Expectation Maximization And Gaussian Model Based Segmentation on Histology Slides

The importance of the Expectation Maximization (EM) algorithm isincreasing day by day in order to solve Maximum A Posteriori (MAP) estimation problems and Gaussian Mixture Models (GMMs), which are parametric probability density functions, have become more popular in computerized applications due tothe EM algorithm. This article explains an automatic GMM based image segmentation method for histology cell images. For this purpose, the GMM parameters, which are recomputed iteratively starting with initial values, arecalculated by using the EM algorithm which classifies each pixel into the class withthe largest probability distribution using maximum likelihood. The accuracy of this segmentation algorithm depends on how much close the probabilistic model to the gray level distributions of the input images

PCA Based Bayesian Approach for Automatic Multiple Sclerosis Lesion Detection

The classical Bayes rule plays very important role in the field of lesion identification. However, the Bayesian approach is very difficult in high dimensional spaces for lesion detection. An alternative approach is Principle Component Analysis (PCA) for automatic multiple sclerosis lesion detection problems in high dimensional spaces. In this study, PCA based Bayesian approach is explained for automatic multiple sclerosis lesion detection using Markov Random Fields (MRF)and Singular Value Decomposition (SVD). It is shown that PCA approach provides better understanding of data. Although Bayesian approach gives effective results, itis not easy to use in high dimensional spaces. Therefore, PCA based Bayesian detection will give much more accurate results for automatic multiple sclerosis (MS)lesion detection

A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function

Thesis (Doctoral)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2013Includes bibliographical references (leaves: 118-135)Text in English; Abstract: Turkish and Englishxv, 145 leavesDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task due to similar intensity values between adjacent organs, geometrically complex liver structure and injection of contrast media, which causes all tissues to have different gray level values. Several artifacts of pulsation and motion, and partial volume effects also increase difficulties for automatic liver segmentation from magnetic resonance images. In this thesis, we present an overview about liver segmentation methods in magnetic resonance images and show comparative results of seven different liver segmentation approaches chosen from deterministic (K-means based), probabilistic (Gaussian model based), supervised neural network (multilayer perceptron based) and deformable model based (level set) segmentation methods. The results of qualitative and quantitative analysis using sensitivity, specificity and accuracy metrics show that the multilayer perceptron based approach and a level set based approach which uses a distance regularization term and signed pressure force function are reasonable methods for liver segmentation from spectral pre-saturation inversion recovery images. However, the multilayer perceptron based segmentation method requires a higher computational cost. The distance regularization term based automatic level set method is very sensitive to chosen variance of Gaussian function. Our proposed level set based method that uses a novel signed pressure force function, which can control the direction and velocity of the evolving active contour, is faster and solves several problems of other applied methods such as sensitivity to initial contour or variance parameter of the Gaussian kernel in edge stopping functions without using any regularization term

Complex Networks on a Rock Joint

A complex network approach on a rough fracture is developed. In this manner, some hidden metric spaces (similarity measurements) between apertures profiles are set up and a general evolutionary network in two directions (in parallel and perpendicular to the shear direction) is constructed. Also, an algorithm (COmplex Networks on Apertures: CONA) is proposed in which evolving of a network is accomplished using preferential detachments and attachments of edges (based on a competition and game manner) while the number of nodes is fixed. Also, evolving of clustering coefficients and number of edges display similar patterns as well as are appeared in shear stress, hydraulic conductivity and dilation changes, which can be engaged to estimate shear strength distribution of asperities.Comment: ROCKENG09: Proceedings of the 3rd CANUS Rock Mechanics Symposium, Toronto, May 2009 (Ed: M.Diederichs and G. Grasselli

Three-Dimensional Cyclic Behaviour of Interfaces

A new apparatus is used to study the behavior of an interface between dry sand and a rough surface under three dimensional cyclic loading conditions. A simple shear type of soil container is utilized in the experiments to investigate the coupling effects of two orthogonal shear stresses acting on the interface. The significance of the results are discussed with reference to the behavior of axially-laterally loaded piles

Simple Shear Versus Direct Shear Tests on Interfaces during Cyclic Loading

An apparatus capable of direct shear type and simple shear type testing of interfaces between soil and structural materials is developed. A series of monotonic and cyclic tests are conducted at the interfaces between dry sand and a rough surface under constant normal stress conditions with both methods. The test results indicate that the peak and residual shear strengths obtained from direct shear and simple shear are approximately the same. However, the simple shear box permits separate measurements of shear deformation of the sand mass and also sliding at the contact surface

A level set method with Sobolev Gradient and Haralick Edge Detection

Variational level set methods, which have been proposed with various energy functionals, mostly use the ordinary L type gradient in gradient descent algorithm to minimize the energy functional. The gradient flow is influenced by both the energy to be minimized and the norms, which are induced from inner products, used to measure the cost of perturbation of the curve. However, there are many undesired properties related to the gradient flows due to the 2 L type inner products. For example, there is not any regularity term in the definition of this inner product that causes non-smooth flows and inaccurate results. Therefore, in this work, Sobolev gradient has been used that is more efficient than the 2 L type gradient for image segmentation and has powerful properties such as regular gradient flows, independency to parameterization of curves, less sensitive to local features and noise in the image and also faster convergence rate than the standard gradient. In addition, Haralick edge detector has been used instead of the edge indicator function in this study. Because, the traditional edge indicator function, which is the absolute of the gradient of the convolved image with the aussian function, is sensitive to noise in level set methods. Experimental results on real images , which are abdominal magnetic resonance images, have been obtained for spleen and kidney segmentation. Quantitative analyses have been performed by using different measurements to evaluate the performance of the proposed approach, which can ignore topological noises and detect boundaries successfully