Robust Retinal Image Enhancement via Dual-Tree Complex Wavelet Transform and Morphology-Based Method
Retinal image processing is very important in the field of clinical medicine. As the first step in retinal image processing, image enhancement is essential. Because the details of a retinal image are complex and difficult to enhance, we present a robust retinal image enhancement algorithm via a dual...
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doaj-d895ac2ec97e4d05b42ed9aaebc6ae512021-03-29T22:28:14ZengIEEEIEEE Access2169-35362019-01-017473034731610.1109/ACCESS.2019.29097888684202Robust Retinal Image Enhancement via Dual-Tree Complex Wavelet Transform and Morphology-Based MethodDongming Li0https://orcid.org/0000-0001-7364-6070Lijuan Zhang1Changming Sun2Tingting Yin3Chen Liu4Jinhua Yang5College of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun, ChinaData61, CSIRO, Epping, NSW, AustraliaData61, CSIRO, Epping, NSW, AustraliaCollege of Computer Science and Engineering, Changchun University of Technology, Changchun, ChinaSchool of Information Technology, Jilin Agricultural University, Changchun, ChinaCollege of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun, ChinaRetinal image processing is very important in the field of clinical medicine. As the first step in retinal image processing, image enhancement is essential. Because the details of a retinal image are complex and difficult to enhance, we present a robust retinal image enhancement algorithm via a dual-tree complex wavelet transform (DTCWT) and morphology-based method in this paper. To begin with, we utilize the pre-processing method to the captured retinal images. Then, the DTCWT is applied to decompose the gray retinal image to obtain high-pass subbands and low-pass subbands. Then, a Contourlet-based enhancement method is applied to the high-pass subbands. For the low-pass subbands, we improve the morphology top-hat transform by adding dynamic multi-scale parameters to achieve an equivalent percentage enhancement and at the same time achieve multi-scale transforms in multiple directions. Finally, we develop the inverse DTCWT method to obtain the enhanced retinal image after processing the low-frequency subimages and high-frequency subimages. We compare this approach with enhancement based on the adaptive unsharp masking, histogram equalization, and multi-scale retinex. We present the test results of our algorithm on 440 retinal images from the DRIVE and the STARE databases. The experimental results show that the proposed approach can achieve better results, and might be helpful for vessel segmentation.https://ieeexplore.ieee.org/document/8684202/Retinal imagedual-tree complex wavelet transformtop-hat transformimage enhancement |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dongming Li Lijuan Zhang Changming Sun Tingting Yin Chen Liu Jinhua Yang |
spellingShingle |
Dongming Li Lijuan Zhang Changming Sun Tingting Yin Chen Liu Jinhua Yang Robust Retinal Image Enhancement via Dual-Tree Complex Wavelet Transform and Morphology-Based Method IEEE Access Retinal image dual-tree complex wavelet transform top-hat transform image enhancement |
author_facet |
Dongming Li Lijuan Zhang Changming Sun Tingting Yin Chen Liu Jinhua Yang |
author_sort |
Dongming Li |
title |
Robust Retinal Image Enhancement via Dual-Tree Complex Wavelet Transform and Morphology-Based Method |
title_short |
Robust Retinal Image Enhancement via Dual-Tree Complex Wavelet Transform and Morphology-Based Method |
title_full |
Robust Retinal Image Enhancement via Dual-Tree Complex Wavelet Transform and Morphology-Based Method |
title_fullStr |
Robust Retinal Image Enhancement via Dual-Tree Complex Wavelet Transform and Morphology-Based Method |
title_full_unstemmed |
Robust Retinal Image Enhancement via Dual-Tree Complex Wavelet Transform and Morphology-Based Method |
title_sort |
robust retinal image enhancement via dual-tree complex wavelet transform and morphology-based method |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Retinal image processing is very important in the field of clinical medicine. As the first step in retinal image processing, image enhancement is essential. Because the details of a retinal image are complex and difficult to enhance, we present a robust retinal image enhancement algorithm via a dual-tree complex wavelet transform (DTCWT) and morphology-based method in this paper. To begin with, we utilize the pre-processing method to the captured retinal images. Then, the DTCWT is applied to decompose the gray retinal image to obtain high-pass subbands and low-pass subbands. Then, a Contourlet-based enhancement method is applied to the high-pass subbands. For the low-pass subbands, we improve the morphology top-hat transform by adding dynamic multi-scale parameters to achieve an equivalent percentage enhancement and at the same time achieve multi-scale transforms in multiple directions. Finally, we develop the inverse DTCWT method to obtain the enhanced retinal image after processing the low-frequency subimages and high-frequency subimages. We compare this approach with enhancement based on the adaptive unsharp masking, histogram equalization, and multi-scale retinex. We present the test results of our algorithm on 440 retinal images from the DRIVE and the STARE databases. The experimental results show that the proposed approach can achieve better results, and might be helpful for vessel segmentation. |
topic |
Retinal image dual-tree complex wavelet transform top-hat transform image enhancement |
url |
https://ieeexplore.ieee.org/document/8684202/ |
work_keys_str_mv |
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1724191558087999488 |