NDLI: Enhanced classification of malignant melanoma lesions via the integration of physiological features from dermatological photographs

Content Provider	IEEE Xplore Digital Library
Author	Haider, S. Cho, D. Amelard, R. Wong, A. Clausi, D.A.
Copyright Year	2014
Description	Author affiliation: Dept. of Syst. Design Eng., Univ. of Waterloo, Waterloo, ON, Canada (Haider, S.; Cho, D.; Amelard, R.; Wong, A.; Clausi, D.A.)
Abstract	Traditional methods for early detection of melanoma rely upon a dermatologist to visually assess a skin lesion using the ABCDE (Asymmetry, Border irregularity, Color variegation, Diameter, Evolution) criteria before confirmation can be done through biopsy by a pathologist. However, this visual assessment strategy taken by dermatologists is hampered by clinician subjectivity and suffers from low sensitivity. Computer-aided diagnostic methods based on dermatological photographs are being developed to aid in the melanoma diagnosis process, but most of these methods rely only on superficial, topographic features that can be limiting in characterizing melanoma. In this work, a hybrid feature model is introduced for characterizing skin lesions that combines low-level and high-level features, and augments them with a set of physiological features extracted from dermatological photographs using a nearest-neighbor nonlinear model to improve classification performance. The physiological features extracted from the lesion for the proposed hybrid feature model include those based on: i) eumelanin concentrations, ii) pheomelanin concentrations, and iii) blood oxygen saturation. The proposed hybrid feature model was evaluated on 206 dermatological photographs of skin lesions (119 confirmed melanoma cases, 87 confirmed non-melanoma cases) using a cross validation scheme. The experimental results show that the proposed hybrid feature model, with integrated physiological features, provided improved sensitivity, specificity, precision and accuracy for the purpose of melanoma classification.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	6455
Ending Page	6458
File Size	813782
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6945106
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lesions Malignant tumors Feature extraction Physiology Skin Cancer Sensitivity
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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