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- W4385354985 abstract "The paper presents a hybrid and automated segmentation algorithm to precisely separate the affected region from a melanoma skin lesion. Melanoma is one of the types of skin cancer, which cannot be diagnosed at the early stage. Many computer-aided diagnosis systems are developed in the literature and it comprises many phases like (1) image acquisition, (2) image preprocessing, (3) image segmentation, (4) feature extraction, (5) classification, and (6) prediction. This paper focuses on the segmentation phase, which will help to acquire the affected region, and if the exact region is segmented, from the region, significant features can be extracted for classification and prediction. A major issue that impacts accurate segmentation is the irregular and disconnected borders of the skin lesion. To overcome this problem, a hybrid and automated segmentation algorithm is proposed and also compared with existing approaches in this paper. Initially, the algorithm acquires the enhanced melanoma skin lesion, and the image is represented using chain codes to trace the border of the image. Then, Euclidean Distance based Region Selection (EDRS) is carried out to remove the negligible regions from the input image. After region selection, active contours are deployed, and the affected region is extracted precisely. The algorithm is evaluated by estimating the dice similarity coefficient between the segmented image and the ground truth images. Promising results are acquired when the algorithm is compared with the other existing segmentation algorithms." @default.
- W4385354985 created "2023-07-29" @default.
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- W4385354985 date "2023-01-01" @default.
- W4385354985 modified "2023-09-26" @default.
- W4385354985 title "Hybrid and automated segmentation algorithm for malignant melanoma using chain codes and active contours" @default.
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- W4385354985 doi "https://doi.org/10.1016/b978-0-443-19413-9.00018-7" @default.
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