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Статті в журналах з теми "Ultrasound Imaging, 2-D Imaging, 3-D Imaging, Automated Image Analysis"
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Chen, Tainsong, Tzu-Pei Chen, and Liang Miin Tsai. "Computerized Quantification Analysis of Left Ventricular Wall Motion from Echocardiograms." Ultrasonic Imaging 19, no. 2 (April 1997): 138–44. http://dx.doi.org/10.1177/016173469701900204.
Повний текст джерелаАнотація:
Two-dimensional echocardiography (2-D echo) imaging is a more attractive clinical tool than other modalities that either involve radiation exposure or are too slow to image heart motion in real-time. Computer-aided analysis of left ventricular (LV) wall motion provides quantitative parameters for diagnosis. This study presents a computerized model for quantitative analysis of left ventricular wall motion from two-dimensional echocardiography by the application of image processing algorithms, including automatic threshold estimation, contrast stretching, boundary detection and border smoothing. The wall motion measurements rely primarily on sequential changes from end-diastolic to end-systolic frames in the left ventricular contours of apical four-chamber view echocardiograms. Left ventricular wall motion was analyzed on the 30 segments of 5 patients with acute myocardial infarction. The results from the computerized model were compared to those obtained from qualitative analysis of echocardiograms by an experienced clinical cardiologist who was unaware of the results of quantitative data.
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Yamashita, Mary, Rachel F. Brem, Lauren Baker, Taylor F. Mahoney, Patrick Walker, Rachel M. Treat, and Linda Hovanessian Larsen. "Abstract P3-04-13: Patient experience with automated SoftVue 3D whole breast tomographic ultrasound." Cancer Research 83, no. 5_Supplement (March 1, 2023): P3–04–13—P3–04–13. http://dx.doi.org/10.1158/1538-7445.sabcs22-p3-04-13.
Повний текст джерелаАнотація:
Abstract Authors: Mary Yamashita, MD; Rachel Brem, MD; Lauren Baker, Ph.D; MD; Taylor Mahoney, PhD; Patrick Walker, PharmD, MPH; Rachel Treat, MA; Linda Hovanessian Larsen, MD. Title: Patient experience with automated SoftVue 3D whole breast tomographic ultrasound. Purpose: SoftVue (SV) is an automated, 3D whole breast ultrasound tomographic imaging device which is FDA PMA approved as adjunct to mammography for women with dense breasts. Screening with handheld US is labor intensive and with ABUS is associated with significant patient discomfort. A benefit of SV is that image acquisition is not operator dependent and does not require compression. Because acceptance by patients is crucial to implementation of US screening, we evaluated patients’ experience with SV, specifically, asymptomatic women with BI-RADS c or d density undergoing FFDM. Materials and Methods: As part of a prospective, 10-site study, 7,439 asymptomatic women with BI-RADS density category c or d were screened on the same day with FFDM and SV. Each patient’s experience was assessed for perceived pain, discomfort and anxiety, discretion and modesty, overall satisfaction, and whether they would recommend it to others. The responses were measured on a Likert scale with 5 choices from strongly agree to strongly disagree, then studied by Chi-Squared analysis. Results: The mean age was 53.9 ± 9.7 yo, mostly white women (87.7%). The median BMI was 24.4. Majority had no personal history of breast cancer (97.2%), but 24.8% had a previous biopsy and almost half (46.3%) had a family history of breast cancer. Almost all patients (99.6%) completed the survey. SV was perceived as significantly more comfortable than FFDM (83.7% vs 52.2%, p< 0.001), was painless (94.9% vs 53.1%, p< 0.001), and was associated with less anxiety during the procedure (95.1% vs 79.9, p< 0.001). Lastly, 99.3% felt the experience was private and discreet, and 95% would recommend the SV exam to other women. Conclusion: Pain, fear, anxiety, and modesty concerns are some of the barriers preventing widespread implementation of screening breast US. This data suggests that SV, an FDA PMA approved adjunctive screening exam for women with dense breast tissue, is painless, offers a private and discreet scan that limits anxiety, and is well accepted by patients. Clinical relevance statement: SV is a novel automated US tomographic screening technology that is comfortable, well-accepted, FDA PMA approved, and will likely result in improved implementation of screening breast ultrasound in women with dense breasts. Citation Format: Mary Yamashita, Rachel F. Brem, Lauren Baker, Taylor F. Mahoney, Patrick Walker, Rachel M. Treat, Linda Hovanessian Larsen. Patient experience with automated SoftVue 3D whole breast tomographic ultrasound [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-04-13.
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Frellesen, Claudia, Julian L. Wichmann, Patricia Tischendorf, Jan-Erik Scholtz, Martin Beeres, Thomas J. Vogl, and Ralf W. Bauer. "High-pitch dual-source paranasal sinus CT in agitated patients with maxillofacial trauma: analysis of image quality, motion artifacts, and dose aspects." Acta Radiologica 59, no. 8 (November 6, 2017): 909–16. http://dx.doi.org/10.1177/0284185117740931.
Повний текст джерелаАнотація:
Background Image quality benefits from high-pitch scanning in agitated patients by reducing acquisition time. Purpose To compare image quality and exposure parameters in patients with maxillofacial trauma on second- and third-generation dual-source computed tomography (DSCT). Material and Methods Four groups were compared. Group 1 was examined on second-generation DSCT (120 kV/50 mAs, pitch 3.0). The other three groups were examined on third-generation DSCT. Group 2 was scanned with 120 kV/50 mAs, pitch 2.2. Automated exposure control (AEC) was used in group 3 and group 4 with pitch factors of 2.2 and 3.0, respectively. Images of third-generation DSCT were reconstructed with iterative reconstruction (IR), of second-generation DSCT with filtered back-projection. CTDIvol, acquisition time, and image quality were compared. Results Thirty patients were included in each group. Average CTDIvol (2.76 ± 0.00 mGy, 2.66 ± 0.00 mGy, 0.74 ± 0.23 mGy, and 0.75 ± 0.17 mGy) was significantly lower on third-generation DSCT with AEC ( P < 0.001). Subjective image quality was rated worst in group 4 due to strong high-pitch artifacts, while in the remaining three groups it was rated good or very good with good inter-observer agreement (k > 0.64). Average acquisition time was significantly shorter with third-generation DSCT (0.47 s, 0.36 s, 0.38 s, 0.30 s; P < 0.001). Conclusion Third-generation DSCT yields faster acquisition times and substantial dose reduction with AEC. A pitch of 2.2 should be preferred, as it results in fewer artifacts. If AEC is used, latest IR ensures that diagnostic image quality is guaranteed.
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Fang, W., Y. An, H. Sugimori, S. Kiuch, and T. Kamishima. "POS1391 DEEP LEARNING-BASED PANNUS LOCALIZATION SOFTWARE IN THE HANDS OF INFLAMMATORY ARTHRITIS USING TIME-INTENSITY CURVE (TIC) SHAPE CLASSIFICATION ON DYNAMIC MRI DATASET." Annals of the Rheumatic Diseases 81, Suppl 1 (May 23, 2022): 1036.1–1036. http://dx.doi.org/10.1136/annrheumdis-2022-eular.2196.
Повний текст джерелаАнотація:
BackgroundRheumatoid arthritis (RA) is a chronic inflammatory disease associated with significant functional impairment and disability, linked to inflammatory and structural articular and peri-articular damage [1]. Synovitis is a characteristic feature of RA, and is considered an important factor in disease activity and the best predictive marker of joint damage [2]. Therefore, accurate quantification of synovitis can play an important role in clinical evaluation and treatment serving as a biomarker. Pixel-by-pixel time–intensity curve (TIC) shape analysis is a dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technique to help visualize differently shaped TICs [3]. Pixels having TIC shape type 4, which is characterized as early enhancement followed by washout phase, were regarded as synovitis pixel [4]. A deep residual network called ResNet-50 model, a convolutional neural network (CNN) that is 50 layers deep, might effectively classify TIC shapes.ObjectivesThis study aimed to develop software that can automatically demonstrate the distribution of enhancing synovial pannus of patients with inflammatory arthritis on DCE-MRI.MethodsModified ResNet-50 was used on MATLAB. Two investigators drew regions of interest (ROIs) of muscle, bone, and synovitis under expert guidance and obtained TICs for each tissue on DCE-MRI of ten rheumatoid patients. Through cross-validation and batch evaluation, we verified the confusion matrices for the performance of the classifier. Then we identified the pixels of enhancing synovial pannus by image classification from the obtained pixel-by-pixel TIC shape and displayed them in color. The software performance was evaluated using a visual assessment on seven hand joints of one patient.Results150 ROIs for muscles, 150 ROIs for bones, and 59 ROIs for synovitis on DCE-MRI of the hand joints were drawn in ten patients and obtained 4049, 3825, and 1041 TIC shape images, respectively. The classifier’s accuracy, precision, sensitivity, and specificity were 99.6%, 99.3%, 98.4%, and 99.7%, respectively. Out of seven joints, four were assessed as good, two as fair, and one as fail.Figure 1.Software performance on contrast enhanced dynamic MRI of the wristConclusionOur classifier showed high accuracy, precision, sensitivity, and specificity. And through the comparison between manual outlining and the result of software, our software had relatively good performance. This automatic software developed using deep learning with CNN might accurately display the enhancing synovial pannus in RA.References[1]Micu MC, Fodor D. Concepts in monitoring the treatment in rheumatoid arthritis- the role of musculoskeletal ultrasound. Part I: synovitis. Med Ultrason. 2015;17(3):367-76.[2]Carotti M, Galeazzi V, Catucci F, Zappia M, Arrigoni F, Barile A, et al. Clinical utility of eco-color-power Doppler ultrasonography and contrast enhanced magnetic resonance imaging for interpretation and quantification of joint synovitis: a review. Acta Biomed. 2018;89(1-s):48-77.[3]Sakashita T, Kamishima T, Kobayashi Y, Sugimori H, Tang M, Sutherland K, et al. Accurate quantitative assessment of synovitis in rheumatoid arthritis using pixel-by-pixel, time-intensity curve shape analysis. Br J Radiol. 2016;89(1061):20151000.[4]Kobayashi Y, Kamishima T, Sugimori H, Ichikawa S, Noguchi A, Kono M, et al. Quantification of hand synovitis in rheumatoid arthritis: Arterial mask subtraction reinforced with mutual information can improve accuracy of pixel-by-pixel time-intensity curve shape analysis in dynamic MRI. J Magn Reson Imaging. 2018.Disclosure of InterestsNone declared
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Kakileti, Siva Teja, Himanshu Madhu, Richa Bansal, Akshita Singh, Sudhakar Sampangi, Bharat Aggarwal, and Geetha Manjunath. "Abstract P3-03-25: An Automated Risk Stratification System for Breast Cancer Screening using Thermalytix." Cancer Research 83, no. 5_Supplement (March 1, 2023): P3–03–25—P3–03–25. http://dx.doi.org/10.1158/1538-7445.sabcs22-p3-03-25.
Повний текст джерелаАнотація:
Abstract Background: As opposed to conventional age-based population-level breast screening strategies, risk-stratified breast screening programs are emerging as a new approach to balanced population screening methodology where the screening frequency and choice of modality (mammography/tomosynthesis or magnetic resonance imaging) is determined based on accurate personalized estimation of an individual’s risk score. A woman identified with low risk can now be screened less frequently, avoiding repeated mammography screening where radiation risk outweighs the benefits in that particular individual. The standard questionnaire based risk stratification is found to be less reliable and imaging based risk stratification mechanisms are being explored in recent years. In this study, we evaluate the performance of a new computer-aided image analysis technique called Thermalytix that automatically generates a personalized risk score using a combination of imaging and questionnaire information for risk stratification of women. Methodology: Thermalytix is an artificial intelligence system that uses thermal imaging and questionnaire data for predicting the risk of breast cancer. Thermalytix analyzes spatio-thermal signatures and vascular patterns in the breast region along with patients’ complaints and age to generate a score called B-Score (or BHARATI Score) which ranges from 1 to 5. B-Score of 1 indicates low risk of malignancy and a B-Score of 5 indicates the highest risk of malignancy. To evaluate the effectiveness of risk stratification using B-Scores, we performed retrospective analysis of thermal and participants’ data acquired from two registered clinical studies. One study (CTRI/2017/10/0 10 115) is a multi-site study conducted in Bangalore, India, and the other study (NCT04688086) is a single site study conducted in Delhi, India. Both these study sites are geographically distant with 2000 KM apart from each other and comprise a diversified population from India. Results: In total, 717 eligible women were considered in this study with age varying from 18 years to 80 years. Reports from standard of care procedures involving mammography, ultrasound and biopsy (as needed), were collectively considered by a radiologist to determine the ground truth for malignancy. Out of 717 women, 85 women were thus concluded as malignant. When used in a blinded fashion, Thermalytix graded 275 women as B-Score 1 (lowest risk), 225 women as B-Score 2, 44 women as B-Score 3, 137 women as B-Score 4 and 36 women as B-Score 5 (highest risk). The fraction of malignancies in the cohorts corresponding to B-Score categories from 1 to 5 were found to be progressively higher (0.36%, 1.33%, 29.55%, 33.58% and 61.11%, respectively) - showing the correctness of the proposed personalized risk scoring methodology. Conclusion: Thermalytix test, a low-cost, radiation-free, contactless and privacy aware test was used as a technique to determine the breast cancer risk of a woman.. The results obtained in the study show that a high B-score of 5 indicates a high risk for malignancy with 61.11% chance of breast cancer. Likewise the lowest B-Score of 1 indicates low risk for malignancy with just 0.36% percentage of women in the cohort found with malignancy. These results combined with other experiential benefits of Thermalytix test makes it a promising risk stratification mechanism enabling differential frequency of screening while balancing the cost and risk versus benefit. Large scale studies, however, need to be conducted to see the ground benefits of the proposed approach in a screening program implementation. Distribution of study population in different risk cohorts Higher risk correlates with higher malignancy rate Citation Format: Siva Teja Kakileti, Himanshu Madhu, Richa Bansal, Akshita Singh, Sudhakar Sampangi, Bharat Aggarwal, Geetha Manjunath. An Automated Risk Stratification System for Breast Cancer Screening using Thermalytix [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-03-25.
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Roysam, Badrinath, Hakan Ancin, Douglas E. Becker, Robert W. Mackin, Matthew M. Chestnut, Gregg M. Ridder, Thomas E. Dufresne, Donald H. Szarowski, and James N. Turner. "Going Beyond 3-D Imaging: Automated 3-D Montaged image Analysis of Cytological Specimens." Proceedings, annual meeting, Electron Microscopy Society of America 54 (August 11, 1996): 282–83. http://dx.doi.org/10.1017/s0424820100163873.
Повний текст джерелаАнотація:
This paper summarizes recent advances made by this group in the automated three-dimensional (3-D) image analysis of cytological specimens that are much thicker than the depth of field, and much wider than the field of view of the microscope. The imaging of thick samples is motivated by the need to sample large volumes of tissue rapidly, make more accurate measurements than possible with 2-D sampling, and also to perform analysis in a manner that preserves the relative locations and 3-D structures of the cells. The motivation to study specimens much wider than the field of view arises when measurements and insights at the tissue, rather than the cell level are needed.The term “analysis” indicates a activities ranging from cell counting, neuron tracing, cell morphometry, measurement of tracers, through characterization of large populations of cells with regard to higher-level tissue organization by detecting patterns such as 3-D spatial clustering, the presence of subpopulations, and their relationships to each other. Of even more interest are changes in these parameters as a function of development, and as a reaction to external stimuli. There is a widespread need to measure structural changes in tissue caused by toxins, physiologic states, biochemicals, aging, development, and electrochemical or physical stimuli. These agents could affect the number of cells per unit volume of tissue, cell volume and shape, and cause structural changes in individual cells, inter-connections, or subtle changes in higher-level tissue architecture. It is important to process large intact volumes of tissue to achieve adequate sampling and sensitivity to subtle changes. It is desirable to perform such studies rapidly, with utmost automation, and at minimal cost. Automated 3-D image analysis methods offer unique advantages and opportunities, without making simplifying assumptions of tissue uniformity, unlike random sampling methods such as stereology.12 Although stereological methods are known to be statistically unbiased, they may not be statistically efficient. Another disadvantage of sampling methods is the lack of full visual confirmation - an attractive feature of image analysis based methods.
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Clendenen, Steven R. "Needle Placement for Piriformis Injection Using 3-D Imaging." May 2013 3;16, no. 3;5 (May 14, 2013): E301—E310. http://dx.doi.org/10.36076/ppj.2013/16/e301.
Повний текст джерелаАнотація:
Piriformis syndrome is a pain syndrome originating in the buttock and is attributed to 6% – 8% of patients referred for the treatment of back and leg pain. The treatment for piriformis syndrome using fluoroscopy, computed tomography (CT), electromyography (EMG), and ultrasound (US) has become standard practice. The treatment of Piriformis Syndrome has evolved to include fluoroscopy and EMG with CT guidance. We present a case study of 5 successful piriformis injections using 3-D computer-assisted electromagnet needle tracking coupled with ultrasound. A 6-degree of freedom electromagnetic position tracker was attached to the ultrasound probe that allowed the system to detect the position and orientation of the probe in the magnetic field. The tracked ultrasound probe was used to find the posterior superior iliac spine. Subsequently, 3 points were captured to register the ultrasound image with the CT or magnetic resonance image scan. Moreover, after the registration was obtained, the navigation system visualized the tracked needle relative to the CT scan in real-time using 2 orthogonal multi-planar reconstructions centered at the tracked needle tip. Conversely, a recent study revealed that fluoroscopically guided injections had 30% accuracy compared to ultrasound guided injections, which tripled the accuracy percentage. This novel technique exhibited an accurate needle guidance injection precision of 98% while advancing to the piriformis muscle and avoiding the sciatic nerve. The mean (± SD) procedure time was 19.08 (± 4.9) minutes. This technique allows for electromagnetic instrument tip tracking with realtime 3-D guidance to the selected target. As with any new technique, a learning curve is expected; however, this technique could offer an alternative, minimizing radiation exposure. Key words: Piriformis, electromagnetic, ultrasound, fluoroscopy, injection, 3-D imaging.
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van der Hulst, J. M., D. Punzo, and J. B. T. M. Roerdink. "3-D interactive visualisation tools for Hispectral line imaging." Proceedings of the International Astronomical Union 12, S325 (October 2016): 305–10. http://dx.doi.org/10.1017/s174392131700134x.
Повний текст джерелаАнотація:
AbstractUpcoming HI surveys will deliver such large datasets that automated processing using the full 3-D information to find and characterize HI objects is unavoidable. Full 3-D visualization is an essential tool for enabling qualitative and quantitative inspection and analysis of the 3-D data, which is often complex in nature. Here we presentSlicerAstro, an open-source extension of3DSlicer, a multi-platform open source software package for visualization and medical image processing, which we developed for the inspection and analysis of HI spectral line data. We describe its initial capabilities, including 3-D filtering, 3-D selection and comparative modelling.
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Awad, Samer I., and Jesse T. Yen. "3D Strain Imaging Using a Rectilinear 2D Array." Ultrasonic Imaging 29, no. 4 (October 2007): 220–30. http://dx.doi.org/10.1177/016173460702900403.
Повний текст джерелаАнотація:
Under mechanical compression, tissue movements are inherently three-dimensional. 2-D strain imaging can suffer from decorrelation noise caused by out-of-plane tissue movement in elevation. With 3-D strain imaging, all tissue movements can be estimated and compensated, hence minimizing out-of-plane decorrelation noise. Promising 3-D strain imaging results have been shown using 1 -D arrays with mechanical translation in elevation. However, the relatively large slice thickness and mechanical translation can degrade image quality. Using 2-D arrays, an improved elevational resolution can be achieved with electronic focusing. Furthermore, scanning with 2-D arrays is also done electronically, which eliminates the need for mechanical translation. In this paper, we demonstrate the feasibility of 3-D strain imaging using a 4 cm × 4 cm ultrasonic sparse rectilinear 2-D array operating at 5MHz. The signal processing combinations of 2-D or 3-D beamforming followed by 2-D or 3-D strain imaging are studied and compared to each other to evaluate the performance of our 3-D strain imaging system. 3-D beamforming followed by 3-D strain imaging showed best performance in all experiments.
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Sasaki, Katsunari, Kohji Kakefuda, Kenji Masuda, Ging-Ho Hsiue, and Chain-Shu Hsu. "Application of Imaging Plate for Polymer Analysis." Advances in X-ray Analysis 36 (1992): 387–96. http://dx.doi.org/10.1154/s0376030800019005.
Повний текст джерелаАнотація:
AbstractThe Fuji Imaging Plate (IP) is a 2-Dimensional X-ray detector on which a latent X-ray image is stored as a distribution of color centers in a photo-stimulable phosphor(BaFBr:Eu) screen. It has excellent characteristics such as a wide dynamic range of five or more digits and an order of magnitude higher sensitivity than X-ray film. Thus it has been actively used in the field of X-ray single crystal structure analysis.For polymer studies, 2-D information is useful to analyse a sample's orientation or periodic structure, and some system such as 2-D position sensitive detector (PSD) are widely used. But in spite of the superior performance of the IP which will give significant advantages in various measurements, few applications have been reported in this field, because most conventional IP based systems are specialized for the single crystal structure analysis,Therefore we developed the R-AXIS II D (Rigaku Automated X-ray Imaging System II D), an IP reader for general X-ray diffractometry which has a removable IP in order for exposure with external X-ray optics, and software which converts 2-D data to conventional 2theta-intensity or beta-intensity data for analysis of crystallinity or orientation. In this paper, we report the performance of R-AXIS II D and its applications to polymer studies and thin film analyses.
Дисертації з теми "Ultrasound Imaging, 2-D Imaging, 3-D Imaging, Automated Image Analysis"
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Xiao, Guofang. "3-D free-hand ultrasound imaging and image analysis of the breast." Thesis, University of Oxford, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.393987.
Повний текст джерелаЧастини книг з теми "Ultrasound Imaging, 2-D Imaging, 3-D Imaging, Automated Image Analysis"
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Nakajima, Yoshikazu, Yuichi Tamura, Yoshinobu Sato, Takahito Tashiro, Nobuhiko Sugano, Kazuo Yonenobu, Hideki Yoshikawa, Takahiro Ochi, and Shinichi Tamura. "Preoperative Analysis of Optimal Imaging Orientation in Fluoroscopy for Voxel-Based 2-D/3-D Registration." In Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002, 485–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45787-9_61.
Повний текст джерела
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Krishnaraj, Nishanth, A. Mary Mekala, Bhaskar M., Ruban Nersisson, and Alex Noel Joseph Raj. "Layer-Wise Tumor Segmentation of Breast Images Using Convolutional Neural Networks." In Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, 1084–98. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7544-7.ch056.
Повний текст джерелаАнотація:
Early prediction of cancer type has become very crucial. Breast cancer is common to women and it leads to life threatening. Several imaging techniques have been suggested for timely detection and treatment of breast cancer. More research findings have been done to accurately detect the breast cancer. Automated whole breast ultrasound (AWBUS) is a new breast imaging technology that can render the entire breast anatomy in 3-D volume. The tissue layers in the breast are segmented and the type of lesion in the breast tissue can be identified which is essential for cancer detection. In this chapter, a u-net convolutional neural network architecture is used to implement the segmentation of breast tissues from AWBUS images into the different layers, that is, epidermis, subcutaneous, and muscular layer. The architecture was trained and tested with the AWBUS dataset images. The performance of the proposed scheme was based on accuracy, loss and the F1 score of the neural network that was calculated for each layer of the breast tissue.
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Krishnaraj, Nishanth, A. Mary Mekala, Bhaskar M., Ruban Nersisson, and Alex Noel Joseph Raj. "Layer-Wise Tumor Segmentation of Breast Images Using Convolutional Neural Networks." In Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments, 70–84. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6690-9.ch004.
Повний текст джерелаАнотація:
Early prediction of cancer type has become very crucial. Breast cancer is common to women and it leads to life threatening. Several imaging techniques have been suggested for timely detection and treatment of breast cancer. More research findings have been done to accurately detect the breast cancer. Automated whole breast ultrasound (AWBUS) is a new breast imaging technology that can render the entire breast anatomy in 3-D volume. The tissue layers in the breast are segmented and the type of lesion in the breast tissue can be identified which is essential for cancer detection. In this chapter, a u-net convolutional neural network architecture is used to implement the segmentation of breast tissues from AWBUS images into the different layers, that is, epidermis, subcutaneous, and muscular layer. The architecture was trained and tested with the AWBUS dataset images. The performance of the proposed scheme was based on accuracy, loss and the F1 score of the neural network that was calculated for each layer of the breast tissue.
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Srikanth M. V., V. V. K. D. V. Prasad, and K. Satya Prasad. "An Improved Firefly Algorithm-Based 2-D Image Thresholding for Brain Image Fusion." In Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, 341–79. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7544-7.ch018.
Повний текст джерелаАнотація:
In this article, an attempt is made to diagnose brain diseases like neoplastic, cerebrovascular, Alzheimer's, and sarcomas by the effective fusion of two images. The two images are fused in three steps. Step 1. Segmentation: The images are segmented on the basis of optimal thresholding, the thresholds are optimized with an improved firefly algorithm (pFA) by assuming Renyi entropy as an objective function. Earlier, image thresholding was performed with a 1-D histogram, but it has been recently observed that a 2-D histogram-based thresholding is better. Step 2: the segmented features are extracted with the scale invariant feature transform (SIFT) algorithm. The SIFT algorithm is good in extracting the features even after image rotation and scaling. Step 3: The fusion rules are made on the basis of an interval type-2 fuzzy set (IT2FL), where uncertainty effects are minimized unlike type-1. The novelty of the proposed work is tested on different benchmark image fusion data sets and has proven better in all measuring parameters.
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Sarkar, Kanchan, and Bohang Li. "Deep Learning for Medical Image Segmentation." In Deep Learning Applications in Medical Imaging, 40–77. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5071-7.ch002.
Повний текст джерелаАнотація:
Pixel accurate 2-D, 3-D medical image segmentation to identify abnormalities for further analysis is on high demand for computer-aided medical imaging applications. Various segmentation algorithms have been studied and applied in medical imaging for many years, but the problem remains challenging due to growing a large number of variety of applications starting from lung disease diagnosis based on x-ray images, nucleus detection, and segmentation based on microscopic pictures to kidney tumour segmentation. The recent innovation in deep learning brought revolutionary advances in computer vision. Image segmentation is one such area where deep learning shows its capacity and improves the performance by a larger margin than its successor. This chapter overviews the most popular deep learning-based image segmentation techniques and discusses their capabilities and basic advantages and limitations in the domain of medical imaging.
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Sarkar, Kanchan, and Bohang Li. "Deep Learning for Medical Image Segmentation." In Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, 861–91. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7544-7.ch044.
Повний текст джерелаАнотація:
Pixel accurate 2-D, 3-D medical image segmentation to identify abnormalities for further analysis is on high demand for computer-aided medical imaging applications. Various segmentation algorithms have been studied and applied in medical imaging for many years, but the problem remains challenging due to growing a large number of variety of applications starting from lung disease diagnosis based on x-ray images, nucleus detection, and segmentation based on microscopic pictures to kidney tumour segmentation. The recent innovation in deep learning brought revolutionary advances in computer vision. Image segmentation is one such area where deep learning shows its capacity and improves the performance by a larger margin than its successor. This chapter overviews the most popular deep learning-based image segmentation techniques and discusses their capabilities and basic advantages and limitations in the domain of medical imaging.
Тези доповідей конференцій з теми "Ultrasound Imaging, 2-D Imaging, 3-D Imaging, Automated Image Analysis"
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Mahmoud, Ahmed M., Phoebe A. Stapleton, Jefferson C. Frisbee, and Osama M. Mukdadi. "Noninvasive Measurement of Brachial Wall Mechanics During Flow-Mediated Vasodilation Using 2D Ultrasound Strain Tensor Imaging." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-192837.
Повний текст джерелаАнотація:
Atherosclerosis has become one of the contributing factors of cardiovascular diseases. Endothelial dysfunction is considered a key factor in the development of atherosclerosis [1]. Flow-mediated vasodilatation (FMD) measurement in brachial and other conduit arteries has become a common method to asses the endothelial function in vivo [2]. Fluid shear-stress increases due to blood flow increases, thus stimulating endothelial cell production and release of nitric oxide, a potent endogenous vasodilator. The mechanical behavior of the arterial wall during vasodilatation is considered an indication for endothelial health. In FMD measurement, the endothelium-dependent variation in arterial diameter in response to reactive ischemia-induced hyperemia is measured by comparing the luminal diameter of the brachial artery before and after the ischemia of the forearm induced by pressurizing a cuff [3]. Ultrasound imaging modalities has been widely used in the FMD analysis as a noninvasive low-cost tool, which can be used to track the arterial diameter change with time. Most of the FMD measurements in the literature are based on tracing the vessel wall boundary manually. Since this process is time consuming and may introduce human errors, automatic measurement techniques have been implemented [3,4]. These techniques utilize image processing algorithms to identify the edges of arterial walls, and then calculate the relative displacement change with time.
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Theodoracatos, Vassilios E., and Dale E. Calkins. "A 3-D Vision System Model for Automatic Object Surface Sensing." In ASME 1992 Design Technical Conferences. American Society of Mechanical Engineers, 1992. http://dx.doi.org/10.1115/detc1992-0166.
Повний текст джерелаАнотація:
Abstract The development of a “light striping” (structured light) based three-dimensional vision system for automatic surface sensing is presented. The three-dimensional world-point reconstruction process and system modeling methodology involves homogeneous coordinate transformations applied in two independent stages; the video imaging stage using three-dimensional perspective transformations, and the mechanical scanning stage, using three-dimensional affine transformations. Concatenation of the two independent matrix models leads to a robust four-by-four matrix system model. The independent treatment of the two-dimensional imaging process from the three-dimensional modeling process, has reduced the number of unknown internal and external geometrical parameters. The reconstructed sectional contours (light stripes) are automatically and in real-time registered with respect to a common world coordinate system in a format compatible with B-spline surface approximation. The reconstruction process is demonstrated by measuring the surface of a 19.5-ft long by 2 feet beam rowing shell. A detailed statistical accuracy and precision analysis shows an average error, 0.2 percent (0.002), of an object’s largest dimension within the the camera’s field-of-view. System sensitivity analysis reveals a nonlinear increase for angles between the normals of the image and laser planes higher than 45 degrees.
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Wilson, Nathan M., Raymond Q. Migrino, Leanne Harmann, Robert W. Prost, and John F. LaDisa. "Modeling and Realistic Simulation of the Carotid Artery Birfurcation Using 3-D Image Segmentation Implemented in a Commercial Software Package for Hemodynamic Simulation (cvSim™)." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-193258.
Повний текст джерелаАнотація:
Stroke is the third leading cause of death and a major cause of disability in the United States. Extracranial carotid artery disease is a major risk factor for stroke. Local hemodynamic forces are important in the development and progression of atherogenesis with areas of low and oscillatory wall shear stress (WSS) such as those occurring in the carotid bifurcation being more prone to atheroma development. Despite the importance of WSS in atherosclerosis, there is currently no practical means of measuring this variable clinically. Computational fluid dynamics (CFD) simulations of patient-specific models built from imaging data may provide a clinically relevant solution [1]. For CFD results to be clinically applicable, they need to replicate hemodynamic and imaging measurements to provide physiologic WSS values and the simulation and quantification process must be conducted in a time-frame consistent with the short duration needed for plaque and intima-media thickness assessment. LaDisa, Migrino and colleagues recently reported on a rapid and practical means of generating WSS maps associated with carotid atherosclerosis using patient-specific CFD models derived from 2D and Doppler ultrasound for flow information and MRI for 3D structure before and after 6 months of statin treatment [2]. Although these results were achieved after 17±8 hours/patient instead of days or weeks for prior models, model construction, quantification of results and simulation time were the most time consuming portions of the simulation process with CFD model construction being the most user-intensive portion of the process.
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Gundert, Timothy J., Paul Hayden, Raymond Q. Migrino, and John F. LaDisa. "Visualization of CFD Results in a Virtual Reality Environment." In ASME 2009 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2009. http://dx.doi.org/10.1115/sbc2009-205067.
Повний текст джерелаАнотація:
Imaging modalities such as computed tomography, 3D ultrasound and magnetic resonance imaging (MRI) facilitate detailed viewing of vascular geometries [1], but lack the ability to directly measure important hemodynamic parameters associated with the onset and progression of cardiovascular disease (i.e. pressure, wall shear stress) [2]. Computational fluid dynamics (CFD) is a noninvasive tool to quantify these indices in vessels reconstructed from imaging data. Although image-based CFD can be used to relate altered hemodynamics to vascular disease, a disjunction exists between information gathered from 4-D CFD (3 spatial dimensions and time) and the 2-D screens where results are typically displayed. In contrast, 3D virtual reality environments can be used to visualize CFD results in a comprehensive manner.
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Evans, Alan C. "Brain mapping with MRI and PET." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1993. http://dx.doi.org/10.1364/oam.1993.tho.2.
Повний текст джерелаАнотація:
Recently, 3-D imaging and computer-intensive analysis have revolutionized the study of normal human neuroanatomy and information processing. 3-D images of anatomy (MRI) and function (PET, MRI) are resampled into a standardized coordinate space which allows quantitative cross-subject comparison. Coordinate transformation may be linear or employ image-warping algorithms to overcome residual anatomical differences evident in the MRI data. Within the standardized space, image segmentation algorithms label voxels according to tissue type (e.g. gray or white matter), anatomical region (e.g. thalamus) or cortical feature (e.g. pre-central sulcus). Population analysis then generates probabilistic atlases for each sub-class.
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Gautam, Vivek, and A. K. Gupta. "Spectroscopic Analysis of Fuel Lean Flames for Propulsion Applications." In ASME 2004 Power Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/power2004-52074.
Повний текст джерелаАнотація:
Chemiluminescence and thermal imaging techniques have been used to examine the chemical and thermal behavior of turbulent flames with practical application to TBCC propulsion systems. The present study examines single swirler using an experimental double concentric swirl burner that simulates one swirler in a practical gas turbine combustor. The optical emission spectroscopy (OES) technique have been used to provide information on selected species in flames that mark the flame reaction zone and heat release rate. The instantaneous images are then integrated to obtain time-averaged information. Spatial distribution of OH, CH, C2 species from within the flames have been obtained at selected wavelengths using an ICCD camera and narrow band interference filters. The vibrational temperature distributions are obtained from the ratio of intensities of two discrete C2 bands of 470nm and 515nm. The time-averaged spatial distribution of flame generated radicals is processed using the Abel Inversion technique to project the initial 2-D image to represent the 3-D distribution of species and temperature in the flame. The results show that swirl distribution affects the shape of the spatial distribution by spreading the high intensity regions radially outwards with increase in swirl strength at inner regions of the fuel injector. Co-swirl distribution in the burner provided decrease in temperature and species intensity due to greater entrainment of the surrounding fluid. Calculated flame thermal strain rates were found to be significantly different for the co- and counter-swirl flames.