ORCID Profile
0000-0002-7477-1591
Current Organisation
UNSW Sydney
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 05-2023
Publisher: Springer Science and Business Media LLC
Date: 10-06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 08-2023
Publisher: Springer Science and Business Media LLC
Date: 05-10-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Elsevier BV
Date: 12-2016
Publisher: Springer Science and Business Media LLC
Date: 08-05-2017
Publisher: Elsevier BV
Date: 09-2023
Publisher: MDPI AG
Date: 12-01-2021
DOI: 10.3390/DIAGNOSTICS11010114
Abstract: Automated detection of vision threatening eye disease based on high resolution retinal fundus images requires accurate segmentation of the blood vessels. In this regard, detection and segmentation of finer vessels, which are obscured by a considerable degree of noise and poor illumination, is particularly challenging. These noises include (systematic) additive noise and multiplicative (speckle) noise, which arise due to various practical limitations of the fundus imaging systems. To address this inherent issue, we present an efficient unsupervised vessel segmentation strategy as a step towards accurate classification of eye diseases from the noisy fundus images. To that end, an ensemble block matching 3D (BM3D) speckle filter is proposed for removal of unwanted noise leading to improved detection. The BM3D-speckle filter, despite its ability to recover finer details (i.e., vessels in fundus images), yields a pattern of checkerboard artifacts in the aftermath of multiplicative (speckle) noise removal. These artifacts are generally ignored in the case of satellite images however, in the case of fundus images, these artifacts have a degenerating effect on the segmentation or detection of fine vessels. To counter that, an ensemble of BM3D-speckle filter is proposed to suppress these artifacts while further sharpening the recovered vessels. This is subsequently used to devise an improved unsupervised segmentation strategy that can detect fine vessels even in the presence of dominant noise and yields an overall much improved accuracy. Testing was carried out on three publicly available databases namely Structured Analysis of the Retina (STARE), Digital Retinal Images for Vessel Extraction (DRIVE) and CHASE_DB1. We have achieved a sensitivity of 82.88, 81.41 and 82.03 on DRIVE, SATARE, and CHASE_DB1, respectively. The accuracy is also boosted to 95.41, 95.70 and 95.61 on DRIVE, SATARE, and CHASE_DB1, respectively. The performance of the proposed methods on images with pathologies was observed to be more convincing than the performance of similar state-of-the-art methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: MDPI AG
Date: 13-11-2019
DOI: 10.3390/S19224949
Abstract: The assessment of transformations in the retinal vascular structure has a strong potential in indicating a wide range of underlying ocular pathologies. Correctly identifying the retinal vessel map is a crucial step in disease identification, severity progression assessment, and appropriate treatment. Marking the vessels manually by a human expert is a tedious and time-consuming task, thereby reinforcing the need for automated algorithms capable of quick segmentation of retinal features and any possible anomalies. Techniques based on unsupervised learning methods utilize vessel morphology to classify vessel pixels. This study proposes a directional multi-scale line detector technique for the segmentation of retinal vessels with the prime focus on the tiny vessels that are most difficult to segment out. Constructing a directional line-detector, and using it on images having only the features oriented along the detector’s direction, significantly improves the detection accuracy of the algorithm. The finishing step involves a binarization operation, which is again directional in nature, helps in achieving further performance improvements in terms of key performance indicators. The proposed method is observed to obtain a sensitivity of 0.8043, 0.8011, and 0.7974 for the Digital Retinal Images for Vessel Extraction (DRIVE), STructured Analysis of the Retina (STARE), and Child Heart And health Study in England (CHASE_DB1) datasets, respectively. These results, along with other performance enhancements demonstrated by the conducted experimental evaluation, establish the validity and applicability of directional multi-scale line detectors as a competitive framework for retinal image segmentation.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 07-03-2023
Publisher: Public Library of Science (PLoS)
Date: 30-01-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: MDPI AG
Date: 30-11-2022
Abstract: In recent years, convolutional neural network architectures have become increasingly complex to achieve improved performance on well-known benchmark datasets. In this research, we have introduced G-Net light, a lightweight modified GoogleNet with improved filter count per layer to reduce feature overlaps, hence reducing the complexity. Additionally, by limiting the amount of pooling layers in the proposed architecture, we have exploited the skip connections to minimize the spatial information loss. The suggested architecture is analysed using three publicly available datasets for retinal vessel segmentation, namely DRIVE, CHASE and STARE datasets. The proposed G-Net light achieves an average accuracy of 0.9686, 0.9726, 0.9730 and F1-score of 0.8202, 0.8048, 0.8178 on DRIVE, CHASE, and STARE datasets, respectively. The proposed G-Net light achieves state-of-the-art performance and outperforms other lightweight vessel segmentation architectures with fewer trainable number of parameters.
Publisher: The Scientific and Technological Research Council of Turkey (TUBITAK-ULAKBIM) - DIGITAL COMMONS JOURNALS
Date: 25-09-2020
DOI: 10.3906/ELK-1907-113
Publisher: Springer Science and Business Media LLC
Date: 10-10-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Location: Australia
No related grants have been discovered for Tariq Mahmood Khan.