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

• Published in: PloS one Vol. 11; no. 7; p. e0158996
• Main Authors: BahadarKhan, Khan, A Khaliq, Amir, Shahid, Muhammad
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 21.07.2016; Public Library of Science (PLoS)
• Subjects: Abnormalities; Adaptive filters; Algorithms; Analysis; Area Under Curve; Automation; Biology and Life Sciences; Blood; Blood vessels; Care and treatment; Complications and side effects; Computer applications; Databases as Topic; Diabetes mellitus; Diabetic retinopathy; Digital imaging; Eigenvalues; Equalization; Ground truth; Hessian matrices; Humans; Illnesses; Image classification; Image enhancement; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Medical imaging; Medical screening; Medicine and Health Sciences; Methods; Morphological filters; Morphology; Noise; Noise reduction; Pattern recognition; Physical Sciences; Physiological aspects; Pixels; Publishing; Research and Analysis Methods; Retina; Retinal images; Retinal vessels; Retinal Vessels - anatomy & histology; Retinopathy; Risk factors; Segmentation; Segmentation (Morphology); Sicknesses; Time Factors; Vision disorders; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0158996

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.