High intensity focused ultrasound (HIFU), has applications in treating various cancers, such as prostate, liver and breast cancer. In order for HIFU to be effective and efficient it needs to be guided by an imaging modality. While there are several options for guiding HIFU treatment, one of the most promising is ultrasound elastography. Current commercial devices use Brightness-Mode (B-mode) imaging or MRI, and are manual processes. Ultrasound elastography, allows complete automation of HIFU treatment due to the enhanced image, that elastography provides. The elastic image provides more information and less noise. To show that segmentation was possible on elastic images, nine algorithms were implemented in matlab and used on three distinct images for object detection. The three images used, have varying properties regarding object intensity and placement, as well as different noise patterns. Using PSNR, to gauge the effectiveness of each algorithm, it was shown that segmentation was possible on all images using different algorithms. The bilateral-shock-bilateral algorithm proved to be an overall effective algorithm in every situation with a PSNR of 83.87db on the phantom image. The segmentation results clearly highlight any object in the images. Future work includes fine tuning the algorithm with different phantom images and in-vivo images to distinguish between noise and desired object.