Hybrid retinal image registration using mutual information and salient features

Jaeyong Ju; Murray Loew; Bonhwa Ku; Hanseok Ko

doi:10.1587/transinf.2015EDL8265

Hybrid retinal image registration using mutual information and salient features

Jaeyong Ju, Murray Loew, Bonhwa Ku, Hanseok Ko

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

This paper presents a method for registering retinal images. Retinal image registration is crucial for the diagnoses and treatments of various eye conditions and diseases such as myopia and diabetic retinopathy. Retinal image registration is challenging because the images have non-uniform contrasts and intensity distributions, as well as having large homogeneous non-vascular regions. This paper provides a new retinal image registration method by effectively combining expectation maximization principal component analysis based mutual information (EMPCA-MI) with salient features. Experimental results show that our method is more efficient and robust than the conventional EMPCA-MI method.

Original language	English
Pages (from-to)	1729-1732
Number of pages	4
Journal	IEICE Transactions on Information and Systems
Volume	E99D
Issue number	6
DOIs	https://doi.org/10.1587/transinf.2015EDL8265
Publication status	Published - 2016 Jun

Bibliographical note

Publisher Copyright:
Copyright © 2016 The Institute of Electronics, Information and Communication Engineers.

Keywords

Medical Imaging
Mutual Information
Retinal Image Registration
Salient Features

ASJC Scopus subject areas

Software
Hardware and Architecture
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering
Artificial Intelligence

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1587/transinf.2015EDL8265

Cite this

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abstract = "This paper presents a method for registering retinal images. Retinal image registration is crucial for the diagnoses and treatments of various eye conditions and diseases such as myopia and diabetic retinopathy. Retinal image registration is challenging because the images have non-uniform contrasts and intensity distributions, as well as having large homogeneous non-vascular regions. This paper provides a new retinal image registration method by effectively combining expectation maximization principal component analysis based mutual information (EMPCA-MI) with salient features. Experimental results show that our method is more efficient and robust than the conventional EMPCA-MI method.",

keywords = "Medical Imaging, Mutual Information, Retinal Image Registration, Salient Features",

author = "Jaeyong Ju and Murray Loew and Bonhwa Ku and Hanseok Ko",

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year = "2016",

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doi = "10.1587/transinf.2015EDL8265",

language = "English",

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AU - Ju, Jaeyong

AU - Loew, Murray

AU - Ku, Bonhwa

AU - Ko, Hanseok

PY - 2016/6

Y1 - 2016/6

N2 - This paper presents a method for registering retinal images. Retinal image registration is crucial for the diagnoses and treatments of various eye conditions and diseases such as myopia and diabetic retinopathy. Retinal image registration is challenging because the images have non-uniform contrasts and intensity distributions, as well as having large homogeneous non-vascular regions. This paper provides a new retinal image registration method by effectively combining expectation maximization principal component analysis based mutual information (EMPCA-MI) with salient features. Experimental results show that our method is more efficient and robust than the conventional EMPCA-MI method.

AB - This paper presents a method for registering retinal images. Retinal image registration is crucial for the diagnoses and treatments of various eye conditions and diseases such as myopia and diabetic retinopathy. Retinal image registration is challenging because the images have non-uniform contrasts and intensity distributions, as well as having large homogeneous non-vascular regions. This paper provides a new retinal image registration method by effectively combining expectation maximization principal component analysis based mutual information (EMPCA-MI) with salient features. Experimental results show that our method is more efficient and robust than the conventional EMPCA-MI method.

KW - Medical Imaging

KW - Mutual Information

KW - Retinal Image Registration

KW - Salient Features

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DO - 10.1587/transinf.2015EDL8265

M3 - Article

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JO - IEICE Transactions on Information and Systems

JF - IEICE Transactions on Information and Systems

IS - 6

ER -

Hybrid retinal image registration using mutual information and salient features

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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