Conference or Workshop Item #18223

(2019) A New Texture-Based Segmentation Method for Optical Coherence Tomography Images. In: 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019, 23 July 2019through 27 July 2019, Berlin.

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Abstract

Optical Coherence Tomography (OCT) is an imaging modality which facilitates capturing pictures from biological organs like retina. Accurate segmentation and verification of OCT images leads to the identification and treatment of harmful retinal diseases such as glaucoma. The main fact used for segmentation in this paper is that a considerable number of boundary pixels have similar features from texture point-of-view. Thus, a novel low-complexity segmentation method for OCT images is proposed paying attention to the texture feature of pixels on the boundaries. The simulation results show that the proposed method provides acceptable values for mean signed and unsigned errors compared to the result of manual segmentation. © 2019 IEEE.

Item Type: Conference or Workshop Item (Paper)
Keywords: intra-retinal layer segmentation Optical coherence tomography (OCT) texture analysis Biological organs Image segmentation Image texture Ophthalmology Pixels Textures Tomography Imaging modality Manual segmentation Retinal disease Retinal layers Segmentation methods Texture features Texture-based segmentations Optical tomography algorithm automated pattern recognition diagnostic imaging human optical coherence tomography retina retina disease Algorithms Humans Pattern Recognition, Automated Retinal Diseases Tomography, Optical Coherence
Subjects: WN Radiology. Diagnostic Imaging > WN 180-240 Diagnostic Imaging
WW Ophthalmology > WW 101-290 Eye
Divisions: Medical Image and Signal Processing Research Center
School of Advanced Technologies in Medicine
Page Range: pp. 4750-4753
Journal Index: Scopus
Publisher: Institute of Electrical and Electronics Engineers Inc.
Identification Number: https://doi.org/10.1109/EMBC.2019.8856610
ISBN: 1557170X (ISSN); 9781538613115 (ISBN)
Depositing User: Zahra Otroj
URI: http://eprints.mui.ac.ir/id/eprint/18223

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