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

<div><p>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.</p></div

Performance metrics for evaluation of the proposed method.

<p>Performance metrics for evaluation of the proposed method.</p

Accuracy (Acc), Sensitivity (Sn) and Specificity (Sp) statistics of the proposed system on the DRIVE, STARE and HRF databases.

<p>Accuracy (Acc), Sensitivity (Sn) and Specificity (Sp) statistics of the proposed system on the DRIVE, STARE and HRF databases.</p

Analysis of Frangi filtering enhancement using DRIVE dataset.

<p>(a) Thin vessel enhanced image (b) Thin binary image (c) Thick vessel enhanced image (d) Thick binary image.</p

Analysis of Frangi filtering enhancement using STARE dataset.

<p><b>A</b> (a) Thin vessel enhanced image (b) Thin binary image (c) Thick vessel enhanced image (d) Thick binary image.</p

Visual presentation of the Proposed system major processing stages.

<p>(a) Input RGB photograph from <b>HRF</b> database (b) Green channel (c) CLAHE applied result (d) Difference image (e) Otsu threshold resultant image (f) Postprocessed dilated image (g) Frangi filter enhanced image (h) Final image using AND Operation.</p

Performance evaluations of various retinal vascular extraction algorithms.

<p>Performance evaluations of various retinal vascular extraction algorithms.</p

Visual appearance of the proposed technique utilizing STARE dataset.

<p>(a) RGB photograph (b) Manual segmentation (c) Proposed technique segmented image.</p

Pictorial representation for unhealthy retinal image from the STARE dataset.

<p>(a) RGB image (b) Manual segmentation (c) Proposed scheme final result.</p