Journal
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
Volume 2022, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2022/4348235
Keywords
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Funding
- Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia [PNURSP2022R239]
- Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
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Malignant melanoma, if left untreated, is one of the deadliest skin diseases. The paper proposes using the active contour method to address the challenge of unclear skin images and utilizes Gaussian filter-based maximum entropy and morphological processing methods to find automatic seed points for segmentation. The methodology shows promising results in the detection of lesions from dermoscopic images.
Malignant melanoma is considered one of the deadliest skin diseases if ignored without treatment. The mortality rate caused by melanoma is more than two times that of other skin malignancy diseases. These facts encourage computer scientists to find automated methods to discover skin cancers. Nowadays, the analysis of skin images is widely used by assistant physicians to discover the first stage of the disease automatically. One of the challenges the computer science researchers faced when developing such a system is the un-clarity of the existing images, such as noise like shadows, low contrast, hairs, and specular reflections, which complicates detecting the skin lesions in that images. This paper proposes the solution to the problem mentioned earlier using the active contour method. Still, seed selection in the dynamic contour method has the main drawback of where it should start the segmentation process. This paper uses Gaussian filter-based maximum entropy and morphological processing methods to find automatic seed points for active contour. By incorporating this, it can segment the lesion from dermoscopic images automatically. Our proposed methodology tested quantitative and qualitative measures on standard dataset dermis and used to test the proposed method's reliability which shows encouraging results.
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