Color Textured Image Segmentation Using ICICM - Interval Type-2 Fuzzy C-Means Clustering Hybrid Approach

Segmentation is an essential process in image because of its wild application such as image analysis, medical image analysis, pattern reorganization, etc. Color and texture are most significant low-level features in an image. Normally, color-textured image segmentation consists of two steps: (i) extracting the feature and (ii) clustering the feature vector. This paper presents the hybrid approach for color texture segmentation using Haralick features extracted from the Integrated Color and Intensity Co-occurrence Matrix (ICICM). Then, Extended- Interval Type-2 Fuzzy C-means clustering algorithm is used to cluster the obtained feature vectors into several classes corresponding to the different regions of the textured image. Experimental results show that the proposed hybrid approach could obtain better cluster quality and segmentation results compared to state-of-art image segmentation algorithms

Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images

Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) are important health issues affecting millions of American women. Investigation of the cause of SUI and POP requires a better understand of the anatomy of female pelvic floor. In addition, pre-surgical planning and individualized treatment plans require development of patient-specific three-dimensional or virtual reality models. The biggest challenge in building those models is to segment pelvic floor structures from magnetic resonance images because of their complex shapes, which make manual segmentation labor-intensive and inaccurate. In this dissertation, a quick and reliable semi-automatic segmentation method based on a shape model is proposed. The model is built on statistical analysis of the shapes of structures in a training set. A local feature map of the target image is obtained by applying a filtering pipeline, including contrast enhancement, noise reduction, smoothing, and edge extraction. With the shape model and feature map, automatic segmentation is performed by matching the model to the border of the structure using an optimization technique called evolution strategy. Segmentation performance is evaluated by calculating a similarity coefficient between semi-automatic and manual segmentation results. Taguchi analysis is performed to investigate the significance of segmentation parameters and provide tuning trends for better performance. The proposed method was successfully tested on both two-dimensional and three-dimensional image segmentation using the levator ani and obturator muscles as examples. Although the method is designed for segmentation of female pelvic floor structures, it can also be applied to other structures or organs without large shape variatio

A framework for tumor segmentation and interactive immersive visualization of medical image data for surgical planning

This dissertation presents the framework for analyzing and visualizing digital medical images. Two new segmentation methods have been developed: a probability based segmentation algorithm, and a segmentation algorithm that uses a fuzzy rule based system to generate similarity values for segmentation. A visualization software application has also been developed to effectively view and manipulate digital medical images on a desktop computer as well as in an immersive environment.;For the probabilistic segmentation algorithm, image data are first enhanced by manually setting the appropriate window center and width, and if needed a sharpening or noise removal filter is applied. To initialize the segmentation process, a user places a seed point within the object of interest and defines a search region for segmentation. Based on the pixels\u27 spatial and intensity properties, a probabilistic selection criterion is used to extract pixels with a high probability of belonging to the object. To facilitate the segmentation of multiple slices, an automatic seed selection algorithm was developed to keep the seeds in the object as its shape and/or location changes between consecutive slices.;The second segmentation method, a new segmentation method using a fuzzy rule based system to segment tumors in a three-dimensional CT data was also developed. To initialize the segmentation process, the user selects a region of interest (ROI) within the tumor in the first image of the CT study set. Using the ROI\u27s spatial and intensity properties, fuzzy inputs are generated for use in the fuzzy rules inference system. Using a set of predefined fuzzy rules, the system generates a defuzzified output for every pixel in terms of similarity to the object. Pixels with the highest similarity values are selected as tumor. This process is automatically repeated for every subsequent slice in the CT set without further user input, as the segmented region from the previous slice is used as the ROI for the current slice. This creates a propagation of information from the previous slices, used to segment the current slice. The membership functions used during the fuzzification and defuzzification processes are adaptive to the changes in the size and pixel intensities of the current ROI. The proposed method is highly customizable to suit different needs of a user, requiring information from only a single two-dimensional image.;Segmentation results from both algorithms showed success in segmenting the tumor from seven of the ten CT datasets with less than 10% false positive errors and five test cases with less than 10% false negative errors. The consistency of the segmentation results statistics also showed a high repeatability factor, with low values of inter- and intra-user variability for both methods.;The visualization software developed is designed to load and display any DICOM/PACS compatible three-dimensional image data for visualization and interaction in an immersive virtual environment. The software uses the open-source libraries DCMTK: DICOM Toolkit for parsing of digital medical images, Coin3D and SimVoleon for scenegraph management and volume rendering, and VRJuggler for virtual reality display and interaction. A user can apply pseudo-coloring in real time with multiple interactive clipping planes to slice into the volume for an interior view. A windowing feature controls the tissue density ranges to display. A wireless gamepad controller as well as a simple and intuitive menu interface control user interactions. The software is highly scalable as it can be used on a single desktop computer to a cluster of computers for an immersive multi-projection virtual environment. By wearing a pair of stereo goggles, the surgeon is immersed within the model itself, thus providing a sense of realism as if the surgeon is inside the patient.;The tools developed in this framework are designed to improve patient care by fostering the widespread use of advanced visualization and computational intelligence in preoperative planning, surgical training, and diagnostic assistance. Future work includes further improvements to both segmentation methods with plans to incorporate the use of deformable models and level set techniques to include tumor shape features as part of the segmentation criteria. For the surgical planning components, additional controls and interactions with the simulated endoscopic camera and the ability to segment the colon or a selected region of the airway for a fixed-path navigation as a full virtual endoscopy tool will also be implemented. (Abstract shortened by UMI.

Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images

Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) are important health issues affecting millions of American women. Investigation of the cause of SUI and POP requires a better understand of the anatomy of female pelvic floor. In addition, pre-surgical planning and individualized treatment plans require development of patient-specific three-dimensional or virtual reality models. The biggest challenge in building those models is to segment pelvic floor structures from magnetic resonance images because of their complex shapes, which make manual segmentation labor-intensive and inaccurate. In this dissertation, a quick and reliable semi-automatic segmentation method based on a shape model is proposed. The model is built on statistical analysis of the shapes of structures in a training set. A local feature map of the target image is obtained by applying a filtering pipeline, including contrast enhancement, noise reduction, smoothing, and edge extraction. With the shape model and feature map, automatic segmentation is performed by matching the model to the border of the structure using an optimization technique called evolution strategy. Segmentation performance is evaluated by calculating a similarity coefficient between semi-automatic and manual segmentation results. Taguchi analysis is performed to investigate the significance of segmentation parameters and provide tuning trends for better performance. The proposed method was successfully tested on both two-dimensional and three-dimensional image segmentation using the levator ani and obturator muscles as examples. Although the method is designed for segmentation of female pelvic floor structures, it can also be applied to other structures or organs without large shape variatio

Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images

Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) are important health issues affecting millions of American women. Investigation of the cause of SUI and POP requires a better understand of the anatomy of female pelvic floor. In addition, pre-surgical planning and individualized treatment plans require development of patient-specific three-dimensional or virtual reality models. The biggest challenge in building those models is to segment pelvic floor structures from magnetic resonance images because of their complex shapes, which make manual segmentation labor-intensive and inaccurate. In this dissertation, a quick and reliable semi-automatic segmentation method based on a shape model is proposed. The model is built on statistical analysis of the shapes of structures in a training set. A local feature map of the target image is obtained by applying a filtering pipeline, including contrast enhancement, noise reduction, smoothing, and edge extraction. With the shape model and feature map, automatic segmentation is performed by matching the model to the border of the structure using an optimization technique called evolution strategy. Segmentation performance is evaluated by calculating a similarity coefficient between semi-automatic and manual segmentation results. Taguchi analysis is performed to investigate the significance of segmentation parameters and provide tuning trends for better performance. The proposed method was successfully tested on both two-dimensional and three-dimensional image segmentation using the levator ani and obturator muscles as examples. Although the method is designed for segmentation of female pelvic floor structures, it can also be applied to other structures or organs without large shape variatio

A Hybrid Model for Segmentation of Images Generated by X-Ray Computed Tomography

Kompjuterizovana tomografija (CT) je u poslednje vreme ušla na velika vrata sa razvojem industrijskih CT sistema, usled njene primene u različitim oblastima, a uveliko ulazi i u polje koodinatne metrologije. Zbog karakterizacije objekata sačinjenih od različitih materijala (najčešće metala i plastike), javljaju se određeni problem u vidu nastanka artefakata kod rezultata dimenzionalnih merenja. Istraživanja koja su sprovedena u ovoj doktorskoj disertaciji se bave problemom redukcije uticaja tih artefakata i segmentacije 2D snimaka. Razvijen je novi model koji je baziran na primeni hibridne metode gde je izvršena kombinacija dve metode za obradu slike, a to su fazi klasterizacija i rast regiona. Aksenat je stavljen na primeni ove hibridne metode radi dobijanja tačnijih rezultata segmentacije, što direktno utiče i na rekonstrukciju dimenzionalno tačnijih 3D modela.Computed tomography (CT) has recently entered a large door with the development of industrial CT systems, due to its application in many different areas, and it is already entering the field of coordinate metrology. Due to its ability to non-destructively characterize objects made of different materials (typicaly metals and plastics), a certain problem arises in the form of artefacts that are present in the results. Research carried out in this dissertation deals with the problem of reducing the impact of these artefacts and the 2D image segmentation. A new model was developed based on the application of the hybrid method where a combination of two methods for image processing was performed, which are fuzzy clustering and region growing. The accent is emphasized in the application of this hybrid method in order to obtain more accurate segmentation results, which directly affects the reconstruction of dimensionally more accurate 3D models

Sistema integrado de modelação para apoio à prevenção e mitigação de acidentes de hidrocarbonetos em estuários e orla costeira

Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Oceanografia), Universidade de Lisboa, Faculdade de Ciências, 2010A temática dos derrames de hidrocarbonetos continua a ser um assunto de extrema importância dados os graves impactes continuadamente causados no meio marinho. O desenvolvimento de ferramentas para auxilio às autoridades responsáveis pela gestão costeira no combate e mitigação deste tipo de acidentes é prioritário nos planos estratégicos governamentais. Neste trabalho propõe-se um novo sistema integrado de análise de hidrocarbonetos desenvolvido para aplicação a derrames em zonas costeiras, portuárias e oceânicas. Desenvolveuse uma nova metodologia baseada na sinergia da modelação numérica com a detecção remota por satélites. Este sistema integra uma componente de modelação numérica flexível (2D/3D) e um novo algoritmo de segmentação de manchas de hidrocarbonetos observadas em imagens SAR. O algoritmo de detecção remota foi concebido para validar os resultados do sistema de modelos. O sistema de modelação baseia-se numa abordagem Euleriana- Lagrangeana para a resolução dos processos de evolução dos hidrocarbonetos, utilizando malhas não-estruturadas para a representação dos domínios de estudo numa perspectiva multi-escala. O modelo de hidrocarbonetos inclui a maioria dos processos relevantes num derrame à superfície e na coluna de água e inclui um novo algoritmo para a retenção costeira que considera a dinâmica intertidal para aplicação a domínios praias, lagunas e estuários. Realizaram-se diversas aplicações sintéticas e reais que comprovaram a precisão, robustez, fiabilidade e flexibilidade do sistema integrado, com custos computacionais e níveis de complexidade variável. A aplicação do sistema ao caso Prestige permitiu demonstrar que a sinergia entre a modelação numérica e a detecção remota é uma mais-valia para a previsão de derrames de hidrocarbonetos no mar e serviu como base para uma análise qualitativa da influência da precisão da previsão do vento como agente forçador. Futuramente, o sistema poderá ser aplicado na optimização de planos de contingência, sustentando análises de risco, e em sistemas de alerta e aviso para acidentes de poluição.The severe impact of oil spill accidents in the marine environment reinforces the great importance of these environmental catastrophes. The development of tools to assist coastal management authorities in the prevention and mitigation of such accidents remains a priority in governmental strategic plans. This work proposes a new integrated system of analysis developed for application to oil spills in coastal areas, harbours and ocean. A new methodology was developed based on the synergy of numerical modeling with satellite remote sensing. The proposed system integrates a flexible component of numerical modeling (2D/3D) and a new segmentation algorithm for hydrocarbons spills observed in SAR images. The remote sensing algorithm was designed to validate the results of the modeling system. The modeling system is based on an Eulerian-Lagrangian approach for solving the oil spill processes, using unstructured grids for the discretization of the study area in a multi-scale perspective. Most processes occurring at the sea surface and in the water column during an oil spill are included in the integrated model. A new algorithm for costal retention that considers the intertidal dynamics for application in beaches, coastal lagoons and estuaries was also developed. Several synthetic and real applications were performed to verify the accuracy, robustness, reliability and flexibility of the integrated models, with varying computational costs and degrees of complexity. The application of the methodology to the Prestige accident demonstrated that the synergy between remote sensing and numerical modeling is an asset to the prediction of oil spills fate in the marine environment, and was the basis for a qualitative analysis of the wind forcings accuracy e ect. In the future, the new system can be applied in the prevention, prediction and optimization of contingency plans, supporting risk analysis studies, and as a key element in alert and warning systems for coastal pollution accidents.Fundação para a Ciência e a Tecnologia (SFRH/BD/22124/2005); Laboratório Nacional de Engenharia Civil pela cedência de todos os recursos indispensáveis à realização deste trabalho (destacando os projectos "G-Cast: Aplicação da computação GRID num sistema de simulação e previsão da morfodinâmica em zonas costeiras"( GRID/GRI/81733/2006) e "Distribuição e paralelização de modelos numéricos em Hidráulica e Ambiente" pela disponibilização de formação e acesso aos recursos de HPC

A Clustering Fuzzy Approach for Image Segmentation

Segmentation is a fundamental step in image description or classification. In recent years, several computational models have been used to implement segmentation methods but without establishing a single analytic solution. However, the intrinsic properties of neural networks make them an interesting approach, despite some measure of inefficiency. This paper presents a clustering approach for image segmentation based on a modified fuzzy approach for image segmentation (ART) model. The goal of the proposed approach is to find a simple model able to instance a prototype for each cluster avoiding complex post-processing phases. Results and comparisons with other similar models presented in the literature (like self-organizing maps and original fuzzy ART) are also discussed. Qualitative and quantitative evaluations confirm the validity of the approach proposed. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved

A clustering fuzzy approach for image segmentation

Segmentation is a fundamental step in image description or classification. In recent years, several computational models have been used to implement segmentation methods but without establishing a single analytic solution. However, the intrinsic properties of neural networks make them an interesting approach, despite some measure of inefficiency. This paper presents a clustering approach for image segmentation based on a modi ed fuzzy approach for image segmentation (ART) model. The goal of the proposed approach is to nd a simple model able to instance a prototype for each cluster avoiding complex post-processing phases. Results and comparisons with other similar models presented in the literature (like self-organizing maps and original fuzzy ART) are also discussed. Qualitative and quantitative evaluations con rm the validity of the approach proposed