Curvelet transform based myelin quantification from real time clinical data

Myelin segmentation from real-time conventional MRI is a useful and challenging task in the medical field. In this work, myelin is segmented from real-time T1-weighted MRI after the application of suitable pre-processing methods. Myelin quantification from conventional magnetic resonance imaging (MRI) such as T1-weighted MRI is an innovative and challenging task in the research field. In the literature, no effort is accessible to segment and quantify the myelin from conventional MRI. Working with clinical data is an immensely demanding task. It is impossible to segment and quantify myelin directly from clinical data. It was necessary to employ pre-processing procedures before segmentation. In this task, Curvelet transform based adaptive Gaussian notch filtering with dynamic stretching is used before segmentation. Different image enhancement methods are compared, When compared to other image enhancement methods if we apply dynamic stretching then the segmented area is very nearer to the values calculated by the radiologists. The segmentation accuracy and other metrics also high in this case. The area from segmented myelin is calculated and the values are compared to the values calculated by the radiologist. The values are nearer to manual calculation in the case of Curvelet transform based adative Gaussian notch filtering with dynamic stretching.