Title :
Automatic recognition of malignant lesions in ultrasound images by artificial neural networks
Author :
Ruggiero, C. ; Bagnoli, Franco ; Sacile, Roberto ; Calabrese, M. ; Rescinito, G. ; Sardanelli, F.
Author_Institution :
Dept. of Inf., Syst. & Telematics, Genoa Univ., Italy
fDate :
29 Oct-1 Nov 1998
Abstract :
A method for the automatic classification of lesions in ultrasound images by artificial neural nets is presented. The parameters used for training of the network are texture related indicators and shape related indicators. Three lesions have been considered: cysts, fibroadenomas and carcinomas. Solid lesions have been separated from cysts in the first step and carcinomas have been separated from fibroadenomas in a second step. A satisfactory classification between cysts and solid lesions can be achieved using texture parameters only, whereas shape parameters appear to be the most significant ones when classifying between carcinomas and fibroadenomas
Keywords :
biomedical ultrasonics; cancer; image recognition; image texture; mammography; medical image processing; neural nets; artificial neural networks; automatic recognition; breast cancer diagnosis; carcinomas; cysts; fibroadenomas; malignant lesions; medical diagnostic imaging; shape parameters; shape related indicators; solid lesions; texture related indicators; ultrasound images; Artificial neural networks; Biomedical imaging; Cancer; Image recognition; Intelligent networks; Lesions; Radiology; Shape; Solids; Ultrasonic imaging;
Conference_Titel :
Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE
Conference_Location :
Hong Kong
Print_ISBN :
0-7803-5164-9
DOI :
10.1109/IEMBS.1998.745577