A Fuzzy Tumor Volume Estimation Approach Based On Fuzzy Segmentation of MR Images

Quantitative measurements of tumor in general and tumor volume in particular, become more realistic with the use of Magnetic Resonance imaging, especially when the tumor morphological changes become irregular and difficult to assess by clinical examination. However, tumor volume estimation strongly depends on the image segmentation, which is fuzzy by nature. In this paper a fuzzy approach is presented for tumor volume segmentation based on the fuzzy connectedness algorithm. The fuzzy affinity matrix resulting from segmentation is then used to estimate a fuzzy volume based on a certainty parameter, an Alpha Cut, defined by the user. The proposed method was shown to highly affect treatment decisions. A statistical analysis was performed in this study to validate the results based on a manual method for volume estimation and the importance of using the Alpha Cut is further explained.

• Sara A.Yones
• Ahmed S. Moussa

• Alpha Cut
• Fuzzy Connectedness
• Magnetic Resonance Imaging
• Tumor volume estimation.

[1] Sanjay Saini, "Radiological Measurement of Tumor Size in Clinical Trials: Past, Present, and Future," AJR: 176 February 2001.
[2] Jinaguo Liu, Jayaram K. Udupa, Dewey Odhner, David Hackney, and Gul Moonis ," A system for brain tumor volume estimation via MR
imaging and fuzzy connectedness," Computerized Medical Imaging and
Graphics, Elsevier , vol. 29, pp. 21-34, 2005.
[3] Yasser M.Salman, "Modified technique for volumetric brain tumor
measurements," J Biomedical Science and Engineering, Scientific
Research Publishing, vol. 2, pp.16-19, 2009.
[4] Gul Moonis, Jianguo Liu, Jayaram K. Udupa, and David B. Hackney,
"Estimation of Tumor Volume with Fuzzy Connectedness Segmentation
of MR Images," AJNR Am J Neuroradiol, vol. 23, pp.356-363,
American Society of Neuroradiology, March 2002.
[5] Fletcher-Heath LM, Hall LO, Goldgof DB, Murtagh FR, "Automatic
segmentation of non-enhancing brain tumors in magnetic resonance
images," Artif Intell Med, vol 21, pp. 43-63, 2001.
[6] Zhu Y, Yan H. "Computerized tumor boundary detection using a
Hopfield neural network," IEEE Trans Med Imag. vol. 16, no. 1, pp. 55-
67, February 1997.
[7] Rai A, and Juluru K, "Visualization and segmentation of liver tumors
using dynamic contrast MRI," Conf Proc IEEE Eng Med Biol Soc, vol
2009, pp. 6985-6989, 2009.
[8] Rodrigus, Vilaca, J.L, and Fonseca, J, "An image processing application
for liver tumor segmentation," Bioengineering (ENBENG), 2011.
[9] Zheng Y, Baloch S, Englander S, Schnall MD, and Shen D,
"Segmentation and classification of breast tumor using dynamic
contrast-enhanced MR images," Med Image Comput Comput Assist
Interv, vol 10, pp. 393-401, 2007.
[10] http://orfeo-toolbox.org/doxygencurrent/
FuzzyConnectednessImageFilter_8cxx_source.html
[11] http://www.itk.org/