Automatic detection of lesion border and edge-related structures in dermoscopy images
Keywords and Phrases
"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.
Moss, Randy Hays, 1953-
Beetner, Daryl G.
Samaranayake, V. A.
Stanley, R. Joe
Electrical and Computer Engineering
Ph. D. in Electrical Engineering
University of Missouri--Rolla
Journal article titles appearing in thesis/dissertation
- Skin lesion segmentation by an adaptive watershed flooding approach
- Adaptive morphological line structure segmentation on skin lesion images - SharpRazor, a software improvement of DullRazorʼ
- Detection of atypical texture features in early malignant melanoma
xi, 83 pages
© 2007 Xiaohe Chen, All rights reserved.
Dissertation - Citation
Melanoma -- Diagnosis
Skin -- Cancer -- Diagnosis
Print OCLC #
Link to Catalog Record
Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.http://merlin.lib.umsystem.edu/record=b6432862~S5
Chen, Xiaohe, "Automatic detection of lesion border and edge-related structures in dermoscopy images" (2007). Doctoral Dissertations. 1752.
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