A new algorithm for massive lesion detection in mammography is presented. The algorithm consists in three main steps : 1) reduction of the dimension of the image to be processed through the identifi cation of regions of interest (rois) as candidates for massive lesions ; 2) characterization of the roi by means of suitable feature extraction ; 3) pattern classifi cation through supervised neural networks. Suspect regions are detected by searching for local maxima of the pixel grey level intensity. A ring of increasing radius, centered on a maximum, is considered until the mean intensity in the ring decreases to a defi ned fraction of the maximum. The rois thus obtained are described by average, variance, skewness and kurtosis of the intensity distributions at diff erent fractions of the radius. A neural network approach is adopted to classify suspect pathological and healthy pattern. The software has been designed in the framework of the infn (Istituto Nazionale Fisica Nucleare) research project gpcalma (Grid Platform for calma) which recruits physicists and radiologists from diff erent Italian Research Institutions and hospitals to develop software for breast and lung cancer detection.
A massive lesion detection algorithm in mammography
DE MITRI, IVAN
2005-01-01
Abstract
A new algorithm for massive lesion detection in mammography is presented. The algorithm consists in three main steps : 1) reduction of the dimension of the image to be processed through the identifi cation of regions of interest (rois) as candidates for massive lesions ; 2) characterization of the roi by means of suitable feature extraction ; 3) pattern classifi cation through supervised neural networks. Suspect regions are detected by searching for local maxima of the pixel grey level intensity. A ring of increasing radius, centered on a maximum, is considered until the mean intensity in the ring decreases to a defi ned fraction of the maximum. The rois thus obtained are described by average, variance, skewness and kurtosis of the intensity distributions at diff erent fractions of the radius. A neural network approach is adopted to classify suspect pathological and healthy pattern. The software has been designed in the framework of the infn (Istituto Nazionale Fisica Nucleare) research project gpcalma (Grid Platform for calma) which recruits physicists and radiologists from diff erent Italian Research Institutions and hospitals to develop software for breast and lung cancer detection.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
