Segmentation of anatomical structures of the heart based on echocardiography

Nowadays, many practical applications in the field of medical image processing require valid and reliable segmentation of images in the capacity of input data. Some of the commonly used imaging techniques are ultrasound, CT, and MRI. However, the main difference between the other medical imaging equipment and EchoCG is that it is safer, low cost, non-invasive and non-traumatic. Three-dimensional EchoCG is a non-invasive imaging modality that is complementary and supplementary to two-dimensional imaging and can be used to examine the cardiovascular function and anatomy in different medical settings. The challenging problems, presented by EchoCG image processing, such as speckle phenomena, noise, temporary non-stationarity of processes, unsharp boundaries, attenuation, etc. forced us to consider and compare existing methods and then to develop an innovative approach that can tackle the problems connected with clinical applications. Actual studies are related to the analysis and development of a cardiac parameters automatic detection system by EchoCG that will provide new data on the dynamics of changes in cardiac parameters and improve the accuracy and reliability of the diagnosis. Research study in image segmentation has highlighted the capabilities of image-based methods for medical applications. The focus of the research is both theoretical and practical aspects of the application of the methods. Some of the segmentation approaches can be interesting for the imaging and medical community. Performance evaluation is carried out by comparing the borders, obtained from the considered methods to those manually prescribed by a medical specialist. Promising results demonstrate the possibilities and the limitations of each technique for image segmentation problems. The developed approach allows: to eliminate errors in calculating the geometric parameters of the heart; perform the necessary conditions, such as speed, accuracy, reliability; build a master model that will be an indispensable assistant for operations on a beating heart

Ray-based segmentation algorithm for medical imaging

In this study, we present a segmentation algorithm based on ray casting and border point detection. The algorithm’s main parameter is the number of emitted rays, which defines the resolution of the object’s boundary. The value of this parameter depends on the shape of the target region. For instance, 8 rays are enough to segment the left ventricle with the average Dice similarity coefficient approximately equal to 85%. Having gathered the data of rays, the training datasets had a relatively high level of class imbalance (up to 90%). To cope with this issue, ensemble-based classifiers used to manage imbalanced datasets such as AdaBoost.M2, RUSBoost, UnderBagging, SMOTEBagging, SMOTEBoost were used for border detection. For estimation of the accuracy and processing time, the proposed algorithm used a cardiac MRI dataset of the University of York and brain tumour dataset of Southern Medical University. The highest Dice similarity coefficients for the heart and brain tumour segmentation, equal to 86.5 ± 6.9% and 89.5 ± 6.7%, respectively, were achieved by the proposed algorithm. The segmentation time of a cardiac frame equals 4.1 ± 2.3 ms and 20.2 ± 23.6 ms for 8 and 64 rays, respectively. Brain tumour segmentation took 5.1 ± 1.1 ms and 16.0 ± 3.0 ms for 8 and 64 rays respectively. By testing the different medical imaging cases, the proposed algorithm is not time-consuming and highly accurate for convex and closed objects. The scalability of the algorithm allows implementing different border detection techniques working in parallel