Segmentation Methods in Ultrasound Images

2011 ◽  
pp. 377-390
Author(s):  
Farhang Sahba
Keyword(s):  
Image Segmentation ◽  
Automatic Segmentation ◽  
Transrectal Ultrasound ◽  
Ultrasound Image ◽  
The Body ◽  
Ultrasound Images ◽  
Sound Waves ◽  
Medical Test ◽  
Segmentation Methods ◽  
Segmentation Task

Ultrasound imaging now has widespread clinical use. It involves exposing a part of the body to highfrequency sound waves in order to generate images of the inside of the body. Because it is a real-time procedure, the ultrasound images show the movement of the body’s internal structure as well. It is usually a painless medical test and its procedures seem to be safe. Despite recent improvement in the quality of information from an ultrasound device, these images are still a challenging case for segmentation. Thus, there is much interest in understanding how to apply an image segmentation task to ultrasound data and any improvements in this regard are desirable. Many methods have been introduced in existing literature to facilitate more accurate automatic or semi-automatic segmentation of ultrasound images. This chapter is a basic review of the works on ultrasound image segmentation classified by application areas, including segmentation of prostate transrectal ultrasound (TRUS), breast ultrasound, and intravascular ultrasound (IVUS) images.

Segmentation Methods in Ultrasound Images

2009 ◽  
pp. 473-487
Author(s):  
Farhang Sahba
Keyword(s):  
Image Segmentation ◽  
Automatic Segmentation ◽  
Transrectal Ultrasound ◽  
Ultrasound Image ◽  
The Body ◽  
Ultrasound Images ◽  
Sound Waves ◽  
Medical Test ◽  
Segmentation Methods ◽  
Segmentation Task

Ultrasound imaging now has widespread clinical use. It involves exposing a part of the body to highfrequency sound waves in order to generate images of the inside of the body. Because it is a real-time procedure, the ultrasound images show the movement of the body’s internal structure as well. It is usually a painless medical test and its procedures seem to be safe. Despite recent improvement in the quality of information from an ultrasound device, these images are still a challenging case for segmentation. Thus, there is much interest in understanding how to apply an image segmentation task to ultrasound data and any improvements in this regard are desirable. Many methods have been introduced in existing literature to facilitate more accurate automatic or semi-automatic segmentation of ultrasound images. This chapter is a basic review of the works on ultrasound image segmentation classified by application areas, including segmentation of prostate transrectal ultrasound (TRUS), breast ultrasound, and intravascular ultrasound (IVUS) images.

scholarly journals WDTISeg: One-Stage Interactive Segmentation for Breast Ultrasound Image Using Weighted Distance Transform and Shape-Aware Compound Loss

2021 ◽  
Vol 11 (14) ◽  
pp. 6279
Author(s):  
Xiaokang Li ◽  
Mengyun Qiao ◽  
Yi Guo ◽  
Jin Zhou ◽  
Shichong Zhou ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Ultrasound Image ◽  
Breast Ultrasound ◽  
Distance Transform ◽  
Interactive Segmentation ◽  
User Interactions ◽  
Weighted Distance ◽  
One Stage ◽  
Segmentation Methods ◽  
Segmentation Task

Accurate tumor segmentation is important for aided diagnosis using breast ultrasound. Interactive segmentation methods can obtain highly accurate results by continuously optimizing the segmentation result via user interactions. However, traditional interactive segmentation methods usually require a large number of interactions to make the result meet the requirements due to the performance limitations of the underlying model. With greater ability in extracting image information, convolutional neural network (CNN)-based interactive segmentation methods have been shown to effectively reduce the number of user interactions. In this paper, we proposed a one-stage interactive segmentation framework (interactive segmentation using weighted distance transform, WDTISeg) for breast ultrasound image using weighted distance transform and shape-aware compound loss. First, we used a pre-trained CNN to attain an initial automatic segmentation, based on which the user provided interaction points of mis-segmented areas. Then, we combined Euclidean distance transform and geodesic distance transform to convert interaction points into weighted distance maps to transfer segmentation guidance information to the model. The same CNN accepted the input image, the initial segmentation, and weighted distance maps as a concatenation input and provided a refined result, without another additional segmentation network. In addition, a shape-aware compound loss function using prior knowledge was designed to reduce the number of user interactions. In the testing phase on 200 cases, our method achieved a dice of 82.86 ± 16.22 (%) for automatic segmentation task and a dice of 94.45 ± 3.26 (%) for interactive segmentation task after 8 interactions. The results of comparative experiments proved that our method could obtain higher accuracy with fewer simple interactions than other interactive segmentation methods.

scholarly journals Ultrasound Image Denoising using Multiscale Ridgelet Transform with Hard and NeighCoeff Thresholding

2013 ◽  
pp. 150-155
Author(s):  
Preeti Goel ◽  
H. P. Sinha ◽  
Harpreet Singh
Keyword(s):  
Adaptive Filters ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
The Body ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Sound Waves ◽  
Edge Preservation ◽  
Ridgelet Transform ◽  
Visual Enhancement

Ultrasound imaging utilizes sound waves reflected from different organs of the body to give local details and important diagnostic information on the human body. However, using ultrasound images for diagnosis is difficult because of the existence of speckle noise in the image. The speckle noise is due to interference between coherent waves which are backscattered by targeted surfaces and arrive out of phase at the sensor. This hampers the perception and the extraction of fine details from the image. Speckle reduction/filtering i.e. visual enhancement techniques are used for enhancing the visual quality of the images. The multscale ridgelet transform based denoising algorithm for Ultrasound images is proposed for effective edge preservation in comparison to filtering techniques using the Adaptive Filters.

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.

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.

Ultrasound Image Intensity Nonuniformity Correction by Combining Intensity and Spatial Information

2013 ◽  
Vol 373-375 ◽  
pp. 552-557
Author(s):  
Wei Dong Song ◽  
Gao Feng Liang ◽  
Xiao Li Yang ◽  
Xiao Wei Son ◽  
Jing Yu Guo ◽  
...  
Keyword(s):  
Spatial Information ◽  
Automatic Segmentation ◽  
Ultrasound Image ◽  
Degradation Process ◽  
Image Features ◽  
The Body ◽  
Image Degradation ◽  
Spatial Image ◽  
Body Tissues ◽  
Intensity Nonuniformity

Medical ultrasonic B-scans often suffer from intensity inhomogeneities that originates from the nonuniform attenuation properties of the sonic beam within the body. In order to correct signal attenuation in the tissue, time gain compensation (TGC) is routinely used in medical ultrasound scanners. However, TGC assumes a uniform attenuation coefficient for all body tissues. Since this assumption is evidently inaccurate, over-amplification or under-amplification sometimes appear. This is a major problem for intensity-based, automatic segmentation of video-intensity images because conventional threshold-based or intensity-statistic-based approaches do not work well in the presence of such image distortions. The main contribution of this paper is that additional spatial image features are incorporated to improve inhomogeneity correction and to make it more dynamic besides most commonly used intensity features, so that local intensity variations can be corrected more efficiently. The degraded image is corrected by the inverse of the image degradation model. The image degradation process is described by a linear model, consisting of a multiplicative and an additive component which are modeled by a combination of smoothly varying basis functions. Spatial information about intensity nonuniformity is obtained using cubic spline smoothing and entropy minimizing. Gray-level histogram information of the image corrupted by intensity inhomogeneity is exploited from a signal processing perspective. We explain how this model can be related to the ultrasonic physics of image formation to justify our approach. Experiments are presented on synthetic images and real US data to evaluate quantitatively the accuracy of the method.

Ultrasound Physics and Ultrasound Machine Functionality

2021 ◽  
pp. 3-10
Author(s):  
Brooke Albright-Trainer
Keyword(s):  
Ultrasound Image ◽  
The Body ◽  
Sound Waves ◽  
Still Image ◽  
Ultrasound Machine ◽  
Operating Modes ◽  
Imaging Tool ◽  
Image Optimization ◽  
Tools And Techniques

Chapter 1 reviews basic ultrasound physics and introduces ultrasound machine functionality. Ultrasound medical imaging (also known as sonography) is a diagnostic imaging tool that uses high-frequency sound waves to create images of structures in the body. It can show details that a still image like a radiograph cannot, such as blood flow or needle guidance to a nerve. Several tools and techniques are useful in acquiring the best ultrasound image. The chapter covers the functions of many ultrasound machine knobs, machine operation, ultrasound operating modes, and ultrasound image optimization. It also examines different types of ultrasound probes and their uses.

Research on CCD Infrared Image Threshold Segmentation

2012 ◽  
Vol 220-223 ◽  
pp. 1292-1297
Author(s):  
Xing Ma ◽  
Jun Li Han ◽  
Chang Shun Liu
Keyword(s):  
Image Segmentation ◽  
Automatic Segmentation ◽  
Infrared Image ◽  
Experimental System ◽  
Segmentation Method ◽  
Gray Scale ◽  
Image Detection ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Characteristics Analysis

In recent years, the gray-scale thresholding segmentation has emerged as a primary tool for image segmentation. However, the application of segmentation algorithms to an image is often disappointing. Based on the characteristics analysis of infrared image, this paper develops several gray-scale thresholding segmentation methods capable of automatic segmentation in regions of pedestrians of infrared image. The approaches of gray-scale thresholding segmentation method are described. Then the experimental system is established by using the infrared CCD device for pedestrian image detection. The image segmentation results generated by the algorithm in the experiment demonstrate that the Otsu thresholding segmentation method has achieved a kind of algorithm on automatic detection and segmentation of infrared image information in regions of interest of image.

scholarly journals Body Navigation-loaded Ultrasound Acquisition Technology: a Pilot Comparison With Conventional Ultrasound

2021 ◽  
Author(s):  
Ki Choon Sim ◽  
Beom Jin Park ◽  
Byunjun Kim ◽  
Yeo Eun Han ◽  
Na Yeon Han ◽  
...  
Keyword(s):  
Imaging System ◽  
Ultrasound Image ◽  
Abdominal Ultrasound ◽  
The Body ◽  
Rank Test ◽  
Ultrasound Images ◽  
Rater Agreement ◽  
Kappa Test ◽  
Institutional Review ◽  
Conventional Ultrasound

Abstract Background: To investigate the usefulness of body navigation-loaded ultrasound including a real time transducer location and the inspection site compared with conventional ultrasound images.Methods: Under the approval of institutional review board, we prospectively enrolled total 29 healthy adult volunteers. One gastrointestinal radiologist performed abdominal ultrasound simultaneously using Ultrasound Navigation Image Convergence System developed by researchers. Subsequently, an equivalent conventional ultrasound image set was prepared. Three radiologists independently evaluated the two ultrasound image sets regarding the recognition of the target organ (2-points), the transducer location (2-points), and the transducer orientation (1-point). At intervals of two-weeks, conventional ultrasound images were analyzed first, and body navigation-loaded images were later analyzed. The score differences between the first and second evaluations were compared using the Wilcoxon signed rank test. Inter-rater agreement of three reviewers was obtained by the Fleiss’ Kappa test.Results: A total of 1402 navigation-loaded ultrasound images were obtained. Ultrasound operator carefully selected a total of 203 images for analysis. In all three reviewers, the interpretation score of each evaluation was significantly increased in the second analysis using the body navigation-loaded ultrasound image (in reviewer A, from 4.07±1.56 to 4.79±0.69 points; in reviewer B, from 3.83±1.59 to 4.49±0.88 points; in reviewer C, from 3.43±1.60 to 4.19±1.01 points; P<.0001). The inter-rater agreement of each evaluation also increased significantly in the second analysis using the body navigation-loaded ultrasound image (P<.0001).Conclusion: The body navigation-loaded ultrasound imaging system allows other medical staffs to easily and accurately interpret ultrasound images.

scholarly journals A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

2020 ◽  
Vol 1 (3) ◽  
pp. 78-91
Author(s):  
Muhammad Muhammad ◽  
Diyar Zeebaree ◽  
Adnan Mohsin Abdulazeez Brifcani ◽  
Jwan Saeed ◽  
Dilovan Asaad Zebari
Keyword(s):  
Breast Cancer ◽  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Region Of Interest ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Clustering Methods ◽  
Advantages And Disadvantages ◽  
Ultrasound Image Segmentation

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

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