Automatic segmentation of coronary arteries using Gabor filters and thresholding based on multiobjective optimization

Cruz-Aceves, Iván; Oloumi, Faraz; Rangayyan, Rangaraj M.; Aviña-Cervantes, Juan Gabriel; Hernández-Aguirre, Arturo

doi:10.1016/j.bspc.2015.11.001

Cited by 43 publications

(31 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reformulated Gabor filter depends only on two parameters: τ and l. The diameter of the features to be detected by the Gabor filter is controlled by τ, while l governs the elongation of a segment that shares a similar orientation. For the detection of vessel-like structures in multiple directions, the Gabor Under the same principle that the single-scale Gabor filter was proposed to detect blood vessels in the retina, this method has been successfully applied to the detection of coronary arteries in X-ray angiograms [16]. Although single-scale Gabor filters are capable of detecting the main vessels in coronary angiograms, the response obtained tends to ignore vessels of diameters lower than τ.…”

Section: Multiscale Gabor Filtersmentioning

confidence: 99%

“…Gabor filters have shown the best detection performance in coronary arteries compared to the state-of-the-art approaches [15,16], followed in efficiency by the Gaussian matched filters [11]. Because these two methods have obtained superior performance to carry out the automatic detection of blood vessels in medical images, in this work they are thoroughly studied.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Segmentation of Coronary Arteries in X-ray Angiograms using Multiscale Analysis and Artificial Neural Networks

et al. 2019

Self Cite

View full text Add to dashboard Cite

This paper presents a novel method for the automatic segmentation of coronary arteries in X-ray angiograms, based on multiscale analysis and neural networks. The multiscale analysis is performed by using Gaussian filters in the spatial domain and Gabor filters in the frequency domain, which are used as inputs by a multilayer perceptron (MLP) for the enhancement of vessel-like structures. The optimal design of the MLP is selected following a statistical comparative analysis, using a training set of 100 angiograms, and the area under the ROC curve ( A z ) for assessment of the detection performance. The detection results of the proposed method are compared with eleven state-of-the-art blood vessel enhancement methods, obtaining the highest performance of A z = 0.9775 , with a test set of 30 angiograms. The database of 130 X-ray coronary angiograms has been outlined by a specialist and approved by a medical ethics committee. On the other hand, the vessel extraction technique was selected from fourteen binary classification algorithms applied to the multiscale filter response. Finally, the proposed segmentation method is compared with twelve state-of-the-art vessel segmentation methods in terms of six binary evaluation metrics, where the proposed method provided the most accurate coronary arteries segmentation with a classification rate of 0.9698 and Dice coefficient of 0.6857 , using the test set of angiograms. In addition to the experimental results, the performance in the detection and segmentation steps of the proposed method have also shown that it can be highly suitable for systems that perform computer-aided diagnosis in X-ray imaging.

show abstract

Section: Multiscale Gabor Filtersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Coronary Arteries in X-ray Angiograms using Multiscale Analysis and Artificial Neural Networks

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…The structure tensor represents image gradient or edge information and because these are first‐order partial derivatives, structure tensor‐based filters are more robust against noise than second‐order Hessian filters. Other filters employed include shifted Gaussian kernel‐based linear filters, quadrature filters, directional filter banks, morphological filters, bi‐Gaussian filters, Gabor filters, and matched filters …”

Section: Image Processingmentioning

confidence: 99%

Reconstruction of coronary circulation networks: A review of methods

et al. 2019

View full text Add to dashboard Cite

Building anatomically accurate models of the coronary vascular system enables potentially deeper understandings of coronary circulation. To achieve this, (a) images at different levels of vascular network-arteries, arterioles, capillaries, venules, and veins-need to be obtained through suitable imaging modalities; and (b) from images, morphological and topological information needs to be extracted using image processing techniques. While there are several modalities that enable the imaging of large vessels, microcirculation imaging-capturing vessels having diameter lesser than 100 μm-has to date been typically confined to small regions of the heart. This spatially limited microcirculatory information has often been used within cardiac models, with the potentially erroneous assumption that it is representative of the whole organ. However, with the recent advancements in imaging and image processing, it is rapidly becoming feasible to acquire, process, and quantify microcirculation data at the scale of whole organ. In this review, we summarize the progress toward this goal followed through a presentation of the current state-of-the-art imaging and image processing techniques in the context of coronary microcirculation extraction, prominently but not exclusively, from small animals. K E Y W O R D Sconfocal imaging, coronary vascular system, deep learning, image processing, vessel extraction

show abstract

“…A myriad of unsupervised methods have been proposed to address the vessel segmentation problem in different 3D scenarios, such as angiograms (Cruz-Aceves et al, 2016) and CTA scans (Metz et al, 2009, Friman et al, 2010 of the coronary arteries, CT scans of the lung (Zhai et al, 2016), and MRA (El-Baz et al, 2012) and CTA (Babin et al, 2013) scans of the brain. The characteristics of the proposed methodologies is often related with the complexity of the vessel network in each case.…”

Section: Related Workmentioning

confidence: 99%

Computer aided detection of deep inferior epigastric perforators in computed tomography angiography scans

Araújo

Garrido

Baraças

et al. 2019

Computerized Medical Imaging and Graphics

View full text Add to dashboard Cite

The deep inferior epigastric artery perforator (DIEAP) flap is the most common free flap used for breast reconstruction after a mastectomy. It makes use of the skin and fat of the lower abdomen to build a new breast mound either at the same time of the mastectomy or in a second surgery. This operation subcutaneous region of each perforator was extracted, by means of a tracking procedure, whereas the intramuscular portion was detected through a minimum cost approach. Both were subsequently compared with the radiologist manual annotation.Results showed that the semi-automatic procedure was able to correctly detect the course of the DIEAPs with a minimum error (average error of 0.64 mm and 0.50 mm regarding the extraction of subcutaneous and intramuscular paths, respectively). The objective methodology is a promising tool in the automatic detection of perforators in CTA and can contribute to spare human resources and reduce subjectivity in the aforementioned task.

show abstract

Automatic segmentation of coronary arteries using Gabor filters and thresholding based on multiobjective optimization

Cited by 43 publications

References 21 publications

Automatic Segmentation of Coronary Arteries in X-ray Angiograms using Multiscale Analysis and Artificial Neural Networks

Automatic Segmentation of Coronary Arteries in X-ray Angiograms using Multiscale Analysis and Artificial Neural Networks

Reconstruction of coronary circulation networks: A review of methods

Computer aided detection of deep inferior epigastric perforators in computed tomography angiography scans

Contact Info

Product

Resources

About