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
Real-time Burn Classification using Ultrasound Imaging
Sangrock Lee,Rahul,Hanglin Ye,Deepak Roy Chittajallu,Uwe Kruger,Tatiana Boyko,James K. Lukan,Andinet Enquobahrie,Jack Norfleet,Suvranu De +9 more
TL;DR: The proposed method is shown to have the potential to assist with the real-time clinical assessment of burn degrees, particularly for discriminating between superficial and deep second degree burns, which is challenging in clinical practice.
Journal ArticleDOI
Automatic breast lesion segmentation in phase preserved DCE-MRIs
TL;DR: In this article , a framework for automatically and accurately segmenting breast lesions from Dynamic Contrast Enhanced (DCE) MRI was proposed. But the proposed method is based on max flow and min cut problems in the continuous domain over phase-preserving denoised images.
Posted Content
An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial Learning
Vivek Kumar Singh,Hatem A. Rashwan,Mohamed Abdel-Nasser,Md. Mostafa Kamal Sarker,Farhan Akram,Nidhi Pandey,Santiago Romani,Domenec Puig +7 more
TL;DR: An atrous convolution layer is proposed to be added to the conditional generative adversarial network (cGAN) segmentation model to learn tumor features at different resolutions of BUS images to automatically re-balance the relative impact of each of the highest level encoded features.
Journal ArticleDOI
Grey Relational Analysis based Keypoints Selection in Bag-of-Features for Histopathological Image Classification
TL;DR: A new Grey relational analysis-based bag- of-features method which improves the efficiency of vector quantization step of the standard bag-of- Features method and is validated in terms of precision, recall, G-mean, F1 score, and radar charts.
Journal ArticleDOI
Methods for the segmentation and classification of breast ultrasound images: a review
TL;DR: In this paper, a review of the segmentation and classification methods for tumours detected in breast ultrasound images is presented, where the authors selected old and recent studies on segmenting and classifying tumours in ultrasound images.
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.