"Approximately 40% of all cancers diagnosed in the United States are skin cancers. Malignant melanoma is the most deadly form of skin cancer. This year there is a lifetime risk of developing melanoma 1 in 68, compared with the 1 in 150 lifetime risk reported in 1985. Prevention, early diagnosis, and appropriate management are crucial to limiting this death toll.
The dermoscopic diagnosis of pigmented skin lesions is based on various analytic approaches or algorithms that have been set forth in the last few years, namely, pattern analysis, the ABCD rule [2,3] and the seven-point checklist [2,3] to quote but a few. The common denominator of all these diagnostic methods are particular dermoscopic features or, better, dermoscopic criteria that represent the backbone for the morphologic diagnosis of pigmented skin lesions.
In this thesis, the primary features of interest are areas of regression and granularity. Areas of regression are regions inside the tumor boundaries that exhibit low color variation. Granularity is a fine texture, which can be characterized as a highly localized noisy gray pixel pattern sometimes found close to areas of regression"-Abstract p. iii
Randy H. Moss
R. Joe Stanley
William Van Stoecker
Electrical and Computer Engineering
M.S. in Computer Engineering
University of Missouri--Rolla
viii, 56 pages
© 2003 Sreenu Tatikonda, All rights reserved.
Thesis - Restricted Access
Melanoma -- Diagnosis
Computer vision -- Technique -- Research
Print OCLC #
Tatikonda, Sreenu, "Automatic detection of regression and granularity in dermatoscopy images of skin cancer" (2003). Masters Theses. 2247.
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