TY - GEN
T1 - A new approach to two-dimensional filter for segmenting retinal vascular network from fundus images of premature born
AU - Intriago-Pazmino, Monserrate
AU - Ibarra-Fiallo, Julio
AU - Alonso-Calvo, Raul
AU - Crespo, Jose
AU - Criollo-Ramos, Antonio
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/6/18
Y1 - 2018/6/18
N2 - There is a wide interest in the segmentation of retinal vessel structure from fundus images of preterm infants. This has lead to focus on the processing of dark or low resolution images acquired with portable medical cameras. The concept of signal matched filter (MF) has been used to recognize the retinal vascular structure and the matched filter and first-order derivative of Gaussian (MFFDOG) method was introduced to get a binarized vascular network. In this paper we present a new variation of MF for segmenting retinal vessels network. We apply the original MF to distinguish the vessels network in a gray scale image. Then we obtain the vascular network as a binary image using an adaptive thresholding based on our denominated hard kernel (MFHK). We have also generalized the application of matched filter with any filter function and any domain. We assessed both methods MFFDOG and MFHK in a set of fifty images of children with retinopathy of prematurity (ROP).
AB - There is a wide interest in the segmentation of retinal vessel structure from fundus images of preterm infants. This has lead to focus on the processing of dark or low resolution images acquired with portable medical cameras. The concept of signal matched filter (MF) has been used to recognize the retinal vascular structure and the matched filter and first-order derivative of Gaussian (MFFDOG) method was introduced to get a binarized vascular network. In this paper we present a new variation of MF for segmenting retinal vessels network. We apply the original MF to distinguish the vessels network in a gray scale image. Then we obtain the vascular network as a binary image using an adaptive thresholding based on our denominated hard kernel (MFHK). We have also generalized the application of matched filter with any filter function and any domain. We assessed both methods MFFDOG and MFHK in a set of fifty images of children with retinopathy of prematurity (ROP).
KW - fundus image analysis
KW - medical image processing
KW - retinal vessels segmentation
KW - retinopathy of prematurity
UR - https://www.scopus.com/pages/publications/85050160081
U2 - 10.1109/ISSPIT.2017.8388677
DO - 10.1109/ISSPIT.2017.8388677
M3 - Contribución a la conferencia
AN - SCOPUS:85050160081
T3 - 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
SP - 405
EP - 409
BT - 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
Y2 - 18 December 2017 through 20 December 2017
ER -