Finding Similar 2D X-Ray Coronary Angiograms

Syeda-Mahmood, Tanveer; Wang, Fei; Kumar, Ritwik; Beymer, David; Lundstrom, Robert J.; McNulty, Edward

doi:10.1007/978-3-642-33454-2_62

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…In this step, the initial vessel layer sequence is further processed to compute the vessel mask regions via vessel segmentation. Part of this vessel segmentation step uses a similar strategy with the works in [26,27]. Since most vessels have similar ridge shapes, we can highlight vessel structures using ridge detection filters.…”

Section: Methodsmentioning

confidence: 99%

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Accurate vessel extraction via tensor completion of background layer in X-ray coronary angiograms

Qin

Jin

Hao

et al. 2019

Pattern Recognition

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This paper proposes an effective method for accurately recovering vessel structures and intensity information from the X-ray coronary angiography (XCA) images of moving organs or tissues. Specifically, a global logarithm transformation of XCA images is implemented to fit the X-ray attenuation sum model of vessel/background layers into a low-rank, sparse decomposition model for vessel/background separation. The contrast-filled vessel structures are extracted by distinguishing the vessels from the low-rank backgrounds by using a robust principal component analysis and by constructing a vessel mask via Radon-like feature filtering plus spatially adaptive thresholding. Subsequently, the low-rankness and inter-frame spatio-temporal connectivity in the complex and noisy backgrounds are used to recover the vessel-masked background regions using tensor completion of all other background regions, while the twist tensor nuclear norm is minimized to complete the background layers. Finally, the method is able to accurately extract vessels’ intensities from the noisy XCA data by subtracting the completed background layers from the overall XCA images. We evaluated the vessel visibility of resulting images on real X-ray angiography data and evaluated the accuracy of vessel intensity recovery on synthetic data. Experiment results show the superiority of the proposed method over the state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

“…edges) [25]. This RLF filter has been applied to the vessel segmentation in coronary angiograms and achieved satisfying performances [26,27]. Though these filter-based methods highlight vascular structures and suppress noises, they can distort the intensities of vessels.…”

Section: Introductionmentioning

confidence: 99%

Accurate vessel extraction via tensor completion of background layer in X-ray coronary angiograms

Qin

Jin

Hao

et al. 2019

Pattern Recognition

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“…Coronary artery tree extraction is done by employing several steps in image processing including adaptive thresholding, and skeletonization to detect blood vessel intersections. Gradient shifting [12]was utilized because of its capacity to feature coronary corridors, counting minor fragments, while suppressing noise. Test pictures for each preparing venture can be extracted.…”

Section: Coronary Tree Extractionmentioning

confidence: 99%

Stenosis Detection Algorithm for Coronary Angiograms

Sambath¹,

Aravindhar²,

Nayar³

2019

IJITEE

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Automatic detection of blocks in the angiographic images is a challenging task. The features such as contrast and gradient of the vessels and the background image are playing a vital role in the detection of the blocks in the X-Ray angiograms. Nowadays, doctors manually identify blocks in the coronary vessels. The automation tool is necessary to identify the blocks in the blood vessels of the heart to help the doctors in the diagnosing process. Spatiotemporal nature of the angiography sequences is used to isolate the coronary artery tree. The coronary artery segment is tracked and in each image frame by frame and the arterial width surface is detected. The stenosis identification is done by using coronary vessel surface’s persistent minima and blob analysis. The proposed method is experimented on 42 patients’ dataset. The performance of the proposed method was evaluated by comparing the blocks identified by the algorithm with the hand-labelled ground truth images given by the experts. The proposed method provides an accuracy of 95.5% on 42 patients with a total of 60 image runs.

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