Capillary detection for clinical images of basal cell carcinoma

Adam Huang, Wen Yu Chang, Hsin Yi Liu, Gwo Shing Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Dilated capillaries are an important characteristic of basal cell carcinoma (BCC). Detecting capillaries in images can improve a computer-aided skin cancer diagnosis system. In this study, we investigate the feasibility to extract capillaries from clinical images of skin lesions recorded by a regular digital camera. First, we used a compact set of 1 curvilinear and 2 color parameters to train a support vector machine (SVM) classifier to identify capillary pixels. Second, the identified pixels were grouped by a region-growing algorithm to form capillary candidates. Last, the likelihood to be a true capillary was estimated based on the distance to the red color in the CIE Lab color space. The method was tested on a dataset of 21 BCC images with visible capillaries and 28 benign pigmented lesions without visible capillaries. The accuracy, sensitivity, and specificity of the proposed method were 89.8% (44/49), 90.5% (19/21), and 89.3% (25/28) respectively. We found capillaries recorded by a regular digital camera can be detected successfully.

Original languageEnglish
Title of host publication2012 9th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2012 - Proceedings
Pages306-309
Number of pages4
DOIs
StatePublished - 2012
Event2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona, Spain
Duration: 2 May 20125 May 2012

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012
Country/TerritorySpain
CityBarcelona
Period2/05/125/05/12

Keywords

  • basal cell carcinoma
  • color segmentation
  • Computer-aided detection
  • support vector machines
  • vascular pattern detection

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