{"id":{"repo_id":"must-thes","oai_identifier":"oai:scholarsmine.mst.edu:doctoral_dissertations-2754"},"canonical_url":"https://search.dev.ndltd.org/etd/must-thes/oai:scholarsmine.mst.edu:doctoral_dissertations-2754","repository":{"repo_id":"must-thes","name":"Missouri University of Science and Technology","base_url":"https://scholarsmine.mst.edu/do/oai/"},"display":{"title":"Automatic detection of lesion border and edge-related structures in dermoscopy images","abstract":"\"Early detection of malignant melanoma is critical because early stage diagnosis results in a higher survival rate. As pre-processing steps of digital dermoscopy images in an automatic diagnosis system, line structure identification and lesion border identification algorithms are important for accuracy of diagnosis. This work presents the methodology and procedures to process dermoscopy images using computer vision and data mining methods. A watershed-based adaptive skin lesion border finder was developed and implemented for dermoscopy images...An improved SharpRazor algorithm was developed to remove hairs from dermoscopy images, based on a previously existing DullRazorʼ algorithm for hair removal...Work is also reported here on classifiers for detecting atypical pigment network in skin lesions based on texture\"--Abstract, page iv.","abstract_html":""Early detection of malignant melanoma is critical because early stage diagnosis results in a higher survival rate. As pre-processing steps of digital dermoscopy images in an automatic diagnosis system, line structure identification and lesion border identification algorithms are important for accuracy of diagnosis. This work presents the methodology and procedures to process dermoscopy images using computer vision and data mining methods. A watershed-based adaptive skin lesion border finder was developed and implemented for dermoscopy images...An improved SharpRazor algorithm was developed to remove hairs from dermoscopy images, based on a previously existing DullRazorʼ algorithm for hair removal...Work is also reported here on classifiers for detecting atypical pigment network in skin lesions based on texture"--Abstract, page iv.","abstract_has_math":false,"creators":["Chen, Xiaohe"],"institution":"University of Missouri--Rolla","degree_name":"Ph. D. in Electrical Engineering","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-02-10T08:00:00Z","date_published":"2016-02-10T08:00:00Z","updated_at":"2026-07-24T03:20:12Z","subjects":["Dermoscopy","Electrical and Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsmine.mst.edu/doctoral_dissertations/1752","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Chen, Xiaohe"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-02-10T08:00:00Z"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation - Citation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. D. in Electrical Engineering"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Missouri--Rolla"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Dermoscopy","Electrical and Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarsmine.mst.edu/doctoral_dissertations/1752"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["\"Early detection of malignant melanoma is critical because early stage diagnosis results in a higher survival rate. As pre-processing steps of digital dermoscopy images in an automatic diagnosis system, line structure identification and lesion border identification algorithms are important for accuracy of diagnosis. This work presents the methodology and procedures to process dermoscopy images using computer vision and data mining methods. A watershed-based adaptive skin lesion border finder was developed and implemented for dermoscopy images...An improved SharpRazor algorithm was developed to remove hairs from dermoscopy images, based on a previously existing DullRazorʼ algorithm for hair removal...Work is also reported here on classifiers for detecting atypical pigment network in skin lesions based on texture\"--Abstract, page iv."]},{"key":"dc:title","label":"Title","values":["Automatic detection of lesion border and edge-related structures in dermoscopy images"]}]}],"canonical_facts":{"dc:creator":["Chen, Xiaohe"],"dc:date.available":["2016-02-10T08:00:00Z"],"dc:description.abstract":["\"Early detection of malignant melanoma is critical because early stage diagnosis results in a higher survival rate. As pre-processing steps of digital dermoscopy images in an automatic diagnosis system, line structure identification and lesion border identification algorithms are important for accuracy of diagnosis. This work presents the methodology and procedures to process dermoscopy images using computer vision and data mining methods. A watershed-based adaptive skin lesion border finder was developed and implemented for dermoscopy images...An improved SharpRazor algorithm was developed to remove hairs from dermoscopy images, based on a previously existing DullRazorʼ algorithm for hair removal...Work is also reported here on classifiers for detecting atypical pigment network in skin lesions based on texture\"--Abstract, page iv."],"dc:identifier":["https://scholarsmine.mst.edu/doctoral_dissertations/1752"],"dc:subject":["Dermoscopy","Electrical and Computer Engineering"],"dc:title":["Automatic detection of lesion border and edge-related structures in dermoscopy images"],"dc:type":["Dissertation - Citation"],"thesis:degree_name":["Ph. D. in Electrical Engineering"],"thesis:institution_name":["University of Missouri--Rolla"]},"updated_at":"2026-07-24T03:20:12Z"}