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Skin Lesion Segmentation Techniques for Melanoma Diagnosis: Comparative Studies

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thesis
posted on 22.05.2021, 17:07 by Farzad Nowroozipour
In recent years, melanoma skin cancer has been one of the rapidest risings of all cancers, which has a high risk of spread. This deadliest form of skin cancer must be diagnosed early for effective treatment. Due to the difficulty and subjectivity of human interpretation, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the segmentation of skin lesion. In this research, we create different new algorithms for the skin lesion segmentation in dermoscopic images. The segmentation algorithms compared are a modified automatic Seeded Region Growing based on Particle swarm optimization image clustering which was used for breast MRI Tumours segmentation, Generalized rough fuzzy c-means algorithm which has been used for brain MR image segmentation, a Support Vector Machine (SVM) and Self-Organizing Map (SOM) with Genetic Algorithm. We used two different datasets with their masks to evaluate the accuracy, sensitivity, and specificity of various segmentation techniques. The results shows that a modified automatic Seeded Region Growing based on Particle swarm optimization image clustering has the highest accuracy (92%) compares with the other algorithms.

History

Language

eng

Degree

Bachelor of Engineering

Program

Electrical and Computer Engineering

Granting Institution

Ryerson University

LAC Thesis Type

Thesis