The threshold filter is a frequently used technique in ultrasound B-scan to reject the small echoes contributed from backscattering that blur the tissue interface and reduce the image contrast. Note that using the threshold based on one value would simultaneously destroy local waveform features of the reflection echoes with amplitudes larger than threshold value. To resolve this problem, we developed an adaptive threshold filter based on the noise-assisted empirical mode decomposition (EMD). Computer simulations at 7.5 MHz using a single-element transducer with a bandwidth of 60% and a pulselength of 0.5 micros were carried out to explore the feasibility of the algorithm. Image measurements on the carotid artery using a 7.5 MHz, 128 elements, 1D linear array transducer with the same characteristics as those in simulations were used to verify the performance of the algorithm in practice. Compared to the result from the conventional threshold technique, the adaptive threshold filter is able to successfully suppress the smaller backscattering signals without changing the local waveform features of the preserved significant echoes due to reflection.
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http://dx.doi.org/10.1016/j.ultras.2008.10.007 | DOI Listing |
Sci Rep
January 2025
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China.
Bearings are critical in mechanical systems, as their health impacts system reliability. Proactive monitoring and diagnosing of bearing faults can prevent significant safety issues. Among various diagnostic methods that analyze bearing vibration signals, deep learning is notably effective.
View Article and Find Full Text PDFAnal Chem
January 2025
Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast BT9 5DL, Northern Ireland.
Maximizing the extraction of true, high-quality, nonredundant features from biofluids analyzed via LC-MS systems is challenging. Here, the R packages IPO and AutoTuner were used to optimize XCMS parameter settings for the retrieval of metabolite or lipid features in both ionization modes from either faecal or urine samples from two cohorts ( = 621). The feature lists obtained were compared with those where the parameter values were selected manually.
View Article and Find Full Text PDFSci Rep
January 2025
School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, China.
China's wind power generation is rich in resources and mature technology, but has the problems of harsh power generation environment, high operation and maintenance costs due to complex operating conditions, and serious consequences of failures. For this reason, this paper proposes a more efficient defect identification method for wind turbine blades that have the longest downtime due to faults. Firstly, starting from the characteristics that the blade defects are darker than the surrounding and distributed in block or point shape, the blade images taken by UAV cruise are processed by grey scaling, filtering, histogram equalization and Grab-cut foreground segmentation.
View Article and Find Full Text PDFJ Nutr Sci Vitaminol (Tokyo)
January 2025
Mental stress is a known risk factor for lifestyle-related diseases. Previously, we reported that short-term stress sharpens the sense of taste and dulls the sense of pungency, but in this study, we examined the effects of chronic mental stress on taste and pungency by comparing normal days with end-of-semester examination days. Furthermore, the relationship between pungency measured on the tongue and the corresponding skin current value causing forearm pain was investigated.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Computer Science, National Textile University, Faisalabad, Pakistan.
Accurate diagnosis of pancreatic cancer using CT scan images is critical for early detection and treatment, potentially saving numerous lives globally. Manual identification of pancreatic tumors by radiologists is challenging and time-consuming due to the complex nature of CT scan images and variations in tumor shape, size, and location of the pancreatic tumor also make it challenging to detect and classify different types of tumors. Thus, to address this challenge we proposed a four-stage framework of computer-aided diagnosis systems.
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