Journal ArticleDOI
A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram
TLDR
A comparative study between wavelet and curvelet transform for breast cancer diagnosis in digital mammogram suggests that curvelettransform outperforms wavelet transform and the difference is statistically significant.About:
This article is published in Computers in Biology and Medicine.The article was published on 2010-04-01. It has received 120 citations till now. The article focuses on the topics: Curvelet & Wavelet transform.read more
Citations
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Journal ArticleDOI
Computer-Aided Breast Cancer Detection Using Mammograms: A Review
TL;DR: This review aims at providing an overview about recent advances and developments in the field of Computer-Aided Diagnosis (CAD) of breast cancer using mammograms, specifically focusing on the mathematical aspects of the same, aiming to act as a mathematical primer for intermediates and experts inThe field.
Journal ArticleDOI
Mass Classification in Mammograms Using Selected Geometry and Texture Features, and a New SVM-Based Feature Selection Method
Xiaoming Liu,Jinshan Tang +1 more
TL;DR: A support vector machine (SVM)-based recursive feature elimination procedure with a normalized mutual information feature selection (NMIFS) procedure is integrated to avoid their singular disadvantages, and a new feature selection method, which is called the SVM-RFE with an NMIFS filter (SRN), is proposed.
Journal ArticleDOI
Three-Class Mammogram Classification Based on Descriptive CNN Features.
TL;DR: A novel classification technique for large data set of mammograms using a deep learning method that targets a three-class classification study (normal, malignant, and benign cases).
Journal ArticleDOI
A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation
TL;DR: This paper presents a method for breast cancer diagnosis in digital mammogram images that depends on extracting the features that can maximize the ability to discriminate between different classes and is validated using 5-fold cross validation.
Journal ArticleDOI
An evaluation of image descriptors combined with clinical data for breast cancer diagnosis
TL;DR: A new descriptor based on the divergence of the gradient (HGD) was demonstrated to be a feasible predictor of breast masses’ diagnosis, demonstrating promising capabilities to describe masses.
References
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Journal ArticleDOI
A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Journal ArticleDOI
Approximate statistical tests for comparing supervised classification learning algorithms
TL;DR: This article reviews five approximate statistical tests for determining whether one learning algorithm outperforms another on a particular learning task and measures the power (ability to detect algorithm differences when they do exist) of these tests.
Journal ArticleDOI
Fast Discrete Curvelet Transforms
TL;DR: This paper describes two digital implementations of a new mathematical transform, namely, the second generation curvelet transform in two and three dimensions, based on unequally spaced fast Fourier transforms, while the second is based on the wrapping of specially selected Fourier samples.
Journal ArticleDOI
Computer-aided detection and classification of microcalcifications in mammograms: a survey
TL;DR: The high correlation between the appearance of the microcalcification clusters and the diseases show that the CAD (computer aided diagnosis) systems for automated detection/classification of MCCs will be very useful and helpful for breast cancer control.
Proceedings Article
Image segmentation
TL;DR: An axiomatic definition for the notion of "segmentation" in image processing is proposed, which is based on the idea of a maximal partition and a key theorem links segmentation with connection, on the one hand, and with connective criteria on the other one.