Abstract
We are in the era of Big Data. Data are everywhere! They are part of the information processing system of all sectors, from science to government, from healthcare to media, from university to real time commerce. In healthcare, in particular, the increasing use of medical devices, such as the Computed Tomography (CT) and the Magnetic Resonance Imaging (MRI) has led to the generation of large amounts of data, including image data. Bioinformatics solutions provide an effective approach for image data processing techniques whose final aim is to support scientists and physicians in diagnosis and therapies. This paper surveys bioinformatics toolkits for medical imaging.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Erickson, B.J., Korfiatis, P., Akkus, Z., Kline, T., Philbrick, K.: Toolkits and Libraries for Deep Learning. J. Digit. Imaging (2017)
Muller, P., Schurmann, M., Guck, J.: ODTbrain: a Python library for full-view, dense diffraction tomography. BMC Bioinf. (2015)
Uhlmann, V., Singh, S., Carpenter, A.E.: CP-CHARM: segmentation-free image classification made accessible. BMC Bioinf. (2016)
Orlov, N., Shamir, L., Macura, T., Johnston, J., Eckley, D.M., Goldberg, I.G.: WND-CHARM: multi-purpose image classification using compound image transforms. Pattern Recognit. Lett. (2008)
Dao, D., Fraser, A.N., Hung, J., Ljosa, V., Singh, S., Carpenter, A.E.: Cell ProfilerAnalyst: interactive data exploration, analysis and classification of large biological image sets. Bioinf. (2016)
Hiner, M.C., Rueden, C.T., Eliceiri, K.W.: SCIFIO: an extensible framework to support scientific image formats. BMC Bioinf. (2016)
Gardner, D., et al.: The neuroscience information framework: a data and knowledge environment for neuroscience. Neuroinformatics (2008)
Kolling, J., Langenkamper, D., Abouna, S., Khan, M., Nattkemper, T.W.: WHIDE’a web tool for visual data mining colocation patterns in multivariate bioimages. Bioinf. (2012)
Nunez-Iglesias, J., Kennedy, R., Plaza, S.M., Chakraborty, A., Katz, W.T.: Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages. Front Neuroinform. (2014)
Campagnola, L., Kratz, M.B., Manis, P.B.: ACQ4: an open-source software platform for data acquisition and analysis in neurophysiology research. Front Neuroinform. (2014)
Olsson, T.S., Hartley, M.: jicbioimage: a tool for automated and reproducible bioimage analysis. PeerJ (2016)
Ljosa, V., Sokolnicki, K.L., Carpenter, A.E.: Annotated high-throughput microscopy image sets for validation. Nat. Methods 9(7), 637 (2012)
Uhlen, M., et al.: Towards a knowledge-based human protein atlas. Nat. Biotechnol. 28(12), 1248–50 (2010)
Turano, S., et al.: ReCaTuR - rare cancer network calabria - implementing a software system based on 3D stereoscopic imaging
Iaquinta, P., et al.: eIMES 3D mobile: A mobile application for diagnostic procedures. In: BIBM 2017, pp. 1634–1641
Iaquinta, P., et al.: eIMES 3D: an innovative medical images analysis tool to support diagnostic and surgical intervention. Proc. Comput. Sci. 110, 459–464 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Caroprese, L. et al. (2018). Software Tools for Medical Imaging Extended Abstract. In: Benczúr, A., et al. New Trends in Databases and Information Systems. ADBIS 2018. Communications in Computer and Information Science, vol 909. Springer, Cham. https://doi.org/10.1007/978-3-030-00063-9_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-00063-9_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00062-2
Online ISBN: 978-3-030-00063-9
eBook Packages: Computer ScienceComputer Science (R0)