Trainable Regularization in Dense Image Matching Problems

This study examines the development of specialized models designed to solve image-matching problems. The purpose of this research is to develop a technique based on energy tensor aggregation for dense image matching. This task is relevant within the framework of computer systems since image comparison makes it possible to solve current problems such as reconstructing a three-dimensional model of an object, creating a panorama scene, ensuring object recognition, etc. This paper examines in detail the key features of the image matching process based on the use of binocular stereo reconstruction and the features of calculating energies during this process, and establishes the main parts of the proposed method in the form of diagrams and formulas. This research develops a machine learning model that provides solutions to image matching problems for real data using parallel programming tools. A detailed description of the architecture of the convolutional recurrent neural network that underlies this method is given. Appropriate computational experiments were conducted to compare the results obtained with the methods proposed in the scientific literature. The method discussed in this article is characterized by better efficiency, both in terms of the speed of work execution and the number of possible errors. Doi: 10.28991/HIJ-2023-04-03-011 Full Text: PD

Comparison of the Performance of Different Time Delay Estimation Techniques for Ultrasound Elastography

Elastography is a non-invasive medical imaging modality that is used as a diagnostic tool for the early detection of several pathological changes in soft tissues. Elastography techniques provide the local strain distributions experienced by soft tissues due to compression. The resulting strain images are called “elastograms”. In elastography, the local tissue strains are usually estimated as the gradient of local tissue displacement. The local tissue displacements are estimated from the time delays between gated pre- and post-compression echo signals. The quality of the resulting elastograms is highly dependent on the accuracy of these local displacement estimates. While several time delay estimation (TDE) techniques have been proposed for elastography applications, there is a lack of systematic study that statistically compares the performance of these techniques. This information could prove to be of great importance to improve currently employed elastographic clinical methods. This study investigates the performance of selected time delay estimators for elastography applications. Time delay estimators based on Generalized Cross Correlation (GCC), Sum of Squared Differences (SSD) and Sum of Absolute Differences (SAD) are proposed and implemented. Within the class of GCC algorithms, we further consider: an FFT-based cross correlation algorithm (GCC-FFT), a hybrid time-domain and frequency domain cross correlation algorithm with prior estimates (GCC-PE) and an algorithm based on the use of fractional Fourier transform to compute the cross correlation (GCC -FRFT) . Image quality factors of the elastograms obtained using the different TDE techniques are analyzed and the results are compared using standard statistical tools. The results of this research suggests that correlation based techniques outperform SSD and SAD techniques in terms of SNRe, CNRe, dynamic range and robustness. The sensitivity of GCC-FFT and SSD were statistically similar and statistically higher than those of all other methods. Within the class of GCC methods, there is no statistically significant difference between SNRe of GCC-FFT, GCC-PE and GCC –FRFT for most of the strain values considered in this study. However, in terms of CNRe, GCC-FFT and GCC-FRFT were significantly better than other TDE algorithms. Based on these results, it is concluded that correlation-based algorithms are the most effective in obtaining high quality elastograms

Accuracy Assessment of Time Delay Estimation in Ultrasound Elastography

The accuracy of time-delay estimation (TDE) in ultrasound elastography is usually measured by calculating the value of the normalized cross correlation (NCC) at the estimated displacement. NCC value is usually high if the TDE is correct. However, it could be very high at a displacement estimate with large error, a well-known problem in TDE referred to as peak-hopping. Furthermore, NCC value could suffer from jitter error, which is due to electric noise and signal decorrelation. In this thesis, we propose a novel method to assess the accuracy of TDE by investigating the NCC profile around the estimated time-delay in a supervised approach. First, we extract seven features from the NCC profile, and utilize a linear support vector machine (SVM) to classify the peak-hopping and jitter error. The results on simulation, phantom and in-vivo data show the significant improvement in the classification accuracy realizing from the proposed algorithm compared to the obtained form the state of the art techniques. Second, we build on our model by utilizing the continuity features in the axial and lateral directions as a prior knowledge. We show that these features also improve the sensitivity and specificity of the classifier. After extracting the continuity features in addition to the seven features, we show the performance improvement of the proposed model on the available data sets. Furthermore, we show that our proposed model could be trained by other elastography methods in future, since we use a new elastography algorithm to train the model. Third, we compare the performance of the method developed using well-known classifiers in the literature and then study the importance of the proposed features using the mean decrease impurity method of the random forest classifier

Novel Ultrasound Elastography Imaging System for Breast Cancer Assessment

Abstract Most conventional methods of breast cancer screening such as X-ray, Ultrasound (US) and MRI have some issues ranging from weaknesses associated with tumour detection or classification to high cost or excessive time of image acquisition and reconstruction. Elastography is a non- invasive technique to visualize suspicious areas in soft tissues such as the breast, prostate and myocardium using tissue stiffness as image contrast mechanism. In this study, a breast Elastography system based on US imaging is proposed. This technique is fast, expected to be cost effective and more sensitive and specific compared to conventional US imaging. Unlike current Elastography techniques that image relative elastic modulus, this technique is capable of imaging absolute Young\u27s modulus (YM). In this technique, tissue displacements and surface forces used to mechanically stimulate the tissue are acquired and used as input to reconstruct the tissue YM distribution. For displacements acquisition, two techniques were used in this research: 1) a modified optical flow technique, which estimates the displacement of each node from US pre- and post-compression images and 2) Radio Frequency (RF) signal cross-correlation technique. In the former, displacements are calculated in 2 dimensions whereas in the latter, displacements are calculated in the US axial direction only. For improving the quality of elastography images, surface force data was used to calculate the stress distribution throughout the organ of interest by using an analytical model and a statistical numerical model. For force data acquisition, a system was developed in which load cells are used to measure forces on the surface of the breast. These forces are input into the stress distribution models to estimate the tissue stress distribution. By combining the stress field with the strain field calculated from the acquired displacements using Hooke\u27s law, the YM can be reconstructed efficiently. To validate the proposed technique, numerical and tissue mimicking phantom studies were conducted. For the numerical phantom study, a 3D breast-shape phantom was created with synthetic US pre- and post-compression images where the results showed the feasibility of reconstructing the absolute value of YM of tumour and background. In the tissue mimicking study, a block shape gelatine- agar phantom was constructed with a cylindrical inclusion. Results obtained from this study also indicated reasonably accurate reconstruction of the YM. The quality of the obtained elasticity images shows that image quality is improved by incorporating the adapted stress calculation techniques. Furthermore, the proposed elastography system is reasonably fast and can be potentially used in real-time clinical applications

New Image Processing Methods for Ultrasound Musculoskeletal Applications

In the past few years, ultrasound (US) imaging modalities have received increasing interest as diagnostic tools for orthopedic applications. The goal for many of these novel ultrasonic methods is to be able to create three-dimensional (3D) bone visualization non-invasively, safely and with high accuracy and spatial resolution. Availability of accurate bone segmentation and 3D reconstruction methods would help correctly interpreting complex bone morphology as well as facilitate quantitative analysis. However, in vivo ultrasound images of bones may have poor quality due to uncontrollable motion, high ultrasonic attenuation and the presence of imaging artifacts, which can affect the quality of the bone segmentation and reconstruction results. In this study, we investigate the use of novel ultrasonic processing methods that can significantly improve bone visualization, segmentation and 3D reconstruction in ultrasound volumetric data acquired in applications in vivo. Specifically, in this study, we investigate the use of new elastography-based, Doppler-based and statistical shape model-based methods that can be applied to ultrasound bone imaging applications with the overall major goal of obtaining fast yet accurate 3D bone reconstructions. This study is composed to three projects, which all have the potential to significantly contribute to this major goal. The first project deals with the fast and accurate implementation of correlation-based elastography and poroelastography techniques for real-time assessment of the mechanical properties of musculoskeletal tissues. The rationale behind this project is that, iii in the future, elastography-based features can be used to reduce false positives in ultrasonic bone segmentation methods based on the differences between the mechanical properties of soft tissues and the mechanical properties of hard tissues. In this study, a hybrid computation model is designed, implemented and tested to achieve real time performance without compromise in elastographic image quality . In the second project, a Power Doppler-based signal enhancement method is designed and tested with the intent of increasing the contrast between soft tissue and bone while suppressing the contrast between soft tissue and connective tissue, which is often a cause of false positives in ultrasonic bone segmentation problems. Both in-vitro and in-vivo experiments are performed to statistically analyze the performance of this method. In the third project, a statistical shape model based bone surface segmentation method is proposed and investigated. This method uses statistical models to determine if a curve detected in a segmented ultrasound image belongs to a bone surface or not. Both in-vitro and in-vivo experiments are performed to statistically analyze the performance of this method. I conclude this Dissertation with a discussion on possible future work in the field of ultrasound bone imaging and assessment

Deformation correction in ultrasound imaging in an elastography framework

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 77-80).Tissue deformation in ultrasound imaging is an inevitable phenomenon and poses challenges to the development of many techniques related to ultrasound image registration, including multimodal image fusion, freehand three-dimensional ultrasound, and quantitative measurement of tissue geometry. In this thesis, a novel trajectory-based method to correct tissue deformation in ultrasound B-mode imaging and elastography is developed in the framework of elastography. To characterize the change of tissue deformation with contact force, a force sensor is used to provide contact force measurement. Correlation-based displacement estimation techniques are applied to ultrasound images acquired under different contact forces. Based on the estimation results, a two-dimensional trajectory field is constructed, where pixel coordinates in each scan are plotted against the corresponding contact force. Interpolation or extrapolation by polynomial curve fitting is then applied to each trajectory to estimate the image under a specified contact force. The performance of displacement estimation and polynomial curve fitting are analyzed in a simulation framework incorporating FEM and ultrasound simulation. Influences of parameter selection are also examined. It is found that in displacement estimation, the coarse-to-fine approach outperforms single-level template search, and correlation filtering in coarse scale provides noticeable improvement in estimation performance. The strategies of image acquisition and order selection in polynomial curve fitting are also evaluated. Additionally, a finer force resolution is found to give better performance in predicting pixel positions under zero force. Deformation correction in both B-mode imaging and elastography is demonstrated through simulation and in-vitro experiments. The performance of correction is quantified by translational offset and area estimation of the tissue inclusions. It is found that, for both B-mode and elastography images, those performance metrics are significantly improved after correction. Moreover, it is shown that a finer resolution in force control gives better performance in deformation correction, which agrees with the analysis of polynomial curve fitting.by Shih-Yu Sun.S.M