BackgroundThe identification and segmentation of inhomogeneous image regions is one of the most challenging issues nowadays. The surface vessels of the human heart are important for the surgeons to locate the region where to perform the surgery and to avoid surgical injuries. In addition, such identification, segmentation, and visualisation helps novice surgeons in the training phase of cardiac surgery.MethodsThis article introduces a new mechanism for identifying the position of vessels leading to the performance of surgery by enhancement of the input image. In addition, develop a 3D vessel reconstruction out of a single-view of a real human heart colour image obtained during open-heart surgery.ResultsReduces the time required for locating the vessel region of interest (ROI). The vessel ROI must appear clearly for the surgeons. Furthermore, reduces the time required for training cardiac surgery of the novice surgeons. The 94.42% accuracy rate of the proposed vessel segmentation method using RGB colour space compares to other colour spaces.ConclusionsThe advantage of this mechanism is to help the surgeons to perform surgery in less time, avoid surgical errors, and to reduce surgical effort. Moreover, the proposed technique can reconstruct the 3D vessel model from a single image to facilitate learning of the heart anatomy as well as training of cardiac surgery for the novice surgeons. Furthermore, extensive experiments have been conducted which reveal the superior performance of the proposed mechanism compared to the state of the art methods.Electronic supplementary materialThe online version of this article (doi:10.1186/s13019-014-0161-1) contains supplementary material, which is available to authorized users.

The 3D reconstruction from a single-view image is a longstanding issue in computer vision literature, especially in the medical field. Traditional medical imaging techniques that provide information about the heart and which are used to reconstruct the heart model, include Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images. However, in some cases, they are not available and the applications that use these techniques to model the human heart only produce acceptable results after a long process, which involves acquiring the input data, as well as the segmentation process, the matching process, effort and cost. Therefore, it would be useful to be able to use a 2D single image to reconstruct the 3D heart surface model. We introduce an image-based human heart surface reconstruction from a single image as input. To model the surface of the heart, the proposed method, first, detects and corrects the specular reflection from the heart's surface, which causes deformation of the surface in the R3. Second, it extrudes the three axes for each image pixel (e.g., x, y and z axes) from the input image, in which the z-axis is calculated using the intensity value. Finally, a 3D reconstruction of the heart surface is created to help the novice cardiac surgeon to reduce the period of time in learning cardiac surgery and to enhance their perception of the operating theatre. The experimental results for images of the heart show the efficiency of the proposed method compared to the existing methods.

BackgroundComputerized tomographic angiography (3D data representing the coronary arteries) and X-ray angiography (2D X-ray image sequences providing information about coronary arteries and their stenosis) are standard and popular assessment tools utilized for medical diagnosis of coronary artery diseases. At present, the results of both modalities are individually analyzed by specialists and it is difficult for them to mentally connect the details of these two techniques. The aim of this work is to assist medical diagnosis by providing specialists with the relationship between computerized tomographic angiography and X-ray angiography.MethodsIn this study, coronary arteries from two modalities are registered in order to create a 3D reconstruction of the stenosis position. The proposed method starts with coronary artery segmentation and labeling for both modalities. Then, stenosis and relevant labeled artery in X-ray angiography image are marked by a specialist. Proper control points for the marked artery in both modalities are automatically detected and normalized. Then, a geometrical transformation function is computed using these control points. Finally, this function is utilized to register the marked artery from the X-ray angiography image on the computerized tomographic angiography and get the 3D position of the stenosis lesion.ResultsThe result is a 3D informative model consisting of stenosis and coronary arteries’ information from the X-ray angiography and computerized tomographic angiography modalities. The results of the proposed method for coronary artery segmentation, labeling and 3D reconstruction are evaluated and validated on the dataset containing both modalities.ConclusionsThe advantage of this method is to aid specialists to determine a visual relationship between the correspondent coronary arteries from two modalities and also set up a connection between stenosis points from an X-ray angiography along with their 3D positions on the coronary arteries from computerized tomographic angiography. Moreover, another benefit of this work is that the medical acquisition standards remain unchanged, which means that no calibration in the acquisition devices is required. It can be applied on most computerized tomographic angiography and angiography devices.

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3D reconstruction with specular reflection remover is one of the vital and robust tools that provide aid in many fields, especially medical filed. This article presents a novel method for reconstruction a real human heart surface from a single view image with a remover specular reflection while keeping the image structure. Reconstruct a heart model from numbers of real images is difficult task and time consuming especially involve reflections, resulted from moisten of the human heart surface. In this paper, we propose a novel method for reconstruct a human heart from a single image while detecting and correcting the specular reflection. The process start with acquired the real heart image by a digital camera in cardiac surgery. Second, processed the image to extract the x, y, and z axes for each pixel and automatic detect the specularities using the difference of the maximum blue color channel and standard deviation of the RGB color channels. Later proceeded with the correction process by the Lshape inverse (Γ) to recover losing information saturated by lights in the operation theater. Finally, the reconstructed of the 3D model for the heart. Experimental results on the heart images show the efficiency of the proposed method comparing to the existing methods.