Comparison of active contour models for image segmentation in X-ray coronary angiogram images

Devi, S. Nirmala; Kumaravel, N.

doi:10.1080/09687630801889440

Cited by 21 publications

(5 citation statements)

References 10 publications

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“…A[44] new external force for active contours is presented, which solves both the problems. An extension of the gradient vector flow snake (GVF snake) method is presented.…”

Section: Vessel Segmentationmentioning

confidence: 99%

A review of coronary vessel segmentation algorithms

2011

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Coronary heart disease has been one of the main threats to human health. Coronary angiography is taken as the gold standard; for the assessment of coronary artery disease. However, sometimes, the images are difficult to visually interpret because of the crossing and overlapping of vessels in the angiogram. Vessel extraction from X-ray angiograms has been a challenging problem for several years. There are several problems in the extraction of vessels, including: weak contrast between the coronary arteries and the background, unknown and easily deformable shape of the vessel tree, and strong overlapping shadows of the bones. In this article we investigate the coronary vessel extraction and enhancement techniques, and present capabilities of the most important algorithms concerning coronary vessel segmentation.

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“…A[44] new external force for active contours is presented, which solves both the problems. An extension of the gradient vector flow snake (GVF snake) method is presented.…”

Section: Vessel Segmentationmentioning

confidence: 99%

A review of coronary vessel segmentation algorithms

2011

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“…Several segmentation frameworks have been proposed that incorporate vessel features for vessel segmentation [17][18][19][20]. Unsupervised methods for automatic coronary vessel segmentation can be broadly categorized into tracking-based, model-based, and filter-based approaches [21].…”

Section: Introductionmentioning

confidence: 99%

Coronary Vessel Segmentation in X-ray Angiography Images using Edge-based Tracking Method

Lalinia,

Sahafi

2023

Preprint

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Automated extraction of coronary arteries is an essential process in the diagnosis of treatment for coronary artery disease (CAD) with computer assistance. Accurately outlining the coronary artery is difficult when using X-ray coronary angiography (XCA) because of the low signal-to-noise ratio and the presence of interfering background structures. In this paper, a new approach for segmenting vessels in angiograms is presented, specifically designed to tackle the difficulties arising from non-uniform illumination, artifacts, and noise present in angiographic images. The proposed method employs an edge-based tracking tool to generate an initial probability map for segmentation. A segmentation method based on coronary vessel tracking is presented for finding the border and centerline of the vessel. The proposed method is designed based on two main components: preprocessing and tracking. In the preprocessing stage, a guided filter and edge-sharpening algorithms are used to enhance the features of the original image. In the tracking stage, an initial point is selected, and using the Gaussian property, a semi-circle operator is applied to track the line perpendicular to the vessel. The proposed method demonstrated remarkable performance in terms of sensitivity and specificity, achieving values of 86.93 and 99.61, respectively. Additionally, the method achieved an accuracy rate of 97.81. Notably, the proposed method outperformed existing state-of-the-art segmentation methods, as indicated by its higher dice score. These impressive results signify a significant advancement in the field of vessel segmentation, highlighting the effectiveness and superiority of the proposed approach.

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“…Typically, the curves are affected by both an internal force and external force. An active contour can locate object contours well, once an appropriate initialization is done (Nirmala Devi & Kumaravel, 2008). However, because the energy minimization is done locally, the contours that are found can be trapped by a local minimum.…”

Section: Introductionmentioning

confidence: 99%

Lung Chest X-Ray Image Segmentation for Detection of Pneumonia using Convolutional Neural Network

et al. 2022

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Pneumonia has been identified as the top cause of mortality in children under the age of five, as well as in elderly with comorbidities. According to the World Health Organization, pneumonia reported 14% fatalities in children under the age of five nationwide in 2019. Chest x-ray (CXR) has been commonly used for detection of pneumonia. However, factor such as noise with low levels of intensity and low contrast between the images and the boundary representation can modify CXR images and it also requires highly skilled medical practitioners to accurately interpret the CXR images. Therefore, the goal of this study is to develop an automatic segmentation model to segment the region of interest (ROI) of pneumonia lung CXR images using U-Net architecture. Image enhancement using Contrast Limited Adaptive Histogram Equalisation (CLAHE) and gamma-correction based enhancement technique were applied to increase the quality of CXR images. Statistical analysis on features extracted from the segmented lung CXR images was performed to analyze the performance of the model was developed. The U-Net segmentation model achieves 95.58%, 95.82% and 95.48% accuracy for normal CXR while the model achieves 86.76%, 87.98% and 86.21% accuracy for pneumonia CXR which indicate that the U-Net segmentation for CLAHE x-ray images has better performance in segmenting the ROI of the lungs. As a conclusion, the segmentation model proposed shown to be able to overcome the disadvantages of manual segmentation where the model can be used to perform segmentation automatically on many CXRs at a time.

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Comparison of active contour models for image segmentation in X-ray coronary angiogram images

Cited by 21 publications

References 10 publications

A review of coronary vessel segmentation algorithms

A review of coronary vessel segmentation algorithms

Coronary Vessel Segmentation in X-ray Angiography Images using Edge-based Tracking Method

Lung Chest X-Ray Image Segmentation for Detection of Pneumonia using Convolutional Neural Network

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