A new method for MR grayscale inhomogeneity correction

Intensity inhomogeneity is a smooth intensity change inside originally homogeneous regions. Filter-based inhomogeneity correction methods have been commonly used in literatures. However, there are few literatures which compare effectiveness of these methods for inhomogeneity correction. In this paper, a new filter-based inhomogeneity correction method is proposed and the effectiveness of the proposed method and other filter-based inhomogeneity correction methods are compared. The methods with different kernel sizes are applied on MRI brain images and the quality of inhomogeneity correction of different methods are compared quantitatively. Experimental results show the proposed method in a kernel size of 20 * 20 performs almost better than or equal the performance of other methods in all kernel sizes

An imperialist competitive algorithm for the winner determination problem in combinatorial auction

Winner Determination problem (WDP) in combinatorial auction is an NP-complete problem. The NP-complete problems are often solved by using heuristic methods and approximation algorithms. This paper presents an imperialist competitive algorithm (ICA) for solving winner determination problem. Combinatorial auction (CA) is an auction that auctioneer considers many goods for sale and the bidder bids on the bundle of items. In this type of auction, the goal is finding winning bids that maximize the auctioneer’s income under the constraint that each item can be allocated to at most one bidder. To demonstrate, the postulated algorithm is applied over various benchmark problems. The ICA offers competitive results and finds good-quality solution in compare to genetic algorithm (GA), Memetic algorithm (MA), Nash equilibrium search approach (NESA) and Tabu search

Medical image segmentation using fuzzy c-mean (FCM), Bayesian method and user interaction

Image segmentation is one of the most important parts of clinical diagnostic tools. Medical images mostly contain noise and in homogeneity. Therefore, accurate segmentation of medical images is a very difficult task. However, the process of accurate segmentation of these images is very important and crucial for a correct diagnosis by clinical tools. In this paper a new method is proposed which is robust against in-homogeneousness and noisiness of images. The user selects training data for each target class. Noise is reduced in image using Stationary wavelet Transform (SWT) then FCM clusters input image to the n clusters where n is the number of target classes. User selects some of the clusters to be partitioned again. FCM clusters each user selected cluster to two sub clusters. This process continues until user to be satisfied. Each cluster is considered as a sub-class. Posterior probability of data to each sub class is calculated using data in those sub-classes. Probability density of each target class at sub classes is calculated using training data. Probability of data to each target class is calculated using probability density of each subclass at input data and probability of each subclass to each target class. At last, the image is clustered using probability of data to each target class. Segmentation of several simulated and real images are demonstrated to show the effectiveness of the new method

Review of brain MRI image segmentation methods

Brain image segmentation is one of the most important parts of clinical diagnostic tools. Brain images mostly contain noise, inhomogeneity and sometimes deviation. Therefore, accurate segmentation of brain images is a very difficult task. However, the process of accurate segmentation of these images is very important and crucial for a correct diagnosis by clinical tools. We presented a review of the methods used in brain segmentation. The review covers imaging modalities, magnetic resonance imaging and methods for noise reduction, inhomogeneity correction and segmentation. We conclude with a discussion on the trend of future research in brain segmentation