Watershed segmentation with boundary curvature ratio based merging criterion

This paper proposes to incorporate boundary curvature ratio, region homogeneity and boundary smoothness into a single new merging criterion to improve the oversegmentation of marker-controlled watershed segmentation algorithm. The result is a more refined segmentation result with smooth boundaries and regular shapes. To pursue a final segmentation result with higher inter-variance and lower intra-variance, an optimal number of segments could be self-determined by a proposed formula. Experimental results are presented to demonstrate the merits of this method.postprintThe 9th IASTED International Conference on Signal and Image Processing (SIP 2007), Honolulu, HI., 20-22 August 2007. In Proceedings of SIP, 2007, p. 7-1

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

An adaptive speed term based on homogeneity for level-set segmentation

We tested on an edge map computed from a local homogeneity measurement, which is a potential replacement for the traditional gradient-based edge map in level-set segmentation. In existing level-set methods, the gradient information is used as a stopping criteria for curve evolution, and also provides the attracting force to the zero level-set from the target boundary. However, in a discrete implementation, the gradient-based term can never fully stop the level-set evolution even for ideal edges, leakage is often unavoidable. Also the effective distance of the attracting force and blurring of edges become a trade-off in choosing the shape and support of the smoothing filter. The proposed homogeneity measurement provides easier and more robust edge estimation, and the possibility of fully stopping the level-set evolution. The homogeneity term decreasing from a homogenous region to the boundary, which dramatically increases the effective distance of the attracting force and also provides additional measurement of the overall approximation to the target boundary. Therefore, it provides a reliable criteria of adaptively changing the advent speed. By using this term, the leakage problem was avoided effectively in most cases compared to traditional level-set methods. The computation of the homogeneity is fast and its extension to the 3D case is straightforward

Using contour information and segmentation for object registration, modeling and retrieval

This thesis considers different aspects of the utilization of contour information and syntactic and semantic image segmentation for object registration, modeling and retrieval in the context of content-based indexing and retrieval in large collections of images. Target applications include retrieval in collections of closed silhouettes, holistic w ord recognition in handwritten historical manuscripts and shape registration. Also, the thesis explores the feasibility of contour-based syntactic features for improving the correspondence of the output of bottom-up segmentation to semantic objects present in the scene and discusses the feasibility of different strategies for image analysis utilizing contour information, e.g. segmentation driven by visual features versus segmentation driven by shape models or semi-automatic in selected application scenarios. There are three contributions in this thesis. The first contribution considers structure analysis based on the shape and spatial configuration of image regions (socalled syntactic visual features) and their utilization for automatic image segmentation. The second contribution is the study of novel shape features, matching algorithms and similarity measures. Various applications of the proposed solutions are presented throughout the thesis providing the basis for the third contribution which is a discussion of the feasibility of different recognition strategies utilizing contour information. In each case, the performance and generality of the proposed approach has been analyzed based on extensive rigorous experimentation using as large as possible test collections

Contributions to the segmentation of dermoscopic images

Tese de mestrado. Mestrado em Engenharia Biomédica. Faculdade de Engenharia. Universidade do Porto. 201

Yüksek uzaysal çözünürlüklü uydu görüntülerinin watershed kullanılarak çok ölçekli otomatik bölütlenmesi.

Useful information extraction from satellite images for the use of other higher level applications such as road network extraction and update, city planning etc. is a very important and active research area. It is seen that pixel-based techniques becomes insufficient for this task with increasing spatial resolution of satellite imaging sensors day by day. Therefore, the use of object-based techniques becomes indispensable and the segmentation method selection is very crucial for object-based techniques. In this thesis, various segmentation algorithms applied in remote sensing literature are presented and a segmentation process that is based on watersheds and multi-scale segmentation is proposed to use as the segmentation step of an object-based classifier. For every step of the proposed segmentation process, qualitative and quantitative comparisons with alternative approaches are done. The ones which provide best performance are incorporated into the proposed algorithm. Also, an unsupervised segmentation accuracy metric to determine all parameters of the algorithm is proposed. By this way, the proposed segmentation algorithm has become a fully automatic approach. Experiments that are done on a database formed with images taken from Google Earth® software provide promising results.M.S. - Master of Scienc

Approche robuste pour la segmentation et la classification d’images m´edicales

Image segmentation is a vital process in various fields, including robotics, object recognition, and medical imaging. In medical imaging, accurate segmentation of brain tissues from MRI images is crucial for diagnosing and treating brain disorders such as Alzheimer’s disease, epilepsy, schizophrenia, multiple sclerosis, and cancer. This thesis proposes an automatic fuzzy method for brain MRI segmentation. Firstly, the proposed method aims to improve the efficiency of the Fuzzy C-Means (FCM) algorithm by reducing the need for manual intervention in cluster initialization and determining the number of clusters. For this purpose, we introduce an adaptive splitmerge technique that effectively divides the image into several homogeneous regions using a multi-threshold method based on entropy information. During the merge process, a new distance metric is introduced to combine the regions that are both highly similar within the merged region and effectively separated from others. The cluster centers and numbers obtained from the adaptive split-merge step serve as the initial parameters for the FCM algorithm. The obtained fuzzy partitions are evaluated using a novel proposed validity index. Secondly, we present a novel method to address the challenge of noisy pixels in the FCM algorithm by incorporating spatial information. Specifically, we assign a crucial role to the central pixel in the clustering process, provided it is not corrupted with noise. However, if it is corrupted with noise, its influence is reduced. Furthermore, we propose a novel quantitative metric for replacing the central pixel with one of its neighbors if it can improve the segmentation result in terms of compactness and separation. To evaluate the effectiveness of the proposed method, a thorough comparison with existing clustering techniques is conducted, considering cluster validity functions, segmentation accuracy, and tissue segmentation accuracy. The evaluation comprises comprehensive qualitative and quantitative assessments, providing strong evidence of the superior performance of the proposed approach