Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
A Measure for Objective Evaluation of Image Segmentation Algorithms
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Acoustic feature selection for automatic emotion recognition from speech
Information Processing and Management: an International Journal
Candidate working set strategy based SMO algorithm in support vector machine
Information Processing and Management: an International Journal
Accurate segmentation of dermoscopic images by image thresholding based on type-2 fuzzy logic
IEEE Transactions on Fuzzy Systems
Automatic histogram threshold using fuzzy measures
IEEE Transactions on Image Processing
A segmentation framework for abdominal organs from CT scans
Artificial Intelligence in Medicine
Suppressed fuzzy-soft learning vector quantization for MRI segmentation
Artificial Intelligence in Medicine
Information Processing and Management: an International Journal
Type-2 Fuzzy Logic: A Historical View
IEEE Computational Intelligence Magazine
Impulse Noise Removal From Digital Images by a Detail-Preserving Filter Based on Type-2 Fuzzy Logic
IEEE Transactions on Fuzzy Systems
An Enhanced Type-Reduction Algorithm for Type-2 Fuzzy Sets
IEEE Transactions on Fuzzy Systems
A target-based color space for sea target detection
Applied Intelligence
Perceptual Segmentation: Combining Image Segmentation With Object Tagging
IEEE Transactions on Image Processing
Optimizing the modified fuzzy ant-miner for efficient medical diagnosis
Applied Intelligence
Transductive cost-sensitive lung cancer image classification
Applied Intelligence
Image retrieval based on augmented relational graph representation
Applied Intelligence
Multi-circle detection on images inspired by collective animal behavior
Applied Intelligence
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This paper discusses a new approach to segment different types of skin cancers using fuzzy logic approach. The traditional skin cancer segmentation involves the analysis of image features to delineate the cancerous region from the normal skin. Using low level features such as colour and intensity, segmentation can be done by obtaining a threshold level to separate the two regions. Methods like Otsu optimisation provide a quick and simple process to optimise such threshold level; however this process is prone to the lighting and skin tone variations. Fuzzy clustering algorithm has also been widely used in image processing due to its ability to model the fuzziness of human visual perception. Classical fuzzy C means (FCM) clustering algorithm has been applied to image segmentation with good results; however, the classical FCM is based on type-1 fuzzy sets and is unable to handle uncertainties in the images. In this paper, we proposed an optimum threshold segmentation algorithm based on type-2 fuzzy sets algorithms to delineate the cancerous area from the skin images. By using the 3D colour constancy algorithm, the effect of colour changes and shadows due to skin tone variation in the image can be significantly reduced in the preprocessing stage. We applied the optimum thresholding technique to the preprocessed image over the RGB channels, and combined individual results to achieve the overall skin cancer segmentation. Compared to the Otsu algorithm, the proposed method is less affected by the shadows and skin tone variations. The results also showed more tolerance at the boundary of the cancerous area. Compared with the type-1 FCM algorithm, the proposed method significantly reduced the segmentation error at the normal skin regions.