Multifilters-Based Unsupervised Method for Retinal Blood Vessel Segmentation

Muzammil, Nayab; Shah, Syed Ayaz Ali; Shahzad, Aamir; Khan, Muhammad Amir; Ghoniem, Rania M.

doi:10.3390/app12136393

Cited by 18 publications

(3 citation statements)

References 42 publications

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“…It analyzes content of a specified frequency with a specific direction only in an image. Specifications of orientation and frequency of the Gabor filter are similar to those of the visual system of humans [33], [34]. The Gabor filter creates two important features (magnitude and phase).…”

Section: Design and Implementationmentioning

confidence: 94%

An Efficient System for Diagnosis of Human Blindness Using Image-Processing and Machine-Learning Methods

Alomari

2023

Int. J. Onl. Eng.

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The two main causes of blindness are diabetes and glaucoma. Routine diagnosis of blindness is based on the conventional robust mass-screening method. However, despite being cost-effective, this method has some problems as a human eye-disease detection method because there are many types of eye disease that are similar or that result in no visual changes in the eye image. These issues make it highly difficult to recognize blindness and control it. Moreover, the color of the macula of the spot can be very close to that of the affected macula in a variety of eye diseases, which suggests that the color of the macula spot can indicate various possibilities, rather than one. This paper discusses the shortcomings of current blindness-screening and monitoring systems and presents a feature-based blindness diagnosis approach using digital eye fundus images for the purpose of automated diagnosis of eye disorders, considering three conditions: healthy eye, diabetic retinopathy (DR), and glaucoma. As such, this paper develops a computer-aided diagnosis (CAD) method for automated detection of human blindness. The proposed approach integrates Gabor filter features, statistical features, colored features, morphological features, and local binary pattern features, then compares them with features drawn from a standard dataset of 1580 fundus images. Several classification techniques were applied to the extracted-features neural network (NN), support vector machine (SVM), naïve bias (NB). SVM classifiers show the most promising accuracy. They achieved 93.3% over the other classifiers.

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Section: Design and Implementationmentioning

confidence: 94%

An Efficient System for Diagnosis of Human Blindness Using Image-Processing and Machine-Learning Methods

Alomari

2023

Int. J. Onl. Eng.

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“…Muzammil et al's 2022 framework [19] discusses a method for pre-processing and segmenting blood vessels in color retinal images. The pre-processing stage involves applying two techniques in parallel to improve image contrast: FBHE and CLAHE.…”

Section: Aicecs-2023mentioning

confidence: 99%

A Systematic Review of Deep Learning Approaches for Vessel Segmentation in Retinal Fundus Images

Hegde,

Prabhu,

Gupta

et al. 2023

J. Phys.: Conf. Ser.

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Retinal vessel segmentation (RVS) is important to accurately differentiate retinal vasculature for diagnosing and monitoring various ocular and systemic diseases. The traditional methods for RVS have mostly involved supervised learning, although semi-supervised and unsupervised techniques are on the rise. This paper reviews the increase in complexity of developments in RVS primarily after 2020. The methods were chosen to cover both the gradual transition over time and a variety of unorthodox or combinatorial approaches. This includes convolutional neural networks, encoder-decoder models, generative models, and other multi-modal or hybrid techniques. CNN approaches discussed employ Zero Phase Component Analysis, Global Contrast Normalization, and reinforcement learning. Encoder-decoder models include approaches such as the use of skip and residual connections, spatial attention, and atrous enhancement U-Net. Generative models propose short link connections, recurrent residual blocks, and multi-scale features to refine convolutional blocks. Hybrid methods involve the use of connectivity features, the MISODATA Algorithm, cross-domain adaptation, and multiple filters (such as morphological, match, and Gabor). All the frameworks are compared based on their performance on the benchmark dataset DRIVE to provide a comprehensive understanding of the current state of RVS.

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“…It used a deep convolutional neural network (VGG16) and the CLAHE method in pre-processing. Other medical applications of the CLAHE algorithm include image quality improvement systems for: fundus images [13,14], ultrasound images [15], and mammographic images [16].…”

Section: Applications Of Clahementioning

confidence: 99%

Real-Time CLAHE Algorithm Implementation in SoC FPGA Device for 4K UHD Video Stream

et al. 2022

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One of the problems encountered in the field of computer vision and video data analysis is the extraction of information from low-contrast images. This problem can be addressed in several ways, including the use of histogram equalisation algorithms. In this work, a method designed for this purpose—the Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm—is implemented in hardware. An FPGA platform is used for this purpose due to the ability to run parallel computations and very low power consumption. To enable the processing of a 4K resolution (UHD, 3840 × 2160 pixels) video stream at 60 fps (frames per second) by using the CLAHE method, it is necessary to use a vector data format and process multiple pixels simultaneously. The algorithm realised in this work can be a component of a larger vision system, such as in autonomous vehicles or drones, but it can also support the analysis of underwater, thermal, or medical images both by humans and in an automated system.

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Multifilters-Based Unsupervised Method for Retinal Blood Vessel Segmentation

Cited by 18 publications

References 42 publications

An Efficient System for Diagnosis of Human Blindness Using Image-Processing and Machine-Learning Methods

An Efficient System for Diagnosis of Human Blindness Using Image-Processing and Machine-Learning Methods

A Systematic Review of Deep Learning Approaches for Vessel Segmentation in Retinal Fundus Images

Real-Time CLAHE Algorithm Implementation in SoC FPGA Device for 4K UHD Video Stream

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