# Fundus Image Registration Technique Based on Local Feature of Retinal Vessels
## 4 Experimental Setup
### Datasets  
A total of five public datasets at the original image size were employed in the evaluation. The original image size was used in the experiment as decreasing the spatial resolution of the fundus image can degrade its quality and led to an inaccurate diagnosis and treatment of retinal diseases [49]. Four of the datasets evaluated the feature extraction performance, namely, CHASE_DB1 [33; 34], DRIVE [35; 36], HRF [37; 38] and STARE [39; 40]. These datasets contain fundus images affected by pathological cases. The provided ground truth images are in the form of the segmented vessels performed by experts. This enables the evaluation of the extracted feature points on the retinal vessels. The details of CHASE_DB1, DRIVE, HRF and STARE datasets are described in Table 2.  
The registration performance of CURVE-SIFT is evaluated in the Fundus Image Registration dataset (FIRE) [41]. This dataset is the only public fundus image registration dataset with ground truth annotation. The FIRE dataset consists of 134 image pairs divided into super-resolution, image mosaicking and longitudinal study applications, as described in Table 3. All image pairs are affected by diabetic retinopathy where vessel tortuosity, microaneuryss and cotton-wood are visible on the images. Each image pair includes 10 corresponding ground truth annotations identified by experts. Registering image pairs from super-resolution, image mosaicking and longitudinal study applications involve a combination of several challenges, namely, overlapping area and rotation. The overlapping area is an intersection region between fixed and moving images. A small overlapping area limits the amount of similar information between images, which can be insufficient to estimate an accurate geometrical transformation. The rotation in the fundus image is introduced to access part of the retina or due to involuntary movement by the patient. The rotation alters the orientation of similar information between images. This alteration can be challenging for the feature-based RIR technique to establish correspondences. The super-resolution application combines multiple fundus images with a large overlapping area and small rotation. The super-resolution application is performed to increase \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Descriptions**} & \multicolumn{4}{c}{**Datasets**} \\ \cline{2-5}  & **CHASE\_DB1** & **DRIVE** & **HRF** & **STARE** \\ \hline Total images & 28 & 40 & 45 & 20 \\ Image size (pixels) & 999 \(\times\) 960 & 564 \(\times\) 584 & 3504 \(\times\) 2336 & 605 \(\times\) 700 \\ Total patients & 14 & 40 & 45 & 20 \\ Age (Years) & 9–10 & 25–90 & N/A & N/A \\ \cline{4-5}  & & & 15 images of healthy & \\ \cline{4-5}  & & 33 images without sign of & patients & Abnormalities that \\ Pathological cases & Vessel & diabetic retinopathy & 15 images of diabetic & obscure the blood \\  & tortuosity & 7 images with mild early & retinopathy & vessel appearance, such \\  & & diabetic retinopathy & 15 images of & as hemorrhaging, etc. \\  & & & glaucomatous & \\ Field of view & 30\({}^{\circ}\) & 45\({}^{\circ}\) & 45\({}^{\circ}\) & 35\({}^{\circ}\) \\ Year & 2012 & 2004 & 2009 & 2000 \\ Ground truth images & 56 & 60 & 45 & 40 \\ Intensity distribution 1  & 22.6136 & 49.3307 & 34.9433 & 49.5126 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptions of CHASE_DB1, DRIVE, HRF and STARE datasets for evaluating feature extraction performance.