Journal ArticleDOI
Breast tumor classification in ultrasound images using texture analysis and super-resolution methods
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TLDR
It is shown that the super-resolution-based approach improves the performance of the evaluated texture methods and thus outperforms the state of the art in benign/malignant tumor classification.About:
This article is published in Engineering Applications of Artificial Intelligence.The article was published on 2017-03-01. It has received 89 citations till now. The article focuses on the topics: Local binary patterns & Phase congruency.read more
Citations
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Journal ArticleDOI
Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning
TL;DR: A biophysical feature-based machine learning method for breast cancer detection is proposed to improve the performance beyond a benchmark deep learning algorithm and to further-more provide a color overlay visual map of the probability of malignancy within a lesion.
Proceedings ArticleDOI
Fusion Framework for Morphological and Multispectral Textural Features for Identification of Endometrial Tuberculosis
TL;DR: In this article, a fusion framework model is proposed where the extracted image features are fused and an optimal subset of features is obtained for identification of endometrial tuberculosis from Transvaginal ultrasound (TVUS) images.
Journal ArticleDOI
Multi-scale convolution based breast cancer image segmentation with attention mechanism in conjunction with war search optimization
Journal ArticleDOI
Learning-based Framework for US Signals Super-resolution
TL;DR: In this paper , a deep learning framework was proposed for super-resolution ultrasound images and videos in terms of spatial resolution and line reconstruction, where the acquired low-resolution image was up-sampled through a vision-based interpolation method; then, a learning-based model was trained to improve the quality of the upsampling.
Proceedings ArticleDOI
Reducing Dark Region Artifacts in Short-Lag Spatial Coherence (SLSC) Beamforming by Coherence Filtering of the Aperture-Domain Data
Luzhen Nie,Thomas M. Carpenter,Harry R. Clegg,James R. McLaughlan,David M. J. Cowell,Steven Freear +5 more
TL;DR: In this paper, a short-lag spatial coherence (SLSC) beamformer was proposed to suppress the artificial dropout and keep the image uniformity through depths by filtering the aperture-domain data.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
Journal ArticleDOI
The Fractal Geometry of Nature
TL;DR: A blend of erudition (fascinating and sometimes obscure historical minutiae abound), popularization (mathematical rigor is relegated to appendices) and exposition (the reader need have little knowledge of the fields involved) is presented in this article.