scholarly journals A REVIEW PAPER ON MEDICAL IMAGE PROCESSING

2017 ◽  
Vol 5 (4RACSIT) ◽  
pp. 21-29
Author(s):  
Shruthishree ◽  
Harshvardhan Tiwari
Keyword(s):  
Image Processing ◽  
Medical Imaging ◽  
Medical Image ◽  
Applied Mathematics ◽  
Correct Diagnosis ◽  
Medical Science ◽  
Medical Image Processing ◽  
Research Field ◽  
The Body ◽  
Biomedical Image Processing

Biomedical image processing has experienced dramatic expansion, and has been an interdisciplinary research field attracting expertise from applied mathematics, computer sciences, engineering, statistics, physics, biology and medicine. Computer-aided diagnostic processing has already become an important part of clinical routine. Accompanied by a rush of new development of high technology and use of various imaging modalities, more challenges arise; for example, how to process and analyze a significant volume of images so that high quality information can be produced for disease diagnoses and treatment. The principal objectives of this course are to provide an introduction to basic concepts and techniques for medical image processing and to promote interests for further study and research in medical imaging processing.The rapid progress of medical science and the invention of various medicines have benefited mankind and the whole civilization. Modern science also has been doing wonders in the surgical field. But, the proper and correct diagnosis of diseases is the primary necessity before the treatment. The more sophisticate the bio-instruments are, better diagnosis will be possible. The medical image plays an important role in clinical diagnosis and therapy of doctor and teaching and researching etc. Medical imaging is often thought of as a way to represent anatomical structures of the body with the help of X-ray computed tomography and magnetic resonance imaging. But often it is more useful for physiologic function rather than anatomy. With the growth of computer and image technology medical imaging has greatly influenced medical field. As the quality of medical imaging affects diagnosis the medical image processing has become a hotspot and the clinical applications wanting to store and retrieve images for future purpose needs some convenient process to store those images in details.

Deep Survey and Comparative Analysis of Medical Image Processing

2020 ◽  
Vol 17 (5) ◽  
pp. 2321-2329
Author(s):  
P. Durgadevi ◽  
S. Vijayalakshmi
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Research Work ◽  
Medical Image Processing ◽  
General Concept ◽  
Research Field ◽  
The Body ◽  
Beam Radiation ◽  
Daily Schedule ◽  
Deep Survey

Biomedical picture handling has encountered emotional extension, and has been an interdisciplinary research field drawing in skill from connected arithmetic, PC sciences, building, insights, material science, science and medication. PC helped indicative handling has just turned into a critical piece of clinical daily schedule. Joined by a surge of new advancement of high innovation and utilization of different imaging modalities, more difficulties emerge; for instance, how to process and dissect a huge volume of pictures so top notch data can be delivered for sickness findings and treatment. The foremost destinations of this course are to give a prologue to essential ideas and strategies for medicinal picture preparing and to advance interests for further examination and research in restorative imaging handling. We will present the Medical Image Processing and abridge related research work here and portray late cutting edge strategies Restorative imaging is regularly seen to assign the arrangement of procedures that noninvasively produce pictures of the inside part of the body. The arrangement of numerical opposite issues has been done with the help of therapeutic imaging. The ultrasonic weight waves and echoes that go inside the tissue to demonstrate the inward structure in medical ultrasonography. In projectional radiography X-beam radiation, which is grouped with respect to its rates and tissue types such as bone, muscle, and fat. As the quality of medical imaging affects analysis that medical image processing has become a vital and the clinical applications wants to store and retrieve images for future purpose needs some suitable process to store those images in details. The paper discusses the general concept of medical image processing.

Biomedical Image Processing Overview

2016 ◽  
pp. 1-12 ◽  
Author(s):  
Monia Mannai Mannai ◽  
Wahiba Ben Abdessalem Karâa
Keyword(s):  
Image Processing ◽  
Medical Imaging ◽  
Medical Image Processing ◽  
Main Process ◽  
Nuclear Medicine Imaging ◽  
Biomedical Image Processing ◽  
Biomedical Image ◽  
X Ray Computed ◽  
Magnetic Resonance Imaging Mri ◽  
High Level

Over the years, there are different sorts of medical imaging have been developed. Where the most known are: X-ray, computed tomography (CT), nuclear medicine imaging (PET, SPECT), ultrasound and magnetic resonance imaging (MRI), each one has its different utilities. Various studies in biomedical informatics present a process to analyze images for extracting the hidden information which can be used after that. Image analysis combines several fields that are classified into two categories; the process of low-level, that requires very little information about the content image and the process of high-level, which may need information about the image content. The topic of this chapter is to introduce the different techniques for medical image processing and to present many research studies in this domain. It includes four stages, firstly, we introduce the most important medical imaging modalities and secondly, we outline the main process of biomedical image.

Recent Advances in Medical Image Processing

2020 ◽  
pp. 1-14
Author(s):  
Zhen Huang ◽  
Qiang Li ◽  
Ju Lu ◽  
Junlin Feng ◽  
Jiajia Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Artificial Intelligence Technology ◽  
Deep Convolution Neural Network

<b><i>Background:</i></b> Application and development of the artificial intelligence technology have generated a profound impact in the field of medical imaging. It helps medical personnel to make an early and more accurate diagnosis. Recently, the deep convolution neural network is emerging as a principal machine learning method in computer vision and has received significant attention in medical imaging. <b><i>Key Message:</i></b> In this paper, we will review recent advances in artificial intelligence, machine learning, and deep convolution neural network, focusing on their applications in medical image processing. To illustrate with a concrete example, we discuss in detail the architecture of a convolution neural network through visualization to help understand its internal working mechanism. <b><i>Summary:</i></b> This review discusses several open questions, current trends, and critical challenges faced by medical image processing and artificial intelligence technology.

scholarly journals Application of CNN Algorithm Based on Chaotic Recursive Diagonal Model in Medical Image Processing

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fangfang Ye ◽  
Sen Xu ◽  
Ting Wang ◽  
Zhangquan Wang ◽  
Tiaojuan Ren
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Medical Image ◽  
Medical Science ◽  
Medical Image Processing ◽  
Living Standards ◽  
Disease Rate ◽  
Application Research ◽  
Paper Briefly ◽  
Gradual Improvement

With the gradual improvement of people’s living standards, the production and drinking of all kinds of food is increasing. People’s disease rate has increased compared with before, which leads to the increasing number of medical image processing. Traditional technology cannot meet most of the needs of medicine. At present, convolutional neural network (CNN) algorithm using chaotic recursive diagonal model has great advantages in medical image processing and has become an indispensable part of most hospitals. This paper briefly introduces the use of medical science and technology in recent years. The hybrid algorithm of CNN in chaotic recursive diagonal model is mainly used for technical research, and the application of this technology in medical image processing is analysed. The CNN algorithm is optimized by using chaotic recursive diagonal model. The results show that the chaotic recursive diagonal model can improve the structure of traditional neural network and improve the efficiency and accuracy of the original CNN algorithm. Then, the application research and comparison of medical image processing are performed according to CNN algorithm and optimized CNN algorithm. The experimental results show that the CNN algorithm optimized by chaotic recursive diagonal model can help medical image automatic processing and patient condition analysis.

Special Issue on Information Medical Science

2012 ◽  
Vol 16 (1) ◽  
pp. 25-25
Author(s):  
Yutaka Hatakeyama
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Social Impact ◽  
Medical Science ◽  
Medical Image Processing ◽  
Medical Data ◽  
Navigation Systems ◽  
Special Issue ◽  
Hospital Data ◽  
Analytical Approaches

One important and interesting application in computational intelligence technology is medical application. Medical engineering research covers such areas as medical image processing. With most health information now described electronically, hospitals are accumulating large amounts of medical data, including imaging, text, and structured materials. This has made ginformation medical scienceh vastly more important in improving healthcare technology. This special issue introduces an application based on hospital data, medical care support systems, and medical image processing. The application deals with analytical approaches, navigation systems, medical data management, and text mining for summary data. The support system shows diagnosis and robotassisted therapy. Medical imaging targets X-ray, blood cell, and ultrasonography images. As guest editors, we can assure the readers that the papers in this special issue have great social impact in this research area and encourage good relations with engineering and medical practice approaches. We thank the contributors and reviewers for introduce these latest achievements in this exciting field.

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