A fast and accurate method for automatic coronary arterial tree extraction in angiograms

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 relevance of lean thinking to sustainable improvement of public office buildings in Nigeria

This study looked into the relevance of lean thinking, particularly the application of muda as a supplement to the sustainable improvement diagnosis technique of existing office buildings, for a fuller assessment of user's requirement in Nigeria. The impact of muda as related to the triple bottom line of sustainable development on perceived job productivity and design features was estimated from end-user's perspective, using diagnostic POE as data acquiring tool while the confirmatory analysis was done through AMOS, SPSS and MS Excel to explain the relationship between the different variables. The findings showed that muda is inherent in public office buildings and it has highly significant causal effects of 0.66 and 0.76, respectively on perceived job productivity and design features; it also has strong effect sizes of 44 and 58% in explaining both their variances, respectively. The result revealed that users require more improvement in facilities as against spatial plan and structures while there is a medium and positive correlation of 0.48 between perceived job productivity and design features implying that the improvement of one will consequently lead to the improvement of the other. The study concludes that lean thinking is relevant to building improvement and could serve as good supplement to the current improvement diagnosis of existing public office buildings but not as a substitute since data were only collected from users who are not able to provide the required technical data that would otherwise warrant use of equipment

Integration of heterogeneous databases for medical experts

This paper presents an initial work on the conceptual architecture of medical databases integration. The problem with current medical institutions are that most of them have variety of systems across various departments where most of the systems have existing and incompatible database management system that are tied together in a network. All these systems stored patients' information in an isolated and stand-alone environment and as such, a user who wish to use the system would have to log-in and log-out from one system to another in order to monitor or trace a patient's medical record. To overcome the problem above, we proposed an integration of heterogeneous medical databases that can eliminate data duplication and save cost, and at the same time can assist medical experts either before or after a surgery

Automatic detection of the end-diastolic and end-systolic from 4D echocardiographics images

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

Measurements of mitral annular displacement in 2D echocardiography images

Mitral Annular Displacement (MAD) in echocardiography has been described as a variation in mitral annulus position between the enddiastolic and the end-systolic in a complete cardiac cycle. It could be used as a rapid and reproducible method of determining the LV global systolic function and could be an easily detectable index for wall motion abnormalities. In this study, a computational method of MAD was implemented based on the mitral annulus motion tracking at both sides; namely the lateral side and the septal side using 2D-Echocardiographic (2DE) datasets. This method comprises three main processing stages: 2DE dataset preparation, Region Of Interest (ROI) selection and MAD measurements. For each 2DE dataset, MAD was computed as the movement distance toward the LV apex at both sides individually in twoconsecutive frames using the ‘Euclidian distance' method. Then, the maximum displacement occurs during a complete cardiac cycle was measured in millimetres (mm) for each side. The overall datasets used are 178 original 2D-echocardiography images in 4-chamber view. The experimental results for MAD measurements were compared with results that obtained by TMQ Advanced technique using QLAB software. The comparative analysis was done qualitatively by visual observation of two expert and the comparison scores show that the proposed method of MAD measurements has high acceptability of 85%. Furthermore, the quantitative analysis of the MAD method is comparable with TMAD measurements by QLAB and there is no significant differences in displacement measurements

Coronary artery segmentation in angiograms with pattern recognition techniques - a survey

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

Online Adaptive Coronary Heart Disease Risk Prediction Model

Coronary Heart Disease (CHD) is the leading causes of death worldwide. Life style changing is one of the important methods to delay the incidence of CHD. The awareness of life style changing is however still low. In order to improve awareness of life style changing, some CHD risk prediction models have been introduced. The existing models however either not well structured, not completed, static or offline. This paper introduces a new online CHD risk prediction model. The model is structured according to three risk factor groups including molecular structure, body system vital sign and bioenergy symphony. The model had also been compared with 5 existing models. Comparison results show that the model has better structure, adaptability and accessibility. Validation test using 120 subjects shows that the model prediction accuracy is 96.2%. This shows that the model is suitable to be used widely for CHD risk prediction both healthy and risk subjects as a preventive method in getting CHD in the earlier age

Online Adaptive Coronary Heart Disease Risk Prediction Model

Coronary Heart Disease (CHD) is the leading causes of death worldwide. Life style changing is one of the important methods to delay the incidence of CHD. The awareness of life style changing is however still low. In order to improve awareness of life style changing, some CHD risk prediction models have been introduced. The existing models however either not well structured, not completed, static or offline. This paper introduces a new online CHD risk prediction model. The model is structured according to three risk factor groups including molecular structure, body system vital sign and bioenergy symphony. The model had also been compared with 5 existing models. Comparison results show that the model has better structure, adaptability and accessibility. Validation test using 120 subjects shows that the model prediction accuracy is 96.2%. This shows that the model is suitable to be used widely for CHD risk prediction both healthy and risk subjects as a preventive method in getting CHD in the earlier age

Automatic boundary detection of wall motion in two-dimensional echocardiography images

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