Using Adaptive Thresholding and Skewness Correction to Detect Gray Areas in Melanoma in Situ Images


The incidence of melanoma in situ (MIS) is growing significantly. Detection at the MIS stage provides the highest cure rate for melanoma, but reliable detection of MIS with dermoscopy alone is not yet possible. Adjunct dermoscopic instrumentation using digital image analysis may allow more accurate detection of MIS. Gray areas are a critical component of MIS diagnosis, but automatic detection of these areas remains difficult because similar gray areas are also found in benign lesions. This paper proposes a novel adaptive thresholding technique for automatically detecting gray areas specific to MIS. The proposed model uses only MIS dermoscopic images to precisely determine gray area characteristics specific to MIS. To this aim, statistical histogram analysis is employed in multiple color spaces. It is demonstrated that skew deviation due to an asymmetric histogram distorts the color detection process. We introduce a skew estimation technique that enables histogram asymmetry correction facilitating improved adaptive thresholding results. These histogram statistical methods may be extended to detect any local image area defined by histograms.


Electrical and Computer Engineering

Second Department


Keywords and Phrases

Adaptive thresholding; Automatic Detection; Benign lesion; Color detection; Critical component; Cure rate; Dermoscopy; Digital image analysis; Estimation techniques; Histogram analysis; In-situ; Multiple color spaces; Reliable detection; Skew estimation; skewed histogram; Dermatology; Diagnosis; Graphic methods; Image analysis; Image segmentation; Medical imaging; Oncology; Statistical methods

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version


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© 2012 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jul 2012