In computer vision, image segmentation is the process of partitioning a digital image into multiple segments according to sets of pixels. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image. The main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image recognition, image analysis, information retrieval, bioinformatics, data compression and computer graphics. Many clustering algorithms are used for analyzing an image. In this paper we have used two algorithms k-means clustering and Adaptive k-means clustering. K-means clustering algorithm belongs to Partitional clustering. It is widely used in image processing specially in image segmentation. K-means clustering segments the images but not gives satisfactory results of desire objects. To overcome the problems we used Adaptive k-means clustering algorithm. Both the algorithms implemented on images of different resolutions and compared the performance of the segmented images. The performances are compared based on some parameters. The parameters are PSNR, RMSE, SSIM, Elapsed time period. From each and every comparison we have concluded that the Adaptive k-means clustering gives better result as compared to k-means clustering. By using these techniques we can isolate any target object from medical images specially detection of cancerous data. Both the algorithms also implemented in other fields like telecommunication, scientific especially high resolution images.