Abstract
Segmentation of an image is most important and essential task in medical image processing, specifically while analyzing magnetic resonance (MR) image of brain clinically. during the clinical investigation of brain MRI images. Lot of research has been carried out for MRI segmentation but still it is challenging task. Hybrid approach which uses enhanced normalized cut and watershed transform to segment brain MRI images is developed in this paper. Watershed transform is used for the initial partitioning of the MRI, which creates primitive regions. In the next stage these primitive regions resembled for graph depiction and then the normalized cut method is used for segmenting an image. Variety of simulated and actual MR images are being segmented by using proposed algorithm to test its efficiency, in addition to it segmentation results are also compared with the other available techniques of brain MRI segmentation.
Highlights
Dimensional analysis and variance among the soft tissues are the main important features of an advanced magnetic resonance imaging (MRI) technique [1]
We have proposed a novel hybrid approach based on enhanced normalize cut and watershed transform for segmentation of brain MRI
Segmentation results for brain MR image by using Fuzzy C-Means (FCM) and Watershed Enhanced Normalized Cut Algorithm (WENCA) for distinct noise levels are illustrated in figures 2
Summary
Dimensional analysis and variance among the soft tissues are the main important features of an advanced magnetic resonance imaging (MRI) technique [1]. Brain MRI segmentation indicates assignment of the tissue type to every pixel of two dimensional and three-dimensional region by observing brain MRI images and the earlier history. It is the initial phase in numerous medical investigation and medical applications, viz. Classification and region-based methods have restrictions on performing well due to intensity differences, noise disturbances and counterfeit arcs affects the quality of segmentation in the methods based on boundary Rather than these elementary segmentation methods, some additional approaches are addressed in the literature. We have proposed a novel hybrid approach based on enhanced normalize cut and watershed transform for segmentation of brain MRI.
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