Abstract
For timely diagnosis of retinal disease, routine retinal monitoring of people with high risk should be put in place. To assist the ophthalmologists in performing retinal analysis efficiently and accurately, numerous studies have been conducted to propose an automated retinal diagnosis system. One of the crucial steps for such a system is accurate detection of retinal blood vessels from retinal image. In this paper, we investigated the use of automatic binarization methods on pre-processed fundus image to detect retinal blood vessels. Three methods for binarization were investigated in this study, namely Otsu's method, ISODATA and K-means clustering method. The resulting binarized output indicated good detection of large vessels but most of the smaller vessels were left undetected. To address this issue, Gabor wavelet filter was used to enhance the small blood vessel structures before binarization of the filter output. Combining the binary images from both binarization with and without Gabor filter resulted in significant improvement of the overall detection rate of the retinal blood vessels. The proposed method proved to be comparable to other unsupervised techniques in the literature when validated using the publicly available fundus image database, DRIVE.
| Original language | English |
|---|---|
| Pages (from-to) | 163-167 |
| Number of pages | 5 |
| Journal | International Journal of Engineering and Technology(UAE) |
| Volume | 7 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 1 Jan 2018 |
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Keywords
- Binarization
- Blood vessel
- Gabor wavelet
- Retinal image
- Segmentation
ASJC Scopus subject areas
- Biotechnology
- Computer Science (miscellaneous)
- Environmental Engineering
- Chemical Engineering(all)
- Engineering(all)
- Hardware and Architecture
Cite this
Segmenting retinal blood vessels with gabor filter and automatic binarization. / Ali, Aziah; Hussain, Aini; Wan Zaki, Wan Mimi Diyana.
In: International Journal of Engineering and Technology(UAE), Vol. 7, No. 4, 01.01.2018, p. 163-167.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Segmenting retinal blood vessels with gabor filter and automatic binarization
AU - Ali, Aziah
AU - Hussain, Aini
AU - Wan Zaki, Wan Mimi Diyana
PY - 2018/1/1
Y1 - 2018/1/1
N2 - For timely diagnosis of retinal disease, routine retinal monitoring of people with high risk should be put in place. To assist the ophthalmologists in performing retinal analysis efficiently and accurately, numerous studies have been conducted to propose an automated retinal diagnosis system. One of the crucial steps for such a system is accurate detection of retinal blood vessels from retinal image. In this paper, we investigated the use of automatic binarization methods on pre-processed fundus image to detect retinal blood vessels. Three methods for binarization were investigated in this study, namely Otsu's method, ISODATA and K-means clustering method. The resulting binarized output indicated good detection of large vessels but most of the smaller vessels were left undetected. To address this issue, Gabor wavelet filter was used to enhance the small blood vessel structures before binarization of the filter output. Combining the binary images from both binarization with and without Gabor filter resulted in significant improvement of the overall detection rate of the retinal blood vessels. The proposed method proved to be comparable to other unsupervised techniques in the literature when validated using the publicly available fundus image database, DRIVE.
AB - For timely diagnosis of retinal disease, routine retinal monitoring of people with high risk should be put in place. To assist the ophthalmologists in performing retinal analysis efficiently and accurately, numerous studies have been conducted to propose an automated retinal diagnosis system. One of the crucial steps for such a system is accurate detection of retinal blood vessels from retinal image. In this paper, we investigated the use of automatic binarization methods on pre-processed fundus image to detect retinal blood vessels. Three methods for binarization were investigated in this study, namely Otsu's method, ISODATA and K-means clustering method. The resulting binarized output indicated good detection of large vessels but most of the smaller vessels were left undetected. To address this issue, Gabor wavelet filter was used to enhance the small blood vessel structures before binarization of the filter output. Combining the binary images from both binarization with and without Gabor filter resulted in significant improvement of the overall detection rate of the retinal blood vessels. The proposed method proved to be comparable to other unsupervised techniques in the literature when validated using the publicly available fundus image database, DRIVE.
KW - Binarization
KW - Blood vessel
KW - Gabor wavelet
KW - Retinal image
KW - Segmentation
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U2 - 10.14419/ijet.v7i4.11.20794
DO - 10.14419/ijet.v7i4.11.20794
M3 - Article
AN - SCOPUS:85054374810
VL - 7
SP - 163
EP - 167
JO - International Journal of Engineering and Technology(UAE)
JF - International Journal of Engineering and Technology(UAE)
SN - 2227-524X
IS - 4
ER -