TY - JOUR
T1 - Local descriptor for retinal fundus image registration
AU - Ramli, Roziana
AU - Idris, Mohd Yamani Idna
AU - Hasikin, Khairunnisa
AU - Karim, Noor Khairiah A.
AU - Wahab, Ainuddin Wahid Abdul
AU - Ahmedy, Ismail
AU - Ahmedy, Fatimah
AU - Arof, Hamzah
N1 - Funding information: This work was supported by the Postgraduate Research Grant under grant PG039-2015B, MyBrain15 scheme of the Ministry of Higher Education, Malaysia and RU Geran RF010A-2018.
PY - 2020/6/1
Y1 - 2020/6/1
N2 - A feature-based retinal image registration (RIR) technique aligns multiple fundus images and composed of pre-processing, feature point extraction, feature descriptor, matching and geometrical transformation. Challenges in RIR include difference in scaling, intensity and rotation between images. The scale and intensity differences can be minimised with consistent imaging setup and image enhancement during the pre-processing, respectively. The rotation can be addressed with feature descriptor method that robust to varying rotation. Therefore, a feature descriptor method is proposed based on statistical properties (FiSP) to describe the circular region surrounding the feature point. From the experiments on public Fundus Image Registration dataset, FiSP established 99.227% average correct matches for rotations between 0° and 180°. Then, FiSP is paired with Harris corner, scale-invariant feature transform (SIFT), speeded-up robust feature (SURF), Ghassabi's and D-Saddle feature point extraction methods to assess its registration performance and compare with the existing feature-based RIR techniques, namely generalised dual-bootstrap iterative closet point (GDB-ICP), Harris-partial intensity invariant feature descriptor (PIIFD), Ghassabi's-SIFT, H-M 16, H-M 17 and D-Saddle-histogram of oriented gradients (HOG). The combination of SIFT-FiSP registered 64.179% of the image pairs and significantly outperformed other techniques with mean difference between 25.373 and 60.448% (p = <0.001*).
AB - A feature-based retinal image registration (RIR) technique aligns multiple fundus images and composed of pre-processing, feature point extraction, feature descriptor, matching and geometrical transformation. Challenges in RIR include difference in scaling, intensity and rotation between images. The scale and intensity differences can be minimised with consistent imaging setup and image enhancement during the pre-processing, respectively. The rotation can be addressed with feature descriptor method that robust to varying rotation. Therefore, a feature descriptor method is proposed based on statistical properties (FiSP) to describe the circular region surrounding the feature point. From the experiments on public Fundus Image Registration dataset, FiSP established 99.227% average correct matches for rotations between 0° and 180°. Then, FiSP is paired with Harris corner, scale-invariant feature transform (SIFT), speeded-up robust feature (SURF), Ghassabi's and D-Saddle feature point extraction methods to assess its registration performance and compare with the existing feature-based RIR techniques, namely generalised dual-bootstrap iterative closet point (GDB-ICP), Harris-partial intensity invariant feature descriptor (PIIFD), Ghassabi's-SIFT, H-M 16, H-M 17 and D-Saddle-histogram of oriented gradients (HOG). The combination of SIFT-FiSP registered 64.179% of the image pairs and significantly outperformed other techniques with mean difference between 25.373 and 60.448% (p = <0.001*).
KW - Feature descriptor
KW - Image registration
KW - Fundus image
UR - http://www.scopus.com/inward/record.url?scp=85084927299&partnerID=8YFLogxK
U2 - 10.1049/iet-cvi.2019.0623
DO - 10.1049/iet-cvi.2019.0623
M3 - Article
AN - SCOPUS:85084927299
SN - 1751-9632
VL - 14
SP - 144
EP - 153
JO - IET Computer Vision
JF - IET Computer Vision
IS - 4
ER -