Survey on Enhancement of Nuclear Cardiology Images Based on Image Processing Techniques for Diagnosis of Ischemic Patients

A blockage of the blood vessels feeding the area causes ischemia, which is defined as a localized absence of blood flow. If an organ is not getting enough oxygen and blood flow, such as the heart, or brain it is said to be ischemic. To describe the progress made in the detection, characterization, and prediction of cardiac ischemia using Machine Learning (ML)-based Artificial Intelligence (AI) processes including together Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET). In the relatively recent past, the use of machine learning algorithms in the area of cardiology has increasingly centered on image processing for the goals of diagnosis, prognosis, and type identification analysis. The main objective of this study was to improve Nuclear Cardiology (NC) images for cardiac ischemia patients using Image Processing techniques. Clinical research is being significantly changed by the AI application. Through the examination of very big datasets and the recent convergence of potent ML algorithms and rising computer capacity, it has been shown that experimental categorization as well as prediction may be improved through examining extremely high-dimensional non-linear features. Machine learning is improving the identification of perfusion abnormalities in myocardial ischemia and predicting adverse cardiovascular events at the patient level.