Advances in Medical Image Computing

2009 ◽  
Vol 48 (04) ◽  
pp. 311-313 ◽  
Author(s):  
T. M. Deserno ◽  
H. Handels ◽  
H.-P. Meinzer ◽  
T. Tolxdorff
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Image Data ◽  
Medical Diagnostics ◽  
Patient Treatment ◽  
High Tech ◽  
Medical Image Computing ◽  
Tools And Techniques ◽  
Medical Disciplines

Summary Objectives: Medical image computing has become a key technology in high-tech applications in medicine and an ubiquitous part of modern imaging systems and the related processes of clinical diagnosis and intervention. Over the past years significant progress has been made in the field, both on methodological and on application level. Despite this progress there are still big challenges to meet in order to establish image processing routinely in health care. In this issue, selected contributions of the German Conference on Medical Image Processing (BVM) are assembled to present latest advances in the field of medical image computing. Methods: The winners of scientific awards of the German Conference on Medical Image Processing (BVM) 2008 were invited to submit a manuscript on their latest developments and results for possible publication in Methods of Information in Medicine. Finally, seven excellent papers were selected to describe important aspects of recent advances in the field of medical image processing. Results: The selected papers give an impression of the breadth and heterogeneity of new developments. New methods for improved image segmentation, non-linear image registration and modeling of organs are presented together with applications of image analysis methods in different medical disciplines. Furthermore, state-of-the-art tools and techniques to support the development and evaluation of medical image processing systems in practice are described. Conclusions: The selected articles describe different aspects of the intense development in medical image computing. The image processing methods presented enable new insights into the patient’s image data and have the future potential to improve medical diagnostics and patient treatment.

Advances in Medical Image Computing

2007 ◽  
Vol 46 (03) ◽  
pp. 251-253 ◽  
Author(s):  
A. Horsch ◽  
H.-P. Meinzer ◽  
H. Handels
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Medical Diagnostics ◽  
High Tech ◽  
Computer Assisted ◽  
Daily Routine ◽  
The Future ◽  
Medical Image Computing ◽  
Medical Disciplines

Summary Objectives: Medical image computing has become a key technology in high-tech applicationsin medicine. Nowadays, medical image computing techniques are applied in daily routine in various medical disciplines. However, further developments are needed to improve computer-aided diagnoses and computer-assisted therapy planning and performance in the future. In this issue selected contributions of the German Conference on Medical Image Processing (BVM) are assembled to present latest advances in the field of medical image computing. Methods: The winners of scientific awards of the German Conferences on Medical Image Processing (BVM) 2005 and 2006 were invited to submit a manuscript on their latest developments and results for possible publication in Methods of Information in Medicine. Finally, eleven excellent papers were selected to describe important aspects of recent advances in the field of medical image computing. Results: The selected papers give an impression of the broad range and heterogeneity of new developments in the field of medical image computing. New methods for improved image reconstruction, image segmentation, modelling of organs, as well as methodical improvements of non-linear image registration algorithms are presented together with applications of image analysis methods in different medical disciplines. Conclusions: The selected articles describe different aspects of the intense development in medical image computing. The image computing methods presented enable new insights into the patient’s image data and have the future potential to improve medical diagnostics and patient treatment.

scholarly journals MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation

2019 ◽  
Author(s):  
J-Donald Tournier ◽  
Robert Smith ◽  
David Raffelt ◽  
Rami Tabbara ◽  
Thijs Dhollander ◽  
...  
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Image Data ◽  
Data Access ◽  
General Purpose ◽  
Computational Performance ◽  
Cross Platform ◽  
High Level ◽  
New Applications

AbstractMRtrix3 is an open-source, cross-platform software package for medical image processing, analysis and visualization, with a particular emphasis on the investigation of the brain using diffusion MRI. It is implemented using a fast, modular and flexible general-purpose code framework for image data access and manipulation, enabling efficient development of new applications, whilst retaining high computational performance and a consistent command-line interface between applications. In this article, we provide a high-level overview of the features of the MRtrix3 framework and general-purpose image processing applications provided with the software.

Deep Learning Applications in Medical Imaging

2021 ◽  
pp. 156-177
Author(s):  
Janani Viswanathan ◽  
N. Saranya ◽  
Abinaya Inbamani
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Processing ◽  
Medical Diagnostics ◽  
Healthcare Sector ◽  
Computational Techniques ◽  
Healthcare Applications ◽  
Medical Sector

Deep learning (DL) and artificial intelligence (AI) are emerging tools in the healthcare sector for medical diagnostics. This chapter elaborates on general reasons for the popularity of computational techniques such as deep learning and machine learning (ML) applications in the medical image processing domain. The initial part of this chapter focuses on reviewing the fundamental concepts of DL algorithms, competence with machine learning, need in healthcare, applications, and challenges in medical image processing. Doing so allows understanding the reasons for the construction of all of them and offers a different view on various domains in the medical sector. The tools and technology required for DL, selection, implementation, optimization, and testing are discussed with respect to an application of cancer detection. Thus, this chapter gives an overall vision of deep learning concepts related to biomedical research.

Medical Image Computing for Computer-supported Diagnostics and Therapy

2009 ◽  
Vol 48 (01) ◽  
pp. 11-17 ◽  
Author(s):  
J. Ehrhardt ◽  
H. Handels
Keyword(s):  
Virtual Reality ◽  
Image Analysis ◽  
Medical Image ◽  
Image Data ◽  
Computing Methods ◽  
Patient Treatment ◽  
Clinical Workflow ◽  
The Future ◽  
Medical Image Computing ◽  
Segmentation Image

Summary Objectives: Medical image computing has become one of the most challenging fields in medical informatics. In image-based diagnostics of the future software assistance will become more and more important, and image analysis systems integrating advanced image computing methods are needed to extract quantitative image parameters to characterize the state and changes of image structures of interest (e.g. tumors, organs, vessels, bones etc.) in a reproducible and objective way. Furthermore, in the field of software-assisted and navigated surgery medical image computing methods play a key role and have opened up new perspectives for patient treatment. However, further developments are needed to increase the grade of automation, accuracy, reproducibility and robustness. Moreover, the systems developed have to be integrated into the clinical workflow. Methods: For the development of advanced image computing systems methods of different scientific fields have to be adapted and used in combination. The principal methodologies in medical image computing are the following: image segmentation, image registration, image analysis for quantification and computer assisted image interpretation, modeling and simulation as well as visualization and virtual reality. Especially, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients and will gain importance in diagnostic and therapy of the future. Results: From a methodical point of view the authors identify the following future trends and perspectives in medical image computing: development of optimized application-specific systems and integration into the clinical workflow, enhanced computational models for image analysis and virtual reality training systems, integration of different image computing methods, further integration of multimodal image data and biosignals and advanced methods for 4D medical image computing. Conclusions: The development of image analysis systems for diagnostic support or operation planning is a complex interdisciplinary process. Image computing methods enable new insights into the patient’s image data and have the future potential to improve medical diagnostics and patient treatment.

Medical Image Region Extraction and 3D Modeling Based on Approximating Curves

2011 ◽  
Vol 222 ◽  
pp. 285-288 ◽  
Author(s):  
Katrina Bolochko ◽  
Aleksandrs Sisojevs ◽  
Aleksandrs Glazs ◽  
Ardis Platkajis
Keyword(s):  
Image Processing ◽  
3D Modeling ◽  
Medical Image ◽  
3D Visualization ◽  
Medical Image Processing ◽  
Region Of Interest ◽  
Medical Diagnostics ◽  
Image Region ◽  
Different Types ◽  
Region Extraction

This work describes several methods that intend to solve such medical image processing tasks as extraction and 3D visualization of the region of interest (ROI). The proposed methods were tested on the medical images of a brain acquired by computer tomography and proven to be applicable to different types of ROI, resulting in a possible visualization of several ROI at once, i.e. pathology and the head of a patient. The results can be used to provide practical improvements to the reliability of medical diagnostics.

Image Conversion from DICOM into Bitmap Based on VC Platform

2012 ◽  
Vol 591-593 ◽  
pp. 2487-2490
Author(s):  
Wen Juan Gu ◽  
Li Nan Fan ◽  
Shen Shen Sun ◽  
Xiang Li Hu ◽  
Xin Wang
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Rapid Development ◽  
Processing System ◽  
Medical Image Processing ◽  
Image Data ◽  
Research Direction ◽  
Image Conversion ◽  
Medical Image Data ◽  
Processing Platform

With the rapid development of medical equipment (such as CT, MRT, PACS), medical image data that need to process has become increasingly rich, which makes the design of medical image processing platform become a popular research direction. Through analyzing one medical image with communication standard (the DICOM protocol) in this paper. Research in-depth how medical images are stored in computer, and how to transform a medical image into the format of bitmap so that to see a medical image on screen , and it is good to software workers and doctors ,for it can help them have a clearer understanding of the medical image, and help them to see diseases clearly, it is also the basis for the subsequent design of medical image processing system .

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

scholarly journals The Constantly Evolving Role of Medical Image Processing in Oncology: From Traditional Medical Image Processing to Imaging Biomarkers and Radiomics

2021 ◽  
Vol 7 (8) ◽  
pp. 124
Author(s):  
Kostas Marias
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Medical Image ◽  
Biomarker Discovery ◽  
Medical Image Processing ◽  
Machine Learning Techniques ◽  
Imaging Biomarker ◽  
Imaging Biomarkers ◽  
Precision Oncology

The role of medical image computing in oncology is growing stronger, not least due to the unprecedented advancement of computational AI techniques, providing a technological bridge between radiology and oncology, which could significantly accelerate the advancement of precision medicine throughout the cancer care continuum. Medical image processing has been an active field of research for more than three decades, focusing initially on traditional image analysis tasks such as registration segmentation, fusion, and contrast optimization. However, with the advancement of model-based medical image processing, the field of imaging biomarker discovery has focused on transforming functional imaging data into meaningful biomarkers that are able to provide insight into a tumor’s pathophysiology. More recently, the advancement of high-performance computing, in conjunction with the availability of large medical imaging datasets, has enabled the deployment of sophisticated machine learning techniques in the context of radiomics and deep learning modeling. This paper reviews and discusses the evolving role of image analysis and processing through the lens of the abovementioned developments, which hold promise for accelerating precision oncology, in the sense of improved diagnosis, prognosis, and treatment planning of cancer.

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