Thresholding methods for lesion segmentation of basal cell carcinoma in dermoscopy images

Abstract

The proposed algorithms, which include solutions for image vignetting correction and border expansion to achieve dermatologist-like borders, provide more inclusive and feature-preserving border detection, favoring better BCC classification accuracy, in future work.

Automatic lesion border selection in dermoscopy images using morphology and color features

“…These algorithms are implemented based on their performance on widely varying skin lesion images in a large and diverse image set. These border segmentation methods are described elsewhere …”

“…The final four borders are histogram thresholding methods modified from original methods, with details available in Kaur, et al . The image thresholding method of Huang and Wang minimizes fuzziness measures in a dermoscopy skin lesion image.…”

“…Sforza et al and Kaur et al introduced new metrics to recognize relative border accuracy and border inclusiveness. Kaur defined three border grades: 0, 1, and 2.…”

“…These border segmentation methods are described elsewhere. 28,[31][32][33][34][35][36][37][38][39][40]…”

“…The final four borders are histogram thresholding methods modified from original methods, [36][37][38][39][40] with details available in Kaur, et al 40 The image thresholding method of Huang and Wang 36 minimizes fuzziness measures in a dermoscopy skin lesion image. The segmentation method of Li and Tam 38 is based on minimum cross entropy thresholding, where threshold selection minimizes the cross entropy between the dermoscopy image and its segmented version.…”