Md. Abu Sayed

Md. Abu Sayed

🎓 Ph.D. Student at UNR ‌|| Robotic Vision, Computer Vision

Publications

An Innovate Approach for Retinal Blood Vessel Segmentation using Mixture of Supervised and Unsupervised Methods.

Published in IET Image Processing, 2020

Segmentation of retinal blood vessels is a very important diagnostic procedure in ophthalmology. Segmenting blood vessels in the presence of pathological lesions is a major challenge. In this paper, we propose an innovative approach to segment the retinal blood vessel in the presence of pathology. The method combines both supervised and unsupervised approaches in the retinal imaging context. Two innovative descriptors named Local Haar Pattern and modified Speeded Up Robust Features are also proposed. Experiments are conducted on three publicly available datasets named: DRIVE, STARE, and CHASE DB1, and the proposed method has been compared against the state-of-the-art methods. The proposed method is found about 1% more accurate than the best performing supervised method and 2% more accurate than the state-of-the-art Nguyen et al.’s method.

Recommended citation: Sayed M.A., Saha S., Rahaman G.M.A., Ghosh T.K., Kanagasingam Y. (2020). "An Innovate Approach for Retinal Blood Vessel Segmentation using Mixture of Supervised and Unsupervised Methods." IET Image Processing. https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/ipr2.12018

Retinal Blood Vessel Segmentation: A Semi-supervised Approach

Published in Iberian Conference on Pattern Recognition and Image Analysis, 2019

Segmentation of retinal blood vessels is an important step in several retinal image analysis tasks. State-of-the-art papers are still incapable to segment retinal vessels correctly, especially, in presence of pathology. In this paper an innovative descriptor named Robust Feature Descriptor (RFD) is proposed to describe vessel pixels more uniquely in the presence of pathology. For accurate segmentation of blood vessels, the method combines both supervised and unsupervised approaches. Extensive experiments have been conducted on three publicly available datasets namely DRIVE, STARE and CHASE_DB1; and the method has been compared with other state-of-the-art methods. The proposed method achieves an overall segmentation accuracy of 0.961, 0.960 and 0.955 respectively on DRIVE, STARE and CHASE_DB1 datasets, which are better than the state-of-the-art methods in comparison. The sensitivity, specificity and area under curve (AUC) of the method are respectively 0.737, 0.981, 0.859 on DRIVE dataset; 0.805, 0.972, 0.889 on STARE dataset; and 0.763, 0.969, 0.866 on CHASE_DB1 dataset.

Recommended citation: Ghosh T.K., Saha S., Rahaman G.M.A., Sayed M.A., Kanagasingam Y. (2019) Retinal Blood Vessel Segmentation: A Semi-supervised Approach. In: Morales A., Fierrez J., Sánchez J., Ribeiro B. (eds) Pattern Recognition and Image Analysis. IbPRIA 2019. Lecture Notes in Computer Science, vol 11868. Springer, Cham https://link.springer.com/chapter/10.1007/978-3-030-31321-0_9

A Semi-supervised Approach to Segment Retinal Blood Vessels in Color Fundus Photographs

Published in Conference on Artificial Intelligence in Medicine in Europe, 2019

Segmentation of retinal blood vessels is an important diagnostic procedure in ophthalmology. In this paper we propose an automated blood vessels segmentation method that combines both supervised and un-supervised approaches. A novel descriptor named Local Haar Pattern (LHP) is proposed to describe retinal pixel of interest. The performance of the proposed method has been evaluated on three publicly available DRIVE, STARE and CHASE_DB1 datasets. The proposed method achieves an overall segmentation accuracy of 96%, 96% and 95% respectively on DRIVE, STARE, and CHASE DB1 datasets, which are better than the state-of-the-art methods.

Recommended citation: Sayed M.A., Saha S., Rahaman G.M.A., Ghosh T.K., Kanagasingam Y. (2019) A Semi-supervised Approach to Segment Retinal Blood Vessels in Color Fundus Photographs. In: Riaño D., Wilk S., ten Teije A. (eds) Artificial Intelligence in Medicine. AIME 2019. Lecture Notes in Computer Science, vol 11526. Springer, Cham https://link.springer.com/chapter/10.1007/978-3-030-21642-9_44