BRAIN TUMOR DETECTION AND BRAIN TUMOR AREA CALCULATION WITH MATLAB

Yıl 2023, Sayı: 052, 352 - 364, 29.03.2023

Burak Kapusız , Yusuf Uzun , Sabri Koçer , Özgür Dündar

https://doi.org/10.59313/jsr-a.1142783

Cited By: 2

Öz

Brain tumors that impair the functionality of the person in daily life occur for many different reasons. Treatment of a brain tumor depends on accurately identifying the type, location, size and boundaries of the tumor. Magnetic Resonance Imaging (MRI) technique is used to diagnose the disease. However, this method cannot detect tumors below a certain size due to its nature. The aim of this study is to calculate the area of the tumor region through the successful method after determining which of the Fuzzy C-Means (FCM), Herbaceous Method, Region Growing and Self-Organizing Maps (SOM) methods are more successful in the analysis of MR images. The threshold values of the algorithms used, the number of clusters and the similarity coefficients of jaccard and dice were determined one by one by changing the index codes in the software.The highest similarity index was found in the K-means 10 cluster numbered segmentation in all trials.In general, K-means and Very Grassy Threshold gave very close results. In this context, advanced imaging technique was used by separating the MR image; Tumor spots and brain fluids were detected. Fuzzy C Mean (FCM) was found to be the best method during detection. Brain fluid pushes segmentations used in area calculations to miscalculate. For this reason, while calculating the tumor area, the brain fluids that appear in white spots are completed by point filling. Then, after the tumor zone was identified, the area of this region was used to produce the volume of the region by using Watershed, Graph-Cut and Active Counter segments. It is aimed to determine the number of tumors in which the tumor is in the detection area.

Anahtar Kelimeler

Image processing, Magnetic Resonance Imaging, Glioma, Image Segmentation

Teşekkür

I would like to express my endless thanks to my esteemed advisor Asst. Prof. Yusuf UZUN, who shared his valuable information in the realization of this study, and to my dear wife, Bilgehan İYİGÖREN KAPUSIZ, who did not spare me a moment of help throughout my study, who faced all the difficulties with me during my study and supported me at every stage of my life.

Kaynakça

• [1] Şimşek, A. B. and Dicle, A. (2013), Primer beyin tümörü: klinik özellikler, tedavi ve bakım, Dokuz Eylül Üniversitesi Hemşirelik Yüksekokulu Elektronik Dergisi, 6 (2), 102-113.
• [2] Dandıl, E, Çakıroğlu, M. and Elşi, Z. (2015), Computer-aided diagnosis of malign nad benign brain tumors on mr ımages, Advances in Intelligent Systems and Computing, 3(11), 155-166.
• [3] Tümer, E. A., Edebali, S. and Gülcü, Ş. (2020), Modeling of removal of chromium (vi) from aqueous solutions using artificial neural network, Iranian Journal of Chemistry and Chemical Engineering, 39(1), 163-175. Doi: 10.30492/ijcce.2020.33257.
• [4] Gülcü, Ş. (2022), An improved animal migration optimization algorithm to train the feed-forward artificial neural networks, Arabian Journal for Science and Engineering, 47(2022), 9557-9581. Doi: 10.1007/s13369-021-06286-z.
• [5] Castelman, R.K. (1994), Digital ımage processing, 1st Eddition, Prentice Hall, Englwood Cliffs, New Jersey, USA.
• [6] Bulut, M. and İstanbullu, A. (2004), Bulanık c-ortalama (fcm) algoritmasına dayalı yeni görüntü bölütleme sisteminin geliştirilmesi, Teknoloji, 7(3), 361-367.
• [7] Pal, R.N. and Pal, S. K. (1993), A review on ımage segmentation techniques, Pattern Recognition, 26(9),1277-1294.
• [8] Yaman, K., Sarucan, A., Atak, M. and Aktürk, N. (2001), Dinamik çizelgeleme için görüntü işleme ve arıma modelleri yardımıyla veri hazırlama, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 16(1), 19-40.
• [9] https://paperswithcode.com/dataset/brats-2018-1. (Erişim Tarihi: Şubat, 2022).
• [10] Aslantaş, A, Dandıl, E and Çakiroğlu, M, (2014), Detection of bone metastases using fcm and edge detection algorithm, International Journal of Information and Electronics Engineering, 4(6), 423-427.
• [11] http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/cmeans.html. (Erişim Tarihi: Aralık, 2021).
• [12] Kamdi, S. and Krishna, R.K. (2008), Image segmentation and region growing algorithm, International Journal of Computer Technology and Electronics Engineering (IJCTEE), 2(1), 13- 25.
• [13] [13] Kohonen, T. (2001), Self-Organizing Maps, 3rd Edition, Springer.
• [14] Dandıl, E, Çakıroglu, M, Eksi, Z, Ozkan, M, Kurt, O.K. and Canan, A. (2014), Artificial neural network- based classification system for lung nodules on computed tomography scans, In Soft Computing and Pattern Recognition (SoCPaR), 6th International Conference of IEEE, 382-386.
• [15] Karakoyun, M., Gülcü, Ş. and Kodaz, H. (2021), D-MOSG: Discrete multi-objective shuffled gray wolf optimizer for multi-level image thresholding, Engineering Science and Technology, An International Journal, 24(2021), 1455-1466.
• [16] Darga, S., Evirgen, H., Çakıroğlu, M. and Dandıl, E. (2014), BT görüntüleri üzerinde pankreasın bölge büyütme yöntemi ile yarı otomatik segmentasyonu, International Conference and Exhibition on Electronic, Computer and Automation Technologies (ICEECAT 14), May 9-11, Konya, Turkey, 55- 58