SU-FF-I-02: Computer-Aided Mass Detection On Digitized Mammograms Using Adaptive Thresholding and Fuzzy Entropy

2007 ◽  
Vol 34 (6Part2) ◽  
pp. 2338-2338
Author(s):  
N Riyahi-Alam ◽  
F Younesi ◽  
R Aghaiizadeh Zoroofi ◽  
A Ahmadian ◽  
M Guiti
Keyword(s):  
Fuzzy Entropy ◽  
Adaptive Thresholding ◽  
Mass Detection ◽  
Computer Aided

Combining two mammographic projections in a computer aided mass detection method

2007 ◽  
Vol 34 (3) ◽  
pp. 898-905 ◽  
Author(s):  
Saskia van Engeland ◽  
Nico Karssemeijer
Keyword(s):  
Detection Method ◽  
Mass Detection ◽  
Computer Aided

Mass detection algorithm for digital mammograms based on an adaptive thresholding technique utilizing multi-resolution processing

2003 ◽  
pp. 334-338
Author(s):  
Satoshi Kasai ◽  
Daisuke Kaji ◽  
Akiko Kano ◽  
Hiroshi Fujita ◽  
Takeshi Hara ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Adaptive Thresholding ◽  
Mass Detection

False-positive reduction in computer-aided mass detection using mammographic texture analysis and classification

2018 ◽  
Vol 160 ◽  
pp. 75-83 ◽  
Author(s):  
Sami Dhahbi ◽  
Walid Barhoumi ◽  
Jaroslaw Kurek ◽  
Bartosz Swiderski ◽  
Michal Kruk ◽  
...  
Keyword(s):  
Texture Analysis ◽  
False Positive ◽  
Mass Detection ◽  
False Positive Reduction ◽  
Computer Aided

Mammographic mass detection by adaptive thresholding and region growing

2000 ◽  
Vol 11 (5) ◽  
pp. 340-346 ◽  
Author(s):  
Yong Jin Lee ◽  
Jeong Mi Park ◽  
Hyun Wook Park
Keyword(s):  
Region Growing ◽  
Adaptive Thresholding ◽  
Mass Detection

Computer-Aided Mass Detection Based on Ipsilateral Multiview Mammograms

2007 ◽  
Vol 14 (5) ◽  
pp. 530-538 ◽  
Author(s):  
Wei Qian ◽  
Dansheng Song ◽  
Minshan Lei ◽  
Ravi Sankar ◽  
Edward Eikman
Keyword(s):  
Mass Detection ◽  
Computer Aided

scholarly journals Optimization of Network Topology in Computer-Aided Detection Schemes Using Phased Searching with NEAT in a Time-Scaled Framework

2014 ◽  
Vol 13s1 ◽  
pp. CIN.S13885
Author(s):  
Maxine Tan ◽  
Jiantao Pu ◽  
Bin Zheng
Keyword(s):  
Selection Process ◽  
Learning Task ◽  
Classification Performance ◽  
Optimal Topology ◽  
Support Vector ◽  
Mass Detection ◽  
Detection Scheme ◽  
Linear Discriminant ◽  
Computer Aided ◽  
Fixed Topology

In the field of computer-aided mammographic mass detection, many different features and classifiers have been tested. Frequently, the relevant features and optimal topology for the artificial neural network (ANN)-based approaches at the classification stage are unknown, and thus determined by trial-and-error experiments. In this study, we analyzed a classifier that evolves ANNs using genetic algorithms (GAs), which combines feature selection with the learning task. The classifier named “Phased Searching with NEAT in a Time-Scaled Framework” was analyzed using a dataset with 800 malignant and 800 normal tissue regions in a 10-fold cross-validation framework. The classification performance measured by the area under a receiver operating characteristic (ROC) curve was 0.856 ± 0.029. The result was also compared with four other well-established classifiers that include fixed-topology ANNs, support vector machines (SVMs), linear discriminant analysis (LDA), and bagged decision trees. The results show that Phased Searching outperformed the LDA and bagged decision tree classifiers, and was only significantly outperformed by SVM. Furthermore, the Phased Searching method required fewer features and discarded superfluous structure or topology, thus incurring a lower feature computational and training and validation time requirement. Analyses performed on the network complexities evolved by Phased Searching indicate that it can evolve optimal network topologies based on its complexification and simplification parameter selection process. From the results, the study also concluded that the three classifiers – SVM, fixed-topology ANN, and Phased Searching with NeuroEvolution of Augmenting Topologies (NEAT) in a Time-Scaled Framework – are performing comparably well in our mammographic mass detection scheme.

scholarly journals Bilateral analysis based false positive reduction for computer-aided mass detection

2007 ◽  
Vol 34 (8) ◽  
pp. 3334-3344 ◽  
Author(s):  
Yi-Ta Wu ◽  
Jun Wei ◽  
Lubomir M. Hadjiiski ◽  
Berkman Sahiner ◽  
Chuan Zhou ◽  
...  
Keyword(s):  
False Positive ◽  
Mass Detection ◽  
False Positive Reduction ◽  
Computer Aided
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