Precise segmentation of underlying objects in an image is very important especially for biomedical image analysis. We present an integrated approach for boundary finding using region and curvature information along with the gradient. Unlike the previous methods, where smoothing is enforced by penalizing curvature, here the grey level curvature is used as an extra source of information. However, information fusion may not be useful unless used properly. To address that, we present results that highlight the pros and cons of using the various sources of information and indicate when one should get precedence over the others.