Advance Techniques in Medical Imaging under Big Data Analysis: Covid-19 Images

S Zimeras

doi:10.4236/act.2021.101001

Abstract

Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. Accurate segmentation is required for volume determination, 3D rendering, radiation therapy, and surgery planning. In medical images, segmentation has traditionally been done by human experts. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore, automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. Many methods have been proposed to detect and segment 2D shapes, most of which involve template matching. Advanced segmentation techniques called Snakes or active contours have been used, considering deformable models or templates. The main purpose of this work is to apply segmentation techniques for the definition of 3D organs (anatomical structures) when big data information has been stored and must be organized by the doctors for medical diagnosis. The processes would be implemented in the CT images from patients with COVID-19.

Highlights

Segmentation is the process that separates an image into its different parts based on the image characteristics or the region of interest that doctors have been considered
Segmentation has traditionally been done by human experts
Automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use

Summary

Introduction

Segmentation is the process that separates an image into its different parts based on the image characteristics (properties) or the region of interest (organ of interest) that doctors have been considered. In cases where the organs have been moved (due to the body position) or the tumor have been spread inside the body effecting close organs the shape of the region of interest must be segmented very careful due to the importance of the doctor examination. In this case more reflex segmentation techniques must be introduced where they take advantage of the image properties and the elasticity of the potions. These models are very important in a number of inverse visual problems such as the segmentation and reconstruction of objects from images in mathematical ill-posed problems

Main Focus of the Chapter

Solutions and Recommendations

Future Research Directions

Conclusions