Binarization of MRI with Intensity Inhomogeneity Using K-Means Clustering for Segmenting Hippocampus

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Medical image segmentation plays a crucial role in identifying the shape and structure of human anatomy. The most widely used image segmentation algorithms are edge-based and typically rely on the intensity inhomogeneity of the image at the edges, which often fail to provide accurate segmentation results. This paper proposes a boundary detection technique for segmenting the hippocampus (the subcortical structure in medial temporal lobe) from MRI with intensity inhomogeneity without ruining its boundary and structure. The image is pre-processed using a noise filter and morphology based operations. An optimal intensity threshold is then computed using K-means clustering technique. Our method has been validated on human brain axial MRI and found to give satisfactory performance in the presence of intensity inhomogeneity. The proposed method works well even for weak edge. Our method can be used to detect boundary for accurate segmentation of hippocampus.