Paper Info
Title
Segmentation and classification of breast lesions using dynamic and textural features in Dynamic Contrast Enhanced-Magnetic Resonance Imaging
Abstract
The aim of this study is to propose an approach, based on Multi Layer Perceptron classification of dynamic and textural features, for breast lesions segmentation and classification using Dynamic Contrast Enhanced-Magnetic Resonance Imaging data. We compared the performance obtainable with dynamic, textural and spatio-temporal features. In particular, 98 dynamic features, 60 textural features and 72 spatio-temporal features were considered. The dataset included 20 breast lesions, 10 benign and 10 malignant. The performance of lesion segmentation have been evaluated with respect to manual segmentation provided by an expert radiologist. Results of lesion classification were compared to histological findings. Our results indicate that Multi Layer Perceptron can achieve better results in terms of sensitivity, specificity and accuracy when dynamic features are considered both for lesion segmentation and classification (accuracy of 91 % and 70 %, respectively).
Year
DOI
Venue
2012
10.1109/CBMS.2012.6266312
Computer-Based Medical Systems
Keywords
Field
DocType
biomedical MRI,image classification,image segmentation,medical image processing,multilayer perceptrons,breast lesions,breast lesions classification,breast lesions segmentation,dynamic contrast enhanced-magnetic resonance imaging,dynamic features,multi layer perceptron classification,spatio-temporal features,textural features
Computer vision,Contrast-enhanced Magnetic Resonance Imaging,Lesion,Computer science,Segmentation,Image segmentation,Multilayer perceptron,Artificial intelligence,Contextual image classification,Lesion segmentation
Conference
ISSN
ISBN
Citations 
1063-7125
978-1-4673-2049-8
2
PageRank 
References 
Authors
0.43
3
4
Name
Order
Citations
PageRank
Roberta Fusco1406.68
Mario Sansone2619.22
C. Sansone3156994.00
Antonella Petrillo43311.89