Computer aided analysis of epi-illumination and transillumination images of skin lesions for diagnosis of skin cancers.

Brian D'Alessandro, Atam P. Dhawan, Nizar Mullani

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Skin lesion pigmentation area from surface, or, epi-illumination (ELM) images and blood volume area from transillumination (TLM) images are useful features to aid a dermatologist in the diagnosis of melanoma and other skin cancers in early curable stages. However, segmentation of these areas is difficult. In this work, we present an automatic segmentation tool for ELM and TLM images that also provides additional choices for user selection and interaction with adaptive learning. Our tool uses a combination of k-means clustering, wavelet analysis, and morphological operations to segment the lesion and blood volume, and then presents the user with six segmentation suggestions for both ELM and TLM images. The final selection of segmentation boundary may then be iteratively improved through scoring by multiple users. The ratio of TLM to ELM segmented areas is an indicator of dysplasia in skin lesions for detection of skin cancers, and this ratio is found to show a statistically significant trend in association with lesion dysplasia on a set of 81 pathologically validated lesions (p = 0.0058). We then present a support vector machine classifier using the results from the interactive segmentation method along with ratio, color, texture, and shape features to characterize skin lesions into three degrees of dysplasia with promising accuracy.

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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