Dynamic segmentation of the cerebral cortex in MR data using implicit active contours (original) (raw)
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Segmenting brain magnetic resonance (MRI) images of the brain into white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) is an important problem in medical image analysis. The study of these regions can be useful for determining different brain disorders, assisting brain surgery, post-surgical analysis, saliency detection and for studying regions of interest. This paper presents a segmentation method that partitions a given brain MRI image into WM, GM and CSF regions through a multiphase region-based active contour method followed by a pixel correction thresholding stage. The proposed region-based active contour method is applied in order to partition the input image into four different regions. Three of those regions within the brain area are then chosen by intersecting a hand-drawn binary mask with the computed contours. Finally, an efficient thresholding-based pixel correction method is applied to the computed WM, GM and CSF regions to increase their accuracy. The segmentation results are compared with ground truths to show the performance of the proposed method.
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Various image segmentation methods are widely used for finding diseases and illness. Detection of any kind of brain tumors from magnetic resonance imaging (MRI) is very important for radiologists and image processing researchers. This paper described a segmentation method based on region based active contour model using level set approach to be useful for region of interest (ROI) based image compression system. The brain tumors (ROI) may be anywhere in MR brain images. The aim of this paper is to segment an image into non-intersecting regions, region of interest and other than region of interest and background for region based medical image compression system. In this system, the initial mask is firstly created. The initial curve can be anywhere in the images and interior contours are automatically detected. This method performs two main steps, curve evolution and segmenting process. Curve evolution is done by using level set method and active contour model segments the region. The ...
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