Acoustic Inverse Scattering for Breast Cancer Microcalcification Detection. Addendum

2011 ◽  
Author(s):  
Matthew A. Lewis
Keyword(s):  
Breast Cancer ◽  
Inverse Scattering ◽  
Microcalcification Detection ◽  
Acoustic Inverse Scattering

Acoustic Inverse Scattering Images from Simulated Higher Contrast Objects and from Laboratory Test Objects

1988 ◽  
pp. 353-371 ◽  
Author(s):  
M. J. Berggren ◽  
S. A. Johnson ◽  
W. W. Kim ◽  
D. T. Borup ◽  
R. S. Eidens ◽  
...  
Keyword(s):  
Laboratory Test ◽  
Inverse Scattering ◽  
Test Objects ◽  
Acoustic Inverse Scattering

scholarly journals Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Gabriele Valvano ◽  
Gianmarco Santini ◽  
Nicola Martini ◽  
Andrea Ripoli ◽  
Chiara Iacconi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
False Positive ◽  
False Positive Rate ◽  
Novel Approach ◽  
Positive Rate ◽  
Microcalcification Detection ◽  
Microcalcification Clusters

Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammograms to train and validate our model, obtaining an accuracy of 99.99% on microcalcification detection and a false positive rate of 0.005%. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.

scholarly journals Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM

Algorithms ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 135
Author(s):  
Cai ◽  
Liu ◽  
Luo ◽  
Du ◽  
Tang
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Early Breast Cancer ◽  
Detection Algorithm ◽  
Support Vector ◽  
Important Landmark ◽  
Professional Doctor ◽  
Breast Microcalcification ◽  
Adaptive Support ◽  
Microcalcification Detection

Microcalcification is the most important landmark information for early breast cancer. At present, morphological artificial observation is the main method for clinical diagnosis of such diseases, but it is easy to cause misdiagnosis and missed diagnosis. The present study proposes an algorithm for detecting microcalcification on mammography for early breast cancer. Firstly, the contrast characteristics of mammograms are enhanced by Contourlet transformation and morphology (CTM). Secondly, split the ROI by the improved K-means algorithm. Thirdly, calculate grayscale feature, shape feature, and Histogram of Oriented Gradient (HOG) for the ROI region. The Adaptive support vector machine (ASVM) is used as a tool to classify the rough calcification point and the false calcification point. Under the guidance of a professional doctor, 280 normal images and 120 calcification images were selected for experimentation, of which 210 normal images and 90 images with calcification images were used for training classification. The remaining 100 are used to test the algorithm. It is found that the accuracy of the automatic classification results of the Adaptive support vector machine (ASVM) algorithm reaches 94%, and the experimental results are superior to similar algorithms. The algorithm overcomes various difficulties in microcalcification detection and has great clinical application value.

Approximate linearized inversion by optimal scaling of prestack depth migration

Geophysics ◽  
2008 ◽  
Vol 73 (2) ◽  
pp. R23-R35 ◽  
Author(s):  
William W. Symes
Keyword(s):  
Inverse Problem ◽  
Inverse Scattering ◽  
Optimal Scaling ◽  
Scale Factor ◽  
Prestack Depth Migration ◽  
Depth Migration ◽  
Acoustic Inverse Scattering

Linearized inversion provides one possible sense of image amplitude correctness. An image or bandlimited model perturbation has correct amplitudes if it is an approximate inversion, that is, if linearized modeling (demigration), with the image as input, reproduces the data approximately. The theory of linearized acoustic inverse scattering with slowly varying background or macromodel shows that an approximate inversion may be recovered from the output of prestack depth migration by a combination of scaling and filtering. The necessary filter is completely specified by the theory, and the scale factor may be estimated via filtering, linearized modeling, a second migration, and the solution of a small auxiliary inverse problem.

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