A Study on Various Segmentation Approaches using Echocardiogram images for promoting Heart Disease Detection

Image segmentation is applicable in various fields, especially medical imaging applications facilitate or automates the depiction of anatomical structures and other regions of interest in various imaging modalities for exploring the internal parts. In the medical field, various imaging modalities like “digital mammography, computed tomography (CT), Magnetic resonance imaging (MRI), Echocardiography (ECG)”, and other imaging modalities offer an efficient way for non-invasively determining the anatomy of a patient. These methodologies have hugely amplified the knowledge of diseased and normal anatomy for clinical research. In processing the cardiac images, automatic segmentation of anatomical structures is a complicated issue owing to the artifacts and acoustic interferences inherited in ECG images. Moreover, the techniques to perform segmentations differ broadly based on the particular imaging modality, application, and also other constraints. There is recently no single segmentation approach that has achieved promising outcomes for each medical image. On the other hand, the selection of a suitable segmentation approach is another complicated issue in the medical field. Thus, this research work explores various segmentation techniques utilized for processing and segmenting ECG images with the aid of detecting heart diseases. The goal of this research work is to provide a tutorial for the technologists and clinicians to provide a huge range of segmentation methods to perform an effective segmentation of ECG images to provide faster and more reliable outcomes for offering timely treatment. Moreover, analyzing different segmentation methods systematically and comprehensively with their performance analysis offers better cardiac or heart disease functional evaluation.