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.

Artificial Intelligence Surgery is a surgical journal that focuses on the interface of computers and the art of surgery. The aim of this journal is to create a space for discourse in terms of topics related to artificial intelligence (AI) in surgery such as smart surgical technology/digital surgery, computer-assisted surgical systems and surgical data science. The intention research direction on robotics includes clinical and technological work on tele-manipulation, robotic-assisted technologies, robotic Natural Orifice Transluminal Endoscopic Surgery (NOTES) and fully autonomous robots.

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.

This paper presents a novel method for improving the training step of the single-scale Gabor filters by using the Boltzmann univariate marginal distribution algorithm (BUMDA) in X-ray angiograms. Since the single-scale Gabor filters (SSG) are governed by three parameters, the optimal selection of the SSG parameters is highly desirable in order to maximize the detection performance of coronary arteries while reducing the computational time. To obtain the best set of parameters for the SSG, the area (Az) under the receiver operating characteristic curve is used as fitness function. Moreover, to classify vessel and nonvessel pixels from the Gabor filter response, the interclass variance thresholding method has been adopted. The experimental results using the proposed method obtained the highest detection rate withAz=0.9502over a training set of 40 images andAz=0.9583with a test set of 40 images. In addition, the experimental results of vessel segmentation provided an accuracy of0.944with the test set of angiograms.