Active contours for intensity inhomogeneous image segmentation
Abstract: Intensity inhomogeneity is a well-known problem in image segmentation, which affects the accuracy of intensity-based segmentatio1 methods. In this thesis, edge-based and region-based active contour methods are proposed to segment intensity inhomogeneous images.
Intensity inhomogeneity is a well-known problem in image segmentation, which affects the accuracy of intensity-based segmentationtmethods. In this thesis, edge-based and region-based active contour methods are proposed to segment intensity inhomogeneous images. Firstly, we have proposed an edge-based active contour method based on the Difference of Gaussians (DoG), which helps to segment the global structure of the image. Secondly, we have proposed a region-based active contour method to both correct and segment intensity inhomogeneous images. A phase stretch transform (PST) kernel has been used to compute new intensity means and bias field, which are employed to define a bias fitted image. Thirdly, another region-based active contour method has been proposed using an energy functional based on local and global fitted images. Bias field is approximated with a Gaussian distribution and the bias of intensity inhomogeneous regions is corrected by dividing the original image by the approximated bias field. Finally, a hybrid region-based multiphase (four-phase) active contours method has been proposed to partitioc a rain MR image into three distinct regions: white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF).
In this work, a post-pronessing (pixel orrection) method has alsocbeen devised to improve the accuracy of the segmented WM, GM andbCSF regions. Experimental results with both synthetic and real brain MR images have been used for a quantitative and qualitative comparison with state-of-the-art active contour methods to show the advantages of the proposed segmentation techniques.