MIA - A free and open source software for gray scale medical image analysis

Abstract

BackgroundGray scale images make the bulk of data in bio-medical image analysis, and hence, the main focus of many image processing tasks lies in the processing of these monochrome images. With ever improving acquisition devices, spatial and temporal image resolution increases, and data sets become very large.Various image processing frameworks exists that make the development of new algorithms easy by using high level programming languages or visual programming. These frameworks are also accessable to researchers that have no background or little in software development because they take care of otherwise complex tasks. Specifically, the management of working memory is taken care of automatically, usually at the price of requiring more it. As a result, processing large data sets with these tools becomes increasingly difficult on work station class computers.One alternative to using these high level processing tools is the development of new algorithms in a languages like C++, that gives the developer full control over how memory is handled, but the resulting workflow for the prototyping of new algorithms is rather time intensive, and also not appropriate for a researcher with little or no knowledge in software development.Another alternative is in using command line tools that run image processing tasks, use the hard disk to store intermediate results, and provide automation by using shell scripts. Although not as convenient as, e.g. visual programming, this approach is still accessable to researchers without a background in computer science. However, only few tools exist that provide this kind of processing interface, they are usually quite task specific, and don’t provide an clear approach when one wants to shape a new command line tool from a prototype shell script.ResultsThe proposed framework, MIA, provides a combination of command line tools, plug-ins, and libraries that make it possible to run image processing tasks interactively in a command shell and to prototype by using the according shell scripting language. Since the hard disk becomes the temporal storage memory management is usually a non-issue in the prototyping phase. By using string-based descriptions for filters, optimizers, and the likes, the transition from shell scripts to full fledged programs implemented in C++ is also made easy. In addition, its design based on atomic plug-ins and single tasks command line tools makes it easy to extend MIA, usually without the requirement to touch or recompile existing code.ConclusionIn this article, we describe the general design of MIA, a general purpouse framework for gray scale image processing. We demonstrated the applicability of the software with example applications from three different research scenarios, namely motion compensation in myocardial perfusion imaging, the processing of high resolution image data that arises in virtual anthropology, and retrospective analysis of treatment outcome in orthognathic surgery. With MIA prototyping algorithms by using shell scripts that combine small, single...

On Post-Acquisition Motion Compensation for Prostate Perfusion Analysis

“…Note that on one hand, the redistribution of the data used is restricted because of privacy laws, and on the other hand, that the authors no longer have access to this data. Also note that the implementation of the ICA-based motion compensation method [2] used in this work has since been changed in [3] to follow [4]. Nevertheless, since the claims made in the paper are only of a qualitative nature, it should still be possible to replicate the results.…”

“…In this work, we only put the focus on an qualitative analysis ofthe registration analysis based on visual inspection of time-profiles and videos. In addition, we provide a free and open source implementation of all the tested methods [3].…”

“…The following motion compensation schemes were considered and implemented by using the a freely available toolkit for gray scale image processing [3]: a) Serial:: Here, only images in temporal succession are registered and then the transformations are applied accumulated. As registration criterion we used a weighted combination of NGF and SSD as proposed in [8].…”

Comparison of Linear and Non-linear 2D+T Registration Methods for DE-MRI Cardiac Perfusion Studies

“…For best reproducibility the experiments were obtained by running the motion compensation within a virtualized Ubuntu Linux 14.04 (i386) environment that can be downloaded as virtual hard disk [13], but with MIA [1] Specifically, in many cases QUASI-P fails to provide motion compensation at the beginning of the series and even deforms the image area containing the LV myocardium beyond recognition ( Fig. 1 (a)), a problem with QUASI-P that that can be attributed to the generation of synthetic references by using linear interpolation between time steps [3].…”

“…All these methods were implemented by using a freely available toolkit for gray scale image processing MIA [1],…”