Samuel Helrich¹, Wesley Pate¹, Nishant Garg¹, Philip Barbosa², Shaun Wason¹

Canadian Journal of Urology, Vol.28, No.6, pp. 10889-10899, 2021

Abstract Introduction: To compare pelvic ultrasound (PUS) and transrectal ultrasound (TRUS) to magnetic resonance imaging (MRI) in the estimation of prostate size.
Materials and methods: After IRB approval, we performed a single-center, retrospective study of 91 patients who had prostate sizing between August 2013 and June 2017. Correlation, reliability, and agreement between PUS, TRUS, and MRI were calculated through the Pearson coefficient, intraclass correlation coefficient, and Bland-Altman analysis, respectively. Data was stratified by prostate size, body mass index, and time between imaging acquisition.
Results: A total of 91 patients underwent all three imaging methods. Median age was 64, median… More >

Wenqian Li¹, Xing Deng^1,2,*, Haijian Shao¹, Xia Wang³

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.1, pp. 65-98, 2021, DOI:10.32604/cmes.2021.016981 - 24 August 2021

Abstract The COVID-19 has resulted in catastrophic situation and the deaths of millions of people all over the world. In this paper, the predictions of epidemiological propagation models, such as SIR and SEIR, are introduced to analyze the earlier COVID-19 propagation. The deep learning methods combined with transfer learning are familiar with classification-detection approaches based on chest X-ray and CT images are presented in detail. Besides, deep learning approaches have also been applied to lung ultrasound (LUS), which has been shown to be more sensitive than chest X-ray and CT images in detecting COVID-19. In the… More > Graphic Abstract

Ke Song^1,*, Paul Liu², Dongquan Liu³

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 1133-1150, 2021, DOI:10.32604/cmes.2021.016008 - 11 August 2021

Abstract A novel beamforming algorithm named Delay Multiply and Sum (DMAS), which excels at enhancing the resolution and contrast of ultrasonic image, has recently been proposed. However, there are nested loops in this algorithm, so the calculation complexity is higher compared to the Delay and Sum (DAS) beamformer which is widely used in industry. Thus, we proposed a simple vector-based method to lower its complexity. The key point is to transform the nested loops into several vector operations, which can be efficiently implemented on many parallel platforms, such as Graphics Processing Units (GPUs), and multi-core Central… More >

Mohamed Esmail Karar^1,2, Omar Reyad^1,3, Mohammed Abd-Elnaby⁴, Abdel-Haleem Abdel-Aty^5,6, Marwa Ahmed Shouman^7,*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2295-2312, 2021, DOI:10.32604/cmc.2021.018671 - 21 July 2021

Abstract Lightweight deep convolutional neural networks (CNNs) present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 patients. Recently, advantages of portable Ultrasound (US) imaging such as simplicity and safe procedures have attracted many radiologists for scanning suspected COVID-19 cases. In this paper, a new framework of lightweight deep learning classifiers, namely COVID-LWNet is proposed to identify COVID-19 and pneumonia abnormalities in US images. Compared to traditional deep learning models, lightweight CNNs showed significant performance of real-time vision applications by using mobile devices with limited hardware resources. Four main lightweight deep learning… More >

Yanling An¹, Shaohai Hu^1,*, Shuaiqi Liu^2,3, Jie Zhao^2,3,*, Yu-Dong Zhang⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 203-222, 2021, DOI:10.32604/cmes.2021.015159 - 28 June 2021

Abstract This paper proposes a computer-aided diagnosis system which can automatically detect thyroid nodules (TNs) and discriminate them as benign or malignant. The system firstly uses variational level set active contour with gradients and phase information to complete automatic extraction of the boundaries of thyroid nodules images. Then according to thyroid ultrasound images and clinical diagnostic criteria, a new feature extraction method based on the fusion of shape, gray and texture is explored. Due to the imbalance of thyroid sample classes, this paper introduces a weight factor to improve support vector machine, offering different classes of More >

Shuai Feng¹, Liu Jin¹, Yadan Wang¹, Wei Zhao^1,2, Hu Peng^1,*, Heyuan Qiao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.2, pp. 739-754, 2021, DOI:10.32604/cmes.2021.014027 - 21 January 2021

Abstract The high frame rate (HFR) imaging technique requires only one emission event for imaging. Therefore, it can achieve ultrafast imaging with frame rates up to the kHz regime, which satisfies the frame rate requirements for imaging moving tissues in scientific research and clinics. Lu’s Fourier migration method is based on a non-diffraction beam to obtain HFR images and can improve computational speed and efficiency. However, in order to obtain high-quality images, Fourier migration needs to make full use of the spectrum of echo signals for imaging, which requires a large number of Fast Fourier Transform… More >

Diyar Qader Zeebaree¹, Adnan Mohsin Abdulazeez², Dilovan Asaad Zebari^3,*, Habibollah Haron⁴, Haza Nuzly Abdull Hamed⁴

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3363-3382, 2021, DOI:10.32604/cmc.2021.013314 - 28 December 2020

Abstract Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging. Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise, an enhanced technique is not achieved. The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern (LBP) and filtered noise reduction. To surmount the above limitations and achieve the aim of the study, a new descriptor that enhances the LBP features based on the new threshold has been proposed.… More >

Ihsan Jasim Hussein¹, M. A. Burhanuddin², Mazin Abed Mohammed^3,*, Mohamed Elhoseny⁴, Begonya Garcia-Zapirain⁵, Marwah Suliman Maashi⁶, Mashael S. Maashi⁷

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3161-3182, 2021, DOI:10.32604/cmc.2021.012691 - 28 December 2020

Abstract One of the most complex tasks for computer-aided diagnosis (Intelligent decision support system) is the segmentation of lesions. Thus, this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images. The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases. In addition, proposed an approach that can efficiently generate region-of-interest (ROI) and new features that can be used in characterizing lesion boundaries. This study uses two databases in training and testing the proposed… More >

Nathan Perlis^1,2, Bishoy Lawendy¹, Jack Barkin^1,3

Canadian Journal of Urology, Vol.27, No.2, pp. 10185-10191, 2020

Abstract There is increasing evidence to support the use of multiparametric magnetic resonance imaging (MRI) in men at risk for clinically significant prostate cancer to help identify lesions and inform biopsy. Randomized, level 1 evidence demonstrates that men who are managed with MRI and MRI-ultrasound fusion targeted biopsy (MRF-TB) have more clinically significant prostate cancer and less clinically insignificant prostate cancer detected and avoid biopsy altogether more often than men who undergo systematic, whole-gland prostate biopsy (SPB). Furthermore, strategies that incorporate MRF-TB have lower rates of upgrading on radical prostatectomy compared to SPB. However, generalizing this… More >

Mo Hou^1,*, Weiyu Kevin Chiang^2,*, Weiqiang Hong¹, Maoyun Yang¹, Wenhua Yu^3,4

Molecular & Cellular Biomechanics, Vol.17, No.4, pp. 165-178, 2020, DOI:10.32604/mcb.2020.010805 - 20 October 2020

Abstract To achieve the image registration/fusion and perfect the quality of the integration, with dual modality contrast agent (DMCA), a novel multi-scale representation registration method between ultrasound imaging (US) and magnetic resonance imaging (MRI) is presented in the paper, and how DMCA influence on registration accuracy is chiefly discussed. Owing to US’s intense speckle noise, it is a tremendous challenge to register US with any other modality images. How to improve the algorithms for US processing has become the bottleneck, and in the short term it is difficult to have a breakthrough. In that case, DMCA… More >