Title
Semivariogram applied for classification of benign and malignant tissues in mammography
Abstract
This work analyzes the application of the semivariogram function to the characterization of breast tissue as malignant or benign in mammographic images. The method characterization is based on a process that selects, using stepwise technique, from all computed semivariance which best discriminate between the benign and malignant tissues. Then, a multilayer perceptron neural network is used to evaluate the ability of these features to predict the classification for each tissue sample. To verify this application we also describe tests that were carried out using a set of 117 tissues samples, 67 benign and 50 malignant. The result analysis has given a sensitivity of 92.8%, a specificity of 83.3% and an accuracy above 88.0%, which means encouraging results. The preliminary results of this approach are very promising in characterizing breast tissue.
Year
DOI
Venue
2006
10.1007/11867661_51
ICIAR (2)
Keywords
Field
DocType
result analysis,breast tissue,tissues sample,preliminary result,mammographic image,computed semivariance,method characterization,malignant tissue,tissue sample,multilayer perceptron neural network,breast cancer,multilayer perceptron,neural network
Mammography,Semivariance,Variogram,Pattern recognition,Computer science,Image processing,Multilayer perceptron neural network,Multilayer perceptron,Artificial intelligence,Artificial neural network
Conference
Volume
ISSN
ISBN
4142
0302-9743
3-540-44894-2
Citations 
PageRank 
References 
6
0.62
3
Authors
4