K-mean clustering and local binary pattern techniques for automatic brain tumor detection

Faiq Sabbar Baji, Saleema Baji Abdullah, Fatimah S. Abdulsattar

Abstract


Tumors in brains are caused by the unregulated emergence of tissue cells inside the brain. The early diagnosis and determining the precise location of the tumor in magnetic resonance imaging (MRI) and its size are essential for the teams of physicians. Image segmentation is often considered a preliminary step in medical image analyses. K-means clustering has been widely adopted for brain tumor detection. The result of this technique is a list of cluster images. The challenge of this method is the difficulty of selecting the appropriate cluster section that depicts the tumor. In this work, we analyze the influence of different image clusters. Each cluster is then split into the left and right parts. After that, the texture features are depicted in each part. Furthermore, the bilateral symmetry measure is applied to estimate the cluster that contains the tumor. Finally, the connected component labeling is employed to determine the target cluster for brain tumor detection. The developed technique is applied to 30 MRI images. The encouraging accuracy of 87% is obtained.

Keywords


Connected component labeling; K-means clustering; Local texture feature; Symmetry analysis; Tumor detection

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DOI: https://doi.org/10.11591/eei.v12i3.4404

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