A robust technique based on VLM and Frangi filter for retinal vessel extraction and denoising

Abstract

The exploration of retinal vessel structure is colossally important on account of numerous diseases including stroke, Diabetic Retinopathy (DR) and coronary heart diseases, which can damage the retinal vessel structure. The retinal vascular network is very hard to be extracted due to its spreading and diminishing geometry and contrast variation in an image. The proposed technique consists of unique parallel processes for denoising and extraction of blood vessels in retinal images. In the preprocessing section, an adaptive histogram equalization enhances dissimilarity between the vessels and the background and morphological top-hat filters are employed to eliminate macula and optic disc, etc. To remove local noise, the difference of images is computed from the top-hat filtered image and the high-boost filtered image. Frangi filter is applied at multi scale for the enhancement of vessels possessing diverse widths. Segmentation is performed by using improved Otsu thresholding on the high-boost filtered image and Frangi’s enhanced image, separately. In the postprocessing steps, a Vessel Location Map (VLM) is extracted by using raster to vector transformation. Postprocessing steps are employed in a novel way to reject misclassified vessel pixels. The final segmented image is obtained by using pixel-by-pixel AND operation between VLM and Frangi output image. The method has been rigorously analyzed on the STARE, DRIVE and HRF datasets.

Blood Vessels Segmentation in Retinal Fundus Image using Hybrid Method of Frangi Filter, Otsu Thresholding and Morphology

“…Performance measurement is done by using two datasets, namely DRIVE and STARE. Performance parameters are measured by reference to equations (15)(16)(17), namely sensitivity, specificity, and AUC. The data used amounted to 20 retinal images, the results of segmentation using the proposed model were compared with the results of segmentation done manually by experts available in both datasets.…”

“…The parameters used are sensitivity, specificity, accuracy, and area under the curve. These parameters can be shown in a formula in equations (15)(16)(17). Testing is done using two datasets, namely DRIVE and STARE.…”

Morphometrical evaluation of the choriocapillaris imaged by swept-source optical coherence tomography angiography

“…AngioTool is open-source software using a fast, multiscale, Hessian-based filter with a fast recursive Gaussian filter for enhancement of the vessel structure. 9 , 10 , 16 , 17 After vessel segmentation, the image was skeletonized and the morphometric parameters were analyzed. The recursive Gaussian filter is applied to choose the optimal size of the vessels to be measured, which is specified as the vessel thickness on the graphical user interface (GUI).…”

Improving Retinal Image Quality Using the Contrast Stretching, Histogram Equalization, and CLAHE Methods with Median Filters

“…The max is the maximum, the min is the minimum value, and the average is the gray level. And y (m, n) is the center of manipulation of the median filter [13]. The median filter will see the pixel value of its neighbor and will match the pixel value that will be represented by the neighbor's pixel value.…”