Digital Image Processing Employing Image Enhancement and Segmentation Techniques Assignment | Get Paper Help

I’ve attached a project brief to out line the project. I just need a literature review on MATLAB, Digital image processing, Different thresholding techniques (adaptive, multilevel, otsu), literature about types of medical scans (including CT, MRI, Ultrasound etc).

Should have around 40-60 different literature entries.

Digital Image Processing Employing Image Enhancement and Segmentation Techniques

Digital image processing consists of the manipulation of images using digital computers. Its use has been increasing exponentially in the last decades. Its applications range from medicine to entertainment, remote sensing, etc.  The discipline of digital image processing is a vast one, encompassing digital signal processing techniques that are specific to images. The processing of digital images can be divided into several classes: image enhancement, image restoration, image analysis, and image compression. In image enhancement, an image is manipulated, mostly by heuristic techniques, so that a human viewer can extract useful information from it. Image restoration techniques aim at processing corrupted images from which there is a statistical or mathematical description of the degradation so that it can be reverted. Image analysis techniques permit that an image be processed so that information can be automatically extracted from it. Examples of image analysis are image segmentation, edge extraction, and texture and motion analysis. An important characteristic of images is the huge amount of information required to represent them.

Image segmentation is an essential process that partition a digital image into multiple segments (sets of pixels, also known as image objects). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyse. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.

Challenges:

Image processing applied to medical research has made many clinical diagnosis protocols and treatment plans more efficient and accurate. For example, a sophisticated detection algorithm applied to digital mammogram images can aid in the early detection of breast cancer. However, image processing applications usually require significant implementation and evaluation effort before they can be accepted for clinical use. The common necessities during the implementation and evaluation of these applications are image data and the workstations that allow the display and manipulation of the images.

Expected outcomes:

A powerful algorithm needs to develop that would provide meaningful insight about the numerous sample images for testing and debugging the algorithm. This could lead for efficient disease-specific diagnostic analysis and in support of training purposes for automation in future