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To avoid these problems when developing new algorithms, one can try to prototype by working with small example data sets, and then translate the prototyped algorithm to an implementation that gives the developer full control over memory handling. This makes it difficult to implement algorithms that are supposed to work on large data sets with languages that use this kind of automatic memory management. When working with large data sets an image processing task may easily require more memory that is installed in the work station, resulting in the corresponding program being terminated by the operating system before it can finish processing the data. Hence, when running algorithms that allocate memory temporarily, the amount of working memory that is actually used may vastly exceed the amount of memory that the algorithm theoretically requires.
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In addition, researchers inexperienced in structured and object oriented programming tend to write code where temporary variables are kept alive, even though they are no longer used. Without this kind of control the developer can not specify when memory is to be released, instead this is taken care of by, e.g., a garbage collector (cf. how and when working memory is allocated, reused, and released. One of the reasons why these frameworks are easy to use is that they hide the difficulties of memory management, i.e.
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These frameworks make it easy to develop new algorithms also for scientists who have little or no experience in software development. , or the image processing toolbox in Matlab. Image processing frameworks exist that focus on easy prototyping, like SciLab As a result, the software that is used to process this data must handle working memory efficiently. On the other hand, the temporal resolution of data acquisition increases making it possible to monitor dynamic processes, like the beating heart, in real time, which also results in large data sets. Likewise, in medical research 7-Tesla MR scanners that offer imaging at high resolution with a reasonable acquisition time are more and more used and will most likely soon find their way into the clinical routine. For instance, in anthropological research high resolution data sets of teeth that are obtained by using micro CT ( μCT) can easily reach 20GB. On one hand, the spatial resolution is increasing. Common to these imaging modalities is that they all provide gray scale images, and although image fusion steadily gains importance in image analysis, the focus is still on processing these gray scale image, albeit only to properly fuse modalities.Ĭhallenges arise from the ever increasing improvement of the imaging devices. Imaging modalities like ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET) make out the bulk of images used in bio-medical imaging and image processing. 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.
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visual programming, this approach is still accessable to researchers without a background in computer science. 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.Īnother 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. As a result, processing large data sets with these tools becomes increasingly difficult on work station class computers. Specifically, the management of working memory is taken care of automatically, usually at the price of requiring more it. These frameworks are also accessable to researchers that have no background or little in software development because they take care of otherwise complex tasks. Various image processing frameworks exists that make the development of new algorithms easy by using high level programming languages or visual programming. With ever improving acquisition devices, spatial and temporal image resolution increases, and data sets become very large.
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Gray 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.