Background: MRI which stands for Magnetic Resonance Imaging is commonly used to
capture images of internal body organs, functionality and structure. Manual analysis is usually performed
by Radiologists on a large set of MR images in order to detect brain tumor.
Aims: This research aims to improve automated brain MR image classification and tumor segmentation
using phase congruency.
Methods: The skull part is removed from brain MR image by applying converging square algorithm
and phase congruency based edge detection method. Features are then extracted from the
segmented brain portion using discrete wavelet transforms. In order to minimize the extracted feature
set, we applied the principal Component Analysis algorithm. The MR images are classified
into tumorous and non-tumorous using Multilayer perceptron and compared with other classifiers
such as K-Nearest Neighbor, Naïve Bayes, and Support Vector Machines (SVM) along with discrete
cosine and discrete cosine transform features. The tumor is segmented using Fuzzy C-mean
and reconstructed tumor 3D model to measure the volume, location and shape accurately.
Results & Conclusion: Experimental results are obtained by testing the proposed method on a dataset
of 19 patients with a total number of 2920 brain MR images. The proposed method achieved
an accuracy of 99.43% for classification which is higher as compared to other current studies.