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Cervical Cancer Histology Image Feature Extraction And Classification, Peng Guo
Cervical Cancer Histology Image Feature Extraction And Classification, Peng Guo
Masters Theses
"Cervical cancer, the second most common cancer affecting women worldwide and the most common in developing countries can be cured if detected early and treated. Expert pathologists routinely visually examine histology slides for cervix tissue abnormality assessment. In previous research, an automated, localized, fusion-based approach was investigated for classifying squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) based on image analysis of 62 digitized histology images obtained through the National Library of Medicine. In this research, CIN grade assessments from two pathologists are analyzed and are used to facilitate atypical cell concentration feature development …
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 …