Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A variational level set approach to multiphase motion
Journal of Computational Physics
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
Segmentation and Feature Extraction to Evaluate the Stomach Dynamic
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Research on Volume Segmentation Algorithm for Medical Image Based on Clustering
KAM '08 Proceedings of the 2008 International Symposium on Knowledge Acquisition and Modeling
Application in Stomach Epidermis Tumors Segmentation by GVF Snake Model
FBIE '08 Proceedings of the 2008 International Seminar on Future BioMedical Information Engineering
IITA '09 Proceedings of the 2009 Third International Symposium on Intelligent Information Technology Application - Volume 01
Automatic segmentation of lung lobes in CT images based on fissures, vessels, and bronchi
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Distance regularized level set evolution and its application to image segmentation
IEEE Transactions on Image Processing
A simple unsupervised MRF model based image segmentation approach
IEEE Transactions on Image Processing
Performance evaluation of finite normal mixture model-based image segmentation techniques
IEEE Transactions on Image Processing
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Internal organs of a human body have very complex structure owing to their anatomic organization. Several image segmentation techniques fail to segment the various organs from medical images due to simple biases. Here, a modified version of the level set method is employed to segment the stomach from CT images. Level set is a model based segmentation method that incorporates a numerical scheme. For the sake of stability of the evolving zero'th level set contour, instead of periodic reinitialization of the signed distance function, a distance regularization term is included. This term is added to the energy optimization function which when solved with gradient flow algorithms, generates a solution with minimum energy and maximum stability. Evolution of the contour is controlled by the edge indicator function. The results show that the algorithm is able to detect inner boundaries in the considered CT stomach images. It appears that it is also possible to extract outer boundaries as well. The results of this approach are reported in this paper.