Search

Recently, the use of Model-Driven Engineering (MDE) via metamodeling approach is gaining more attention for software applications development. The community from the healthcare domain also attempts to employ the metamodel approach for producing quality healthcare applications. Healthcare applications have become an imperative in every attempt to improve healthcare management. Numerous studies reported that the healthcare domain is seen as a complex and unique domain, which involves dynamic characteristics. In addition, it is widely recognized that the increase of information exchange in the healthcare domain is caused by the diversity of healthcare data. This has led to the increase use of information technologies in the healthcare industry so as to enhance the healthcare delivery process via healthcare applications. However, the complexity of healthcare information leads to ineffective models and design of healthcare applications. Modeling the healthcare processes and developing healthcare applications are challenging tasks.  Hence, the advances of MDE have influenced the use of the metamodeling technique in the development of healthcare applications. Various metamodels are developed as a solution to provide a clear healthcare process model and a correct healthcare application. The aim of this paper is to analyse the use of the metamodel-based approach in designing healthcare applications. We believe that the metamodel-based approach would improve the development of healthcare applications.&nbsp

Exploring the computer systems and accessing a software and performing a surgery at the same time is a quite heavy task to achieve by surgeons in the operating theatre. This paper presents a way to design a system which utilises voice recognition to select, visualise and fully control cardiovascular angiography and their 3D models. The work starts by choosing an angiography image using voice only. Next, the image is processed to extract complete tree of arteries. Finally, the system processes the segmented arteries to display the reconstructed 3D arteries. The software is able to fully control the reconstructed 3D coronary arteries by voice in order to rotate, zoom-in and out, speed-up and down the rotation, spelling out the voice commands, voice-on and off, and terminating the system. The system was tested by a consultant heart surgeon on a real data and he approved the credibility of the system.

In order to utilize both computed tomography (CT) and echocardiography images of the heart for medical applications such as diagnosis and image guided intervention concurrently, non-rigid registration is an essential task. A challenging but important problem in image registration is evaluating the performance of a registration algorithm. The direct quantitative approach is to compare the deformation field solution with the ground truth transformation (at all or some landmark pixels). However, in clinical data, the ground truth is typically unknown. To deal with the absence of ground truth, some methods opted to estimate registration accuracy by using uncertainty measures as a surrogate for quantitative registration error. In this paper, we define the registration uncertainty and explore its use for diagnostic purposes. We use uncertainty estimation for improving accuracy of a hybrid registration which register a pre-operative CT to an intra-operative echocardiography images. In other words, uncertainty estimation is used to evaluate the registration algorithm performance which integrates intensity-based and feature-based methods. This registration can potentially be used to improve the diagnosis of cardiac disease by augmenting echocardiography images with high-resolution CT images and to facilitate intra-operative image fusion for minimally invasive cardio-thoracic surgical navigation. Here, we show how to determine the registration uncertainty, by using uncertainty quantification regarding to abnormal intensity and geometry distribution. The result indicates that registration uncertainty is a good predictor for the functional abnormality of subjects.

Accurate detection of the End-Diastolic (ED) and End-Systolic (ES) frames of a cardiac cycle are significant factors that may affect the accuracy of abnormality assessment of a ventricle. This process is a routine step of the ventricle assessment procedure as most of the time in clinical reports many parameters are measured in these two frames to help in diagnosing and dissection making. According to the previous works the process of detecting the ED and ES remains a challenge in that the ED and ES frames for the cavity are usually determined manually by review of individual image phases of the cavity and/or tracking the tricuspid valve. The proposed algorithm aims to automatically determine the ED and ES frames from the four Dimensional Echocardiographic images (4DE) of the Right Ventricle (RV) from one cardiac cycle. By computing the area of three slices along one cardiac cycle and selecting the maximum area as the ED frame and the minimum area as the ES frame. This method gives an accurate determination for the ED and ES frames, hence avoid the need for time consuming, expert contributions during the process of computing the cavity stroke volume.

This paper presents a hybrid method to extract endocardial contour of the right ventricular (RV) in 4-slices from 3D echocardiography dataset. The overall framework comprises four processing phases. In Phase I, the region of interest (ROI) is identified by estimating the cavity boundary. Speckle noise reduction and contrast enhancement were implemented in Phase II as preprocessing tasks. In Phase III, the RV cavity region was segmented by generating intensity threshold which was used for once for all frames. Finally, Phase IV is proposed to extract the RV endocardial contour in a complete cardiac cycle using a combination of shape-based contour detection and improved radial search algorithm. The proposed method was applied to 16 datasets of 3D echocardiography encompassing the RV in long-axis view. The accuracy of experimental results obtained by the proposed method was evaluated qualitatively and quantitatively. It has been done by comparing the segmentation results of RV cavity based on endocardial contour extraction with the ground truth. The comparative analysis results show that the proposed method performs efficiently in all datasets with overall performance of 95% and the root mean square distances (RMSD) measure in terms of mean ± SD was found to be 2.21 ± 0.35 mm for RV endocardial contours.

Coronary arterial tree extraction in angiograms is an essential component of each cardiac image processing system. Once physicians decide to check up coronary arteries from x-ray angiograms, extraction must be done precisely, fast, automatically and including whole arterial tree to help diagnosis or treatment during the cardiac surgical operation. This application is very helpful for the surgeon on deciding the target vessels prior to coronary artery bypass graft surgery. Some techniques and algorithms are proposed for extracting coronary arteries in angiograms. However, most of them suffer from some disadvantages such as time complexity, low accuracy, extracting only parts of main arteries instead of the full coronary arterial tree, need manual segmentation, appearance of artifacts and so forth. This study presents a new method for extracting whole coronary arterial tree in angiography images using Starlet wavelet transform. To this end, firstly we remove noise from raw angiograms and then sharpen the coronary arteries. Then coronary arterial tree is extracted by applying a modified Starlet wavelet transform and afterwards the residual noises and artifacts are cleaned. For evaluation, we measure proposed method performance on our created data set from 4932 Left Coronary Artery (LCA) and Right Coronary Artery (RCA) angiograms and compared with some state-of-the-art approaches. The proposed method shows much higher accuracy 96% for LCA and 97% for RCA, higher sensitivity 86% for LCA and 89% for RCA, higher specificity 98% for LCA and 99% for RCA and also higher precision 87% for LCA and 93% for RCA angiograms.

The present invention relates to a method for automatic motion tracking of ventricular endocardial surface in three dimensional (3D) echocardiography, characterized by the steps of extracting a plurality of ventricular endocardial contours over a complete cardiac cycle; identifying a plurality of landmarks on each ventricular endocardial contour; measuring displacement vector flow (DVF) for each landmark by comparing a pair of consecutive ventricular endocardial contours; measuring velocity vector flow (VVF) for each landmark from end-diastolic (ED) to end-systolic (ES) and vice versa; identifying at least four landmarks from the plurality of landmarks on each ventricular endocardial contour to represent anatomical landmarks of left lateral surface, right lateral surface, inferior wall and anterior wall by using geometrical distance calculation (GDC) algorithm; analysing ventricular endocardial motion direction using a fuzzy logic analyzer (FLA) for the four landmarks identified; updating values of the displacement vector flow (DVF) and velocity vector flow (VVF) based on the ventricular endocardial motion direction; and generating graphical curves of time versus values of the displacement vector flow (DVF) and velocity vector flow (VVF) for the four landmarks identified.

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.

The extraction and tracking of the vessels and their centerlines of coronary artery vessels in 2D and 3D angiograms has been vital part of many clinical analysis studies. This paper presents a new approach to extract the centerlines of vessels using novel center of gravity equations. The new equations depend on the intensity value as their main factor to track the vessels and by applying the center of gravity technique it can lead to centerline extraction. The new algorithm is called New Center of Gravity (NCOG). NCOG algorithm consists of four stages. First stage is angiogram partitioning using Recursive data structure technique. The second stage is to calculate the gray pixels in each partition and compare them with a threshold value (T). The third stage is center of gravity (COG) calculation. The fourth and last stage is connecting the final COG points by lines. The algorithm using the new COG equations were applied on a raw of clinical data and the results showed high robustness in extract the centerlines of vessels. We can conclude that our approach is robust, time saving, and helpful tool in surgery management and scientific researches.

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.

Medical image processing is nowadays one of the best tools to make an informative model from a raw image of each part of the body, and segmentation is the most important step in which used to extract significant features. Coronary artery segmentation algorithm in angiograms is a fundamental component of each cardiac image processing system. There are lots of techniques and algorithms proposed for extracting coronary arteries in angiograms. But based on our knowledge, there is not any review paper to categorize and compare them together. In this paper, we have divided these algorithms into five major classes and propose a survey for the main class, pattern recognition, which is a famous technique in this manner. We studied all the papers in the pattern recognition class and defined six categories for them: (1) Multi scale approaches (2) Region growing approaches (3) Matching filters approaches (4) Mathematical morphology approaches (5) Skeleton based approaches and (6) Ridge based approaches. Finally, we made a table to compare all the algorithms in each category against criteria such as: user interaction, angiography types, dimensionality, enhancement method, full coronary artery output, whole tree output, and 3D reconstruction ability.

Hospitals are known for having separate information systems across various departments, which are usually heterogeneous in nature. Medical experts could benefit greatly from the integration of these islands, but the heterogeneity of the sources/databases often impedes this. Most cardiothoracic units use isolated heterogeneous systems to keep patients records as well as to support the experts when deciding for cardiothoracic surgery. These include patient’s information system, lab results for x-ray, angiography, echocardiogram, blood etc. All these information are stored in heterogeneous systems and as such, a user would have to log-in and log-out from one system to another, to monitor/trace a patient’s medical record. As the solution, we proposed a clinical database system in which the system incorporates the overall patient’s management, from ward admission through Operation Theatre, Coronary Intensive Care Unit (CICU), recovery and discharge in which is divided into several modules. The purpose of this paper is to presents the conceptual database design for the Smart Medical Clinical System which include features that could alert the healthcare personnel regarding any abnormal trend in the data, so that medical experts need not have to repeat the same investigations.

Patients in Cardiothoracic Intensive Care Units (CICU) are physiologically weak and require watchful monitoring and support. Such monitoring generates massive amount of data that enable early detection of changes in the patient's condition and provide information that help medical staff to give the treatment and evaluate the response to medical interventions. The countless data gathered from monitoring systems and clinical information systems have created a challenge and are time consuming for clinicians to analyze. This paper discusses the implementation of an intelligent system that has been designed to improve interpretation of clinical data which will then increase the quality and efficiency of the working environment in CICU. The implementation is based on the description state from the cardiologist. This research work extends the cardiologist approach by providing the heuristic rules-based approach to address the treatment. The system is intended to help physicians and CICU staffs to diagnose and track the conditions of patients.

The present invention relates to a method of processing a three-dimensional (3D) echocardiography video with a complete cardiac cycle encompassing a ventricular. The method comprises step of obtaining a set of voxels representing a total scanned volume from the three-dimensional (3D) echocardiography video and performing segmentation on the set of voxels representing the total scanned volume.

The present invention relates to a method for determining right ventricle stroke volume comprising the steps of: providing a plurality of three-dimensional images of right cardiac ventricle of a patient over a cardiac cycle; identically slicing each of the plurality of images; selecting an image from the plurality of images, for each time unit of the cardiac cycle; determining a region of interest in each slice of the selected images, wherein the region of interest shows the right ventricle of the patient; determining area of the region of interest; determining volume of the region of interest; determining total volume of the region of interest for each time unit, wherein the maximum total volume is end diastolic volume and the minimum total volume is end systolic volume; and determining the right ventricle stroke volume using an equation: Right Ventricle Stroke Volume = end diastolic volume - end systolic volume (3).

In this work we present a technique to extract the heart contours from noisy echocardiograph images. Our technique is based on improving the image before applying contours detection to reduce heavy noise and get better image quality. To perform that, we combine many pre-processing techniques (filtering, morphological operations, and contrast adjustment) to avoid unclear edges and enhance low contrast of echocardiograph images, after implementing these techniques we can get legible detection for heart boundaries and valves movement by traditional edge detection methods.

Problem statement: Medical image analysis is a particularly difficult problem because the inherent characteristics of these images, including low contrast, speckle noise, signal dropouts and complex anatomical structures. An accurate analysis of wall motion in Two-dimensional echocardiography images is "important clinical diagnosis parameter for many cardiovascular diseases". A challenge most researchers faced is how to speed up the clinical decisions and reduce human error of estimating accurately the true wall movements boundaries if can be done automatically will be a useful tool for assessing these diseases qualitatively and quantitatively. Approach: The proposed method involves three stages: First, the pre-processing stage for image contrast enhancement to reduce speckle-noise and to highlight certain features of interest (i.e., myocardial tissue). In the second stage, we applied the segmentation process using thresholding technique by considering a mean value of pixels intensity as a threshold value to distinct image features (e.g., Background and object). After thresholding implementation, the two most common mathematical morphology operators 'erosion' and 'dilation' are applied to improve the efficiency of the wall boundary detection process. Finally, Robert's operator is used as edge detector to identify the wall boundaries. Results: For accuracy measurement, the experimental results of the proposed method are compared to that obtained from medical QLab system qualitatively and quantitatively. Conclusion: The results showed that our proposed method is reliable and its performance accuracy percentages are 50% more acceptable and 42% better than QLab system results.

Whereas most of the conventional techniques propose using multiview cineangiograms to reconstruct 3D objects this article proposes to integrate a Three Dimension(3D) model of the coronary artery tree using a standard single-view cineangiogram. Splitting the cineangiograms into non-sequenced and different angle views is how the data is supplied in this method. Each single view can be used to reconstruct a robust 3D model of the coronary artery from that angle of view. Although the dynamic variations of blood vessels curvature have been difficult to study in Two Dimension (2D) angiograms, there is both experimental and clinical evidence showing that 3D coronary reconstruction is very useful for surgery planning and clinical study. Approach: The algorithm has three stages. The first stage is the vessel extraction and labeling for each view for the purpose of constructing the 3D model, while in the second stage, the vessels information (x, y and z) will be saved in a data file to be forwarded to the next stage. Finally, we input the x, y and z of a specific coronary artery tree to the OPENGL library included in the software, which we developed and called Fast 3D (F3D) and which is displayed in R3. Results: Experimental evaluation has been done to clinical raw data sets where the experimental results revealed that the proposed algorithm has a robust 3D output. Conclusion: Results showed that our proposed algorithm has high robustness for a variety of image resolutions and voxel anisotropy.

Problem statement: Three-Dimension (3D) reconstruction is one of the vital and robust tools that provide aid in many fields, especially medicine. This article is about 3D shape similarity and it presents a comparison approach between principal curvature methods of 3D output. Our approach follows the concept of using the gray scale value as the z dimension and the other approach is a standard one. A comparison of the curvature of the 3D outputs will be made between the standard approach and our proposed one to prove its correctness. We propose to use the standard deviation technique to compare the output features of the 3D coronary artery trees. We applied a standard approach of 3D shape similarity and compared the features with ours. The standard approach was published in 1998 as a study comparing certain 3D curvature measurement algorithms. Approach: Our approach consists of three major steps: (1) Apply the paraboloid fitting technique from the standard approach; (2) Apply the 3D reconstruction algorithm proposed in this research on the same data in step (1) and (3) Apply the Standard Deviation technique on both outputs from (1) and (2) and compare the outputs. Results: Experimental evaluation has been done on clinical raw data sets where the experimental results revealed that both outputs are totally matched. Conclusion: The match in the output refers to the correctness of the proposed 3D output and subsequently its coronary artery tree curvature as well.

Echocardiography is a widely used imaging technique to examine various cardiac functions, especially to detect the left ventricular wall motion abnormality. Unfortunately the quality of echocardiograph images and complexities of underlying motion captured, makes it difficult for an in-experienced physicians/ radiologist to describe the motion abnormalities in a crisp way, leading to possible errors in diagnosis. In this study, we present a method to analyze left ventricular wall motion, by using optical flow to estimate velocities of the left ventricular wall segments and find relation between these segments motion. The proposed method will be able to present real clinical help to verify the left ventricular wall motion diagnosis.

An integrated healthcare management system accessible by users comprising a plurality user end interfaces (131,132,133,134). The user end interface (131,132,133,134) are connected through a first network (120) to web based applications hosted on one or more application servers (104) comprising one or more modules adapted to perform one or more user functions. There may be preferably one for each user group (131,132,133,134), interacting with each other in real time and selectively accessible to one or more user groups (131,132,133,134). The application server (104) is further connected to a plurality of servers through a second network (110). The plurality of servers include at least one server (101) to store and manages the data selectively accessible at the user end (131,132,133,134) and at least one server to store images (102) selectively accessible at the user end (131,132,133,134).

A method of constructing at least one 3 dimensional image from plurality of data is provided, the method includes the steps of extracting data centerline using a center of gravity formula for at least one image, partitioning the at least one image into a plurality of portions, determining a plurality of gray scale points in each portion, compaing the plurality of gray scale points with a threshold value, determining the center of gravity for each portion and connecting a plurality of center of gravity. A 3 dimensional image (100) constructable from a plurality of data is also provided, the image (100) includes a plurality of 3 dimensional points linearly connectable to form at least one cloud of 3 dimensional points.

Contours extraction from two dimensional echocardiographic images has been a challenge in digital image processing. This is essentially due to the heavy noise, poor quality of these images and some artifacts like papillary muscles, intra-cavity structures as chordate, and valves that can interfere with the endocardial border tracking. In this paper, we will present a technique to extract the contours of heart boundaries from a sequence of echocardiographic images, where it started with pre-processing to reduce noise and produce better image quality. By pre-processing the images, the unclear edges are avoided, and we can get an accurate detection of both heart boundary and movement of heart valves.

A voice activated data retrieval and storage medical system is provided wherein the voice activated medical care system includes a processor, a database, wherein the database is controlled by the processor, at least one medical care device, wherein the medical care device receives commands from the processor, wherein the medical care device stores data in the database, and a speech recognition module, wherein the speech recognition module receives commands from the processor and sends commands to the processor, wherein the speech recognition module stores data in the database.