Dermoscopic Image Segmentation using Machine Learning Algorithm

Problem statement: Malignant melanoma is the most frequent type of skin cancer. Its incidence has been rapidly increasing over the last few decades. Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. Approach: This study explains the task of segmenting skin lesions in Dermoscopy images based on intelligent systems such as Fuzzy and Neural Networks clustering techniques for the early diagnosis of Malignant Melanoma. The various intelligent systems based clustering techniques used were Fuzzy C Means Algorithm (FCM), Possibilistic C Means Algorithm (PCM), Hierarchical C Means Algorithm (HCM); C-mean based Fuzzy Hopfield Neural Network, Adaline Neural Network and Regression Neural Network. Results: The segmented images were compared with the ground truth image using various parameters such as False Positive Error (FPE), False Negative Error (FNE) Coefficient of similarity, spatial overlap and their performance was evaluated. Conclusion: The experimental results show that Hierarchical C Means algorithm( Fuzzy) provides better segmentation than other (Fuzzy C Means, Possibilistic C Means, Adaline Neural Network, FHNN and GRNN) clustering algorithms. Hierarchical C Means approach can handle uncertainties that exist in the data efficiently and useful for the lesion segmentation in a computer aided diagnosis system to assist the clinical diagnosis of dermatologists.