Image segmentation is one of the most important problems in computer vision. Recently, Fairfield proposed an interesting approach to image segmentation using toboggan enhancement followed by naive contrast segmentation, which is a noniterative, linear execution time method. The way it operates can be thought of as a man tobogganing in the first derivative terrain, i.e., the graph surface of a discontinuity measure computed by the first derivative of the image intensity. The segmentation results it produced appeared equal in quality to that of other complex optimal region growing methods. In this paper, an improved version of Fairfield's method, called keep-sliding toboggan segmentation, is presented. With our method, the toboggan will keep sliding on a plane in the derivative terrain, where the original toboggan method will stop sliding. Therefore, our method produces far less regions than the original. Other improvements achieved are as follows: Instead of being followed by contrast segmentation post-process, our keep-sliding tobogganing process is preceded by a prefiltering process which suppresses small fluctuations in the first derivative terrain. Because of this prefiltering operation, our tobogganing process can automatically merge the regions having small intercontrast. Also, a new discontinuity measure is proposed to allow the detection of small target regions without ever-segmenting the images. Experimental results indicate that the segmentations produced by the keep-sliding toboggan method are less noisy, and, therefore, it is more appropriate to use them as initial segmentations for higher level image segmentation techniques.