1. A Deep Learning Based Approach to Skin Lesion Border Extraction with a Novel Edge Detector in Dermoscopy Images
- Author
-
Abder-Rahman Ali, Sally Jane O’Shea, Guang Yang, Thomas Trappenberg, Xujiong Ye, and Jingpeng Li
- Subjects
Edge detector ,integumentary system ,Computer science ,business.industry ,Deep learning ,02 engineering and technology ,medicine.disease ,Lesion ,03 medical and health sciences ,0302 clinical medicine ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,020201 artificial intelligence & image processing ,Computer vision ,Artificial intelligence ,Skin cancer ,medicine.symptom ,Skin lesion ,business ,030217 neurology & neurosurgery - Abstract
Lesion border detection is considered a crucial step in diagnosing skin cancer. However, performing such a task automatically is challenging due to the low contrast between the surrounding skin and lesion, ambiguous lesion borders, and the presence of artifacts such as hair. In this paper we propose a two-stage approach for skin lesion border detection: (i) segmenting the skin lesion dermoscopy image using U-Net, and (ii) extracting the edges from the segmented image using a novel approach we call FuzzEdge. The proposed approach is compared with another published skin lesion border detection approach, and the results show that our approach performs better in detecting the main borders of the lesion and is more robust to artifacts that might be present in the image. The approach is also compared with the manual border drawings of a dermatologist, resulting in an average Dice similarity of 87.7%.
- Published
- 2019