Robust Hidden Markov Model based intelligent blood vessel detection of fundus images. (November 2017)

• Record Type: Journal Article
• Authors: Hassan, Mehdi; Amin, Muhammad; Murtza, Iqbal; Khan, Asifullah; Chaudhry, Asmatullah
• Is Part Of: Computer methods and programs in biomedicine. Volume 151(2017)
• Page Start: 193
• Page End: 201
• Publication Date: 2017-11
• Subjects: HMM -- Retinal Fundus Image -- Retina -- Biometrics -- Vasculature; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.08.023
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A new robust fundus vessel tracking technique has been proposed. The proposed technique tackles the challenging problem of blood vessels network detection in human retinal image. Challenging occlusion problem has been resolved by incorporating of Hidden Markov Mode. The proposed technique has been tested at publically available DRIVE dataset. The proposed approach successfully segregated the retinal blood vessels in fundus images. Abstract: In this paper, we consider the challenging problem of detecting retinal vessel networks. Precise detection of retinal vessel networks is vital for accurate eye disease diagnosis. Most of the blood vessel tracking techniques may not properly track vessels in presence of vessels' occlusion. Owing to problem in sensor resolution or acquisition of fundus images, it is possible that some part of vessel may occlude. In this scenario, it becomes a challenging task to accurately trace these vital vessels. For this purpose, we have proposed a new robust and intelligent retinal vessel detection technique on Hidden Markov Model. The proposed model is able to successfully track vessels in the presence of occlusion. The effectiveness of the proposed technique is evaluated on publically available standard DRIVE dataset of the fundus images. The experiments show that the proposed technique not only outperforms the other state of the art methodologies of retinal blood vessels segmentation, but it is also capable of accurate occlusion handling in retinal vessel networks. The proposed technique offers better average classification accuracy, sensitivity, specificity, and area under the curve (AUC) of 95.7%, 81.0%, 97.0%, and 90.0% respectively, which shows the usefulness of the proposed technique.