In the image processing area, aligning two images captured or scanned from different time points is usually an image registration issue. In this paper, two image registration solutions are proposed for facing different image qualities of retinal images (as in , the image in the left panel showing good quality with full blood vessels; the image in the right panel showing good quality in its optic disk region but lack of blood vessel information in its peripheral region) in order to make the registration methods more robust and feasible in our system. Solution 1 is a blood vessel based registration method, solution 2 optic-disk-region intensity based registration method. Both methods use green-channel images extracted from their original colour fundus images (around 615x615 pixels in the visual field region) as candidates in the following registration processes.
Retinal images with and without enough blood vessel information.
Solution 1: Retinal blood vessel based image registration
Retinal blood vessels can cover the majority of a retinal image and generally retain a static retinal location even in a diseased eye. This feature makes retinal blood vessels ideal for retinal image registration. The blood vessel based image registration method requires that the blood vessels can be extracted from the retinal image correctly and used as a feature structure in the registration procedure. Therefore, good image quality is necessary.
Prior to the registration, a novel blood vessel enhancement step is proposed for the image pre-processing. A local entropy thresholding method is applied for extracting blood vessels from the processed image.
Black Top-hat method is firstly used for the initial enhancement of the blood vessels in the green-channel image. The morphological method computes the difference between the closing of the image and itself to enhance the objects (blood vessels) whose diameters are less than that of the structuring element used in the morphological method. A Gaussian matched filtering method is then applied for further enhancing the blood vessels in the output of the Black Top-hat operation.According to Chaudhun et al15
, the intensity profile of the cross-section of blood vessels can be approximated by a Gaussian curve. A 2-Dimensional Gaussian matched filter is applied as:
where L is the common length of the segment of a piece blood vessel. Twelve directional Gaussian kernels are designed (15 degree interval between the neighbouring kernels) to process the image for matching the blood vessel segments with arbitrary orientations. The final enhanced image is constructed by assigning each pixel with the maximum grey value from the 12 filtered images at the same position.
A local entropy thresholding method7
is applied for the blood vessel extraction from the above enhanced retinal image. If the blood vessels are considered as the object to be detected, the local entropy thresholding method is to maximise a second-order local entropy of the object and the background, for computing an optimal threshold, as:
are the second-order object entropy and second-order background entropy and P is normalised co-occurrence probability of pixel intensities. A binary operation can be easily implemented on the enhanced image by the computed threshold.
An image registration method is developed to apply a translation registration firstly for approaching an initial alignment of the two blood vessel images then applying an affine registration further for a more accurate image registration. Considering the candidates are binary images with blood vessels (foreground) and their background, in the translation registration procedure, One-Plus-One Evolutionary strategy is chosen as the optimiser, and the similarity of matched cardinality as the metric. In the affine registration procedure, a step gradient descent method is chosen as its optimiser and a mean squares difference is used as the metric. In the registration process, nearest-neighbour interpolation method is adopted because of the binary images.
Solution 2: Optic-disk-region intensity based registration
Solution 2 is a complementary to solution 1, in the situation that the blood vessels cannot be extracted fully in the retinal visual field for some kind of images with low image quality or illumination in the peripheral region (right image in ).
The solution can be briefly described as two steps: (1) searching the optic-disk region in each image and defining a rectangular region, including the optic disk and its surrounding region, as registration candidate; (2) applying image intensity based translation and affine registration methods on the detected optic-disk-region candidates from two images for their image registration.
The difference of the registration process from that in solution 1 is that the mutual information computed from the two optic-disk-region candidates is used as the metric for both translation and affine registrations. A linear interpolation method is adopted during the registration because of the grey value images.
The obtained transformation matrices from the above registration methods are then applied to warp the original colour image or green-channel image for aligning two images for comparison and diagnostic purpose.