote sensing images. First, line segments are extracted in image pyramids to unify the scales of the reference image and the test image. Then, a line descriptor based on the gradient distribution of local areas is constructed, and the segments are matched in image pyramids. Lastly, triplets of intersections of matching lines are selected to estimate affine transformation between two images. Additional corresponding intersections are provided based on the estimated transformation, and an iterative process is adopted to remove outliers. The performance of the proposed method is tested on a variety of optical remote sensing image pairs, including synthetic and real data. Compared with existing methods, our method can provide more accurate registration results, even in images with significant background changes.

  15. Interactive co-segmentation of objects in image collections

    CERN Document Server

    Batra, Dhruv; Parikh, Devi

    2011-01-01

    The authors survey a recent technique in computer vision called Interactive Co-segmentation, which is the task of simultaneously extracting common foreground objects from multiple related images. They survey several of the algorithms, present underlying common ideas, and give an overview of applications of object co-segmentation.

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

    Science.gov (United States)

    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.

  17. Image segmentation evaluation for very-large datasets

    Science.gov (United States)

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

    2016-03-01

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

  18. Comparison of algorithms for ultrasound image segmentation without ground truth

    Science.gov (United States)

    Sikka, Karan; Deserno, Thomas M.

    2010-02-01

    Image segmentation is a pre-requisite to medical image analysis. A variety of segmentation algorithms have been proposed, and most are evaluated on a small dataset or based on classification of a single feature. The lack of a gold standard (ground truth) further adds to the discrepancy in these comparisons. This work proposes a new methodology for comparing image segmentation algorithms without ground truth by building a matrix called region-correlation matrix. Subsequently, suitable distance measures are proposed for quantitative assessment of similarity. The first measure takes into account the degree of region overlap or identical match. The second considers the degree of splitting or misclassification by using an appropriate penalty term. These measures are shown to satisfy the axioms of a quasi-metric. They are applied for a comparative analysis of synthetic segmentation maps to show their direct correlation with human intuition of similar segmentation. Since ultrasound images are difficult to segment and usually lack a ground truth, the measures are further used to compare the recently proposed spectral clustering algorithm (encoding spatial and edge information) with standard k-means over abdominal ultrasound images. Improving the parameterization and enlarging the feature space for k-means steadily increased segmentation quality to that of spectral clustering.

  19. Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution

    Directory of Open Access Journals (Sweden)

    I. Cruz-Aceves

    2013-01-01

    Full Text Available This paper presents a novel automatic image segmentation method based on the theory of active contour models and estimation of distribution algorithms. The proposed method uses the univariate marginal distribution model to infer statistical dependencies between the control points on different active contours. These contours have been generated through an alignment process of reference shape priors, in order to increase the exploration and exploitation capabilities regarding different interactive segmentation techniques. This proposed method is applied in the segmentation of the hollow core in microscopic images of photonic crystal fibers and it is also used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, respectively. Moreover, to evaluate the performance of the medical image segmentations compared to regions outlined by experts, a set of similarity measures has been adopted. The experimental results suggest that the proposed image segmentation method outperforms the traditional active contour model and the interactive Tseng method in terms of segmentation accuracy and stability.

  20. Spectral segmentation of polygonized images with normalized cuts

    Energy Technology Data Exchange (ETDEWEB)

    Matsekh, Anna [Los Alamos National Laboratory; Skurikhin, Alexei [Los Alamos National Laboratory; Rosten, Edward [UNIV OF CAMBRIDGE

    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.

  1. Skin lesion image segmentation using Delaunay Triangulation for melanoma detection.

    Science.gov (United States)

    Pennisi, Andrea; Bloisi, Domenico D; Nardi, Daniele; Giampetruzzi, Anna Rita; Mondino, Chiara; Facchiano, Antonio

    2016-09-01

    Developing automatic diagnostic tools for the early detection of skin cancer lesions in dermoscopic images can help to reduce melanoma-induced mortality. Image segmentation is a key step in the automated skin lesion diagnosis pipeline. In this paper, a fast and fully-automatic algorithm for skin lesion segmentation in dermoscopic images is presented. Delaunay Triangulation is used to extract a binary mask of the lesion region, without the need of any training stage. A quantitative experimental evaluation has been conducted on a publicly available database, by taking into account six well-known state-of-the-art segmentation methods for comparison. The results of the experimental analysis demonstrate that the proposed approach is highly accurate when dealing with benign lesions, while the segmentation accuracy significantly decreases when melanoma images are processed. This behavior led us to consider geometrical and color features extracted from the binary masks generated by our algorithm for classification, achieving promising results for melanoma detection.

  2. Segmentation techniques for extracting humans from thermal images

    CSIR Research Space (South Africa)

    Dickens, JS

    2011-11-01

    Full Text Available A pedestrian detection system for underground mine vehicles is being developed that requires the segmentation of people from thermal images in underground mine tunnels. A number of thresholding techniques are outlined and their performance on a...

  3. Software Agent with Reinforcement Learning Approach for Medical Image Segmentation

    Institute of Scientific and Technical Information of China (English)

    Mahsa Chitsaz; Chaw Seng Woo

    2011-01-01

    Many image segmentation solutions are problem-based. Medical images have very similar grey level and texture among the interested objects. Therefore, medical image segmentation requires improvements although there have been researches done since the last few decades. We design a self-learning framework to extract several objects of interest simultaneously from Computed Tomography (CT) images. Our segmentation method has a learning phase that is based on reinforcement learning (RL) system. Each RL agent works on a particular sub-image of an input image to find a suitable value for each object in it. The RL system is define by state, action and reward. We defined some actions for each state in the sub-image. A reward function computes reward for each action of the RL agent. Finally, the valuable information, from discovering all states of the interest objects, will be stored in a Q-matrix and the final result can be applied in segmentation of similar images. The experimental results for cranial CT images demonstrated segmentation accuracy above 95%.

  4. Segmentation of Sloped Roofs from Airborne LiDAR Point Clouds Using Ridge-Based Hierarchical Decomposition

    Directory of Open Access Journals (Sweden)

    Hongchao Fan

    2014-04-01

    Full Text Available This paper presents a new approach for roof facet segmentation based on ridge detection and hierarchical decomposition along ridges. The proposed approach exploits the fact that every roof can be composed of a set of gabled roofs and single facets which are separated by the gabled roofs. In this work, firstly, building footprints stored in OpenStreetMap are used to extract 3D points on roofs. Then, roofs are segmented into roof facets. The algorithm starts with detecting roof ridges using RANSAC since they are parallel to the horizon and situated on the top of the roof. The roof ridges are utilized to indicate the location and direction of the gabled roof. Thus, points on the two roof facets along a roof ridge can be identified based on their connectivity and coplanarity. The results of the segmentation benefit the further process of roof reconstruction because many parameters, including the position, angle and size of the gabled roof can be calculated and used as priori knowledge for the model-driven approach, and topologies among the point segments are made known for the data-driven approach. The algorithm has been validated in the test sites of two towns next to Bavaria Forest national park. The experimental results show that building roofs can be segmented with both high correctness and completeness simultaneously.

  5. HIERARCHICAL REGULARIZATION OF POLYGONS FOR PHOTOGRAMMETRIC POINT CLOUDS OF OBLIQUE IMAGES

    Directory of Open Access Journals (Sweden)

    L. Xie

    2017-05-01

    Full Text Available Despite the success of multi-view stereo (MVS reconstruction from massive oblique images in city scale, only point clouds and triangulated meshes are available from existing MVS pipelines, which are topologically defect laden, free of semantical information and hard to edit and manipulate interactively in further applications. On the other hand, 2D polygons and polygonal models are still the industrial standard. However, extraction of the 2D polygons from MVS point clouds is still a non-trivial task, given the fact that the boundaries of the detected planes are zigzagged and regularities, such as parallel and orthogonal, cannot preserve. Aiming to solve these issues, this paper proposes a hierarchical polygon regularization method for the photogrammetric point clouds from existing MVS pipelines, which comprises of local and global levels. After boundary points extraction, e.g. using alpha shapes, the local level is used to consolidate the original points, by refining the orientation and position of the points using linear priors. The points are then grouped into local segments by forward searching. In the global level, regularities are enforced through a labeling process, which encourage the segments share the same label and the same label represents segments are parallel or orthogonal. This is formulated as Markov Random Field and solved efficiently. Preliminary results are made with point clouds from aerial oblique images and compared with two classical regularization methods, which have revealed that the proposed method are more powerful in abstracting a single building and is promising for further 3D polygonal model reconstruction and GIS applications.

  6. Hierarchical Regularization of Polygons for Photogrammetric Point Clouds of Oblique Images

    Science.gov (United States)

    Xie, L.; Hu, H.; Zhu, Q.; Wu, B.; Zhang, Y.

    2017-05-01

    Despite the success of multi-view stereo (MVS) reconstruction from massive oblique images in city scale, only point clouds and triangulated meshes are available from existing MVS pipelines, which are topologically defect laden, free of semantical information and hard to edit and manipulate interactively in further applications. On the other hand, 2D polygons and polygonal models are still the industrial standard. However, extraction of the 2D polygons from MVS point clouds is still a non-trivial task, given the fact that the boundaries of the detected planes are zigzagged and regularities, such as parallel and orthogonal, cannot preserve. Aiming to solve these issues, this paper proposes a hierarchical polygon regularization method for the photogrammetric point clouds from existing MVS pipelines, which comprises of local and global levels. After boundary points extraction, e.g. using alpha shapes, the local level is used to consolidate the original points, by refining the orientation and position of the points using linear priors. The points are then grouped into local segments by forward searching. In the global level, regularities are enforced through a labeling process, which encourage the segments share the same label and the same label represents segments are parallel or orthogonal. This is formulated as Markov Random Field and solved efficiently. Preliminary results are made with point clouds from aerial oblique images and compared with two classical regularization methods, which have revealed that the proposed method are more powerful in abstracting a single building and is promising for further 3D polygonal model reconstruction and GIS applications.

  7. Segmentation of Fingerprint Images Using Linear Classifier

    Directory of Open Access Journals (Sweden)

    Xinjian Chen

    2004-04-01

    Full Text Available An algorithm for the segmentation of fingerprints and a criterion for evaluating the block feature are presented. The segmentation uses three block features: the block clusters degree, the block mean information, and the block variance. An optimal linear classifier has been trained for the classification per block and the criteria of minimal number of misclassified samples are used. Morphology has been applied as postprocessing to reduce the number of classification errors. The algorithm is tested on FVC2002 database, only 2.45% of the blocks are misclassified, while the postprocessing further reduces this ratio. Experiments have shown that the proposed segmentation method performs very well in rejecting false fingerprint features from the noisy background.

  8. Local and global evaluation for remote sensing image segmentation

    Science.gov (United States)

    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. Fuzzy local Gaussian mixture model for brain MR image segmentation.

    Science.gov (United States)

    Ji, Zexuan; Xia, Yong; Sun, Quansen; Chen, Qiang; Xia, Deshen; Feng, David Dagan

    2012-05-01

    Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of noise and intensity inhomogeneity in brain MR images, many segmentation algorithms suffer from limited accuracy. In this paper, we assume that the local image data within each voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM) algorithm for automated brain MR image segmentation. This algorithm estimates the segmentation result that maximizes the posterior probability by minimizing an objective energy function, in which a truncated Gaussian kernel function is used to impose the spatial constraint and fuzzy memberships are employed to balance the contribution of each GMM. We compared our algorithm to state-of-the-art segmentation approaches in both synthetic and clinical data. Our results show that the proposed algorithm can largely overcome the difficulties raised by noise, low contrast, and bias field, and substantially improve the accuracy of brain MR image segmentation.

  10. Medical image segmentation using level set and watershed transform

    Science.gov (United States)

    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. Automatic Image Segmentation based on MRF-MAP

    CERN Document Server

    Qiyang, Zhao

    2012-01-01

    Solving the Maximum a Posteriori on Markov Random Field, MRF-MAP, is a prevailing method in recent interactive image segmentation tools. Although mathematically explicit in its computational targets, and impressive for the segmentation quality, MRF-MAP is hard to accomplish without the interactive information from users. So it is rarely adopted in the automatic style up to today. In this paper, we present an automatic image segmentation algorithm, NegCut, based on the approximation to MRF-MAP. First we prove MRF-MAP is NP-hard when the probabilistic models are unknown, and then present an approximation function in the form of minimum cuts on graphs with negative weights. Finally, the binary segmentation is taken from the largest eigenvector of the target matrix, with a tuned version of the Lanczos eigensolver. It is shown competitive at the segmentation quality in our experiments.

  12. Hierarchical segmentation of the Malawi Rift: The influence of inherited lithospheric heterogeneity and kinematics in the evolution of continental rifts

    Science.gov (United States)

    Laó-Dávila, Daniel A.; Al-Salmi, Haifa S.; Abdelsalam, Mohamed G.; Atekwana, Estella A.

    2015-12-01

    We used detailed analysis of Shuttle Radar Topography Mission-digital elevation model and observations from aeromagnetic data to examine the influence of inherited lithospheric heterogeneity and kinematics in the segmentation of largely amagmatic continental rifts. We focused on the Cenozoic Malawi Rift, which represents the southern extension of the Western Branch of the East African Rift System. This north trending rift traverses Precambrian and Paleozoic-Mesozoic structures of different orientations. We found that the rift can be hierarchically divided into first-order and second-order segments. In the first-order segmentation, we divided the rift into Northern, Central, and Southern sections. In its Northern Section, the rift follows Paleoproterozoic and Neoproterozoic terrains with structural grain that favored the localization of extension within well-developed border faults. The Central Section occurs within Mesoproterozoic-Neoproterozoic terrain with regional structures oblique to the rift extent. We propose that the lack of inherited lithospheric heterogeneity favoring extension localization resulted in the development of the rift in this section as a shallow graben with undeveloped border faults. In the Southern Section, Mesoproterozoic-Neoproterozoic rocks were reactivated and developed the border faults. In the second-order segmentation, only observed in the Northern Section, we divided the section into five segments that approximate four half-grabens/asymmetrical grabens with alternating polarities. The change of polarity coincides with flip-over full-grabens occurring within overlap zones associated with ~150 km long alternating border faults segments. The inherited lithospheric heterogeneity played the major role in facilitating the segmentation of the Malawi Rift during its opening resulting from extension.

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

    Science.gov (United States)

    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.

  14. Road Scene Segmentation from a Single Image

    NARCIS (Netherlands)

    J.M. Alvarez; T. Gevers; Y. LeCun; A.M. Lopez

    2012-01-01

    Road scene segmentation is important in computer vision for different applications such as autonomous driving and pedestrian detection. Recovering the 3D structure of road scenes provides relevant contextual information to improve their understanding. In this paper, we use a convolutional neural net

  15. Road Scene Segmentation from a Single Image

    NARCIS (Netherlands)

    Alvarez, J.M.; Gevers, T.; LeCun, Y.; Lopez, A.M.

    2012-01-01

    Road scene segmentation is important in computer vision for different applications such as autonomous driving and pedestrian detection. Recovering the 3D structure of road scenes provides relevant contextual information to improve their understanding. In this paper, we use a convolutional neural

  16. Clustering based segmentation of text in complex color images

    Institute of Scientific and Technical Information of China (English)

    毛文革; 王洪滨; 张田文

    2004-01-01

    We propose a novel scheme based on clustering analysis in color space to solve text segmentation in complex color images. Text segmentation includes automatic clustering of color space and foreground image generation. Two methods are also proposed for automatic clustering: The first one is to determine the optimal number of clusters and the second one is the fuzzy competitively clustering method based on competitively learning techniques. Essential foreground images obtained from any of the color clusters are combined into foreground images. Further performance analysis reveals the advantages of the proposed methods.

  17. Segmentation of Bacteria Image Based on Level Set Method

    Institute of Scientific and Technical Information of China (English)

    WANG Hua; CHEN Chun-xiao; HU Yong-hong; YANG Wen-ge

    2008-01-01

    In biology ferment engineering, accurate statistics of the quantity of bacte-ria is one of the most important subjects. In this paper, the quantity of bacteria which was observed traditionally manuauy can be detected automatically. Image acquisition and pro-cessing system is designed to accomplish image preprocessing, image segmentation and statistics of the quantity of bacteria. Segmentation of bacteria images is successfully real-ized by means of a region-based level set method and then the quantity of bacteria is com-puted precisely, which plays an important role in optimizing the growth conditions of bac-teria.

  18. Image Segmentation by Discounted Cumulative Ranking on Maximal Cliques

    CERN Document Server

    Carreira, Joao; Sminchisescu, Cristian

    2010-01-01

    We propose a mid-level image segmentation framework that combines multiple figure-ground hypothesis (FG) constrained at different locations and scales, into interpretations that tile the entire image. The problem is cast as optimization over sets of maximal cliques sampled from the graph connecting non-overlapping, putative figure-ground segment hypotheses. Potential functions over cliques combine unary Gestalt-based figure quality scores and pairwise compatibilities among spatially neighboring segments, constrained by T-junctions and the boundary interface statistics resulting from projections of real 3d scenes. Learning the model parameters is formulated as rank optimization, alternating between sampling image tilings and optimizing their potential function parameters. State of the art results are reported on both the Berkeley and the VOC2009 segmentation dataset, where a 28% improvement was achieved.

  19. Image Segmentation using Multi-Coloured Active Illumination

    Directory of Open Access Journals (Sweden)

    Tze Ki Koh

    2007-11-01

    Full Text Available In this paper, the use of active illumination is extended to image segmentation, specifically in the case of overlapping particles. This work is based on Multi-Flash Imaging (MFI, originally developed by Mitsubishi Electric Labs, to detect depth discontinuities. Illuminations of different wavelengths are projected from multiple positions, providing additional information about a scene compared to conventional segmentation techniques. Shadows are used to identify true object edges. The identification of nonoccluded particles is made possible by exploiting the fact that shadows are cast on underlying particles. Implementation issues such as selecting the appropriate colour model and number of illuminations are discussed. Image segmentation using the proposed method, canny edge detection and watershed transform are performed on overlapping beach pebbles and granite. Evaluation results confirm that the proposed method is a successful extension of the original method and reveals its increased accuracy when compared with conventional segmentation techniques.

  20. Multiphase Image Segmentation Using the Deformable Simplicial Complex Method

    DEFF Research Database (Denmark)

    Dahl, Vedrana Andersen; Christiansen, Asger Nyman; Bærentzen, Jakob Andreas

    2014-01-01

    The deformable simplicial complex method is a generic method for tracking deformable interfaces. It provides explicit interface representation, topological adaptivity, and multiphase support. As such, the deformable simplicial complex method can readily be used for representing active contours in...... in image segmentation based on deformable models. We show the benefits of using the deformable simplicial complex method for image segmentation by segmenting an image into a known number of segments characterized by distinct mean pixel intensities.......The deformable simplicial complex method is a generic method for tracking deformable interfaces. It provides explicit interface representation, topological adaptivity, and multiphase support. As such, the deformable simplicial complex method can readily be used for representing active contours...

  1. PHISHING WEB IMAGE SEGMENTATION BASED ON IMPROVING SPECTRAL CLUSTERING

    Institute of Scientific and Technical Information of China (English)

    Li Yuancheng; Zhao Liujun; Jiao Runhai

    2011-01-01

    Abstract This paper proposes a novel phishing web image segmentation algorithm which based on improving spectral clustering.Firstly,we construct a set of points which are composed of spatial location pixels and gray levels from a given image.Secondly,the data is clustered in spectral space of the similar matrix of the set points,in order to avoid the drawbacks of K-means algorithm in the conventional spectral clustering method that is sensitive to initial clustering centroids and convergence to local optimal solution,we introduce the clone operator,Cauthy mutation to enlarge the scale of clustering centers,quantum-inspired evolutionary algorithm to find the global optimal clustering centroids.Compared with phishing web image segmentation based on K-means,experimental results show that the segmentation performance of our method gains much improvement.Moreover,our method can convergence to global optimal solution and is better in accuracy of phishing web segmentation.

  2. Vehicles Recognition Using Fuzzy Descriptors of Image Segments

    CERN Document Server

    Płaczek, Bartłomiej

    2011-01-01

    In this paper a vision-based vehicles recognition method is presented. Proposed method uses fuzzy description of image segments for automatic recognition of vehicles recorded in image data. The description takes into account selected geometrical properties and shape coefficients determined for segments of reference image (vehicle model). The proposed method was implemented using reasoning system with fuzzy rules. A vehicles recognition algorithm was developed based on the fuzzy rules describing shape and arrangement of the image segments that correspond to visible parts of a vehicle. An extension of the algorithm with set of fuzzy rules defined for different reference images (and various vehicle shapes) enables vehicles classification in traffic scenes. The devised method is suitable for application in video sensors for road traffic control and surveillance systems.

  3. Image Mosaic Method Based on SIFT Features of Line Segment

    Directory of Open Access Journals (Sweden)

    Jun Zhu

    2014-01-01

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

  4. Evaluating the impact of image preprocessing on iris segmentation

    Directory of Open Access Journals (Sweden)

    José F. Valencia-Murillo

    2014-08-01

    Full Text Available Segmentation is one of the most important stages in iris recognition systems. In this paper, image preprocessing algorithms are applied in order to evaluate their impact on successful iris segmentation. The preprocessing algorithms are based on histogram adjustment, Gaussian filters and suppression of specular reflections in human eye images. The segmentation method introduced by Masek is applied on 199 images acquired under unconstrained conditions, belonging to the CASIA-irisV3 database, before and after applying the preprocessing algorithms. Then, the impact of image preprocessing algorithms on the percentage of successful iris segmentation is evaluated by means of a visual inspection of images in order to determine if circumferences of iris and pupil were detected correctly. An increase from 59% to 73% in percentage of successful iris segmentation is obtained with an algorithm that combine elimination of specular reflections, followed by the implementation of a Gaussian filter having a 5x5 kernel. The results highlight the importance of a preprocessing stage as a previous step in order to improve the performance during the edge detection and iris segmentation processes.

  5. A New Approach to Lung Image Segmentation using Fuzzy Possibilistic C-Means Algorithm

    CERN Document Server

    Gomathi, M

    2010-01-01

    Image segmentation is a vital part of image processing. Segmentation has its application widespread in the field of medical images in order to diagnose curious diseases. The same medical images can be segmented manually. But the accuracy of image segmentation using the segmentation algorithms is more when compared with the manual segmentation. In the field of medical diagnosis an extensive diversity of imaging techniques is presently available, such as radiography, computed tomography (CT) and magnetic resonance imaging (MRI). Medical image segmentation is an essential step for most consequent image analysis tasks. Although the original FCM algorithm yields good results for segmenting noise free images, it fails to segment images corrupted by noise, outliers and other imaging artifact. This paper presents an image segmentation approach using Modified Fuzzy C-Means (FCM) algorithm and Fuzzy Possibilistic c-means algorithm (FPCM). This approach is a generalized version of standard Fuzzy CMeans Clustering (FCM) ...

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

    NARCIS (Netherlands)

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

    2012-01-01

    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

  7. Automatic segmentation of the striatum and globus pallidus using MIST: Multimodal Image Segmentation Tool

    Science.gov (United States)

    Visser, Eelke; Keuken, Max C.; Douaud, Gwenaëlle; Gaura, Veronique; Bachoud-Levi, Anne-Catherine; Remy, Philippe; Forstmann, Birte U.; Jenkinson, Mark

    2016-01-01

    Accurate segmentation of the subcortical structures is frequently required in neuroimaging studies. Most existing methods use only a T1-weighted MRI volume to segment all supported structures and usually rely on a database of training data. We propose a new method that can use multiple image modalities simultaneously and a single reference segmentation for initialisation, without the need for a manually labelled training set. The method models intensity profiles in multiple images around the boundaries of the structure after nonlinear registration. It is trained using a set of unlabelled training data, which may be the same images that are to be segmented, and it can automatically infer the location of the physical boundary using user-specified priors. We show that the method produces high-quality segmentations of the striatum, which is clearly visible on T1-weighted scans, and the globus pallidus, which has poor contrast on such scans. The method compares favourably to existing methods, showing greater overlap with manual segmentations and better consistency. PMID:26477650

  8. An entropy-based approach to automatic image segmentation of satellite images

    CERN Document Server

    Barbieri, A L; Rodrigues, F A; Bruno, O M; Costa, L da F

    2009-01-01

    An entropy-based image segmentation approach is introduced and applied to color images obtained from Google Earth. Segmentation refers to the process of partitioning a digital image in order to locate different objects and regions of interest. The application to satellite images paves the way to automated monitoring of ecological catastrophes, urban growth, agricultural activity, maritime pollution, climate changing and general surveillance. Regions representing aquatic, rural and urban areas are identified and the accuracy of the proposed segmentation methodology is evaluated. The comparison with gray level images revealed that the color information is fundamental to obtain an accurate segmentation.

  9. Laplacian forests: semantic image segmentation by guided bagging.

    Science.gov (United States)

    Lombaert, Herve; Zikic, Darko; Criminisi, Antonio; Ayache, Nicholas

    2014-01-01

    This paper presents a new, efficient and accurate technique for the semantic segmentation of medical images. The paper builds upon the successful random decision forests model and improves on it by modifying the way in which randomness is injected into the tree training process. The contribution of this paper is two-fold. First, we replace the conventional bagging procedure (the uniform sampling of training images) with a guided bagging approach, which exploits the inherent structure and organization of the training image set. This allows the creation of decision trees that are specialized to a specific sub-type of images in the training set. Second, the segmentation of a previously unseen image happens via selection and application of only the trees that are relevant to the given test image. Tree selection is done automatically, via the learned image embedding, with more precisely a Laplacian eigenmap. We, therefore, call the proposed approach Laplacian Forests. We validate Laplacian Forests on a dataset of 256, manually segmented 3D CT scans of patients showing high variability in scanning protocols, resolution, body shape and anomalies. Compared with conventional decision forests, Laplacian Forests yield both higher training efficiency, due to the local analysis of the training image space, as well as higher segmentation accuracy, due to the specialization of the forest to image sub-types.

  10. Learning evaluation of ultrasound image segmentation using combined measures

    Science.gov (United States)

    Fang, Mengjie; Luo, Yongkang; Ding, Mingyue

    2016-03-01

    Objective evaluation of medical image segmentation is one of the important steps for proving its validity and clinical applicability. Although there are many researches presenting segmentation methods on medical image, while with few studying the evaluation methods on their results, this paper presents a learning evaluation method with combined measures to make it as close as possible to the clinicians' judgment. This evaluation method is more quantitative and precise for the clinical diagnose. In our experiment, the same data sets include 120 segmentation results of lumen-intima boundary (LIB) and media-adventitia boundary (MAB) of carotid ultrasound images respectively. And the 15 measures of goodness method and discrepancy method are used to evaluate the different segmentation results alone. Furthermore, the experimental results showed that compared with the discrepancy method, the accuracy with the measures of goodness method is poor. Then, by combining with the measures of two methods, the average accuracy and the area under the receiver operating characteristic (ROC) curve of 2 segmentation groups are higher than 93% and 0.9 respectively. And the results of MAB are better than LIB, which proved that this novel method can effectively evaluate the segmentation results. Moreover, it lays the foundation for the non-supervised segmentation evaluation system.

  11. Automated Segmentation and Retrieval System for CT Head Images

    Science.gov (United States)

    Tong, Hau-Lee; Ahmad Fauzi, Mohammad Faizal; Komiya, Ryoichi

    In this paper, automatic segmentation and retrieval of medical images are presented. For the segmentation, different unsupervised clustering techniques are employed to partition the Computed Tomography (CT) brain images into three regions, which are the abnormalities, cerebrospinal fluids (CSF) and brain matters. The novel segmentation method proposed is a dual level segmentation approach. The first level segmentation, which purpose is to acquire abnormal regions, uses the combination of fuzzy c-means (FCM) and k-means clustering. The second level segmentation performs either the expectation-maximization (EM) technique or the modified FCM with population-diameter independent (PDI) to segment the remaining intracranial area into CSF and brain matters. The system automatically determines which algorithm to be utilized in order to produce optimum results. The retrieval of the medical images is based on keywords such as "no abnormal region", "abnormal region(s) adjacent to the skull" and "abnormal region(s) not adjacent to the skull". Medical data from collaborating hospital are experimented and promising results are observed.

  12. Image Segmentation for Food Quality Evaluation Using Computer Vision System

    Directory of Open Access Journals (Sweden)

    Nandhini. P

    2014-02-01

    Full Text Available Quality evaluation is an important factor in food processing industries using the computer vision system where human inspection systems provide high variability. In many countries food processing industries aims at producing defect free food materials to the consumers. Human evaluation techniques suffer from high labour costs, inconsistency and variability. Thus this paper provides various steps for identifying defects in the food material using the computer vision systems. Various steps in computer vision system are image acquisition, Preprocessing, image segmentation, feature identification and classification. The proposed framework provides the comparison of various filters where the hybrid median filter was selected as the filter with the high PSNR value and is used in preprocessing. Image segmentation techniques such as Colour based binary Image segmentation, Particle swarm optimization are compared and image segmentation parameters such as accuracy, sensitivity , specificity are calculated and found that colour based binary image segmentation is well suited for food quality evaluation. Finally this paper provides an efficient method for identifying the defected parts in food materials.

  13. An adaptive spatial clustering method for automatic brain MR image segmentation

    Institute of Scientific and Technical Information of China (English)

    Jingdan Zhang; Daoqing Dai

    2009-01-01

    In this paper, an adaptive spatial clustering method is presented for automatic brain MR image segmentation, which is based on a competitive learning algorithm-self-organizing map (SOM). We use a pattern recognition approach in terms of feature generation and classifier design. Firstly, a multi-dimensional feature vector is constructed using local spatial information. Then, an adaptive spatial growing hierarchical SOM (ASGHSOM) is proposed as the classifier, which is an extension of SOM, fusing multi-scale segmentation with the competitive learning clustering algorithm to overcome the problem of overlapping grey-scale intensities on boundary regions. Furthermore, an adaptive spatial distance is integrated with ASGHSOM, in which local spatial information is considered in the cluster-ing process to reduce the noise effect and the classification ambiguity. Our proposed method is validated by extensive experiments using both simulated and real MR data with varying noise level, and is compared with the state-of-the-art algorithms.

  14. Weakly supervised histopathology cancer image segmentation and classification.

    Science.gov (United States)

    Xu, Yan; Zhu, Jun-Yan; Chang, Eric I-Chao; Lai, Maode; Tu, Zhuowen

    2014-04-01

    Labeling a histopathology image as having cancerous regions or not is a critical task in cancer diagnosis; it is also clinically important to segment the cancer tissues and cluster them into various classes. Existing supervised approaches for image classification and segmentation require detailed manual annotations for the cancer pixels, which are time-consuming to obtain. In this paper, we propose a new learning method, multiple clustered instance learning (MCIL) (along the line of weakly supervised learning) for histopathology image segmentation. The proposed MCIL method simultaneously performs image-level classification (cancer vs. non-cancer image), medical image segmentation (cancer vs. non-cancer tissue), and patch-level clustering (different classes). We embed the clustering concept into the multiple instance learning (MIL) setting and derive a principled solution to performing the above three tasks in an integrated framework. In addition, we introduce contextual constraints as a prior for MCIL, which further reduces the ambiguity in MIL. Experimental results on histopathology colon cancer images and cytology images demonstrate the great advantage of MCIL over the competing methods.

  15. 3D Medical Image Segmentation Based on Rough Set Theory

    Institute of Scientific and Technical Information of China (English)

    CHEN Shi-hao; TIAN Yun; WANG Yi; HAO Chong-yang

    2007-01-01

    This paper presents a method which uses multiple types of expert knowledge together in 3D medical image segmentation based on rough set theory. The focus of this paper is how to approximate a ROI (region of interest) when there are multiple types of expert knowledge. Based on rough set theory, the image can be split into three regions:positive regions; negative regions; boundary regions. With multiple knowledge we refine ROI as an intersection of all of the expected shapes with single knowledge. At last we show the results of implementing a rough 3D image segmentation and visualization system.

  16. Image segmentation by using the localized subspace iteration algorithm

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    An image segmentation algorithm called"segmentation based on the localized subspace iterations"(SLSI)is proposed in this paper.The basic idea is to combine the strategies in Ncut algorithm by Shi and Malik in 2000 and the LSI by E,Li and Lu in 2007.The LSI is applied to solve an eigenvalue problem associated with the affinity matrix of an image,which makes the overall algorithm linearly scaled.The choices of the partition number,the supports and weight functions in SLSI are discussed.Numerical experiments for real images show the applicability of the algorithm.

  17. An image segmentation based method for iris feature extraction

    Institute of Scientific and Technical Information of China (English)

    XU Guang-zhu; ZHANG Zai-feng; MA Yi-de

    2008-01-01

    In this article, the local anomalistic blocks such ascrypts, furrows, and so on in the iris are initially used directly asiris features. A novel image segmentation method based onintersecting cortical model (ICM) neural network was introducedto segment these anomalistic blocks. First, the normalized irisimage was put into ICM neural network after enhancement.Second, the iris features were segmented out perfectly and wereoutput in binary image type by the ICM neural network. Finally,the fourth output pulse image produced by ICM neural networkwas chosen as the iris code for the convenience of real timeprocessing. To estimate the performance of the presentedmethod, an iris recognition platform was produced and theHamming Distance between two iris codes was computed tomeasure the dissimilarity between them. The experimentalresults in CASIA vl.0 and Bath iris image databases show thatthe proposed iris feature extraction algorithm has promisingpotential in iris recognition.

  18. Medical image segmentation based on cellular neural network

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The application of cellular neural network (CNN) has made great progress in image processing. When the selected objects extraction (SOE) CNN is applied to gray scale images, its effects depend on the choice of initial points. In this paper, we take medical images as an example to analyze this limitation. Then an improved algorithm is proposed in which we can segment any gray level objects regardless of the limitation stated above. We also use the gradient information and contour detection CNN to determine the contour and ensure the veracity of segmentation effectively. Finally, we apply the improved algorithm to tumor segmentation of the human brain MR image. The experimental results show that the algorithm is practical and effective.

  19. Automated Segmentation of Cardiac Magnetic Resonance Images

    DEFF Research Database (Denmark)

    Stegmann, Mikkel Bille; Nilsson, Jens Chr.; Grønning, Bjørn A.

    2001-01-01

    Magnetic resonance imaging (MRI) has been shown to be an accurate and precise technique to assess cardiac volumes and function in a non-invasive manner and is generally considered to be the current gold-standard for cardiac imaging [1]. Measurement of ventricular volumes, muscle mass and function...

  20. Semiautomatic segmentation of liver metastases on volumetric CT images

    Energy Technology Data Exchange (ETDEWEB)

    Yan, Jiayong [Department of Biomedical Engineering, Shanghai University of Medicine & Health Sciences, 101 Yingkou Road, Yang Pu District, Shanghai 200093 (China); Schwartz, Lawrence H.; Zhao, Binsheng, E-mail: bz2166@cumc.columbia.edu [Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, New York 10032 (United States)

    2015-11-15

    Purpose: Accurate segmentation and quantification of liver metastases on CT images are critical to surgery/radiation treatment planning and therapy response assessment. To date, there are no reliable methods to perform such segmentation automatically. In this work, the authors present a method for semiautomatic delineation of liver metastases on contrast-enhanced volumetric CT images. Methods: The first step is to manually place a seed region-of-interest (ROI) in the lesion on an image. This ROI will (1) serve as an internal marker and (2) assist in automatically identifying an external marker. With these two markers, lesion contour on the image can be accurately delineated using traditional watershed transformation. Density information will then be extracted from the segmented 2D lesion and help determine the 3D connected object that is a candidate of the lesion volume. The authors have developed a robust strategy to automatically determine internal and external markers for marker-controlled watershed segmentation. By manually placing a seed region-of-interest in the lesion to be delineated on a reference image, the method can automatically determine dual threshold values to approximately separate the lesion from its surrounding structures and refine the thresholds from the segmented lesion for the accurate segmentation of the lesion volume. This method was applied to 69 liver metastases (1.1–10.3 cm in diameter) from a total of 15 patients. An independent radiologist manually delineated all lesions and the resultant lesion volumes served as the “gold standard” for validation of the method’s accuracy. Results: The algorithm received a median overlap, overestimation ratio, and underestimation ratio of 82.3%, 6.0%, and 11.5%, respectively, and a median average boundary distance of 1.2 mm. Conclusions: Preliminary results have shown that volumes of liver metastases on contrast-enhanced CT images can be accurately estimated by a semiautomatic segmentation

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

    Science.gov (United States)

    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.

  2. Sparse representation-based spectral clustering for SAR image segmentation

    Science.gov (United States)

    Zhang, Xiangrong; Wei, Zhengli; Feng, Jie; Jiao, Licheng

    2011-12-01

    A new method, sparse representation based spectral clustering (SC) with Nyström method, is proposed for synthetic aperture radar (SAR) image segmentation. Different from the conventional SC, this proposed technique is developed by using the sparse coefficients which obtained by solving l1 minimization problem to construct the affinity matrix and the Nyström method is applied to alleviate the segmentation process. The advantage of our proposed method is that we do not need to select the scaling parameter in the Gaussian kernel function artificially. We apply the proposed method, k-means and the classic spectral clustering algorithm with Nyström method to SAR image segmentation. The results show that compared with the other two methods, the proposed method can obtain much better segmentation results.

  3. FBIH financial market segmentation on the basis of image factors

    Directory of Open Access Journals (Sweden)

    Arnela Bevanda

    2008-12-01

    Full Text Available The aim of the study is to recognize, single out and define market segments useful for future marketing strategies, using certain statistical techniques on the basis of influence of various image factors of financial institutions. The survey included a total of 500 interviewees: 250 bank clients and 250 clients of insurance companies. Starting from the problem area and research goal, the following hypothesis has been formulated: Basic preferences of clients in regard of image factors while selecting financial institutions are different enough to be used as such for differentiating significant market segments of clients. Two segments have been singled out by cluster analysis and named, respectively, traditionalists and visualists. Results of the research confirmed the established hypothesis and pointed to the fact that managers in the financial institutions of the Federation of Bosnia and Herzegovina (FBIH must undertake certain corrective actions, especially when planning and implementing communication strategies, if they wish to maintain their competitiveness in serving both selected segments.

  4. A New Algorithm for Interactive Structural Image Segmentation

    CERN Document Server

    Noma, Alexandre; Consularo, Luis Augusto; Cesar, Roberto M Jr; Bloch, Isabelle

    2008-01-01

    This paper proposes a novel algorithm for the problem of structural image segmentation through an interactive model-based approach. Interaction is expressed in the model creation, which is done according to user traces drawn over a given input image. Both model and input are then represented by means of attributed relational graphs derived on the fly. Appearance features are taken into account as object attributes and structural properties are expressed as relational attributes. To cope with possible topological differences between both graphs, a new structure called the deformation graph is introduced. The segmentation process corresponds to finding a labelling of the input graph that minimizes the deformations introduced in the model when it is updated with input information. This approach has shown to be faster than other segmentation methods, with competitive output quality. Therefore, the method solves the problem of multiple label segmentation in an efficient way. Encouraging results on both natural and...

  5. Variational segmentation problems using prior knowledge in imaging and vision

    DEFF Research Database (Denmark)

    Fundana, Ketut

    This dissertation addresses variational formulation of segmentation problems using prior knowledge. Variational models are among the most successful approaches for solving many Computer Vision and Image Processing problems. The models aim at finding the solution to a given energy functional defined......, prior knowledge is needed to obtain the desired solution. The introduction of shape priors in particular, has proven to be an effective way to segment objects of interests. Firstly, we propose a prior-based variational segmentation model to segment objects of interest in image sequences, that can deal...... pose invariant parameters complicate the optimization of the model. To overcome the common numerical problems associated with the step size of the pose parameters in the discretization of the pose model, we propose a novel gradient procedure for the pose estimation based on the construction...

  6. Polarization image segmentation of radiofrequency ablated porcine myocardial tissue

    Science.gov (United States)

    Ahmad, Iftikhar; Gribble, Adam; Murtza, Iqbal; Ikram, Masroor; Pop, Mihaela; Vitkin, Alex

    2017-01-01

    Optical polarimetry has previously imaged the spatial extent of a typical radiofrequency ablated (RFA) lesion in myocardial tissue, exhibiting significantly lower total depolarization at the necrotic core compared to healthy tissue, and intermediate values at the RFA rim region. Here, total depolarization in ablated myocardium was used to segment the total depolarization image into three (core, rim and healthy) zones. A local fuzzy thresholding algorithm was used for this multi-region segmentation, and then compared with a ground truth segmentation obtained from manual demarcation of RFA core and rim regions on the histopathology image. Quantitative comparison of the algorithm segmentation results was performed with evaluation metrics such as dice similarity coefficient (DSC = 0.78 ± 0.02 and 0.80 ± 0.02), sensitivity (Sn = 0.83 ± 0.10 and 0.91 ± 0.08), specificity (Sp = 0.76 ± 0.17 and 0.72 ± 0.17) and accuracy (Acc = 0.81 ± 0.09 and 0.71 ± 0.10) for RFA core and rim regions, respectively. This automatic segmentation of parametric depolarization images suggests a novel application of optical polarimetry, namely its use in objective RFA image quantification. PMID:28380013

  7. Breast image pre-processing for mammographic tissue segmentation.

    Science.gov (United States)

    He, Wenda; Hogg, Peter; Juette, Arne; Denton, Erika R E; Zwiggelaar, Reyer

    2015-12-01

    During mammographic image acquisition, a compression paddle is used to even the breast thickness in order to obtain optimal image quality. Clinical observation has indicated that some mammograms may exhibit abrupt intensity change and low visibility of tissue structures in the breast peripheral areas. Such appearance discrepancies can affect image interpretation and may not be desirable for computer aided mammography, leading to incorrect diagnosis and/or detection which can have a negative impact on sensitivity and specificity of screening mammography. This paper describes a novel mammographic image pre-processing method to improve image quality for analysis. An image selection process is incorporated to better target problematic images. The processed images show improved mammographic appearances not only in the breast periphery but also across the mammograms. Mammographic segmentation and risk/density classification were performed to facilitate a quantitative and qualitative evaluation. When using the processed images, the results indicated more anatomically correct segmentation in tissue specific areas, and subsequently better classification accuracies were achieved. Visual assessments were conducted in a clinical environment to determine the quality of the processed images and the resultant segmentation. The developed method has shown promising results. It is expected to be useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.

  8. A simple shape prior model for iris image segmentation

    Science.gov (United States)

    Bishop, Daniel A.; Yezzi, Anthony, Jr.

    2011-06-01

    In order to make biometric systems faster and more user-friendly, lower-quality images must be accepted. A major hurdle in this task is accurate segmentation of the boundaries of the iris in these images. Quite commonly, circle-fitting is used to approximate the boundaries of the inner (pupil) and outer (limbic) boundaries of the iris, but this assumption does not hold for off-axis or otherwise non-circular boundaries. In this paper we present a novel, foundational method for elliptical segmentation of off-axis iris images. This method uses active contours with constrained flow to achieve a simplified form of shape prior active contours. This is done by calculating a region-based contour evolution and projecting it upon a properly chosen set of vectors to confine it to a class of shapes. In this case, that class of shapes is ellipses. This serves to regularize the contour, simplifying the curve evolution and preventing the development of irregularities that present challenges in iris segmentation. The proposed method is tested using images from the UBIRIS v.1 and CASIA-IrisV3 image data sets, with both near-ideal and off-axis images. Additional testing has been performed using the WVU Off Axis/Angle Iris Dataset, Release 1. By avoiding many of the assumptions commonly used in iris segmentation methods, the proposed method is able to accurately fit elliptical boundaries to off-axis images.

  9. Dynamic Programming Using Polar Variance for Image Segmentation.

    Science.gov (United States)

    Rosado-Toro, Jose A; Altbach, Maria I; Rodriguez, Jeffrey J

    2016-10-06

    When using polar dynamic programming (PDP) for image segmentation, the object size is one of the main features used. This is because if size is left unconstrained the final segmentation may include high-gradient regions that are not associated with the object. In this paper, we propose a new feature, polar variance, which allows the algorithm to segment objects of different sizes without the need for training data. The polar variance is the variance in a polar region between a user-selected origin and a pixel we want to analyze. We also incorporate a new technique that allows PDP to segment complex shapes by finding low-gradient regions and growing them. The experimental analysis consisted on comparing our technique with different active contour segmentation techniques on a series of tests. The tests consisted on robustness to additive Gaussian noise, segmentation accuracy with different grayscale images and finally robustness to algorithm-specific parameters. Experimental results show that our technique performs favorably when compared to other segmentation techniques.

  10. Interactive Image Segmentation Framework Based On Control Theory.

    Science.gov (United States)

    Zhu, Liangjia; Kolesov, Ivan; Karasev, Peter; Tannenbaum, Allen

    2015-02-21

    Segmentation of anatomical structures in medical imagery is a key step in a variety of clinical applications. Designing a generic, automated method that works for various structures and imaging modalities is a daunting task. Instead of proposing a new specific segmentation algorithm, in this paper, we present a general design principle on how to integrate user interactions from the perspective of control theory. In this formulation, Lyapunov stability analysis is employed to design and analyze an interactive segmentation system. The effectiveness and robustness of the proposed method are demonstrated.

  11. Cardiac MR image segmentation using CHNN and level set method

    Institute of Scientific and Technical Information of China (English)

    王洪元; 周则明; 王平安; 夏德深

    2004-01-01

    Although cardiac magnetic resonance imaging (MRI) can provide high spatial resolution image, the area gray level inhomogenization, weak boundary and artifact often can be found in MR images. So, the MR images segmentation using the gradient-based methods is poor in quality and efficiency. An algorithm, based on the competitive hopfield neural network (CHNN) and the curve propagation, is proposed for cardiac MR images segmentation in this paper. The algorithm is composed of two phases. In first phase, a CHNN is used to classify the image objects, and to make gray level homogenization and to recognize weak boundaries in objects. In second phase, based on the classified results, the level set velocity function is created and the object boundaries are extracted with the curve propagation algorithm of the narrow band-based level set. The test results are promising and encouraging.

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

    Science.gov (United States)

    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.

  13. Image Segmentation of Historical Handwriting from Palm Leaf Manuscripts

    Science.gov (United States)

    Surinta, Olarik; Chamchong, Rapeeporn

    Palm leaf manuscripts were one of the earliest forms of written media and were used in Southeast Asia to store early written knowledge about subjects such as medicine, Buddhist doctrine and astrology. Therefore, historical handwritten palm leaf manuscripts are important for people who like to learn about historical documents, because we can learn more experience from them. This paper presents an image segmentation of historical handwriting from palm leaf manuscripts. The process is composed of three steps: 1) background elimination to separate text and background by Otsu's algorithm 2) line segmentation and 3) character segmentation by histogram of image. The end result is the character's image. The results from this research may be applied to optical character recognition (OCR) in the future.

  14. Sub-Markov Random Walk for Image Segmentation.

    Science.gov (United States)

    Dong, Xingping; Shen, Jianbing; Shao, Ling; Van Gool, Luc

    2016-02-01

    A novel sub-Markov random walk (subRW) algorithm with label prior is proposed for seeded image segmentation, which can be interpreted as a traditional random walker on a graph with added auxiliary nodes. Under this explanation, we unify the proposed subRW and other popular random walk (RW) algorithms. This unifying view will make it possible for transferring intrinsic findings between different RW algorithms, and offer new ideas for designing novel RW algorithms by adding or changing auxiliary nodes. To verify the second benefit, we design a new subRW algorithm with label prior to solve the segmentation problem of objects with thin and elongated parts. The experimental results on both synthetic and natural images with twigs demonstrate that the proposed subRW method outperforms previous RW algorithms for seeded image segmentation.

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

    Science.gov (United States)

    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.

  16. An Improved Image Segmentation Algorithm Based on MET Method

    Directory of Open Access Journals (Sweden)

    Z. A. Abo-Eleneen

    2012-09-01

    Full Text Available Image segmentation is a basic component of many computer vision systems and pattern recognition. Thresholding is a simple but effective method to separate objects from the background. A commonly used method, Kittler and Illingworth's minimum error thresholding (MET, improves the image segmentation effect obviously. Its simpler and easier to implement. However, it fails in the presence of skew and heavy-tailed class-conditional distributions or if the histogram is unimodal or close to unimodal. The Fisher information (FI measure is an important concept in statistical estimation theory and information theory. Employing the FI measure, an improved threshold image segmentation algorithm FI-based extension of MET is developed. Comparing with the MET method, the improved method in general can achieve more robust performance when the data for either class is skew and heavy-tailed.

  17. Anterior Segment Imaging in Ocular Surface Squamous Neoplasia

    Directory of Open Access Journals (Sweden)

    Sally S. Ong

    2016-01-01

    Full Text Available Recent advances in anterior segment imaging have transformed the way ocular surface squamous neoplasia (OSSN is diagnosed and monitored. Ultrasound biomicroscopy (UBM has been reported to be useful primarily in the assessment of intraocular invasion and metastasis. In vivo confocal microscopy (IVCM shows enlarged and irregular nuclei with hyperreflective cells in OSSN lesions and this has been found to correlate with histopathology findings. Anterior segment optical coherence tomography (AS-OCT demonstrates thickened hyperreflective epithelium with an abrupt transition between abnormal and normal epithelium in OSSN lesions and this has also been shown to mimic histopathology findings. Although there are limitations to each of these imaging modalities, they can be useful adjunctive tools in the diagnosis of OSSN and could greatly assist the clinician in the management of OSSN patients. Nevertheless, anterior segment imaging has not replaced histopathology’s role as the gold standard in confirming diagnosis.

  18. Automatic Segmentation of Dermoscopic Images by Iterative Classification

    Directory of Open Access Journals (Sweden)

    Maciel Zortea

    2011-01-01

    Full Text Available Accurate detection of the borders of skin lesions is a vital first step for computer aided diagnostic systems. This paper presents a novel automatic approach to segmentation of skin lesions that is particularly suitable for analysis of dermoscopic images. Assumptions about the image acquisition, in particular, the approximate location and color, are used to derive an automatic rule to select small seed regions, likely to correspond to samples of skin and the lesion of interest. The seed regions are used as initial training samples, and the lesion segmentation problem is treated as binary classification problem. An iterative hybrid classification strategy, based on a weighted combination of estimated posteriors of a linear and quadratic classifier, is used to update both the automatically selected training samples and the segmentation, increasing reliability and final accuracy, especially for those challenging images, where the contrast between the background skin and lesion is low.

  19. An adaptive multi-feature segmentation model for infrared image

    Science.gov (United States)

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

    2016-04-01

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

  20. Automated Segmentability Index for Layer Segmentation of Macular SD-OCT Images

    NARCIS (Netherlands)

    Lee, K.; Buitendijk, G.H.; Bogunovic, H.; Springelkamp, H.; Hofman, A.; Wahle, A.; Sonka, M.; Vingerling, J.R.; Klaver, C.C.W.; Abramoff, M.D.

    2016-01-01

    PURPOSE: To automatically identify which spectral-domain optical coherence tomography (SD-OCT) scans will provide reliable automated layer segmentations for more accurate layer thickness analyses in population studies. METHODS: Six hundred ninety macular SD-OCT image volumes (6.0 x 6.0 x 2.3 mm3) we

  1. SAR image segmentation using MSER and improved spectral clustering

    Science.gov (United States)

    Gui, Yang; Zhang, Xiaohu; Shang, Yang

    2012-12-01

    A novel approach is presented for synthetic aperture radar (SAR) image segmentation. By incorporating the advantages of maximally stable extremal regions (MSER) algorithm and spectral clustering (SC) method, the proposed approach provides effective and robust segmentation. First, the input image is transformed from a pixel-based to a region-based model by using the MSER algorithm. The input image after MSER procedure is composed of some disjoint regions. Then the regions are treated as nodes in the image plane, and a graph structure is applied to represent them. Finally, the improved SC is used to perform globally optimal clustering, by which the result of image segmentation can be generated. To avoid some incorrect partitioning when considering each region as one graph node, we assign different numbers of nodes to represent the regions according to area ratios among the regions. In addition, K-harmonic means instead of K-means is applied in the improved SC procedure in order to raise its stability and performance. Experimental results show that the proposed approach is effective on SAR image segmentation and has the advantage of calculating quickly.

  2. Computer Based Melanocytic and Nevus Image Enhancement and Segmentation

    Science.gov (United States)

    Jamil, Uzma; Khalid, Shehzad; Abbas, Sarmad; Saleem, Kashif

    2016-01-01

    Digital dermoscopy aids dermatologists in monitoring potentially cancerous skin lesions. Melanoma is the 5th common form of skin cancer that is rare but the most dangerous. Melanoma is curable if it is detected at an early stage. Automated segmentation of cancerous lesion from normal skin is the most critical yet tricky part in computerized lesion detection and classification. The effectiveness and accuracy of lesion classification are critically dependent on the quality of lesion segmentation. In this paper, we have proposed a novel approach that can automatically preprocess the image and then segment the lesion. The system filters unwanted artifacts including hairs, gel, bubbles, and specular reflection. A novel approach is presented using the concept of wavelets for detection and inpainting the hairs present in the cancer images. The contrast of lesion with the skin is enhanced using adaptive sigmoidal function that takes care of the localized intensity distribution within a given lesion's images. We then present a segmentation approach to precisely segment the lesion from the background. The proposed approach is tested on the European database of dermoscopic images. Results are compared with the competitors to demonstrate the superiority of the suggested approach.

  3. Computer Based Melanocytic and Nevus Image Enhancement and Segmentation

    Directory of Open Access Journals (Sweden)

    Uzma Jamil

    2016-01-01

    Full Text Available Digital dermoscopy aids dermatologists in monitoring potentially cancerous skin lesions. Melanoma is the 5th common form of skin cancer that is rare but the most dangerous. Melanoma is curable if it is detected at an early stage. Automated segmentation of cancerous lesion from normal skin is the most critical yet tricky part in computerized lesion detection and classification. The effectiveness and accuracy of lesion classification are critically dependent on the quality of lesion segmentation. In this paper, we have proposed a novel approach that can automatically preprocess the image and then segment the lesion. The system filters unwanted artifacts including hairs, gel, bubbles, and specular reflection. A novel approach is presented using the concept of wavelets for detection and inpainting the hairs present in the cancer images. The contrast of lesion with the skin is enhanced using adaptive sigmoidal function that takes care of the localized intensity distribution within a given lesion’s images. We then present a segmentation approach to precisely segment the lesion from the background. The proposed approach is tested on the European database of dermoscopic images. Results are compared with the competitors to demonstrate the superiority of the suggested approach.

  4. Hybrid Method for 3D Segmentation of Magnetic Resonance Images

    Institute of Scientific and Technical Information of China (English)

    ZHANGXiang; ZHANGDazhi; TIANJinwen; LIUJian

    2003-01-01

    Segmentation of some complex images, especially in magnetic resonance brain images, is often difficult to perform satisfactory results using only single approach of image segmentation. An approach towards the integration of several techniques seems to be the best solution. In this paper a new hybrid method for 3-dimension segmentation of the whole brain is introduced, based on fuzzy region growing, edge detection and mathematical morphology, The gray-level threshold, controlling the process of region growing, is determined by fuzzy technique. The image gradient feature is obtained by the 3-dimension sobel operator considering a 3×3×3 data block with the voxel to be evaluated at the center, while the gradient magnitude threshold is defined by the gradient magnitude histogram of brain magnetic resonance volume. By the combined methods of edge detection and region growing, the white matter volume of human brain is segmented perfectly. By the post-processing using mathematical morphological techniques, the whole brain region is obtained. In order to investigate the validity of the hybrid method, two comparative experiments, the region growing method using only gray-level feature and the thresholding method by combining gray-level and gradient features, are carried out. Experimental results indicate that the proposed method provides much better results than the traditional method using a single technique in the 3-dimension segmentation of human brain magnetic resonance data sets.

  5. Pixel Intensity Clustering Algorithm for Multilevel Image Segmentation

    Directory of Open Access Journals (Sweden)

    Oludayo O. Olugbara

    2015-01-01

    Full Text Available Image segmentation is an important problem that has received significant attention in the literature. Over the last few decades, a lot of algorithms were developed to solve image segmentation problem; prominent amongst these are the thresholding algorithms. However, the computational time complexity of thresholding exponentially increases with increasing number of desired thresholds. A wealth of alternative algorithms, notably those based on particle swarm optimization and evolutionary metaheuristics, were proposed to tackle the intrinsic challenges of thresholding. In codicil, clustering based algorithms were developed as multidimensional extensions of thresholding. While these algorithms have demonstrated successful results for fewer thresholds, their computational costs for a large number of thresholds are still a limiting factor. We propose a new clustering algorithm based on linear partitioning of the pixel intensity set and between-cluster variance criterion function for multilevel image segmentation. The results of testing the proposed algorithm on real images from Berkeley Segmentation Dataset and Benchmark show that the algorithm is comparable with state-of-the-art multilevel segmentation algorithms and consistently produces high quality results. The attractive properties of the algorithm are its simplicity, generalization to a large number of clusters, and computational cost effectiveness.

  6. HIERARCHICAL CLASSIFICATION OF POLARIMETRIC SAR IMAGE BASED ON STATISTICAL REGION MERGING

    Directory of Open Access Journals (Sweden)

    F. Lang

    2012-07-01

    Full Text Available Segmentation and classification of polarimetric SAR (PolSAR imagery are very important for interpretation of PolSAR data. This paper presents a new object-oriented classification method which is based on Statistical Region Merging (SRM segmentation algorithm and a two-level hierarchical clustering technique. The proposed method takes full advantage of the polarimetric information contained in the PolSAR data, and takes both effectiveness and efficiency into account according to the characteristic of PolSAR. A modification of over-merging to over-segmentation technique and a post processing of segmentation for SRM is proposed according to the application of classification. And a revised symmetric Wishart distance is derived from the Wishart PDF. Segmentation and classification results of AirSAR L-band PolSAR data over the Flevoland test site is shown to demonstrate the validity of the proposed method.

  7. Visual Attention Shift based on Image Segmentation Using Neurodynamic System

    Directory of Open Access Journals (Sweden)

    Lijuan Duan

    2011-01-01

    Full Text Available A method of predicting visual attention shift is proposed based on image segmentation using neurodynamic system in this paper. The input image is mapped to a neural oscillator network. Each oscillator corresponding to a pixel is modeled by means of simplified Wilson-Cowan equations, and is coupled with its 8-nearest neighbors. Then the image is segmented by classifying the oscillation curves of the excitatory groups of all the oscillators. The classifier is constructed based on features of frequency, offset, phase and amplitude of the curves. The visual attention shift between the regions on the image is predicted according to the saliency strength of each region. Referring to the mechanism of winner-take-all competition, the saliency of a region is the aggregation of the dissimilarities between this region and all the other ones. Experimental results on images show the effectiveness of our method.

  8. Automatic Segmentation and Inpainting of Specular Highlights for Endoscopic Imaging

    Directory of Open Access Journals (Sweden)

    Arnold Mirko

    2010-01-01

    Full Text Available Minimally invasive medical procedures have become increasingly common in today's healthcare practice. Images taken during such procedures largely show tissues of human organs, such as the mucosa of the gastrointestinal tract. These surfaces usually have a glossy appearance showing specular highlights. For many visual analysis algorithms, these distinct and bright visual features can become a significant source of error. In this article, we propose two methods to address this problem: (a a segmentation method based on nonlinear filtering and colour image thresholding and (b an efficient inpainting method. The inpainting algorithm eliminates the negative effect of specular highlights on other image analysis algorithms and also gives a visually pleasing result. The methods compare favourably to the existing approaches reported for endoscopic imaging. Furthermore, in contrast to the existing approaches, the proposed segmentation method is applicable to the widely used sequential RGB image acquisition systems.

  9. A dendritic lattice neural network for color image segmentation

    Science.gov (United States)

    Urcid, Gonzalo; Lara-Rodríguez, Luis David; López-Meléndez, Elizabeth

    2015-09-01

    A two-layer dendritic lattice neural network is proposed to segment color images in the Red-Green-Blue (RGB) color space. The two layer neural network is a fully interconnected feed forward net consisting of an input layer that receives color pixel values, an intermediate layer that computes pixel interdistances, and an output layer used to classify colors by hetero-association. The two-layer net is first initialized with a finite small subset of the colors present in the input image. These colors are obtained by means of an automatic clustering procedure such as k-means or fuzzy c-means. In the second stage, the color image is scanned on a pixel by pixel basis where each picture element is treated as a vector and feeded into the network. For illustration purposes we use public domain color images to show the performance of our proposed image segmentation technique.

  10. Neuron Segmentation in Electron Microscopy Images Using Partial Differential Equations.

    Science.gov (United States)

    Jones, Cory; Sayedhosseini, Mojtaba; Ellisman, Mark; Tasdizen, Tolga

    2013-01-01

    In connectomics, neuroscientists seek to identify the synaptic connections between neurons. Segmentation of cell membranes using supervised learning algorithms on electron microscopy images of brain tissue is often done to assist in this effort. Here we present a partial differential equation with a novel growth term to improve the results of a supervised learning algorithm. We also introduce a new method for representing the resulting image that allows for a more dynamic thresholding to further improve the result. Using these two processes we are able to close small to medium sized gaps in the cell membrane detection and improve the Rand error by as much as 9% over the initial supervised segmentation.

  11. Topography Image Segmentation Based on Improved Chan-Vese Model

    Institute of Scientific and Technical Information of China (English)

    ZHAO Min-rong; ZHANG Xi-wen; JIANG Juan-na

    2013-01-01

    Aiming to solve the inefficient segmentation in traditional C-V model for complex topography image and time-consuming process caused by the level set function solving with partial differential, an improved Chan-Vese model is presented in this paper. With the good performances of maintaining topological properties of the traditional level set method and avoiding the numerical so-lution of partial differential, the same segmentation results could be easily obtained. Thus, a stable foundation for rapid segmenta-tion-based on image reconstruction identification is established.

  12. Constrained segmentation of cardiac MR image sequences

    NARCIS (Netherlands)

    Üzümcü, Mehmet

    2007-01-01

    Cardiovascular diseases are highly prevalent in the western world. With the aging of the population, the number of people suffering from CVD is still increasing. Therefore, the amount of diagnostic assessments and thus, the number of image acquisitions will increase accordingly. Considering the high

  13. Image Segmentation for Improvised Explosive Devices

    Science.gov (United States)

    2012-12-01

    Soltani and Wong in 1988 [23]. Also, Sezgin and Sankur 2004 and Shapiro 2001 12 Figure 2.3: Comparing the usefulness of histograms to define the threshold...S. Soltani , and A. K. Wong. A survey of thresholding techniques. Computer Vision, Graphics, and Image Processing, 41(2):233–260, 1988. [24] M

  14. Automatic image segmentation for treatment planning in radiotherapy; Segmentation automatique des images pour la planifi cation dosimetrique en radiotherapie

    Energy Technology Data Exchange (ETDEWEB)

    Pasquiera, D. [Centre Galilee, polyclinique de la Louviere, 59 - Lille (France); Peyrodie, L. [Ecole des hautes etudes d' ingenieur, 59 - Lille (France); Laboratoire d' automatique, genie informatique et signal (LAGIS), Cite scientifi que, 59 - Villeneuve d' Ascq (France); Denis, F. [Centre Jean-Bernard, 72 - Le Mans (France); Pointreau, Y.; Bera, G. [Clinique d' oncologie radiotherapie, Centre Henry-S.-Kaplan, CHU Bretonneau, 37 - Tours (France); Lartigau, E. [Departement universitaire de radiotherapie, Centre O. Lambret, Universite Lille 2, 59 - Lille (France)

    2010-07-01

    One drawback of the growth in conformal radiotherapy and image-guided radiotherapy is the increased time needed to define the volumes of interest. This also results in inter- and intra-observer variability. However, developments in computing and image processing have enabled these tasks to be partially or totally automated. This article will provide a detailed description of the main principles of image segmentation in radiotherapy, its applications and the most recent results in a clinical context. (authors)

  15. Segmentation of fluorescence microscopy cell images using unsupervised mining.

    Science.gov (United States)

    Du, Xian; Dua, Sumeet

    2010-05-28

    The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. In this study, we have developed and evaluated the performance of multiple unsupervised data mining techniques in cell image segmentation. We adapt four distinctive, yet complementary, methods for unsupervised learning, including those based on k-means clustering, EM, Otsu's threshold, and GMAC. Validation measures are defined, and the performance of the techniques is evaluated both quantitatively and qualitatively using synthetic and recently published real data. Experimental results demonstrate that k-means, Otsu's threshold, and GMAC perform similarly, and have more precise segmentation results than EM. We report that EM has higher recall values and lower precision results from under-segmentation due to its Gaussian model assumption. We also demonstrate that these methods need spatial information to segment complex real cell images with a high degree of efficacy, as expected in many medical informatics applications.

  16. Adaptive geodesic transform for segmentation of vertebrae on CT images

    Science.gov (United States)

    Gaonkar, Bilwaj; Shu, Liao; Hermosillo, Gerardo; Zhan, Yiqiang

    2014-03-01

    Vertebral segmentation is a critical first step in any quantitative evaluation of vertebral pathology using CT images. This is especially challenging because bone marrow tissue has the same intensity profile as the muscle surrounding the bone. Thus simple methods such as thresholding or adaptive k-means fail to accurately segment vertebrae. While several other algorithms such as level sets may be used for segmentation any algorithm that is clinically deployable has to work in under a few seconds. To address these dual challenges we present here, a new algorithm based on the geodesic distance transform that is capable of segmenting the spinal vertebrae in under one second. To achieve this we extend the theory of the geodesic distance transforms proposed in1 to incorporate high level anatomical knowledge through adaptive weighting of image gradients. Such knowledge may be provided by the user directly or may be automatically generated by another algorithm. We incorporate information 'learnt' using a previously published machine learning algorithm2 to segment the L1 to L5 vertebrae. While we present a particular application here, the adaptive geodesic transform is a generic concept which can be applied to segmentation of other organs as well.

  17. Automated segmentation of three-dimensional MR brain images

    Science.gov (United States)

    Park, Jonggeun; Baek, Byungjun; Ahn, Choong-Il; Ku, Kyo Bum; Jeong, Dong Kyun; Lee, Chulhee

    2006-03-01

    Brain segmentation is a challenging problem due to the complexity of the brain. In this paper, we propose an automated brain segmentation method for 3D magnetic resonance (MR) brain images which are represented as a sequence of 2D brain images. The proposed method consists of three steps: pre-processing, removal of non-brain regions (e.g., the skull, meninges, other organs, etc), and spinal cord restoration. In pre-processing, we perform adaptive thresholding which takes into account variable intensities of MR brain images corresponding to various image acquisition conditions. In segmentation process, we iteratively apply 2D morphological operations and masking for the sequences of 2D sagittal, coronal, and axial planes in order to remove non-brain tissues. Next, final 3D brain regions are obtained by applying OR operation for segmentation results of three planes. Finally we reconstruct the spinal cord truncated during the previous processes. Experiments are performed with fifteen 3D MR brain image sets with 8-bit gray-scale. Experiment results show the proposed algorithm is fast, and provides robust and satisfactory results.

  18. Automatic comic page image understanding based on edge segment analysis

    Science.gov (United States)

    Liu, Dong; Wang, Yongtao; Tang, Zhi; Li, Luyuan; Gao, Liangcai

    2013-12-01

    Comic page image understanding aims to analyse the layout of the comic page images by detecting the storyboards and identifying the reading order automatically. It is the key technique to produce the digital comic documents suitable for reading on mobile devices. In this paper, we propose a novel comic page image understanding method based on edge segment analysis. First, we propose an efficient edge point chaining method to extract Canny edge segments (i.e., contiguous chains of Canny edge points) from the input comic page image; second, we propose a top-down scheme to detect line segments within each obtained edge segment; third, we develop a novel method to detect the storyboards by selecting the border lines and further identify the reading order of these storyboards. The proposed method is performed on a data set consisting of 2000 comic page images from ten printed comic series. The experimental results demonstrate that the proposed method achieves satisfactory results on different comics and outperforms the existing methods.

  19. Remote sensing images segmentation by Deriche's filter and neural network

    Science.gov (United States)

    Koffi, Raphael K.; Solaiman, Basel; Mouchot, Marie-Catherine

    1994-12-01

    An image segmentation method for remote sensing data using hybride techniques is proposed. Edge detection approach for segmentation is considered in our study. Our aim is to integrate segmentation results in further processing namely classification. Images of the land from satellite are often corrupted by noise. On one hand, optimal edge detectors insure good noise immunity. On the other hand, the multi-layer perceptron (MLP) neural network has been found to be suited for classification. So we propose to combine these two techniques to improve segmentation process. Satellites for remote sensing provide several images for the same area, coded differently according to spectral bands. In order to bear in mind spectral and spatial information, neighborhood relation of pixels and different bands are taken into consideration during the classification realized by the neural network. Samples which constituate the training set for the MLP are selected from the third, fourth and fifth band and represent edge and non-edge patterns. Each sample vector is composed of the value of a current pixel in the local maxima image (enhancement image obtained by Deriche's filter) and its 8 nearest neighbors. The proposed method provides satisfactory results for our application and compared to other similar methods.

  20. Automatic segmentation of lumbar vertebrae in CT images

    Science.gov (United States)

    Kulkarni, Amruta; Raina, Akshita; Sharifi Sarabi, Mona; Ahn, Christine S.; Babayan, Diana; Gaonkar, Bilwaj; Macyszyn, Luke; Raghavendra, Cauligi

    2017-03-01

    Lower back pain is one of the most prevalent disorders in the developed/developing world. However, its etiology is poorly understood and treatment is often determined subjectively. In order to quantitatively study the emergence and evolution of back pain, it is necessary to develop consistently measurable markers for pathology. Imaging based measures offer one solution to this problem. The development of imaging based on quantitative biomarkers for the lower back necessitates automated techniques to acquire this data. While the problem of segmenting lumbar vertebrae has been addressed repeatedly in literature, the associated problem of computing relevant biomarkers on the basis of the segmentation has not been addressed thoroughly. In this paper, we propose a Random-Forest based approach that learns to segment vertebral bodies in CT images followed by a biomarker evaluation framework that extracts vertebral heights and widths from the segmentations obtained. Our dataset consists of 15 CT sagittal scans obtained from General Electric Healthcare. Our main approach is divided into three parts: the first stage is image pre-processing which is used to correct for variations in illumination across all the images followed by preparing the foreground and background objects from images; the next stage is Machine Learning using Random-Forests, which distinguishes the interest-point vectors between foreground or background; and the last step is image post-processing, which is crucial to refine the results of classifier. The Dice coefficient was used as a statistical validation metric to evaluate the performance of our segmentations with an average value of 0.725 for our dataset.

  1. Split-remerge method for eliminating processing window artifacts in recursive hierarchical segmentation

    Science.gov (United States)

    Tilton, James C. (Inventor)

    2010-01-01

    A method, computer readable storage, and apparatus for implementing recursive segmentation of data with spatial characteristics into regions including splitting-remerging of pixels with contagious region designations and a user controlled parameter for providing a preference for merging adjacent regions to eliminate window artifacts.

  2. Hierarchical part-type segmentation using voxel-based curve skeletons

    NARCIS (Netherlands)

    Reniers, Dennie; Telea, Alexandru

    2008-01-01

    We present an effective framework for segmenting 3D shapes into meaningful components using the curve skeleton. Our algorithm identifies a number of critical points on the efficiently computed curve skeleton, either fully automatically as the junctions of the curve skeleton, or based on user input.

  3. Statistical Segmentation of Regions of Interest on a Mammographic Image

    Science.gov (United States)

    Adel, Mouloud; Rasigni, Monique; Bourennane, Salah; Juhan, Valerie

    2007-12-01

    This paper deals with segmentation of breast anatomical regions, pectoral muscle, fatty and fibroglandular regions, using a Bayesian approach. This work is a part of a computer aided diagnosis project aiming at evaluating breast cancer risk and its association with characteristics (density, texture, etc.) of regions of interest on digitized mammograms. Novelty in this paper consists in applying and adapting Markov random field for detecting breast anatomical regions on digitized mammograms whereas most of previous works were focused on masses and microcalcifications. The developed method was tested on 50 digitized mammograms of the mini-MIAS database. Computer segmentation is compared to manual one made by a radiologist. A good agreement is obtained on 68% of the mini-MIAS mammographic image database used in this study. Given obtained segmentation results, the proposed method could be considered as a satisfying first approach for segmenting regions of interest in a breast.

  4. Superpixel Segmentation for Endmember Detection in Hyperspectral Images

    Science.gov (United States)

    Thompson, D. R.; de Granville, C.; Gilmore, M. S.; Castano, R.

    2009-12-01

    "Superpixel segmentation" is a novel approach to facilitate statistical analyses of hyperspectral image data with high spatial resolution and subtle spectral features. The method oversegments the image into homogeneous regions each comprised of several contiguous pixels. This can reduce noise by exploiting scene features' spatial contiguity: isolated spectral features are likely to be noise, but spectral features that appear in several adjacent pixels probably indicate real materials in the scene. The mean spectra from each superpixel define a smaller, noise-reduced dataset. This preprocessing step improves endmember detection for the images in our study. Our endmember detection approach presumes a linear (geographic) mixing model for image spectra. We generate superpixels with the Felzenszwalb/Huttenlocher graph-based segmentation [1] with a Euclidean distance metric. This segmentation shatters the image into thousands of superpixels, each with an area of approximately 20 image pixels. We then apply Symmetric Maximum Angle Convex Cone (SMACC) endmember detection algorithm to the data set consisting of the mean spectrum from all superpixels. We evaluated the approach for several images from the Compact Reconnaissance Imaging Spectrometer (CRISM) [2]. We used the 1000-2500nm wavelengths of images frt00003e12 and frt00003fb9. We cleaned the images with atmospheric correction based on Olympus Mons spectra [3] and preprocessed with a radius-1 median filter in the spectral domain. Endmembers produced with and without the superpixel reduction are compared to the representative (mean) spectra of five representative mineral classes identified in an expert analysis of each scene. Expert-identified minerals include mafic minerals and phyllosilicate deposits that in some cases subtended just a few tens of pixels. Only the endmembers from the superpixel approach reflected all major mineral constituents in the images. Additionally, the superpixel endmembers are more

  5. Filter Design and Performance Evaluation for Fingerprint Image Segmentation.

    Directory of Open Access Journals (Sweden)

    Duy Hoang Thai

    Full Text Available Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: 'true' foreground can be labeled as background and features like minutiae can be lost, or conversely 'true' background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available.

  6. Quantitative measure in image segmentation for skin lesion images: A preliminary study

    Science.gov (United States)

    Azmi, Nurulhuda Firdaus Mohd; Ibrahim, Mohd Hakimi Aiman; Keng, Lau Hui; Ibrahim, Nuzulha Khilwani; Sarkan, Haslina Md

    2014-12-01

    Automatic Skin Lesion Diagnosis (ASLD) allows skin lesion diagnosis by using a computer or mobile devices. The idea of using a computer to assist in diagnosis of skin lesions was first proposed in the literature around 1985. Images of skin lesions are analyzed by the computer to capture certain features thought to be characteristic of skin diseases. These features (expressed as numeric values) are then used to classify the image and report a diagnosis. Image segmentation is often a critical step in image analysis and it may use statistical classification, thresholding, edge detection, region detection, or any combination of these techniques. Nevertheless, image segmentation of skin lesion images is yet limited to superficial evaluations which merely display images of the segmentation results and appeal to the reader's intuition for evaluation. There is a consistent lack of quantitative measure, thus, it is difficult to know which segmentation present useful results and in which situations they do so. If segmentation is done well, then, all other stages in image analysis are made simpler. If significant features (that are crucial for diagnosis) are not extracted from images, it will affect the accuracy of the automated diagnosis. This paper explore the existing quantitative measure in image segmentation ranging in the application of pattern recognition for example hand writing, plat number, and colour. Selecting the most suitable segmentation measure is highly important so that as much relevant features can be identified and extracted.

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

    Science.gov (United States)

    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.

  8. Effect of image scaling and segmentation in digital rock characterisation

    Science.gov (United States)

    Jones, B. D.; Feng, Y. T.

    2016-04-01

    Digital material characterisation from microstructural geometry is an emerging field in computer simulation. For permeability characterisation, a variety of studies exist where the lattice Boltzmann method (LBM) has been used in conjunction with computed tomography (CT) imaging to simulate fluid flow through microscopic rock pores. While these previous works show that the technique is applicable, the use of binary image segmentation and the bounceback boundary condition results in a loss of grain surface definition when the modelled geometry is compared to the original CT image. We apply the immersed moving boundary (IMB) condition of Noble and Torczynski as a partial bounceback boundary condition which may be used to better represent the geometric definition provided by a CT image. The IMB condition is validated against published work on idealised porous geometries in both 2D and 3D. Following this, greyscale image segmentation is applied to a CT image of Diemelstadt sandstone. By varying the mapping of CT voxel densities to lattice sites, it is shown that binary image segmentation may underestimate the true permeability of the sample. A CUDA-C-based code, LBM-C, was developed specifically for this work and leverages GPU hardware in order to carry out computations.

  9. Segmentation and separation of venous vasculatures in liver CT images

    Science.gov (United States)

    Wang, Lei; Hansen, Christian; Zidowitz, Stephan; Hahn, Horst K.

    2014-03-01

    Computer-aided analysis of venous vasculatures including hepatic veins and portal veins is important in liver surgery planning. The analysis normally consists of two important pre-processing tasks: segmenting both vasculatures and separating them from each other by assigning different labels. During the acquisition of multi-phase CT images, both of the venous vessels are enhanced by injected contrast agent and acquired either in a common phase or in two individual phases. The enhanced signals established by contrast agent are often not stably acquired due to non-optimal acquisition time. Inadequate contrast and the presence of large lesions in oncological patients, make the segmentation task quite challenging. To overcome these diffculties, we propose a framework with minimal user interactions to analyze venous vasculatures in multi-phase CT images. Firstly, presented vasculatures are automatically segmented adopting an efficient multi-scale Hessian-based vesselness filter. The initially segmented vessel trees are then converted to a graph representation, on which a series of graph filters are applied in post-processing steps to rule out irrelevant structures. Eventually, we develop a semi-automatic workow to refine the segmentation in the areas of inferior vena cava and entrance of portal veins, and to simultaneously separate hepatic veins from portal veins. Segmentation quality was evaluated with intensive tests enclosing 60 CT images from both healthy liver donors and oncological patients. To quantitatively measure the similarities between segmented and reference vessel trees, we propose three additional metrics: skeleton distance, branch coverage, and boundary surface distance, which are dedicated to quantifying the misalignment induced by both branching patterns and radii of two vessel trees.

  10. Enhanced Image Segmentation Using Fuzzy Logic

    OpenAIRE

    Manpreet singh

    2013-01-01

    This research work proposed an improved edge detection techniques using fuzzy sets. The problem is to find edges in the image, as a first step in the process of scene reconstruction. Edges are scale-dependent and an edge may comprise other edges, but at a definite scale, an edge still has no width. This paper has presented different edge detection operators and their benefit when they merge with fuzzy logic theory. This paper has achieved the accuracy of edge detection up to 94.89 %. The prop...

  11. Segmentation of MR images using multiple-feature vectors

    Science.gov (United States)

    Cole, Orlean I. B.; Daemi, Mohammad F.

    1996-04-01

    Segmentation is an important step in the analysis of MR images (MRI). Considerable progress has been made in this area, and numerous reports on 3D segmentation, volume measurement and visualization have been published in recent years. The main purpose of our study is to investigate the power and use of fractal techniques in extraction of features from MR images of the human brain. These features which are supplemented by other features are used for segmentation, and ultimately for the extraction of a known pathology, in our case multiple- sclerosis (MS) lesions. We are particularly interested in the progress of the lesions and occurrence of new lesions which in a typical case are scattered within the image and are sometimes difficult to identify visually. We propose a technique for multi-channel segmentation of MR images using multiple feature vectors. The channels are proton density, T1-weighted and T2-weighted images containing multiple-sclerosis (MS) lesions at various stages of development. We first represent each image as a set of feature vectors which are estimated using fractal techniques, and supplemented by micro-texture features and features from the gray-level co-occurrence matrix (GLCM). These feature vectors are then used in a feature selection algorithm to reduce the dimension of the feature space. The next stage is segmentation and clustering. The selected feature vectors now form the input to the segmentation and clustering routines and are used as the initial clustering parameters. For this purpose, we have used the classical K-means as the initial clustering method. The clustered image is then passed into a probabilistic classifier to further classify and validate each region, taking into account the spatial properties of the image. Initially, segmentation results were obtained using the fractal dimension features alone. Subsequently, a combination of the fractal dimension features and the supplementary features mentioned above were also obtained

  12. RESEARCH ON SEGMENTATION OF WEED IMAGES BASED ON COMPUTER VISION

    Institute of Scientific and Technical Information of China (English)

    Liu Yajing; Yang Fan; Yang Ruixia; Jia Kejin; Zhang Hongtao

    2007-01-01

    In this letter, a segment algorithm based on color feature of images is proposed. The algorithm separates the weed area from soil background according to the color eigenvalue, which is obtained by analyzing the color difference between the weeds and background in three color spaces RGB, rgb and HSI. The results of the experiment show that it can get notable effect in segmentation according to the color feature, and the possibility of successful segmentation is 87%-93%. This method can also be widely used in other fields which are complicated in the background of the image and facilely influenced in illumination, such as weed identification, tree species discrimination, fruit picking and so on.

  13. Medical image segmentation using object atlas versus object cloud models

    Science.gov (United States)

    Phellan, Renzo; Falcão, Alexandre X.; Udupa, Jayaram K.

    2015-03-01

    Medical image segmentation is crucial for quantitative organ analysis and surgical planning. Since interactive segmentation is not practical in a production-mode clinical setting, automatic methods based on 3D object appearance models have been proposed. Among them, approaches based on object atlas are the most actively investigated. A key drawback of these approaches is that they require a time-costly image registration process to build and deploy the atlas. Object cloud models (OCM) have been introduced to avoid registration, considerably speeding up the whole process, but they have not been compared to object atlas models (OAM). The present paper fills this gap by presenting a comparative analysis of the two approaches in the task of individually segmenting nine anatomical structures of the human body. Our results indicate that OCM achieve a statistically significant better accuracy for seven anatomical structures, in terms of Dice Similarity Coefficient and Average Symmetric Surface Distance.

  14. Statistical Characterization and Segmentation of Drusen in Fundus Images

    Energy Technology Data Exchange (ETDEWEB)

    Santos-Villalobos, Hector J [ORNL; Karnowski, Thomas Paul [ORNL; Aykac, Deniz [ORNL; Giancardo, Luca [ORNL; Li, Yaquin [University of Tennessee, Knoxville (UTK); Nichols, Trent L [ORNL; Tobin Jr, Kenneth William [ORNL; Chaum, Edward [University of Tennessee, Knoxville (UTK)

    2011-01-01

    Age related Macular Degeneration (AMD) is a disease of the retina associated with aging. AMD progression in patients is characterized by drusen, pigmentation changes, and geographic atrophy, which can be seen using fundus imagery. The level of AMD is characterized by standard scaling methods, which can be somewhat subjective in practice. In this work we propose a statistical image processing approach to segment drusen with the ultimate goal of characterizing the AMD progression in a data set of longitudinal images. The method characterizes retinal structures with a statistical model of the colors in the retina image. When comparing the segmentation results of the method between longitudinal images with known AMD progression and those without, the method detects progression in our longitudinal data set with an area under the receiver operating characteristics curve of 0.99.

  15. Message Segmentation to Enhance the Security of LSB Image Steganography

    Directory of Open Access Journals (Sweden)

    Dr. Mohammed Abbas Fadhil Al-Husainy

    2012-03-01

    Full Text Available Classic Least Significant Bit (LSB steganography technique is the most used technique to hide secret information in the least significant bit of the pixels in the stego-image. This paper proposed a technique by splitting the secret message into set of segments, that have same length (number of characters, and find the best LSBs of pixels in the stego-image that are matched to each segment. The main goal of this technique is to minimize the number of LSBs that are changed when substituting them with the bits of characters in the secret message. This will lead to decrease the distortion (noise that is occurred in the pixels of the stego-image and as result increase the immunity of the stego-image against the visual attack. The experiment shows that the proposed technique gives good enhancement to the Classic Least Significant Bit (LSB technique

  16. Crowdsourcing the creation of image segmentation algorithms for connectomics

    Directory of Open Access Journals (Sweden)

    Ignacio eArganda-Carreras

    2015-11-01

    Full Text Available To stimulate progress in automating the reconstruction of neural circuits,we organized the first international challenge on 2D segmentationof electron microscopic (EM images of the brain. Participants submittedboundary maps predicted for a test set of images, and were scoredbased on their agreement with ground truth from human experts. Thewinning team had no prior experience with EM images, and employeda convolutional network. This ``deep learning'' approach has sincebecome accepted as a standard for segmentation of EM images. The challengehas continued to accept submissions, and the best so far has resultedfrom cooperation between two teams. The challenge has probably saturated,as algorithms cannot progress beyond limits set by ambiguities inherentin 2D scoring. Retrospective evaluation of the challenge scoring systemreveals that it was not sufficiently robust to variations in the widthsof neurite borders. We propose a solution to this problem, which shouldbe useful for a future 3D segmentation challenge.

  17. Fingerprint Image Segmentation Algorithm Based on Contourlet Transform Technology

    Directory of Open Access Journals (Sweden)

    Guanghua Zhang

    2016-09-01

    Full Text Available This paper briefly introduces two classic algorithms for fingerprint image processing, which include the soft threshold denoise algorithm of wavelet domain based on wavelet domain and the fingerprint image enhancement algorithm based on Gabor function. Contourlet transform has good texture sensitivity and can be used for the segmentation enforcement of the fingerprint image. The method proposed in this paper has attained the final fingerprint segmentation image through utilizing a modified denoising for a high-frequency coefficient after Contourlet decomposition, highlighting the fingerprint ridge line through modulus maxima detection and finally connecting the broken fingerprint line using a value filter in direction. It can attain richer direction information than the method based on wavelet transform and Gabor function and can make the positioning of detailed features more accurate. However, its ridge should be more coherent. Experiments have shown that this algorithm is obviously superior in fingerprint features detection.

  18. Statistical characterization and segmentation of drusen in fundus images.

    Science.gov (United States)

    Santos-Villalobos, H; Karnowski, T P; Aykac, D; Giancardo, L; Li, Y; Nichols, T; Tobin, K W; Chaum, E

    2011-01-01

    Age related Macular Degeneration (AMD) is a disease of the retina associated with aging. AMD progression in patients is characterized by drusen, pigmentation changes, and geographic atrophy, which can be seen using fundus imagery. The level of AMD is characterized by standard scaling methods, which can be somewhat subjective in practice. In this work we propose a statistical image processing approach to segment drusen with the ultimate goal of characterizing the AMD progression in a data set of longitudinal images. The method characterizes retinal structures with a statistical model of the colors in the retina image. When comparing the segmentation results of the method between longitudinal images with known AMD progression and those without, the method detects progression in our longitudinal data set with an area under the receiver operating characteristics curve of 0.99.

  19. MEDICAL IMAGE SEGMENTATION BASED ON A MODIFIED LEVEL SET ALGORITHM

    Institute of Scientific and Technical Information of China (English)

    Yang Yong; Lin Pan; Zheng Chongxun; Gu Jianwen

    2005-01-01

    Objective To present a novel modified level set algorithm for medical image segmentation. Methods The algorithm is developed by substituting the speed function of level set algorithm with the region and gradient information of the image instead of the conventional gradient information. This new algorithm has been tested by a series of different modality medical images. Results We present various examples and also evaluate and compare the performance of our method with the classical level set method on weak boundaries and noisy images. Conclusion Experimental results show the proposed algorithm is effective and robust.

  20. Automatic tissue segmentation of breast biopsies imaged by QPI

    Science.gov (United States)

    Majeed, Hassaan; Nguyen, Tan; Kandel, Mikhail; Marcias, Virgilia; Do, Minh; Tangella, Krishnarao; Balla, Andre; Popescu, Gabriel

    2016-03-01

    The current tissue evaluation method for breast cancer would greatly benefit from higher throughput and less inter-observer variation. Since quantitative phase imaging (QPI) measures physical parameters of tissue, it can be used to find quantitative markers, eliminating observer subjectivity. Furthermore, since the pixel values in QPI remain the same regardless of the instrument used, classifiers can be built to segment various tissue components without need for color calibration. In this work we use a texton-based approach to segment QPI images of breast tissue into various tissue components (epithelium, stroma or lumen). A tissue microarray comprising of 900 unstained cores from 400 different patients was imaged using Spatial Light Interference Microscopy. The training data were generated by manually segmenting the images for 36 cores and labelling each pixel (epithelium, stroma or lumen.). For each pixel in the data, a response vector was generated by the Leung-Malik (LM) filter bank and these responses were clustered using the k-means algorithm to find the centers (called textons). A random forest classifier was then trained to find the relationship between a pixel's label and the histogram of these textons in that pixel's neighborhood. The segmentation was carried out on the validation set by calculating the texton histogram in a pixel's neighborhood and generating a label based on the model learnt during training. Segmentation of the tissue into various components is an important step toward efficiently computing parameters that are markers of disease. Automated segmentation, followed by diagnosis, can improve the accuracy and speed of analysis leading to better health outcomes.

  1. Image Segmentation Based on Period Difference of the Oscillation

    Institute of Scientific and Technical Information of China (English)

    王直杰; 张珏; 范宏; 柯克峰

    2004-01-01

    A new method for image segmentation based on pulse neural network is proposed. Every neuron in the network represents one pixel in the image and the network is locally connected.Each group of the neurons that correspond to each object synchronizes while different gronps of the neurons oscillate at different period. Applying this period difference,different objects are divided. In addition to simulation, an analysis of the mechanism of the method is presented in this paper.

  2. Color Image Segmentation Method Based on Improved Spectral Clustering Algorithm

    OpenAIRE

    Dong Qin

    2014-01-01

    Contraposing to the features of image data with high sparsity of and the problems on determination of clustering numbers, we try to put forward an color image segmentation algorithm, combined with semi-supervised machine learning technology and spectral graph theory. By the research of related theories and methods of spectral clustering algorithms, we introduce information entropy conception to design a method which can automatically optimize the scale parameter value. So it avoids the unstab...

  3. Ontology-Based Knowledge Organization for the Radiograph Images Segmentation

    Directory of Open Access Journals (Sweden)

    MATEI, O.

    2008-04-01

    Full Text Available The quantity of thoracic radiographies in the medical field is ever growing. An automated system for segmenting the images would help doctors enormously. Some approaches are knowledge-based; therefore we propose here an ontology for this purpose. Thus it is machine oriented, rather than human-oriented. That is all the structures visible on a thoracic image are described from a technical point of view.

  4. Segmentation of Camera Captured Business Card Images for Mobile Devices

    CERN Document Server

    Mollah, Ayatullah Faruk; Nasipuri, Mita

    2011-01-01

    Due to huge deformation in the camera captured images, variety in nature of the business cards and the computational constraints of the mobile devices, design of an efficient Business Card Reader (BCR) is challenging to the researchers. Extraction of text regions and segmenting them into characters is one of such challenges. In this paper, we have presented an efficient character segmentation technique for business card images captured by a cell-phone camera, designed in our present work towards developing an efficient BCR. At first, text regions are extracted from the card images and then the skewed ones are corrected using a computationally efficient skew correction technique. At last, these skew corrected text regions are segmented into lines and characters based on horizontal and vertical histogram. Experiments show that the present technique is efficient and applicable for mobile devices, and the mean segmentation accuracy of 97.48% is achieved with 3 mega-pixel (500-600 dpi) images. It takes only 1.1 se...

  5. Contourlet-based active contour model for PET image segmentation

    NARCIS (Netherlands)

    Abdoli, M.; Dierckx, R. A. J. O.; Zaidi, H.

    Purpose: PET-guided radiation therapy treatment planning, clinical diagnosis, assessment of tumor growth, and therapy response rely on the accurate delineation of the tumor volume and quantification of tracer uptake. Most PET image segmentation techniques proposed thus far are suboptimal in the

  6. Unsupervised segmentation of predefined shapes in multivariate images

    NARCIS (Netherlands)

    Noordam, J.C.; Broek, van den W.H.A.M.; Buydens, L.M.C.

    2003-01-01

    Fuzzy C-means (FCM) is an unsupervised clustering technique that is often used for the unsupervised segmentation of multivariate images. In traditional FCM the clustering is based on spectral information only and the geometrical relationship between neighbouring pixels is not used in the clustering

  7. Contourlet-based active contour model for PET image segmentation

    NARCIS (Netherlands)

    Abdoli, M.; Dierckx, R. A. J. O.; Zaidi, H.

    2013-01-01

    Purpose: PET-guided radiation therapy treatment planning, clinical diagnosis, assessment of tumor growth, and therapy response rely on the accurate delineation of the tumor volume and quantification of tracer uptake. Most PET image segmentation techniques proposed thus far are suboptimal in the pres

  8. Optimization-Based Image Segmentation by Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    H. Laurent

    2008-05-01

    Full Text Available Many works in the literature focus on the definition of evaluation metrics and criteria that enable to quantify the performance of an image processing algorithm. These evaluation criteria can be used to define new image processing algorithms by optimizing them. In this paper, we propose a general scheme to segment images by a genetic algorithm. The developed method uses an evaluation criterion which quantifies the quality of an image segmentation result. The proposed segmentation method can integrate a local ground truth when it is available in order to set the desired level of precision of the final result. A genetic algorithm is then used in order to determine the best combination of information extracted by the selected criterion. Then, we show that this approach can either be applied for gray-levels or multicomponents images in a supervised context or in an unsupervised one. Last, we show the efficiency of the proposed method through some experimental results on several gray-levels and multicomponents images.

  9. Optimization-Based Image Segmentation by Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Rosenberger C

    2008-01-01

    Full Text Available Abstract Many works in the literature focus on the definition of evaluation metrics and criteria that enable to quantify the performance of an image processing algorithm. These evaluation criteria can be used to define new image processing algorithms by optimizing them. In this paper, we propose a general scheme to segment images by a genetic algorithm. The developed method uses an evaluation criterion which quantifies the quality of an image segmentation result. The proposed segmentation method can integrate a local ground truth when it is available in order to set the desired level of precision of the final result. A genetic algorithm is then used in order to determine the best combination of information extracted by the selected criterion. Then, we show that this approach can either be applied for gray-levels or multicomponents images in a supervised context or in an unsupervised one. Last, we show the efficiency of the proposed method through some experimental results on several gray-levels and multicomponents images.

  10. AN IMPROVED FUZZY CLUSTERING ALGORITHM FOR MICROARRAY IMAGE SPOTS SEGMENTATION

    Directory of Open Access Journals (Sweden)

    V.G. Biju

    2015-11-01

    Full Text Available An automatic cDNA microarray image processing using an improved fuzzy clustering algorithm is presented in this paper. The spot segmentation algorithm proposed uses the gridding technique developed by the authors earlier, for finding the co-ordinates of each spot in an image. Automatic cropping of spots from microarray image is done using these co-ordinates. The present paper proposes an improved fuzzy clustering algorithm Possibility fuzzy local information c means (PFLICM to segment the spot foreground (FG from background (BG. The PFLICM improves fuzzy local information c means (FLICM algorithm by incorporating typicality of a pixel along with gray level information and local spatial information. The performance of the algorithm is validated using a set of simulated cDNA microarray images added with different levels of AWGN noise. The strength of the algorithm is tested by computing the parameters such as the Segmentation matching factor (SMF, Probability of error (pe, Discrepancy distance (D and Normal mean square error (NMSE. SMF value obtained for PFLICM algorithm shows an improvement of 0.9 % and 0.7 % for high noise and low noise microarray images respectively compared to FLICM algorithm. The PFLICM algorithm is also applied on real microarray images and gene expression values are computed.

  11. A New Wavelet-Based Document Image Segmentation Scheme

    Institute of Scientific and Technical Information of China (English)

    赵健; 李道京; 俞卞章; 耿军平

    2002-01-01

    The document image segmentation is very useful for printing, faxing and data processing. An algorithm is developed for segmenting and classifying document image. Feature used for classification is based on the histogram distribution pattern of different image classes. The important attribute of the algorithm is using wavelet correlation image to enhance raw image's pattern, so the classification accuracy is improved. In this paper document image is divided into four types: background, photo, text and graph. Firstly, the document image background has been distingusished easily by former normally method; secondly, three image types will be distinguished by their typical histograms, in order to make histograms feature clearer, each resolution' s HH wavelet subimage is used to add to the raw image at their resolution. At last, the photo, text and praph have been devided according to how the feature fit to the Laplacian distrbution by -X2 and L. Simulations show that classification accuracy is significantly improved. The comparison with related shows that our algorithm provides both lower classification error rates and better visual results.

  12. Embryonic Heart Morphogenesis from Confocal Microscopy Imaging and Automatic Segmentation

    Directory of Open Access Journals (Sweden)

    Hongda Mao

    2013-01-01

    Full Text Available Embryonic heart morphogenesis (EHM is a complex and dynamic process where the heart transforms from a single tube into a four-chambered pump. This process is of great biological and clinical interest but is still poorly understood for two main reasons. On the one hand, the existing imaging modalities for investigating EHM suffered from either limited penetration depth or limited spatial resolution. On the other hand, current works typically adopted manual segmentation, which was tedious, subjective, and time consuming considering the complexity of developing heart geometry and the large size of images. In this paper, we propose to utilize confocal microscopy imaging with tissue optical immersion clearing technique to image the heart at different stages of development for EHM study. The imaging method is able to produce high spatial resolution images and achieve large penetration depth at the same time. Furthermore, we propose a novel convex active contour model for automatic image segmentation. The model has the ability to deal with intensity fall-off in depth which is characterized by confocal microscopy images. We acquired the images of embryonic quail hearts from day 6 to day 14 of incubation for EHM study. The experimental results were promising and provided us with an insight view of early heart growth pattern and also paved the road for data-driven heart growth modeling.

  13. From acoustic segmentation to language processing: evidence from optical imaging.

    Science.gov (United States)

    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.

  14. Lung tumor segmentation in PET images using graph cuts.

    Science.gov (United States)

    Ballangan, Cherry; Wang, Xiuying; Fulham, Michael; Eberl, Stefan; Feng, David Dagan

    2013-03-01

    The aim of segmentation of tumor regions in positron emission tomography (PET) is to provide more accurate measurements of tumor size and extension into adjacent structures, than is possible with visual assessment alone and hence improve patient management decisions. We propose a segmentation energy function for the graph cuts technique to improve lung tumor segmentation with PET. Our segmentation energy is based on an analysis of the tumor voxels in PET images combined with a standardized uptake value (SUV) cost function and a monotonic downhill SUV feature. The monotonic downhill feature avoids segmentation leakage into surrounding tissues with similar or higher PET tracer uptake than the tumor and the SUV cost function improves the boundary definition and also addresses situations where the lung tumor is heterogeneous. We evaluated the method in 42 clinical PET volumes from patients with non-small cell lung cancer (NSCLC). Our method improves segmentation and performs better than region growing approaches, the watershed technique, fuzzy-c-means, region-based active contour and tumor customized downhill.

  15. A general approach to liver lesion segmentation in CT images

    Science.gov (United States)

    Cao, Li; Udupa, Jayaram K.; Odhner, Dewey; Huang, Lidong; Tong, Yubing; Torigian, Drew A.

    2016-03-01

    Lesion segmentation has remained a challenge in different body regions. Generalizability is lacking in published methods as variability in results is common, even for a given organ and modality, such that it becomes difficult to establish standardized methods of disease quantification and reporting. This paper makes an attempt at a generalizable method based on classifying lesions along with their background into groups using clinically used visual attributes. Using an Iterative Relative Fuzzy Connectedness (IRFC) delineation engine, the ideas are implemented for the task of liver lesion segmentation in computed tomography (CT) images. For lesion groups with the same background properties, a few subjects are chosen as the training set to obtain the optimal IRFC parameters for the background tissue components. For lesion groups with similar foreground properties, optimal foreground parameters for IRFC are set as the median intensity value of the training lesion subset. To segment liver lesions belonging to a certain group, the devised method requires manual loading of the corresponding parameters, and correct setting of the foreground and background seeds. The segmentation is then completed in seconds. Segmentation accuracy and repeatability with respect to seed specification are evaluated. Accuracy is assessed by the assignment of a delineation quality score (DQS) to each case. Inter-operator repeatability is assessed by the difference between segmentations carried out independently by two operators. Experiments on 80 liver lesion cases show that the proposed method achieves a mean DQS score of 4.03 and inter-operator repeatability of 92.3%.

  16. Segmentation of multiple sclerosis lesions in MR images: a review

    Energy Technology Data Exchange (ETDEWEB)

    Mortazavi, Daryoush; Kouzani, Abbas Z. [Deakin University, School of Engineering, Geelong, Victoria (Australia); Soltanian-Zadeh, Hamid [Henry Ford Health System, Image Analysis Laboratory, Radiology Department, Detroit, MI (United States); University of Tehran, Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, Tehran (Iran, Islamic Republic of); School of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics (IPM), Tehran (Iran, Islamic Republic of)

    2012-04-15

    Multiple sclerosis (MS) is an inflammatory demyelinating disease that the parts of the nervous system through the lesions generated in the white matter of the brain. It brings about disabilities in different organs of the body such as eyes and muscles. Early detection of MS and estimation of its progression are critical for optimal treatment of the disease. For diagnosis and treatment evaluation of MS lesions, they may be detected and segmented in Magnetic Resonance Imaging (MRI) scans of the brain. However, due to the large amount of MRI data to be analyzed, manual segmentation of the lesions by clinical experts translates into a very cumbersome and time consuming task. In addition, manual segmentation is subjective and prone to human errors. Several groups have developed computerized methods to detect and segment MS lesions. These methods are not categorized and compared in the past. This paper reviews and compares various MS lesion segmentation methods proposed in recent years. It covers conventional methods like multilevel thresholding and region growing, as well as more recent Bayesian methods that require parameter estimation algorithms. It also covers parameter estimation methods like expectation maximization and adaptive mixture model which are among unsupervised techniques as well as kNN and Parzen window methods that are among supervised techniques. Integration of knowledge-based methods such as atlas-based approaches with Bayesian methods increases segmentation accuracy. In addition, employing intelligent classifiers like Fuzzy C-Means, Fuzzy Inference Systems, and Artificial Neural Networks reduces misclassified voxels. (orig.)

  17. Image Segmentation Using Two Step Splitting Function

    Directory of Open Access Journals (Sweden)

    Gopal Kumar Jha

    2013-12-01

    Full Text Available Image processing and computer vision is widely using Level Set Method (LSM. In conventional level set formulation, irregularities are developed during evolution of level set function, which cause numerical errors and eventually destroy the stability of the evolution. Therefore a numerical remedy called re-initialization is typically applied periodically to replace the degraded level set function. However re –initialization raises serious problem that is when and how it should be performed and also affects numerical accuracy in an undesirable way. To overcome this drawback of re-initialization process, a new variation level set formulation called Distance regularization level set evolution (DRLSE is introduced in which the regularity of the level set function is internally maintained during the level set evolution. DRLSE allows more general and effective initialization of the level set function. But DRLSE uses relatively large number of steps to ensure efficient numerical accuracy. Here in this thesis we are implementing faster and equally efficient computation technique called two step splitting method (TSSM. TSSM is physio-chemical reaction diffusion equation in which firstly LSE equation get iterated and then regularize the level set function from the first step to ensure the stability and hence re-initialization is completely eliminated from LSE which also satisfy DRLSE.

  18. Automated 3D renal segmentation based on image partitioning

    Science.gov (United States)

    Yeghiazaryan, Varduhi; Voiculescu, Irina D.

    2016-03-01

    Despite several decades of research into segmentation techniques, automated medical image segmentation is barely usable in a clinical context, and still at vast user time expense. This paper illustrates unsupervised organ segmentation through the use of a novel automated labelling approximation algorithm followed by a hypersurface front propagation method. The approximation stage relies on a pre-computed image partition forest obtained directly from CT scan data. We have implemented all procedures to operate directly on 3D volumes, rather than slice-by-slice, because our algorithms are dimensionality-independent. The results picture segmentations which identify kidneys, but can easily be extrapolated to other body parts. Quantitative analysis of our automated segmentation compared against hand-segmented gold standards indicates an average Dice similarity coefficient of 90%. Results were obtained over volumes of CT data with 9 kidneys, computing both volume-based similarity measures (such as the Dice and Jaccard coefficients, true positive volume fraction) and size-based measures (such as the relative volume difference). The analysis considered both healthy and diseased kidneys, although extreme pathological cases were excluded from the overall count. Such cases are difficult to segment both manually and automatically due to the large amplitude of Hounsfield unit distribution in the scan, and the wide spread of the tumorous tissue inside the abdomen. In the case of kidneys that have maintained their shape, the similarity range lies around the values obtained for inter-operator variability. Whilst the procedure is fully automated, our tools also provide a light level of manual editing.

  19. Repair approach for DMC images based on hierarchical location using edge curve

    Institute of Scientific and Technical Information of China (English)

    PAN Jun; WANG Mi; LI DeRen; FENG TianTian

    2009-01-01

    The color composite digital mapping camera (DMC) images are produced by the post-processing software of Z/I imaging. But the failure of radiometric correction in post-processing leads to residual radiometric differences between CCD images, which then affect the quality of the images in further applications. This paper, via analyzing the characters and causes of such a phenomenon, proposes a repair approach based on hierarchical location using edge curve. The approach employs a hierarchical strategy to locate the transition area and seam-line automatically and then repair the image through the global reconstruction between CCD images and the local reconstruction in the transition area. Experiments indicate that the approach proposed by this paper is feasible and can improve the quality of images effectively.

  20. Segmentation of MRI Using Hierarchical Markov Random Field%基于层次MRF的MR图像分割(英文)

    Institute of Scientific and Technical Information of China (English)

    张红梅; 袁泽剑; 蔡忠闽; 卞正中

    2002-01-01

    核磁共振图像(MRI)的定量分析在神经疾病的早期治疗中有很重要作用.提出了一种基于层次Markov随机场模型的MRI图像分割新方法.在高层次的标记图象中采用了混合模型,即区域的内部用各向同性均匀MRF来建模,边界用各向异性非均匀MRF来建模.所以方向性被引入到边界信息中,这样可以更准确的表达标记图象的特性;在低层次的像素图像中,不同区域中像素的灰度分布用不同的高斯纹理来描述.分割问题可以被转换成一种最大后验概率估计问题.采用基于直方图的DAEM算法来估计SNFM参数的全局最优值;并基于MRF先验参数的实际意义,提出一种近似的方法来简化这些参数的估计,实验显示该方法能获得更好的结果.%Magnetic Resonance Image (MRI) segmentation plays a major role in the tissue quantitative analysis which benefits the early treatment of neurological diseases. In this paper, a new approach to MRI segmentation based on hierarchical Markov random field (MRF) model is proposed: In higher-level MRF, a new mixture model is presented to describe the label image, that is, the interior of region is modeled by homogenous and isotropic MRF while the boundary is modeled by inhomogeneous and anisotropic MRF. So the orientation is incorporated into the boundary information and the characteristic of label image can be more accurately represented. In lower-level MRF, the different Gauss texture is filled in each region to describe pixel image. Then the segmentation problem is formulated as Maximum a Posterior Probability (MAP) estimation rule. A histogram based DAEM algorithm is used, which is able to find the global optima of the standard finite normal mixture (SFNM) parameters. Based on the meaning of prior MRF parameter, an approximate method is proposed to simplify the estimation of those parameters. Experiments on the pathological MRI show that our approach can achieve better results.

  1. Comparison of perceptual color spaces for natural image segmentation tasks

    Science.gov (United States)

    Correa-Tome, Fernando E.; Sanchez-Yanez, Raul E.; Ayala-Ramirez, Victor

    2011-11-01

    Color image segmentation largely depends on the color space chosen. Furthermore, spaces that show perceptual uniformity seem to outperform others due to their emulation of the human perception of color. We evaluate three perceptual color spaces, CIELAB, CIELUV, and RLAB, in order to determine their contribution to natural image segmentation and to identify the space that obtains the best results over a test set of images. The nonperceptual color space RGB is also included for reference purposes. In order to quantify the quality of resulting segmentations, an empirical discrepancy evaluation methodology is discussed. The Berkeley Segmentation Dataset and Benchmark is used in test series, and two approaches are taken to perform the experiments: supervised pixelwise classification using reference colors, and unsupervised clustering using k-means. A majority filter is used as a postprocessing stage, in order to determine its contribution to the result. Furthermore, a comparison of elapsed times taken by the required transformations is included. The main finding of our study is that the CIELUV color space outperforms the other color spaces in both discriminatory performance and computational speed, for the average case.

  2. Segmentation of color images based on the gravitational clustering concept

    Science.gov (United States)

    Lai, Andrew H.; Yung, H. C.

    1998-03-01

    A new clustering algorithm derived from the Markovian model of the gravitational clustering concept is proposed that works in the RGB measurement space for color image. To enable the model to be applicable in image segmentation, the new algorithm imposes a clustering constraint at each clustering iteration to control and determine the formation of multiple clusters. Using such constraint to limit the attraction between clusters, a termination condition can be easily defined. The new clustering algorithm is evaluated objectively and subjectively on three different images against the K-means clustering algorithm, the recursive histogram clustering algorithm for color, the Hedley-Yan algorithm, and the widely used seed-based region growing algorithm. From the evaluation, it is observed that the new algorithm exhibits the following characteristics: (1) its objective measurement figures are comparable with the best in this group of segmentation algorithms; (2) it generates smoother region boundaries; (3) the segmented boundaries align closely with the original boundaries; and (4) it forms a meaningful number of segmented regions.

  3. Interactive segmentation for geographic atrophy in retinal fundus images.

    Science.gov (United States)

    Lee, Noah; Smith, R Theodore; Laine, Andrew F

    2008-10-01

    Fundus auto-fluorescence (FAF) imaging is a non-invasive technique for in vivo ophthalmoscopic inspection of age-related macular degeneration (AMD), the most common cause of blindness in developed countries. Geographic atrophy (GA) is an advanced form of AMD and accounts for 12-21% of severe visual loss in this disorder [3]. Automatic quantification of GA is important for determining disease progression and facilitating clinical diagnosis of AMD. The problem of automatic segmentation of pathological images still remains an unsolved problem. In this paper we leverage the watershed transform and generalized non-linear gradient operators for interactive segmentation and present an intuitive and simple approach for geographic atrophy segmentation. We compare our approach with the state of the art random walker [5] algorithm for interactive segmentation using ROC statistics. Quantitative evaluation experiments on 100 FAF images show a mean sensitivity/specificity of 98.3/97.7% for our approach and a mean sensitivity/specificity of 88.2/96.6% for the random walker algorithm.

  4. Superpixel Cut for Figure-Ground Image Segmentation

    Science.gov (United States)

    Yang, Michael Ying; Rosenhahn, Bodo

    2016-06-01

    Figure-ground image segmentation has been a challenging problem in computer vision. Apart from the difficulties in establishing an effective framework to divide the image pixels into meaningful groups, the notions of figure and ground often need to be properly defined by providing either user inputs or object models. In this paper, we propose a novel graph-based segmentation framework, called superpixel cut. The key idea is to formulate foreground segmentation as finding a subset of superpixels that partitions a graph over superpixels. The problem is formulated as Min-Cut. Therefore, we propose a novel cost function that simultaneously minimizes the inter-class similarity while maximizing the intra-class similarity. This cost function is optimized using parametric programming. After a small learning step, our approach is fully automatic and fully bottom-up, which requires no high-level knowledge such as shape priors and scene content. It recovers coherent components of images, providing a set of multiscale hypotheses for high-level reasoning. We evaluate our proposed framework by comparing it to other generic figure-ground segmentation approaches. Our method achieves improved performance on state-of-the-art benchmark databases.

  5. SEGMENTATION OF RANGE IMAGE BASED ON KOHONEN NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    Zou Ning; Liu Jian; Zhou Manli; Li Qing

    2001-01-01

    This paper presents an unsupervised range image segmentation based on Kohonen neural network. At first, the derivative and partial derivative of each point are calculated and the normal in each points is gotten. With the character vectors including normal and range value, self-organization map is introduced to cluster. The normal analysis is used to eliminate over-segmentation and the last result is gotten. This method avoid selecting original seeds and uses fewer samples, moreover computes rapidly. The experiment shows the better performance.

  6. Sequence-independent segmentation of magnetic resonance images.

    Science.gov (United States)

    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.

  7. An eSnake model for medical image segmentation

    Institute of Scientific and Technical Information of China (English)

    L(U) Hongyu; YUAN Kehong; BAO Shanglian; ZU Donglin; DUAN Chaijie

    2005-01-01

    A novel scheme of external force for detecting the object boundary of medical image based on Snakes (active contours)is introduced in the paper. In our new method, an electrostatic field on a template plane above the original image plane is designed to form the map of the external force. Compared with the method of Gradient Vector Flow (GVF), our approach has clear physical meanings. It has stronger ability to conform to boundary concavities, is simple to implement, and reliable for shape segmenting. Additionally, our method has larger capture range for the external force and is useful for medical image preprocessing in various applications. Finally, by adding the balloon force to the electrostatic field model, our Snake is able to represent long tube-like shapes or shapes with significant protrusions or bifurcations, and it has the specialty to prevent Snake leaking from large gaps on image edge by using a two-stage segmentation technique introduced in this paper. The test of our models proves that our methods are robust, precise in medical image segmentation.

  8. Cryo-EM Structure Determination Using Segmented Helical Image Reconstruction.

    Science.gov (United States)

    Fromm, S A; Sachse, C

    2016-01-01

    Treating helices as single-particle-like segments followed by helical image reconstruction has become the method of choice for high-resolution structure determination of well-ordered helical viruses as well as flexible filaments. In this review, we will illustrate how the combination of latest hardware developments with optimized image processing routines have led to a series of near-atomic resolution structures of helical assemblies. Originally, the treatment of helices as a sequence of segments followed by Fourier-Bessel reconstruction revealed the potential to determine near-atomic resolution structures from helical specimens. In the meantime, real-space image processing of helices in a stack of single particles was developed and enabled the structure determination of specimens that resisted classical Fourier helical reconstruction and also facilitated high-resolution structure determination. Despite the progress in real-space analysis, the combination of Fourier and real-space processing is still commonly used to better estimate the symmetry parameters as the imposition of the correct helical symmetry is essential for high-resolution structure determination. Recent hardware advancement by the introduction of direct electron detectors has significantly enhanced the image quality and together with improved image processing procedures has made segmented helical reconstruction a very productive cryo-EM structure determination method.

  9. Automatic character detection and segmentation in natural scene images

    Institute of Scientific and Technical Information of China (English)

    ZHU Kai-hua; QI Fei-hu; JIANG Ren-jie; XU Li

    2007-01-01

    We present a robust connected-component (CC) based method for automatic detection and segmentation of text in real-scene images. This technique can be applied in robot vision, sign recognition, meeting processing and video indexing. First, a Non-Linear Niblack method (NLNiblack) is proposed to decompose the image into candidate CCs. Then, all these CCs are fed into a cascade of classifiers trained by Adaboost algorithm. Each classifier in the cascade responds to one feature of the CC. Proposed here are 12 novel features which are insensitive to noise, scale, text orientation and text language. The classifier cascade allows non-text CCs of the image to be rapidly discarded while more computation is spent on promising text-like CCs. The CCs passing through the cascade are considered as text components and are used to form the segmentation result. A prototype system was built,with experimental results proving the effectiveness and efficiency of the proposed method.

  10. Minimum description length synthetic aperture radar image segmentation.

    Science.gov (United States)

    Galland, Frédéric; Bertaux, Nicolas; Réfrégier, Philippe

    2003-01-01

    We present a new minimum description length (MDL) approach based on a deformable partition--a polygonal grid--for automatic segmentation of a speckled image composed of several homogeneous regions. The image segmentation thus consists in the estimation of the polygonal grid, or, more precisely, its number of regions, its number of nodes and the location of its nodes. These estimations are performed by minimizing a unique MDL criterion which takes into account the probabilistic properties of speckle fluctuations and a measure of the stochastic complexity of the polygonal grid. This approach then leads to a global MDL criterion without an undetermined parameter since no other regularization term than the stochastic complexity of the polygonal grid is necessary and noise parameters can be estimated with maximum likelihood-like approaches. The performance of this technique is illustrated on synthetic and real synthetic aperture radar images of agricultural regions and the influence of different terms of the model is analyzed.

  11. Image segmentation for enhancing symbol recognition in prosthetic vision.

    Science.gov (United States)

    Horne, Lachlan; Barnes, Nick; McCarthy, Chris; He, Xuming

    2012-01-01

    Current and near-term implantable prosthetic vision systems offer the potential to restore some visual function, but suffer from poor resolution and dynamic range of induced phosphenes. This can make it difficult for users of prosthetic vision systems to identify symbolic information (such as signs) except in controlled conditions. Using image segmentation techniques from computer vision, we show it is possible to improve the clarity of such symbolic information for users of prosthetic vision implants in uncontrolled conditions. We use image segmentation to automatically divide a natural image into regions, and using a fixation point controlled by the user, select a region to phosphenize. This technique improves the apparent contrast and clarity of symbolic information over traditional phosphenization approaches.

  12. Multi-scale classification based lesion segmentation for dermoscopic images.

    Science.gov (United States)

    Abedini, Mani; Codella, Noel; Chakravorty, Rajib; Garnavi, Rahil; Gutman, David; Helba, Brian; Smith, John R

    2016-08-01

    This paper presents a robust segmentation method based on multi-scale classification to identify the lesion boundary in dermoscopic images. Our proposed method leverages a collection of classifiers which are trained at various resolutions to categorize each pixel as "lesion" or "surrounding skin". In detection phase, trained classifiers are applied on new images. The classifier outputs are fused at pixel level to build probability maps which represent lesion saliency maps. In the next step, Otsu thresholding is applied to convert the saliency maps to binary masks, which determine the border of the lesions. We compared our proposed method with existing lesion segmentation methods proposed in the literature using two dermoscopy data sets (International Skin Imaging Collaboration and Pedro Hispano Hospital) which demonstrates the superiority of our method with Dice Coefficient of 0.91 and accuracy of 94%.

  13. Underwater acoustic image segmentation based on deformable template

    Institute of Scientific and Technical Information of China (English)

    SANG Enfang; LIU Zhuofu

    2005-01-01

    In order to solve the problem of deformation and blurred edge in underwater acoustic image segmentation, an approach based on the deformable template is presented. Compared with the energy minimization of the Snake model, the energy function is redefined by adding a shape restriction. This improves the noise-resistance ability so that robustness and high segmentation efficiency are acquired. The energy optimization problem is tackled using the Dijkstra Algorithm. This method has been successfully tested on the filled-in acoustic images.The results show that this algorithm is efficient, precise and very immune to image deformation and noise when compared to results obtained from the Snake model and several traditional optimization methods.

  14. Segmentation of Sinus Images for Grading of Severity of Sinusitis

    Science.gov (United States)

    Iznita Izhar, Lila; Sagayan Asirvadam, Vijanth; Lee, San Nien

    Sinusitis is commonly diagnosed with techniques such as endoscopy, ultrasound, X-ray, Computed Tomography (CT) scan and Magnetic Resonance Imaging (MRI). Out of these techniques, imaging techniques are less invasive while being able to show blockage of sinus cavities. This project attempts to develop a computerize system by developing algorithm for the segmentation of sinus images for the detection of sinusitis. The sinus images were firstly undergo noise removal process by median filtering followed by Contrast Limited Adapted Histogram Equalisation (CLAHE) for image enhancement. Multilevel thresholding algorithm were then applied to segment the enhanced images into meaningful regions for the detection and diagnosis of severity of sinusitis. The multilevel thresholding algorithms based on Otsu method were able to extract three distinct and important features namely bone region, hollow and mucous areas from the images. Simulations were performed on images of healthy sinuses and sinuses with sinusitis. The developed algorithms are found to be able to differentiate and evaluate healthy sinuses and sinuses with sinusitis effectively.

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

    Science.gov (United States)

    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.

  16. The method of parallel-hierarchical transformation for rapid recognition of dynamic images using GPGPU technology

    Science.gov (United States)

    Timchenko, Leonid; Yarovyi, Andrii; Kokriatskaya, Nataliya; Nakonechna, Svitlana; Abramenko, Ludmila; Ławicki, Tomasz; Popiel, Piotr; Yesmakhanova, Laura

    2016-09-01

    The paper presents a method of parallel-hierarchical transformations for rapid recognition of dynamic images using GPU technology. Direct parallel-hierarchical transformations based on cluster CPU-and GPU-oriented hardware platform. Mathematic models of training of the parallel hierarchical (PH) network for the transformation are developed, as well as a training method of the PH network for recognition of dynamic images. This research is most topical for problems on organizing high-performance computations of super large arrays of information designed to implement multi-stage sensing and processing as well as compaction and recognition of data in the informational structures and computer devices. This method has such advantages as high performance through the use of recent advances in parallelization, possibility to work with images of ultra dimension, ease of scaling in case of changing the number of nodes in the cluster, auto scan of local network to detect compute nodes.

  17. Image segmentation based on scaled fuzzy membership functions

    DEFF Research Database (Denmark)

    Jantzen, Jan; Ring,, P.; Christiansen, Pernille

    1993-01-01

    As a basis for an automated interpretation of magnetic resonance images, the authors propose a fuzzy segmentation method. The method uses five standard fuzzy membership functions: small, small medium, medium, large medium, and large. The method fits these membership functions to the modes...... of interest in the image histogram by means of a piecewise-linear transformation. A test example is given concerning a human head image, including a sensitivity analysis based on the fuzzy area measure. The method provides a rule-based interface to the physician...

  18. Control of multiple excited image states around segmented carbon nanotubes

    Energy Technology Data Exchange (ETDEWEB)

    Knörzer, J., E-mail: johannes.knoerzer@physnet.uni-hamburg.de; Fey, C., E-mail: christian.fey@physnet.uni-hamburg.de [Zentrum für Optische Quantentechnologien, Universität Hamburg, Luruper Chaussee 149, Hamburg 22761 (Germany); Sadeghpour, H. R. [ITAMP, Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, Massachusetts 02138 (United States); Schmelcher, P. [Zentrum für Optische Quantentechnologien, Universität Hamburg, Luruper Chaussee 149, Hamburg 22761 (Germany); The Hamburg Centre for Ultrafast Imaging, Luruper Chaussee 149, Hamburg 22761 (Germany)

    2015-11-28

    Electronic image states around segmented carbon nanotubes can be confined and shaped along the nanotube axis by engineering the image potential. We show how several such image states can be prepared simultaneously along the same nanotube. The inter-electronic distance can be controlled a priori by engineering tubes of specific geometries. High sensitivity to external electric and magnetic fields can be exploited to manipulate these states and their mutual long-range interactions. These building blocks provide access to a new kind of tailored interacting quantum systems.

  19. Colour image segmentation using unsupervised clustering technique for acute leukemia images

    Science.gov (United States)

    Halim, N. H. Abd; Mashor, M. Y.; Nasir, A. S. Abdul; Mustafa, N.; Hassan, R.

    2015-05-01

    Colour image segmentation has becoming more popular for computer vision due to its important process in most medical analysis tasks. This paper proposes comparison between different colour components of RGB(red, green, blue) and HSI (hue, saturation, intensity) colour models that will be used in order to segment the acute leukemia images. First, partial contrast stretching is applied on leukemia images to increase the visual aspect of the blast cells. Then, an unsupervised moving k-means clustering algorithm is applied on the various colour components of RGB and HSI colour models for the purpose of segmentation of blast cells from the red blood cells and background regions in leukemia image. Different colour components of RGB and HSI colour models have been analyzed in order to identify the colour component that can give the good segmentation performance. The segmented images are then processed using median filter and region growing technique to reduce noise and smooth the images. The results show that segmentation using saturation component of HSI colour model has proven to be the best in segmenting nucleus of the blast cells in acute leukemia image as compared to the other colour components of RGB and HSI colour models.

  20. An image processing pipeline to detect and segment nuclei in muscle fiber microscopic images.

    Science.gov (United States)

    Guo, Yanen; Xu, Xiaoyin; Wang, Yuanyuan; Wang, Yaming; Xia, Shunren; Yang, Zhong

    2014-08-01

    Muscle fiber images play an important role in the medical diagnosis and treatment of many muscular diseases. The number of nuclei in skeletal muscle fiber images is a key bio-marker of the diagnosis of muscular dystrophy. In nuclei segmentation one primary challenge is to correctly separate the clustered nuclei. In this article, we developed an image processing pipeline to automatically detect, segment, and analyze nuclei in microscopic image of muscle fibers. The pipeline consists of image pre-processing, identification of isolated nuclei, identification and segmentation of clustered nuclei, and quantitative analysis. Nuclei are initially extracted from background by using local Otsu's threshold. Based on analysis of morphological features of the isolated nuclei, including their areas, compactness, and major axis lengths, a Bayesian network is trained and applied to identify isolated nuclei from clustered nuclei and artifacts in all the images. Then a two-step refined watershed algorithm is applied to segment clustered nuclei. After segmentation, the nuclei can be quantified for statistical analysis. Comparing the segmented results with those of manual analysis and an existing technique, we find that our proposed image processing pipeline achieves good performance with high accuracy and precision. The presented image processing pipeline can therefore help biologists increase their throughput and objectivity in analyzing large numbers of nuclei in muscle fiber images.

  1. Hybrid Artificial Root Foraging Optimizer Based Multilevel Threshold for Image Segmentation

    Science.gov (United States)

    Liu, Yang; Liu, Junfei

    2016-01-01

    This paper proposes a new plant-inspired optimization algorithm for multilevel threshold image segmentation, namely, hybrid artificial root foraging optimizer (HARFO), which essentially mimics the iterative root foraging behaviors. In this algorithm the new growth operators of branching, regrowing, and shrinkage are initially designed to optimize continuous space search by combining root-to-root communication and coevolution mechanism. With the auxin-regulated scheme, various root growth operators are guided systematically. With root-to-root communication, individuals exchange information in different efficient topologies, which essentially improve the exploration ability. With coevolution mechanism, the hierarchical spatial population driven by evolutionary pressure of multiple subpopulations is structured, which ensure that the diversity of root population is well maintained. The comparative results on a suit of benchmarks show the superiority of the proposed algorithm. Finally, the proposed HARFO algorithm is applied to handle the complex image segmentation problem based on multilevel threshold. Computational results of this approach on a set of tested images show the outperformance of the proposed algorithm in terms of optimization accuracy computation efficiency. PMID:27725826

  2. Intra-patient semi-automated segmentation of the cervix-uterus in CT-images for adaptive radiotherapy of cervical cancer

    Science.gov (United States)

    Luiza Bondar, M.; Hoogeman, Mischa; Schillemans, Wilco; Heijmen, Ben

    2013-08-01

    For online adaptive radiotherapy of cervical cancer, fast and accurate image segmentation is required to facilitate daily treatment adaptation. Our aim was twofold: (1) to test and compare three intra-patient automated segmentation methods for the cervix-uterus structure in CT-images and (2) to improve the segmentation accuracy by including prior knowledge on the daily bladder volume or on the daily coordinates of implanted fiducial markers. The tested methods were: shape deformation (SD) and atlas-based segmentation (ABAS) using two non-rigid registration methods: demons and a hierarchical algorithm. Tests on 102 CT-scans of 13 patients demonstrated that the segmentation accuracy significantly increased by including the bladder volume predicted with a simple 1D model based on a manually defined bladder top. Moreover, manually identified implanted fiducial markers significantly improved the accuracy of the SD method. For patients with large cervix-uterus volume regression, the use of CT-data acquired toward the end of the treatment was required to improve segmentation accuracy. Including prior knowledge, the segmentation results of SD (Dice similarity coefficient 85 ± 6%, error margin 2.2 ± 2.3 mm, average time around 1 min) and of ABAS using hierarchical non-rigid registration (Dice 82 ± 10%, error margin 3.1 ± 2.3 mm, average time around 30 s) support their use for image guided online adaptive radiotherapy of cervical cancer.

  3. Pulmonary Vascular Tree Segmentation from Contrast-Enhanced CT Images

    CERN Document Server

    Helmberger, M; Pienn, M; Balint, Z; Olschewski, A; Bischof, H

    2013-01-01

    We present a pulmonary vessel segmentation algorithm, which is fast, fully automatic and robust. It uses a coarse segmentation of the airway tree and a left and right lung labeled volume to restrict a vessel enhancement filter, based on an offset medialness function, to the lungs. We show the application of our algorithm on contrast-enhanced CT images, where we derive a clinical parameter to detect pulmonary hypertension (PH) in patients. Results on a dataset of 24 patients show that quantitative indices derived from the segmentation are applicable to distinguish patients with and without PH. Further work-in-progress results are shown on the VESSEL12 challenge dataset, which is composed of non-contrast-enhanced scans, where we range in the midfield of participating contestants.

  4. Optimal segmentation of pupillometric images for estimating pupil shape parameters.

    Science.gov (United States)

    De Santis, A; Iacoviello, D

    2006-12-01

    The problem of determining the pupil morphological parameters from pupillometric data is considered. These characteristics are of great interest for non-invasive early diagnosis of the central nervous system response to environmental stimuli of different nature, in subjects suffering some typical diseases such as diabetes, Alzheimer disease, schizophrenia, drug and alcohol addiction. Pupil geometrical features such as diameter, area, centroid coordinates, are estimated by a procedure based on an image segmentation algorithm. It exploits the level set formulation of the variational problem related to the segmentation. A discrete set up of this problem that admits a unique optimal solution is proposed: an arbitrary initial curve is evolved towards the optimal segmentation boundary by a difference equation; therefore no numerical approximation schemes are needed, as required in the equivalent continuum formulation usually adopted in the relevant literature.

  5. Automatic segmentation of trophectoderm in microscopic images of human blastocysts.

    Science.gov (United States)

    Singh, Amarjot; Au, Jason; Saeedi, Parvaneh; Havelock, Jon

    2015-01-01

    Accurate assessment of embryos viability is an extremely important task in the optimization of in vitro fertilization treatment outcome. One of the common ways of assessing the quality of a human embryo is grading it on its fifth day of development based on morphological quality of its three main components (Trophectoderm, Inner Cell Mass, and the level of expansion or the thickness of its Zona Pellucida). In this study, we propose a fully automatic method for segmentation and measurement of TE region of blastocysts (day-5 human embryos). Here, we eliminate the inhomogeneities of the blastocysts surface using the Retinex theory and further apply a level-set algorithm to segment the TE regions. We have tested our method on a dataset of 85 images and have been able to achieve a segmentation accuracy of 84.6% for grade A, 89.0% for grade B, and 91.7% for grade C embryos.

  6. Color Image Segmentation using Kohonen Self-Organizing Map (SOM

    Directory of Open Access Journals (Sweden)

    I Komang Ariana

    2014-05-01

    Full Text Available Color image segmentation using Kohonen Self-Organizing Map (SOM, is proposed in this study. RGB color space is used as input in the process of clustering by SOM. Measurement of the distance between weight vector and input vector in learning and recognition stages in SOM method, uses Normalized Euclidean Distance. Then, the validity of clustering result is tested by Davies-Bouldin Index (DBI and Validity Measure (VM to determine the most optimal number of cluster. The clustering result, according to the most optimal number of cluster, then is processed with spatial operations. Spatial operations are used to eliminate noise and small regions which are formed from the clustering result. This system allows segmentation process become automatic and unsupervised. The segmentation results are close to human perception.

  7. Object-Based and Semantic Image Segmentation Using MRF

    Directory of Open Access Journals (Sweden)

    Feng Li

    2004-06-01

    Full Text Available The problem that the Markov random field (MRF model captures the structural as well as the stochastic textures for remote sensing image segmentation is considered. As the one-point clique, namely, the external field, reflects the priori knowledge of the relative likelihood of the different region types which is often unknown, one would like to consider only two-pairwise clique in the texture. To this end, the MRF model cannot satisfactorily capture the structural component of the texture. In order to capture the structural texture, in this paper, a reference image is used as the external field. This reference image is obtained by Wold model decomposition which produces a purely random texture image and structural texture image from the original image. The structural component depicts the periodicity and directionality characteristics of the texture, while the former describes the stochastic. Furthermore, in order to achieve a good result of segmentation, such as improving smoothness of the texture edge, the proportion between the external and internal fields should be estimated by regarding it as a parameter of the MRF model. Due to periodicity of the structural texture, a useful by-product is that some long-range interaction is also taken into account. In addition, in order to reduce computation, a modified version of parameter estimation method is presented. Experimental results on remote sensing image demonstrating the performance of the algorithm are presented.

  8. An Image Matching Algorithm Integrating Global SRTM and Image Segmentation for Multi-Source Satellite Imagery

    Directory of Open Access Journals (Sweden)

    Xiao Ling

    2016-08-01

    Full Text Available This paper presents a novel image matching method for multi-source satellite images, which integrates global Shuttle Radar Topography Mission (SRTM data and image segmentation to achieve robust and numerous correspondences. This method first generates the epipolar lines as a geometric constraint assisted by global SRTM data, after which the seed points are selected and matched. To produce more reliable matching results, a region segmentation-based matching propagation is proposed in this paper, whereby the region segmentations are extracted by image segmentation and are considered to be a spatial constraint. Moreover, a similarity measure integrating Distance, Angle and Normalized Cross-Correlation (DANCC, which considers geometric similarity and radiometric similarity, is introduced to find the optimal correspondences. Experiments using typical satellite images acquired from Resources Satellite-3 (ZY-3, Mapping Satellite-1, SPOT-5 and Google Earth demonstrated that the proposed method is able to produce reliable and accurate matching results.

  9. On the Splitting Algorithm Based on Multi-target Model for Image Segmentation

    OpenAIRE

    Yuezhongyi Sun

    2014-01-01

    Against to the different regions of membership functions indicated image in the traditional image segmentation variational model, resulting segmentation is not clear, de-noising effect is not obvious problems, this paper proposes multi-target model for image segmentation and the splitting algorithm. The model uses a sparse regularization method to maintain the boundaries of segmented regions, to overcome the disadvantages of segmentation fuzzy boundaries resulting from total variation regular...

  10. Joint graph cut and relative fuzzy connectedness image segmentation algorithm.

    Science.gov (United States)

    Ciesielski, Krzysztof Chris; Miranda, Paulo A V; Falcão, Alexandre X; Udupa, Jayaram K

    2013-12-01

    We introduce an image segmentation algorithm, called GC(sum)(max), which combines, in novel manner, the strengths of two popular algorithms: Relative Fuzzy Connectedness (RFC) and (standard) Graph Cut (GC). We show, both theoretically and experimentally, that GC(sum)(max) preserves robustness of RFC with respect to the seed choice (thus, avoiding "shrinking problem" of GC), while keeping GC's stronger control over the problem of "leaking though poorly defined boundary segments." The analysis of GC(sum)(max) is greatly facilitated by our recent theoretical results that RFC can be described within the framework of Generalized GC (GGC) segmentation algorithms. In our implementation of GC(sum)(max) we use, as a subroutine, a version of RFC algorithm (based on Image Forest Transform) that runs (provably) in linear time with respect to the image size. This results in GC(sum)(max) running in a time close to linear. Experimental comparison of GC(sum)(max) to GC, an iterative version of RFC (IRFC), and power watershed (PW), based on a variety medical and non-medical images, indicates superior accuracy performance of GC(sum)(max) over these other methods, resulting in a rank ordering of GC(sum)(max)>PW∼IRFC>GC.

  11. Segmentation and intensity estimation for microarray images with saturated pixels

    Directory of Open Access Journals (Sweden)

    Yang Yan

    2011-11-01

    Full Text Available Abstract Background Microarray image analysis processes scanned digital images of hybridized arrays to produce the input spot-level data for downstream analysis, so it can have a potentially large impact on those and subsequent analysis. Signal saturation is an optical effect that occurs when some pixel values for highly expressed genes or peptides exceed the upper detection threshold of the scanner software (216 - 1 = 65, 535 for 16-bit images. In practice, spots with a sizable number of saturated pixels are often flagged and discarded. Alternatively, the saturated values are used without adjustments for estimating spot intensities. The resulting expression data tend to be biased downwards and can distort high-level analysis that relies on these data. Hence, it is crucial to effectively correct for signal saturation. Results We developed a flexible mixture model-based segmentation and spot intensity estimation procedure that accounts for saturated pixels by incorporating a censored component in the mixture model. As demonstrated with biological data and simulation, our method extends the dynamic range of expression data beyond the saturation threshold and is effective in correcting saturation-induced bias when the lost information is not tremendous. We further illustrate the impact of image processing on downstream classification, showing that the proposed method can increase diagnostic accuracy using data from a lymphoma cancer diagnosis study. Conclusions The presented method adjusts for signal saturation at the segmentation stage that identifies a pixel as part of the foreground, background or other. The cluster membership of a pixel can be altered versus treating saturated values as truly observed. Thus, the resulting spot intensity estimates may be more accurate than those obtained from existing methods that correct for saturation based on already segmented data. As a model-based segmentation method, our procedure is able to identify inner

  12. From acoustic segmentation to language processing: evidence from optical imaging

    Directory of Open Access Journals (Sweden)

    Hellmuth Obrig

    2010-06-01

    Full Text Available 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, fMRI 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.

  13. Brain MR image segmentation improved algorithm based on probability

    Science.gov (United States)

    Liao, Hengxu; Liu, Gang; Guo, Xiantang

    2017-08-01

    Local weight voting algorithm is a kind of current mainstream segmentation algorithm. It takes full account of the influences of the likelihood of image likelihood and the prior probabilities of labels on the segmentation results. But this method still can be improved since the essence of this method is to get the label with the maximum probability. If the probability of a label is 70%, it may be acceptable in mathematics. But in the actual segmentation, it may be wrong. So we use the matrix completion algorithm as a supplement. When the probability of the former is larger, the result of the former algorithm is adopted. When the probability of the later is larger, the result of the later algorithm is adopted. This is equivalent to adding an automatic algorithm selection switch that can theoretically ensure that the accuracy of the algorithm we propose is superior to the local weight voting algorithm. At the same time, we propose an improved matrix completion algorithm based on enumeration method. In addition, this paper also uses a multi-parameter registration model to reduce the influence that the registration made on the segmentation. The experimental results show that the accuracy of the algorithm is better than the common segmentation algorithm.

  14. STUDY OF IMAGE SEGMENTATION TECHNIQUES ON RETINAL IMAGES FOR HEALTH CARE MANAGEMENT WITH FAST COMPUTING

    Directory of Open Access Journals (Sweden)

    Srikanth Prabhu

    2012-02-01

    Full Text Available The role of segmentation in image processing is to separate foreground from background. In this process, the features become clearly visible when appropriate filters are applied on the image. In this paper emphasis has been laid on segmentation of biometric retinal images to filter out the vessels explicitly for evaluating the bifurcation points and features for diabetic retinopathy. Segmentation on images is performed by calculating ridges or morphology. Ridges are those areas in the images where there is sharp contrast in features. Morphology targets the features using structuring elements. Structuring elements are of different shapes like disk, line which is used for extracting features of those shapes. When segmentation was performed on retinal images problems were encountered during image pre-processing stage. Also edge detection techniques have been deployed to find out the contours of the retinal images. After the segmentation has been performed, it has been seen that artifacts of the retinal images have been minimal when ridge based segmentation technique was deployed. In the field of Health Care Management, image segmentation has an important role to play as it determines whether a person is normal or having any disease specially diabetes. During the process of segmentation, diseased features are classified as diseased one’s or artifacts. The problem comes when artifacts are classified as diseased ones. This results in misclassification which has been discussed in the analysis Section. We have achieved fast computing with better performance, in terms of speed for non-repeating features, when compared to repeating features.

  15. Binary image segmentation based on optimized parallel K-means

    Science.gov (United States)

    Qiu, Xiao-bing; Zhou, Yong; Lin, Li

    2015-07-01

    K-means is a classic unsupervised learning clustering algorithm. In theory, it can work well in the field of image segmentation. But compared with other segmentation algorithms, this algorithm needs much more computation, and segmentation speed is slow. This limits its application. With the emergence of general-purpose computing on the GPU and the release of CUDA, some scholars try to implement K-means algorithm in parallel on the GPU, and applied to image segmentation at the same time. They have achieved some results, but the approach they use is not completely parallel, not take full advantage of GPU's super computing power. K-means algorithm has two core steps: label and update, in current parallel realization of K-means, only labeling is parallel, update operation is still serial. In this paper, both of the two steps in K-means will be parallel to improve the degree of parallelism and accelerate this algorithm. Experimental results show that this improvement has reached a much quicker speed than the previous research.

  16. a Method of Tomato Image Segmentation Based on Mutual Information and Threshold Iteration

    Science.gov (United States)

    Wu, Hongxia; Li, Mingxi

    Threshold Segmentation is a kind of important image segmentation method and one of the important preconditioning steps of image detection and recognition, and it has very broad application during the research scopes of the computer vision. According to the internal relation between segment image and original image, a tomato image automatic optimization segmentation method (MI-OPT) which mutual information associate with optimum threshold iteration was presented. Simulation results show that this method has a better image segmentation effect on the tomato images of mature period and little background color difference or different color.

  17. Segmentation and Content-Based Watermarking for Color Image and Image Region Indexing and Retrieval

    Directory of Open Access Journals (Sweden)

    Nikolaos V. Boulgouris

    2002-04-01

    Full Text Available In this paper, an entirely novel approach to image indexing is presented using content-based watermarking. The proposed system uses color image segmentation and watermarking in order to facilitate content-based indexing, retrieval and manipulation of digital images and image regions. A novel segmentation algorithm is applied on reduced images and the resulting segmentation mask is embedded in the image using watermarking techniques. In each region of the image, indexing information is additionally embedded. In this way, the proposed system is endowed with content-based access and indexing capabilities which can be easily exploited via a simple watermark detection process. Several experiments have shown the potential of this approach.

  18. Image Segmentation in Liquid Argon Time Projection Chamber Detector

    CERN Document Server

    Płoński, Piotr; Sulej, Robert; Zaremba, Krzysztof

    2015-01-01

    The Liquid Argon Time Projection Chamber (LAr-TPC) detectors provide excellent imaging and particle identification ability for studying neutrinos. An efficient and automatic reconstruction procedures are required to exploit potential of this imaging technology. Herein, a novel method for segmentation of images from LAr-TPC detectors is presented. The proposed approach computes a feature descriptor for each pixel in the image, which characterizes amplitude distribution in pixel and its neighbourhood. The supervised classifier is employed to distinguish between pixels representing particle's track and noise. The classifier is trained and evaluated on the hand-labeled dataset. The proposed approach can be a preprocessing step for reconstructing algorithms working directly on detector images.

  19. Removing the twin image in digital holography by segmented filtering of in-focus twin image

    OpenAIRE

    2008-01-01

    We propose and investigate a new digital method for the reduction of twin-image noise from digital Fresnel holograms. For the case of in-line Fresnel holography the unwanted twin is present as a highly corruptive noise when the object image is numerically reconstructed. We propose to firstly reconstruct the unwanted twin-image when it is in-focus and in this plane we calculate a segmentation mask that borders this in focus image. The twin-image is then segmented and removed by sim...

  20. Joint Image Reconstruction and Segmentation Using the Potts Model

    CERN Document Server

    Storath, Martin; Frikel, Jürgen; Unser, Michael

    2014-01-01

    We propose a new algorithmic approach to the non-smooth and non-convex Potts problem (also called piecewise-constant Mumford-Shah problem) for inverse imaging problems. We derive a suitable splitting into specific subproblems that can all be solved efficiently. Our method does not require a priori knowledge on the gray levels nor on the number of segments of the reconstruction. Further, it avoids anisotropic artifacts such as geometric staircasing. We demonstrate the suitability of our method for joint image reconstruction and segmentation from limited data in x-ray and photoacoustic tomography. For instance, our method is able to reconstruct the Shepp-Logan phantom from $7$ angular views only. We demonstrate the practical applicability in an experiment with real PET data.

  1. Structured Learning of Tree Potentials in CRF for Image Segmentation.

    Science.gov (United States)

    Liu, Fayao; Lin, Guosheng; Qiao, Ruizhi; Shen, Chunhua

    2017-04-13

    We propose a new approach to image segmentation, which exploits the advantages of both conditional random fields (CRFs) and decision trees. In the literature, the potential functions of CRFs are mostly defined as a linear combination of some predefined parametric models, and then, methods, such as structured support vector machines, are applied to learn those linear coefficients. We instead formulate the unary and pairwise potentials as nonparametric forests--ensembles of decision trees, and learn the ensemble parameters and the trees in a unified optimization problem within the large-margin framework. In this fashion, we easily achieve nonlinear learning of potential functions on both unary and pairwise terms in CRFs. Moreover, we learn classwise decision trees for each object that appears in the image. Experimental results on several public segmentation data sets demonstrate the power of the learned nonlinear nonparametric potentials.

  2. Point based interactive image segmentation using multiquadrics splines

    Science.gov (United States)

    Meena, Sachin; Duraisamy, Prakash; Palniappan, Kannappan; Seetharaman, Guna

    2017-05-01

    Multiquadrics (MQ) are radial basis spline function that can provide an efficient interpolation of data points located in a high dimensional space. MQ were developed by Hardy to approximate geographical surfaces and terrain modelling. In this paper we frame the task of interactive image segmentation as a semi-supervised interpolation where an interpolating function learned from the user provided seed points is used to predict the labels of unlabeled pixel and the spline function used in the semi-supervised interpolation is MQ. This semi-supervised interpolation framework has a nice closed form solution which along with the fact that MQ is a radial basis spline function lead to a very fast interactive image segmentation process. Quantitative and qualitative results on the standard datasets show that MQ outperforms other regression based methods, GEBS, Ridge Regression and Logistic Regression, and popular methods like Graph Cut,4 Random Walk and Random Forest.6

  3. Segmentation and Classification of Remotely Sensed Images: Object-Based Image Analysis

    Science.gov (United States)

    Syed, Abdul Haleem

    Land-use-and-land-cover (LULC) mapping is crucial in precision agriculture, environmental monitoring, disaster response, and military applications. The demand for improved and more accurate LULC maps has led to the emergence of a key methodology known as Geographic Object-Based Image Analysis (GEOBIA). The core idea of the GEOBIA for an object-based classification system (OBC) is to change the unit of analysis from single-pixels to groups-of-pixels called `objects' through segmentation. While this new paradigm solved problems and improved global accuracy, it also raised new challenges such as the loss of accuracy in categories that are less abundant, but potentially important. Although this trade-off may be acceptable in some domains, the consequences of such an accuracy loss could be potentially fatal in others (for instance, landmine detection). This thesis proposes a method to improve OBC performance by eliminating such accuracy losses. Specifically, we examine the two key players of an OBC system: Hierarchical Segmentation and Supervised Classification. Further, we propose a model to understand the source of accuracy errors in minority categories and provide a method called Scale Fusion to eliminate those errors. This proposed fusion method involves two stages. First, the characteristic scale for each category is estimated through a combination of segmentation and supervised classification. Next, these estimated scales (segmentation maps) are fused into one combined-object-map. Classification performance is evaluated by comparing results of the multi-cut-and-fuse approach (proposed) to the traditional single-cut (SC) scale selection strategy. Testing on four different data sets revealed that our proposed algorithm improves accuracy on minority classes while performing just as well on abundant categories. Another active obstacle, presented by today's remotely sensed images, is the volume of information produced by our modern sensors with high spatial and

  4. Multi-atlas segmentation of biomedical images: A survey.

    Science.gov (United States)

    Iglesias, Juan Eugenio; Sabuncu, Mert R

    2015-08-01

    Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, et al. (2004), Klein, et al. (2005), and Heckemann, et al. (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of "atlases" (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003-2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.

  5. Interactive segmentation for geographic atrophy in retinal fundus images

    OpenAIRE

    Lee, Noah; SMITH, R. THEODORE; Laine, Andrew F.

    2008-01-01

    Fundus auto-fluorescence (FAF) imaging is a non-invasive technique for in vivo ophthalmoscopic inspection of age-related macular degeneration (AMD), the most common cause of blindness in developed countries. Geographic atrophy (GA) is an advanced form of AMD and accounts for 12–21% of severe visual loss in this disorder [3]. Automatic quantification of GA is important for determining disease progression and facilitating clinical diagnosis of AMD. The problem of automatic segmentation of patho...

  6. Image segmentation based on Mumford-Shah functional

    Institute of Scientific and Technical Information of China (English)

    CHEN Xu-feng(陈旭锋); GUAN Zhi-cheng(管志成)

    2004-01-01

    In this paper, the authors propose a new model for active contours segmentation in a given image, based on Mumford-Shah functional (Mumford and Shah, 1989). The model is composed of a system of differential and integral equations. By the experimental results we can keep the advantages of Chan and Vese's model (Chan and Vese, 2001) and avoid the regularization for Dirac function. More importantly, in theory we prove that the system has a unique viscosity solution.

  7. Joint Hierarchical Category Structure Learning and Large-Scale Image Classification

    Science.gov (United States)

    Qu, Yanyun; Lin, Li; Shen, Fumin; Lu, Chang; Wu, Yang; Xie, Yuan; Tao, Dacheng

    2017-09-01

    We investigate the scalable image classification problem with a large number of categories. Hierarchical visual data structures are helpful for improving the efficiency and performance of large-scale multi-class classification. We propose a novel image classification method based on learning hierarchical inter-class structures. Specifically, we first design a fast algorithm to compute the similarity metric between categories, based on which a visual tree is constructed by hierarchical spectral clustering. Using the learned visual tree, a test sample label is efficiently predicted by searching for the best path over the entire tree. The proposed method is extensively evaluated on the ILSVRC2010 and Caltech 256 benchmark datasets. Experimental results show that our method obtains significantly better category hierarchies than other state-of-the-art visual tree-based methods and, therefore, much more accurate classification.

  8. Hierarchical prediction and context adaptive coding for lossless color image compression.

    Science.gov (United States)

    Kim, Seyun; Cho, Nam Ik

    2014-01-01

    This paper presents a new lossless color image compression algorithm, based on the hierarchical prediction and context-adaptive arithmetic coding. For the lossless compression of an RGB image, it is first decorrelated by a reversible color transform and then Y component is encoded by a conventional lossless grayscale image compression method. For encoding the chrominance images, we develop a hierarchical scheme that enables the use of upper, left, and lower pixels for the pixel prediction, whereas the conventional raster scan prediction methods use upper and left pixels. An appropriate context model for the prediction error is also defined and the arithmetic coding is applied to the error signal corresponding to each context. For several sets of images, it is shown that the proposed method further reduces the bit rates compared with JPEG2000 and JPEG-XR.

  9. Multi-scale visual words for hierarchical medical image categorisation

    Science.gov (United States)

    Markonis, Dimitrios; Seco de Herrera, Alba G.; Eggel, Ivan; Müller, Henning

    2012-02-01

    The biomedical literature published regularly has increased strongly in past years and keeping updated even in narrow domains is difficult. Images represent essential information of their articles and can help to quicker browse through large volumes of articles in connection with keyword search. Content-based image retrieval is helping the retrieval of visual content. To facilitate retrieval of visual information, image categorisation can be an important first step. To represent scientific articles visually, medical images need to be separated from general images such as flowcharts or graphs to facilitate browsing, as graphs contain little information. Medical modality classification is a second step to focus search. The techniques described in this article first classify images into broad categories. In a second step the images are further classified into the exact medical modalities. The system combines the Scale-Invariant Feature Transform (SIFT) and density-based clustering (DENCLUE). Visual words are first created globally to differentiate broad categories and then within each category a new visual vocabulary is created for modality classification. The results show the difficulties to differentiate between some modalities by visual means alone. On the other hand the improvement of the accuracy of the two-step approach shows the usefulness of the method. The system is currently being integrated into the Goldminer image search engine of the ARRS (American Roentgen Ray Society) as a web service, allowing concentrating image search onto clinically relevant images automatically.

  10. SAR and Oblique Aerial Optical Image Fusion for Urban Area Image Segmentation

    Science.gov (United States)

    Fagir, J.; Schubert, A.; Frioud, M.; Henke, D.

    2017-05-01

    The fusion of synthetic aperture radar (SAR) and optical data is a dynamic research area, but image segmentation is rarely treated. While a few studies use low-resolution nadir-view optical images, we approached the segmentation of SAR and optical images acquired from the same airborne platform - leading to an oblique view with high resolution and thus increased complexity. To overcome the geometric differences, we generated a digital surface model (DSM) from adjacent optical images and used it to project both the DSM and SAR data into the optical camera frame, followed by segmentation with each channel. The fused segmentation algorithm was found to out-perform the single-channel version.

  11. Refinement of Hyperspectral Image Classification with Segment-Tree Filtering

    Directory of Open Access Journals (Sweden)

    Lu Li

    2017-01-01

    Full Text Available This paper proposes a novel method of segment-tree filtering to improve the classification accuracy of hyperspectral image (HSI. Segment-tree filtering is a versatile method that incorporates spatial information and has been widely applied in image preprocessing. However, to use this powerful framework in hyperspectral image classification, we must reduce the original feature dimensionality to avoid the Hughes problem; otherwise, the computational costs are high and the classification accuracy by original bands in the HSI is unsatisfactory. Therefore, feature extraction is adopted to produce new salient features. In this paper, the Semi-supervised Local Fisher (SELF method of discriminant analysis is used to reduce HSI dimensionality. Then, a tree-structure filter that adaptively incorporates contextual information is constructed. Additionally, an initial classification map is generated using multi-class support vector machines (SVMs, and segment-tree filtering is conducted using this map. Finally, a simple Winner-Take-All (WTA rule is applied to determine the class of each pixel in an HSI based on the maximum probability. The experimental results demonstrate that the proposed method can improve HSI classification accuracy significantly. Furthermore, a comparison between the proposed method and the current state-of-the-art methods, such as Extended Morphological Profiles (EMPs, Guided Filtering (GF, and Markov Random Fields (MRFs, suggests that our method is both competitive and robust.

  12. Combining Constraint Types From Public Data in Aerial Image Segmentation

    DEFF Research Database (Denmark)

    Jacobsen, Thomas Stig; Jensen, Jacob Jon; Jensen, Daniel Rune

    2013-01-01

    detect the number of clusters using the elongated K-means algorithm instead of using the standard spectral clustering approach of a predefined number of clusters. The results are refined by filtering out noise with a binary morphological operator. Point data is used for semi-supervised labelling......We introduce a method for image segmentation that constraints the clustering with map and point data. The method is showcased by applying the spectral clustering algorithm on aerial images for building detection with constraints built from a height map and address point data. We automatically...

  13. A Bayesian Approach for Segmentation in Stereo Image Sequences

    Directory of Open Access Journals (Sweden)

    Tzovaras Dimitrios

    2002-01-01

    Full Text Available Stereoscopic image sequence processing has been the focus of considerable attention in recent literature for videoconference applications. A novel Bayesian scheme is proposed in this paper, for the segmentation of a noisy stereoscopic image sequence. More specifically, occlusions and visible foreground and background regions are detected between the left and the right frame while the uncovered-background areas are identified between two successive frames of the sequence. Combined hypotheses are used for the formulation of the Bayes decision rule which employs a single intensity-difference measurement at each pixel. Experimental results illustrating the performance of the proposed technique are presented and evaluated in videoconference applications.

  14. Fully automatic segmentation of complex organ systems: example of trachea, esophagus and heart segmentation in CT images

    Science.gov (United States)

    Meyer, Carsten; Peters, Jochen; Weese, Jürgen

    2011-03-01

    Automatic segmentation is a prerequisite to efficiently analyze the large amount of image data produced by modern imaging modalities. Many algorithms exist to segment individual organs or organ systems. However, new clinical applications and the progress in imaging technology will require the segmentation of more and more complex organ systems composed of a number of substructures, e.g., the heart, the trachea, and the esophagus. The goal of this work is to demonstrate that such complex organ systems can be successfully segmented by integrating the individual organs into a general model-based segmentation framework, without tailoring the core adaptation engine to the individual organs. As an example, we address the fully automatic segmentation of the trachea (around its main bifurcation, including the proximal part of the two main bronchi) and the esophagus in addition to the heart with all chambers and attached major vessels. To this end, we integrate the trachea and the esophagus into a model-based cardiac segmentation framework. Specifically, in a first parametric adaptation step of the segmentation workflow, the trachea and the esophagus share global model transformations with adjacent heart structures. This allows to obtain a robust, approximate segmentation for the trachea even if it is only partly inside the field-of-view, and for the esophagus in spite of limited contrast. The segmentation is then refined in a subsequent deformable adaptation step. We obtained a mean segmentation error of about 0.6mm for the trachea and 2.3mm for the esophagus on a database of 23 volumetric cardiovascular CT images. Furthermore, we show by quantitative evaluation that our integrated framework outperforms individual esophagus segmentation, and individual trachea segmentation if the trachea is only partly inside the field-of-view.

  15. Brain tumor segmentation using holistically nested neural networks in MRI images.

    Science.gov (United States)

    Zhuge, Ying; Krauze, Andra V; Ning, Holly; Cheng, Jason Y; Arora, Barbara C; Camphausen, Kevin; Miller, Robert W

    2017-07-24

    Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic resonance imaging (MRI) is a widely used technique employed in the diagnosis and management of gliomas in clinical practice. MRI is also the standard imaging modality used to delineate the brain tumor target as part of treatment planning for the administration of radiation therapy. Despite more than 20 yr of research and development, computational brain tumor segmentation in MRI images remains a challenging task. We are presenting a novel method of automatic image segmentation based on holistically nested neural networks that could be employed for brain tumor segmentation of MRI images. Two preprocessing techniques were applied to MRI images. The N4ITK method was employed for correction of bias field distortion. A novel landmark-based intensity normalization method was developed so that tissue types have a similar intensity scale in images of different subjects for the same MRI protocol. The holistically nested neural networks (HNN), which extend from the convolutional neural networks (CNN) with a deep supervision through an additional weighted-fusion output layer, was trained to learn the multiscale and multilevel hierarchical appearance representation of the brain tumor in MRI images and was subsequently applied to produce a prediction map of the brain tumor on test images. Finally, the brain tumor was obtained through an optimum thresholding on the prediction map. The proposed method was evaluated on both the Multimodal Brain Tumor Image Segmentation (BRATS) Benchmark 2013 training datasets, and clinical data from our institute. A dice similarity coefficient (DSC) and sensitivity of 0.78 and 0.81 were achieved on 20 BRATS 2013 training datasets with high-grade gliomas (HGG), based on a two-fold cross-validation. The HNN model built on the BRATS 2013 training data was applied to ten clinical datasets with HGG from a locally developed database. DSC and sensitivity of

  16. Medical Image Segmentation through Bat-Active Contour Algorithm

    Directory of Open Access Journals (Sweden)

    Rabiu O. Isah

    2017-01-01

    Full Text Available In this research work, an improved active contour method called Bat-Active Contour Method (BAACM using bat algorithm has been developed. The bat algorithm is incorporated in order to escape local minima entrapped into by the classical active contour method, stabilize contour (snake movement and accurately, reach boundary concavity. Then, the developed Bat-Active Contour Method was applied to a dataset of medical images of the human heart, bone of knee and vertebra which were obtained from Auckland MRI Research Group (Cardiac Atlas Website, University of Auckland. Set of similarity metrics, including Jaccard index and Dice similarity measures were adopted to evaluate the performance of the developed algorithm. Jaccard index values of 0.9310, 0.9234 and 0.8947 and Dice similarity values of 0.8341, 0.8616 and 0.9138 were obtained from the human heart, vertebra and bone of knee images respectively. The results obtained show high similarity measures between BA-ACM algorithm and expert segmented images. Moreso, traditional ACM produced Jaccard index values 0.5873, 0.5601, 0.6009 and Dice similarity values of 0.5974, 0.6079, 0.6102 in the human heart, vertebra and bone of knee images respectively. The results obtained for traditional ACM show low similarity measures between it and expertly segmented images. It is evident from the results obtained that the developed algorithm performed better compared to the traditional ACM

  17. Jacquard image segmentation using Mumford-Shah model

    Institute of Scientific and Technical Information of China (English)

    FENG Zhi-lin; YIN Jian-wei; CHEN Gang; DONG Jin-xiang

    2006-01-01

    Jacquard image segmentation is one of the primary steps in image analysis for jacquard pattern identification. The main aim is to recognize homogeneous regions within a jacquard image as distinct, which belongs to different patterns. Active contour models have become popular for finding the contours of a pattem with a complex shape. However, the performance of active contour models is often inadequate under noisy environment. In this paper, a robust algorithm based on the Mumford-Shah model is proposed for the segmentation of noisy jacquard images. First, the Mumford-Shah model is discretized on piecewise linear finite element spaces to yield greater stability. Then, an iterative relaxation algorithm for numerically solving the discrete version of the model is presented. In this algorithm, an adaptiye triangular mesh is refined to generate Delaunay type triangular mesh defined on structured triangulations, and then a quasi-Newton numerical method is applied to find the absolute minimum of the discrete model. Experimental results on noisy jacquard images demonstrated the efficacy of the proposed algorithm.

  18. Method, Software and Aparatus for Segmenting a Series of 2D or 3D Images

    NARCIS (Netherlands)

    Noble, Nicholas M.I.; Spreeuwers, Lieuwe Jan; Breeuwer, Marcel

    2005-01-01

    he invention relates to an apparatus having means for segmenting a series of 2D or 3D images obtained by monitoring a patient's organ or other body part, wherein a first segmentation is carried out on a first image of the series of images and wherein the first segmentation is used for the subsequent

  19. Method, Software and Aparatus for Segmenting a Series of 2D or 3D Images

    NARCIS (Netherlands)

    Noble, Nicholas Michael Ian; Spreeuwers, Lieuwe Jan; Breeuwer, Marcel

    2010-01-01

    he invention relates to an apparatus having means for segmenting a series of 2D or 3D images obtained by monitoring a patient's organ or other body part, wherein a first segmentation is carried out on a first image of the series of images and wherein the first segmentation is used for the subsequent

  20. Fast Graph Partitioning Active Contours for Image Segmentation Using Histograms

    Directory of Open Access Journals (Sweden)

    Nath SumitK

    2009-01-01

    Full Text Available Abstract We present a method to improve the accuracy and speed, as well as significantly reduce the memory requirements, for the recently proposed Graph Partitioning Active Contours (GPACs algorithm for image segmentation in the work of Sumengen and Manjunath (2006. Instead of computing an approximate but still expensive dissimilarity matrix of quadratic size, , for a 2D image of size and regular image tiles of size , we use fixed length histograms and an intensity-based symmetric-centrosymmetric extensor matrix to jointly compute terms associated with the complete dissimilarity matrix. This computationally efficient reformulation of GPAC using a very small memory footprint offers two distinct advantages over the original implementation. It speeds up convergence of the evolving active contour and seamlessly extends performance of GPAC to multidimensional images.

  1. Magnetic Resonance Image Segmentation and its Volumetric Measurement

    Directory of Open Access Journals (Sweden)

    Rahul R. Ambalkar

    2013-02-01

    Full Text Available Image processing techniques make it possible to extract meaningful information from medical images. Magnetic resonance (MR imaging has been widely applied in biological research and diagnostics because of its excellent soft tissue contrast, non-invasive character, high spatial resolution and easy slice selection at any orientation. The MRI-based brain volumetric is concerned with the analysis of volumes and shapes of the structural components of the human brain. It also provides a criterion, by which we recognize the presence of degenerative diseases and characterize their rates of progression to make the diagnosis and treatments as a easy task. In this paper we have proposed an automated method for volumetric measurement of Magnetic Resonance Imaging and used Self Organized Map (SOM clustering method for their segmentations. We have used the MRI data set of 61 slices of 256×256 pixels in DICOM standard format

  2. Filler segmentation of SEM paper images based on mathematical morphology.

    Science.gov (United States)

    Ait Kbir, M; Benslimane, Rachid; Princi, Elisabetta; Vicini, Silvia; Pedemonte, Enrico

    2007-07-01

    Recent developments in microscopy and image processing have made digital measurements on high-resolution images of fibrous materials possible. This helps to gain a better understanding of the structure and other properties of the material at micro level. In this paper SEM image segmentation based on mathematical morphology is proposed. In fact, paper models images (Whatman, Murillo, Watercolor, Newsprint paper) selected in the context of the Euro Mediterranean PaperTech Project have different distributions of fibers and fillers, caused by the presence of SiAl and CaCO3 particles. It is a microscopy challenge to make filler particles in the sheet distinguishable from the other components of the paper surface. This objectif is reached here by using switable strutural elements and mathematical morphology operators.

  3. Medical image segmentation based on SLIC superpixels model

    Science.gov (United States)

    Chen, Xiang-ting; Zhang, Fan; Zhang, Ruo-ya

    2017-01-01

    Medical imaging has been widely used in clinical practice. It is an important basis for medical experts to diagnose the disease. However, medical images have many unstable factors such as complex imaging mechanism, the target displacement will cause constructed defect and the partial volume effect will lead to error and equipment wear, which increases the complexity of subsequent image processing greatly. The segmentation algorithm which based on SLIC (Simple Linear Iterative Clustering, SLIC) superpixels is used to eliminate the influence of constructed defect and noise by means of the feature similarity in the preprocessing stage. At the same time, excellent clustering effect can reduce the complexity of the algorithm extremely, which provides an effective basis for the rapid diagnosis of experts.

  4. A Hierarchical Bayesian M/EEG Imaging Method Correcting for Incomplete Spatio-Temporal Priors

    DEFF Research Database (Denmark)

    Stahlhut, Carsten; Attias, Hagai T.; Sekihara, Kensuke;

    2013-01-01

    In this paper we present a hierarchical Bayesian model, to tackle the highly ill-posed problem that follows with MEG and EEG source imaging. Our model promotes spatiotemporal patterns through the use of both spatial and temporal basis functions. While in contrast to most previous spatio-temporal ...

  5. A new iterative triclass thresholding technique in image segmentation.

    Science.gov (United States)

    Cai, Hongmin; Yang, Zhong; Cao, Xinhua; Xia, Weiming; Xu, Xiaoyin

    2014-03-01

    We present a new method in image segmentation that is based on Otsu's method but iteratively searches for subregions of the image for segmentation, instead of treating the full image as a whole region for processing. The iterative method starts with Otsu's threshold and computes the mean values of the two classes as separated by the threshold. Based on the Otsu's threshold and the two mean values, the method separates the image into three classes instead of two as the standard Otsu's method does. The first two classes are determined as the foreground and background and they will not be processed further. The third class is denoted as a to-be-determined (TBD) region that is processed at next iteration. At the succeeding iteration, Otsu's method is applied on the TBD region to calculate a new threshold and two class means and the TBD region is again separated into three classes, namely, foreground, background, and a new TBD region, which by definition is smaller than the previous TBD regions. Then, the new TBD region is processed in the similar manner. The process stops when the Otsu's thresholds calculated between two iterations is less than a preset threshold. Then, all the intermediate foreground and background regions are, respectively, combined to create the final segmentation result. Tests on synthetic and real images showed that the new iterative method can achieve better performance than the standard Otsu's method in many challenging cases, such as identifying weak objects and revealing fine structures of complex objects while the added computational cost is minimal.

  6. Automatic segmentation of brain images: selection of region extraction methods

    Science.gov (United States)

    Gong, Leiguang; Kulikowski, Casimir A.; Mezrich, Reuben S.

    1991-07-01

    In automatically analyzing brain structures from a MR image, the choice of low level region extraction methods depends on the characteristics of both the target object and the surrounding anatomical structures in the image. The authors have experimented with local thresholding, global thresholding, and other techniques, using various types of MR images for extracting the major brian landmarks and different types of lesions. This paper describes specifically a local- binary thresholding method and a new global-multiple thresholding technique developed for MR image segmentation and analysis. The initial testing results on their segmentation performance are presented, followed by a comparative analysis of the two methods and their ability to extract different types of normal and abnormal brain structures -- the brain matter itself, tumors, regions of edema surrounding lesions, multiple sclerosis lesions, and the ventricles of the brain. The analysis and experimental results show that the global multiple thresholding techniques are more than adequate for extracting regions that correspond to the major brian structures, while local binary thresholding is helpful for more accurate delineation of small lesions such as those produced by MS, and for the precise refinement of lesion boundaries. The detection of other landmarks, such as the interhemispheric fissure, may require other techniques, such as line-fitting. These experiments have led to the formulation of a set of generic computer-based rules for selecting the appropriate segmentation packages for particular types of problems, based on which further development of an innovative knowledge- based, goal directed biomedical image analysis framework is being made. The system will carry out the selection automatically for a given specific analysis task.

  7. Effect of hierarchical deformable motion compensation on image enhancement for DSA acquired via C-ARM

    Science.gov (United States)

    Wei, Liyang; Shen, Dinggang; Kumar, Dinesh; Turlapati, Ram; Suri, Jasjit S.

    2008-02-01

    DSA images suffer from challenges like system X-ray noise and artifacts due to patient movement. In this paper, we present a two-step strategy to improve DSA image quality. First, a hierarchical deformable registration algorithm is used to register the mask frame and the bolus frame before subtraction. Second, the resulted DSA image is further enhanced by background diffusion and nonlinear normalization for better visualization. Two major changes are made in the hierarchical deformable registration algorithm for DSA images: 1) B-Spline is used to represent the deformation field in order to produce the smooth deformation field; 2) two features are defined as the attribute vector for each point in the image, i.e., original image intensity and gradient. Also, for speeding up the 2D image registration, the hierarchical motion compensation algorithm is implemented by a multi-resolution framework. The proposed method has been evaluated on a database of 73 subjects by quantitatively measuring signal-to-noise (SNR) ratio. DSA embedded with proposed strategies demonstrates an improvement of 74.1% over conventional DSA in terms of SNR. Our system runs on Eigen's DSA workstation using C++ in Windows environment.

  8. Hierarchical spatial genetic structure in a distinct population segment of greater sage-grouse

    Science.gov (United States)

    Oyler-McCance, Sara J.; Casazza, Michael L.; Fike, Jennifer A.; Coates, Peter S.

    2014-01-01

    Greater sage-grouse (Centrocercus urophasianus) within the Bi-State Management Zone (area along the border between Nevada and California) are geographically isolated on the southwestern edge of the species’ range. Previous research demonstrated that this population is genetically unique, with a high proportion of unique mitochondrial DNA (mtDNA) haplotypes and with significant differences in microsatellite allele frequencies compared to populations across the species’ range. As a result, this population was considered a distinct population segment (DPS) and was recently proposed for listing as threatened under the U.S. Endangered Species Act. A more comprehensive understanding of the boundaries of this genetically unique population (where the Bi-State population begins) and an examination of genetic structure within the Bi-State is needed to help guide effective management decisions. We collected DNA from eight sampling locales within the Bi-State (N = 181) and compared those samples to previously collected DNA from the two most proximal populations outside of the Bi-State DPS, generating mtDNA sequence data and amplifying 15 nuclear microsatellites. Both mtDNA and microsatellite analyses support the idea that the Bi-State DPS represents a genetically unique population, which has likely been separated for thousands of years. Seven mtDNA haplotypes were found exclusively in the Bi-State population and represented 73 % of individuals, while three haplotypes were shared with neighboring populations. In the microsatellite analyses both STRUCTURE and FCA separate the Bi-State from the neighboring populations. We also found genetic structure within the Bi-State as both types of data revealed differences between the northern and southern part of the Bi-State and there was evidence of isolation-by-distance. STRUCTURE revealed three subpopulations within the Bi-State consisting of the northern Pine Nut Mountains (PNa), mid Bi-State, and White Mountains (WM) following a

  9. Hybrid of Fuzzy Logic and Random Walker Method for Medical Image Segmentation

    National Research Council Canada - National Science Library

    Jasdeep Kaur; Manish Mahajan

    2015-01-01

    ...’ treatment are based on information extracted from radiological images. Several algorithms and techniques have developed for image segmentation and have their own advantages and disadvantages...

  10. Segmentation of the thoracic aorta in noncontrast cardiac CT images.

    Science.gov (United States)

    Avila-Montes, Olga C; Kurkure, Uday; Nakazato, Ryo; Berman, Daniel S; Dey, Damini; Kakadiaris, Ioannis A

    2013-09-01

    Studies have shown that aortic calcification is associated with cardiovascular disease. In this study, a method for localization, centerline extraction, and segmentation of the thoracic aorta in noncontrast cardiac-computed tomography (CT) images, toward the detection of aortic calcification, is presented. The localization of the right coronary artery ostium slice is formulated as a regression problem whose input variables are obtained from simple intensity features computed from a pyramid representation of the slice. The localization, centerline extraction, and segmentation of the aorta are formulated as optimal path detection problems. Dynamic programming is applied in the Hough space for localizing key center points in the aorta which guide the centerline tracing using a fast marching-based minimal path extraction framework. The input volume is then resampled into a stack of 2-D cross-sectional planes orthogonal to the obtained centerline. Dynamic programming is again applied for the segmentation of the aorta in each slice of the resampled volume. The obtained segmentation is finally mapped back to its original volume space. The performance of the proposed method was assessed on cardiac noncontrast CT scans and promising results were obtained.

  11. Adaptive distance metric learning for diffusion tensor image segmentation.

    Science.gov (United States)

    Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C N; Chu, Winnie C W

    2014-01-01

    High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.

  12. Adaptive distance metric learning for diffusion tensor image segmentation.

    Directory of Open Access Journals (Sweden)

    Youyong Kong

    Full Text Available High quality segmentation of diffusion tensor images (DTI is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.

  13. Alternative Fuzzy Cluster Segmentation of Remote Sensing Images Based on Adaptive Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Jing; TANG Jilong; LIU Jibin; REN Chunying; LIU Xiangnan; FENG Jiang

    2009-01-01

    Remote sensing image segmentation is the basis of image understanding and analysis. However, the precision and the speed of segmentation can not meet the need of image analysis, due to strong uncertainty and rich texture details of remote sensing images. We proposed a new segmentation method based on Adaptive Genetic Algorithm (AGA) and Alternative Fuzzy C-Means (AFCM). Segmentation thresholds were identified by AGA. Then the image was segmented by AFCM. The results indicate that the precision and the speed of segmentation have been greatly increased, and the accuracy of threshold selection is much higher compared with traditional Otsu and Fuzzy C-Means (FCM) segmentation methods. The segmentation results also show that multi-thresholds segmentation has been achieved by combining AGA with AFCM.

  14. OBIA based hierarchical image classification for industrial lake water.

    Science.gov (United States)

    Uca Avci, Z D; Karaman, M; Ozelkan, E; Kumral, M; Budakoglu, M

    2014-07-15

    Water management is very important in water mining regions for the sustainability of the natural environment and for industrial activities. This study focused on Acigol Lake, which is an important wetland for sodium sulphate (Na2SO4) production, a significant natural protection area and habitat for local bird species and endemic species of this saline environment, and a stopover for migrating flamingos. By a hierarchical classification method, ponds representing the industrial part were classified according to in-situ measured Baumé values, and lake water representing the natural part was classified according to in-situ measurements of water depth. The latter is directly related to the water level, which should not exceed a critical level determined by the regulatory authorities. The resulting data, produced at an accuracy of around 80%, illustrates the status in two main regions for a single date. The output of the analysis may be meaningful for firms and environmental researchers, and authorizations can provide a good perspective for decision making for sustainable resource management in the region which has uncommon and specific ecological characteristics.

  15. A quantum mechanics-based framework for image processing and its application to image segmentation

    Science.gov (United States)

    Youssry, Akram; El-Rafei, Ahmed; Elramly, Salwa

    2015-10-01

    Quantum mechanics provides the physical laws governing microscopic systems. A novel and generic framework based on quantum mechanics for image processing is proposed in this paper. The basic idea is to map each image element to a quantum system. This enables the utilization of the quantum mechanics powerful theory in solving image processing problems. The initial states of the image elements are evolved to the final states, controlled by an external force derived from the image features. The final states can be designed to correspond to the class of the element providing solutions to image segmentation, object recognition, and image classification problems. In this work, the formulation of the framework for a single-object segmentation problem is developed. The proposed algorithm based on this framework consists of four major steps. The first step is designing and estimating the operator that controls the evolution process from image features. The states associated with the pixels of the image are initialized in the second step. In the third step, the system is evolved. Finally, a measurement is performed to determine the output. The presented algorithm is tested on noiseless and noisy synthetic images as well as natural images. The average of the obtained results is 98.5 % for sensitivity and 99.7 % for specificity. A comparison with other segmentation algorithms is performed showing the superior performance of the proposed method. The application of the introduced quantum-based framework to image segmentation demonstrates high efficiency in handling different types of images. Moreover, it can be extended to multi-object segmentation and utilized in other applications in the fields of signal and image processing.

  16. Automatic Scheme for Fused Medical Image Segmentation with Nonsubsampled Contourlet Transform

    OpenAIRE

    Ch.Hima Bindu; Dr. K. Satya Prasad

    2012-01-01

    Medical image segmentation has become an essential technique in clinical and research- oriented applications. Because manual segmentation methods are tedious, and semi-automatic segmentation lacks the flexibility, fully-automatic methods have become the preferred type of medical image segmentation. This work proposes a robust fully automatic segmentation scheme based on the modified contouring technique. The entire scheme consists of three stages. In the first stage, the Nonsubsampled Contour...

  17. Replica inference approach to unsupervised multiscale image segmentation.

    Science.gov (United States)

    Hu, Dandan; Ronhovde, Peter; Nussinov, Zohar

    2012-01-01

    We apply a replica-inference-based Potts model method to unsupervised image segmentation on multiple scales. This approach was inspired by the statistical mechanics problem of "community detection" and its phase diagram. Specifically, the problem is cast as identifying tightly bound clusters ("communities" or "solutes") against a background or "solvent." Within our multiresolution approach, we compute information-theory-based correlations among multiple solutions ("replicas") of the same graph over a range of resolutions. Significant multiresolution structures are identified by replica correlations manifest by information theory overlaps. We further employ such information theory measures (such as normalized mutual information and variation of information), thermodynamic quantities such as the system entropy and energy, and dynamic measures monitoring the convergence time to viable solutions as metrics for transitions between various solvable and unsolvable phases. Within the solvable phase, transitions between contending solutions (such as those corresponding to segmentations on different scales) may also appear. With the aid of these correlations as well as thermodynamic measures, the phase diagram of the corresponding Potts model is analyzed at both zero and finite temperatures. Optimal parameters corresponding to a sensible unsupervised segmentations appear within the "easy phase" of the Potts model. Our algorithm is fast and shown to be at least as accurate as the best algorithms to date and to be especially suited to the detection of camouflaged images.

  18. Fuzzy Markov random fields versus chains for multispectral image segmentation.

    Science.gov (United States)

    Salzenstein, Fabien; Collet, Christophe

    2006-11-01

    This paper deals with a comparison of recent statistical models based on fuzzy Markov random fields and chains for multispectral image segmentation. The fuzzy scheme takes into account discrete and continuous classes which model the imprecision of the hidden data. In this framework, we assume the dependence between bands and we express the general model for the covariance matrix. A fuzzy Markov chain model is developed in an unsupervised way. This method is compared with the fuzzy Markovian field model previously proposed by one of the authors. The segmentation task is processed with Bayesian tools, such as the well-known MPM (Mode of Posterior Marginals) criterion. Our goal is to compare the robustness and rapidity for both methods (fuzzy Markov fields versus fuzzy Markov chains). Indeed, such fuzzy-based procedures seem to be a good answer, e.g., for astronomical observations when the patterns present diffuse structures. Moreover, these approaches allow us to process missing data in one or several spectral bands which correspond to specific situations in astronomy. To validate both models, we perform and compare the segmentation on synthetic images and raw multispectral astronomical data.

  19. Segmentation of the ovine lung in 3D CT Images

    Science.gov (United States)

    Shi, Lijun; Hoffman, Eric A.; Reinhardt, Joseph M.

    2004-04-01

    Pulmonary CT images can provide detailed information about the regional structure and function of the respiratory system. Prior to any of these analyses, however, the lungs must be identified in the CT data sets. A popular animal model for understanding lung physiology and pathophysiology is the sheep. In this paper we describe a lung segmentation algorithm for CT images of sheep. The algorithm has two main steps. The first step is lung extraction, which identifies the lung region using a technique based on optimal thresholding and connected components analysis. The second step is lung separation, which separates the left lung from the right lung by identifying the central fissure using an anatomy-based method incorporating dynamic programming and a line filter algorithm. The lung segmentation algorithm has been validated by comparing our automatic method to manual analysis for five pulmonary CT datasets. The RMS error between the computer-defined and manually-traced boundary is 0.96 mm. The segmentation requires approximately 10 minutes for a 512x512x400 dataset on a PC workstation (2.40 GHZ CPU, 2.0 GB RAM), while it takes human observer approximately two hours to accomplish the same task.

  20. Segmentation of interstitial lung disease patterns in HRCT images

    Science.gov (United States)

    Dash, Jatindra K.; Madhavi, Vaddepalli; Mukhopadhyay, Sudipta; Khandelwal, Niranjan; Kumar, Prafulla

    2015-03-01

    Automated segmentation of pathological bearing region is the first step towards the development of lung CAD. Most of the work reported in the literature related to automated analysis of lung tissue aims towards classification of fixed sized block into one of the classes. This block level classification of lung tissues in the image never results in accurate or smooth boundaries between different regions. In this work, effort is taken to investigate the performance of three automated image segmentation algorithms those results in smooth boundaries among lung tissue patterns commonly encountered in HRCT images of the thorax. A public database that consists of HRCT images taken from patients affected with Interstitial Lung Diseases (ILDs) is used for the evaluation. The algorithms considered are Markov Random Field (MRF), Gaussian Mixture Model (GMM) and Mean Shift (MS). 2-fold cross validation approach is followed for the selection of the best parameter value for individual algorithm as well as to evaluate the performance of all the algorithms. Mean shift algorithm is observed as the best performer in terms of Jaccard Index, Modified Hausdorff Distance, accuracy, Dice Similarity Coefficient and execution speed.

  1. Practical contour segmentation algorithm for small animal digital radiography image

    Science.gov (United States)

    Zheng, Fang; Hui, Gong

    2008-12-01

    In this paper a practical, automated contour segmentation technique for digital radiography image is described. Digital radiography is an imaging mode based on the penetrability of x-ray. Unlike reflection imaging mode such as visible light camera, the pixel brightness represents the summation of the attenuations on the photon thoroughfare. It is not chromophotograph but gray scale picture. Contour extraction is of great importance in medical applications, especially in non-destructive inspection. Manual segmentation techniques include pixel selection, geometrical boundary selection and tracing. But it relies heavily on the experience of the operators, and is time-consuming. Some researchers try to find contours from the intensity jumping characters around them. However these characters also exist in the juncture of bone and soft tissue. The practical way is back to the primordial threshold algorithm. This research emphasizes on how to find the optimal threshold. A high resolution digital radiography system is used to provide the oriental gray scale image. A mouse is applied as the sample of this paper to show the feasibility of the algorithm.

  2. Proximity graphs based multi-scale image segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Skurikhin, Alexei N [Los Alamos National Laboratory

    2008-01-01

    We present a novel multi-scale image segmentation approach based on irregular triangular and polygonal tessellations produced by proximity graphs. Our approach consists of two separate stages: polygonal seeds generation followed by an iterative bottom-up polygon agglomeration into larger chunks. We employ constrained Delaunay triangulation combined with the principles known from the visual perception to extract an initial ,irregular polygonal tessellation of the image. These initial polygons are built upon a triangular mesh composed of irregular sized triangles and their shapes are ad'apted to the image content. We then represent the image as a graph with vertices corresponding to the polygons and edges reflecting polygon relations. The segmentation problem is then formulated as Minimum Spanning Tree extraction. We build a successive fine-to-coarse hierarchy of irregular polygonal grids by an iterative graph contraction constructing Minimum Spanning Tree. The contraction uses local information and merges the polygons bottom-up based on local region-and edge-based characteristics.

  3. Semantic Segmentation of Aerial Images with AN Ensemble of Cnns

    Science.gov (United States)

    Marmanis, D.; Wegner, J. D.; Galliani, S.; Schindler, K.; Datcu, M.; Stilla, U.

    2016-06-01

    This paper describes a deep learning approach to semantic segmentation of very high resolution (aerial) images. Deep neural architectures hold the promise of end-to-end learning from raw images, making heuristic feature design obsolete. Over the last decade this idea has seen a revival, and in recent years deep convolutional neural networks (CNNs) have emerged as the method of choice for a range of image interpretation tasks like visual recognition and object detection. Still, standard CNNs do not lend themselves to per-pixel semantic segmentation, mainly because one of their fundamental principles is to gradually aggregate information over larger and larger image regions, making it hard to disentangle contributions from different pixels. Very recently two extensions of the CNN framework have made it possible to trace the semantic information back to a precise pixel position: deconvolutional network layers undo the spatial downsampling, and Fully Convolution Networks (FCNs) modify the fully connected classification layers of the network in such a way that the location of individual activations remains explicit. We design a FCN which takes as input intensity and range data and, with the help of aggressive deconvolution and recycling of early network layers, converts them into a pixelwise classification at full resolution. We discuss design choices and intricacies of such a network, and demonstrate that an ensemble of several networks achieves excellent results on challenging data such as the ISPRS semantic labeling benchmark, using only the raw data as input.

  4. An Unsupervised Dynamic Image Segmentation using Fuzzy Hopfield Neural Network based Genetic Algorithm

    CERN Document Server

    Halder, Amiya

    2012-01-01

    This paper proposes a Genetic Algorithm based segmentation method that can automatically segment gray-scale images. The proposed method mainly consists of spatial unsupervised grayscale image segmentation that divides an image into regions. The aim of this algorithm is to produce precise segmentation of images using intensity information along with neighborhood relationships. In this paper, Fuzzy Hopfield Neural Network (FHNN) clustering helps in generating the population of Genetic algorithm which there by automatically segments the image. This technique is a powerful method for image segmentation and works for both single and multiple-feature data with spatial information. Validity index has been utilized for introducing a robust technique for finding the optimum number of components in an image. Experimental results shown that the algorithm generates good quality segmented image.

  5. Efficient color representation for image segmentation under nonwhite illumination

    Science.gov (United States)

    Park, Jae Byung

    2003-10-01

    Color image segmentation algorithms often consider object color to be a constant property of an object. If the light source dominantly exhibits a particular color, however, it becomes necessary to consider the color variation induced by the colored illuminant. This paper presents a new approach to segmenting color images that are photographed under non-white illumination conditions. It also addresses how to estimate the color of illuminant in terms of the standard RGB color values rather than the spectrum of the illuminant. With respect to the illumination axis that goes through the origin and the centroid of illuminant color clusters (prior given by the estimation process), the RGB color space is transformed into our new color coordinate system. Our new color scheme shares the intuitiveness of the HSI (HSL or HSV) space that comes from the conical (double-conical or cylindrical) structure of hue and saturation aligned with the intensity variation at its center. It has been developed by locating the ordinary RGB cube in such a way that the illumination axis aligns with the vertical axis (Z-axis) of a larger Cartesian (XYZ) space. The work in this paper uses the dichromatic reflection model [1] to interpret the physics about light and optical effects in color images. The linearity proposed in the dichromatic reflection model is essential and is well preserved in the RGB color space. By proposing a straightforward color model transduction, we suggest dimensionality reduction and provide an efficient way to analyze color images of dielectric objects under non-white illumination conditions. The feasibility of the proposed color representation has been demonstrated by our experiment that is twofold: 1) Segmentation result from a multi-modal histogram-based thresholding technique and 2) Color constancy result from discounting illumination effect further by color balancing.

  6. Asymmetric similarity-weighted ensembles for image segmentation

    DEFF Research Database (Denmark)

    Cheplygina, V.; Van Opbroek, A.; Ikram, M. A.

    2016-01-01

    the images, thus representative data might not be available. Transfer learning techniques can be used to account for these differences, thus taking advantage of all the available data acquired with different protocols. We investigate the use of classifier ensembles, where each classifier is weighted...... according to the similarity between the data it is trained on, and the data it needs to segment. We examine 3 asymmetric similarity measures that can be used in scenarios where no labeled data from a newly introduced scanner or scanning protocol is available. We show that the asymmetry is informative...... and the direction of measurement needs to be chosen carefully. We also show that a point set similarity measure is robust across different studies, and outperforms state-of-the-art results on a multi-center brain tissue segmentation task....

  7. Automatic airline baggage counting using 3D image segmentation

    Science.gov (United States)

    Yin, Deyu; Gao, Qingji; Luo, Qijun

    2017-06-01

    The baggage number needs to be checked automatically during baggage self-check-in. A fast airline baggage counting method is proposed in this paper using image segmentation based on height map which is projected by scanned baggage 3D point cloud. There is height drop in actual edge of baggage so that it can be detected by the edge detection operator. And then closed edge chains are formed from edge lines that is linked by morphological processing. Finally, the number of connected regions segmented by closed chains is taken as the baggage number. Multi-bag experiment that is performed on the condition of different placement modes proves the validity of the method.

  8. An Enhanced Level Set Segmentation for Multichannel Images Using Fuzzy Clustering and Lattice Boltzmann Method

    Directory of Open Access Journals (Sweden)

    Savita Agrawal

    2015-11-01

    Full Text Available In the last decades, image segmentation has proved its applicability in various areas like satellite image processing, medical image processing and many more. In the present scenario the researchers tries to develop hybrid image segmentation techniques to generates efficient segmentation. Due to the development of the parallel programming, the lattice Boltzmann method (LBM has attracted much attention as a fast alternative approach for solving partial differential equations. In this project work, first designed an energy functional based on the fuzzy c-means objective function which incorporates the bias field that accounts for the intensity in homogeneity of the real-world image. Using the gradient descent method, corresponding level set equations are obtained from which we deduce a fuzzy external force for the LBM solver based on the model by Zhao. The method is speedy, robust for denoise, and does not dependent on the position of the initial contour, effective in the presence of intensity in homogeneity, highly parallelizable and can detect objects with or without edges. For the implementation of segmentation techniques defined for gray images, most of the time researchers determines single channel segments of the images and superimposes the single channel segment information on color images. This idea leads to provide color image segmentation using single channel segments of multi channel images. Though this method is widely adopted but doesn’t provide complete true segmentation of multichannel ie color images because a color image contains three different channels for Red, green and blue components. Hence segmenting a color image, by having only single channel segments information, will definitely loose important segment regions of color images. To overcome this problem this paper work starts with the development of Enhanced Level Set Segmentation for single channel Images Using Fuzzy Clustering and Lattice Boltzmann Method. For the

  9. An Enhanced Level Set Segmentation for Multichannel Images Using Fuzzy Clustering and Lattice Boltzmann Method

    Directory of Open Access Journals (Sweden)

    Savita Agrawal

    2014-05-01

    Full Text Available In the last decades, image segmentation has proved its applicability in various areas like satellite image processing, medical image processing and many more. In the present scenario the researchers tries to develop hybrid image segmentation techniques to generates efficient segmentation. Due to the development of the parallel programming, the lattice Boltzmann met hod (LBM has attracted much attention as a fast alternative approach for solving partial differential equations. In this project work, first designed an energy functional based on the fuzzy c-means objective function which incorporates the bias field that accounts for the intensity in homogeneity of the real-world image. Using the gradient descent method, corresponding level set equations are obtained from which we deduce a fuzzy external force for the LBM solver based on the model by Zhao. The method is speedy, robust for denoise, and does not dependent on the position of the initial contour, effective in the presence of intensity in homogeneity, highly parallelizable and can detect objects with or without edges. For the implementation of segmentation techniques defined for gr ay images, most of the time researchers determines single channel segments of the images and superimposes the single channel segment information on color images. This idea leads to provide color image segmentation using single channel segments of multi chann el images. Though this method is widely adopted but doesn’t provide complete true segmentation of multichannel ie color images because a color image contains three different channels for Red, green and blue components. Hence segmenting a color image, b y having only single channel segments information, will definitely loose important segment regions of color images. To overcome this problem this paper work starts with the development of Enhanced Level Set Segmentation for single channel Images Using Fuzzy Clustering and Lattice Boltzmann Method. For the

  10. Infrared image segmentation based on region of interest extraction with Gaussian mixture modeling

    Science.gov (United States)

    Yeom, Seokwon

    2017-05-01

    Infrared (IR) imaging has the capability to detect thermal characteristics of objects under low-light conditions. This paper addresses IR image segmentation with Gaussian mixture modeling. An IR image is segmented with Expectation Maximization (EM) method assuming the image histogram follows the Gaussian mixture distribution. Multi-level segmentation is applied to extract the region of interest (ROI). Each level of the multi-level segmentation is composed of the k-means clustering, the EM algorithm, and a decision process. The foreground objects are individually segmented from the ROI windows. In the experiments, various methods are applied to the IR image capturing several humans at night.

  11. Color image segmentation using watershed and Nyström method based spectral clustering

    Science.gov (United States)

    Bai, Xiaodong; Cao, Zhiguo; Yu, Zhenghong; Zhu, Hu

    2011-11-01

    Color image segmentation draws a lot of attention recently. In order to improve efficiency of spectral clustering in color image segmentation, a novel two-stage color image segmentation method is proposed. In the first stage, we use vector gradient approach to detect color image gradient information, and watershed transformation to get the pre-segmentation result. In the second stage, Nyström extension based spectral clustering is used to get the final result. To verify the proposed algorithm, it is applied to color images from the Berkeley Segmentation Dataset. Experiments show our method can bring promising results and reduce the runtime significantly.

  12. Adaptive Image Restoration and Segmentation Method Using Different Neighborhood Sizes

    Directory of Open Access Journals (Sweden)

    Chengcheng Li

    2003-04-01

    Full Text Available The image restoration methods based on the Bayesian's framework and Markov random fields (MRF have been widely used in the image-processing field. The basic idea of all these methods is to use calculus of variation and mathematical statistics to average or estimate a pixel value by the values of its neighbors. After applying this averaging process to the whole image a number of times, the noisy pixels, which are abnormal values, are filtered out. Based on the Tea-trade model, which states that the closer the neighbor, more contribution it makes, almost all of these methods use only the nearest four neighbors for calculation. In our previous research [1, 2], we extended the research on CLRS (image restoration and segmentation by using competitive learning algorithm to enlarge the neighborhood size. The results showed that the longer neighborhood range could improve or worsen the restoration results. We also found that the autocorrelation coefficient was an important factor to determine the proper neighborhood size. We then further realized that the computational complexity increased dramatically along with the enlargement of the neighborhood size. This paper is to further the previous research and to discuss the tradeoff between the computational complexity and the restoration improvement by using longer neighborhood range. We used a couple of methods to construct the synthetic images with the exact correlation coefficients we want and to determine the corresponding neighborhood size. We constructed an image with a range of correlation coefficients by blending some synthetic images. Then an adaptive method to find the correlation coefficients of this image was constructed. We restored the image by applying different neighborhood CLRS algorithm to different parts of the image according to its correlation coefficient. Finally, we applied this adaptive method to some real-world images to get improved restoration results than by using single

  13. A Markov Random Field Model for Image Segmentation of Rice Planthopper in Rice Fields

    OpenAIRE

    Hongwei Yue; Ken Cai; Hanhui Lin; Hong Man; Zhaofeng Zeng

    2016-01-01

    It is meaningful to develop the automation segmentation of rice planthopper pests based on imaging technology in precision agriculture. However, rice planthopper images affected by light and complicated backgrounds in open rice fields make the segmentation difficult. This study proposed a segmentation approach of rice planthopper images based on the Markov random field to conduct effective segmentation. First, fractional order differential was introduced into the extraction proces...

  14. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool

    OpenAIRE

    Taha, Abdel Aziz; Hanbury, Allan

    2015-01-01

    Background Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the literature of multiple definitions for certain metrics, inefficiency of the metric calculation implementations leading to difficulties with large volumes, and lack of support for fuzzy segmentation by ...

  15. Hybrid of Fuzzy Logic and Random Walker Method for Medical Image Segmentation

    OpenAIRE

    Jasdeep Kaur; Manish Mahajan

    2015-01-01

    The procedure of partitioning an image into various segments to reform an image into somewhat that is more significant and easier to analyze, defined as image segmentation. In real world applications, noisy images exits and there could be some measurement errors too. These factors affect the quality of segmentation, which is of major concern in medical fields where decisions about patients’ treatment are based on information extracted from radiological images. Several algorithms and technique...

  16. Quantitative Assessment of Image Segmentation Quality by Random Walk Relaxation Times

    Science.gov (United States)

    Andres, Björn; Köthe, Ullrich; Bonea, Andreea; Nadler, Boaz; Hamprecht, Fred A.

    The purpose of image segmentation is to partition the pixel grid of an image into connected components termed segments such that (i) each segment is homogenous and (ii) for any pair of adjacent segments, their union is not homogenous. (If it were homogenous the segments should be merged). We propose a rigorous definition of segment homogeneity which is scale-free and adaptive to the geometry of segments. We motivate this definition using random walk theory and show how segment homogeneity facilitates the quantification of violations of the conditions (i) and (ii) which are referred to as under-segmentation and over-segmentation, respectively. We describe the theoretical foundations of our approach and present a proof of concept on a few natural images.

  17. Unsupervised Classification of SAR Images using Hierarchical Agglomeration and EM

    NARCIS (Netherlands)

    Kayabol, K.; Krylov, V.; Zerubia, J.; Salerno, E.; Cetin, A.E.; Salvetti, O.

    2012-01-01

    We implement an unsupervised classification algorithm for high resolution Synthetic Aperture Radar (SAR) images. The foundation of algorithm is based on Classification Expectation-Maximization (CEM). To get rid of two drawbacks of EM type algorithms, namely the initialization and the model order sel

  18. Segmentation of complex objects' sonar images using parameter-fixed MRF model

    Institute of Scientific and Technical Information of China (English)

    YAO Bin; LI Hai-sen; ZHOU Tian; SUN SHENG-he

    2006-01-01

    The effective method of the recognition of underwater complex objects in sonar image is to segment sonar image into target, shadow and sea-bottom reverberation regions and then extract the edge of the object. Because of the time-varying and space-varying characters of underwater acoustics environment, the sonar images have poor quality and serious speckle noise, so traditional image segmentation is unable to achieve precise segmentation. In the paper, the image segmentation process based on MRF (Markov random field) model is studied, and a practical method of estimating model parameters is proposed. Through analyzing the impact of chosen model parameters, a sonar imagery segmentation algorithm based on fixed parameters' MRF model is proposed. Both of the segmentation effect and the low computing load are gained. By applying the algorithm to the synthesized texture image and actual side-scan sonar image, the algorithm can be achieved with precise segmentation result.

  19. Development of image-processing software for automatic segmentation of brain tumors in MR images

    Directory of Open Access Journals (Sweden)

    C Vijayakumar

    2011-01-01

    Full Text Available Most of the commercially available software for brain tumor segmentation have limited functionality and frequently lack the careful validation that is required for clinical studies. We have developed an image-analysis software package called ′Prometheus,′ which performs neural system-based segmentation operations on MR images using pre-trained information. The software also has the capability to improve its segmentation performance by using the training module of the neural system. The aim of this article is to present the design and modules of this software. The segmentation module of Prometheus can be used primarily for image analysis in MR images. Prometheus was validated against manual segmentation by a radiologist and its mean sensitivity and specificity was found to be 85.71±4.89% and 93.2±2.87%, respectively. Similarly, the mean segmentation accuracy and mean correspondence ratio was found to be 92.35±3.37% and 0.78±0.046, respectively.

  20. An Interactive Java Statistical Image Segmentation System: GemIdent.

    Science.gov (United States)

    Holmes, Susan; Kapelner, Adam; Lee, Peter P

    2009-06-01

    Supervised learning can be used to segment/identify regions of interest in images using both color and morphological information. A novel object identification algorithm was developed in Java to locate immune and cancer cells in images of immunohistochemically-stained lymph node tissue from a recent study published by Kohrt et al. (2005). The algorithms are also showing promise in other domains. The success of the method depends heavily on the use of color, the relative homogeneity of object appearance and on interactivity. As is often the case in segmentation, an algorithm specifically tailored to the application works better than using broader methods that work passably well on any problem. Our main innovation is the interactive feature extraction from color images. We also enable the user to improve the classification with an interactive visualization system. This is then coupled with the statistical learning algorithms and intensive feedback from the user over many classification-correction iterations, resulting in a highly accurate and user-friendly solution. The system ultimately provides the locations of every cell recognized in the entire tissue in a text file tailored to be easily imported into R (Ihaka and Gentleman 1996; R Development Core Team 2009) for further statistical analyses. This data is invaluable in the study of spatial and multidimensional relationships between cell populations and tumor structure. This system is available at http://www.GemIdent.com/ together with three demonstration videos and a manual.