Image Analysis for Segmentation of Psoriasis Lesion

Psoriasis, a hereditary inflammatory skin condition that currently affects 2 - 3 % of the world's population, is marked by reddish, scaly rashes or lesions covered with scabs of dead skin. lbis dermatosis is currently not curable but the symptoms can be effectively controlled through an accurate assessment scheme and well-integrated medical care therapy. The National Psoriasis Foundation Medical Board has published a guideline that categorizes the severity of psoriasis - mild, moderate and severe, each characterized by the percentage of lesions on an individual's body surface area. However the caveat remains that the distinction between the different categories of severity is largely influenced by the clinical practitioner's subjectivity. As a result, PASI scoring is introduced. PASI (Psoriasis Area and Severity Index) is currently the gold standard method to measure psoriasis severity by evaluating the area, erythema, scaliness and thickness of the lesions. These 4 parameters require the lesions to be first segmented from the skin patches before they can be assessed and scored individually. lbis report thus investigates digital image analysis techniques to segment psoriasis lesions. In this work, 90 patients are categorized into groups of differing skin tones based on mean values in the L * component. The 1000 new colours obtained through clustering of pixel values in the R, G and B component are used to construct three different skin lesion models. The validation of the three models is done by comparing the mean values of constructed models and original image, which are found to be the same. For segmentation of skin into involved and non-involved regions, iterative thresholding and Otsu's method are applied in 3 colour spaces, namely, I 1hh. CIE L * a* b* and HSI. The average segmentation errors in the 3 colour spaces are then compared to select the best colour channel in which to perform the segmentation for either thresholding. The specificity and sensitivity analysis with the accompanying Type I error and Type II error are conducted as well. From the segmentation results of skin lesion models, it is found that segmentation in the h colour channel (for fair skin tone), !3 and b colour channels (for middle skin tone) and lz, b and S colour channels (for dark skin tone) yields high accuracy. The same thresholding method in corresponding colour channels is then applied on 20 real skin samples. The segmented images are compared with the reference images to measure the accuracy of the proposed lesion segmentation method in different colour channels. Out of 20 cases, the segmentation method achieved accuracies of higher than 95% for 19 cases. The lowest accuracy obtained is for a particular skin-lesion patch with accuracies of 92 - 93%. The lower accuracy is due to wrinkled skin areas which have been exposed to unequally distributed light leading to misclassification as lesions. For each different skin tone, the overall accuracy results show that the proposed colour channels are appropriate and accurate to carry out the Otsu' s method for segmentation of psoriasis lesions