Validation of Performance Homogeneity of Chan-Vese Model on Selected Tumour Cells
Loading...
Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
International Journal of E-Health and Medical Communications
Abstract
This study aims to analyze the Chan-Vese model’s performance using a variety of tumor images. The
processes involve the tumors’ segmentation, detecting the tumors, identifying the segmented tumor
region, and extracting the features before classification occurs. In the findings, the Chan-Vese model
performed well with brain and breast tumor segmentation. The model on the skin performed poorly.
The brain recorded DSC 0.6949903, Jaccard 0.532558; the time elapsed 7.389940 with an iteration
of 100. The breast recorded a DSC of 0.554107, Jaccard 0.383228; the time elapsed 9.577161 with an
iteration of 100. According to this study, a higher DSC does not signify a well-segmented image, as
the breast had a lower DSC than the skin. The skin recorded a DSC of 0.620420, Jaccard 0.449717;
the time elapsed 17.566681 with an iteration of 200.
Description
Research Article
Keywords
Chan-Vese, Deformable Models, Dice Similarity Coefficient, Level Set, Tumor