Improved Segmentation of Cardiac MRI Using Efficient Pre-Processing Techniques

Abstract

Cardiac Magnetic Resonance Imaging is a popular non-invasive technique used for assessing the cardiac performance. Automating the segmentation helps in increased diagnosis accuracy in considerably less time and effort. In this paper a novel approach has been proposed to improve the automated segmentation process by increasing the accuracy of segmentation and laying focus on efficient pre-processing of the cardiac Magnetic Resonance (MR) image. The pre-processing module in the proposed method includes noise estimation and efficient denoising of images using discrete total variation based Non local means method.Segmentation accuracy is evaluated using measures such as average perpendicular distance and dice similarity coefficient. The performance of all the segmentation techniques is improved. Further segmentation comparison has also been performed using other state-of-the art noise removal techniques for pre-processing and it was observed that the proposed pre-processing technique outperformed other noise removal techniques in improving the segmentation accuracy.

Revolutionizing Echocardiography: A Comparative Study of Advanced AI Models for Precise Left Ventricular Segmentation

“…Thus, this is not a work focused on high-resolution imaging for further cardiac diagnostics; rather, this is work on the transformational potential that automated segmentation techniques may provide. Such methodologies are designed for the ease of the clinician for better diagnosis and assessment of cardiac health [19], [20]. This work contributed highly to the accuracy and effectiveness of the cardiac function test by using state-of-the-art artificial intelligence models that accurately segment the left ventricle.…”