Delineation of malignant skin tumors by hyperspectral imaging using diffusion maps dimensionality reduction

Valery Zheludev, Ilkka Pölönen, Noora Neittaanmäki-Perttu, Amir Averbuch, Pekka Neittaanmäki, Mari Grönroos, Heikki Saari

Research output: Contribution to journalArticlepeer-review

Abstract

A new non-invasive method for delineation of lentigo maligna and lentigo maligna melanoma is demonstrated. The method is based on the analysis of the hyperspectral images taken in vivo before surgical excision of the lesions. For this, the characteristic features of the spectral signatures of diseased pixels and healthy pixels are extracted, which combine the intensities in a few selected wavebands with the coefficients of the wavelet frame transforms of the spectral curves. To reduce dimensionality and to reveal the internal structure of the datasets, the diffusion maps technique is applied. The averaged Nearest Neighbor and the Classification and Regression Tree (CART) classifiers are utilized as the decision units. To reduce false alarms by the CART classifier, the Aisles procedure is used.

Original languageEnglish
Pages (from-to)48-60
Number of pages13
JournalBiomedical Signal Processing and Control
Volume16
DOIs
StatePublished - Feb 2015

Keywords

  • Delineation
  • Framelet
  • Hyperspectral imaging
  • Malignant
  • Tumor

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