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Automatic segmentation of scaling in 2-D psoriasis skin images
- Source :
- IEEE transactions on medical imaging. 32(4)
- Publication Year :
- 2013
-
Abstract
- Psoriasis is a chronic inflammatory skin disease that affects over 3% of the population. Various methods are currently used to evaluate psoriasis severity and to monitor therapeutic response. The PASI system of scoring is widely used for evaluating psoriasis severity. It employs a visual analogue scale to score the thickness, redness (erythema), and scaling of psoriasis lesions. However, PASI scores are subjective and suffer from poor inter- and intra-observer concordance. As an integral part of developing a reliable evaluation method for psoriasis, an algorithm is presented for segmenting scaling in 2-D digital images. The algorithm is believed to be the first to localize scaling directly in 2-D digital images. The scaling segmentation problem is treated as a classification and parameter estimation problem. A Markov random field (MRF) is used to smooth a pixel-wise classification from a support vector machine (SVM) that utilizes a feature space derived from image color and scaling texture. The training sets for the SVM are collected directly from the image being analyzed giving the algorithm more resilience to variations in lighting and skin type. The algorithm is shown to give reliable segmentation results when evaluated with images with different lighting conditions, skin types, and psoriasis types.
- Subjects :
- Support Vector Machine
Databases, Factual
Feature vector
Population
Feature extraction
Scale-space segmentation
Image processing
Image texture
Image Interpretation, Computer-Assisted
Image Processing, Computer-Assisted
Humans
Psoriasis
Computer vision
Electrical and Electronic Engineering
education
Mathematics
Skin
education.field_of_study
Radiological and Ultrasound Technology
Contextual image classification
business.industry
Pattern recognition
Image segmentation
Markov Chains
Computer Science Applications
Artificial intelligence
business
Software
Subjects
Details
- ISSN :
- 1558254X
- Volume :
- 32
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- IEEE transactions on medical imaging
- Accession number :
- edsair.doi.dedup.....61ccd0e8cf46a0feeb7023199c19ea04