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.

scholarly journals MIPP: A Medical Image Processing Platform based on ITK, VTK and Eclipse RCP

2007 ◽  
Author(s):  
Roland Swoboda ◽  
Gerald Zwettler ◽  
Franz Pfeifer ◽  
Werner Backfrieder
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Software Systems ◽  
Modular Architecture ◽  
Application Data ◽  
New Applications ◽  
2D And 3D ◽  
Processing Platform ◽  
Eclipse Rcp

A novel platform for realizing software prototypes in the area of medical image processing, analysis and visualization is introduced: The "Medical Image Processing Platform" (MIPP). A novel feature is the combination of ITK/VTK with Java/Eclipse RCP, four potential technologies complementing each other. While ITK and VTK provide comprehensive functionality for image processing and visualization, Java and Eclipse RCP facilitate the development of a modern and rich application.RCP offers everything for creating plug-in based software. The benefits of integrating ITK and VTK in such a modular architecture are reusability and extensibility. Functionality usually found in software for medical image processing and visualization (2D and 3D viewers, DICOM and application data management, etc.) is provided by the MIPP platform as plug-ins, can be easily reused by new applications and needn't be re-implemented. Developers can extend the platform's features by contributing new plug-ins and, again, make these available for others.The paper describes the architecture of the MIPP platform and outlines its most important functionality. Finally, three clinical software systems implemented with MIPP are presented.

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.

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.

Recent advances in medical image processing for the evaluation of chronic kidney disease

2021 ◽  
Vol 69 ◽  
pp. 101960
Author(s):  
Israa Alnazer ◽  
Pascal Bourdon ◽  
Thierry Urruty ◽  
Omar Falou ◽  
Mohamad Khalil ◽  
...  
Keyword(s):  
Image Processing ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Medical Image ◽  
Medical Image Processing ◽  
Recent Advances
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