THALIA - An automatic hierarchical analysis system to detect drusen lesion images for amd assessment

Damon W.K. Wong, Jiang Liu, Xiangang Cheng, Jielin Zhang, Fengshou Yin, Mayuri Bhargava, Gemmy C.M. Cheung, Tien Yin Wong

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

16 Citations (Scopus)

Abstract

Age-related macular degeneration (AMD) is a leading cause of permanent blindness. In its early stage AMD is characterized by drusen which are extracellelur deposits in the retina. In this paper, we present THALIA, an automatic system for the detection of drusen images for AMD assessment. First, the macular region of interest is detected using a seeded mode tracking approach. The macular region of interest is then mapped into a new representation using a hierarchicial word transform (HWI). In HWI, dense sampling is first carried out to generate structured pixels which embed local context. These structured pixels are then clustered using hierarchical k-means. The HWI image is subsequently classified using a SVM-based classifier. We have tested THALIA on a dataset of 350 images and obtained an accuracy of 95.46%. Results are promising for further validation of the THALIA system.

Original languageEnglish
Title of host publicationISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro
Pages884-887
Number of pages4
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 - San Francisco, CA, United States
Duration: 7 Apr 201311 Apr 2013

Publication series

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

Conference

Conference2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
Country/TerritoryUnited States
CitySan Francisco, CA
Period7/04/1311/04/13

Keywords

  • AMD
  • drusen
  • retinal image

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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