Title :
Semiautomated breast cancer classification from ultrasound video
Author :
Bocchi, L. ; Gritti, F. ; Manfredi, C. ; Giannotti, E. ; Nori, J.
Author_Institution :
Dept. of Electron. & Telecommun., Univ. of Florence, Florence, Italy
Abstract :
The present work presents a new approach, based on the elaboration of a whole video clip of the ecoghraphic acquisition describing the lesion from different viewpoints. The focus of the paper is in comparing performances which can be obtained, using a simple classification algorithm, using the full video clip against the more common single frame approach. The system consists of five modules: preprocessing, semiautomatic segmentation, extraction of morphological features, classification of each frame and integration to obtain the classification of the video clip. Results show that the data integration allows an increase in the correct classification rate from approximately 89%, on a frame-by-frame basis, up to 97% on the whole video clip.
Keywords :
biological organs; biomedical ultrasonics; cancer; data integration; feature extraction; gynaecology; image classification; image segmentation; medical image processing; video signal processing; data integration; ecoghraphic acquisition; frame-by-frame basis; morphological feature extraction; semiautomated breast cancer classification algorithm; semiautomatic segmentation; ultrasound video clip; Breast; Cancer; Feature extraction; Lesions; Manuals; Training; Ultrasonic imaging; Breast ultrasound; classification; video;
Conference_Titel :
Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4577-1857-1
DOI :
10.1109/ISBI.2012.6235754