Lesion border detection in dermoscopy images.

M Emre Celebi, Hitoshi Iyatomi, Gerald Schaefer, William V Stoecker
Author Information
  1. M Emre Celebi: Dept. of Computer Science, Louisiana State Univ., Shreveport, LA, USA. ecelebi@lsus.edu

Abstract

BACKGROUND: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders.
METHODS: In this article, we present a systematic overview of the recent border detection methods in the literature paying particular attention to computational issues and evaluation aspects.
CONCLUSION: Common problems with the existing approaches include the acquisition, size, and diagnostic distribution of the test image set, the evaluation of the results, and the inadequate description of the employed methods. Border determination by dermatologists appears to depend upon higher-level knowledge, therefore it is likely that the incorporation of domain knowledge in automated methods will enable them to perform better, especially in sets of images with a variety of diagnoses.

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Grants

  1. R44 CA101639/NCI NIH HHS
  2. R44 CA101639-02A2/NCI NIH HHS
  3. SBIR #2R44 CA-101639-02A2/NCI NIH HHS

MeSH Term

Artificial Intelligence
Dermoscopy
Humans
Image Interpretation, Computer-Assisted
Image Processing, Computer-Assisted
Pattern Recognition, Automated
Skin Neoplasms

Word Cloud

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