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Robust unsupervised texture segmentation for motion analysis in ultrasound images

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2025年第1期20卷 97-106页

作者： Brignol, Arnaud Cheriet, Farida Aubin-Fournier, Jean-Francois Fortin, Carole Laporte, Catherine Ecole Technol Super Dept Elect Engn 1100 Rue Notre Dame Ouest Montreal PQ H3C 1K3 Canada Polytech Montreal Dept Comp Engn & Software Engn 2900 boul Edouard Montpetit Montreal PQ H3T 1J4 Canada CHU Sainte Justine Ctr Readaptat Marie Enfant 5200 Rue Belanger Est Montreal PQ H1T 1C9 Canada Ecole Readaptat Fac Med 6128 Succursale Ctr ville Montreal PQ H3C 3J7 Canada

Purpose: Ultrasound imaging has emerged as a promising cost-effective and portable non-irradiant modality for the diagnosis and follow-up of diseases. Motion analysis can be performed by segmenting anatomical structures of interest before tracking them over time. However, doing so in a robust way is challenging as ultrasound images often display a low contrast and blurry boundaries. Methods: In this paper, a robust descriptor inspired from the fractal dimension is presented to locally characterize the gray-level variations of an image. This descriptor is an adaptive grid pattern whose scale locally varies as the gray-level variations of the image. Robust features are then located based on the gray-level variations, which are more likely to be consistently tracked over time despite the presence of noise. Results: The method was validated on three datasets: segmentation of the left ventricle on simulated echocardiography (Dice coefficient, DC), accuracy of diaphragm motion tracking for healthy subjects (mean sum of distances, MSD) and for a scoliosis patient (root mean square error, RMSE). Results show that the method segments the left ventricle accurately ( DC=0.84DC=0.84 ) and robustly tracks the diaphragm motion for healthy subjects ( MSD=1.10MSD=1.10 mm) and for the scoliosis patient ( RMSE=1.22RMSE=1.22 mm). Conclusions: This method has the potential to segment structures of interest according to their texture in an unsupervised fashion, as well as to help analyze the deformation of tissues. Possible applications are not limited to US image. The same principle could also be applied to other medical imaging modalities such as MRI or CT scans.

关键词： Ultrasound images texture segmentation Tissue motion Fractal dimension Landmarks

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texture segmentation using image decomposition and local self-similarity of different features

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MULTIMEDIA TOOLS AND APPLICATIONS 2015年第15期74卷 6069-6089页

作者： Yang, Hongbo Hou, Xia Beijing Informat Sci & Technol Univ Sch Automat Expt Teaching Ctr Elect Informat & Control Beijing 100192 Peoples R China Beijing Informat Sci & Technol Univ Sch Comp Beijing 100192 Peoples R China

In this paper, we propose a new two channels feature space active contours model for texture segmentation by using image decomposition and local self-similarity descriptor of textures. The piece-wise smooth component of image decomposition is regarded as one channel of feature space for segmentation. Defined as a symmetry matrix and a kind of features fusion tool, the local self-similarity descriptor SSM captures the internal geometric layout of local repetitive pattern regions and is computed on different features of textures including oscillatory component of image decomposition, phase congruency and log-Gabor filters responses. A distance map dSSM can measure the similarities between the descriptor of template and the local windows surrounding every pixel on the texture image. And then dSSM is set as another channel of feature space for segmentation. Based on the space, texture segmentation is performed by using active contours and level set technology. In addition, the accuracy of texture boundary localization and the template searching inside initial contour are also concerned in this paper. Compared with some recent approaches, our method is more convincing and works well for synthetic textured images and ones in the real world.

关键词： texture segmentation Local self-similarity Image decomposition

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texture segmentation BASED ON A HIERARCHICAL MARKOV RANDOM FIELD MODEL

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SIGNAL PROCESSING 1992年第3期26卷 285-305页

作者： HU, RM FAHMY, MM [a]Department of Electrical Engineering Queen's University Kingston Ontario Canada K7L 3N6

This paper presents a new texture segmentation technique for both supervised and unsupervised segmentation. The textured images under study are modeled by a proposed hierarchical Markov random field (MRF) model. This model is formed by combining the binomial model for textures and the multi-level logistic model for region distributions. The supervised segmentation is achieved by a new algorithm which can reach the global maxima of the posteriori distribution even if the textures are modeled by an MRF model. For unsupervised segmentation, a new parameter estimation scheme is proposed to estimate the model parameters directly from a given image. The new technique is verified by a variety of textured images, such as synthesized textures, natural textures and aerial images, in both supervised and unsupervised segmentation cases.

关键词： IMAGE ANALYSIS IMAGE segmentation texture segmentation texture MODELING MARKOV RANDOM FIELDS PARAMETER ESTIMATION SIMULATED ANNEALING

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texture segmentation by frequency-sensitive elliptical competitive learning

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IMAGE AND VISION COMPUTING 2001年第9-10期19卷 639-648页

作者： De Backer, S Scheunders, P Univ Antwerp Dept Phys Vis Lab B-2020 Antwerp Belgium

In this paper, a new learning algorithm is proposed with the purpose of texture segmentation. The algorithm is a competitive clustering scheme with two specific features: elliptical clustering is accomplished by incorporating the Mahalanobis distance measure into the learning rules and under-utilization of smaller clusters is avoided by incorporating a frequency-sensitive term. In the paper, an efficient learning rule that incorporates these features is elaborated. In the experimental section, several experiments demonstrate the usefulness of the proposed technique for the segmentation of textured images. On the compositions of textured images, Gabor filters were applied to generate texture features. The segmentation performance is compared to k-means clustering with and without the use of the Mahalanobis distance and to the ordinary competitive learning scheme. It is demonstrated that the proposed algorithm outperforms the others. A fuzzy version of the technique is introduced, and experimentally compared with fuzzy versions of the k-means and competitive clustering algorithms. The same conclusions as for the hard clustering case hold. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： unsupervised classification competitive learning mixture modelling texture segmentation

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texture segmentation based on MRMRF modeling

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PATTERN RECOGNITION LETTERS 2000年第2期21卷 189-200页

作者： Wang, L Liu, J Nanyang Technol Univ Div Informat Engn Sch Elect & Elect Engn Singapore 639798 Singapore

texture segmentation remains a fundamental issue in low-level image analysis, pattern recognition and computer vision. texture segmentation problem can be solved in two directions: model fitting and non-parametric classification. In this paper, we propose to use multiresolution MRF (MRMRF) modeling in texture segmentation. A novel MRMRF parameter estimation method based on MCMC approach is presented. The experimental result shows that the method is suitable to segment textured images. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： multiresolution MRF (MRMRF) Markov chain Monte Carlo (MCMC) texture segmentation parameter estimation

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texture segmentation using Gaussian-Markov random fields and neural oscillator networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2001年第2期12卷 394-404页

作者： Çesmeli, E Wang, DL Ohio State Univ Ctr Biomed Engn Columbus OH 43210 USA Ohio State Univ Dept Comp & Informat Sci Columbus OH 43210 USA Ohio State Univ Ctr Cognit Sci Columbus OH 43210 USA

We propose an image segmentation method based on texture analysis. Our method is composed of two parts;The first part determines a novel set of texture features derived from a Gaussian-Markov random fields (GMRF) model. Unlike a GMRF-based approach, our method does not employ model parameters as features or require the extraction of features for a fixed set of texture types a priori. The second part is a two-dimensional (2-D) array of locally excitatory globally inhibitory oscillator networks (LEGION). After being filtered for noise suppression, features are used to determine the local couplings in the network,When LEGION runs, the oscillators corresponding to the same texture tend to synchronize, whereas different texture regions tend to correspond to distinct phases. In simulations, a large system of differential equations is solved for the first time using a recently proposed method for integrating relaxation oscillator networks. We provide results on real texture images to demonstrate the performance of our method.

关键词： dynamical systems Gaussian Markov random fields LEGION neural networks relaxation oscillators texture segmentation

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texture segmentation: Do the processing units on the saliency map increase with eccentricity?

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VISION RESEARCH 2011年第1期51卷 1-12页

作者： Schade, Ursula Meinecke, Cristina Univ Erlangen Nurnberg Inst Psychol D-91054 Erlangen Germany

The saliency map is a computational model and has been constructed for simulating human saliency processing, e.g. pop-out target detection (e.g. Itti & Koch, 2000). In this study the spatial structure on the saliency map was investigated. It is proposed that the saliency map is structured into processing units whose size is increasing with retinal eccentricity. In two experiments the distance between a target in the stimulus and an irrelevant structure in the mask was varied systematically. Our findings had two main points. Firstly, in texture segmentation tasks the saliency signals from two texture irregularities interfere, when these irregularities appear within a critical spatial distance. Second, the critical distances increase with target eccentricity. The eccentricity-dependent critical distances can be interpreted as crowding effects. It is assumed that additionally to the target eccentricity, also the strength of a saliency signal can determine the spatial area of its impairing influence. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Early vision Saliency map Detection texture segmentation Eccentricity Crowding Critical distance

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texture segmentation USING GABOR MODULATION DEMODULATION

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PATTERN RECOGNITION LETTERS 1987年第4期6卷 261-267页

作者： CLARK, M BOVIK, AC GEISLER, WS UNIV TEXAS DEPT ELECT & COMP ENGNAUSTINTX 78712 UNIV TEXAS DEPT PSYCHOLAUSTINTX 78712

Two-dimensional Gabor filters are used to segment images into regions of specific spatial frequency or orientation characteristic. The images are transformed into a modulated narrowband signal whose envelope coincides... 详细信息

关键词： computer vision Gabor filter image demodulation image modulation texture segmentation

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texture segmentation USING VORONOI POLYGONS

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1990年第2期12卷 211-216页

作者： TUCERYAN, M JAIN, AK Dept. of Comput. Sci. Michigan State Univ. East Lansing MI USA

textures are defined in terms of primitives called tokens. A texture segmentation algorithm based on the Voronoi tessellation is discussed. The algorithm first builds the Voronoi tessellation of the tokens that make up the textured image. It then computes a feature vector for each Voronoi polygon. These feature vectors are used in a probabilistic relaxation labeling on the tokens, to identify the interior and the border regions of the textures. Some experimental results are shown.

关键词： Image segmentation Shape Labeling Statistics Surface texture Data mining Computer vision Machine vision Computer science Focusing INSPEC: Controlled Indexing picture processing computational geometry pattern recognition INSPEC: Non-Controlled Indexing probabilistic relaxation labeling picture processing pattern recognition Voronoi polygons tokens texture segmentation Voronoi tessellation textured image

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texture segmentation USING 2-D GABOR ELEMENTARY-FUNCTIONS

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1994年第2期16卷 130-149页

作者： DUNN, D HIGGINS, WE WAKELEY, J PENN STATE UNIV DEPT ELECT & COMP ENGNUNIV PKPA 16802 PENN STATE UNIV APPL RES LABUNIV PKPA 16802

Many texture-segmentation schemes use an elaborate bank of filters to decompose a textured image into a joint space/spatial-frequency representation. Although these schemes show promise, and although some analytical work work has been done, the relationship between texture differences and the filter configurations required to distinguish them remain largely unknown. This paper examines the issue of designing individual filters. Using a 2-D texture model, we show analytically that applying a properly configured bandpass filter to a textured image produces distinct output discontinuities at texture boundaries;the analysis is based on Gabor elementary functions, but it is the bandpass nature of the filter that is essential. Depending on the type of texture difference, these discontinuities form one of four characteristic signatures: a step, ridge, valley, or a step change in average local output variation. Accompanying experimental evidence indicates that these signatures are useful for segmenting an image. The analysis indicates those texture characteristics that are responsible for each signature type. Detailed criteria are provided for designing filters that can produce quality output signatures. We also illustrate occasions when asymmetric filters are beneficial, an issue not previously addressed.

关键词： texture segmentation texture DISCRIMINATION COMPUTER VISION GABOR FUNCTIONS IMAGE segmentation

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