Estimation of Displacement Tissues in Breast Ultrasound Ultrasound Elastography

Taher Slimi^1*, Ines Marzouk Moussa^2,3, Tarek Kraiem^1,3,4 and Halima Mahjoubi¹

¹University of Tunis El Manar, High Institute of Medical Technologies of Tunis, Laboratory of Biophysics and Medical Technologies, 9th Dr. Zouhair Essafi Street, 1006 Tunis, Tunisia

²University Hospital Center of Monji Slim, Department of Medical Imaging and Radiology, 2046 Marsa, Tunisia

³University of Tunis El Manar, Faculty of Medicine of Tunis, Department of Biophysics, 1007 Rabta, Tunisia

⁴Department of National Radiation Protection Center, Bab Sadoun Children’s Hospital, 1006 Tunisia

*Corresponding Author : Taher Slimi
University of Tunis El Manar, High Institute of Medical Technologies of Tunis, Laboratory of Biophysics and Medical Technologies, 9th Dr. Zouhair Essafi Street, 1006 Tunis, Tunisia
Tel: 00216 22 352 994
E-mail: slimi.taher@hotmail.com

Received: May 18, 2017 Accepted: June 06, 2017 Published: June 11, 2017

Citation: Slimi T, Moussa IM, Kraiem T, Mahjoubi H (2017) Estimation of Displacement Tissues in Breast Ultrasound Ultrasound Elastography. J Diagn Tech Biomed Anal 6:2. doi: 10.4172/2469-5653.1000124

Displacement estimation in ultrasound imaging is a topic of crucial research in diagnosis of diseases related to tissue stiffness that to analyze expression differences between normal and tumor cells of various tissue. In this work, we investigate a new clinical application of the monogenic signal method. The new case report focuses on the study of breast organ of which the estimated displacement is done by monogenic signal method. This method has proven effective in the case of heart and thyroid organs but it is never applied to estimate breast tissue displacement. In this context, we adopt this technique for breast case study analyzing, that to be used as a future application in estimation of breast tissue elasticity. Our proposed method is used that to excerpt ultrasound breast image features such as local orientation and phase using pre and post compression image, image features are used to estimate analytically the displacement fields. By Adapting this model on breast organ, we show that it has proven effective sub pixel accuracy in breast tissues, it gives better values in term of standard deviation, better contrast to noise ratio (CNR)and much faster than Block Matching (BM) method.