Detection of Granularity in Dermoscopy Images of Malignant Melanoma using Color and Texture Features
Abstract
Granularity, also called peppering and multiple blue-grey dots, is defined as an accumulation of tiny, blue-grey granules in dermoscopy images. Granularity is most closely associated with a diagnosis of malignant melanoma. This study analyzes areas of granularity with color and texture measures to discriminate granularity in melanoma from similar areas in non-melanoma skin lesions. The granular areas in dermoscopy images of 74 melanomas and 14 melanomas in situ were identified and manually selected. For 200 non-melanoma dermoscopy images, those areas which most closely resembled granularity in color and texture were similarly selected. Ten texture and twenty-two color measures were studied. The texture measures consisted of the average and range of energy, inertia, correlation, inverse difference, and entropy. The color measures consisted of absolute and relative RGB averages, absolute and relative RGB chromaticity averages, absolute and relative G/B averages, CIE X, Y, Z, X/Y, X/Z and Y/Z averages, R variance, and luminance. These measures were calculated for each granular area of the melanomas and the comparable areas in the non-melanoma images. Receiver operating characteristic (ROC) curve analysis showed that the best separation of melanoma images from non-melanoma images by granular area features was obtained with a combination of color and texture measures. Comparison of ROC results showed greater separation of melanoma from benign lesions using relative color than using absolute color. Statistical analysis showed that the four most significant measures of granularity in melanoma are two color measures and two texture measures averaged over the spots: relative blue, relative green, texture correlation, and texture energy range. The best feature set, utilizing texture and relative color measures, achieved an accuracy of 96.4% based on area under the receiver operating characteristic curve.
Recommended Citation
W. V. Stoecker and M. Wronkiewiecz and R. H. Chowdhury and R. J. Stanley and J. Xu and A. Bangert and B. Shrestha and D. A. Calcara and H. S. Rabinovitz and M. C. Oliviero and F. Ahmed and L. A. Perry and R. J. Drugge, "Detection of Granularity in Dermoscopy Images of Malignant Melanoma using Color and Texture Features," Computerized Medical Imaging and Graphics, vol. 35, no. 2, pp. 144 - 147, Elsevier, Mar 2011.
The definitive version is available at https://doi.org/10.1016/j.compmedimag.2010.09.005
Department(s)
Chemistry
Second Department
Electrical and Computer Engineering
Keywords and Phrases
Area Feature; Benign Lesion; Color and Texture Features; Dermoscopy; Dermoscopy Images; Feature Sets; Granular; Granularity; In-situ; Inverse Differences; Malignant Melanoma; Melanoma; Receiver Operating Characteristic Curve Analysis; Receiver Operating Characteristic Curves; Relative Color; Skin Lesion; Statistical Analysis; Texture Energy; Two-color; Color; Diagnosis; Image Analysis; Oncology; Textures; Dermatology; Benign Skin Tumor; Colorimetry; Diagnostic Accuracy; Energy; Entropy; Epiluminescence Microscopy; Human; Image Quality; Luminance; Priority Journal; Algorithms; Humans; Image Enhancement; Image Interpretation; Computer-assisted; Pattern Recognition; Automated; Reproducibility of Results; Sensitivity and Specificity; Skin Neoplasms
International Standard Serial Number (ISSN)
0895-6111
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2011 Elsevier, All rights reserved.
Publication Date
01 Mar 2011
