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A Semi-Vectorial Hybrid Morphological Segmentation of Multicomponent Images Based on Multithreshold Analysis of Multidimensional Compact Histogram  [PDF]
Adles Kouassi, Sié Ouattara, Jean-Claude Okaingni, Wognin J. Vangah, Alain Clement
Open Journal of Applied Sciences (OJAppS) , 2017, DOI: 10.4236/ojapps.2017.711043
Abstract: In this work, we propose an original approach of semi-vectorial hybrid morphological segmentation for multicomponent images or multidimensional data by analyzing compact multidimensional histograms based on different orders. Its principle consists first of segment marginally each component of the multicomponent image into different numbers of classes fixed at K. The segmentation of each component of the image uses a scalar segmentation strategy by histogram analysis; we mainly count the methods by searching for peaks or modes of the histogram and those based on a multi-thresholding of the histogram. It is the latter that we have used in this paper, it relies particularly on the multi-thresholding method of OTSU. Then, in the case where i) each component of the image admits exactly K classes, K vector thresholds are constructed by an optimal pairing of which each component of the vector thresholds are those resulting from the marginal segmentations. In addition, the multidimensional compact histogram of the multicomponent image is computed and the attribute tuples or ‘colors’ of the histogram are ordered relative to the threshold vectors to produce (K + 1) intervals in the partial order giving rise to a segmentation of the multidimensional histogram into K classes. The remaining colors of the histogram are assigned to the closest class relative to their center of gravity. ii) In the contrary case, a vectorial spatial matching between the classes of the scalar components of the image is produced to obtain an over-segmentation, then an interclass fusion is performed to obtain a maximum of K classes. Indeed, the relevance of our segmentation method has been highlighted in relation to other methods, such as K-means, using unsupervised and supervised quantitative segmentation evaluation criteria. So the robustness of our method relatively to noise has been tested.
High-Resolution Algorithm for Image Segmentation in the Presence of Correlated Noise  [PDF]
Haiping Jiang,Salah Bourennane,Caroline Fossati
Journal of Electrical and Computer Engineering , 2010, DOI: 10.1155/2010/630768
Abstract: Multiple line characterization is a most common issue in image processing. A specific formalism turns the contour detection issue of image processing into a source localization issue of array processing. However, the existing methods do not address correlated noise. As a result, the detection performance is degraded. In this paper, we propose to improve the subspace-based high-resolution methods by computing the fourth-order slice cumulant matrix of the received signals instead of second-order statistics, and we estimate contour parameters out of images impaired with correlated Gaussian noise. Simulation results are presented and show that the proposed methods improve line characterization performance compared to second-order statistics.
Modified Watershed Segmentation with Denoising of Medical Images  [PDF]
International Journal of Innovative Research in Science, Engineering and Technology , 2013,
Abstract: De-noising and segmentation are fundamental steps in processing of images. They can be used as pre-processing and post-processing step. They are used to enhance the image quality. Various medical imaging that are used in these days are Magnetic Resonance Images (MRI), Ultrasound, X-Ray, CT Scan etc. Various types of noises affect the quality of images which may lead to unpredictable results. Various noises like speckle noise, Gaussian noise and Rician noise is present in ultrasound, MRI respectively. With the segmentation region required for analysis and diagnosis purpose is extracted. Various algorithm for segmentation like watershed, K-mean clustering, FCM, thresholding, region growing etc. exist. In this paper, we propose an improved watershed segmentation using de-noising filter. First of all, image will be de-noised with morphological opening-closing technique then watershed transform using linear correlation and convolution operations is applied to improve efficiency, accuracy and complexity of the algorithm. In this paper, watershed segmentation and various techniques which are used to improve the performance of watershed segmentation are discussed and comparative analysis is done.
Markov Random Field Segmentation of Brain MR Images  [PDF]
Karsten Held,Elena Rota Kops,Bernd J. Krause,William M. Wells III,Ron Kikinis,Hans-Wilhelm Mueller-Gaertner
Physics , 2009, DOI: 10.1109/42.650883
Abstract: We describe a fully-automatic 3D-segmentation technique for brain MR images. Using Markov random fields the segmentation algorithm captures three important MR features, i.e. non-parametric distributions of tissue intensities, neighborhood correlations and signal inhomogeneities. Detailed simulations and real MR images demonstrate the performance of the segmentation algorithm. The impact of noise, inhomogeneity, smoothing and structure thickness is analyzed quantitatively. Even single echo MR images are well classified into gray matter, white matter, cerebrospinal fluid, scalp-bone and background. A simulated annealing and an iterated conditional modes implementation are presented. Keywords: Magnetic Resonance Imaging, Segmentation, Markov Random Fields
Automatic Segmentation for Synthetic Aperture Radar Images

Li Ying,Shi Qing-feng,Zhang Yan-ning,Zhao Rong-chun,

电子与信息学报 , 2006,
Abstract: The multiplicative nature of the speckle noise in SAR images is a big problem in SAR image segmentation. A novel method for automatic segmentation of SAR images is proposed. The wavelet energy is used to extract texture features, the regional statistics is used to extract gray-level features and the edge preserving mean of gray-level features is used to ensure the accuracy of classification of pixels near to the edge. Three representative kinds of features of SAR image are extracted, so the segmentation performance is enhanced. Besides, an improved unsupervised clustering algorithm is proposed for image segmentation, which can determine the number of classes automatically. Segmentation results on real SAR image demonstrate the effectiveness of the proposed method.
Image Segmentation in the Presence of Intensity in Homogeneities by Using Level Set Method with MRI and Satellite Images
D.Mohammed Elias,,Sri. P.Lakshmi Devi
International Journal of Innovative Technology and Exploring Engineering , 2012,
Abstract: This paper proposes a novel region-based method for image segmentation, which is able to deal with intensity inhomogeneities in the segmentation. Intensity inhomogeneity often occurs in real-world images, which presents a considerable challenge in image segmentation. Here we can take both mri images and also satellite images. First, based on the model of images with intensity inhomogeneities, we derive a local intensity clustering property of the image intensities, and define a local clustering criterion function for the image intensities in a neighborhood of each point. This local clustering criterion function is then integrated with respect to the neighborhood center to give a global criterion of image segmentation. Our method has been validated on synthetic images and real images of various modalities, with desirable performance in the presence of intensity inhomogeneities. Experiments show that our method is more robust to initialization, faster and more accurate than the well-known piecewise smooth model. As an application, our method has been used for segmentation and bias correction of magnetic resonance (MR) images with promising results.
Unsupervised Segmentation Method of Multicomponent Images based on Fuzzy Connectivity Analysis in the Multidimensional Histograms  [PDF]
Sié Ouattara, Georges Laussane Loum, Alain Clément
Engineering (ENG) , 2011, DOI: 10.4236/eng.2011.33024
Abstract: Image segmentation denotes a process for partitioning an image into distinct regions, it plays an important role in interpretation and decision making. A large variety of segmentation methods has been developed; among them, multidimensional histogram methods have been investigated but their implementation stays difficult due to the big size of histograms. We present an original method for segmenting n-D (where n is the number of components in image) images or multidimensional images in an unsupervised way using a fuzzy neighbourhood model. It is based on the hierarchical analysis of full n-D compact histograms integrating a fuzzy connected components labelling algorithm that we have realized in this work. Each peak of the histo- gram constitutes a class kernel, as soon as it encloses a number of pixels greater than or equal to a secondary arbitrary threshold knowing that a first threshold was set to define the degree of binary fuzzy similarity be- tween pixels. The use of a lossless compact n-D histogram allows a drastic reduction of the memory space necessary for coding it. As a consequence, the segmentation can be achieved without reducing the colors population of images in the classification step. It is shown that using n-D compact histograms, instead of 1-D and 2-D ones, leads to better segmentation results. Various images were segmented; the evaluation of the quality of segmentation in supervised and unsupervised of segmentation method proposed compare to the classification method k-means gives better results. It thus highlights the relevance of our approach, which can be used for solving many problems of segmentation.
Hybrid Image Segmentation & Energy minimization technique for the images with non-uniform light intensity
Chandradatta Verma,Chandrahas Sahu
International Journal of Electronics and Computer Science Engineering , 2012,
Abstract: - Image segmentation is a classical problem in computer vision and is of paramount importance to medical imaging. The segmentation is complicated by lack of clarity, the overlap of intensities and many other factors. We present a hybrid algorithm for obtaining segmentation of images that are subject to noise and multiplicative intensity nonuniformity. The algorithm is formulated by the combination of bias field correction algorithm and level set segmentation method. An adaptive local clustering criteria function can be integrated for the determination of the intensity nonuniformity pattern. This non-intensity pattern taken off from image using the conventional level set segmentation method. Energy minimization of the level set function enables us to simultaneously segment and correct the image. A MATLAB code has been implemented as per the proposed method and it gives us good result of segmentation in most of the cases. Our method simply increases the performance in terms of reduced complexity, reduced time and grater segmentation accuracy.
Multicomponent MR Image Denoising  [PDF]
José V. Manjón,Neil A. Thacker,Juan J. Lull,Gracian Garcia-Martí,Luís Martí-Bonmatí,Montserrat Robles
International Journal of Biomedical Imaging , 2009, DOI: 10.1155/2009/756897
Abstract: Magnetic Resonance images are normally corrupted by random noise from the measurement process complicating the automatic feature extraction and analysis of clinical data. It is because of this reason that denoising methods have been traditionally applied to improve MR image quality. Many of these methods use the information of a single image without taking into consideration the intrinsic multicomponent nature of MR images. In this paper we propose a new filter to reduce random noise in multicomponent MR images by spatially averaging similar pixels using information from all available image components to perform the denoising process. The proposed algorithm also uses a local Principal Component Analysis decomposition as a postprocessing step to remove more noise by using information not only in the spatial domain but also in the intercomponent domain dealing in a higher noise reduction without significantly affecting the original image resolution. The proposed method has been compared with similar state-of-art methods over synthetic and real clinical multicomponent MR images showing an improved performance in all cases analyzed.
Improved algorithm of watershed segmentation based on fusion and adaptive morphological filtering

XU Guo-bao,YIN Yi-xin,WANG Ji,SU Zhi-bin,XIE Shi-yi,

计算机应用研究 , 2009,
Abstract: To overcome the over-segmentation for the watershed, this paper presented a novel algorithm of watershed segmentation based on fusion and adaptive morphological filtering. The algorithm first executed the adaptive morphological filtering of fusion to restrain dark noise and texture details of the images. Secondly, in order to enhance the contrast of the object and background, carried out the Tophat and Bothat transformations. Finally, achieved the image segmentation using the watershed algorithm based on immersion model. The experimental results show that the proposed algorithm, compared with general segmentation algorithms, can effectively overcome the segmentation impact on all kinds of noise, has a strong performance against over-segmentation, and achieves quickly and efficiently the image segmentation.
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