Advanced Algorithms for Medical Image Processing
T. Rajendran1, K. P. Sridhar2, S. Manimurugan3, *, S. Deepa4
Identifiers and Pagination:Year: 2019
Issue: Suppl-1, M1
First Page: 102
Last Page: 102
Publisher Id: TOBEJ-13-102
Article History:Electronic publication date: 17/12/2019
Collection year: 2019
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Medical image processing in healthcare and biomedical research centers has demonstrated predominant advantages to the world . Lately, a lot of research articles were published, featuring the need for Medical image processing in various health issues of the human body and its major internal organs. The quintessence of diagnostic imaging algorithms is to facilitate the process of observing a patient's body and its major internal organs with higher efficiency and accuracy in recognition diseases or injuries . Normally, diagnostic reports of patients incorporate radiographic representation of human body or its parts; utilization of various medical image processing techniques improves the procedure applied to the patients. Algorithms plays a major role in the artifacts filtering, segmentation, feature extraction and classification, which will expedite the disease diagnosis. In the recent two decades, deep learning algorithms played a vital role in the medical image processing with improved performance than the conventional machine learning algorithms, such advanced algorithms that show greater accuracy in the classification of cancer cells, lesions, organ segmentation and medical image enhancement with an average accuracy ranges of 96% to 98% . In the mere future, advanced computational and learning approaches like Deep learning and hybrid deep learning approaches will play a vital role in the field of diagnostic imaging with most substantial clinical impact on medical imaging examinations to provide improved decision support in medical image interpretation and analysis [4-10].