Thresholding Methods for Lesion Segmentation of Basal Cell Carcinoma in Dermoscopy Images
Purpose: Algorithms employed for pigmented lesion segmentation perform poorly on dermoscopy images of basal cell carcinoma (BCC), the most common skin cancer. The main objective was to develop better methods for BCC segmentation.
Methods: Fifteen thresholding methods were implemented for BCC lesion segmentation. We propose two error metrics that better measure the type II error: Relative XOR Error and Lesion Capture Ratio.
Results: On training/test sets of 305 and 34 BCC images, respectively, five new techniques outperform two state-of-the-art methods used in segmentation of melanomas, based on the new error metrics.
Conclusion: 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.
R. Kaur et al., "Thresholding Methods for Lesion Segmentation of Basal Cell Carcinoma in Dermoscopy Images," Skin Research and Technology, vol. 23, no. 3, pp. 416-428, Blackwell Publishing, Aug 2017.
The definitive version is available at https://doi.org/10.1111/srt.12352
Electrical and Computer Engineering
Keywords and Phrases
Dermatology; Diagnosis; Diseases; Errors; Image Analysis, Basal Cell Carcinoma; Dermoscopy; Huang Method; Isodata; Lesion Segmentations; Otsu Method; Shanbhag Method; Skin Cancers; Thresholding, Image Segmentation, Algorithm; Article; Automation; Basal Cell Carcinoma; Cancer Classification; Dermatologist; Dysplastic Nevus; Entropy; Epiluminescence Microscopy; Human; Image Processing; Image Segmentation; Imaging Software; Melanoma; Nevus; Seborrheic Keratosis; Image Analysis; Isodata Method; Li Method; Shanbhag Method; Thresholding
International Standard Serial Number (ISSN)
Article - Journal
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