Image Segmentation Using Semi-Supervised k-Means

Extracting the region of interest is a very challenging task in Image Processing. Image segmentation is an important technique for image processing which aims at partitioning the image into different homogeneous regions or clusters. Lots of general-purpose techniques and algorithms have been developed and widely applied in various application areas. In this paper, a Semi-Supervised k-means segmentation method is proposed. First, an image thresholding has been performed to get the optimal threshold value of the image which categorizes the image in to two main parts. This optimal threshold value is then used to label the objects in the image to be initialized as initial cluster centroids in Semi-Supervised k-means algorithm. At the end of clustering, a mask of labeled parts of image has been created. To evaluate the results and compare them with k-means simple algorithm, PSNR criteria of the images are used. Evaluations show that this method has better accuracy in comparison with the unsupervised k-means.