The difference in echogenicity between cancerous and normal tissues is not quite distinguishable in ultrasound B-mode imaging. However, tumor or cancer in breast or prostate tends to be stiffer than the surrounding normal tissue. Thus, imaging the stiffness contrast between the two different tissue types is helpful for quantitative diagnosis, and such a method of imaging the elasticity of human tissue is collectively referred to as ultrasound elasticity imaging. Recently, elasticity imaging has established itself as an effective diagnostic modality in addition to ultrasound B-mode imaging. The purpose of this paper is to present various elasticity imaging methods that have been reported up to now and to describe their principles of operation and characteristics.

Nowadays, ultrasound Doppler imaging is widely used in assessing cardiovascular functions in the human body. However, a major drawback of ultrasonic Doppler methods is that they can provide information on blood flow velocity along the ultrasound beam propagation direction only. Thus, the blood flow velocity is estimated differently depending on the angle between the ultrasound beam and the flow direction. In order to overcome this limitation, there have been many researches devoted to estimating both axial and lateral velocities. The purpose of this article is to survey various two-dimensional velocity estimation methods in the context of Doppler imaging. Some velocity vector estimation methods can also be applied to determine tissue motion as required in elastography. The discussion is mainly concerned with the case of estimating a two-dimensional in-plane velocity vector involving the axial and lateral directions.

This article reviews recent developments in the emerging field of cellular-level biomedical ultrasonics with the specific focus on the mechanics of ultrasound-cell interaction. Due to the nature of the field at its relative infancy, the review poses more questions than it provides answers. Discussed are topics such as the basic structure of a biological cell, the origin of cell's elasticity, a theoretical framework for ultrasound-cell interaction, and shape deformation of cells and its measurement, Some interesting problems for future study are proposed.

This paper is based on our comments and proposed amendments to the documents, Annex A, Phantom for determining Maximum Depth of Penetration, and Annex B, Local Dynamic Range Using Acoustical Test Objects 87/400/CDV. IEC 61391-2 Ed. 1.0 200X, prepared by IEC technical Committee 87; Ultrasonics. The documents are concerned with the influence of microstructure of reference target material on the ultrasonic backscattering. Previous works on the attenuation due to backreflection and backscattering of reference target materials are reviewed. The drawback to the use of ungraded stainless steel and metallic materials without microstructural data such as, crystal structure, basic acoustic data of sound velocity and attenuation, grain size, roughness and elastic constants has been discussed. The analysis suggested that the insightful conclusion can be made by differentiating the influence arising from target size and microstructure on the backscattering measurements. The microstructural parameters are associated with physical, geometrical, acoustical and mechanical origins of variation with frequency. Further clarification of such a diverse source mechanisms for ultrasonic backscattering would make the target material and its application for medical diagnosis and therapy simpler and more reliable.

In this paper, we present the use of the Features Weighted Mahalanobis Distance (FWMD) in improving the performance of Likelihood Maximizing Beamforming (Limabeam) algorithm in speech recognition for microphone array. The proposed approach is based on the replacement of the traditional distance measure in a Gaussian classifier with adding weight for different features in the Mahalanobis distance according to their distances after the variance normalization. By using Features Weighted Mahalanobis Distance for Limabeam algorithm (FWMD-Limabeam), we obtained correct word recognition rate of 90.26% for calibrate Limabeam and 87.23% for unsupervised Limabeam, resulting in a higher rate of 3% and 6% respectively than those produced by the original Limabearn. By implementing a HM-Net speech recognition strategy alternatively, we could save memory and reduce computation complexity.