A massive lesion detection algorithm in mammography.
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 identification of regions of interest (roi) as candidates for massive lesions; 2) characterization of the RoI by means of suitable feature extraction; 3) pattern classification 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 defined fraction of the maximum. The ROIS thus obtained are described by average, variance, skewness and kurtosis of the intensity distributions at different 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 different Italian Research Institutions and hospitals to develop software for breast cancer detection.
Fauci
Francesco
F
Dipartimento di Fisica e Tecnologie Relative dell'Università di Palermo and INFN-Sezione di Catania (Italy).
Raso
Giuseppe
G
Magro
Rosario
R
Forni
Giustina
G
Lauria
Adele
A
Bagnasco
Stefano
S
Cerello
Piergiorgio
P
Cheran
Sorin C
SC
Lopez Torres
Ernesto
E
Bellotti
Robero
R
Carlo
Francesco De
FD
Gargano
Gianfranco
G
Tangaro
Sonia
S
Mitri
Ivan De
ID
Nunzio
Giorgio De
GD
Cataldo
Rossella
R