Modified technique for volumetric brain tumor measurements

Yasser M. Salman

Egyptian Armed Forces.

DOI: 10.4236/jbise.2009.21003                

Abstract

Quantitative measurements of tumor response rate in three dimensions (3D) become more re-alistic with the use of advanced technology im-aging during therapy, especially when the tumor morphological changes remain subtle, irregular and difficult to assess by clinical examination. These quantitative measurements depend strongly on the accuracy of the segmentations methods used. Improvements on such methods yield to increase the accuracy of the segmentation process. Recently, the essential modification in the Traditional Region Growing (T-RG) method has been developed and a “Modified Region Growing Method” (MRGM) has been presented and gives more accurate boundary detection and holes filling after segmentation. In this pa-per, the new automatic calculation of the volu-metric size of brain tumor has been imple-mented based on Modified Region Growing Method. A comparative study and statistical analysis performed in this work show that the modified method gives more accurate and better performance for 3D volume measurements. The method was tested by 7 fully investigated pa-tients of different tumor type and shape, and better accurate results were reported using MRGM.

Keywords

Region Growing, Modified Region growing, and Volumetric Brain Tumor Measurements

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	H. Hricak, C. G. Lacey, L. G. Sandles, Y. C. F. Chang, M. L. Winkler, J. L. Stern. (1988) Invasive cervical carcinoma: Compari-son of MR imaging and surgical findings, Radiology 166: 623-631.
[2]	K. Hatano, Y. Sekiya, H. Araki, et al. (1999) Evaluation of the therapeutic effect of radiotherapy on cervical cancer using magnetic resonance imaging, Int. J. Radiat. Oncol. Biol. Phys. 45: 639-644.
[3]	N. A. Mayr, W. T. C. Yuh, et al. (1997) Tumor size evaluated by pelvic examination compared with 3-D MR quantitative analysis in the prediction of outcome for cervical cancer, Int J Radiat. Oncol. Biol. Phys. 39: 395-404.
[4]	H. Hricak, J. Quivey, et al. (1993) Phillips T. Carcinoma of the cervix: Predictive value of clinical and magnetic resonance (MR) imaging assessment of prognostic factors, Int. J. Radiat. Oncol. Biol. Phys. 27: 791-801.
[5]	N. A. Mayr, E. T. Tali, et al. (1993) Cervical cancer: Application of MR imaging in radiation therapy, Radiology 189: 601-608.
[6]	M. Kass, A. Witkin, and D. Terzopoulos, (1987) “Snakes: Active contour models,” Int. J. Computer Vision, Vol. 1, No. 4, 321-331.
[7]	R. Adams and L. Bischof, (1994) Seeded Region Growing, IEEE Transactions on Image processing, Vol.16, No.6, 641-47.
[8]	F. Vincent, H. Chong, J. Y. Zhou, B. James, K. Khoo, J. Huang, T. K. Lim. (2004) Tongue carcinoma: Tumor volume measurement, Int. J. Radiation Oncology Biol. Phys., Vol. 59, No. 1, 59-66.
[9]	Y. M. Salman, A. M. Badawi, M. A. Assal, S. M. Alian, (2005) New automatic technique for tracking brain tumor responsem , International Conference on Biological and Medical Physics, UAE.
[10]	Salman Y. M., A. M. Badawi, (2005) Validation Techniques for Quantitative Brain Tumor Measurements, The 27th Annual In-ternational Conference of the IEEE Engineering in Medicine and Biology Society, China, pp. 7048-7051.
[11]	S. C. Zhu and A. Yuille, (1996) Region competition: Unifying snakes, region growing and Bayer/MDL for multiband image segmentation, IEEE Transactions on Pattern Analysis and Ma-chine Intelligence, Vol. 18, No. 9, pp. 884-900.
[12]	M. Sato, S. Lakare, M. Wan, and A. Kaufman, (2000) A Gradient Magnitude Based Region Growing Algorithm For Accurate Segmentation, In Proc. International Conference on Image Proc-essing, Vol.3, 448-451.
[13]	S. Lakare, (2000) 3D Segmentation Techniques for Medical Volumes, Center of Visual Computer, state university of NY, Stony Brooks.
[14]	Jauhiainen, Tommi, Jarvinen, et al., (1998) MR Gradient Echo Volumetric Analysis of Human Cardiac Casts: Focus on the Right Ventricle, Journal of Computer Assisted Tomography, Vol.22, No.6, 899-903.
[15]	J. M. Links , L. D. Beach , B. Subramaniam, (1998) Edge com-plexity and partial volume effects, Journal of Computer Assisted Tomography, Vol. 22, No.3, 450-458.
[16]	R. C. Gonzalez and R. E woods, (1992) Digital Image Process-ing, Addison-Wesley, USA.
[17]	Y. L. Chang, X. Li, (1994) Adaptive Image Region-Growing, IEEE Trans. on Image Processing, Vol. 3, No. 6, 868-872.
[18]	W. Schroeder, K. Martin, and B. Lorensen B, (1998) the Visuali-zation Toolkit. New Jersey, Prentice Hall.
[19]	W. E Lorensen, H. E Cline, (1987) Marching cubes: a high reso-lution 3D surface construction algorithm, Computer Graphics (SIGGRAPH 87 Proceedings), Vol. 21, 1693-169.
[20]	L. Ibanes, W. Schroeder, L. Ng, (2003) Insight Segmentation and Registration Toolkit (ITK) Software Guide.
[21]	B. N. Joe, et al., (1999) Brain Tumor Volume Measurement: Comparison of manual and semi automated methods, Radiology, No.212, 811-816.