A method for vector displacement estimation with ultrasound imaging and its application for thyroid nodular disease.
ABSTRACT Ultrasound elastography is a promising imaging technique that can assist in diagnosis of thyroid cancer. However, the complexity of the tissue movements under freehand compression requires the use of a parametric displacement model and a specific estimation method adapted to sub-pixel motion. Therefore, the aim of this study was to develop a motion estimation method for ultrasound elastography and test its performances compared to a classical block matching technique. The proposed method, referred to as Bilinear Deformable Block Matching (BDBM), uses a bilinear model with eight parameters for controlling the local mesh deformation. In addition, a technique of motion initialization based on a triangle scan of the images adapted to ultrasound elastography is proposed. The BDBM method includes an iterative multi-scale process. This iterative approach is shown to decrease the absolute error of the displacement estimation by a factor of 1.4 when passing from 1 to 2 iterations. The method was tested on simulated images and the results show that absolute displacement estimation error was reduced by a factor of 4 compared to classical block matching. We applied the BDBM method on three experimental sets of data. In the first data set, a phantom designed for ultrasound elastography was used. The two other sets of data involve the thyroid gland and were acquired using freehand tissue compression by ultrasound probe of a clinical ultrasound scanner modified for research. A similarity measurement based on local cross-correlation shows that, for experimental data, the BDBM method outperforms the usual block matching.
- SourceAvailable from: Luminita Moraru[Show abstract] [Hide abstract]
ABSTRACT: Statistical approach is a valuable way to describe texture primitives. The aim of this study is to design and implement a classifier framework to automatically identify the thyroid nodules from ultrasound images. Using rigorous mathematical foundations, this article focuses on developing a discriminative texture analysis method based on texture variations corresponding to four biological areas (normal thyroid, thyroid nodule, subcutaneous tissues, and trachea). Our research follows three steps: automatic extraction of the most discriminative first-order statistical texture features, building a classifier that automatically optimizes and selects the valuable features, and correlating significant texture parameters with the four biological areas of interest based on pixel classification and location characteristics. Twenty ultrasound images of normal thyroid and 20 that present thyroid nodules were used. The analysis involves both the whole thyroid ultrasound images and the region of interests (ROIs). The proposed system and the classification results are validated using the receiver operating characteristics which give a better overall view of the classification performance of methods. It is found that the proposed approach is capable of identifying thyroid nodules with a correct classification rate of 83 % when whole image is analyzed and with a percent of 91 % when the ROIs are analyzed.Journal of Digital Imaging 05/2012; 26(1). DOI:10.1007/s10278-012-9475-5 · 1.20 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: The success rate of medical procedures involving needle insertion is often directly related to needle placement accuracy. Due to inherent limitations of commonly used freehand needle placement techniques, there is a need for a system providing for controlled needle steering for procedures that demand high positional accuracy. This paper describes a robotic system developed for flexible needle steering inside soft tissues under real-time ultrasound imaging. An inverse kinematics algorithm based on a virtual spring model is applied to calculate needle base manipulations required for the tip to follow a curved trajectory while avoiding physiological obstacles. The needle tip position is derived from ultrasound images and is used in calculations to minimize the tracking error, enabling a closed-loop needle insertion. In addition, as tissue stiffness is a necessary input to the control algorithm, a novel method to classify tissue stiffness from localized tissue displacements is proposed and shown to successfully distinguish between soft and stiff tissue. The system performance was experimentally verified by robotic manipulation of the needle base inside a phantom with layers of varying stiffnesses. The closed-loop experiment with updated tissue stiffness parameters demonstrated a needle-tip tracking error of ~ 1 mm and proved to be significantly more accurate than the freehand method.IEEE Transactions on Biomedical Engineering 05/2010; DOI:10.1109/TBME.2009.2030169 · 2.23 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Ultrasonic imaging is often used to estimate blood flow velocity. Estimates are currently performed by Doppler-based techniques but they suffer from some shortcomings. This article compares four vector velocity estimation methods complementary to Doppler. Each method has been applied to six sequences, simulated and experimental, with various flow parameters. Results are presented in several curves and show specificities of each method.Physics Procedia 01/2010; 3(1):225-233. DOI:10.1016/j.phpro.2010.01.031