Hang Ying and Qin Yu, Department of Biomedical Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
International Journal of Advanced Information and Communication Technology
Received On : 12 April 2021
Revised On : 26 June 2021
Accepted On : 28 August 2021
Published On : 05 October 2021
Volume 08, Issue 10
Pages : 213-221
Abstract
The process of extracting clinical images, for instance, heart images from cameras is full of noises and complexities. As such, the general expenditure for processing these images like resources and time is significantly high, mostly for complex and large amounts of data. In that case, this research contribution utilizes the machine vision-centred method to effectively address these issues. The method significantly incorporates four essential stages with various forms of algorithms to handle the clinical heart images. In the first stage, the smoothing algorithm is utilized to minimize some form of noise. In the second stage, the filtering algorithms are applied for the analysis of images to effectively identify the targeted region. In the third stage, more developed algorithm is utilized to evaluate the image characteristics in the targeted region, identifying the basic image outline. Lastly, the reduction algorithm is meant to transform the original image into significantly precise and smooth pictures. The experimental findings indicate that the machine vision-centred clinical image analysis algorithm might significantly extract fundamental data and attain the most reliable results, contrasting with the most ancient image analysis techniques.
Keywords
Machine Vision; Heart Image; Clinical Image Processing; Digitalized Image.
Cite this article
Hang Ying and Qin Yu, “Big Data Techniques for Clinical Image Analysis, ” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp. 213-221, October. 2021.
Copyright
© 2021 Hang Ying and Qin Yu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.