A Comparative Study of Different Segmentation Techniques for Brain Tumour Detection

Brain tumour detection is one of the challenging tasks in medical image processing. The present study discusses in detail the segmentation process by means of histogram clustering, Global thresholding, Watershed segmentation and edge based segmentation. Six MRI images from radiologists were collected and the experiments were conducted for statistical analysis also. A comparative study is made and the results are of great interest and practical utility

Spatial fuzzy c-mean sobel algorithm with grey wolf optimizer for MRI brain image segmentation

Segmentation is the process of dividing the original image into multiple sub regions called segments in such a way that there is no intersection between any two regions. In medical images, the segmentation is hard to obtain due to the intensity similarity among various regions and the presence of noise in medical images. One of the most popular segmentation algorithms is Spatial Fuzzy C-means (SFCM). Although this algorithm has a good performance in medical images, it suffers from two issues. The first problem is lack of a proper strategy for point initialization step, which must be performed either randomly or manually by human. The second problem of SFCM is having inaccurate segmented edges. The goal of this research is to propose a robust medical image segmentation algorithm that overcomes these weaknesses of SFCM for segmenting magnetic resonance imaging (MRI) brain images with less human intervention. First, in order to find the optimum initial points, a histogram based algorithm in conjunction with Grey Wolf Optimizer (H-GWO) is proposed. The proposed H-GWO algorithm finds the approximate initial point values by the proposed histogram based method and then by taking advantage of GWO, which is a soft computing method, the optimum initial values are found. Second, in order to enhance SFCM segmentation process and achieve higher accurate segmented edges, an edge detection algorithm called Sobel was utilized. Therefore, the proposed hybrid SFCM-Sobel algorithm first finds the edges of the original image by Sobel edge detector algorithm and finally extends the edges of SFCM segmented images to the edges that are detected by Sobel. In order to have a robust segmentation algorithm with less human intervention, the H-GWO and SFCM-Sobel segmentation algorithms are integrated to have a semi-automatic robust segmentation algorithm. The results of the proposed H-GWO algorithms show that optimum initial points are achieved and the segmented images of the SFCM-Sobel algorithm have more accurate edges as compared to recent algorithms. Overall, quantitative analysis indicates that better segmentation accuracy is obtained. Therefore, this algorithm can be utilized to capture more accurate segmented in images in the era of medical imaging

Representation and fusion of heterogeneous fuzzy information in the 3D space for model-based structural recognition—Application to 3D brain imaging

AbstractWe present a novel approach to model-based pattern recognition where structural information and spatial relationships have a most important role. It is illustrated in the domain of 3D brain structure recognition using an anatomical atlas. Our approach performs segmentation and recognition of the scene simultaneously. The solution of the recognition task is progressive, processing successively different objects, and using different pieces of knowledge about the object and about relationships between objects. Therefore, the core of the approach is the knowledge representation part, and constitutes the main contribution of this paper. We make use of a spatial representation of each piece of information, as a spatial fuzzy set representing a constraint to be satisfied by the searched object, thanks in particular to fuzzy mathematical morphology operations. Fusion of these constraints allows us to select, segment and recognize the desired object

An Analysis of the Genetic Algorithms for the Multiregional Soft Segmentation Optimization in Application on Medical Image Data

Tato bakalářská práce se zabývá analyzováním genetických algoritmů pro optimalizování multiregionálních soft segmentací v aplikacích na medicínská obrazová data. Konkrétním záměrem aplikace segmentační metody je analýza a modelování faciální teplotní distribuce z IR obrazových dat v průběhu intoxikace alkoholem. Předpokládaným záměrem této analýzy je matematický model, umožňující dynamické sledování postupné intoxikace alkoholem na základě dynamiky teplotní distribuce. V teoretické části práce jsou popsány metody pro zjišťování intoxikace alkoholem pomocí IR obrazu. Dále je uvedena aktivita krve v obličeji po intoxikaci alkoholem a vysvětlení genetických algoritmů. V praktické části práce je na IR obrazech od různých probandů aplikován genetický algoritmus K-means řízený ABC algoritmem, který extrahuje dané oblasti zájmu. Důležitou součástí analýzy je testování parametrů ABC genetického algoritmu s cílem dosažení optimalizovaného modelu teplotní distribuce.This bachelor thesis deals with analysis of genetic algorithms for optimization of multiregional soft segmentations in medical image data applications. The specific purpose of segmentation method application is to analyze and model facial temperature distribution from IR image data during alcohol intoxication. The assumed intent of this analysis is a mathematical model allowing dynamic monitoring of gradual alcohol intoxication based on the temperature distribution dynamics. The theoretical part of the thesis describes methods for detection of alcohol intoxication by IR image. In addition, the activity of blood in the face after alcohol intoxication and explanation of genetic algorithms. In the practical part of the thesis, the K-means algorithm is controlled by an ABC algorithm that extracts the area of interest from the various probands. An important part of the analysis is the testing of ABC genetic algorithm parameters in order to achieve an optimized temperature distribution model.450 - Katedra kybernetiky a biomedicínského inženýrstvívelmi dobř

Fast interactive 2D and 3D segmentation tools.

by Kevin Chun-Ho Wong.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 74-79).Abstract also in Chinese.Chinese Abstract --- p.vAbstract --- p.viAcknowledgements --- p.viiChapter 1 --- Introduction --- p.1Chapter 2 --- Prior Work : Image Segmentation Techniques --- p.3Chapter 2.1 --- Introduction to Image Segmentation --- p.4Chapter 2.2 --- Region Based Segmentation --- p.5Chapter 2.2.1 --- Boundary Based vs Region Based --- p.5Chapter 2.2.2 --- Region growing --- p.5Chapter 2.2.3 --- Integrating Region Based and Edge Detection --- p.6Chapter 2.2.4 --- Watershed Based Methods --- p.8Chapter 2.3 --- Fuzzy Set Theory in Segmentation --- p.8Chapter 2.3.1 --- Fuzzy Geometry Concept --- p.8Chapter 2.3.2 --- Fuzzy C-Means (FCM) Clustering --- p.9Chapter 2.4 --- Canny edge filter with contour following --- p.11Chapter 2.5 --- Pyramid based Fast Curve Extraction --- p.12Chapter 2.6 --- Curve Extraction with Multi-Resolution Fourier transformation --- p.13Chapter 2.7 --- User interfaces for Image Segmentation --- p.13Chapter 2.7.1 --- Intelligent Scissors --- p.14Chapter 2.7.2 --- Magic Wands --- p.16Chapter 3 --- Prior Work : Active Contours Model (Snakes) --- p.17Chapter 3.1 --- Introduction to Active Contour Model --- p.18Chapter 3.2 --- Variants and Extensions of Snakes --- p.19Chapter 3.2.1 --- Balloons --- p.20Chapter 3.2.2 --- Robust Dual Active Contour --- p.21Chapter 3.2.3 --- Gradient Vector Flow Snakes --- p.22Chapter 3.2.4 --- Energy Minimization using Dynamic Programming with pres- ence of hard constraints --- p.23Chapter 3.3 --- Conclusions --- p.25Chapter 4 --- Slimmed Graph --- p.26Chapter 4.1 --- BSP-based image analysis --- p.27Chapter 4.2 --- Split Line Selection --- p.29Chapter 4.3 --- Split Line Selection with Summed Area Table --- p.29Chapter 4.4 --- Neighbor blocks --- p.31Chapter 4.5 --- Slimmed Graph Generation --- p.32Chapter 4.6 --- Time Complexity --- p.35Chapter 4.7 --- Results and Conclusions --- p.36Chapter 5 --- Fast Intelligent Scissor --- p.38Chapter 5.1 --- Background --- p.39Chapter 5.2 --- Motivation of Fast Intelligent Scissors --- p.39Chapter 5.3 --- Main idea of Fast Intelligent Scissors --- p.40Chapter 5.3.1 --- Node position and Cost function --- p.41Chapter 5.4 --- Implementation and Results --- p.42Chapter 5.5 --- Conclusions --- p.43Chapter 6 --- 3D Contour Detection: Volume Cutting --- p.50Chapter 6.1 --- Interactive Volume Cutting with the intelligent scissors --- p.51Chapter 6.2 --- Contour Selection --- p.52Chapter 6.2.1 --- 3D Intelligent Scissors --- p.53Chapter 6.2.2 --- Dijkstra's algorithm --- p.54Chapter 6.3 --- 3D Volume Cutting --- p.54Chapter 6.3.1 --- Cost function for the cutting surface --- p.55Chapter 6.3.2 --- "Continuity function (x,y, z) " --- p.59Chapter 6.3.3 --- Finding the cutting surface --- p.61Chapter 6.3.4 --- Topological problems for the volume cutting --- p.61Chapter 6.3.5 --- Assumptions for the well-conditional contour used in our algo- rithm --- p.62Chapter 6.4 --- Implementation and Results --- p.64Chapter 6.5 --- Conclusions --- p.64Chapter 7 --- Conclusions --- p.71Chapter 7.1 --- Contributions --- p.71Chapter 7.2 --- Future Work --- p.72Chapter 7.2.1 --- Real-time interactive tools with Slimmed Graph --- p.72Chapter 7.2.2 --- 3D slimmed graph --- p.72Chapter 7.2.3 --- Cartoon Film Generation System --- p.7

An Automated Technique for Statistical Characterization of Brain Tissues in Magnetic Resonance Imaging

A procedure for estimating the joint probability density function (pdf) of T1, T2 and proton spin density (PD) for gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) in the brain is presented. The pdf's have numerous applications, including the study of tissue parameter variability in pathology and across populations. The procedure requires a multispectral, spin echo magnetic resonance imaging (MRI) data set of the brain. It consists of five automated steps: (i) preprocess the data to remove extracranial tissue using a sequence of image processing operators; (ii) estimate T1, T2 and PD by fitting the preprocessed data to an imaging equation; (iii) perform a fuzzy c-means clustering on the same preprocessed data to obtain a spatial map representing the membership value of the three tissue classes at each pixel location; (iv) reject estimates which are not from pure tissue or have poor fits in the parameter estimation, and classify the remaining estimates as either GM, WM or CSF; (v) compute statistics on the classified estimates to obtain a probability mass function and a Gaussian joint pdf of the tissue parameters for each tissue class. Some preliminary results are shown comparing computed pdf's of young, elderly and Alzheimer's subjects. Two brief examples applying the joint pdf's to pulse sequence optimization and generation of computational phantoms are also provided

An Automated Technique for Statistical Characterization of Brain Tissues in Magnetic Resonance Imaging

A procedure for estimating the joint probability density function (pdf) of T 1 , T 2 , and proton spin density (P D ) for gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) in the brain is presented. The pdf's have numerous applications, including the study of tissue parameter variability in pathology and across populations. The procedure requires a multispectral, spin echo magnetic resonance imaging (MRI) data set of the brain. It consists of five automated steps: 1) preprocess the data to remove extracranial tissue using a sequence of image processing operators; 2) estimate T 1 , T 2 , and PD by fitting the preprocessed data to an imaging equation; 3) perform a fuzzy c-means clustering on the same preprocessed data to obtain a spatial map representing the membership value of the three tissue classes at each pixel location; 4) reject estimates which are not from pure tissue or have poor fits in the parameter estimation, and classify remaining estimates as either GM, WM..

Segmentation of medical images under topological constraints

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2006.Includes bibliographical references (p. 135-142).Major advances in the field of medical imaging over the past two decades have provided physicians with powerful, non-invasive techniques to probe the structure, function, and pathology of the human body. This increasingly vast and detailed amount of information constitutes a great challenge for the medical imaging community, and requires significant innovations in all aspect of image processing. To achieve accurate and topologically-correct delineations of anatomical structures from medical images is a critical step for many clinical and research applications. In this thesis, we extend the theoretical tools applicable to the segmentation of images under topological control, apply these new concepts to broaden the class of segmentation methodologies, and develop generally applicable and well-founded algorithms to achieve accurate segmentations of medical images under topological constraints. First, we introduce a digital concept that offers more flexibility in controlling the topology of digital segmentations. Second, we design a level set framework that offers a subtle control over the topology of the level set components. Our method constitutes a trade-off between traditional level sets and topology-preserving level sets.(cont.) Third, we develop an algorithm for the retrospective topology correction of 3D digital segmentations. Our method is nested in the theory of Bayesian parameter estimation, and integrates statistical information into the topology correction process. In addition, no assumption is made on the topology of the initial input images. Finally, we propose a genetic algorithm to accurately correct the spherical topology of cortical surfaces. Unlike existing approaches, our method is able to generate several potential topological corrections and to select the maximum-a-posteriori retessellation in a Bayesian framework. Our approach integrates statistical, geometrical, and shape information into the correction process, providing optimal solutions relatively to the MRI intensity profile and the expected curvature.by Florent Ségonne.Ph.D