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Subject: search.filters.subject.Segmentation

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    Genetic Algorithm Based Hybrid Clustering Technique for the Retinal Blood Vessels Segmentation
    (IEEE, 2022-12-09) Dasanayake, D; Athuraliya, N; De Silva, H; Fernando, K; Haddela ., P.S
    Important details about the visual anomaly can be found in the retinal fundus imaging. The segmentation of the blood vessels is crucial and necessary for diagnosing different ocular fundus. The primary and most common causes of blindness are diabetic retinopathy and its effects on the retinal vascular structures. The study suggested a genetic algorithm combined with the K-means clustering technique for unsupervised retinal segmentation. An essential pre-processing step for vessel identification applications is vessel enhancement. The CLAHE filtering method is employed in this work as a preprocessing step for vessel improvement. The improved vessels were grouped together using a genetic approach, and K-means clustering was applied for superior clustering outcomes. DRIVE and IOSTAR databases that are accessible to the public are used to evaluate the suggested strategy. According to the experimental findings, the proposed algorithm successfully separates clusters that are more dense and well-separated than those of other previous findings. Both the Calinski-Harabasz I ndex S core and the Silhouette Index Score are used to validate the proposed algorithm.
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    Melanoma Skin Cancer Detection Using Image Processing and Machine Learning Techniques
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Ahmed Thaajwer, M.A.; Ishanka, U.A.P.
    In humans, skin cancer is the most common and severe type of cancer. Melanoma is a deadly type of skin cancer. If it identifies early stages, it can be easily cured. The formal method for diagnosing melanoma detection is the biopsy method. This method can be a very painful one and a time-consuming process. This study gives a computer-aided detection system for the early identification of melanoma. In this study, image processing techniques and the Support vector machine (SVM) algorithms are used to introduce an efficient diagnosing system. The affected skin image is taken, and it sent under several pre-processing techniques for getting the enhanced image and smoothed image. Then the image is sent through the segmentation process using morphological and thresholding methods. Some essential texture, color and shape features of the skin images are extracted. Gray Level Co-occurrence Matrix (GLCM) methodology is used for extracting texture features. These extracted GLCM, color and shape features are given as input to the SVM classifier. It classifies the given image into malignant melanoma or benign melanoma. High accuracy of 83% is achieved when we combine and apply the shape, color and GLCM features to the classifier.