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

Compare different spatial based fuzzy-C_mean (FCM) extensions for MRI image segmentation

FCM does not use spatial information in clustering process. Therefore, it is not robust against noise and other imaging artefacts. In order to incorporate spatial information, an extension for FCM (FCM_S) is proposed which allows pixel to be labelled by influence of its neighbourhood labels. FCM_S is time-consuming. To over come this problem, FCM_S1 is introduced, which is faster. Then, FCM_EN and FGFCM are proposed which are faster than previous methods. Four spatial based extensions are simulated for FCM: FCM_S, FCM_S1, FCM_EN and FGFCM. In order to compare their quality, they are applied to simulated brain MRI images and similarity index is used to compare their quality quantitatively

Medical image segmentation using Fuzzy C-Mean (FCM) and user specified data

Image segmentation is one of the most important parts of clinical diagnostic tools. Medical images mostly contain noise and inhomogeneity. 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. We proposed a new clustering method based on Fuzzy C-Mean (FCM) and user specified data. In the postulated method, the color image is converted to grey level image and anisotropic filter is applied to decrease noise; User selects training data for each target class, afterwards, the image is clustered using ordinary FCM. Due to inhomogeneity and unknown noise some clusters contain training data for more than one target class. These clusters are partitioned again. This process continues until there are no such clusters. Then, the clusters contain training data for a target class assigned to that target class; mean of intensity in each class is considered as feature for that class, afterwards, feature distance of each unsigned cluster from different class is found then unsigned clusters are signed to target class with least distance from. Experimental result is demonstrated to show effectiveness of new method

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

Medical image segmentation using fuzzy c-mean (FCM) and dominant grey levels of image

Image segmentation is a critical part of clinical diagnostic tools. Medical images mostly contain noise. Therefore, accurate segmentation of medical images is highly challenging; however, accurate segmentation of these images is very important in correct diagnosis by clinical tools. We proposed a new method for image segmentation based on dominant grey level of image and Fuzzy C-Mean (FCM). In the postulated method, the colour image is converted to grey level image and stationary wavelet is applied to decrease noise; the image is clustered using ordinary FCM, afterwards, clusters with error more than a threshold are divided to two sub clusters. This process continues until there remain no such, erroneous, clusters. The dominant connected component of each cluster is obtained -- if existed. In obtained dominant connected components, the n biggest connected components are selected. N is specified based upon considered number of clusters. Averages of grey levels of n selected components, in grey level image, are considered as Dominant grey levels. Dominant grey levels are used as cluster centres. Eventually, the image is clustered using specified cluster centres. Experimental results are demonstrated to show effectiveness of new method