scholarly journals Fabrication and evaluation of bilateral Helmholtz radiofrequency coil for thermo‐stable breast image with reduced artifacts

2021 ◽  
Author(s):  
Young Han Lee ◽  
Kyu‐Ho Song ◽  
Jaemoon Yang ◽  
Won Jun Kang ◽  
Keum Sil Lee ◽  
...  
Keyword(s):  
Radiofrequency Coil ◽  
Breast Image

scholarly journals Double-tuned Radiofrequency Coil for 19F and 1H Imaging

2014 ◽  
Vol 13 (3) ◽  
pp. 199-205 ◽  
Author(s):  
Yosuke OTAKE ◽  
Yoshihisa SOUTOME ◽  
Koji HIRATA ◽  
Hisaaki OCHI ◽  
Yoshitaka BITO
Keyword(s):  
Radiofrequency Coil

Advanced Framework for Effective Denoising the Enhanced Thermal Breast Image

2021 ◽  
pp. 1-14
Author(s):  
A. Arul Edwin Raj ◽  
M. Sundaram ◽  
T. Jaya
Keyword(s):  
Breast Image

Breast image registration by using non-linear local affine transformation

2013 ◽  
Author(s):  
Feiyu Chen ◽  
Peng Zheng ◽  
Penglong Xu ◽  
Andrew D. A. Maidment ◽  
Predrag R. Bakic ◽  
...  
Keyword(s):  
Image Registration ◽  
Affine Transformation ◽  
Breast Image ◽  
Non Linear

Breast Tumor Detection in MR Images Based on Density

2021 ◽  
pp. 2250001
Author(s):  
Neeraj Shrivastava ◽  
Jyoti Bharti
Keyword(s):  
Breast Cancer ◽  
Breast Tumor ◽  
Early Stage ◽  
Region Growing ◽  
Tumor Detection ◽  
Misclassification Rate ◽  
Seed Region ◽  
True Negative ◽  
Multiple Tumor ◽  
Breast Image

Breast cancer is dangerous in women. It is generally found after the symptoms appear. Detecting the breast cancer at an early stage and understanding the treatment are the most important strategies to prevent death from cancer. Generally, for detection of breast cancer, breast Magnetic Resonance Image (MRI) takes place. It is one of the best approaches to detect tumor in women. In this research paper, a combination of selection methods for seed region growing image segmentation is suggested to detect breast tumor. The suggested method has been divided into following parts: First, the pre-processing of breast image is performed. Second, the automatic threshold for binarization process is calculated. Third, the number of seed points and its position in the breast image are determined automatically using density of pixels value. Fourth, a method for calculation of threshold value is proposed for the purpose of region creation in seed region growing. For the evaluation purpose, the proposed method was applied and tested on the RIDER MRI breast dataset from National Biomedical Imaging Archive (NBIA). After the test was performed, it was observed that proposed algorithm gives 90% accuracy, 88% True Negative Fraction, 91% True Positive Fraction, 10% Misclassification Rate, 94% Precision and 86% Relative Overlap which is better than other existing methods. It not only gives better evaluation measure but also provides segmentation method for multiple tumor detection.

Quantitative T 1 and proton density mapping with direct calculation of radiofrequency coil transmit and receive profiles from two-point variable flip angle data

2016 ◽  
Vol 29 (3) ◽  
pp. 349-360 ◽  
Author(s):  
Simon Baudrexel ◽  
Sarah C. Reitz ◽  
Stephanie Hof ◽  
René-Maxime Gracien ◽  
Vinzenz Fleischer ◽  
...  
Keyword(s):  
Direct Calculation ◽  
Flip Angle ◽  
Proton Density ◽  
Variable Flip Angle ◽  
Density Mapping ◽  
Radiofrequency Coil ◽  
Angle Data

A glass compensator filter to improve breast image quality in radiation therapy simulation

2000 ◽  
Vol 46 (4) ◽  
pp. 1061-1064
Author(s):  
Sang Gyu Ju ◽  
Seung Jae Huh ◽  
Kyu Chan Lee ◽  
Inhwan Jason Yeo ◽  
Yong Chan Ahn ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Image Quality ◽  
Breast Image

scholarly journals High-Field Pulsed ENDOR with Intra-cavity Radiofrequency Coil

2020 ◽  
Vol 51 (11) ◽  
pp. 1433-1449
Author(s):  
G. Annino ◽  
H. Moons ◽  
M. Fittipaldi ◽  
S. Van Doorslaer ◽  
E. Goovaerts
Keyword(s):  
Magnetic Field ◽  
High Field ◽  
Conversion Factor ◽  
Signal To Noise Ratio ◽  
Model Systems ◽  
Signal To Noise ◽  
Radiofrequency Coil ◽  
W Band ◽  
Radiofrequency Current ◽  
Field Uniformity

AbstractThis study compares the performance of two coil configurations for W-band pulsed ENDOR using a setup with both a radiofrequency ‘hairpin’ coil internal to a microwave non-radiative resonator and Helmholtz-like coils external to the resonator. Evaluation of the different coil performances is achieved via the ENDOR study of two model systems. The efficiencies of the coil configurations are first investigated numerically, showing that a higher radiofrequency current-to-magnetic field conversion factor can be achieved with the intra-cavity coil, with a similar radiofrequency magnetic field uniformity. This result is then confirmed by the broadband ENDOR spectra acquired with the two coil arrangements. A gain in the signal-to-noise ratio enabled by the internal coil of about a factor 10 was observed. In some cases, the high conversion factor of the intra-cavity coil led to a saturation of the ENDOR transitions. The possibility to implement a similar intra-cavity radiofrequency coil configuration in higher field spectrometers is finally discussed.

scholarly journals Automatic segmentation of multiple lesions in ultrasound breast image

2018 ◽  
Vol 7 (2.7) ◽  
pp. 665
Author(s):  
Chelladurai R ◽  
Selvakumar R ◽  
S Poonguzhali
Keyword(s):  
Breast Lesion ◽  
Automatic Segmentation ◽  
Initial Step ◽  
Ultra Sound ◽  
Ultrasound Images ◽  
Seed Point ◽  
Manual Intervention ◽  
Contour Shape ◽  
Breast Image ◽  
Trained Neural Network

Breast cancer is one of the leading cancer that affects woman all around the world. Nowadays ultra sound imaging technique is used to diagnose various cancer because of its non-ionizing, on-invasive, and cheap cost. Breast lesion region in ultrasound images are classified depending upon the contour, shape, size and textural features of the segmented region. Seed point is the initial step in segmentation of lesion regions and if that point is located outside the lesion region, it leads to wrong segmentation which results in misclassification of the lesion regions. To avoid this, most of the time the seed point is located manually. In order to avoid this manual intervention, we are proposing a novel method in locating the seed point and also segmenting the breast lesion region automatically. In this method, the image is processed with tan function for effective distinguishing of breast lesion and normal region. Then using the trained neural network, the seed point is automatically located inside the lesion region and from the seed point the region of the lesion is grown and segmented automatically. Most of the past works on automatic segmentation of lesion had concentrated only in single lesion region, but using this proposed method, we were able to automatically segment multiple lesion regions in the image. Outcome of the proposed method is to detect automatically and dynamically separate the lesion region in the range between 90% to 97.5% of images. 

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