Specular reflection removal and bloodless vessel segmentation for 3-D heart model reconstruction from single view images

Three Dimensional (3D) human heart model is attracting attention for its role in medical images for education and clinical purposes. Analysing 2D images to obtain meaningful information requires a certain level of expertise. Moreover, it is time consuming and requires special devices to obtain aforementioned images. In contrary, a 3D model conveys much more information. 3D human heart model reconstruction from medical imaging devices requires several input images, while reconstruction from a single view image is challenging due to the colour property of the heart image, light reflections, and its featureless surface. Lights and illumination condition of the operating room cause specular reflections on the wet heart surface that result in noises forming of the reconstruction process. Image-based technique is used for the proposed human heart surface reconstruction. It is important the reflection is eliminated to allow for proper 3D reconstruction and avoid imperfect final output. Specular reflections detection and correction process examine the surface properties. This was implemented as a first step to detect reflections using the standard deviation of RGB colour channel and the maximum value of blue channel to establish colour, devoid of specularities. The result shows the accurate and efficient performance of the specularities removing process with 88.7% similarity with the ground truth. Realistic 3D heart model reconstruction was developed based on extraction of pixel information from digital images to allow novice surgeons to reduce the time for cardiac surgery training and enhancing their perception of the Operating Theatre (OT). Cardiac medical imaging devices such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) images, or Echocardiography provide cardiac information. However,these images from medical modalities are not adequate, to precisely simulate the real environment and to be used in the training simulator for cardiac surgery. The propose method exploits and develops techniques based on analysing real coloured images taken during cardiac surgery in order to obtain meaningful information of the heart anatomical structures. Another issue is the different human heart surface vessels. The most important vessel region is the bloodless, lack of blood, vessels. Surgeon faces some difficulties in locating the bloodless vessel region during surgery. The thesis suggests a technique of identifying the vessels’ Region of Interest (ROI) to avoid surgical injuries by examining an enhanced input image. The proposed method locates vessels’ ROI by using Decorrelation Stretch technique. This Decorrelation Stretch can clearly enhance the heart’s surface image. Through this enhancement, the surgeon become enables effectively identifying the vessels ROI to perform the surgery from textured and coloured surface images. In addition, after enhancement and segmentation of the vessels ROI, a 3D reconstruction of this ROI takes place and then visualize it over the 3D heart model. Experiments for each phase in the research framework were qualitatively and quantitatively evaluated. Two hundred and thirteen real human heart images are the dataset collected during cardiac surgery using a digital camera. The experimental results of the proposed methods were compared with manual hand-labelling ground truth data. The cost reduction of false positive and false negative of specular detection and correction processes of the proposed method was less than 24% compared to other methods. In addition, the efficient results of Root Mean Square Error (RMSE) to measure the correctness of the z-axis values to reconstruction of the 3D model accurately compared to other method. Finally, the 94.42% accuracy rate of the proposed vessels segmentation method using RGB colour space achieved is comparable to other colour spaces. Experimental results show that there is significant efficiency and robustness compared to existing state of the art methods

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

Background: The 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. Methods: This 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. Results: Reduces 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. Conclusions: The 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

Single image reconstruction of human heart surface with specular reflection remover

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 L-shape 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

Three dimensional reconstruction of human heart surface from single image- view under different illumination conditions

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