Analysis of Ultrasound Images of Superficial Organs Based on the Combination of Global Features and Local Features

2021 ◽  
Vol 11 (3) ◽  
pp. 988-995
Author(s):  
Siqin Li ◽  
Huicheng Lv ◽  
Xiaoyan Yu
Keyword(s):  
Ultrasound Image ◽  
Local Features ◽  
Input Image ◽  
Local Feature ◽  
Feature Descriptor ◽  
Ultrasound Images ◽  
Global Features ◽  
Non Invasive ◽  
Malignant Lesions ◽  
Feature Information

Ultrasound imaging has become the preferred method for early detection of superficial organ lesions due to its non-invasive, economical, convenient, and radiation-free features. First, in the superficial organ ultrasound image analysis module based on local features, using Dense Sift as the local feature descriptor, the similarity sequence of the local features of the input image and the images in the sample library is calculated by the BOW algorithm and sorted in descending order of similarity. Secondly, using the wavelet transform’s local feature information representation capabilities in the time and frequency domains, the wavelet transform is performed on the small and identical feature information contained in the image to remove redundant information in each feature map to obtain a salient part of the image’s local features. Finally, through the analysis of elastic ultrasound images, a quantitative index that can be used to evaluate ultrasound images of superficial organs is proposed, which has higher accuracy and reliability than the current clinical methods for evaluating ultrasound images of superficial organs. By analyzing the features of superficial organ ultrasound images, it is proposed that the overall features of superficial organ ultrasound images are more conducive to distinguishing benign and malignant lesions than local features. Based on the initial localization of the lesion, the global features of the superficial organ ultrasound image were combined with the local features of the B-ultrasound image, and the method of combining global features with local features was used to classify and achieved good results.

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 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.

scholarly journals Adaptive Otsu’s Technique for PCOS Segmentation from Ovarian Ultrasound Images

2020 ◽  
Author(s):  
Sheela S ◽  
Sumathi M ◽  
Nirmala Priya S ◽  
Sangeeth Kumar B ◽  
Yukesh Kumar S J ◽  
...  
Keyword(s):  
Circular Polarization ◽  
Mimo System ◽  
Imaging Modality ◽  
Ultrasound Image ◽  
Region Of Interest ◽  
Ultrasound Images ◽  
Non Invasive ◽  
Antenna Performance ◽  
Scanned Image ◽  
Processing Techniques

Infertility is a common and important problem of many women in today’s life. Poly cystic ovarian syndrome (PCOS) is the origin of the infertility. This endocrine disorder affects women’s reproductive system. It also causes other problems like cardiovascular diseases, diabetes mellitus, etc. Among the various imaging modality, Ultrasound plays a major role in the diagnosis of PCOS since it is harmless, painless and non-invasive. Even though ultrasound image has so many advantages, due to poor image quality, inherent noise, overlapping of follicles and operator’s lack of prior knowledge, analyzing the characteristics of the scanned image is more challenging. Now a day, several image processing techniques are available to make this process easier. A commonly used segmentation method is Otsu’s threshold-based segmentation technique. But, it is suitable only for the high contrast image. To make this method suitable for all the images, Adaptive Otsu’s Technique (AOT) is developed and also achieved more desirable segmentation of the region of interest (ROI). In MIMO system , mutual coupling degrades the antenna performance to overcome this we go for circular polarization. In this paper, compact circular polarization and planar

Attention and Depth Perception

Perception ◽  
1986 ◽  
Vol 15 (5) ◽  
pp. 563-572 ◽  
Author(s):  
Nobuo Kawabata
Keyword(s):  
Depth Perception ◽  
Necker Cube ◽  
Line Drawing ◽  
Three Dimensional ◽  
Local Features ◽  
Local Feature ◽  
Two Dimensional ◽  
Front Part ◽  
Global Features ◽  
Dimensional Object

The Necker cube is a line drawing with two possible solutions in depth perception. The process of interpreting a two-dimensional line drawing as a three-dimensional object was investigated using the Necker cube. Attention was directed to a local feature of a briefly presented cube, ie an angle at a vertex. The attended angle was perceived as a front part of the cube and other parts were interpreted so as to match this interpretation. Results show that the local feature to which attention was directed was interpreted first and then global features and other local features were interpreted so as to agree with the local feature interpreted initially. This suggests that the three-dimensional interpretation of the line drawing was made sequentially from the local feature to global structures.

scholarly journals A Review on Various Ultrasound Image Despeckling Techniques

2021 ◽  
Author(s):  
Abhinav Panwar ◽  
Ramesh K. Sunkaria ◽  
Anterpreet Bedi
Keyword(s):  
Ultrasound Imaging ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Imaging Modalities ◽  
Disease Detection ◽  
Ultrasound Images ◽  
Non Invasive ◽  
Important Phenomenon ◽  
Image Despeckling

Ultrasound imaging is considered as one of the most widely used imaging modalities owing to its simple and non-invasive nature. However, ultrasound images are usually manifested with speckle noise, that acts as a hindrance in carrying out any further analysis or disease detection by the radiologists. Despeckling of these images is thus a very important phenomenon to carry out further studies by medical experts. It is of utmost importance that ultrasound images be despeckled, keeping in consideration that no information is lost from the images. This paper covers various despeckling techniques that have been designed for ultrasound images, making sure that no information is lost.

scholarly journals Planar object recognition based on local feature prediction agreements

2018 ◽  
Vol 246 ◽  
pp. 03011 ◽  
Author(s):  
Chengge Gu ◽  
Jianying Bao ◽  
Haonan Sang ◽  
Jinqiua Mo
Keyword(s):  
Object Recognition ◽  
Industrial Application ◽  
Local Features ◽  
Input Image ◽  
Local Feature ◽  
Matching Method ◽  
Recognition Experiment ◽  
Two Stages

Object recognition has drawn great attention in industrial application especially in automated feeding and assembling, for it can greatly improve the line flexibility and save cost. In this paper, a simple but effective method for planar object recognition is presented. This method can deal with objects under complex conditions like occlusion and clutter. The method generates object pose hypothesis from the prediction agreements of different local features in the object. There are two stages contained in our method, offline stage and online stage. At offline stage, the representative parts in the object are chosen as its local features and the recognition template is made. Next at online stage, the matches of different local features are found in the input image. Then the prediction agreements are searched among them in order to generate the final object pose hypothesis. A thin planar object recognition experiment has been conducted under occluded conditions and an improved result is presented compared with the traditional overall matching method.

Experimental Validation of Minimax Entropy Principle in Ultrasound Images

2019 ◽  
Vol 12 (6) ◽  
pp. 487-493
Author(s):  
Neha Mehta ◽  
Svav Prasad ◽  
Leena Arya
Keyword(s):  
Region Of Interest ◽  
Ultrasound Images ◽  
Human Gallbladder ◽  
New Approach ◽  
Non Invasive ◽  
Entropy Principle ◽  
Significant Performance ◽  
Considerable Impact ◽  
Ultrasonic Devices ◽  
Measure Of Uncertainty

Ultrasound imaging is one of the non-invasive imaging, that diagnoses the disease inside a human body and there are numerous ultrasonic devices being used frequently. Entropy as a well known statistical measure of uncertainty has a considerable impact on the medical images. A procedure for minimizing the entropy with respect to the region of interest is demonstrated. This new approach has shown the experiments using Extracted Region Of Interest Based Sharpened image, called as (EROIS) image based on Minimax entropy principle and various filters. In this turn, the approach also validates the versatility of the entropy concept. Experiments have been performed practically on the real-time ultrasound images collected from ultrasound centers and have shown a significant performance. The present approach has been validated with showing results over ultrasound images of the Human Gallbladder.

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.

MO226A NEW DIAGNOSTIC TOOL FOR CKD PATIENTS: CONTRAST-ENHANCED ULTRASONOGRAPHY. A SINGLE CENTER EXPERIENCE

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Mirela Liana Gliga ◽  
Cristian Chirila ◽  
Paula Chirila ◽  
Adriana Gomotarceanu ◽  
Imola Torok ◽  
...  
Keyword(s):  
Diagnostic Tool ◽  
Final Diagnosis ◽  
Medical System ◽  
Focal Lesions ◽  
Non Invasive ◽  
Single Center Experience ◽  
Contrast Enhanced ◽  
Contrast Enhanced Ultrasonography ◽  
Malignant Lesions ◽  
Guidelines And Recommendations

Abstract Background and Aims Contrast-enhanced ultrasonography (CEUS) is a minimally invasive diagnostic tool available for diagnosing microvascular disturbances in tumors and many vascular pathologies. Unlike other radiological contrast agents, it is completely harmless for CKD patients and therefore it is used for the safe diagnosis of many diffuse or focal pathologies. Method We used CEUS examination in 50 CKD patients for the following pathologies: 10 atypical cysts, 15 liver focal lesions, 2 splenic focal lesions, 3 renal infarcts, 12 kidney focal lesions and 8 other organ involvements. Examination was made using a VOLUSON E8 machine (GE Medical System Kreztechnik GmbH Tiefenbach 15, Austria) with a 3.5 MHz convex array abdominal transducer. 2.4 ml of microbubble contrast-agent was administered intravenously and recording of the results were made for 3-5 minutes after injection. Results Depending on the organ vascular characteristics, contrast enhancement and/or wash-out were suggestive for the final diagnosis. In liver lesions there are three phases and in kidneys, spleen, gallbladder, adenopathies there are two vascular phases. We obtained a very good positive predictive value and sensitivity in detecting malignant lesions. Conclusion According to The EFSUMB Guidelines and Recommendations for the Clinical Practice of Contrast-Enhanced Ultrasound they are used both for hepatic and Non-Hepatic Applications. Being non-invasive and non-irradiating it could be the main diagnostic examination in CKD population in the future.

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.

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