Multimodal Brain Tumor Segmentation using Neighboring Image Features

Authors

  • Ghazanfar Latif Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Malaysia. College of Computer Engineering and Sciences, Prince Mohammad bin Fahd University, Khobar, Saudi Arabia.
  • D.N.F. Awang Iskandar Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Malaysia.
  • Arfan Jaffar Department of Computer Science, Al-Imam Muhammad Ibn Saud Islamic University, Riyadh, Saudi Arabia.
  • M. Mohsin Butt Department of Computer Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.

Keywords:

Multimodal Brain Tumor Segmentation, KNN, DWT, MICCAI BraTS,

Abstract

Brain tumor can grow anywhere in the brain with irregular contours and appearance. It is very hard to correctly segment the tumor tissues due to the similarity, noise, complex texture, poor sampling and image distortions. In this article, an enhanced novel technique for brain tumor detection is introduced by using multimodal (T1, T2, T1c, Flair) MR images. The proposed method consists of two main steps. In the first step, supervised binomial classification method is used to classify MR images into tumorous and non-tumorous by extracting Discrete Cosine Transform (DCT) features and applying k-nearest neighbors (KNN) classifier. In the second step, segmented the tumor by manipulating image intensity values and used neighboring image features along with the actual image features. We further enhanced the tumor segmentation by applying region-growing algorithm. The proposed method is tested on MICCAI BraTS 2015, a wellknown standard dataset. Receiver Operating Characteristic (ROC), Dice Similarity Coefficient (DSC) and Mutual Information (MI) are used to measure the performance and achieved 96.91% accuracy for the binomial classification and 93.22% accuracy for the tumor segmentation.

References

Volpe, J. J. (2009). Brain injury in premature infants: a complex amalgam of destructive and developmental disturbances. The Lancet Neurology, 8(1), pp. 110-124.

Choong, M. K., Logeswaran, R., & Bister, M. (2007). Cost-effective handling of digital medical images in the telemedicine environment. international journal of medical informatics, 76(9), pp. 646-654.

Stickrath, C., Druck, J., Hensley, N., Maddox, T. M., & Richlie, D. (2012). Patient and health care provider discussions about the risks of medical imaging: not ready for prime time. Archives of internal medicine, 172(13), pp. 1037-1038.

Hyare, H., Wisco, J. J., Alusi, G., Cohen, M., Nabili, V., Abemayor, E., & Kirsch, C. F. (2010). The anatomy of nasopharyngeal carcinoma spread through the pharyngobasilar fascia to the trigeminal mandibular nerve on 1.5 T MRI. Surgical and radiologic anatomy, 32(10), pp. 937-944.

Foster‐Gareau, P., Heyn, C., Alejski, A., & Rutt, B. K. (2003). Imaging single mammalian cells with a 1.5 T clinical MRI scanner. Magnetic resonance in medicine, 49(5), pp. 968-971.

De Leeuw, F. E., de Groot, J. C., Achten, E., Oudkerk, M., Ramos, L. M. P., Heijboer, R., ... & Breteler, M. M. B. (2001). Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study. Journal of Neurology, Neurosurgery & Psychiatry, 70(1),pp. 9-14.

Pichler, B. J., Kolb, A., Nägele, T., & Schlemmer, H. P. (2010). PET/MRI: paving the way for the next generation of clinical multimodality imaging applications. Journal of Nuclear Medicine, 51(3), pp. 333-336.

Rexilius, J., Hahn, H. K., Klein, J., Lentschig, M. G., & Peitgen, H. O. (2007, March). Multispectral brain tumor segmentation based on histogram model adaptation. In Medical imaging, 6514(1), pp. 1-10.

Qurat-Ul-Ain, G. L., Kazmi, S. B., Jaffar, M. A., & Mirza, A. M. (2010). Classification and segmentation of brain tumor using texture analysis. Recent Advances In Artificial Intelligence, Knowledge Engineering And Data Bases, pp. 147-155.

Ruan, S., Lebonvallet, S., Merabet, A., & Constans, J. M. (2007, April). Tumor segmentation from a multispectral MRI images by using support vector machine classification. In 4th IEEE International Symposium Biomedical Imaging (ISBI): From Nano to Macro, pp. 1236-1239.

Corso, J. J., Sharon, E., Dube, S., El-Saden, S., Sinha, U., & Yuille, A. (2008). Efficient multilevel brain tumor segmentation with integrated bayesian model classification. IEEE transactions on medical imaging, 27(5), pp. 629-640.

Zhu, Y., Young, G. S., Xue, Z., Huang, R. Y., You, H., Setayesh, K., ... & Wong, S. T. (2012). Semi-automatic segmentation software for quantitative clinical brain glioblastoma evaluation. Academic radiology, 19(8), pp. 977-985.

Jaffar, M. A., Zia, S., Latif, G., Mirza, A. M., Mehmood, I., Ejaz, N., & Baik, S. W. (2012). Anisotropic diffusion based brain MRI segmentation and 3D reconstruction. International Journal of Computational Intelligence Systems, 5(3), pp. 494-504.

Wang, T., Cheng, I., & Basu, A. (2009). Fluid vector flow and applications in brain tumor segmentation. IEEE Transactions on Biomedical Engineering, 56(3), pp. 781-789.

Harati, V., Khayati, R., & Farzan, A. (2011). Fully automated tumor segmentation based on improved fuzzy connectedness algorithm in brain MR images. Computers in biology and medicine, 41(7), pp. 483-492.

Festa, J., Pereira, S., Mariz, J. A., Sousa, N., & Silva, C. A. (2013). Automatic brain tumor segmentation of multi-sequence MR images using random decision forests. Proceedings of NCI-MICCAI BRATS, 1, pp. 23-26.

Khachane, M. Y., & Ramteke, R. J. (2016). Modality Based Medical Image Classification. In Emerging Research in Computing, Information, Communication and Applications, pp. 597-606.

Nichat, A. M., & Ladhake, S. A. (2016). Brain Tumor Segmentation and Classification Using Modified FCM and SVM Classifier. International Journal of Advanced Research in Computer and Communication Engineering, 5(4), pp. 73-76.

Griffis, J. C., Allendorfer, J. B., & Szaflarski, J. P. (2016). Voxel-based Gaussian naïve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans. Journal of neuroscience methods, 257, pp. 97-108.

Kalbkhani, H., Salimi, A., & Shayesteh, M. G. (2015, May). Classification of brain MRI using multi-cluster feature selection and KNN classifier. In 23rd Iranian Conference on Electrical Engineering (ICEE), pp. 93-98.

Zhang, Y., Sun, Y., Phillips, P., Liu, G., Zhou, X., & Wang, S. (2016). A multilayer perceptron based smart pathological brain detection system by fractional Fourier entropy. Journal of medical systems, 40(7), pp. 1-11.

Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., ... & Lanczi, L. (2015). The multimodal brain tumor image segmentation benchmark (BRATS). IEEE transactions on medical imaging, 34(10), pp. 1993-2024.

Fan, J. L., & Zhao, F. (2007). Two-dimensional Otsu's curve thresholding segmentation method for gray-level images. Acta Electronica Sinica, 35(4), pp. 751.

Zabir, I., Paul, S., Rayhan, M. A., Sarker, T., Fattah, S. A., & Shahnaz, C. (2015, December). Automatic brain tumor detection and segmentation from multi-modal MRI images based on region growing and level set evolution. In IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), pp. 503-506.

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Published

2017-09-15

How to Cite

Latif, G., Awang Iskandar, D., Jaffar, A., & Butt, M. M. (2017). Multimodal Brain Tumor Segmentation using Neighboring Image Features. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-9), 37–42. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2674

Issue

Vol. 9 No. 2-9: Technology Transforming Lives I

Section

Articles

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