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This paper describes a rough set approach for gray scale image segmentation that can automatically segment an image to its constituents parts. The aim of the proposed method is to produce an efficient segmentation of images using intensity information along with neighborhood relationships. The proposed method mainly consists of spatial segmentation; the spatial segmentation divides each image into different regions with similar properties. Proposed algorithm is based on a modified K-means clustering using rough set theory (RST) for image segmentation, which is further divided into two parts. Initially the cluster centers are determined and then in the next phase they are reduced using RST. K-means clustering algorithm is then applied on the reduced set of cluster centers with the purpose of segmentation of the images. The existing clustering algorithms namely the K-means and the Fuzzy C-Means (FCM) requires initialization of cluster centers whereas the proposed scheme does not require any such prior information to partition the exact regions. This rough set based image segmentation scheme results in satisfactory segmented image and Davies-Bouldin (DB) index which is better than other state-of-the-art image segmentation method.