A Semi-Automatic Method for Segmentation of the Coronary Artery Tree from Angiography

Lara, Daniel S. D.; Faria, Alexandre Wagner Chagas; Araújo, Arnaldo de Albuquerque; Menotti, David

doi:10.1109/sibgrapi.2009.41

Cited by 18 publications

(12 citation statements)

References 28 publications

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“…They often rely on preprocessing steps like histogram standardization [18], [19] and/or foreground/background estimation [19]- [21]. Deformable models like level sets [19], [21] or techniques like region growing [22], [23] have the capability to represent the surface with voxel-level accuracy. However, further processing steps are necessary for retrieving centerline information.…”

Section: A Related Workmentioning

confidence: 99%

“…Semi-automated tracking is used e.g., in [25], [27] (based on cylindric models), [28] (spherical model), [29] (ray burst), [22], [23] (region growing), [10] (derivatives), or [17] (medialness). Fully automated tracking is more challenging because no hint of vessel location, orientation, or scale is given by a user so that it fully relies on the extraction of morphological properties from images.…”

Section: A Related Workmentioning

confidence: 99%

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Efficient Monte Carlo Image Analysis for the Location of Vascular Entity

Skibbe

Reisert

Maeda

et al. 2015

IEEE Trans. Med. Imaging

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Tubular shaped networks appear not only in medical images like X-ray-, time-of-flight MRI- or CT-angiograms but also in microscopic images of neuronal networks. We present EMILOVE (Efficient Monte-carlo Image-analysis for the Location Of Vascular Entity), a novel modeling algorithm for tubular networks in biomedical images. The model is constructed using tablet shaped particles and edges connecting them. The particles encode the intrinsic information of tubular structure, including position, scale and orientation. The edges connecting the particles determine the topology of the networks. For simulated data, EMILOVE was able to accurately extract the tubular network. EMILOVE showed high performance in real data as well; it successfully modeled vascular networks in real cerebral X-ray and time-of-flight MRI angiograms. We also show some promising, preliminary results on microscopic images of neurons.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Efficient Monte Carlo Image Analysis for the Location of Vascular Entity

Skibbe

Reisert

Maeda

et al. 2015

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

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“…Some of those methods are: hit-ormiss transform, 1 single-scale operators, 2,3 single-scale operators with the watershed transform, 4 operators 5 and region-growing with differential geometry. 6 However, morphology-based methods are susceptible to image noise, which reduce their detection performance. Due to the morphological similarities, methods based on Gaussian-shaped matched filters have been applied successfully for the detection of retinal blood vessels 7,8 and coronary arteries.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of coronary artery stenosis in X-ray angiograms using Gaussian filters and genetic algorithms

Cervantes-Sanchez

Cruz-Aceves

Hernández-Aguirre

2016

AIP Conference Proceedings

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Abstract. This paper presents a new automatic coronary artery segmentation method based on Gaussian matched filters (GMF) and genetic algorithms in X-ray angiograms. This method is used for detecting coronary stenosis based on the second-order derivatives of the vessel size, and it consists of the steps of vessel enhancement and vessel segmentation. In the enhancement step, the proposed method uses genetic algorithms to obtain the best performance of the GMF by tuning the optimal parameters. The results of the detection method are compared with those obtained by different state-of-the-art methods based on GMF using the area (A z ) under the receiver operating characteristic curve. Moreover, in the segmentation step, the inter-class variance thresholding method has been applied for classifying vessel and non-vessel pixels. The segmentation results obtained by the proposed method demonstrated high performance achieving an accuracy of 0.962 with a test set of 20 angiograms of a database of 40 angiograms outlined by a specialist. In addition, the detection results of potential cases of coronary stenosis are promising and suggest that the proposed method can be highly suitable for computer-aided diagnosis in cardiology.

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“…(Bouraoui et al, 2008) introduced an automatic segmentation method for coronary angiograms consisting in the application of the hit-or-miss transform and the region-growing strategy. (Lara et al, 2009) proposed an interactive segmentation method based on the region-growing strategy and a differential-geometry approach for coronary arteries in angiograms. In general, these morphological-based methods provide satisfactory results when vessels have high contrast; however, they do not perform well when vessels have different diameters and low contrast.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of coronary arteries using a multiscale Top-Hat operator and multiobjective optimization

Aceves

Aguirre

Cervantes

2015

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Automatic segmentation of coronary arteries using a multiscale Top-Hat operator and multiobjective optimization Segmentación automática de arterias coronarias utilizando un operador Top-Hat multiescala y optimización multiobjetivo ResumenEn este artículo se presenta un nuevo método de segmentación de arterias coronarias en imágenes angiográficas de rayos X consistente en dos etapas. En la primera etapa, un operador top-hat multiescala basado en las propiedades de la matriz Hessiana es introducido para realzar estructuras con forma arterial en el angiograma. Los resultados del operador top-hat multiescala propuesto son comparados con métodos multiescala basados en filtros de correspondencia Gaussiana, matriz Hessiana y operadores morfológicos, los cuales son analizados usando el área ( z A ) bajo la curva característica operativa del receptor. En la segunda etapa, un nuevo método de umbralización basado en optimización multiobjetivo mediante la aproximación de la suma ponderada para clasificar pixeles arteriales y no arteriales es presentado. El desempeño del método multiobjetivo es comparado con siete métodos de umbralización automática utilizando angiogramas delineados por un especialista mediante las medidas de sensibilidad, especificidad y exactitud. Finalmente, el método propuesto es comparado con cinco métodos de segmentación arterial pertenecientes al estado del arte. Los resultados de realzado arterial mediante el operador top-hat multiescala demostraron la mejor exactitud con Keywords: Automatic segmentation, coronary angiograms, Hessian matrix, multiobjective thresholding, vessel enhancement Automatic segmentation of coronary arteries using a multiscale Top-Hat operator and multiobjective optimization

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A Semi-Automatic Method for Segmentation of the Coronary Artery Tree from Angiography

Cited by 18 publications

References 28 publications

Efficient Monte Carlo Image Analysis for the Location of Vascular Entity

Efficient Monte Carlo Image Analysis for the Location of Vascular Entity

Automatic detection of coronary artery stenosis in X-ray angiograms using Gaussian filters and genetic algorithms

Automatic segmentation of coronary arteries using a multiscale Top-Hat operator and multiobjective optimization

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