B-COSFIRE filter and VLM based retinal blood vessels segmentation and denoising

Khan, Khan Bahadar; Khaliq, Amir A.; Shahid, Muhammad

doi:10.1109/icecube.2016.7495210

Cited by 18 publications

(8 citation statements)

References 29 publications

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“…e Hessian matrix and binary pictures are combined together to make an entropy-maximizing threshold for improved vessels. Guo et al and Khan et al [26,27] used a multiscale line detector to segment retinal arteries. A matching filter with signed integers is used to improve the difference among both vascular and nonvascular pixels.…”

Section: Related Workmentioning

confidence: 99%

Retinal Blood Vessels and Optic Disc Segmentation Using U-Net

David

Mahesh

Kumar

et al. 2022

Mathematical Problems in Engineering

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A color fundus image is a photograph obtained using a fundus camera of the inner wall of the eyeball. In the image, doctors may see changes in the retinal vessels, which can be used to diagnose various dangerous disorders such as arteriosclerosis, some macular degeneration related to age, and glaucoma. To diagnose certain disorders as early as possible, automatic segmentation of retinal arteries is used to help the doctors. Also, it is a challenge for the medical community to analyze the image with the right procedure to diagnose the disorders with high accuracy. Furthermore, this will help the doctor to make the right decision on effective treatment. Hence, the authors have implemented an enhanced architecture called U-Net to segment retinal vessels in this paper. The proposed conventional U-Net permits using all the accessible spatial setting information by adding the multiscale input layer and a thick square to the conventional U-Net in terms of improving the accuracy level of image segmentation. It achieved 95.6% accuracy with a comparatively traditional U-Net model. Moreover, the segmentation results have proved that the proposed approach outperformed in detecting most complex low-contrast blood vessels even when they are very thin. The task of segmenting vessels in retinal images is known as retinal vessel segmentation. Blood vessel density can be assessed using dense pixel values. Data augmentation and analytics play a major role in building the true value of eye blood vessels for medical diagnosis. The proposed method is very promising in the automatic segmentation of retinal arteries.

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Section: Related Workmentioning

confidence: 99%

Retinal Blood Vessels and Optic Disc Segmentation Using U-Net

David

Mahesh

Kumar

et al. 2022

Mathematical Problems in Engineering

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“…Azzopardi et al (3) proposed a method of retinal vessel segmentation based on the COSFIRE approach, called B-COSFIRE filter, by constructing two kinds of the B-COSFIRE filter which are selective for vessel and vessel-ending, respectively. Khan et al (18) presented an unsupervised method of vasculature segmentation, by applying pixel AND operation between the vessel location map and B-COSFIRE segmentation image. Bahadarkhan et al (19) proposed a less computational unsupervised automated technique with promising results for the detection of retinal vasculature by using morphological Hessian-based approach and region-based Otsu thresholding.…”

Section: Related Workmentioning

confidence: 99%

“…As shown in Tables 6, 7, the overhead of the relevant supervised methods in the segmentation time is about 1 min. 2) When considering the combined effects of performance and time overhead, some unsupervised (16)(17)(18)(19)(20)(21)30) methods lack efficient application value. Some unsupervised methods have higher time overhead when obtaining higher segmentation performance.…”

Section: Related Workmentioning

confidence: 99%

Retinal Vessel Segmentation Based on B-COSFIRE Filters in Fundus Images

Xiao

et al. 2022

Front. Public Health

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Retinal vessel extraction plays an important role in the diagnosis of several medical pathologies, such as diabetic retinopathy and glaucoma. In this article, we propose an efficient method based on a B-COSFIRE filter to tackle two challenging problems in fundus vessel segmentation: (i) difficulties in improving segmentation performance and time efficiency together and (ii) difficulties in distinguishing the thin vessel from the vessel-like noise. In the proposed method, first, we used contrast limited adaptive histogram equalization (CLAHE) for contrast enhancement, then excerpted region of interest (ROI) by thresholding the luminosity plane of the CIELab version of the original RGB image. We employed a set of B-COSFIRE filters to detect vessels and morphological filters to remove noise. Binary thresholding was used for vessel segmentation. Finally, a post-processing method based on connected domains was used to eliminate unconnected non-vessel pixels and to obtain the final vessel image. Based on the binary vessel map obtained, we attempt to evaluate the performance of the proposed algorithm on three publicly available databases (DRIVE, STARE, and CHASEDB1) of manually labeled images. The proposed method requires little processing time (around 12 s for each image) and results in the average accuracy, sensitivity, and specificity of 0.9604, 0.7339, and 0.9847 for the DRIVE database, and 0.9558, 0.8003, and 0.9705 for the STARE database, respectively. The results demonstrate that the proposed method has potential for use in computer-aided diagnosis.

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“…4. Instead of a normal top-hat transform which induces noise, modified tophat transform [36] is adopted. Modified top-hat ensures better noise removal and suitable feature extraction.…”

Section: A Pre-processingmentioning

confidence: 99%

Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks

Aleem

Sheng

et al. 2019

IEEE Trans. Ind. Inf.

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The expansion of automation techniques and increased risk of identity theft have led emphasis on the tremendous need of automated identification system. Due to the high recognition accuracy and robustness to changes in human physiology, retinal biometric identification system has drawn much attention in this research field. In this paper, we aim to propose an automatic fast and accurate retinal identification system for the multi-sample data set. The proposed approach uses a hybrid segmentation technique to segment out both thick/thin vessels for effectively balancing the difference of wavelet response between thick/thin blood vessels. As a result, recognition accuracy is improved. A PCA (Principle Component Analysis) based feature processing approach is proposed for efficiently reducing the dimensionality of a large number of vessels features. It significantly reduces computation time and accelerates the matching process in the retinal identification system. The proposed technique is validated on DRIVE, STARE, VARIA, RIDB, HRF, Messidor, DIARETDB0, and a large multi-sample per subject database created by authors using the images provided by Dr. Chen (Shanghai Jiao Tong University Affiliated Sixth People Hospital). Experimental results demonstrated that the proposed approach outperforms other existing techniques. Segmentation achieves an overall accuracy of 99.65% with the recognition rate of 99.40% on all these databases.

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B-COSFIRE filter and VLM based retinal blood vessels segmentation and denoising

Cited by 18 publications

References 29 publications

Retinal Blood Vessels and Optic Disc Segmentation Using U-Net

Retinal Blood Vessels and Optic Disc Segmentation Using U-Net

Retinal Vessel Segmentation Based on B-COSFIRE Filters in Fundus Images

Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks

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