Extraction of blood vessels in retinal images using resampling high-order background estimation
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Author list: Paripurana S., Chiracharit W., Chamnongthai K., Saito H.
Publisher: Institute of Electronics, Information and Communication Engineers
Publication year: 2015
Journal: IEICE Transactions on Information and Systems (0916-8532)
Volume number: E98D
Issue number: 3
Start page: 692
End page: 703
Number of pages: 12
ISSN: 0916-8532
eISSN: 1745-1361
Languages: English-Great Britain (EN-GB)
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Abstract
In retinal blood vessel extraction through background removal, the vessels in a fundus image which appear in a higher illumination variance area are often missing after the background is removed. This is because the intensity values of the vessel and the background are nearly the same. Thus, the estimated background should be robust to changes of the illumination intensity. This paper proposes retinal blood vessel extraction using background estimation. The estimated background is calculated by using a weight surface fitting method with a high degree polynomial. Bright pixels are defined as unwanted data and are set as zero in a weight matrix. To fit a retinal surface with a higher degree polynomial, fundus images are reduced in size by different scaling parameters in order to reduce the processing time and complexity in calculation. The estimated background is then removed from the original image. The candidate vessel pixels are extracted from the image by using the local threshold values. To identify the true vessel region, the candidate vessel pixels are dilated from the candidate. After that, the active contour without edge method is applied. The experimental results show that the efficiency of the proposed method is higher than the conventional low-pass filter and the conventional surface fitting method. Moreover, rescaling an image down using the scaling parameter at 0.25 before background estimation provides as good a result as a non-rescaled image does. The correlation value between the non-rescaled image and the rescaled image is 0.99. The results of the proposed method in the sensitivity, the specificity, the accuracy, the area under the receiver operating characteristic (ROC) curve (AUC) and the processing time per image are 0.7994, 0.9717, 0.9543, 0.9676 and 1.8320 seconds for the DRIVE database respectively. Copyright ฉ 2015 The Institute of Electronics, Information and Communication Engineers.
Keywords
High order degree polynomial, Rescaling image, Retinal background estimation, Retinal blood vessel extraction
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