Multi-spectral image analysis and classification of melanoma using fuzzy membership based partitions

Sachin V. Patwardhan, Shuangshuang Dai, Atam P. Dhawan

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)287-296
Number of pages10
JournalComputerized Medical Imaging and Graphics
Issue number4
StatePublished - Jun 2005

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Health Informatics
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


  • Dysplastic nevus
  • Fuzzy membership function
  • Melanoma
  • Nevoscope
  • Tree-structured wavelet transform


Dive into the research topics of 'Multi-spectral image analysis and classification of melanoma using fuzzy membership based partitions'. Together they form a unique fingerprint.

Cite this