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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 technique is mainly used to identify tumor boundaries in Magnetic Resonance Imaging (MRI) images. 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 quantification of this volume require segmentation which in practice is done manually by field expects. Although the manual segmentation can be accurate, it is time consuming and also require qualified professionals which in effect significantly limit the number of segmentation that can be accomplished in practice (Vorontsov, Tang, Roy, Pal, & Kadoury, 2017). In the segmentation of the tumor, it can be classified as malignant or benign. 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). To detect these cells, methods like active contours become useful. Active contour models are subsets of deformable models which have been experimented broadly in image processing and are recognized as one of the most widely used and successful approaches for image segmentation. The class of region-based active contour models works primarily by incorporating special functions called the level set. These functions are deformed under well defined energy functions which traces the evolving curve towards a targeted boundary. Although these integrated functions are incredibly robust for sophisticated medical images, they suffer in accuracy against the contour curve's initialization.. The Chan-Vese model as a variant of the region-based active contour models has gained considerable interest in various image segmentation techniques (Zawish, Siyal, Ahmed, Khalil, & Memon, 2019) despite the generally known problem. Extensive investigations has been made in recent years with their integration to Computer-Aided Detection (CAD) systems for tumor segmentation making it an interesting tool to further investigate their performance across selected tumour cells in this study. Since the manual delineation of tumors is an arduous process with variations in results; it is therefore critical to develop an automated model for segmentation (Kharote, Sankhe, & Patkar, 2019; Chahal, Pandey, & Goel, 2020)).
In this study we seek to analyze the perfornance of the famous Chan-Vese model in an attempt to answer the question “Is Chan-Vese Model generally scalable across different tumor types?” To address this question, appropriate research data is required and study has shown that the predominate datasets sufficient for the current study are MIAS, Navoneel, Diaretdb0, and ISIC. In evaluating the performance of the model, the following metrics are used: Dice Similarity Coefficient (DSC) and Jaccard Index (JI).