Performance comparison of AUC with existing techniques.

<p>Performance comparison of AUC with existing techniques.</p

Pictorial results of different retinal blood vessel segmentation techniques on pathological image of DRIVE dataset.

<p>(a) RGB input image. (b) Manual segmented image. (c) Proposed method. (d) Azzopardi et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158996#pone.0158996.ref035" target="_blank">35</a>]. (e) Dai et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158996#pone.0158996.ref040" target="_blank">40</a>]. (f) Bankhead et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158996#pone.0158996.ref030" target="_blank">30</a>].</p

A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding

<div><p>Diabetic Retinopathy (DR) harm retinal blood vessels in the eye causing visual deficiency. The appearance and structure of blood vessels in retinal images play an essential part in the diagnoses of an eye sicknesses. We proposed a less computational unsupervised automated technique with promising results for detection of retinal vasculature by using morphological hessian based approach and region based Otsu thresholding. Contrast Limited Adaptive Histogram Equalization (CLAHE) and morphological filters have been used for enhancement and to remove low frequency noise or geometrical objects, respectively. The hessian matrix and eigenvalues approach used has been in a modified form at two different scales to extract wide and thin vessel enhanced images separately. Otsu thresholding has been further applied in a novel way to classify vessel and non-vessel pixels from both enhanced images. Finally, postprocessing steps has been used to eliminate the unwanted region/segment, non-vessel pixels, disease abnormalities and noise, to obtain a final segmented image. The proposed technique has been analyzed on the openly accessible DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (STructured Analysis of the REtina) databases along with the ground truth data that has been precisely marked by the experts.</p></div

Comparison of the setting of parameter <i>σ</i> on different scales.

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

Visual inspection of different vessel segmentation methods using DRIVE database.

<p>(a) RGB input image. (b) Manual segmented image. (c) Proposed method final image. (d) Dai et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158996#pone.0158996.ref040" target="_blank">40</a>]. (e) Azzopardi et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158996#pone.0158996.ref035" target="_blank">35</a>]. (f) Bankhead et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158996#pone.0158996.ref030" target="_blank">30</a>]. (g)Vlachos and Dermatas [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158996#pone.0158996.ref041" target="_blank">41</a>]. (h) Martinez-Perez et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158996#pone.0158996.ref017" target="_blank">17</a>].</p

Segmentation results comparison for normal versus abnormal cases of our proposed technique with different segmentation techniques.

<p>Segmentation results comparison for normal versus abnormal cases of our proposed technique with different segmentation techniques.</p

Flow chart of the proposed segmentation framework.

<p>Flow chart of the proposed segmentation framework.</p

Proposed method main processing steps for retinal blood vessel segmentation.

<p>(a) RGB image from <b>STARE</b> database. (b) Green Channel. (c) CLAHE. (d) Morphological filters. (e) Thin vessel enhanced image. (f) Wide vessel enhanced image. (g) Otsu global thresholding output image. (h) Fused image of thin enhanced image and Otsu global thresholding output image. (i) Otsu local thresholding to enhance thin vessels (j) Postprocessed final binary image.</p

Visual results of different thresholding techniques.

<p><b>(a) Proposed Otsu method.</b> (b) TILT. (c) K-means. (d) Moment-preserving thresholding. (e) Niblack local thresholding. (f) Fuzzy ISODATA algorithms.</p

Accuracy (Acc), Sensitivity (Sn) and Specificity (Sp) results of proposed method for 20 retinal images of the DRIVE and the STARE datasets.

<p>Accuracy (Acc), Sensitivity (Sn) and Specificity (Sp) results of proposed method for 20 retinal images of the DRIVE and the STARE datasets.</p