Title :
Exploring Ranklets Performances in Mammographic Mass Classification using Recursive Feature Elimination
Author_Institution :
Dept. of Phys., Univ. of Bologna, Bologna
Abstract :
The ranklet transform is a recently developed image processing technique characterized by a multi-resolution and orientation-selective approach similar to that of the wavelet transform. Yet, differently from the latter, it deals with pixels´ ranks rather than with their gray-level intensity values. In this work, the ranklet coefficients resulting from the application of the ranklet transform to regions of interest (ROIs) found on breast radiographic images are used as classification features to determine whether ROIs contain mass or normal tissue. Performances are explored recursively eliminating some of the less discriminant ranklet coefficients according to the cost function of a support vector machine (SVM) classifier. Experiments show good classification performances (Az values of 0.976 plusmn 0.003) even after a significant reduction of the number of ranklet coefficients.
Keywords :
image classification; image resolution; mammography; medical image processing; support vector machines; transforms; breast radiographic images; gray-level intensity value; image processing; mammographic mass classification; multiresolution approach; orientation-selective approach; ranklet coefficients; ranklet transform; ranklets performance; recursive feature elimination; support vector machine classifier; Breast; Delta-sigma modulation; Diagnostic radiography; Face detection; Image databases; Physics; Spatial databases; Support vector machine classification; Support vector machines; Wavelet transforms;
Conference_Titel :
Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on
Conference_Location :
Arlington, VA
Print_ISBN :
1-4244-0656-0
Electronic_ISBN :
1551-2541
DOI :
10.1109/MLSP.2006.275559