Fast and Robust 3D Numerical Method for Coronary Artery Vesselness Diffusion from CTA Images

Cui, Hengfei

doi:10.1007/978-3-030-00764-5_45

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…Medical images can first be pre-processed by utilizing vessel enhancing techniques [24]. The divergence form of the diffusion equation is commonly given by:…”

Section: Methodologies a Vesselness Enhancement Diffusionmentioning

confidence: 99%

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2D and 3D Vascular Structures Enhancement Via Improved Vesselness Filter and Vessel Enhancing Diffusion

2019

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In medical imaging practice, vascular enhancement filtering has been widely performed before vessel segmentation and centerline detection, which provides important pathological information and holds great significance for vessel quantification. In the literature, numerous well known vesselness filtering approaches have been developed. For example, some techniques explore the Hessian matrix of the original images and construct the vessel filter based on the eigenvalues of the Hessian matrix. In this work we develop a hybrid technique for fast and accurate vascular enhancement filter, which contains two main steps: vesselness diffusion and improved vesselness filter based on the eigenvalues ratio. This novel approach is quantitatively and qualitatively tested on the public 2D retinal datasets and 3D synthetic vascular structure models. Experimental results demonstrate that the proposed filter outperforms other existing approaches for curvilinear structure enhancement from noisy images. Moreover, the novel approach is further evaluated on real patient Coronary Computed Tomography Angiography (CCTA) datasets with ground truth regions labelled by professional cardiologist. Our method is proven to produce more accurate coronary artery segmentation results. Given the accuracy and efficiency, the proposed vesselness filter can be further used in medical practice for vascular structures enhancement before vessel segmentation and quantification.INDEX TERMS Vascular structures detection, improved enhancement filter, Hessian matrix, vesselness diffusion, vessel segmentation.

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“…Medical images can first be pre-processed by utilizing vessel enhancing techniques [24]. The divergence form of the diffusion equation is commonly given by:…”

Section: Methodologies a Vesselness Enhancement Diffusionmentioning

confidence: 99%

“…since both λ 2 and λ 3 are negative. Recall that λ 3 is regularized, we have 24) which requires that the vesselness function ( 21) equals to 1 when λ 2 ≤ λ reg /2. Therefore, it is achieved by substituting λ reg with λ reg − λ 2 in ( 21) and fixing the value to 1 for λ 2 ≥ λ reg /2.…”

Section: B Improved Vessel Filtermentioning

confidence: 99%

2D and 3D Vascular Structures Enhancement Via Improved Vesselness Filter and Vessel Enhancing Diffusion

2019

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“…In addition, it can effectively handle a large number of inputs. This paper is the extension of our previous conference work [15]. The scope of this work is in the first place to investigate and analyze the abilities of deep neural networks for medical image filtering.…”

Section: Introductionmentioning

confidence: 94%

Supervised Filter Learning for Coronary Artery Vesselness Enhancement Diffusion in Coronary CT Angiography Images

Cui¹

2020

IJCIS

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In medical imaging, vesselness diffusion is usually performed to enhance the vessel structures of interest and reduce background noises, before vessel segmentation and analysis. Numerous learning-based techniques have recently become very popular for coronary artery filtering due to their impressive results. In this work, a supervised machine learning method for coronary artery vesselness diffusion with high accuracy and minimal user interaction is designed. The fully discriminative filter learning method jointly learning a classifier the weak learners rely on and the features of the classifier is developed. Experimental results demonstrate that this scheme achieves good isotropic filtering performances on both synthetic and real patient Coronary Computed Tomography Angiography (CCTA) datasets. Furthermore, region growing-based segmentation approach is performed over filtered images obtained by using different schemes. The proposed diffusion scheme is able to achieve higher average performance measures (87.8% ± 1.5% for Dice, 86.5% ± 1.3% for Precision and 88.5% ± 2.6% for Sensitivity). In conclusion, the developed diffusion method is capable of filtering coronary artery structures and suppressing nonvessel tissues, and can be further used in clinical practice as a real-time CCTA images preprocessing tool.

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Fast and Robust 3D Numerical Method for Coronary Artery Vesselness Diffusion from CTA Images

Cited by 2 publications

References 8 publications

2D and 3D Vascular Structures Enhancement Via Improved Vesselness Filter and Vessel Enhancing Diffusion

2D and 3D Vascular Structures Enhancement Via Improved Vesselness Filter and Vessel Enhancing Diffusion

Supervised Filter Learning for Coronary Artery Vesselness Enhancement Diffusion in Coronary CT Angiography Images

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