Cardiac Cavity Segmentation in Echocardiography Using Triangle Equation

In this paper, cardiac cavity segmentation in echocardiography is proposed. The method uses triangle equation algorithms to detect and reconstruct the border. Prior to the application of both algorithms, some preprocessings have to be carried out. The first step is high boost filter to enhance high frequency component while still keeping the low frequency component. The second step is applying morphological and thresholding operations to eliminate noise and convert the image into binary image. The third step is negative laplacian filter to apply edge detector. The fourth step is region filter to eliminate small region. The last step is using triangle equation to detect and reconstruct the imprecise border. This technique is able to perform segmentation and detect border of cardiac cavity from echocardiographics sequences. Keywords: cardiac cavity, high boost filter, morphology, negative laplacian, region filter, and triangle equation

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

A novel myocardium segmentation approach based on neutrosophic active contour model

Automatic delineation of the myocardium in echocardiography can assist ra- diologists to diagnosis heart problems. However, it is still challenging to distinguish myocardium from other tissue due to a low signal-to-noise ratio, low contrast, vague boundary, and speckle noise

Extraction of epi-cardium contours from unseen images using a shape database

Accurate segmentation of the myocardium in cardiac magnetic resonance images can be restricted by image noise and low discrimination between the epi-cardium boundary and other organs. Segmentation of the epi-cardium is important for the calculation of left ventricle mass. In this paper we propose a novel method of epi-cardium segmentation, which firstly segments the left ventricle cavity. The epi-cardium boundary is found using the edge information in the image, and where such information is lacking it enhances the shape with the best fitting scaled segment, taken from a database of expertly assisted hand segmented images. In the final stage the segments are connected using a natural closed spline. The method was evaluated using a leave-one-out strategy on 24 volumes and calculates the coefficient of determination as 0.93 and a root mean square of the point to curve error of 1.54 mm when compared to manually segmented images

Use of Image Processing Techniques for the Analysis of Echocardiographic Images

Echocardiography is a medical imaging modality that uses ultrasound in order to obtain cross sectional views of the heart. The basic problem in the use of echocardiography is the ability to obtain a reliable set of physical parameters related to cardiac status, so that assessment of heart disease can be performed automatically. This work overviews different image processing techniques used in the analysis of two dimensional echocardiographic images. After reviewing how the echocardiographic image formation process works, an outline of the general processing steps from image acquisition to automatic detection of important features is presented. Special emphasis on cardiac image segmentation is presented. In particular, a relaxation algorithm for image segmentation is discussed. Also, echocardiographic image segmentation using temporal analysis and a new algorithm for boundary detection is described. Measurements of left ventricular area, wall thickness, and ejection fraction is also presented. Shape analysis is introduced as a tool for echocardiographic image analysis. A high level description of the left ventricular boundaries using curvature is proposed. Curvature analysis attempts to identify stable landmarks during the beating process, muscles. Tracking these landmarks aids in the detection of abnormal heart contractions. Finally the use of expert systems is proposed in the analysis of echocardiographic images

Doctor of Philosophy

dissertationCongenital heart defects are classes of birth defects that affect the structure and function of the heart. These defects are attributed to the abnormal or incomplete development of a fetal heart during the first few weeks following conception. The overall detection rate of congenital heart defects during routine prenatal examination is low. This is attributed to the insufficient number of trained personnel in many local health centers where many cases of congenital heart defects go undetected. This dissertation presents a system to identify congenital heart defects to improve pregnancy outcomes and increase their detection rates. The system was developed and its performance assessed in identifying the presence of ventricular defects (congenital heart defects that affect the size of the ventricles) using four-dimensional fetal chocardiographic images. The designed system consists of three components: 1) a fetal heart location estimation component, 2) a fetal heart chamber segmentation component, and 3) a detection component that detects congenital heart defects from the segmented chambers. The location estimation component is used to isolate a fetal heart in any four-dimensional fetal echocardiographic image. It uses a hybrid region of interest extraction method that is robust to speckle noise degradation inherent in all ultrasound images. The location estimation method's performance was analyzed on 130 four-dimensional fetal echocardiographic images by comparison with manually identified fetal heart region of interest. The location estimation method showed good agreement with the manually identified standard using four quantitative indexes: Jaccard index, Sørenson-Dice index, Sensitivity index and Specificity index. The average values of these indexes were measured at 80.70%, 89.19%, 91.04%, and 99.17%, respectively. The fetal heart chamber segmentation component uses velocity vector field estimates computed on frames contained in a four-dimensional image to identify the fetal heart chambers. The velocity vector fields are computed using a histogram-based optical flow technique which is formulated on local image characteristics to reduces the effect of speckle noise and nonuniform echogenicity on the velocity vector field estimates. Features based on the velocity vector field estimates, voxel brightness/intensity values, and voxel Cartesian coordinate positions were extracted and used with kernel k-means algorithm to identify the individual chambers. The segmentation method's performance was evaluated on 130 images from 31 patients by comparing the segmentation results with manually identified fetal heart chambers. Evaluation was based on the Sørenson-Dice index, the absolute volume difference and the Hausdorff distance, with each resulting in per patient average values of 69.92%, 22.08%, and 2.82 mm, respectively. The detection component uses the volumes of the identified fetal heart chambers to flag the possible occurrence of hypoplastic left heart syndrome, a type of congenital heart defect. An empirical volume threshold defined on the relative ratio of adjacent fetal heart chamber volumes obtained manually is used in the detection process. The performance of the detection procedure was assessed by comparison with a set of images with confirmed diagnosis of hypoplastic left heart syndrome and a control group of normal fetal hearts. Of the 130 images considered 18 of 20 (90%) fetal hearts were correctly detected as having hypoplastic left heart syndrome and 84 of 110 (76.36%) fetal hearts were correctly detected as normal in the control group. The results show that the detection system performs better than the overall detection rate for congenital heart defect which is reported to be between 30% and 60%