TY - JOUR
T1 - A fast, efficient and automated method to extract vessels from fundus images
AU - Villalobos-Castaldi, Fabiola M.
AU - Felipe-Riverón, Edgardo M.
AU - Sánchez-Fernández, Luis P.
N1 - Funding Information:
Acknowledgments The authors of this paper wish to thank the National Polytechnic Institute, Center for Computing Research and Postgraduate and Research Secretary, Mexico, for their support under the research grant number SIP 20082213.
PY - 2010/8
Y1 - 2010/8
N2 - We present a fast, efficient, and automatic method for extracting vessels from retinal images. The proposed method is based on the second local entropy and on the gray-level co-occurrence matrix (GLCM). The algorithm is designed to have flexibility in the definition of the blood vessel contours. Using information from the GLCM, a statistic feature is calculated to act as a threshold value. The performance of the proposed approach was evaluated in terms of its sensitivity, specificity, and accuracy. The results obtained for these metrics were 0.9648, 0.9480, and 0.9759, respectively. These results show the high performance and accuracy that the proposed method offers. Another aspect evaluated in this method is the elapsed time to carry out the segmentation. The average time required by the proposed method is 3 s for images of size 565×584 pixels. To assess the ability and speed of the proposed method, the experimental results are compared with those obtained using other existing methods.
AB - We present a fast, efficient, and automatic method for extracting vessels from retinal images. The proposed method is based on the second local entropy and on the gray-level co-occurrence matrix (GLCM). The algorithm is designed to have flexibility in the definition of the blood vessel contours. Using information from the GLCM, a statistic feature is calculated to act as a threshold value. The performance of the proposed approach was evaluated in terms of its sensitivity, specificity, and accuracy. The results obtained for these metrics were 0.9648, 0.9480, and 0.9759, respectively. These results show the high performance and accuracy that the proposed method offers. Another aspect evaluated in this method is the elapsed time to carry out the segmentation. The average time required by the proposed method is 3 s for images of size 565×584 pixels. To assess the ability and speed of the proposed method, the experimental results are compared with those obtained using other existing methods.
KW - Blood vessel network
KW - Co-occurrence matrix
KW - Entropy thresholding
KW - Fast automated analysis
KW - Image segmentation
KW - Retinal image analysis
UR - http://www.scopus.com/inward/record.url?scp=84864987495&partnerID=8YFLogxK
U2 - 10.1007/s12650-010-0037-y
DO - 10.1007/s12650-010-0037-y
M3 - Artículo
SN - 1343-8875
VL - 13
SP - 263
EP - 270
JO - Journal of Visualization
JF - Journal of Visualization
IS - 3
ER -