Segmentation of skin lesion using Cohen–Daubechies–Feauveau biorthogonal wavelet

Abstract

This paper presents a novel technique for segmentation of skin lesion in dermoscopic images based on wavelet transform along with morphological operations. The acquired dermoscopic images may include artifacts inform of gel, dense hairs and water bubble which make accurate segmentation more challenging. We have also embodied an efficient approach for artifacts removal and hair inpainting, to enhance the overall segmentation results. In proposed research, color space is also analyzed and selection of blue channel for lesion segmentation have confirmed better performance than techniques which utilizes gray scale conversion. We tackle the problem by finding the most suitable mother wavelet for skin lesion segmentation. The performance achieved with ‘bior6.8’ Cohen–Daubechies–Feauveau biorthogonal wavelet is found to be superior as compared to other wavelet family. The proposed methodology achieves 93.87 % accuracy on dermoscopic images of PH2 dataset acquired at Dermatology Service of Hospital Pedro Hispano, Matosinhos, Portugal.

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images

“…Afterwards, the c-k-means clustering approach was employed for segmentation. Nevertheless, wavelets [18] are sensitive to poor directionality during the analysis of supplementary functions in multi-dimensional applications. Hence, wavelets are relatively ineffectual to represent edges and anisotropic features in the dermoscopic images.…”

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

“…The segmentation of skin lesion in dermoscopic images based on wavelet transform along with morphological analysis found the channel of the color model to give better performance than grayscale conversion [1]. The segmentation of skin lesion based on the , normalized , YIQ, and 1 2 3 color models has been reported to give good results for channel of YIQ and 3 channel of melanoma skin lesion.…”

“…Complementing the methods of saliency computation with other useful analysis methods such as the morphological analysis can significantly improve image segmentation results. The hybrid segmentation of skin lesion in dermoscopic images using wavelet transform along with morphological analysis has been reported [1], while segmentation using saliency combined with Otsu threshold has been discussed [13].…”

“…The ability to spread widely to other parts of the body is a unique characteristic that makes melanoma one of the deadliest skin cancer diseases. Its prevalence is rapidly increasing across the world, as recorded death cases of its patients continue to annually escalate [1,3,4]. Prevention which is better than cure and early detection are recommended as the best strategies for improving outcomes in melanoma and to reduce the induced mortality rate of the disease because treatment at later stages can be hard [1,[4][5][6].…”

“…Melanoma is a cancer of pigment that produces melanocytes and is one of the most serious, complex, aggressive, and fatal forms of all skin cancer related diseases [1]. It is a cancerous skin disease that typically results from environmental factors such as exposure to sunlight [2].…”