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.
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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; · 1.10 Impact Factor
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ABSTRACT: Intravascular ultrasound elastography (IVUSe) could improve the diagnosis of cardiovascular disease by revealing vulnerable plaques through their mechanical tissue properties. To improve the performance of IVUSe, we developed and implemented a non-rigid image-registration method to visualize the radial and circumferential component of strain within vascular tissues. We evaluated the algorithm's performance with four initialization schemes using simulated and experimentally acquired ultrasound images. Applying the registration method to radio-frequency (RF) echo frames improved the accuracy of displacements compared to when B-mode images were employed. However, strain elastograms measured from RF echo frames produce erroneous results when both the zero-initialization method and the mesh-refinement scheme were employed. For most strain levels, the cross-correlation-initialization method produced the best performance. The simulation study predicted that elastograms obtained from vessels with average strains in the range of 3%-5% should have high elastographic signal-to-noise ratio (SNRe)-on the order of 4.5 and 7.5 for the radial and circumferential components of strain, respectively. The preliminary in vivo validation study (phantom and an atherosclerotic rabbit) demonstrated that the non-rigid registration method could produce useful radial and circumferential strain elastograms under realistic physiologic conditions. The results of this investigation were sufficiently encouraging to warrant a more comprehensive in vivo validation.Ultrasound in medicine & biology 12/2012; · 2.46 Impact Factor
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ABSTRACT: OBJECTIVE. The aim of this systematic review was to determine the diagnostic accuracy of sonoelastography in detecting malignant thyroid nodules. MATERIALS AND METHODS. A systematic search in MEDLINE and bibliographic databases was performed for the terms "thyroid nodule" and "sonoelastography." The inclusion criteria were the report of a 4- or 5-point scoring scale for elasticity score by qualitative sonoelastography as the index test and fine-needle aspiration (FNA) cytology or histopathology for thyroid nodules as the reference standard. Studies in which only the strain ratio was reported and studies of patients with underlying medical conditions were excluded. The methodologic quality of the studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool. A meta-analysis of diagnostic accuracy measures for sonoelastography was performed using Meta-DiSc freeware software (version 1.4). RESULTS. A total of 12 studies assessing 1180 thyroid nodules (817 benign and 363 malignant) were included. The most commonly used threshold for characterizing malignancy-that is, elasticity scores between 2 and 3-showed a sensitivity of 86.0% (95% CI, 81.9-89.4%) and specificity of 66.7% (95% CI, 63.4-69.9%) with positive and negative likelihood ratios and a diagnostic odds ratio of 3.82 (95% CI, 2.38-6.13), 0.16 (95% CI, 0.08-0.32), and 27.51 (95% CI, 9.21-82.18), respectively. The highest sensitivity of the test was achieved by a threshold elasticity score of between 1 and 2 with a sensitivity of 98.3% (95% CI, 96.2-99.5%). CONCLUSION. Sonoelastography can be considered as a reliable screening tool for characterizing thyroid nodules. An elasticity score of 1 is indicative of benign pathology in almost all cases and can be used to exclude many patients from further invasive assessments.American Journal of Roentgenology 04/2014; 202(4):W379-89. · 2.90 Impact Factor