Noninvasive diagnosis of melanoma with tensor decomposition-based feature extraction from clinical color image

Ante Jukić, Ivica Kopriva, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

We propose a method for feature extraction from clinical color images, with application in classification of skin lesions. Proposed feature extraction method is based on tensor decomposition of the clinical color image of skin lesion. Since color image is naturally represented as a three-way tensor, it is reasonable to use multi-way techniques to capture the underlying information contained in the image. Extracted features are elements of the core tensor in the corresponding multi-way decomposition, and represent spatial-spectral profile of the lesion. In contrast to common methods that exploit either texture or spectral diversity of the tumor only, the proposed approach simultaneously captures spatial and spectral characteristics. The procedure is tested on a problem of noninvasive diagnosis of melanoma from the clinical color images of skin lesions, with overall sensitivity 82.1% and specificity 86.9%. Our method compares favorably with the state of the art results reported in the literature and provides an interesting alternative to the existing approaches.

Original languageEnglish
Pages (from-to)755-763
Number of pages9
JournalBiomedical Signal Processing and Control
Volume8
Issue number6
DOIs
Publication statusPublished - 2013
Externally publishedYes

Keywords

  • Feature extraction
  • Melanoma
  • Multidimensional signal processing
  • Noninvasive diagnosis
  • Tensor decomposition

Fingerprint

Dive into the research topics of 'Noninvasive diagnosis of melanoma with tensor decomposition-based feature extraction from clinical color image'. Together they form a unique fingerprint.

Cite this