Summary
This paper presents an algorithm for automatic detection of brain tumor using MRI images. The algorithm comprises two phases: image preprocessing and image segmentation. Image preprocessing is performed to obtain the highest effectiveness of the algorithm, its most important phases are: contrast enhancement, Wiener filtering and skull stripping. The aim of image segmentation is to label voxels according to tissue type they represent which includes: white matter, grey matter and brain tumor. The presented algorithm is reliable for all projections of brain images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Damadian, R., Goldsmith, M., Minkoff, L.: NMR in cancer: XVI. Fonar image of the live human body. Physiological Chemistry and Physics 9, 97–100 (1977)
Bernstein, M., Al-Anazi, A.R., Abdul, R., Kucharczyk, W., Manninen, P., Bronskill, M., Henkelman, M.: Brain Tumor Surgery with the Toronto Open Magnetic Resonance Imaging System: Preliminary Results for 36 Patients and Analysis of Advantages, Disadvantages, and Future Prospects. Neurosurgery 46, 900–909 (2000)
Darbane, S., Drai, R., Cherfa, Y., Kabir, Y.: Detection of Welding Defects In X-Ray Images by Image Segmentation. Annales de Chimie - Science des Materiaux 22, 133–141 (1997)
Cherfa, Y., et al.: Segmentation of Magnetic Resonance Brain Images Using Edge and Region Cooperation Characterization of Stroke Lesions. The International Arab Journal of Information Technology 4, 281–288 (2007)
Murugavalli, S., Rajamani, V.: An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Neuro Fuzzy Technique. Journal of Computer Science 3, 841–846 (2007)
Lemieux, L., Wieshmann, U.C., Moran, F.N., Fish, D.R., Shorvon, S.D.: The detection and significance of subtle changes in mixed-signal brain lesions by serial MRI scan matching and spatial normalization. Medical Image Analysis 2, 227–242 (1998)
Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: A brain tumor segmentation framework based on outlier detection. Medical Image Analysis 8, 275–283 (2004)
Zuiderveld, K.: Contrast limited adaptive histogram equalization. Graphics gems IV, pp. 474–485. Academic Press Professional, Inc., London (1994)
Ziółko, M.: Lectures: Digital Signal Processing, Faculty of Electrical Engineering, Automatics, Computer Science and Electronics, AGH University of Science and Technology, http://wavelet.elektro.agh.edu.pl/wyklad/
Waluś, M., Kosmala, J., Saeed, K.: Finger Vein Pattern Extraction Algorithm. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds.) HAIS 2011, Part I. LNCS, vol. 6678, pp. 404–411. Springer, Heidelberg (2011)
Parker, J.R.: Algorithms for Image Processing and Computer Vision, pp. 23–29. John Wiley & Sons, Inc., New York (1997)
Canny, J.: A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 679–698 (1986)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Boberek, M., Saeed, K. (2011). Segmentation of MRI Brain Images for Automatic Detection and Precise Localization of Tumor. In: Choraś, R.S. (eds) Image Processing and Communications Challenges 3. Advances in Intelligent and Soft Computing, vol 102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23154-4_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-23154-4_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23153-7
Online ISBN: 978-3-642-23154-4
eBook Packages: EngineeringEngineering (R0)