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Classification of Skin Lesion Using (Segmentation) Shape Feature Detection
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Author(s): Satheesha T.Y. (Department of Electrical and Computer Engineering, School of Engineering and Technology, CMR University, Bangalore, India)
Copyright: 2020
Pages: 8
Source title:
Biomedical and Clinical Engineering for Healthcare Advancement
Source Author(s)/Editor(s): N. Sriraam (Ramaiah Institute of Technology, India)
DOI: 10.4018/978-1-7998-0326-3.ch011
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Abstract
Malignant melanoma has caused countless deaths in recent years. Many calculation methods have been created for automatic melanoma detection. In this chapter, based on the traditional concept of shape signature and convex hull, an improved boundary description shape signature is developed. The convex defect-based signature (CDBS) proposed in this paper scans contour irregularities and is applied to skin lesion classification in macroscopic images. Border irregularities of skin lesions are the predominant criteria for ABCD (asymmetry, border, color, and diameter) to distinguish between melanoma and nonmelanoma. The performance of the CDBS is compared with popular shape descriptors: shape signature, indentation depth function, invariant elliptic Fourier descriptor (IEFD), and rotation invariant wavelet descriptor (RIWD), where the proposed descriptor shows better results. Multilayer perceptron neural network is used as a classifier in this work. Experimental results show that the proposed approach achieves significant performance with mean accuracy of 90.49%.
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