Hair detection and lesion segmentation in dermoscopic images using domain knowledge

Automated segmentation and dermoscopic hair detection are one of the significant challenges in computer-aided diagnosis (CAD) of melanocytic lesions. Additionally, due to the presence of artifacts and variation in skin texture and smooth lesion boundaries, the accuracy of such methods gets hampered. The objective of this research is to develop an automated hair detection and lesion segmentation algorithm using lesion-specific properties to improve the accuracy. The aforementioned objective is achieved in two ways. Firstly, a novel hair detection algorithm is designed by considering the properties of dermoscopic hair. Second, a novel chroma-based geometric deformable model is used to effectively differentiate the lesion from the surrounding skin. The speed function incorporates the chrominance properties of the lesion to stop evolution at the lesion boundary. Automatic initialization of the initial contour and chrominance-based speed function aids in providing robust and flexible segmentation. The proposed approach is tested on 200 images from PH2 and 900 images from ISBI 2016 datasets. Average accuracy, sensitivity, specificity, and overlap scores of 93.4, 87.6, 95.3, and 11.52% respectively are obtained for the PH2 dataset. Similarly, the proposed method resulted in average accuracy, sensitivity, specificity, and overlap scores of 94.6, 82.4, 97.2, and 7.20% respectively for the ISBI 2016 dataset. Statistical and quantitative analyses prove the reliability of the algorithm for incorporation in CAD systems.