Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector

Ooi, Alexander Ze Hwan; Zunaina, Embong; Hamid, Aini Ismafairus Abd; Zainon, Rafidah; Wang, Shir Li; Ng, Theam Foo; Hamzah, Rostam Affendi; Teoh, Soo Siang; Ibrahim, Haidi

doi:10.3390/s21196380

Cited by 30 publications

(12 citation statements)

References 42 publications

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“…It requires fewer coefficients than ordinary wavelets to fully detect the curvature information in the image. As shown in Figure 5 , To capture the curvature information, wavelet needs about 13 coefficient values, Shearlet needs 4 coefficients, and Bendlet needs only 2 coefficients to fully detect curved regions [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

Brain Tumor Segmentation Based on Bendlet Transform and Improved Chan-Vese Model

Meng

Cattani

Villecco

2022

Entropy

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Automated segmentation of brain tumors is a difficult procedure due to the variability and blurred boundary of the lesions. In this study, we propose an automated model based on Bendlet transform and improved Chan-Vese (CV) model for brain tumor segmentation. Since the Bendlet system is based on the principle of sparse approximation, Bendlet transform is applied to describe the images and map images to the feature space and, thereby, first obtain the feature set. This can help in effectively exploring the mapping relationship between brain lesions and normal tissues, and achieving multi-scale and multi-directional registration. Secondly, the SSIM region detection method is proposed to preliminarily locate the tumor region from three aspects of brightness, structure, and contrast. Finally, the CV model is solved by the Hermite-Shannon-Cosine wavelet homotopy method, and the boundary of the tumor region is more accurately delineated by the wavelet transform coefficient. We randomly selected some cross-sectional images to verify the effectiveness of the proposed algorithm and compared with CV, Ostu, K-FCM, and region growing segmentation methods. The experimental results showed that the proposed algorithm had higher segmentation accuracy and better stability.

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Section: Methodsmentioning

confidence: 99%

Brain Tumor Segmentation Based on Bendlet Transform and Improved Chan-Vese Model

Meng

Cattani

Villecco

2022

Entropy

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“…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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“…Ooi et al [14] developed the operation of semi-automatic image segmentation in retinal images through a user interface based operation that enables distinct edge recognition variables on distinct regions of similar images. Tchinda et al [15] introduces a novel methodology for segmenting blood vessels.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Machine Learning Enabled Retinal Blood Vessel Segmentation and Classification

Alkhaldi¹,

Halawani²

2023

Computers, Materials &Amp; Continua

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Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis. The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal disease. This article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification (GOFED-RBVSC) model. The proposed GOFED-RBVSC model initially employs contrast enhancement process. Besides, GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership functions. The ORB (Oriented FAST and Rotated BRIEF) feature extractor is exploited to generate feature vectors. Finally, Improved Conditional Variational Auto Encoder (ICAVE) is utilized for retinal image classification, shows the novelty of the work. The performance validation of the GOFED-RBVSC model is tested using benchmark dataset, and the comparative study highlighted the betterment of the GOFED-RBVSC model over the recent approaches.

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Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector

Cited by 30 publications

References 42 publications

Brain Tumor Segmentation Based on Bendlet Transform and Improved Chan-Vese Model

Brain Tumor Segmentation Based on Bendlet Transform and Improved Chan-Vese Model

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

Intelligent Machine Learning Enabled Retinal Blood Vessel Segmentation and Classification

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