Melanoma Diagnosis by the Use of Wavelet Analysis based on Morphological Operators
Loading...
Other Title
Authors
Fassihi, Nima
Shanbehzadeh, Jamshid
Sarrafzadeh, Hossein
Ghasemi, Elham
Shanbehzadeh, Jamshid
Sarrafzadeh, Hossein
Ghasemi, Elham
Author ORCID Profiles (clickable)
Degree
Grantor
Date
2011-03
Supervisors
Type
Conference Contribution - Paper in Published Proceedings
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
feature extraction
skin melanoma
segmentation
wavelet transform
skin melanoma
segmentation
wavelet transform
ANZSRC Field of Research Code (2020)
Citation
Fassihi, N., Shanbehzadeh, J., Sarrafzadeh, A., and Ghasemi, E. (2011). Melanoma Diagnosis by the Use of Wavelet Analysis based on Morphological Operators. Proceedings of the International MultiConference of Engineers and Computer Scientists.(Ed.), (pp.193-196). 1.
Abstract
Skin melanoma is the most dangerous type of skin cancer which is curable if diagnosed at the right time.. Drawing distinction between melanoma and mole is a difficult task and needs detailed laboratory tests. Utilizing morphologic operators in segmenting and wavelet analysis in order to extract the features has culminated in better result in melanoma diagnosis. This paper employs coefficients of wavelet decomposition to extract image's features. Melanoma classification is carried out by using the variance and mean of wavelet coefficients of images as the inputs of neural network. Results show 90% ability In distinction between benign and malignant lesions.
Publisher
International MultiConference of Engineers and Computer Scientists.
Permanent link
Link to ePress publication
DOI
Copyright holder
International MultiConference of Engineers and Computer Scientists.
Copyright notice
All rights reserved