Rules and feature extraction for microcalcifications detection in digital mammograms using neuro-symbolic hybrid systems and undecimated filter banks

Authors:
Osslan Osiris Vergara Villegas;Humberto De Jesús Ochoa Domínguez;Vianey Guadalupe Cruz Sánchez;Efrén David Gutiérrez Casas;Gerardo Reyes Salgado
Affiliations:
Industrial and Manufacturing Engineering Department, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chihuahua, Mexico;Electrical and Computer Engineering Department, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chihuahua, Mexico;Computer Science Department, Centro Nacional de Investigación y Desarrollo Tecnológico, Cuernavaca, Morelos, Mexico;Electrical and Computer Engineering Department, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chihuahua, Mexico;Computer Science Department, Centro Nacional de Investigación Desarrollo Tecnológico, Cuernavaca, Morelos, Mexico
Venue:
WSEAS Transactions on Signal Processing
Year:
2008

Citing 4
Cited 1

Filter banks allowing perfect reconstruction

Signal Processing
Multiresolution mammogram analysis in multilevel decomposition

Pattern Recognition Letters
A novel neural-genetic algorithm to find the most significant combination of features in digital mammograms

Applied Soft Computing
A system of microcalcifications detection and evaluation of the radiologist: comparative study of the three main races in Malaysia

Computers in Biology and Medicine

Computational methods used in the study of border breast lesions diagnosed by mammography and ultrasound

MACMESE'10 Proceedings of the 12th WSEAS international conference on Mathematical and computational methods in science and engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a Neuro-Symbolic Hybrid System methodology to improve the recognition stage of benignant or malignant microcalcifications in mammography. At the first stage, we use five different undecimated filter banks in order to detect the microcalcifications. The microcalcifications appear as a small number of high intensity pixels compared with their neighbors. Once the microcalcifications were detected, we extract rules in order to obtain the image features. At the end, we can classify the microcalcification in one of three sets: benign, malign, and normal. The results obtained show that there is no a substantial difference in the number of detected microcalcification among the several filter banks used and the NSHS methodology proposed can improve, in the future, the results of microcalcification recognition.