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

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 intensityinhomogeneity of the image at the edges, which often fail to provide accurate segmentation results. Thispaper proposes a boundary detection technique for segmenting the hippocampus (the subcortical structurein medial temporal lobe) from MRI with intensity inhomogeneity without ruining its boundary andstructure. The image is pre-processed using a noise filter and morphology based operations. An optimalintensity threshold is then computed using K-means clustering technique. Our method has been validatedon human brain axial MRI and found to give satisfactory performance in the presence of intensityinhomogeneity. The proposed method works well even for weak edge. Our method can be used to detectboundary for accurate segmentation of hippocampus.