NDLI: Curvelet analysis of breast masses on dynamic magnetic resonance mammography

Content Provider	IET Digital Library
Author	Nirouei, Mahyar Pouladian, Majid Abdolmaleki, Parviz Akhlaghpoor, Shahram
Abstract	This study is devoted to extracting significant texture features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast using curvelet features and to classify breast masses into malignant and benign using the calculated features. The authors utilised the first generation of curvelet transform in the interpretation of breast tumours on DCE-MRI. The analysis is performed after injecting 23 patients with a contrast agent and 23 mass lesions were extracted from these patients. Then, 288 statistical parameters were extracted by calculating the mean and variance of the curvelet coefficients of tumour texture in sub-band images. Due to a large number of extracted features and the presence of redundant and inter-correlated descriptors, they used a combination of genetic algorithm (GA) and Pearson's correlation for feature selection and a three-layer artificial neural network (ANN) for classification of malignant and benign breast lesions. The GA-ANN model has yielded a good diagnostic accuracy (96%), sensitivity (92%) and specificity (100%). Also, the area under the receiver operating characteristic curve was 0.955. The curvelet transform was able to effectively quantify the distribution of contrast agent in tumour texture, which is different in malignant and benign tumours.
Starting Page	745
Ending Page	750
Page Count	6
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 5, May (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0125
Journal	IET Image Processing
Publisher Date	2017-12-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Benign Breast Lesion Classification Biology And Medical Computing Biomedical Imaging/measurement Biomedical Magnetic Resonance Imaging Biomedical MRI Breast Masses Classification Breast Tumours Computer Vision And Image Processing Technique Contrast Agent Distribution Curvelet Analysis Curvelet Coefficient Curvelet Transform Curvelet Transforms DCE-MRI Dynamic Contrast-enhanced Magnetic Resonance Imaging Dynamic Magnetic Resonance Mammography Feature Extraction Feature Selection Function Theory And Analysis GA-ANN Model Genetic Algorithm Image Classification Image Recognition Image Texture Integral Transforms Inter-correlated Descriptors Malignant Breast Lesion Classification Mammography Mass Lesions Medical Image Processing Medical Magnetic Resonance Imaging And Spectroscopy Neural Computing Technique Neural Nets Optimisation Technique Patient Diagnostic Method And Instrumentation Pearson Correlation Probability Theory Radiography And Computed Tomography Receiver Operating Characteristic Curve Spectroscopy Statistical Analysis Statistical Parameter Statistics Stochastic Linearised SCUC Sub-band Image Texture Texture Feature Extraction Three-layer Artificial Neural Network Tumour Texture Tumours X-Ray Technique X-Rays And Particle Beam
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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