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Feature Extraction Through K-Means Segmentation For Melanoma Detection, Snigdha Priya Bommadevara
Feature Extraction Through K-Means Segmentation For Melanoma Detection, Snigdha Priya Bommadevara
Masters Theses
"Malignant melanoma is responsible for 75% of the deaths caused due to skin cancer annually. However, melanoma detection can be possible through feature extraction and pattern classification, which can lower the risk, if the melanoma is detected at an early stage. Clustering is one of the most useful tools used to differentiate features that can contribute to melanoma. This research work uses the k-means clustering algorithm for implementation of color segmentation. However, k-means clustering requires a predefined value of k, i.e., the number of clusters must be specified at the beginning of the run. This research uses a predefined value …
Feature Extraction Through Median-Split Algorithm Segmentation For Melanoma Detection, Venkata Sai Narasimha Kaushik Ghantasala
Feature Extraction Through Median-Split Algorithm Segmentation For Melanoma Detection, Venkata Sai Narasimha Kaushik Ghantasala
Masters Theses
""Detection of melanoma remains an empirical clinical science. New tools for automatic discrimination of melanoma from benign lesions in digitized dermoscopy images may allow an improvement in early detection of melanoma. This research implements a fast version of the median split algorithm in an open source format and applied to four-color splitting of the lesion area to capture the architectural disorder apparent in melanoma colors. This version of the median split algorithm splits colors along the color axis with maximum range". For a dermoscopy set of 888 images, K-means clustering algorithm is compared with a median split algorithm to find …