Psoriasis Severity Assessment of 2-D Psoriasis Skin Images

DOI

10.2991/pecteam-18.2018.11How to use a DOI?

Keywords

Feature extraction, Markov Random Field (MRF), Support Vector Machine (SVM), Gabor filter, segmentation.

Abstract

Psoriasis, meaning "itchy condition", is a chronic skin disease that is characterized by scaly, reddened patches. It is a recurring disease with varying severity ranging from slight limited flakes to entire body. Psoriasis Area and Severity Index (PASI) is the most conventional method for measuring the severity of this disease. It computes the PASI score, which ranges from 0 to 72, by combining the severity of lesions and area affected into a single computational score. But these scores are not reliable as they vary for the same psoriatic lesion among different physicians and suffer from inter- and intra-observer difference. This paper mainly focuses on assessing the severity index of 2D digital images of psoriasis by removing erythema (redness) from the selected image, thereby considering other skin cells for analysis. It makes use of "Feature Space Scaling" algorithm that relies on color contrast and image texture along with a combination of Support Vector Machine (SVM) classification filters and Markov Random Fields (MRF) to come up with a treatment solution. This algorithm is tested on different psoriasis affected skin images under various lighting conditions and is proved to be reliable.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - S Raaghavi
AU  - M Ragini
PY  - 2018/02
DA  - 2018/02
TI  - Psoriasis Severity Assessment of 2-D Psoriasis Skin Images
BT  - Proceedings of the International Conference for Phoenixes on Emerging Current Trends in Engineering and Management (PECTEAM 2018)
PB  - Atlantis Press
SP  - 56
EP  - 59
SN  - 2352-5401
UR  - https://doi.org/10.2991/pecteam-18.2018.11
DO  - 10.2991/pecteam-18.2018.11
ID  - Raaghavi2018/02
ER  -