SEGMENTATION OF PROSTATE IN MRI IMAGES USING GRAPH-CUT SEGMENTATION

Prostate cancer has become one of the highest cancer-related death cases over the last few years in the West. This cancer affects only men. Statistics has shown that there is a big rise in the number of estimation cases over the last years. The increase in the number of these cases leads to accurate diagnoses at early stages enabling early intervention. Numbers of clinical practices are also introduced. One of these practices is the use of the Magnetic Resonance Imaging (MRI) scanner. However, images produced show a poor contrast of soft tissue between the surrounding tissue and prostate gland. This article aims to use the Graph-cut as the method segmentation of images. Index Terms – Prostate Gland, MRI scanner, MATLA

Remote sensing satellite image processing techniques for image classification: a comprehensive survey

This paper is a brief survey of advance technological aspects of Digital Image Processing which are applied to remote sensing images obtained from various satellite sensors. In remote sensing, the image processing techniques can be categories in to four main processing stages: Image preprocessing, Enhancement, Transformation and Classification. Image pre-processing is the initial processing which deals with correcting radiometric distortions, atmospheric distortion and geometric distortions present in the raw image data. Enhancement techniques are applied to preprocessed data in order to effectively display the image for visual interpretation. It includes techniques to effectively distinguish surface features for visual interpretation. Transformation aims to identify particular feature of earth’s surface and classification is a process of grouping the pixels, that produces effective thematic map of particular land use and land cover

Two and three dimensional segmentation of multimodal imagery

The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes

Variational region-based segmentation using multiple texture statistics

International audienceThis paper investigates variational region-level criterion for supervised and unsupervised texturebased image segmentation. The focus is given to the demonstration of the effectiveness and robustness of this region-based formulation compared to most common variational approaches. The main contributions of this global criterion are twofold. First, the proposed methods circumvent a major problem related to classical texture based segmentation approaches. Existing methods, even if they use different and various texture features, are mainly stated as the optimization of a criterion evaluating punctual pixel likelihoods or similarity measure computed within local neighborhood. The former approaches require sufficient dissimilarity between used feature statistics. The latter involve an additional limitation which is the choice of the neighborhood size and shape. These two parameters and especially the neighborhood size significantly influence the classification performances: the neighborhood must be large enough to capture texture structures and small enough to warrant segmentation accuracy. These parameters are often set experimentally. To address these limitations, the proposed methods are stated at the region-level, both for stating the overall variational criterion and the observation-driven texture criterion. It resorts to an energy criterion on image regions: image regions are characterized by non-parametric distributions of their responses to a set of filters. In supervised case the segmentation algorithm consists in the minimization of a similarity measure between regions features and texture prototypes and a boundary based functional that imposes smoothness and regularity on region boundaries. In unsupervised case, the segmentation consists in the maximization of the dissimilarity between regions. The proposed similarity-based criteria are generic and permit optimally fusing various types of texture features. It is defined as a weighted sum of Kullback-Leibler divergences between feature distributions. The optimization of the proposed variational criteria is carried out using a level-set formulation. The effectiveness and the robustness of this formulation at region-level, compared to classical active contour methods, are evaluated for various Brodatz and natural images