Detail publikace

Segmentace cévního řečiště ve snímcích sítnice pomocí klasifikační metody

ODSTRČILÍK, J. KOLÁŘ, R. HARABIŠ, V. TORNOW, R.

Originální název

Classification-Based Blood Vessel Segmentation in Retinal Images

Český název

Segmentace cévního řečiště ve snímcích sítnice pomocí klasifikační metody

Anglický název

Classification-Based Blood Vessel Segmentation in Retinal Images

Typ

článek ve sborníku

Jazyk

en

Originální abstrakt

Automatic and precise segmentation of retinal blood vessels can help in computer aided as-sessment of various retinal diseases, especially diseases related to cardiovascular system or glaucoma. Hence, an accurate detection of retinal vascular structures is one of the most addressed topic in the field of retinal im-age processing today. Most of the methods are designed directly for utilization with a special dataset only or have problems to segment blurry and noisy images. In this study, we showed that combination of three stand-ard approaches – matched filtering, Hessian-based approach, and morphological processing, together with support vector machine classification technique, can be satisfactorily used as a universal segmentation ap-proach in retinal images acquired using entirely different devices.

Český abstrakt

Automatic and precise segmentation of retinal blood vessels can help in computer aided as-sessment of various retinal diseases, especially diseases related to cardiovascular system or glaucoma. Hence, an accurate detection of retinal vascular structures is one of the most addressed topic in the field of retinal im-age processing today. Most of the methods are designed directly for utilization with a special dataset only or have problems to segment blurry and noisy images. In this study, we showed that combination of three stand-ard approaches – matched filtering, Hessian-based approach, and morphological processing, together with support vector machine classification technique, can be satisfactorily used as a universal segmentation ap-proach in retinal images acquired using entirely different devices.

Anglický abstrakt

Automatic and precise segmentation of retinal blood vessels can help in computer aided as-sessment of various retinal diseases, especially diseases related to cardiovascular system or glaucoma. Hence, an accurate detection of retinal vascular structures is one of the most addressed topic in the field of retinal im-age processing today. Most of the methods are designed directly for utilization with a special dataset only or have problems to segment blurry and noisy images. In this study, we showed that combination of three stand-ard approaches – matched filtering, Hessian-based approach, and morphological processing, together with support vector machine classification technique, can be satisfactorily used as a universal segmentation ap-proach in retinal images acquired using entirely different devices.

Klíčová slova

segmentation, retinal vessels, blood vessels, classification, retinal images

Rok RIV

2015

Vydáno

19.10.2015

Nakladatel

CRC Press Taylor and Francis Group

Místo

London, UK

ISBN

978-1-138-02926-2

Kniha

Computational Vision and Medical Image Processing V

Strany od

95

Strany do

100

Strany počet

6

BibTex


@inproceedings{BUT117935,
  author="Jan {Odstrčilík} and Radim {Kolář} and Vratislav {Harabiš} and Ralf-Peter {Tornow}",
  title="Classification-Based Blood Vessel Segmentation in Retinal Images",
  annote="Automatic and precise segmentation of retinal blood vessels can help in computer aided as-sessment of various retinal diseases, especially diseases related to cardiovascular system or glaucoma. Hence, an accurate detection of retinal vascular structures is one of the most addressed topic in the field of retinal im-age processing today. Most of the methods are designed directly for utilization with a special dataset only or have problems to segment blurry and noisy images. In this study, we showed that combination of three stand-ard approaches – matched filtering, Hessian-based approach, and morphological processing, together with support vector machine classification technique, can be satisfactorily used as a universal segmentation ap-proach in retinal images acquired using entirely different devices.",
  address="CRC Press Taylor and Francis Group",
  booktitle="Computational Vision and Medical Image Processing V",
  chapter="117935",
  howpublished="print",
  institution="CRC Press Taylor and Francis Group",
  year="2015",
  month="october",
  pages="95--100",
  publisher="CRC Press Taylor and Francis Group",
  type="conference paper"
}