Watershed transform is a commonly used image segmentation method. The main problem with this segmentation technique is that of its sensitivity to noise and other irregularities which leads to over-segmentation. In this paper the over-segmentation problem is overcome by combing pre-processing and post-processing techniques along with watershed transform. First multi-scale morphological filtering by reconstruction is used to remove noise and then h minima transform is implemented to extract markers. These markers are then superimposed on gradient image. Watershed transform is then applied on the modified gradient map. Post-processing region merging technique is used to merge the over segmented regions in the final segmented map. Experimental results show that the over-segmentation problem is reduced with the average segmentation accuracy of 0.96.