Preprocessing for improved computer aided detection in medical ultrasound

Computer-Aided Mass Detection Based on Ipsilateral Multiview Mammograms

Due to the bad channel environment and poor image sampling equipment, images are often contaminated by noise in the process of collection, transmission and processing. Speckle noise, which is difficult and complex to eliminate, is one of the common noise appearing in image processing. Denoising methods based on Compressed Sensing (CS) technology have been proved as useful tools in suppressing speckle noise of single-frame images. However, temporal correlation in multi-frame images has not yet been utilized. Considering that the traditional denoising methods do not work satisfactorily in speckle noise reduction, a multi-frame image speckle denoising methods based on compressed sensing using tensor model is proposed. The first step is to use the third-order tensor to represent the blocks of image sequences, then the denoising tensor model is established according to the CS theory and the corresponding optimization problem is raised. The problem is divided into three parts: the sparse representation, the tensor dictionary update and the image reconstruction. A Kruskal tensor-based Orthogonal Matching Pursuit (OMP) and Candecomp/Parafac (CP) analysis are used to solve these problems and get the denoised image. At last, simulations are conducted to compare the CS method and traditional methods. It is shown that the CS-based multi-frame speckle denoising method performs well in noise variance and can significantly enhance the visual quality of the image.

Ultrasound scanning is considered the most important complementar method of breast cancer diagnosis. However, the acquisition format of this diagnostic method uses to introduce a remarkable amount of noise in the image, being the speckle noise of primary importance since it gives the image a granular aspect hindering the detection mainly of small objects in the image. With this feature in view, this work attempts to make a comparison among some of techniques proposed with aims of reducing this type of noise, such as: Frost Filter, Lee Filter, Wiener Filter, and Discreet Wavelet Transform. The results are analyzed by a series of evaluation functions which can be used as figures of merit in the image quality evaluation - Root Mean Square Error, Signal to Noise Ratio rms, and Peak Signal to Noise Ratio. The results have indicated that the Wavelet Transform seems to be more efficient in achieving the main purpose of speckle reduction in breast ultrasound images.KeywordsUltrasoundSpecklebreast imagesimage processsing

The myoelectric signal can be used to control many rehabilitation systems, for instance, prostheses and artificial neuromuscular electrical stimulation toward restoring movement to spinal cord injured subjects. These mobile systems are usually used in different environments and thus are being exposed to different noise levels with characteristics not completely known. In this article, three main techniques for noise reduction were evaluated: wavelet transform (WT), adaptive digital filters, and nonadaptive digital filters. The WT was used to reconstruct the signal with the components without noise information. Adaptive filters were designed using least mean square (LMS) and recursive least square (RLS) algorithms. Finite-impulse response (FIR) and infinite-impulse response (IIR) nonadaptive filters were used for comparison to both the adaptive filters and the signal reconstruction through the WT.

Shannon’s Nyquist theorem has always dictated the conventional signal acquisition policies. Power system is not an exception to this. As per this theory, the sampling rate must be at least twice the maximum frequency present in the signal. Recently, compressive sampling (CS) theory has shown that the signals can be reconstructed from samples obtained at sub-Nyquist rate. Signal reconstruction in this theory is exact for “sparse signals” and is near exact for compressible signals provided certain conditions are satisfied. CS theory has already been applied in communication, medical imaging, MRI, radar imaging, remote sensing, computational biology, machine learning, geophysical data analysis, and so forth. CS is comparatively new in the area of computer based power system monitoring. In this paper, subareas of computer based power system monitoring where compressive sampling theory has been applied are reviewed. At first, an overview of CS is presented and then the relevant literature specific to power systems is discussed.

As an alternative paradigm to the Shannon-Nyquist sampling theorem, compressive sensing enables sparse signals to be acquired by sub-Nyquist analog-to-digital converters thus may launch a revolution in signal collection, transmission and processing. In the practical compressive sensing applications, the sparse signal is always affected by noise and interference, and therefore the recovery performance reduces based on the conventional compressive sensing, especially in the low signal-to-noise scene, the sparse recovery is usually unavailable. In this paper, the influence of noise on recovery performance is analyzed, so as to provide the theoretical basis for the noise folding phenomenon in compressive sensing. From the analysis, we find that the expected noise gain in the random measure process is closely related to the row and column of the measurement matrix. However, only those columns corresponding to the support for the sparse signal contribute to the sparse vector. In the traditional Shannon-Nyquist sampling system, an antialiasing filter is applied before the sampling process, so as to filter the noise beyond the passband of interest. Inspired by the necessity of antialiasing filtering in bandpass signal sampling, we propose a selective measurement scheme, namely adapted compressive sensing, whose measurement matrix can be updated according to the noise information fed back by the processing center. The measurement matrix is specially designed, and the sensing matrix has directivity so that the signal noise can be suppressed. The measurement matrix senses only the spectrum of interest, where the sparse spectrum is most likely to lie. Moreover, we compare the recovery performance of the proposed adaptive scheme with those of the non-adaptive orthogonal matching pursuit algorithm, FOCal underdetermined system solver algorithm, and sarse Bayesian learning algorithm. Extensive numerical experiments show that the proposed scheme has a better improvement in the performance of the sparse signal recovery. From the viewpoint of implementation, the measurement noise should be taken into consideration in the system, and more efficient algorithms will be developed for source pre-estimation at lower signal-to-noise ratio.

The innovative knowledgebase system enables the healthcare system to address patient’s customized requirements. Developing and implementing this system with context-aware modifications is a challenge for developing countries, where it can change the healthcare delivery landscape from diagnosis to treatment. Although many studies have been conducted on the development of a knowledge-based system for early breast cancer detection using a rule-based approach, they lack contextual factors such as cultural aspects and socioeconomic features. This paper proposes a contextually aware machine learning (ML)-based knowledge base system for early breast cancer detection in the Pakistani context. Established risk and preventive breast cancer factors were identified from an extensive literature review and validated by academic and industry experts. Contextual factors were investigated with the assistance of field experts, and predictions from the ML algorithm were duly validated. The nested if-then rule, based on established risk and preventive factors, was combined with ML prediction to form the foundation of the proposed knowledgebase system. This research will improve the performance of healthcare systems in terms of accuracy and cost-effectiveness.

In this study, the noise reduction properties of the adaptive statistical iterative reconstruction (IR) on two different CT scanners of 64 and 256-slice were compared and their differences were assessed. The homogeneous module of the ACR CT phantom was scanned on the 64 and 256slices CT scanners from the same vendor in the range of 15-40mA. On each scanner, the data were reconstructed using filtered back projection (FBP) and at all strengths of IR with the STANDARD kernel. For each reconstruction, a 3D noise power spectrum (NPS) was calculated and the central frequency ratio in the xy plane (CFRxy ), CFR in the z-direction (CFRz ), and noise magnitude ratio (NMR) were derived. CFR is the central frequency ratio of NPS between the denoised image and the FBP image, and NMR is the ratio of the areas under the NPS curves. Ideally, both CFRxy and CFRz should be near 1, indicating minimal texture changes in both xy and z directions, while NMR should be as close to 0 as possible, indicating more noise reduction. When comparing strengths with equivalent impact on noise texture, IR on the 64-slice reduced the noise magnitude in the xy plane more than that on the 256-slice. In the z-direction, the IR on the 256-slice produced a central frequency shift on the 256-slice but not on the 64-slice. In addition, the noise reduction effects of the IR on the 256-slice were affected when radiation exposure was below 2.0mGy, but there was no observable dose-dependence on the 64-slice. Our noise property analysis revealed that iterative reconstructions on different scanner platforms from the same vendor can be distinct, with unique effects on the noise texture and magnitude in CT images. The IR on a 64-slice scanner provides slightly enhanced noise reduction and maintains a noise reduction rate independent of dose, unlike the one on a 256-slice scanner. Notably, the IR on the 64-slice scanner was a 2D noise reduction technique (NRT), while the one on the 256-slice was a 3D NRT. These observations showcase the impact of different NRTs on clinical CT images, even when comparing the same NRT on different scanners.

Environmental noise and data sparsity present major challenges within the field of bioacoustics. The presence of noise degrades the analysis of audio and field recordings commonly containing large quantities of data with sparse vocalisation features. This work explores noise reduction (audio enhancement) techniques in the context of extremely sparse vocalisations (< 1% occurrence rates) of invasive mammalian and marsupial species, and the clear implications for other bioacoustics applications which face similar challenges. This work compares relevant noise reduction techniques and recommends a spectral subtraction approach. Spectral subtraction achieved a 42.1 dB improvement in signal to noise power (SnNR) and a 2.7 dB improvement in noise variance (SR). We also demonstrate reliable noise reduction at bandwidths up to 250 kHz and efficiency improvements compared to alternative methods. We explore the benefits of deep audio enhancement approaches demonstrating comparable noise reduction with improvements in transient noise reduction but also key limitations such as bandwidth, efficiency and data generation in bioacoustics applications. We identify how the contributions of this work can be applied within the broader context of bioacoustics. All data and code is publicly available at https://github.com/BenMcEwen1/Sparse-Noise-Reduction

The dose per exam for a CT of the abdomen and pelvis has decreased by a factor of 2–3 since the 1980s due to a number of technical innovations. This was accomplished in parallel to scan times being reduced from several minutes to several seconds and routine image thicknesses being decreased from 10 mm to 3 – 5 mm. Some of the advances responsible for these improvements included: use of solid‐state scintillating detectors and electronic circuits with lower levels of “background” noise, wider multiple‐detector‐row arrays having thin individual detector elements, more powerful x‐ray tubes and generators which allow greater pre‐patient “beam‐hardening,” and beam shaping filters that vary decrease intensity to the patient periphery or restrict the majority of the dose to the center of the patient for cardiac imaging. A number of additional dose reduction techniques now exist and are gaining wide‐spread use, including automated adjustment of scanner output according to patient size, tube current modulation based on patient attenuation or ECG, automated selection of the most dose‐efficient tube potential, iterative reconstruction methods, increased spiral pitch or non‐spiral cardiac CT acquisition modes, dynamic z‐axis collimation, and novel noise reduction techniques, some of which require raw data and some of which operate on reconstructed images.This presentation will provide an overview of CT dose reduction techniques, emphasizing those with the greatest clinical impact, and new and emerging techniques. The participant will learn about the general classes of CT dose reduction techniques, and examples of specific implementations will be given.Learning Objectives:1. Recognize the general classes of dose reduction techniques in CT2. Understand the basic principles of each general class3. Learn how specific implementations of each class function4. Appreciate the magnitude of dose reduction that can be achieved using the described techniques

This paper addresses the problem of single microphone speech enhancement in noisy environments. State of the art short-time noise reduction techniques are most often expressed as a spectral gain depending on the signal-to-noise ratio (SNR). The well-known decision-directed approach drastically limits the level of musical noise but the estimated a priori SNR is biased since it depends on the speech spectrum estimated in the previous frame. The consequence of this bias is an annoying reverberation effect. We propose a new method, called reliable features selection noise reduction (RFSNR) technique, that is able to classify the a posteriori SNR estimates into two categories: the reliable features leading to speech components and the unreliable ones corresponding to musical noise only. Then it is possible to directly enhance speech using these reliable components leading to an unbiased estimator.

This paper explores noise measurement and reduction techniques as related to automated test equipment (ATE). The potential measurement techniques include various instruments with connections made at external interfaces, instrument interfaces and bus points. The instruments may include standard lab equipment like multimeters, digitizers, spectrum analyzers and power meters depending on the frequency band and signal levels of interest. The potential noise sources include instrument drive levels, conductor parasitics, ground system circulating currents, transmission line effects, instrument leakage, facility power and electromagnetic susceptibility

Background and Aim: Background noise as a serious challenge mainly affects the speech perception in people with hearing loss. One of the methods used to control noise is digital noise reduction (DNR) technique. The present study aimed to investigate the effect of DNR program (activated and deactivated) on the frequency gain of basic and advanced behind-the-ear (BTE) Oticon hearing aids, using different DNR strategies to reduce background noise. Methods: Two behind-the-ear Oticon hearing aids (Opn1 S105 and GetP) were used in this study. The Affinity 2.0 test box was first used to measure their DNR (off/on) gains using the national acoustic laboratories-non linear2 (NAL-NL2) and desired sensation level multi-stage [input/output] (DSLm[I/O]) formulas at sound pressure levels of 45, 65 and 85 dB SPL at a frequency range of 250-8000 Hz for three hearing loss (HL) patterns using the international speech test signal and broad band noise. Results: There was a significant difference in DNR performance between the Opn1 S105 and GetP models for all three HL patterns at 45 and 65 dB SPL and most frequencies. Conclusion: The DNR performance of advanced and basic hearing aids is different for different HLs at 45 and 65 dB SPL and most frequencies. The performance of advanced hearing aids is significant using the DSLm[I/O] formula at most frequencies. Keywords: Digital noise reduction; international speech test signal; frequency response; broad band noise

We describe the use of a graphical mathematical spreadsheet programming environment which can be used to simulate the acquisition and reconstruction processes of an x-ray computed tomography (CT) machine. The simulation is used to study the effect of photon counting statistics on the noise in the reconstructed image. Finally we describe and evaluate a novel technique for noise reduction using a nonlinear wavelet filter in which the filter thresholds are calculated individually from the "measured" projection data. This filter is shown to compare favorably to threshold filters based on global estimates of noise variance.

A novel technique of noise reduction in holographic microscopy has been experimentally studied. It has been shown that significant improvement in the holomicroscopic images of actual low-contrast continuous tone biological objects can be achieved without trade off in image resolution. The technique makes use of holographically produced multidirectional phase gratings used as diffusers and the continuous addition of subchannel holograms. It has been shown that the self-imaging property of this type of diffuser makes the use of these diffusers ideal for microscopic objects. Experimental results have also been presented to demonstrate real-time image processing capability of this technique.