Medical images provide information that can be used to detect and diagnose a variety of diseases and abnormalities. Because cardiovascular disorders are the primary cause of death and cancer is the second, good early identification can aid in the reduction of cancer mortality rates. There are different medical imaging modalities that the radiologists use in order to study the organ or tissue structure. The significance of each imaging modality is changing depending on the medical field. The goal of this research is to give a review that shows new machine learning applications for medical image processing and gives a review of the field’s progress. The classification of medical photographs of various sections of the human body is the focus of this review. Additional information on methodology developed using various machine learning algorithms to aid in the classification of tumors, non-tumors, and other dense masses is available. It begins with an introduction of several medical imaging modalities, followed by a discussion of various machine learning algorithms to segmentation and feature extraction.
The process of segmentation of the cardiac image aims to limit the inner and outer walls of the heart to segment all or portions of the organ's boundaries. Due to its accurate morphological information, magnetic resonance (MR) images are typically used in cardiac segmentation as they provide the best contrast of soft tissues. The data acquired from the resulting cardiac images simplifies not only the laboratory assessment but also other conventional diagnostic techniques that provide several useful measures to evaluate and diagnose cardiovascular disease (CVD). Therefore, scientists have offered numerous segmentation schemes to remedy these issues for producing more accurate diagnosis. This work conducts a comparative study among several medical image segmentation schemes to find the most accurate segmentation quality based on performance measurements such as Hausdorff distance, peak signal-to-noise ratio (PSNR), and similarity Dice coefficient. This paper utilizes a multi-axis Cardiac Magnetic Resonance Image (CMRI) database in three axes for several case studies which provide the results of various segmentation schemes. Additionally, throughout the experiments, the performance time of every segmentation scheme is estimated and utilized in the comparison process as an additional performance factor.
Objective of image segmentation is to group regions with coherent characteristics. This paper presents a comparative study on Random walk techniques for Left ventricle segmentation. In this paper, LV cavity segmentation is demonstrated for each technique using multi-slice MR cardiac images. The quality of segmentation process is measured by comparing the resulted images of different Random walk techniques using DICE coefficient, PSNR and Hausdorff distance. The comparison includes measuring the execution time of each technique.
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