Theses/Dissertations
Author Cheng, Beibei, 1984-

Title Automatic vessel and telangiectases analysis in dermoscopy skin lesion images / by Beibei Cheng.

Published ©2009.
LOCATION CALL # STATUS
 MST DEPOSITORY  THESIS T 9476/9499  MICROFILM    NOT CHECKED OUT
 MST Thesis  THESIS T 9480    NOT CHECKED OUT
Description ix, 47 leaves : illustrations ; 28 cm
Summary "The blood vessels are part of the circulatory system and function to transport blood throughout the body. Vessels have their own features such as distinctive color compared to surrounding skin as well as distinctive curved and/or linear shape. Telangiectases are small dilated blood vessels near the surface of the skin or mucous membranes, measuring between 0.5 and 1 millimeter in diameter. In this research, image analysis techniques are investigated to detect vessels in dermoscopy skin lesion images. Machine vision and neural network methods are explored to discriminate skin lesions containing telangiectases from those containing normal vessels. A vessels Detection technique is implemented firstly to find the possible vessels in dermatology skin lesion images. In addition, a noise filtering technique is applied, which filters out the "noise" such as hair, bubble and so one, according to their own features. Based on the fact that some of the images are fuzzy, a contrast enhancement technique can be added to increase the contrast. After obtaining the final masked regions containing vessel-like structures, features are computed to facilitate the discrimination of skin lesion with normal vessels from lesions containing telangiectases. The features are mostly about the number, shape and size of telangiectases mask"--Abstract, leaf iii.
Notes Vita.
M.S. Missouri University of Science and Technology 2009.
Includes bibliographical references (leaf 46).
Subjects Telangiectasia.
Basal cell carcinoma -- Diagnosis -- Computer programs.
Skin -- Cancer -- Diagnosis -- Computer programs.
Image processing -- Computer programs.
Other Titles MST thesis. Computer Engineering (M.S., 2009).
OCLC/WorldCat Number 436057808