Impact of ICA-Based Image Enhancement Technique on Retinal Blood Vessels Segmentation

Soomro, Toufique Ahmed; Khan, Tariq M.; Khan, Muhammad Mushtaq; Gao, Junbin; Paul, Manoranjan; Zheng, Lihong

doi:10.1109/access.2018.2794463

Cited by 95 publications

(34 citation statements)

References 43 publications

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“…Soomro et al [44] employed Independent Component Analysis (ICA), where images were treated as random variables and their pixels as the traits for contrast enhancement. These traits exhibit a high co-relation probability as two models ICA1 and ICA2 were simultaneously used to enhance finer vessels and suppress noise, respectively.…”

Section: B Unsupervised Learning Methodsmentioning

confidence: 99%

“…The big advantage of this filter is its flexibility towards larger images, as it incorporates a multi-scale instead of a fixed-scale approach. However, the main limitations of detectors such as Frangi filter and line detectors, come in the form of image noise, branching nodes, and vessel crossings which confuse the segmentation algorithm into generating a higher number of false-positives [43], [44], [48]. Another drawback of Frangi filter is the misclassification of pixels near the boundary of a vessel, where non-uniform illumination causes the filter to become weak.…”

Section: B Unsupervised Learning Methodsmentioning

confidence: 99%

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An Improved Retinal Vessel Segmentation Framework Using Frangi Filter Coupled With the Probabilistic Patch Based Denoiser

et al. 2019

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Vessel segmentation has come a long way in terms of matching the experts at detection accuracy, yet there is potential for further improvement. In this regard, the accurate detection of vessels is generally more challenging due to the high variations in vessel contrast, width, and the observed noise level. Most vessel segmentation strategies utilize contrast enhancement as a preprocessing step, which has an inherent tendency to aggravate the noise and therefore, impede accurate vessel detection. To alleviate this problem, we propose to use the state-of-the-art Probabilistic Patch-Based (PPB) denoiser within the framework of an unsupervised retinal vessel segmentation strategy based on the Frangi filter. The PPB denoiser helps preserve vascular structure while effectively dealing with the amplified noise. Also, the modified Frangi filter is evaluated separately for tiny and large vessels, followed by individual segmentation and linear recombination of the binarized outputs. This way, the performance of the modified Frangi filter is significantly enhanced. The performance evaluation of the proposed method is evaluated on two recognized open-access datasets, viz: DRIVE and STARE. The proposed strategy yields competitive results for both preprocessing modalities, i.e., Contrast Limited Adaptive Histogram Equalization (CLAHE) and Generalized Linear Model (GLM). The performance observed for CLAHE over DRIVE and STARE datasets is (Sn = 0.8027, Acc = 0.9561) and (Sn = 0.798, Acc = 0.9561), respectively. For GLM, it is observed to be (Sn = 0.7907, Acc = 0.9603) and (Sn = 0.7860, Acc = 0.9583) over DRIVE and STARE datasets, respectively. Furthermore, based on the conducted comparative study, it is established that the proposed method outperforms various notable vessel segmentation methods available in the existing literature. INDEX TERMS Image denoising, image segmentation, modified Frangi filter, probabilistic patch-based denoiser, retinal vessels.

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Section: B Unsupervised Learning Methodsmentioning

confidence: 99%

Section: B Unsupervised Learning Methodsmentioning

confidence: 99%

An Improved Retinal Vessel Segmentation Framework Using Frangi Filter Coupled With the Probabilistic Patch Based Denoiser

et al. 2019

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“…Later on, with the development of medical imaging technology, intensive and specific efforts have been made to segment various types of medical images. e existent medical image segmentation algorithms can be classified as threshold-based methods [2], region-based methods [9][10][11], edge-based methods [12], active-contour-model-based methods [13][14][15][16][17][18][19][20][21][22], hybrid methods [23][24][25][26], and others [27][28][29][30][31][32].…”

Section: Segmentation Of Femur and Patella Regionsmentioning

confidence: 99%

Automatic Segmentation and Measurement on Knee Computerized Tomography Images for Patellar Dislocation Diagnosis

Sun

Kong

Huang

et al. 2020

Computational and Mathematical Methods in Medicine

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Traditionally, for diagnosing patellar dislocation, clinicians make manual geometric measurements on computerized tomography (CT) images taken in the knee area, which is often complex and error-prone. erefore, we develop a prototype CAD system for automatic measurement and diagnosis. We firstly segment the patella and the femur regions on the CT images and then measure two geometric quantities, patellar tilt angle (PTA), and patellar lateral shift (PLS) automatically on the segmentation results, which are finally used to assist in diagnoses. e proposed quantities are proved valid and the proposed algorithms are proved effective by experiments.

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“…Therefore, the condition of the retinal vessels is an important indicator for the diagnosis of some retinal diseases. For example, the progression of diabetic retinopathy is the most life-threatening because it leads to vision loss due to high sugar levels and hypertension in later stages [1]. Doctors can detect other diseases of the body in advance by examining some eye diseases and make an early diagnosis of these diseases to carry out the corresponding treatment in advance.…”

Section: Introductionmentioning

confidence: 99%

“…Doctors can detect other diseases of the body in advance by examining some eye diseases and make an early diagnosis of these diseases to carry out the corresponding treatment in advance. According to reports, early detection, timely treatment, and appropriate follow-up procedures can prevent about 95% of blindness [1].…”

Section: Introductionmentioning

confidence: 99%

Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks

et al. 2019

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Automated retinal vessel segmentation technology has become an important tool for disease screening and diagnosis in clinical medicine. However, most of the available methods of retinal vessel segmentation still have problems such as poor accuracy and low generalization ability. This is because the symmetrical and asymmetrical patterns between blood vessels are complicated, and the contrast between the vessel and the background is relatively low due to illumination and pathology. Robust vessel segmentation of the retinal image is essential for improving the diagnosis of diseases such as vein occlusions and diabetic retinopathy. Automated retinal vein segmentation remains a challenging task. In this paper, we proposed an automatic retinal vessel segmentation framework using deep fully convolutional neural networks (FCN), which integrate novel methods of data preprocessing, data augmentation, and full convolutional neural networks. It is an end-to-end framework that automatically and efficiently performs retinal vessel segmentation. The framework was evaluated on three publicly available standard datasets, achieving F1 score of 0.8321, 0.8531, and 0.8243, an average accuracy of 0.9706, 0.9777, and 0.9773, and average area under the Receiver Operating Characteristic (ROC) curve of 0.9880, 0.9923 and 0.9917 on the DRIVE, STARE, and CHASE_DB1 datasets, respectively. The experimental results show that our proposed framework achieves state-of-the-art vessel segmentation performance in all three benchmark tests.

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Impact of ICA-Based Image Enhancement Technique on Retinal Blood Vessels Segmentation

Cited by 95 publications

References 43 publications

An Improved Retinal Vessel Segmentation Framework Using Frangi Filter Coupled With the Probabilistic Patch Based Denoiser

An Improved Retinal Vessel Segmentation Framework Using Frangi Filter Coupled With the Probabilistic Patch Based Denoiser

Automatic Segmentation and Measurement on Knee Computerized Tomography Images for Patellar Dislocation Diagnosis

Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks

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