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Proceedings Paper

Acne image analysis: lesion localization and classification
Author(s): Fazly Salleh Abas; Benjamin Kaffenberger; Joseph Bikowski; Metin N. Gurcan
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Paper Abstract

Acne is a common skin condition present predominantly in the adolescent population, but may continue into adulthood. Scarring occurs commonly as a sequel to severe inflammatory acne. The presence of acne and resultant scars are more than cosmetic, with a significant potential to alter quality of life and even job prospects. The psychosocial effects of acne and scars can be disturbing and may be a risk factor for serious psychological concerns. Treatment efficacy is generally determined based on an invalidated gestalt by the physician and patient. However, the validated assessment of acne can be challenging and time consuming. Acne can be classified into several morphologies including closed comedones (whiteheads), open comedones (blackheads), papules, pustules, cysts (nodules) and scars. For a validated assessment, the different morphologies need to be counted independently, a method that is far too time consuming considering the limited time available for a consultation. However, it is practical to record and analyze images since dermatologists can validate the severity of acne within seconds after uploading an image. This paper covers the processes of region-ofinterest determination using entropy-based filtering and thresholding as well acne lesion feature extraction. Feature extraction methods using discrete wavelet frames and gray-level co-occurence matrix were presented and their effectiveness in separating the six major acne lesion classes were discussed. Several classifiers were used to test the extracted features. Correct classification accuracy as high as 85.5% was achieved using the binary classification tree with fourteen principle components used as descriptors. Further studies are underway to further improve the algorithm performance and validate it on a larger database.

Paper Details

Date Published: 24 March 2016
PDF: 9 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97850B (24 March 2016); doi: 10.1117/12.2216444
Show Author Affiliations
Fazly Salleh Abas, The Ohio State Univ. (United States)
Multimedia Univ. (Malaysia)
Benjamin Kaffenberger, The Ohio State Univ. (United States)
Joseph Bikowski, The Ohio State Univ. (United States)
Metin N. Gurcan, The Ohio State Univ. (United States)


Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato, Editor(s)

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