Segmentation of Soft Tissues from MRI Brain Images Using Optimized KPCM Clustering Via Level Set Formulation
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An important approach for medical images is image segmentation. Image segmentation is the way toward to eradicate the area of attentiveness by making various segments in an image. This segmentation helps to analyze the representation of image in an easier manner. The segmentation of an image in medical image analysis is considered as one of the challenging tasks in many clinical applications due to noise, poor illumination changes, and also the intensity inhomogeneity. In order to have segmentation of soft tissues from magnetic resonance imaging (MRI) brain images, a new approach had been proposed known as “optimized kernel possibilistic fuzzy c-means (OKPCM)” algorithm using a level set formulation. The proposed algorithm consists of two stages: In order to improve clustering efficiency in the preprocessing, we introduced a hybrid approach which is called particle swarm optimization (PSO) algorithm followed by kernel possibilistic fuzzy C-means (KPFCM) clustering. Firstly, with the help of PSO algorithm, automatically the optimal cluster centers are calculated. Later, these optimum cluster values acted as a cluster centers for KPFCM clustering in order to ameliorate the clustering efficiency. The membership function (MF) of the conventional clustering algorithms, i.e., FCM, PCM along with KFCM, was sensitive to the noise and outliers. The preprocessing segmentation results suffer from boundary leakages and outliers. So, to overcome these drawbacks, it is necessary to use post-processing where we introduce the level set method. The level set method utilizes the efficient curve deformation which is driven by an external and internal force in order to capture the important structures (usually edges) in an image as well as curve with minimal energy function is defined. The combined approach of both preprocessing and post-processing is called as optimized kernel possibility fuzzy c-means (OKPFCM) clustering using level set method. The accuracy and the noise effect in those images can be upgraded by using this method.
KeywordsImage segmentation Kernel-based possibilistic fuzzy c-mean algorithm Particle swarm optimization Level sets and medical images
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