BEYİN TÜMÖRLERİNİN BİÇİMSEL YAPILANDIRMA KULLANILARAK BİLGİSAYAR DESTEKLİ TESPİTİ

Yıl 2016, Cilt: 21 Sayı: 2, 257 - 268, 28.11.2016

Buket Doğan , Seda Kazdal Çalık Önder Demir

https://doi.org/10.17482/uumfd.270102

Cited By: 3

Öz

Bilgisayar destekli
tespit (BDT) sistemleri görüntü işleme ve örüntü tanıma tekniklerini kullanarak
medikal görüntülerdeki normal olmayan yapıların tespit işlemine yardımcı
olmaktadır. BDT sistemleri karar verme sürecini hızlandırırken bu süreçteki
insan hatası olasılığını da azaltarak fayda sağlamaktadır. Bu çalışmada beyin
MR görüntülerinde tespit edilen ilgi alanlarını biçimsel öznitelikler
kullanılarak yeniden yapılandırılması ve sınıflandırılmasını yapabilen bir BDT
sistemi geliştirilmiştir. Geliştirilen sistem önişleme, bölütleme, ilgi alanı
belirleme ve tümör tespiti olmak üzere dört aşamadan oluşmaktadır. Geliştirilen
sistem 10 hastaya ait 497 kesit görüntüsünden oluşan REMBRANDT veri setiyle
değerlendirilmiştir. Sınıflandırma işleminde sistemin performansı karar
ağaçları ile %93,36, yapay sinir ağları ile %94,89, K-en yakın komşu ile
algoritması ile %96,93 ve Meta-Learner algoritması ile %96,93 doğruluk
oranlarına erişmiştir. Bu sonuçlar önerilen yöntemin MR görüntülerinden beyin
tümörü tespitinde etkin olduğunu ve sınıflandırma işleminin performansını
arttırdığını göstermektedir.  Kullanılan
biçimsel yapılandırma yöntemi diğer BDT uygulamalarına uyarlanabilecek şekilde
geliştirilmiştir.

 

Anahtar Kelimeler

Biyomedikal görüntü işleme, görüntü sınıflandırma, biçimsel yapılandırma, tümör tespiti, bilgisayar destekli tespit

Computer-Aided Detection of Brain Tumors Using Morphological Reconstruction

Öz

Computer aided detection (CAD) systems helps the
detection of abnormalities in medical images using advanced image processing
and pattern recognition techniques. CAD has advantages in accelerating
decision-making and reducing the human error in detection process. In this
study, a CAD system is developed which is based on morphological reconstruction
and classification methods with the use of morphological features of the
regions of interest to detect brain tumors from brain magnetic resonance (MR)
images. The CAD system consists of four stages: the preprocessing, the
segmentation, region of interest specification and tumor detection stages. The
system is evaluated on REMBRANDT dataset with 497 MR image slices of 10
patients. In the classification stage the performance of CAD has achieved
accuracy of 93.36% with Decision Tree Algorithm, 94.89% with Artificial Neural
Network (Multilayer Perceptron), 96.93% with K-Nearest Neighbour Algorithm and
96.93% with  Meta-Learner (Decorate)
Algorithm. These results show that the proposed technique is effective and
promising for detecting tumors in brain MR images and enhances the
classification process to be more accurate. The using morphological
reconstruction method is useful and adaptive than the methods used in other CAD
applications.

Anahtar Kelimeler

Biomedical image processing, Image classification, Morphological reconstruction, Tumor Detection, computer aided detection

Kaynakça

• Abdel-Maksoud, E., Elmogy, M. and Al-Awadi, R. (2015) Brain tumor segmentation based on a hybrid clustering technique. Egyptian Informatics Journal, 16(1), 71-81. doi: 10.1016/j.eij.2015.01.003
• Akram, M. U. and Usman, A. (2011) Computer aided system for brain tumor detection and segmentation. Computer Networks and Information Technology (ICCNIT), 2011 International Conference.
• Ambrosini, R. D., Wang, P. and O'Dell, W. G. (2010) Computer‐aided detection of metastatic brain tumors using automated three‐dimensional template matching. Journal of Magnetic Resonance Imaging, 31(1), 85-93.
• Arimura, H., Magome, T., Yamashita, Y. and Yamamoto, D. (2009) Computer-aided diagnosis systems for brain diseases in magnetic resonance images. Algorithms, 2(3), 925-952. doi: 10.3390/a2030925
• Capelle, A.-S., Alata, O., Fernandez, C., Lefèvre, S. and Ferrie, J. (2000) Unsupervised segmentation for automatic detection of brain tumors in MRI. Image Processing, 2000. Proceedings. 2000 International Conference.
• Castleman, K. R. (1995) Digital Image Processing: Prentice Hall.
• Chan, T. (2007) Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Computerized Medical Imaging and Graphics, 31(4), 285-298. doi: 10.1016/j.compmedimag.2007.02.010
• Clark, M. C., Hall, L. O., Goldgof, D. B., Velthuizen, R., Murtagh, F. R. and Silbiger, M. S. (1998) Automatic tumor segmentation using knowledge-based techniques. Medical Imaging, IEEE Transactions on, 17(2), 187-201. doi: 10.1109/42.700731
• Dougherty, G. (2009) Digital image processing for medical applications: Cambridge University Press.
• El-Dahshan, E.-S. A., Hosny, T. and Salem, A.-B. M. (2010) Hybrid intelligent techniques for MRI brain images classification. Digital Signal Processing, 20(2), 433-441. doi: 10.1016/j.dsp.2009.07.002
• El-Sayed, A., Mohsen, H. M., Revett, K. and Salem, A.-B. M. (2014) Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert Systems with Applications, 41, 5526-5545. doi: 10.1016/j.eswa.2014.01.021.
• Gonzalez, R. C., Woods, R. E. and Eddins, S. L. (2004) Digital image processing using MATLAB: Pearson Education India.
• Gopal, N. N. and Karnan, M. (2010) Diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization techniques. Computational Intelligence and Computing Research (ICCIC), 2010 IEEE International Conference.
• Harati, V., Khayati, R. and Farzan, A. (2011) Fully automated tumor segmentation based on improved fuzzy connectedness algorithm in brain MR images. Computers in biology and medicine, 41(7), 483-492. doi: 10.1016/j.compbiomed.2011.04.010
• Haykin, S. and Lippmann, R. (1994) Neural Networks, A Comprehensive Foundation. International Journal of Neural Systems, 5(4), 363-364.
• Hellman, M. E. (1970) The nearest neighbor classification rule with a reject option. IEEE Transactions on Systems Science and Cybernetics, 6(3), 179-185. doi: 10.1109/TSSC.1970.300339
• Jayachandran, A. and Dhanasekaran, R. (2013) Brain Tumor Detection and Classification of MR Images Using Texture Features and Fuzzy SVM Classifier. Research Journal of Applied Sciences, Engineering and Technology, 6(12), 2264-2269.
• Khotanlou, H., Colliot, O., Atif, J. and Bloch, I. (2009) 3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models. Fuzzy Sets and Systems, 160(10), 1457-1473. doi: 10.1016/j.fss.2008.11.016
• Kittler, J. and Illingworth, J. (1986) Minimum error thresholding. Pattern recognition, 19(1), 41-47.
• Li, H., Wang, Y., Liu, K. R., Lo, S.-C. and Freedman, M. T. (2001) Computerized radiographic mass detection. I. Lesion site selection by morphological enhancement and contextual segmentation. IEEE Transactions on Medical Imaging, 20(4), pp.289-301.20(4), 289-301. doi:10.1109/42.921478
• Lippmann, R. P. (1989) Pattern classification using neural networks. Communications Magazine, IEEE, 27(11), 47-50. doi: 10.1109/35.41401
• Logeswari, T. and Karnan, M. (2010) An enhanced implementation of brain tumor detection using segmentation based on soft computing. Signal Acquisition and Processing, 2010. ICSAP'10. International Conference. doi: 10.1109/ICSAP.2010.55
• Manohar, M. and Ramapriyan, H. (1989) Connected component labeling of binary images on a mesh connected massively parallel processor. Computer vision, graphics, and image processing, 45(2), 133-149. doi: 10.1016/0734-189X(89)90129-1
• Mehmood, I., Ejaz, N., Sajjad, M. and Baik, S. W. (2013) Prioritization of brain MRI volumes using medical image perception model and tumor region segmentation. Computers in biology and medicine, 43(10), 1471-1483. doi:10.1016/j.compbiomed.2013.07.001
• Mei, X., Zheng, Z., Bingrong, W. and Guo, L. (2009) The edge detection of brain tumor. Communications, Circuits and Systems, 2009. ICCCAS 2009. International Conference.
• Melville, P., and Mooney, R. J. (2003). Constructing diverse classifier ensembles using artificial training examples. In IJCAI (Vol. 3, pp. 505-510).
• Mohsen, H., El-Dahshan, E.-S. and Salem, A. (2012) A machine learning technique for MRI brain images. Informatics and Systems (INFOS), 2012 8th International Conference.
• Nabizadeh, N. and Kubat, M. (2015) Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features. Computers & Electrical Engineering, 45, 286-301. doi:10.1016/j.compeleceng.2015.02.007
• Nagashima, H. and Harakawa, T. (2011) Computer‐aided diagnostic (CAD) scheme by use of contralateral subtraction technique. Application to detection of acute cerebral infarctions in brain computed tomography (CT). Electronics and communications in Japan, 94(2), 32-41.
• Naik, J. and Patel, S. (2014) Tumor Detection and Classification using Decision Tree in Brain MRI. International Journal of Computer Science and Network Security, 14(6), 87.
• Ozekes, S. and Camurcu, A. Y. (2006) Rule based detection of lung nodules in ct images. IU-Journal of Electrical & Electronics Engineering, 6(1), 61-67.
• Pal, N. R. and Pal, S. K. (1993) A review on image segmentation techniques. Pattern recognition, 26(9), 1277-1294. doi: 10.1016/0031-3203(93)90135-J
• Pitas, I. (2000) Digital image processing algorithms and applications: John Wiley & Sons.
• Quinlan, J. R. (2014) C4. 5: programs for machine learning: Elsevier.
• Ronse, C. and Devijver, P. A. Connected Components in Binary Images: the Detection Problem, 1984: Research Studies Press/John Wiley & Sons Inc., New York, NY, USA.
• Rulaningtyas, R. and Ain, K. (2009) Edge detection for brain tumor pattern recognition. Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), 2009 International Conference. doi: 10.1109/ICICI-BME.2009.5417299
• Ulku, E. E. and Camurcu, A. Y. (2013) Computer aided brain tumor detection with histogram equalization and morphological image processing techniques. In Electronics, Computer and Computation (ICECCO), 2013 International Conference on. on, pp. 48-51. IEEE, doi: 10.1109/ICECCO.2013.6718225
• Vannier, M. W. and Haller, J. W. (1998) Biomedical image segmentation. Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference.doi: 10.1109/ICIP.1998.723309
• Vrji, K. and Jayakumari, J. (2011) Automatic detection of brain tumor based on magnetic resonance image using CAD System with watershed segmentation. Signal Processing, Communication, Computing and Networking Technologies (ICSCCN), 2011 International Conference.
• Wu, M.-N., Lin, C.-C. and Chang, C.-C. (2007) Brain tumor detection using color-based k-means clustering segmentation. Intelligent Information Hiding and Multimedia Signal Processing, 2007. IIHMSP 2007. Third International Conference.doi: 10.1109/IIHMSP.2007.4457697
• Xuan, X. and Liao, Q. (2007) Statistical structure analysis in MRI brain tumor segmentation. Image and Graphics, 2007. ICIG 2007. Fourth International Conference.
• Zarandi, M. H. F., Zarinbal, M., Zarinbal, A., Turksen, I. and Izadi, M. (2010) Using type-2 fuzzy function for diagnosing brain tumors based on image processing approach. IEEE International Conference on Fuzzy Systems.doi: 10.1109/FUZZY.2010.5584469
• Zikopoulos, P., Parasuraman, K., Deutsch, T., Giles, J. and Corrigan, D. (2012) Harness the power of big data The IBM big data platform: McGraw Hill Professional.