Enhanced Needle Detection in Ultrasound Images using Acoustic Excitation and Ultrasound Image Analyses

2018 ◽  
Author(s):  
Mohammad I. Daoud ◽  
Ahmad Shtaiyat ◽  
Rami Alazrai
Keyword(s):  
Ultrasound Image ◽  
Acoustic Excitation ◽  
Ultrasound Images ◽  
Needle Detection ◽  
Image Analyses

An Automatic Biopsy Needle Detection and Segmentation on Ultrasound Images Using a Convolutional Neural Network

2021 ◽  
pp. 016173462110252
Author(s):  
Agata Wijata ◽  
Jacek Andrzejewski ◽  
Bartłomiej Pyciński
Keyword(s):  
False Negative ◽  
False Negative Rate ◽  
Ultrasound Image ◽  
Ultrasound Images ◽  
Biopsy Needle ◽  
Core Needle ◽  
Moment Estimation ◽  
2D Ultrasound ◽  
Needle Detection ◽  
Fully Automatic

Needle visualization in the ultrasound image is essential to successfully perform the ultrasound-guided core needle biopsy. Automatic needle detection can significantly reduce the procedure time, false-negative rate, and highly improve the diagnosis. In this paper, we present a CNN-based, fully automatic method for detection of core needle in 2D ultrasound images. Adaptive moment estimation optimizer is proposed as CNN architecture. Radon transform is applied to locate the needle. The network’s model was trained and tested on the total of 619 2D images from 91 cases of breast cancer. The model has achieved an average weighted intersection over union (the weighted Jaccard Index) of 0.986, F1 Score of 0.768, and angle RMSE of 3.73°. The obtained results exceed the other solutions by at least 0.27 and 7° in case of F1 score and angle RMSE, respectively. Finally, the needle is detected in a single frame averagely in 21.6 ms on a modern PC.

Automatic Measurement of Pennation Angle from Ultrasound Images using Resnets

2021 ◽  
Vol 43 (2) ◽  
pp. 74-87
Author(s):  
Weimin Zheng ◽  
Shangkun Liu ◽  
Qing-Wei Chai ◽  
Jeng-Shyang Pan ◽  
Shu-Chuan Chu
Keyword(s):  
Muscle Fibers ◽  
Ultrasound Image ◽  
Automatic Measurement ◽  
Pennation Angle ◽  
Reference Line ◽  
Ultrasound Images ◽  
Single Image ◽  
Angle Measuring ◽  
Sequential Image ◽  
Robust Measurements

In this study, an automatic pennation angle measuring approach based on deep learning is proposed. Firstly, the Local Radon Transform (LRT) is used to detect the superficial and deep aponeuroses on the ultrasound image. Secondly, a reference line are introduced between the deep and superficial aponeuroses to assist the detection of the orientation of muscle fibers. The Deep Residual Networks (Resnets) are used to judge the relative orientation of the reference line and muscle fibers. Then, reference line is revised until the line is parallel to the orientation of the muscle fibers. Finally, the pennation angle is obtained according to the direction of the detected aponeuroses and the muscle fibers. The angle detected by our proposed method differs by about 1° from the angle manually labeled. With a CPU, the average inference time for a single image of the muscle fibers with the proposed method is around 1.6 s, compared to 0.47 s for one of the image of a sequential image sequence. Experimental results show that the proposed method can achieve accurate and robust measurements of pennation angle.

scholarly journals Performance Evaluation of Contour Based Segmentation Methods for Ultrasound Images

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
R. J. Hemalatha ◽  
V. Vijaybaskar ◽  
T. R. Thamizhvani
Keyword(s):  
Active Contour ◽  
Performance Metrics ◽  
Ultrasound Image ◽  
Medical Image Segmentation ◽  
Similarity Criteria ◽  
Support Vector ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Segmentation Method ◽  
Region Detection

Active contour methods are widely used for medical image segmentation. Using level set algorithms the applications of active contour methods have become flexible and convenient. This paper describes the evaluation of the performance of the active contour models using performance metrics and statistical analysis. We have implemented five different methods for segmenting the synovial region in arthritis affected ultrasound image. A comparative analysis between the methods of segmentation was performed and the best segmentation method was identified using similarity criteria, standard error, and F-test. For further analysis, classification of the segmentation techniques using support vector machine (SVM) classifier is performed to determine the absolute method for synovial region detection. With these results, localized region based active contour named Lankton method is defined to be the best segmentation method.

scholarly journals Ultrasonography in physiotherapy and rehabilitation

2021 ◽  
pp. 1-3
Author(s):  
Barassi Giovanni ◽  
Guerri Sergio ◽  
Tavani Roberta ◽  
Ricucci Giampiero ◽  
De Luca Giorgia ◽  
...  
Keyword(s):  
Ultrasound Image ◽  
Muscle Stiffness ◽  
Ultrasound Images ◽  
Tendon Tissue ◽  
Invasive Approach ◽  
Know How ◽  
Non Invasive ◽  
Load Transmission ◽  
Intensity Information

There is an interrelation with ultrasound / physiotherapist and the duty of the physiotherapist to know how to perform ultrasound examinations alone, not for diagnostic purposes, to follow the evolution of the therapeutic cycle of physiotherapy. For this reason, ultrasound image analysis (US) is a promising non-invasive approach that uses load-dependent changes in the intensity of the echo to characterize the rigidity of muscle and tendon tissue. The purpose of this contribution is to improve the use of ultrasound images (US) and the role of the physiotherapist, who are able to detect localized changes, in particular in stiffness of the tendon due to partial and full-thickness tendon tears. Image intensity information is less sensitive for identifying load transmission variations resulting from partial thickness cuts initiated on the joint side. Ultrasound images can be useful for quantitatively assessing the variations dependent on the tendon load and muscle stiffness in physiotherapy and that the interruption of the behavior of the acousto-elastic ultrasound images can be indicative of substantial damage to the muscle or tendon.

scholarly journals Texture Based Quality Analysis of Simulated Synthetic Ultrasound Images Using Local Binary Patterns

2017 ◽  
Author(s):  
Prerna Singh ◽  
Ramakrishnan Mukundan ◽  
Rex De Ryke
Keyword(s):  
Quality Assessment ◽  
Image Quality Assessment ◽  
Ultrasound Image ◽  
Texture Feature ◽  
Ground Truth ◽  
Speckle Noise ◽  
Local Binary Patterns ◽  
Quality Analysis ◽  
Ultrasound Images ◽  
Ground Truth Data

Speckle noise reduction is an important area of research in the field of ultrasound image processing. Several algorithms for speckle noise characterization and analysis have been recently proposed in the area. Synthetic ultrasound images can play a key role in noise evaluation methods as they can be used to generate a variety of speckle noise models under different interpolation and sampling schemes, and can also provide valuable ground truth data for estimating the accuracy of the chosen methods. However, not much work has been done in the area of modelling synthetic ultrasound images, and in simulating speckle noise generation to get images that are as close as possible to real ultrasound images. An important aspect of simulated synthetic ultrasound images is the requirement for extensive quality assessment for ensuring that they have the texture characteristics and gray-tone features of real images. This paper presents texture feature analysis of synthetic ultrasound images using local binary patterns (LBP) and demonstrates the usefulness of a set of LBP features for image quality assessment. Experimental results presented in the paper clearly show how these features could provide an accurate quality metric that correlates very well with subjective evaluations performed by clinical experts.

scholarly journals Enhancing ultrasound texture differences for developing an in vivo 'virtual histology' approach to bovine ovarian imaging

2007 ◽  
Vol 19 (8) ◽  
pp. 910 ◽  
Author(s):  
Mark G. Eramian ◽  
Gregg P. Adams ◽  
Roger A. Pierson
Keyword(s):  
Image Processing ◽  
Ultrasound Image ◽  
Ultrasound Images ◽  
Virtual Histology ◽  
Form And Function ◽  
Non Invasive ◽  
Candidate Image ◽  
And Function ◽  
Processing Techniques

A ‘virtual histology’ can be thought of as the ‘staining’ of a digital ultrasound image via image processing techniques in order to enhance the visualisation of differences in the echotexture of different types of tissues. Several candidate image-processing algorithms for virtual histology using ultrasound images of the bovine ovary were studied. The candidate algorithms were evaluated qualitatively for the ability to enhance the visual differences in intra-ovarian structures and quantitatively, using standard texture description features, for the ability to increase statistical differences in the echotexture of different ovarian tissues. Certain algorithms were found to create textures that were representative of ovarian micro-anatomical structures that one would observe in actual histology. Quantitative analysis using standard texture description features showed that our algorithms increased the statistical differences in the echotexture of stroma regions and corpus luteum regions. This work represents a first step toward both a general algorithm for the virtual histology of ultrasound images and understanding dynamic changes in form and function of the ovary at the microscopic level in a safe, repeatable and non-invasive way.

Denoising Ultrasound Medical Images

2017 ◽  
pp. 761-775
Author(s):  
A.S.C.S. Sastry ◽  
P.V.V. Kishore ◽  
Ch. Raghava Prasad ◽  
M.V.D. Prasad
Keyword(s):  
Image Quality ◽  
Medical Images ◽  
Ultrasound Image ◽  
Similarity Index ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Soft Thresholding ◽  
Block Based

Medical ultrasound imaging has revolutioned the diagnostics of human body in the last few decades. The major drawback of ultrasound medical images is speckle noise. Speckle noise in ultrasound images is because of multiple reflections of ultrasound waves from hard tissues. Speckle noise degrades the medical ultrasound images lessening the visible quality of the image. The aim of this paper is to improve the image quality of ultrasound medical images by applying block based hard and soft thresholding on wavelet coefficients. Medical ultrasound image transformation to wavelet domain uses debauchee's mother wavelet. Divide the approximate and detailed coefficients into uniform blocks of size 8×8, 16×16, 32×32 and 64×64. Hard and soft thresholding on these blocks of approximate and detailed coefficients reduces speckle noise. Inverse transformation to original spatial domain produces a noise reduced ultrasound image. Experiments on medical ultrasound images obtained from diagnostic centers in Vijayawada, India show good improvements to ultrasound images visually. Quality of improved images in measured using peak signal to noise ratio (PSNR), image quality index (IQI), structural similarity index (SSIM).

scholarly journals Texture Feature-Based Classification on Transrectal Ultrasound Image for Prostatic Cancer Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaofu Huang ◽  
Ming Chen ◽  
Peizhong Liu ◽  
Yongzhao Du
Keyword(s):  
Prostate Cancer ◽  
Early Detection ◽  
Markov Random Fields ◽  
Transrectal Ultrasound ◽  
Ultrasound Image ◽  
Texture Features ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Malignant Tumours ◽  
Data Set

Prostate cancer is one of the most common cancers in men. Early detection of prostate cancer is the key to successful treatment. Ultrasound imaging is one of the most suitable methods for the early detection of prostate cancer. Although ultrasound images can show cancer lesions, subjective interpretation is not accurate. Therefore, this paper proposes a transrectal ultrasound image analysis method, aiming at characterizing prostate tissue through image processing to evaluate the possibility of malignant tumours. Firstly, the input image is preprocessed by optical density conversion. Then, local binarization and Gaussian Markov random fields are used to extract texture features, and the linear combination is performed. Finally, the fused texture features are provided to SVM classifier for classification. The method has been applied to data set of 342 transrectal ultrasound images obtained from hospitals with an accuracy of 70.93%, sensitivity of 70.00%, and specificity of 71.74%. The experimental results show that it is possible to distinguish cancerous tissues from noncancerous tissues to some extent.

scholarly journals Breast Ultrasound Image Synthesis using Deep Convolutional Generative Adversarial Networks

Diagnostics ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 176 ◽  
Author(s):  
Tomoyuki Fujioka ◽  
Mio Mori ◽  
Kazunori Kubota ◽  
Yuka Kikuchi ◽  
Leona Katsuta ◽  
...  
Keyword(s):  
Ultrasound Image ◽  
Image Synthesis ◽  
Breast Ultrasound ◽  
Generative Adversarial Networks ◽  
Ultrasound Images ◽  
Clinical Value ◽  
Adversarial Networks ◽  
Significant Difference ◽  
Definition Of ◽  
The Masses

Deep convolutional generative adversarial networks (DCGANs) are newly developed tools for generating synthesized images. To determine the clinical utility of synthesized images, we generated breast ultrasound images and assessed their quality and clinical value. After retrospectively collecting 528 images of 144 benign masses and 529 images of 216 malignant masses in the breasts, synthesized images were generated using a DCGAN with 50, 100, 200, 500, and 1000 epochs. The synthesized (n = 20) and original (n = 40) images were evaluated by two radiologists, who scored them for overall quality, definition of anatomic structures, and visualization of the masses on a five-point scale. They also scored the possibility of images being original. Although there was no significant difference between the images synthesized with 1000 and 500 epochs, the latter were evaluated as being of higher quality than all other images. Moreover, 2.5%, 0%, 12.5%, 37.5%, and 22.5% of the images synthesized with 50, 100, 200, 500, and 1000 epochs, respectively, and 14% of the original images were indistinguishable from one another. Interobserver agreement was very good (|r| = 0.708–0.825, p < 0.001). Therefore, DCGAN can generate high-quality and realistic synthesized breast ultrasound images that are indistinguishable from the original images.

Automated location of thyroid nodules in ultrasound images with improved YOLOV3 network

2020 ◽  
pp. 1-16
Author(s):  
Ling Zhang ◽  
Yan Zhuang ◽  
Zhan Hua ◽  
Lin Han ◽  
Cheng Li ◽  
...  
Keyword(s):  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Automatic Segmentation ◽  
Ultrasound Image ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Ultrasound Images ◽  
Semantic Features ◽  
Low Contrast ◽  
Automated Method

BACKGROUND: Thyroid ultrasonography is widely used to diagnose thyroid nodules in clinics. Automatic localization of nodules can promote the development of intelligent thyroid diagnosis and reduce workload of radiologists. However, besides the ultrasound image has low contrast and high noise, the thyroid nodules are diverse in shape and vary greatly in size. Thus, thyroid nodule detection in ultrasound images is still a challenging task. OBJECTIVE: This study proposes an automatic detection algorithm to locate nodules in B ultrasound images and Doppler ultrasound images. This method can be used to screen thyroid nodules and provide a basis for subsequent automatic segmentation and intelligent diagnosis. METHODS: We develop and optimize an improved YOLOV3 model for detecting thyroid nodules in ultrasound images with B-mode and Doppler mode. Improvements include (1) using the high-resolution network (HRNet) as the basic network for gradually extracting high-level semantic features to reduce the missed detection and misdetection, (2) optimizing the loss function for single target detection like nodules, and (3) obtaining the anchor boxes by clustering the candidate frames of real nodules in the dataset. RESULTS: The experimental results of applying to 8000 clinical ultrasound images show that the new method developed and tested in this study can effectively detect thyroid nodules. The method achieves 94.53% mean precision and 95.00% mean recall. CONCLUTIONS: The study demonstrates a new automated method that enables to achieve high detection accuracy and effectively locate thyroid nodules in various ultrasound images without any user interaction, which indicates its potential clinical application value for the thyroid nodule screening.

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