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Journal ArticleDOI

Resolution enhancement in medical ultrasound imaging.

01 Jan 2015-Journal of medical imaging (International Society for Optics and Photonics)-Vol. 2, Iss: 1, pp 017001-017001
TL;DR: It is theoretically shown that a domain change and a multidimensional AR model can be used to achieve super-resolution in ultrasound imaging provided the order is estimated correctly, and it is shown that the method provides better results from a qualitative and a quantitative viewpoint.
Abstract: Image resolution enhancement is a problem of considerable interest in all medical imaging modalities. Unlike general purpose imaging or video processing, for a very long time, medical image resolution enhancement has been based on optimization of the imaging devices. Although some recent works purport to deal with image postprocessing, much remains to be done regarding medical image enhancement via postprocessing, especially in ultrasound imaging. We face a resolution improvement issue in the case of medical ultrasound imaging. We propose to investigate this problem using multidimensional autoregressive (AR) models. Noting that the estimation of the envelope of an ultrasound radio frequency (RF) signal is very similar to the estimation of classical Fourier-based power spectrum estimation, we theoretically show that a domain change and a multidimensional AR model can be used to achieve super-resolution in ultrasound imaging provided the order is estimated correctly. Here, this is done by means of a technique that simultaneously estimates the order and the parameters of a multidimensional model using relevant regression matrix factorization. Doing so, the proposed method specifically fits ultrasound imaging and provides an estimated envelope. Moreover, an expression that links the theoretical image resolution to both the image acquisition features (such as the point spread function) and a postprocessing feature (the AR model) order is derived. The overall contribution of this work is threefold. First, it allows for automatic resolution improvement. Through a simple model and without any specific manual algorithmic parameter tuning, as is used in common methods, the proposed technique simply and exclusively uses the ultrasound RF signal as input and provides the improved B-mode as output. Second, it allows for the a priori prediction of the improvement in resolution via the knowledge of the parametric model order before actual processing. Finally, to achieve the previous goal, while classical parametric methods would first estimate the model order and then the model parameters, our approach estimates the model parameters and the order simultaneously. The effectiveness of the methodology is validated using two-dimensional synthetic and in vivo data. We show that, compared to other techniques, our method provides better results from a qualitative and a quantitative viewpoint.

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Citations
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Journal ArticleDOI
TL;DR: Qualitative analysis of the reconstructed images confirms that the proposed super-resolution technique achieves much better quality HR images than other methods in terms of the human visual system and there is a statistically significant difference between it and others.
Abstract: Ultrasound offers a safe, non-invasive, and inexpensive way of imaging. However, due to its natural intrinsic imaging characteristics, it produces poor quality images with low resolution (LR) compared to other medical imaging modalities. Various image enhancement techniques have been extensively studied to overcome these shortcomings. Super-resolution (SR) is one of these methods, which endeavor to obtain high resolution (HR) images from LR images while enlarging them. Numerous studies have already utilized different SR techniques in various stages of ultrasound imaging (USI). Unlike other studies, which aimed at obtaining SR in the pre-processing phase or early stages of the post-processing phase of USI, we achieved SR on B-mode ultrasound images, which is the last stage of USI. We constructed a deep convolutional neural network (CNN) and trained it with a very large dataset of B-mode ultrasound images for the scale factors 2, 3, 4, and 8. We evaluated the performance of our proposed model quantitatively with eight image quality measures. The quantitative results revealed that our algorithm is much more successful than other methods at each magnification factor. Furthermore, we also verified that there is a statistically significant difference between our approach and others. Besides, qualitative analysis of the reconstructed images also confirms that it produces much better quality HR images than other methods in terms of the human visual system.

13 citations


Cites background from "Resolution enhancement in medical u..."

  • ...Reference [47] employed the idea that RF signal envelopes have to be improved to increase the resolution of images....

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Journal ArticleDOI
Ruoyun Liu1, Shichong Zhou1, Yi Guo1, Yuanyuan Wang1, Cai Chang1 
TL;DR: The proposed joint-training convolutional neural network is of considerable significance for accurate thyroid nodule localization in ultrasound images and can be generalized to other types of nodules, thereby providing trustworthy assistance for clinical diagnosis.
Abstract: The accurate localization of nodules in ultrasound images can convey crucial information to support a reliable diagnosis. However, this is usually challenging due to low contrast and image artifacts, especially in thyroid ultrasound images where nodules are relatively small in most cases. To address these problems, in this paper, we propose a joint-training convolutional neural network (CNN) for thyroid nodule localization in ultrasound images. Considering the advantage of the faster region-based CNN (Faster R-CNN) in detecting natural targets, we adopt it as the basic framework. To boost the representative power and noise suppression capability of the network, the attention mechanism module is embedded for adaptive feature refinement along the channel and spatial dimensions. Furthermore, in the training process, we annotate the training set in a novel way, called joint-training annotation, by exploiting the fake foreground (FFG) area around the nodule as a spatial prior constraint to improve the sensitivity to small nodules. Ablation experiments are conducted to verify the effectiveness of our proposed method. The experimental results show that our method outperforms others by a mean average precision (mAP) of 0.93 and achieves an intersection over union (IoU) of 0.9, indicating that the localization results agree well with the ground truth. Furthermore, extended experiments on breast nodule datasets are also conducted to verify the generalizability of the proposed approach. Above all, the proposed algorithm is of considerable significance for accurate thyroid nodule localization in ultrasound images and can be generalized to other types of nodules, thereby providing trustworthy assistance for clinical diagnosis.

6 citations


Cites methods from "Resolution enhancement in medical u..."

  • ...Histogram equalization methods have been widely used in the field of image processing for improving both contrast and structure visibility, therein utilizing the overall intensity distribution in the image as characterized by the normalized cumulative histogram [31]....

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Proceedings ArticleDOI
TL;DR: Experimental results show that the background noises and artifacts can be efficiently restrained, the wire object has a higher resolution and the contrast ratio can be enhanced for about 12dB to 15dB at different image depth comparing to delay-and-sum (DAS).
Abstract: Ultrasound image quality enhancement is a problem of considerable interest in medical imaging modality and an ongoing challenge to date. This paper investigates a method based on frequency-shift low-pass filtering (FSLF) and least mean square adaptive filtering (LMSAF) for ultrasound image quality enhancement. FSLF is used for processing the ultrasound signal in the frequency domain, while LMSAPF in the time domain. Firstly, FSLF shifts the center frequency of the focused signal to zero. Then the real and imaginary part of the complex data are filtered respectively by finite impulse response (FIR) low-pass filter. Thus the information around the center frequency are retained while the undesired ones, especially background noises are filtered. Secondly, LMSAF multiplies the signals with an automatically adjusted weight vector to further eliminate the noises and artifacts. Through the combination of the two filters, the ultrasound image is expected to have less noises and artifacts and higher resolution, and contrast. The proposed method was verified with the RF data of the CIRS phantom 055A captured by SonixTouch DAQ system. Experimental results show that the background noises and artifacts can be efficiently restrained, the wire object has a higher resolution and the contrast ratio (CR) can be enhanced for about 12dB to 15dB at different image depth comparing to delay-and-sum (DAS).

5 citations

Dissertation
28 Nov 2016
TL;DR: Both a need to improve the quality of TVS scanning and the viability of achieving this objective are suggested by introducing a QI programme driven by metrics gathered by software tools able to analyze the images used to measure ovaries.
Abstract: Research Question This thesis aims to answer the question as to whether software tools might be developed for automating the analysis of images used to measure ovaries in transvaginal sonography (TVS) exams. Such tools would allow the routine collection of independent and objective metrics at low cost and might be used to drive a programme of continuous Quality Improvement (QI) in TVS scanning. The tools will be assessed by processing images from thousands of TVS exams performed by the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Background This research is important because TVS is core to any ovarian cancer (OC) screening strategy yet independent and objective quality control (QC) metrics for this procedure are not routinely obtained due to the high cost of manual image inspection. Improving the quality of TVS in the National Health Service (NHS) would assist in the early diagnosis of the disease and result in improved outcome for some women. Therefore, the research has clear translational potential for the >1.2 million scans performed annually by the NHS. Research Findings A study performed to process images from 1,000 TVS exams has shown the tool produces accurate and reliable QC metrics. A further study revealed that over half of these exams should have been classified as unsatisfactory as an expert review of the images showed that that the sonographer had mistakenly measured a structure that was not an ovary. It also reported a correlation between such ovary visualisation and a novel metric (DCR) measured by the tools from the examination images. Conclusion The research results suggest both a need to improve the quality of TVS scanning and the viability of achieving this objective by introducing a QI programme driven by metrics gathered by software tools able to analyze the images used to measure ovaries.

3 citations

Journal ArticleDOI
TL;DR: In this paper, the authors introduce the principles of the classical power spectral estimation and modern power spectral estimator and analyze their characteristics and application in MATLAB simulation, and verify the analysis of the modern power estimator based on ARmodel is more accurate than the classical estimator.
Abstract: The power spectral estimation is an important element in the random signal analysis. The paper will introduce the principles of the classical power spectral estimation and modern power spectral estimation, analyses their characteristics and application in MATLAB simulation. The variance obtained by the classical power spectral estimation is inversely proportional to its resolution, the resolution of the modern spectral estimation are not subject to this restriction, but also the variance achieve greatly improvement, which is a great importance for improving the accuracy of the power spectral estimation. This paper mainly studies AR model of parametric modeling in the modern spectral estimation, and then uses the simulation between the classical power spectral estimation and modern power spectral estimation for comparison, verifies the analysis of the modern power spectral estimation based on ARmodel is more accurate than the classical power spectral estimation.

3 citations

References
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Book
01 Jan 1987
TL;DR: This new book provides a broad perspective of spectral estimation techniques and their implementation concerned with spectral estimation of discretespace sequences derived by sampling continuousspace signals.

2,731 citations

Journal ArticleDOI
TL;DR: A method for simulation of pulsed pressure fields from arbitrarily shaped, apodized and excited ultrasound transducers is suggested, which relies on the Tupholme-Stepanishen method for calculating pulsing pressure fields and can also handle the continuous wave and pulse-echo case.
Abstract: A method for simulation of pulsed pressure fields from arbitrarily shaped, apodized and excited ultrasound transducers is suggested. It relies on the Tupholme-Stepanishen method for calculating pulsed pressure fields, and can also handle the continuous wave and pulse-echo case. The field is calculated by dividing the surface into small rectangles and then Summing their response. A fast calculation is obtained by using the far-field approximation. Examples of the accuracy of the approach and actual calculation times are given. >

2,340 citations

01 Jan 1987
TL;DR: In this article, a broad perspective of spectral estimation techniques and their implementation is provided, focusing on spectral estimation of discretespace sequences derived by sampling continuous space signals, including parametric methods, minimum variance method, eigenanalysis-based estimators, multichannel methods, and twodimensional methods.
Abstract: This new book provides a broad perspective of spectral estimation techniques and their implementation. It concerned with spectral estimation of discretespace sequences derived by sampling continuousspace signals. Among its key features, the book: · Emphasizes the behavior of each spectral estimator for short data records. · Provides 35 computer programs, including fast algorithms. · Provides the theoretical background and review material in linear systems, Fourier transforms matrix algebra, random processes, and statics. · Summarizes classical spectral estimation as it is practiced today. · Covers Prony’s method, parametric methods, the minimum variance method, eigenanalysis-based estimators, multichannel methods, and twodimensional methods. · Includes problems. · Contains appendices that cover Sunspot Numbers, Complex Test Data, Temperature Data, and Program Conversion for Complex-to-Real Case. Of Special Interest A disk is included that has a double-sides 360kB format readable by any personal computer with an MS-DOS 2 or 3 operating system, such as the IBM XT or AT.

1,975 citations

Journal ArticleDOI

844 citations


"Resolution enhancement in medical u..." refers background in this paper

  • ...and the resolution problem comes to the point of hypothesis testing: R < 0: the two points are resolved R ≥ 0: the two points are not resolved : (14)...

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Journal ArticleDOI
TL;DR: Aspects of transducer development, systems design and tissue properties are presented to provide a foundation for medical and biological applications and speculation on the continuing evolution of ultrasound biomicroscopy is discussed.
Abstract: The visualisation of living tissues at microscopic resolution is attracting attention in several fields. In medicine, the goals are to image healthy and diseased tissue with the aim of providing information previously only available from biopsy samples. In basic biology, the goal may be to image biological models of human disease or to conduct longitudinal studies of small-animal development. High-frequency ultrasonic imaging (ultrasound biomicroscopy) offers unique advantages for these applications. In this paper, the development of ultrasound biomicroscopy is reviewed. Aspects of transducer development, systems design and tissue properties are pre- sented to provide a foundation for medical and biological applications. The majority of applications appear to be developing in the 40 - 60-MHz frequency range, where resolution on the order of 50 mm can be achieved. Doppler processing in this frequency range is beginning to emerge and some examples of current achievements will be highlighted. The current state of the art is reviewed for medical applications in ophthalmology, intravascular ultrasound, dermatology, and cartilage imaging. Ultrasound biomicroscopic studies of mouse embryonic devel- opment and tumour biology are presented. Speculation on the continuing evolution of ultrasound biomicroscopy will be discussed. © 2000 World Federation for Ultrasound in Medicine & Biology.

702 citations


"Resolution enhancement in medical u..." refers background in this paper

  • ...(6), it can be seen that since PSDyðfÞ is directly linked to the frequency variable f via the exponential function, the frequency resolution may be theoretically not limited, provided the am coefficients and the model order M are known....

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  • ...PSDyðfÞ 1⁄4 σ(2) j1þPMm1⁄41 am expð−2πjfmÞj2 ; (6)...

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  • ...(5) and (6) on this inverse Fourier transform leads to a new (better resolved) envelope signal....

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