An Unsupervised Retinal Vessel Segmentation Using Hessian and Intensity Based Approach

Abstract

The structure of blood vessels play a crucial role in diagnoses of the various vision threatening diseases including Glaucoma and Diabetic Retinopathy (DR). The correct segmentation of retinal blood vessels is a crucial step in the study of retinal fundus images. We proposed a simple unsupervised approach by using a combination of Hessian based approach and intensity transformation approach. We have applied CLAHE for enhancing the contrast of the retinal fundus images. An enhanced version of PSO algorithm is applied for contextual region tuning of CLAHE. Morphological filter and Wiener filter are used to denoise the enhanced image. The eigenvalues are obtained from the Hessian matrix at two different scales to extract thick and thin vessel enhanced images separately. The intensity transformation approach is separately applied to the enhanced image to maximize the vessel details. Global Otsu thresholding is applied on intensity transformed image and thick vessel enhanced image whereas ISODATA local thresholding is applied on thin vessel enhanced image. Finally, a simple post-processing step based on the region parameters such as area, eccentricity, and solidity is used. The region parameters are obtained for each connected component in input binary images. The threshold values of region parameters are empirically investigated and applied to each of the three binary images to remove the non-vessel components. The thresholded images are combined by applying logical OR operator, which resulted in the final segmented binary image. We assessed our developed framework on the open-access CHASE_DB1 and DRIVE datasets, achieving a sensitivity of 0.7776 and 0.7851, and an accuracy of 0.9505 and 0.9559 respectively. These results outperform several state-of-the-art unsupervised methods. The reduced computational complexity and significantly improved evaluation metrics advocates for its use in the automated diagnostic systems for retinal image analysis.

Multilevel Augmentation for Identifying Thin Vessels in Diabetic Retinopathy Using UNET Model

“…The threshold levels are fixed over the region parameters by eliminating the irrelevant vessels from the image. The contrast variations and enhancement are balanced by applying hybrid transformation models with thin vessels in a single pixel level [8].…”

An Efficient and Light Weight Deep Learning Model for Accurate Retinal Vessels Segmentation

“…Supervised methods are learned based on the features of the input images and manually marked images. In contrast, unsupervised methods discover the hidden features of blood vessels and do not require manually segmented images [15]. In [13], the authors presented different vessel segmentation techniques for the supervised and unsupervised methods.…”

Deep neural network-based detection and segmentation of intracranial aneurysms on 3D rotational DSA

“…The 3D Hessian filter produces a second-order derivative of the image. 30 This filter is used to make the edge of the vessel more prominent, as the gradient of the pixel intensity at the border of the vessel is much higher than its center. This filtered Hessian volume is stacked together with the original DSA volume and feed as a two-channel image into the model.…”