Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cell image segmentation for diagnostic pathology
Advanced algorithmic approaches to medical image segmentation
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
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Neural Stem Cells (NSCs) have a remarkable capacity to proliferate and differentiate to other cell types. This ability to differentiate to desirable phenotypes has motivated clinical interests, hence the interest here to segment Neural Stem Cell (NSC) clusters to locate the NSC clusters over time in a sequence of frames, and in turn to perform NSC cluster motion analysis. However the manual segmentation of such data is a tedious task. Thus, due to the increasing amount of cell data being collected, automated cell segmentation methods are highly desired. In this paper a novel level set based segmentation method is proposed to accomplish this segmentation. The method is initialization insensitive, making it an appropriate solution for automated segmentation systems. The proposed segmentation method has been successfully applied to NSC cluster segmentation.