Multi-spectral image analysis and classification of melanoma using fuzzy membership based partitions.
The sensitivity and specificity of melanoma diagnosis can be improved by adding the lesion depth and structure information obtained from the multi-spectral, trans-illumination images to the surface characteristic information obtained from the epi-illumination images. Wavelet transform based bi-modal channel energy features obtained from the images are used in the analysis. Methods using both crisp and fuzzy membership based partitioning of the feature space are evaluated. For this purpose, the ADWAT classification method that uses crisp partitioning is extended to handle multi-spectral image data. Also, multi-dimensional fuzzy membership functions with Gaussian and Bell profiles are proposed for classification. Results show that the fuzzy membership functions with Bell profile are more effective than the extended ADWAT method in discriminating melanoma from dysplastic nevus.
Patwardhan
Sachin V
SV
Department of Electrical and Computer Engineering, New Jersey Institute of Technology, 323 Martin Luther King Blvd., University Heights, Newark, NJ 07102, USA.
Dai
Shuangshuang
S
Dhawan
Atam P
AP
D003937:Diagnosis, Differential; D004416:Dysplastic Nevus Syndrome / Q000175:diagnosis; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008545:Melanoma / Q000145:classification* / Q000175:diagnosis; D012878:Skin Neoplasms / Q000145:classification* / Q000175:diagnosis; D014481:United States