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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Proceedings ArticleDOI
Multi-feature Fusion for Ultrasound Breast Image Classification of Benign and Malignant
TL;DR: A method based on a histogram of oriented gradients, local binary pattern and gray-level co-occurrence matrix feature extraction combined with machine learning classifier – support vector machine (SVM) to classify benign and malignant breast tumor ultrasound images is proposed.
Journal ArticleDOI
Bounded-abstaining classification for breast tumors in imbalanced ultrasound images
TL;DR: A bounded-abstaining classification model is proposed that maximizes the area under the ROC curve (AUC) under two abstention constraints and yields a significantly larger AUC and G-mean using imbalanced BUS datasets.
Book ChapterDOI
Amniotic Fluid Segmentation by Pixel Classification in B-Mode Ultrasound Image for Computer Assisted Diagnosis
TL;DR: This study proposed a model segmentation considering the local information from each pixel based upon its neighborhood information considering the mean intensity, deviation standard, skewness, entropy, and property taken based upon the 3 × 3 and 5 × 5 window.
Journal ArticleDOI
Quantitative Ultrasound Texture Analysis to Assess the Spastic Muscles in Stroke Patients
TL;DR: The results showed that echotexture was more inhomogeneous in the paretic BBM than in the non-pareti BBM, and LBP extracted by LBP may be a promising approach for quantitative assessment of the spastic BBM in post-stroke patients.
Journal ArticleDOI
BreastNet: Entropy-Regularized Transferable Multi-task Learning for Classification with Limited Breast Data
TL;DR: A framework to automatically separate malignant from benign breast lesions using limited breast ultrasound data is described, and detailed analysis of the choice of regularizing parameter and visual evidence that introduction of confusion leads to increase in feature generalization are provided.
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.