Paper
22 September 2015 Dermoscopy analysis of RGB-images based on comparative features
Oleg O. Myakinin, Valery P. Zakharov, Ivan A. Bratchenko, Dmitry N. Artemyev, Evgeny Y. Neretin, Sergey V. Kozlov
Author Affiliations +
Abstract
In this paper, we propose an algorithm for color and texture analysis for dermoscopic images of human skin based on Haar wavelets, Local Binary Patterns (LBP) and Histogram Analysis. This approach is a modification of «7-point checklist» clinical method. Thus, that is an “absolute” diagnostic method because one is using only features extracted from tumor’s ROI (Region of Interest), which can be selected manually and/or using a special algorithm. We propose additional features extracted from the same image for comparative analysis of tumor and healthy skin. We used Euclidean distance, Cosine similarity, and Tanimoto coefficient as comparison metrics between color and texture features extracted from tumor’s and healthy skin’s ROI separately. A classifier for separating melanoma images from other tumors has been built by SVM (Support Vector Machine) algorithm. Classification’s errors with and without comparative features between skin and tumor have been analyzed. Significant increase of recognition quality with comparative features has been demonstrated. Moreover, we analyzed two modes (manual and automatic) for ROI selecting on tumor and healthy skin areas. We have reached 91% of sensitivity using comparative features in contrast with 77% of sensitivity using the only “absolute” method. The specificity was the invariable (94%) in both cases.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Oleg O. Myakinin, Valery P. Zakharov, Ivan A. Bratchenko, Dmitry N. Artemyev, Evgeny Y. Neretin, and Sergey V. Kozlov "Dermoscopy analysis of RGB-images based on comparative features", Proc. SPIE 9599, Applications of Digital Image Processing XXXVIII, 95992B (22 September 2015); https://doi.org/10.1117/12.2188165
Lens.org Logo
CITATIONS
Cited by 2 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Skin

Tumors

Melanoma

Cancer

Feature extraction

Skin cancer

Binary data

Back to Top