Electrical and Electronic Engineering - Theses
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ItemMedical image processing with application to psoriasis( 2017)Psoriasis is a chronic, auto-immune and long-lasting skin condition, with no clear cause or cure. Psoriasis affects people of all ages, and in all countries. According to the International Federation of Psoriasis Associations (IFPA), 125 million people worldwide have psoriasis. The severity of psoriasis is determined by clinical assessment of affected areas and how much it affects a person's quality of life. The most common form is plaque psoriasis (at least 80% of cases), which appears as red patches covered with a silvery white build-up of dead skin cells. The current practice of assessing the severity of psoriasis is called "Psoriasis Area Severity Index" (PASI), which is considered the most widely accepted severity index. PASI has four parameters: percentage of body surface area covered, erythema, plaque thickness, and scaliness. Each measure is scored for four different body regions: head, trunk, upper-limbs, and lower-limbs. Although, PASI scores guide the dermatologists to prescribe a treatment, significant inter- and intra- observer variability in PASI scores exist, and are a fact of life. This variability along with the subjectivity and time required to manually determine the final score make the current practice inefficient and unattractive for use in daily clinics. Therefore, developing a computer-aided diagnosis system for psoriasis severity assessment is highly beneficial and long over due. Although, research in the area of medical image analysis has advanced rapidly during the last decade, notable advances in psoriasis image analysis and PASI scoring have been limited and only recently have started to attract the attention. In this thesis, we present the framework of a computer-aided system for PASI scoring using 2D digital skin images by exploring advanced image processing and machine learning techniques. From one side, this will greatly help improve access to early diagnosis and appropriate treatment for psoriasis, by obtaining consistent, precise and reliable severity scoring as well as reducing the inter- and intra- observer variations in clinical practice. From the other side, this can improve the quality of life for psoriasis patients. The framework consists of (i) a novel preprocessing algorithm for removing skin hair and side clinical markers in 2D psoriasis skin images, (ii) psoriasis skin segmentation method, (iii) a fully automated nipple detection approach for psoriasis images, (iv) a semi-supervised approach for erythema severity scoring, (v) a robust, reliable and fully automated superpixel-based method for psoriasis lesion segmentation, and (vi) a new automated scale scoring method using bag of visual words model with different colour and texture descriptors.