Burak KAPUSIZ, Yusuf UZUN, Sabri KOÇER, Özgür DÜNDAR

BRAIN TUMOR DETECTION AND BRAIN TUMOR AREA CALCULATION WITH MATLAB

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.

Keywords:

Image processing, Magnetic Resonance Imaging, Glioma, Image Segmentation,

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