The elevational distance between two ultrasound images can be obtained from the correlation between the two images, leading to sensorless freehand 3D ultrasound systems. Most of these systems rely on the correlation between patches of fully developed speckles (FDS). Previous work has compared different FDS detectors and concluded that the elevational distance measurement limited to the FDS patches obtained by low order moment test yields significantly more accurate results than other FDS detectors. However, small coherent and FDS regions are spread throughout a typical ultrasound image of real tissue. This makes it extremely unlikely to find a regularly shaped (conventionally a rectangle) FDS patch, making it infeasible to estimate elevational distance accurately1. In this work, first we propose a simple and fast algorithm which is capable of detecting arbitrarily irregular FDS regions in an ultrasound image. In vitro experiments on beef liver, beef steak and chicken breast indicates that the proposed algorithm generates remarkably more FDS patches than the current methods. Preliminary results show that the FDS patches obtained by this algorithm generate more accurate elevational distance measurement. Second, we propose a new calibration scheme to generate decorrelation curves. At a particular location in the image, conventional methods acquire one decorrelation curve. We create multiple curves, as a function of particular statistical properties of the patch. The results reveal a theoretically expected relation between the decorrelation curve and the statistical properties of the patch. As a result of this calibration based on the patch statistical properties, improvement in the out of plane motion estimation is expected.