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A crucial task in character recognition systems is the segmentation of the document into text lines and especially if it is handwritten. When dealing with non-Latin document such as Arabic, the challenge becomes greater since in addition to the variability of writing, the presence of diacritical points and the high number of ascender and descender characters complicates more the process of the segmentation. To remedy with this complexity and even to make this difficulty an advantage since the focus is on the Arabic language which is semi-cursive in nature, a method based on the Watershed Transform technique is proposed. Tested on «Handwritten Arabic Proximity Datasets» [21] a segmentation rate of 93% for a 95% of matching score is achieved. [ABSTRACT FROM AUTHOR]

The detection equipment of weapon systems is usually used to detect the noncooperative targets, causing the distribution patterns of observed samples to be unpredictable in feature spaces. The irregular cluster shapes, diversified cluster densities and noise bring great challenges to clustering algorithms. A novel adaptive grid-based clustering algorithm, which consists of a k-nearest neighbor method-based gridding method with spatial resolution adaptability, and an adaptive watershed transform-based method for cluster detection and segmentation in the gridded space are presented. The proposed algorithm could process the clusters with noises and significantly diverse densities, meanwhile keeps the advantages of gird-based clustering, including robustness for cluster shape and no need for cluster number as priori parameter. The effectiveness of the algorithm is tested with simulation and artificial datasets. [ABSTRACT FROM AUTHOR]

The watershed transform is considered as the most appropriate method for image segmentation in the field of mathematical morphology. In the following paper, we present an adapted topological watershed algorithm suited for a rapid and effective implementation on Shared Memory Parallel Machine (SMPM). The introduced algorithm allows a parallel watershed computing while preserving the given topology. No prior minima extraction is needed, nor the use of any sorting step or hierarchical queue. The strategy that guides the parallel watershed computing, labeled SDM-Strategy (equivalent to Split-Distributes and Merge), is also presented. Experimental analyses such as execution time, performance enhancement, cache consumption, efficiency and scalability are also presented and discussed. [ABSTRACT FROM AUTHOR]

High resolution remote sensing image segmentation methods that consider only the spectral information in the region growing process often lead to over segmentation and low boundary precision. To overcome that, a watershed transform algorithm which combines spectral information and texture information is proposed. At first, the spectral intensity gradient and the texture gradient have to be extracted from the input image. For that purpose, a new bilateral filtering model is introduced. This edge preserving algorithm can remove noise of images. Meanwhile, it can also remove texture from images by using a local smoothing scale parameter. By adapting this filtering algorithm on the original image and the Gabor texture feature images, the spectral information and texture information are extracted separately. Then with edge detection algorithm, the spectral intensity gradient and texture gradient are obtained. Finally a gradient fusion strategy by morphological dilation and watershed transform are performed in succession. Experiments are carried out on three high resolution color remote sensing images. Compared with JSEG and multi-resolution segmentation methods, the proposed method has a higher boundary precision and can reduce the over segmentation and under segmentation effects. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the use of graph-cuts to merge the regions of the watershed transform optimally. Watershed is a simple, intuitive and efficient way of segmenting an image. Unfortunately it presents a few limitations such as over-segmentation and poor detection of low boundaries. Our segmentation process merges regions of the watershed over-segmentation by minimizing a specific criterion using graph-cuts optimization. Two methods will be introduced in this paper. The first is based on regions histogram and dissimilarity measures between adjacent regions. The second method deals with efficient approximation of minimal surfaces and geodesics. Experimental results show that these techniques can efficiently be used for large images segmentation when a pre-computed low level segmentation is available. We will present these methods in the context of interactive medical image segmentation.

This paper presents segmentation of p53immunostained tissue images of oral squamous cell carcinoma that consist of cell nuclei segmentation and splitting of overlapping/touching cell structures. In segmentation, the entropy thresholding has been adopted in which the optimum threshold value to each color component of the image is obtained by maximizing the global entropy computed from its gray-level co-occurrence matrix. The segmented image consists of isolated cells and complex nuclei structures. A novel complex nuclei structure detection algorithm is proposed to identify overlapped nuclei structures, which have been further resolved by watershed transform. The performance of the segmentation technique is evaluated using the quantitative metrics, namely mean absolute difference (MAD), dice coefficient (DC) and accuracy. Global entropy thresholding-based segmentation technique achieved the best MAD of 0.478, DC of 0.967 and accuracy of 0.970 compared to state-of-art techniques such as otsu and active contour. Extensive experimental results show that proposed complex nuclei structure detection-based overlapping/touching cells splitting algorithm effectively delineated nuclei with over- and under-segmentation rate of 0.49 %. Therefore, tissue image segmentation method presented has high potential in immunohistochemical (IHC) quantification and also can be easily generalized for images stained with other biomarkers. [ABSTRACT FROM AUTHOR]

Inverse Synthetic Aperture Radar images are playing a significant role in classification of sea and air targets. First we acquire the ISAR images of targets using a sensor like radar and extract the characteristics of targets from the ISAR images in the form of feature vectors. The computed feature vectors are used for classification of targets. In this work, widely used and efficient segmentation tool Watershed transform and the multi resolution technique wavelet transform are explored to derive the target features. An artificial neural network based classifier is used for classification. The Wavelet analysis divides the information of an image into approximation and detail sub signals. The approximate and three detail sub signal values are taken as feature vectors and given as input to the classifier for ship ISAR image classification. The widely used segmentation technique, Watershed transform is applied to the ISAR images. The wavelet coefficients are computed for the segmented ISAR images and used as feature vectors for classification of the ISAR images. Also, the statistical moments mean and standard deviation are computed for the color ISAR images itself, taken in RGB format. These statistical color moments are used as feature vector. The classification accuracy is compared for the feature vectors. [ABSTRACT FROM AUTHOR]

We consider the problem of segmenting triangle meshes endowed with a discrete scalar function f based on the critical points of f. The watershed transform induces a decomposition of the domain of function f into regions of influence of its minima, called catchment basins. The discrete Morse gradient induced by f allows recovering not only catchment basins but also a complete topological characterization of the function and of the shape on which it is defined through a Morse decomposition. Unfortunately, discrete Morse theory and related algorithms assume that the input scalar function has no flat areas, whereas such areas are common in real data and are easily handled by watershed algorithms. We propose here a new approach for building a discrete Morse gradient on a triangulated 3D shape endowed by a scalar function starting from the decomposition of the shape induced by the watershed transform. This allows for treating flat areas without adding noise to the data. Experimental results show that our approach has significant advantages over existing ones, which eliminate noise through perturbation: it is faster and always precise in extracting the correct number of critical elements. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate an algorithm for generating an automatic portal system. This has been accomplished based on a given set of triangles. The proposed solution was designed to enhance the performance of a sound beam-tracing engine. This solution can also be used for other areas where portal systems are applicable. The provided technical solution emphasizes the beam tracing engine's requirements. Our approach is based on the work of Haumont et al. (with additional improvements), resulting in improved scene segmentation and lower computational complexity. We examined voxelization techniques and their properties, and have adjusted these to fit the requirements of a beamtracing engine. As a result of our investigation, a new method for finding portal placement has been developed by adjusting the orientation of the found portals to fit the neighboring scene walls. In addition, we replaced Haumont et al.'s prevoxelization step, which is used for erasing geometrical details (for example, thin walls). This was done by smoothing the distance field that, in effect, eliminated incorrectly positioned portals. The results of our work remove the requirement for walls that separate rooms to have a particular thickness. We also describe a method for building a structure that accelerates real-time queries for determining the area where a given point is located. All of the presented techniques allow for the use of larger sized voxels, which increases performance and reduces memory requirements (not only during the preprocessing phase but also during real-time usage). The proposed solutions were tested using scenarios with scenes of varying complexity. [ABSTRACT FROM AUTHOR]

Image segmentation methodology that has played a vast role in the image processing approach. The segmentation process that will partition the input images into numerous number of parts as needed. The resultant segmented parts applied with certain techniques or algorithms to enhance the required feature works in the process. The general applications of image segmentation encompass object exposure, surgery simulation, tumour location, recognition work etc. The intension of image segmentation is to shorten or alter the image to some form which will effectively results in some other form with more relevant information needed. This will help in the clearer and apparent resolution of the image. This paper presents the focal approach on the analysis of the numerous transformation techniques and algorithm associated with the image segmentation process. [ABSTRACT FROM AUTHOR]

A crucial task in character recognition systems is the segmentation of the document into text lines and especially if it is handwritten. When dealing with non-Latin document such as Arabic, the challenge becomes greater since in addition to the variability of writing, the presence of diacritical points and the high number of ascender and descender characters complicates more the process of the segmentation. To remedy with this complexity and even to make this difficulty an advantage since the focus is on the Arabic language which is semi-cursive in nature, a method based on the Watershed Transform technique is proposed. Tested on «Handwritten Arabic Proximity Datasets» a segmentation rate of 93% for a 95% of matching score is achieved.

Different from natural image, topographic map is a complex manually generated image which has amount of interlaced lines and area features. Because of the frequent intersection and the overlap between geographic elements, the misalignment in scanner and other disturbances like inappropriate preserving, false color, mixed color and color aliasing problems occur in the raster color maps. These problems could cause serious challenges in segmentation process. In this work, we present a color topographic map segmentation method based on superpixel to overcome these problems. Firstly, the finest partition is obtained based on double color-opponent boundary detection method and watershed approach. Then, a strict region merging method is introduced to prevent mis-merging while superpixels generated. This merging method could make the superpixel partition accurately adherent the boundary between different geographic elements. Finally, luminosity, color and texture information are combinative applied to classify the superpixel into different layers based on support vector machine. The experimental results show that the proposed method outperforms other state-of-art topographic map segmentation approaches. [ABSTRACT FROM AUTHOR]

In this paper, a novel superpixel based multi-focus image fusion algorithm for raw images of single-sensor color imaging devices which incorporates the Bayer pattern is proposed. The proposed algorithm is more efficient than traditional fusion schemes since the raw Bayer pattern images are fused before color demosaicing. With the proposed fusion algorithm, the interpolation errors introduced by the repeated demosaicing operation on multi-source images can also be greatly reduced. In detail, a clarity measurement of Bayer pattern image is defined to judge the focus-level of raw Bayer pattern images, and the fusion operator is performed on superpixels which provide powerful grouping cues of local image feature. By comparing the clarities of superpixels, a weight map is constructed and the guided filter is utilized to refine the weight map. The raw images are merged with refined weight map to get the fused Bayer pattern image, which is restored by the demosaicing algorithm to get the full resolution color image. Experimental results demonstrate that the proposed algorithm can obtain better fused results with more natural appearance and fewer artifacts than the traditional algorithms. [ABSTRACT FROM AUTHOR]

This paper presents an automatic scoring method for p53 immunostained tissue images of oral cancer that consist of tissue image segmentation, splitting of clustered nuclei, feature extraction and classification. The tissue images are segmented using entropy thresholding technique in which the optimum threshold value to each color component is obtained by maximizing the global entropy of its gray-level co-occurrence matrix and clustered cells are separated by selectively applying marker-controlled watershed transform. Cell nuclei feature is extracted by maximal separation technique (MS) based on blue component of tissue image and subsequently, each cell is classified into one of four categories using multi-level thresholding. Finally, IHC score of tissue images have been determined using Allred method. A statistical analysis is performed between immuno-score of manual and automatic method, and compared with the scores that have obtained using other MS techniques. According to the performance evaluation, IHC score based on blue component that has high correlation coefficients (CC) of 0.95, low mean difference (MD) of 0.15, and a very close range of 95% confidence interval with manual scores. Therefore, automatic scoring method presented in this paper has high potential to help the pathologist in IHC scoring of tissue images. [ABSTRACT FROM AUTHOR]

Mastitis is an infectious disease associated with the increased number of somatic cells in cow’s milk, and it is one of the most relevant cause of economic losses in dairy farming industries. In this paper, we propose a method capable of determine, with 99.7% accuracy, the number of these cells in microscope slide images. This level of accuracy is achieved by changing the image original RGB format to Lab color space and applying k -means clustering algorithm to remove debris and other background features. A new gray level thresholding is proposed, and the remaining bound cells are separated in the final segmentation step applying Watershed transform. Many microscope slide images with debris, contrast, and hue variation were used to validate the experimental results. Comparison between the proposed method and manual counting indicates that this new approach is a robust and promising solution to be incorporated in a future automated somatic cell counting system using optical microscopy. [ABSTRACT FROM AUTHOR]

This paper proposes an object-based segmentation/classification scheme for remotely sensed images, based on a novel variant of the recently proposed Genetic Sequential Image Segmentation (GeneSIS) algorithm. GeneSIS segments the image in an iterative manner, whereby at each iteration a single object is extracted via a genetic-based object extraction algorithm. Contrary to the previous pixel-based GeneSIS where the candidate objects to be extracted were evaluated through the fuzzy content of their included pixels, in the newly developed region-based GeneSIS algorithm, a watershed-driven fine segmentation map is initially obtained from the original image, which serves as the basis for the forthcoming GeneSIS segmentation. Furthermore, in order to enhance the spatial search capabilities, we introduce a more descriptive encoding scheme in the object extraction algorithm, where the structural search modules are represented by polygonal shapes. Our objectives in the new framework are posed as follows: enhance the flexibility of the algorithm in extracting more flexible object shapes, assure high level classification accuracies, and reduce the execution time of the segmentation, while at the same time preserving all the inherent attributes of the GeneSIS approach. Finally, exploiting the inherent attribute of GeneSIS to produce multiple segmentations, we also propose two segmentation fusion schemes that operate on the ensemble of segmentations generated by GeneSIS. Our approaches are tested on an urban and two agricultural images. The results show that region-based GeneSIS has considerably lower computational demands compared to the pixel-based one. Furthermore, the suggested methods achieve higher classification accuracies and good segmentation maps compared to a series of existing algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a gradient-based multi-hop clustering protocol combined with a mobile sink (MS) solution for efficient data gathering in wireless sensor networks. The main insight for the clustering algorithm is drawn from image processing field and namely from the watershed transform, widely used for image segmentation. The proposed algorithm creates multi-hop clusters whose cluster heads (CHs) as well as cluster members near the CHs have high energy reserves. Specifically, the energy of the sensors in a cluster increases progressively as getting closer to the CH. As the nodes closer to the CH are most burdened with relaying of data from other cluster members, the higher levels of available energy at these nodes prolong the network lifetime eventually. After cluster formation, a MS periodically visits each CH and collects the data from cluster members already gathered at the CH. Simulation results show the higher performance of the proposed scheme in comparison to other competent approaches in the literature. [ABSTRACT FROM AUTHOR]

A fine-grained segmentation of radio or TV broadcasts is an essential step for most multimedia processing tasks. Applying segmentation algorithms to the speech transcripts seems straightforward. Yet, most of these algorithms are not suited when dealing with short segments or noisy data. In this paper, we present a new segmentation technique inspired from the image analysis field and relying on a new way to compute similarities between candidate segments called vectorization. Vectorization makes it possible to match text segments that do not share common words; this property is shown to be particularly useful when dealing with transcripts in which transcription errors and short segments makes the segmentation difficult. This new topic segmentation technique is evaluated on two corpora of transcripts from French TV broadcasts on which it largely outperforms other existing approaches from the state-of-the-art. [ABSTRACT FROM AUTHOR]

Image segmentation is an important process that separates objects from the background and also from each other. Applied to cells, the results can be used for cell counting which is very important in medical diagnosis and treatment, and biological research that is often used by scientists and medical practitioners. Segmenting 3D confocal microscopy images containing cells of different shapes and sizes is still challenging as the nuclei are closely packed. The watershed transform provides an efficient tool in segmenting such nuclei provided a reasonable set of markers can be found in the image. In the presence of low-contrast variation or excessive noise in the given image, the watershed transform leads to over-segmentation (a single object is overly split into multiple objects). The traditional watershed uses the local minima of the input image and will characteristically find multiple minima in one object unless they are specified (marker-controlled watershed). An alternative to using the local minima is by a supervised technique called seeded watershed, which supplies single seeds to replace the minima for the objects. Consequently, the accuracy of a seeded watershed algorithm relies on the accuracy of the predefined seeds. In this paper, we present a segmentation approach based on the geometric morphological properties of the ‘landscape’ using curvatures. The curvatures are computed as the eigenvalues of the Shape matrix, producing accurate seeds that also inherit the original shape of their respective cells. We compare with some popular approaches and show the advantage of the proposed method.

Image segmentation is one of the most important in satellite imaging technology. Remote sensing satellite images are more precise. The objects of these images contain several pixels provide a value information of the area on earth. A segmentation method is applied to form a group of pixels that fit in the same objects before classification. The quality of such a segmentation method is essential to achieve good classification results. Watershed transform is region-based segmentation method. The watershed transform finds watershed ridge lines and catchment basins in an image by considering it, as a surface where light pixels are high and dark pixels are low. Watershed transforms can be accurate if the foreground objects and background locations are to be marked with suitable operations. Mathematical morphological techniques are popular in image processing. A combination of these operations can be marked the neighbor pixels with the similar properties. The inclusion of gradient operators the morphological operations are more efficient analysis of ridge lines and mark the regions. The present work is to design and implementation a new algorithm based on watershed transform using Gradient morphological operations. The algorithm is implemented in MATLAB 2012 and experimented on various remote sensing satellite images. It performs well both in retaining the weak boundary and reducing the undesired over-segmentation. The results shown the proposed method has a good generality in producing segmentation results. [ABSTRACT FROM AUTHOR]

In this paper, a region-merging-based method is proposed for fast segmentation of amplitude-format synthetic aperture radar (SAR) images. It combines the existing fast initial partition by applying the watershed transform to the thresholded ratio edge strength map with fast region merging by using a new merging cost with relative common boundary length penalty (RCBLP) and the nearest neighbor graph (NNG) for fast search minimal edge on a region adjacency graph (RAG). A new statistical similarity measure, which is a scale-invariant and approximately constant false alarm rate with respect to region sizes, is proposed and combined with an RCBLP term to form a new merging cost. The region-merging process starting from the initial partition is fast implemented by means of the RAG and NNG. Several quantitative indexes in optical image segmentation assessment are borrowed for quantitative assessment of segmentation quality. Experiments to synthetic and real SAR images are reported. The results show that the proposed method is fast and attains higher quality segmentation results than the two recent state-of-the-art methods. [ABSTRACT FROM PUBLISHER]

Generalizations of various random tessellation models generated by Poisson point processes are introduced, and their functional probability P(K) is given. They are obtained from Boolean random function models, and alternatively from a geodesic distance, providing a generic way of simulation of a wide range of random tessellations, as illustrated in the paper. [ABSTRACT FROM AUTHOR]

Introduction: Parcellation of the corpus callosum (CC) in the midsagittal cross-section of the brain is of utmost importance for the study of diffusion properties within this structure. The complexity of this operation comes from the absence of macroscopic anatomical landmarks to help in dividing the CC into different callosal areas. In this paper we propose a completely automatic method for CC parcellation using diffusion tensor imaging (DTI). Methods: A dataset of 15 diffusion MRI volumes from normal subjects was used. For each subject, the midsagital slice was automatically detected based on the Fractional Anisotropy (FA) map. Then, segmentation of the CC in the midsgital slice was performed using the hierarchical watershed transform over a weighted FA-map. Finally, parcellation of the CC was obtained through the application of the watershed transform from chosen markers. Results: Parcellation results obtained were consistent for fourteen of the fifteen subjects tested. Results were similar to the ones obtained from tractography-based methods. Tractography confirmed that the cortical regions associated with each obtained CC region were consistent with the literature. Conclusions: A completely automatic DTI-based parcellation method for the CC was designed and presented. It is not based on tractography, which makes it fast and computationally inexpensive. While most of the existing methods for parcellation of the CC determine an average behavior for the subjects based on population studies, the proposed method reflects the diffusion properties specific for each subject. Parcellation boundaries are found based on the diffusion properties within each individual CC, which makes it more reliable and less affected by differences in size and shape among subjects. [ABSTRACT FROM AUTHOR]

Abstract: There are few fully automated methods for liver segmentation in magnetic resonance images (MRI) despite the benefits of this type of acquisition in comparison to other radiology techniques such as computed tomography (CT). Motivated by medical requirements, liver segmentation in MRI has been carried out. For this purpose, we present a new method for liver segmentation based on the watershed transform and stochastic partitions. The classical watershed over-segmentation is reduced using a marker-controlled algorithm. To improve accuracy of selected contours, the gradient of the original image is successfully enhanced by applying a new variant of stochastic watershed. Moreover, a final classifier is performed in order to obtain the final liver mask. Optimal parameters of the method are tuned using a training dataset and then they are applied to the rest of studies (17 datasets). The obtained results (a Jaccard coefficient of 0.91±0.02) in comparison to other methods demonstrate that the new variant of stochastic watershed is a robust tool for automatic segmentation of the liver in MRI. [Copyright &y& Elsevier]

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

This paper presents a hybrid approach for extraction of information from high resolution satellite imagery and also demonstrates the accuracy achieved by the final extracted information. The hybrid technique comprises of improved marker-controlled watershed transforms and a nonlinear derivative method. It overcomes all the disadvantages of existing region-based and edge-based methods by incorporating aforesaid hybrid methods. It preserves the advantages of multi-resolution and multi-scale gradient approaches. Region-based segmentation also incorporates the watershed technique due to its better efficiency in segmentation. In principle, a proper segmentation can be performed perfectly by watershed technique on incorporating ridges. These ridges express as the object's boundaries according to the property of contour detection. On the other hand, the nonlinear derivative method is used for resolving the discrete edge detection problem. Since it automatically selects the best edge localization, which is very much useful for estimation of gradient selection. The main benefit of univocal edge localization is to provide a better direction estimation of the gradient, which helps in producing a confident edge reference map for synthetic images. The practical merit of this proposed method is to derive an impervious surface from emerging urban areas. [ABSTRACT FROM AUTHOR]

Traditional biopsy procedures require invasive tissue removal from a living subject, followed by time-consuming and complicated processes, so noninvasive in vivo virtual biopsy, which possesses the ability to obtain exhaustive tissue images without removing tissues, is highly desired. Some sets of in vivo virtual biopsy images provided by healthy volunteers were processed by the proposed cell segmentation approach, which is based on the watershed-based approach and the concept of convergence index filter for automatic cell segmentation. Experimental results suggest that the proposed algorithm not only reveals high accuracy for cell segmentation but also has dramatic potential for noninvasive analysis of cell nuclear-to-cytoplasmic ratio (NC ratio), which is important in identifying or detecting early symptoms of diseases with abnormal NC ratios, such as skin cancers during clinical diagnosis via medical imaging analysis. [ABSTRACT FROM PUBLISHER]

Abstract: Precise liver segmentation in abdominal MRI images is one of the most important steps for the computer-aided diagnosis of liver pathology. The first and essential step for diagnosis is automatic liver segmentation, and this process remains challenging. Extensive research has examined liver segmentation; however, it is challenging to distinguish which algorithm produces more precise segmentation results that are applicable to various medical imaging techniques. In this paper, we present a new automatic system for liver segmentation in abdominal MRI images. The system includes several successive steps. Preprocessing is applied to enhance the image (edge-preserved noise reduction) by using mathematical morphology. The proposed algorithm for liver region extraction is a combined algorithm that utilizes MLP neural networks and watershed algorithm. The traditional watershed transformation generally results in oversegmentation when directly applied to medical image segmentation. Therefore, we use trained neural networks to extract features of the liver region. The extracted features are used to monitor the quality of the segmentation using the watershed transform and adjust the required parameters automatically. The process of adjusting parameters is performed sequentially in several iterations. The proposed algorithm extracts liver region in one slice of the MRI images and the boundary tracking algorithm is suggested to extract the liver region in other slices, which is left as our future work. This system was applied to a series of test images to extract the liver region. Experimental results showed positive results for the proposed algorithm. [Copyright &y& Elsevier]

This paper presents an optimum user-steered boundary tracking approach for image segmentation, which simulates the behavior of water flowing through a riverbed. The riverbed approach was devised using the image foresting transform with a never-exploited connectivity function. We analyze its properties in the derived image graphs and discuss its theoretical relation with other popular methods such as live wire and graph cuts. Several experiments show that riverbed can significantly reduce the number of user interactions (anchor points), as compared to live wire for objects with complex shapes. This paper also includes a discussion about how to combine different methods in order to take advantage of their complementary strengths. [ABSTRACT FROM AUTHOR]

Abstract: Malaria is a serious disease caused by a blood parasite named Plasmodium spp. The World Health Organization estimates 300-500 million malaria cases and more than 1 million deaths per year (Tek F. B. et al., 2006). Manual counting and classifications of infected erythrocytes is a time-consuming and laborious process (Selena W.S. Sio et al., 2007).The aim of our study to develop a fully automatic system for counting and classification of Malaria parasite infected erythrocytes and detection of life stage of parasites. The system uses Giemsa stained thin blood images for processing; using Otsu''s threshold erythrocytes are segmented form pre-processed images; watershed algorithm is used to separate overlapped cells. Statistical and colour features are extracted and given to the SVM binary classifier which classifies Malaria infected erythrocytes and SVM multi classifier is used for detection of parasite life stages. [Copyright &y& Elsevier]

Abstract: Objective: This paper presents an algorithm based on multi-level watershed segmentation combined with three fuzzy systems to segment a large number of myelinated nerve fibers in microscope images. The method can estimate various geometrical parameters of myelinated nerve fibers in peripheral nerves. It is expected to be a promising tool for the quantitative assessment of myelinated nerve fibers in related research. Materials and methods: A novel multi-level watershed scheme iteratively detects pre-candidate nerve fibers. At each immersion level, watershed segmentation extracts the initial axon locations and obtains meaningful myelinated nerve fiber features. Thereafter, according to a priori characteristics of the myelinated nerve fibers, fuzzy rules reject unlikely pre-candidates and collect a set of candidates. Initial candidate boundaries are then refined by a fuzzy active contour model, which flexibly deforms contours according to the observed features of each nerve fiber. A final scan with a different set of fuzzy rules based on the a priori properties of the myelinated nerve fibers removes false detections. A particle swarm optimization method is employed to efficiently train the large number of parameters in the proposed fuzzy systems. Results: The proposed method can automatically segment the transverse cross-sections of nerve fibers obtained from optical microscope images. Although the microscope image is usually noisy with weak or variable levels of contrast, the proposed system can handle images with a large number of myelinated nerve fibers and achieve a high fiber detection ratio. As compared to manual segmentation by experts, the proposed system achieved an average accuracy of 91% across different data sets. Conclusion: We developed an image segmentation system that automatically handles myelinated nerve fibers in microscope images. Experimental results showed the efficacy of this system and its superiority to other nerve fiber segmentation approaches. Moreover, the proposed method can be extended to other applications of automatic segmentation of microscopic images. [Copyright &y& Elsevier]

Abstract: Automation of chromosome analysis has long been considered as a difficult task. However the advent of Multiplex Fluorescence In Situ Hybridization (M-FISH) made the analysis of chromosomes much easier. Nevertheless, the chromosomes in an M-FISH image do very often partially occlude each other; hence, their segmentation is not trivial and requires the application of a dedicated procedure. In this paper a method is presented for the segmentation of touching and overlapping groups of chromosomes in M-FISH images. Initially, the watershed transform is applied and the image is decomposed into watershed regions. Next, gradient paths starting from points of high concavity are computed for each produced region. Finally, adjacent regions are merged producing the final chromosome areas. To validate our method a benchmark database of 183 M-FISH images has been used. The proposed algorithm resulted in a 90.6% success rate for touching chromosomes and 80.4% for overlapping groups of chromosomes. [ABSTRACT FROM AUTHOR]

Abstract: Automatic image interpretation is often achieved by first performing a segmentation of the image (i.e., gathering neighbouring pixels into homogeneous regions) and then applying a supervised region-based classification. In such a process, the quality of the segmentation step is of great importance in the final classified result. Nevertheless, whereas the classification step takes advantage from some prior knowledge such as learning sample pixels, the segmentation step rarely does. In this paper, we propose to involve such samples through machine learning procedures to improve the segmentation process. More precisely, we consider the watershed transform segmentation algorithm, and rely on both a fuzzy supervised classification procedure and a genetic algorithm in order to respectively build the elevation map used in the watershed paradigm and tune segmentation parameters. We also propose new criteria for segmentation evaluation based on learning samples. We have evaluated our method on remotely sensed images. The results assert the relevance of machine learning as a way to introduce knowledge within the watershed segmentation process. [Copyright &y& Elsevier]

Wing venation characteristics, such as the wing diagram and coordinates of vein junctions, art, species-specific and are usually used in studies of insect identification. Image analysis methods are quick and precise means to measure these characteristics. An image analysis method is proposed that enables extraction of the wing diagram and automatic identification of vein junctions. A color image of an insect wing is converted to a binary image of wing venation based on thresholding. A distance-to-boundary map of the complement of the binary image is computed using a distance transform function. The vein skeleton is obtained by a watershed transform. The lookup table method is applied to find coordinates of vein junctions. The proposed method is capable of producing a smooth vein skeleton diagram and can extract the coordinates of vein junctions. This approach is not limited to high-quality images and is simple because the characteristics are obtained almost simultaneously. The experimental results showed that tire proposed method is repeatable and useful for wing analysis. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a new tensorial color representation, obtained by making a correspondence between color models (HSL, IHSL, HSV, RGB and CIELUV) and tensors. Based on this representation, a proposed tensorial morphological gradient (TMG), defined as the maximum dissimilarity over the neighborhood, was tested using several tensor similarity measures. Experimental results illustrate which color models are more suitable to the proposed tensorial representation and which measures give best results in the TMG computation. The watershed transform was used to demonstrate that the proposed representation and the TMG can be applied to segment color images. A quantitative analysis of segmentation results was also conducted. [Copyright &y& Elsevier]

Abstract: We introduce a framework for synergistic arc-weight estimation, where the user draws markers inside each object (including background), arc weights are estimated from image attributes and object information (pixels under the markers), and a visual feedback guides the user’s next action. We demonstrate the method in several graph-based segmentation approaches as a basic step (which should be followed by some proper approach-specific adaptive procedure) and show its advantage over methods that do not exploit object information and over methods that recompute weights during delineation, which make the user to lose control over the segmentation process. We also validate the method using medical data from two imaging modalities (CT and MRI-T1). [Copyright &y& Elsevier]

Abstract: The microimage of activated carbon fibers (ACFs) obtained from SEM or TEM has low signal to noise ratio (SNR). And the objects in image overlapped together. Image processing methods based on clustering analysis and watershed transform were applied to explore the macropores morphology structure of ACFs. The microimage shows that abundant round-shaped macropores with pore size around 50–200nm, some of which cluster in the form of ring, distribute on ACF surface. Through clustering analysis, we calculated the surface area of macropores and their distributions. The results showed that the pores characterize within three regions, (1) the pore objects smaller than 30 pixels distributing nearly uniformly; (2) those with pixels between 30 and 165 peaking at lower region and (3) those larger than 165 pixels having fewer numbers and provide the main part of clusters. According to watershed transform segmentation analysis, the overlapped pores in one cluster were separated from each other. The number and other morphology parameters of macropores are calculated automatically and accurately in this paper. [Copyright &y& Elsevier]

Digital video recording (DVR) systems are widely used in our daily life because of cost-down of capturing devices. Developing an automatic and intelligent system to detect, track, recognize, and analyze moving objects could save human power in monitoring centers. In this study, the color features of an employee's uniform were extracted to identify the entrance legality in a restricted area of an open space. First of all, a background subtraction technique was used to detect moving objects in image sequences. Three key object features, the position, the size and the color, were extracted to track the detected entrants. After that, the body of an entrant was segmented into three parts for locating the region of interest (ROI) using a watershed transform. Dominant color features extracted from the ROI were classified for preventing the illegal entrance. Some experiments were conducted to show the feasibility and validity of the proposed system. In the final part of the paper, conclusions are drawn and future work is suggested. [ABSTRACT FROM AUTHOR]

Abstract: This paper addresses a study to achieve a method of assessment of coated abrasives for precision belt grinding based on the identification of the prevailing relationships between the changing features of fixed grains on flexible coated belts and grinding performance. A set of parameters was defined which describe the aluminium oxide resin-bonded belt characteristics including active grits density, cutting edge dullness, chip storage space and mean effective indentation. A parametric study was made of the effects of coated belt characteristics on surface finish performance with different grain sizes for grinding different workpiece materials. Experimental results are discussed in relation to the prevailing mechanisms of the process at the belt-work interface which can be separated into cutting and ploughing components. This enables also to describe the grit wear mechanisms operating with coated abrasives when grinding two engineering materials. [Copyright &y& Elsevier]