Three Dimensional Reconstruction of Human Heart Surface From Single Image- View Under Different Illumination Conditions

Abstract

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.

A new human heart vessel identification, segmentation and 3D reconstruction mechanism

“…The reflections affect the perception of the vessels. The reflection can be removed be the method proposed in [ 19 ]. Moreover, the proposed segmentation method initially enhances the original image of the human heart using a Decorrelation Stretch [ 20 ] as shown in Figure 4 b.…”

Specular Reflections Detection and Removal for Endoscopic Images Based on Brightness Classification

“…They first used nonlinear filters for reflection enhancement and then calculated the brightness difference between CIE-XYZ and RGB color spaces to detect the specular reflection. In the context of surface reconstruction, Al-Surmi et al [ 14 ] used a single grayscale threshold to detect specular reflections on the surface of the heart. Marcinczak et al [ 15 ] proved that the accuracy of mirror detection using a single threshold technique is limited, and they proposed a more reliable hybrid detection method based on closed contours and thresholding.…”

“…Many researchers have tried to solve the problem of specular reflection in endoscopic images from two perspectives of highlight detection and highlight removal. Color space-based methods [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ] mainly detect highlights by thresholding different color channels, which usually require empirical threshold setting, and endoscopic images in different scenes may require a large number of parameter searches. Most of these methods focus on processing mid-brightness endoscopic images.…”

Measurements of Mitral Annular Displacement in 2D Echocardiography Images

“…Due to the anatomical structure difficulties in 2D-echocardiography images including speckle noise, low contrast and signal dropouts, not all images provided clear tissues of myocardial wall and in some cases, spatial filtering was needed (Sun et al, 2013;Al-Surmi et al, 2013;Dawood et al, 2012;Hoque and Al-Mahfuz, 2011;Ahmed and Nordin, 2011). In the literature, most of the common tracking methods for measuring MAD which depending on the myocardial tissue structure (Storaa et al, 2004;Pan et al, 2001).…”