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.
Highlights
According to Rajendran & Dhanasekaran (2012), deformable models are among the most widely employed techniques for segmentation especially in the field of medical image analysis
This section discusses the performance of the Chan-Vese model in the detection of tumor from the brain, breast, and skin
From the results reported for the brain, breast, and skin tumor, there is an indication that an increase in iterations do not necessarily affect performance, instead it only introduces time complexity at higher iterations
Summary
According to Rajendran & Dhanasekaran (2012), deformable models are among the most widely employed techniques for segmentation especially in the field of medical image analysis. Though various medical image modalities currently exist with great improvement in the analysis and diagnosis of patient’s condition, MRI techniques are usually preferred as they are sufficient enough in capturing a number of soft tissues in the human body (Kumar & Mankame, 2020) In instances where these images contain tumor, it volume becomes a good indicator to track and prepare suitable treatments for recovery. The benign tumors are homogeneous in structure with the absence of cancerous cells, whereas the malignant tumors are cancerous cells (Sharif et al, 2020) These cancerous cells can rapture and rapidly spread to other parts of the human body making them life-threatening and the need to remove them as soon as detected (Sehgal, Goel, Mangipudi, Mehra, & Tyagi, 2016). Since the manual delineation of tumors is an arduous process with variations in results; it is critical to develop an automated model for segmentation (Kharote, Sankhe, & Patkar, 2019; Chahal, Pandey, & Goel, 2020))
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