A SURVEY OF MEDICAL IMAGE PROCESSING TOOLS

Lee Lay Khoon, Liew Siau Chuin

Abstract


A precise analysis of medical image is an important stage in the contouring phase throughout radiotherapy preparation. Medical images are mostly used as radiographic techniques in diagnosis, clinical studies and treatment planning. Medical image processing tool are also similarly as important. With a medical image processing tool, it is possible to speed up and enhance the operation of the analysis of the medical image. This paper describes medical image processing software tool which attempts to secure the same kind of programmability advantage for exploring applications of the pipelined processors. Terminology and important issues in image analysis are first presented. These tools simulate complete systems consisting of several of the proposed processing components, in a configuration described by a graphical schematic diagram. In this paper, fifteen different medical image processing tools will be compared in several aspects. The main objective of the comparison is to gather and analysis the tool in order to recommend users of different operating systems on what type of medical image tools to be used when analysing different types of imaging. A result table was attached and discussed in the paper and concluded with a discussion on the future of tool in biomedical or computer vision research.

 

Keywords: Image processing; Medical Image; Processing Tool


Full Text:

[PDF]

References


Alexander, G. E., Chen, K., Pietrini, P., Rapoport, S. I., & Reiman, E. M. (2002). Longitudinal PET evaluation of cerebral metabolic decline in dementia: a potential outcome measure in Alzheimer's disease treatment studies. American Journal of Psychiatry, 159(5), 738- 745.

Assaf, Y., & Alexander, D. C. (2014). Advanced Methods to Study White Matter Microstructure.

Awad, T. S., Moharram, H. A., Shaltout, O. E., Asker, D., & Youssef, M. M. (2012). Applications of ultrasound in analysis, processing and quality control of food: A review. Food Research International, 48(2), 410-427.

Avants, B. B., Tustison, N., & Song, G. (2009). Advanced normalization tools (ANTS). Insight J, 2, 1-35.

Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2011). A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage, 54(3), 2033-2044.

Berg, W. A., Zhang, Z., Lehrer, D., Jong, R. A., Pisano, E. D., Barr, R. G., ... & Morton, M. J. (2012). Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk. Jama, 307(13), 1394-1404.

Cook, P. A., Bai, Y., Nedjati-Gilani, S. K. K. S., Seunarine, K. K., Hall, M. G., Parker, G. J., & Alexander, D. C. (2006, May). Camino: open-source diffusion-MRI reconstruction and processing. In 14th scientific meeting of the international society for magnetic resonance in medicine (Vol. 2759). Seattle WA, USA.

Drzezga, A., Souvatzoglou, M., Eiber, M., Beer, A. J., Fürst, S., Martinez-Möller, A., ... & Schwaiger, M. (2012). First clinical experience with integrated whole-body PET/MR: comparison to PET/CT in patients with oncologic diagnoses. Journal of Nuclear Medicine, 53(6), 845-855.

DTI registration in atlas based fiber analysis of infantile Krabbe disease. Neuroimage, 55(4), 1577-1586.

Fagiolo, G., Waldman, A., & Hajnal, J. V. (2014). A simple procedure to improve FMRIb software library brain extraction tool performance. The British journal of radiology.

Gharge, S. (2013). Segmentation of medical images.

Greenberg, A. K., Lu, F., Goldberg, J. D., Eylers, E., Tsay, J. C., Yie, T. A., ... & Addrizzo- Harris, D. (2012). CT scan screening for lung cancer: risk factors for nodules and malignancy in a high-risk urban cohort. PloS one, 7(7), e39403.

Hanwell, M. D., Martin, K. M., Chaudhary, A., & Avila, L. S. (2015). The Visualization Toolkit (VTK): Rewriting the rendering code for modern graphics cards. SoftwareX, 1, 9-12.

Johnsen, S. F., Taylor, Z. A., Clarkson, M. J., Hipwell, J., Modat, M., Eiben, B., ... & Ourselin, S. (2015). NiftySim: A GPU-based nonlinear finite element package for simulation of soft tissue biomechanics. International journal of computer assisted radiology and surgery, 10(7), 1077-1095.

Johnson, H. J., McCormick, M. M., & Ibanez, L. (2014). The ITK Software Guide Book 1: Introduction and Development Guidelines Fourth Edition Updated for ITK version 4.7.

Keihaninejad, S., Zhang, H., Ryan, N. S., Malone, I. B., Modat, M., Cardoso, M. J., ... & Ourselin, S. (2013). An unbiased longitudinal analysis framework for tracking white matter changes using diffusion tensor imaging with application to Alzheimer's disease. NeuroImage, 72, 153-163.

Larrabide, I., Omedas, P., Martelli, Y., Planes, X., Nieber, M., Moya, J. A., ... & Bijnens, B. H. (2009). GIMIAS: an open source framework for efficient development of research tools and clinical prototypes. In Functional Imaging and Modeling of the Heart (pp. 417-426). Springer Berlin Heidelberg.

Lee, L. K., Liew, S. C., & Thong, W. J. (2015). A review of image segmentation methodologies in medical image. In Advanced computer and communication engineering technology (pp. 1069-1080). Springer International Publishing.

Liu, Y., Kot, A., Drakopoulos, F., Yao, C., Fedorov, A., Enquobahrie, A., ... & Chrisochoides, N. P. (2014). An ITK implementation of a physics-based non-rigid registration method for brain deformation in image-guided neurosurgery.

Lu, T., Liang, P., Wu, W. B., Xue, J., Lei, C. L., Li, Y. Y., ... & Liu, F. Y. (2012). Integration of the image-guided surgery toolkit (igstk) into the medical imaging interaction toolkit (mitk). Journal of digital imaging, 25(6), 729-737.

Kerner, G. S., Fischer, A., Koole, M. J., Pruim, J., & Groen, H. J. (2015). Evaluation of elastix- based propagated align algorithm for VOI-and voxel-based analysis of longitudinal 18F- FDG PET/CT data from patients with non-small cell lung cancer (NSCLC). EJNMMI research, 5(1), 15.

Khesin, M., Quenan, D., Jesikiewicz, T., Kenien, D., & Girvan, R. (1997). Demonstration tests of new burner diagnostic system on a 650 MW coal-fired utility boiler (No. CONF- 970456--). Illinois Inst. of Tech., Chicago, IL (United States).

Mengler, L., Khmelinskii, A., Diedenhofen, M., Po, C., Staring, M., Lelieveldt, B. P., & Hoehn, M. (2014). Brain maturation of the adolescent rat cortex and striatum: changes in volume and myelination. Neuroimage, 84, 35-44.

Modat, M., McClelland, J., & Ourselin, S. (2010). Lung registration using the NiftyReg package. Medical Image Analysis for the Clinic-A Grand Challenge, 2010, 33-42.

Nolden, M., Zelzer, S., Seitel, A., Wald, D., Müller, M., Franz, A. M., ... & Maier-Hein, K. H. (2013). The medical imaging interaction toolkit: challenges and advances. International journal of computer assisted radiology and surgery, 8(4), 607-620.

Penny, W. D., Friston, K. J., Ashburner, J. T., Kiebel, S. J., & Nichols, T. E. (Eds.). (2011). Statistical parametric mapping: the analysis of functional brain images: the analysis of functional brain images. Academic press.

Pettit, C., Bishop, I., Sposito, V., Aurambout, J. P., & Sheth, F. (2012). Developing a multi-scale visualisation framework for use in climate change response. Landscape ecology, 27(4), 487-508.

Qasrawi, R., & Ivorra, A. (2015). Impact of Liver Vasculature on Electric Field Distribution during Electroporation Treatments: An Anatomically Realistic Numerical Study. In 6th European Conference of the International Federation for Medical and Biological Engineering (pp. 573-576). Springer International Publishing.

Seitel, A., Yung, K., Mersmann, S., Kilgus, T., Groch, A., dos Santos, T. R., ... & Maier-Hein, L. (2012). MITK-ToF—Range data within MITK. International journal of computer assisted radiology and surgery, 7(1), 87-96.

Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen-Berg, H., ... & Niazy, R. K. (2004). Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23 (Suppl. 1), S208–S219. External Resources Pubmed/Medline (NLM) CrossRef (DOI).

Solomon, C., & Breckon, T. (2011). Fundamentals of Digital Image Processing: A practical approach with examples in Matlab. John Wiley & Sons.

Sonka, M., Hlavac, V., & Boyle, R. (2014). Image processing, analysis, and machine vision. Cengage Learning.

Sowell, E. R., Levitt, J., Thompson, P. M., Holmes, C. J., Blanton, R. E., Kornsand, D. S., ... & Toga, A. W. (2000). Brain abnormalities in early-onset schizophrenia spectrum disorder observed with statistical parametric mapping of structural magnetic resonance images. American Journal of Psychiatry, 157(9), 1475-1484.

Stein, E. A., Pankiewicz, J., Harsch, H. H., Cho, J. K., Fuller, S. A., Hoffmann, R. G., ... &

Bloom, A. S. (1998). Nicotine-induced limbic cortical activation in the human brain: a functional MRI study. American Journal of Psychiatry.

Suryanarayana, C., & Norton, M. G. (2013). X-ray diffraction: a practical approach. Springer Science & Business Media.

TIG. (2014). The TIG, Image processing tool. Retrieved from http://cmictig.cs.ucl.ac.uk/wiki/index.php/Main_Page.

User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage, 31(3), 1116-1128.

Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., & Gerig, G. (2006).


Refbacks

  • There are currently no refbacks.