![]() GRAPE allows graphical development and execution of image analysis algorithms for inline, real-time, and adaptive MRI applications.Īn image processing pipeline to detect and segment nuclei in muscle fiber microscopic images. GRAPE was successfully used to implement and execute three applications in MRI of the brain, performed on a 3.0-T MRI scanner: (i) a multi-parametric pipeline for segmenting the brain tissue and detecting lesions in multiple sclerosis (MS), (ii) patient-specific optimization of the 3D fluid-attenuated inversion recovery MRI scan parameters to enhance the contrast of brain lesions in MS, and (iii) an algebraic image method for combining two MR images for improved lesion contrast. The application of GRAPE is demonstrated in multiple MRI applications, and the software is described in detail for both the user and the developer. The platform provides the tools and infrastructure to design new algorithms, and build and execute an array of image analysis routines, and provides a mechanism to include existing analysis libraries, all within a graphical environment. GRAPE is an open-source project implemented in the Qt C++ framework to enable graphical creation, execution, and debugging of real-time image analysis algorithms integrated with the MRI scanner. We present a platform, GRAphical Pipeline Environment (GRAPE), to facilitate the development of patient-adaptive magnetic resonance imaging (MRI) protocols. Gabr, Refaat E Tefera, Getaneh B Allen, William J Pednekar, Amol S Narayana, Ponnada A GRAPE: a graphical pipeline environment for image analysis in adaptive magnetic resonance imaging. The proposed goal-oriented strategy should help biologists to better apprehend image analysis in the context of their research and should allow them to efficiently interact with image processing specialists. The originality of the proposed approach is to follow an iterative, backwards procedure from the target objectives of analysis. This paper presents a general guideline methodology to rationally address the design of image processing and analysis pipelines. Tens, if not hundreds, of algorithms and methods have been developed and integrated into image analysis software, resulting in a combinatorial explosion of possible image processing sequences. Many commercial or free bioimage analysis software are now available and several textbooks and reviews have presented the mathematical and computational fundamentals of image processing and analysis. Each new application requires the design of a specific image analysis pipeline, by assembling a series of image processing operations. With the progress of microscopy techniques and the rapidly growing amounts of acquired imaging data, there is an increased need for automated image processing and analysis solutions in biological studies. ![]() Designing Image Analysis Pipelines in Light Microscopy: A Rational Approach.Īrganda-Carreras, Ignacio Andrey, Philippe
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