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نتیجه جستجو - Ultrasound imaging

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 Biometric recognition through 3D ultrasound hand geometry
تشخیص بیومتریک از طریق هندسه سونوگرافی سه بعدی دست-2021
Biometric recognition systems based on ultrasonic images have several advantages over other technologies, including the capability of capturing 3D images and detecting liveness. In this work, a recognition system based on hand geometry achieved through ultrasound images is proposed and experimentally evaluated. 3D images of human hand are acquired by performing parallel mechanical scans with a commercial ultrasound probe. Several 2D images are then extracted at increasing under-skin depths and, from each of them, up to 26 distances among key points of the hand are defined and computed to achieve a 2D template. A 3D template is then obtained by combining in several ways 2D templates of two or more images. A preliminary evaluation of the system is achieved by carrying out verification experiments on a home–made database. Results have shown a good recognition accuracy: the Equal Error Rate was 1.15% when a single 2D image is used and improved to 0.98% by using the 3D template. The possibility to upgrade the proposed system to a multimodal system, by extracting from the same volume other features like palmprint and hand veins, as well as possible improvements are finally discussed.
Keywords: Ultrasound imaging | Image processing | Biometry | Hand Geometry
مقاله انگلیسی
2 Automatic fetal biometry prediction using a novel deep convolutional network architecture
پیش بینی بیومتری خودکار جنین با استفاده از معماری شبکه ای پیچیده عمیق جدید-2021
Purpose: Fetal biometric measurements face a number of challenges, including the presence of speckle, limited soft-tissue contrast and difficulties in the presence of low amniotic fluid. This work proposes a convolutional neural network for automatic segmentation and measurement of fetal biometric parameters, including biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur length (FL) from ultra- sound images that relies on the attention gates incorporated into the multi-feature pyramid Unet (MFP-Unet) network.
Methods: The proposed approach, referred to as Attention MFP-Unet, learns to extract/detect salient regions automatically to be treated as the object of interest via the attention gates. After determining the type of anatomical structure in the image using a convolutional neural network, Niblack’s thresholding technique was applied as pre-processing algorithm for head and abdomen identification, whereas a novel algorithm was used for femur extraction. A publicly-available dataset (HC18 grand-challenge) and clinical data of 1334 subjects were utilized for training and evaluation of the Attention MFP-Unet algorithm.
Results: Dice similarity coefficient (DSC), hausdorff distance (HD), percentage of good contours, the conformity coefficient, and average perpendicular distance (APD) were employed for quantitative evaluation of fetal anatomy segmentation. In addition, correlation analysis, good contours, and conformity were employed to evaluate the accuracy of the biometry predictions. Attention MFP-Unet achieved 0.98, 1.14 mm, 100%, 0.95, and0.2 mm for DSC, HD, good contours, conformity, and APD, respectively.
Conclusions: Quantitative evaluation demonstrated the superior performance of the Attention MFP-Unet compared to state-of-the-art approaches commonly employed for automatic measurement of fetal biometric parameters.
Keywords: Fetal biometry | Ultrasound imaging | Deep learning | Convolutional neural network | Image classification
مقاله انگلیسی
3 Ultrasound image segmentation using a novel multi-scale Gaussian kernel fuzzy clustering and multi-scale vector field convolution
قطعه بندی تصویر اولتراسوند با استفاده از یک خوشه بندی فازی هسته ای گاوسی چند مقیاسی و پیچیدگی میدان بردار چند مقیاسی-2019
Ultrasound imaging is most popular technique used for breast cancer screening. Lesion segmentation is challenging step in characterization of breast ultrasound (US) based Computer Aided Diagnosis (CAD) systems due to presence of speckle noise, shadowing effect etc. The aim of this study is to develop an automatic lesion segmentation technique in breast US with high accuracy even in presence of noises, artifacts and multiple lesions. This article presents a novel clustering method called Multi-scale Gaussian Kernel induced Fuzzy C -means (MsGKFCM) for segmentation of lesions in automatically extracted Region of Interest (ROI) in US to delimit the border of the mass. Further, a hybrid approach using MsGKFCM and Multi-scale Vector Field Convolution (MsVFC) is proposed to obtain an accurate lesion margin in breast US images. Initially, the images are filtered using speckle reducing anisotropic diffusion (SRAD) technique. Subsequently, MsGKFCM is applied on filtered images to segment the mass and detect an appropriate cluster center. The detected cluster center is further used by MsVFC to determine the accurate lesion margin. The proposed technique is evaluated on 127 US images using measures such as Jaccard Index, Dice similarity, Shape similarity, Hausdroffdifference, Area difference, Accuracy, F -measure and analysis of variance (ANOVA) test. The empirical results suggest that the proposed approach can be used as an expert system to assist medical professionals by providing objective evidences in breast lesion detection. Results obtained are so far looking promising and effective in comparison to state-of-the-art algorithms.
Keywords: Ultrasound image segmentation | Speckle reduction | Multi-scale Gaussian kernel induced fuzzy | C -means | Multi-scale vector field convolution
مقاله انگلیسی
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