Assessment of hydrocephalus in children based on digital image processing and analysis

• Autorzy: Anna Fabijańska , Tomasz Węgliński , Krzysztof Zakrzewski , Emilia Nowosławska
• Języki publikacji: EN

Abstrakty

EN

Hydrocephalus is a pathological condition of the central nervous system which often affects neonates and young children. It manifests itself as an abnormal accumulation of cerebrospinal fluid within the ventricular system of the brain with its subsequent progression. One of the most important diagnostic methods of identifying hydrocephalus is Computer Tomography (CT). The enlarged ventricular system is clearly visible on CT scans. However, the assessment of the disease progress usually relies on the radiologist's judgment and manual measurements, which are subjective, cumbersome and have limited accuracy. Therefore, this paper regards the problem of semi-automatic assessment of hydrocephalus using image processing and analysis algorithms. In particular, automated determination of popular indices of the disease progress is considered. Algorithms for the detection, semi-automatic segmentation and numerical description of the lesion are proposed. Specifically, the disease progress is determined using shape analysis algorithms. Numerical results provided by the introduced methods are presented and compared with those calculated manually by a radiologist and a trained operator. The comparison proves the correctness of the introduced approach.

Słowa kluczowe

EN

hydrocephalus; computed tomography; image segmentation; Evans index; frontal and occipital horn ratio; ventricular angle; frontal horn radius

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2014
• Tom: 24
• Numer: 2
• Strony: 299-312

Daty

wydano

2014

otrzymano

2013-01-17

poprawiono

2013-12-28

Twórcy

autor

Anna Fabijańska

• Institute of Applied Computer Science, Łódź University of Technology, Stefanowskiego 18/22, 90-924 Łódź, Poland

autor

Tomasz Węgliński

• Institute of Applied Computer Science, Łódź University of Technology, Stefanowskiego 18/22, 90-924 Łódź, Poland

autor

Krzysztof Zakrzewski

• Department of Neurosurgery, Polish Mother's Memorial Hospital, Research Institute in Łódź, Rzgowska 281/289, 93-338 Łódź, Poland

autor

Emilia Nowosławska

• Department of Neurosurgery, Polish Mother's Memorial Hospital, Research Institute in Łódź, Rzgowska 281/289, 93-338 Łódź, Poland

Bibliografia

• Ambarki, K., Wahlin, A., Birgander, R., Eklund, A. and Malm, J., (2011). MR imaging of brain volumes: Evaluation of a fully automatic software, American Journal of Neuroradiology 32(2): 408-412
• Barkovich, A.J., (2005). Pediatric Neuroimaging, Lippincott Williams & Wilkins, New York, NY.
• Bosnjak, A., Montilla, G., Villegas, R. and Jara, I. (2007). 3D segmentation with an application of level set-method using MRI volumes for image guided surgery, Proceedings of the 29th Annual International Conference of the IEEE Engineering and Medicine in Biology Society, Osaka, Japan, pp. 5263-5266.
• Boykov, Y. and Jolly, M.P. (2001). Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images, Proceedings of the International Conference on Computer Vision, Vancouver, Canada, Vol. 1, pp. 105-112.
• Butman, J.A. and Linguraru, M.G. (2008). Assessment of ventricle volume from serial MRI scans in communicating hydrocephalus, Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France, pp. 49-52.
• DICOM (n.d.). DICOM specification, http://medical.nema.org/dicom/2004.html.
• Frąckiewicz, M. and Palus, H. (2011). KHM clustering technique as a segmentation method for endoscopic colour images, International Journal of Applied Mathematics and Computer Science 21(1): 203-209, DOI: 10.2478/v10006011-0015-0.
• Gonzalez, R.C. and Woods, R.E. (2007). Digital Image Processing, 3rd Edition, Prentice Hall, Englewood Cliffs, NJ.
• Gupta, V., Ambrosius, W., Qian, G., Blazejewska, A., Kazmierski, R., Urbanik, A. and Nowinski, W.L. (2010). Automatic segmentation of cerebrospinal fluid, white and gray matter in unenhanced computed tomography images, Academic Radiology 17(11): 1350-1358.
• Halberstadt, W. and Douglas, T.S. (2005) Fuzzy clustering of CT images for the measurement of hydrocephalus associated with tuberculous meningitis, Proceedings of the Annual International Conference of the IEEE on Engineering in Medicine and Biology, Shanghai, China, pp. 4014-4016.
• Hamano, K., Iwasaki, N., Takeya, T. and Takita, H. (1993). A comparative study of linear measurements of the brain and three-dimensional measurement of brain volume using CT scans, Pediatric Radiology 23(3): 165-168.
• Hiraoka, K., Yamasaki, H., Takagi, M., Saito, M., Nishio, Y., Iizuka, O., Kanno, S., Kikuchi, H., Kondo, T. and Mori, E. (2010). Changes in the volumes of the brain and cerebrospinal fluid spaces after shunt surgery in idiopathic normal-pressure hydrocephalus, Journal of the Neurological Sciences 296(1): 7-12.
• Kanayama, S.A., Calderon, A.B., Makita, J.I.C., Ohara, Y.D., Tsunoda, A.D. and Sato, K.D. (1998). Evaluation of noninvasive cerebrospinal fluid volume measurement method with 3D-FASE MRI, Systems and Computers in Japan 29(14): 41-49.
• Kulczycki, P. and Charytanowicz, M. (2010). A complete gradient clustering algorithm formed with kernel estimators, International Journal of Applied Mathematics and Computer Science 20(1): 123-134, DOI: 10.2478/v10006-010-00093.
• Lie, W.-N., Peng, W.-H. and Chuung, C.-H. (2002). Efficient content-based CT brain image retrieval by using region shape features, Proceedings of the IEEE International Symposium on Circuits and Systems, Phoenix-Scottsdale, AZ, USA, Vol. 4, pp. 157-160.
• Luo, F., Evans, J.W., Linney, N.C., Schmidt, M.H. and Gregson, P.H. (2010). Wavelet-based image registration and segmentation framework for the quantitative evaluation of hydrocephalus, Journal of Biomedical Imaging (2010): 1-12, Article ID: 248393.
• O'Hayon, B.B., Drake, J.M., Ossip, M.G., Tuli, S. and Clarke, M. (1998). Frontal and occipital horn ratio: A linear estimate of ventricular size for multiple imaging modalities in pediatric hydrocephalus, Pediatric Neurosurgery 29(5): 245-249.
• Pustkova, R., Kutalek, F., Penhaker, M. and Novak, V. (2010). Measurement and calculation of cerebrospinal fluid in proportion to the skull, Proceedings of the 9th RoEduNet IEEE International Conference, Sibiu, Romania, Vol. 8, pp. 95-99.
• Ruttimann, U.E., Joyce, E.M., Rio, D.E. and Eckardt, M.J. (1993). Fully automated segmentation of cerebrospinal fluid in computed tomography, Psychiatry Research: Neuroimaging 50(2): 101-119.
• Schnack, H.G., Hulshoff Pol, H.E., Baare, W.F.C., Viergever, M.A. and Kahn, R.S. (2001). Automatic segmentation of the ventricular system from MR images of the human brain, NeuroImage 14(2): 95-104.
• Synek, V., Reuben, J.R. and Du Boulay, G.H. (1976). Comparing Evans index and computerized axial tomography in assessing relationship of ventricular size to brain size, Neurology 26(3): 231-233.
• Węgliński, T. and Fabijańska, A. (2012). Min-cut/max-flow segmentation of hydrocephalus in children from CT datasets, Proceedings of the IEEE International Conference on Signals and Electronic Systems, Wrocław, Poland, pp. 1-6
• Węgliński, T. and Fabijańska, A. (2012) Survey over modern image segmentation algorithms on CT scans of hydrocephalic brains, Image Processing and Communications 17(4): 223-230.
• Zang, X., Wang, Y., Yang, J. and Liu, Y. (2010) A novel method of CT brain images segmentation, Proceedings of the International Conference on Medical Image Analysis and Clinical Application, Wuhan, China, pp. 109-112.

Identyfikatory

DOI

10.2478/amcs-2014-0022