Title :
Characterization of lung nodules
Author :
Kaya, Ahmet ; Can, Ahmet Burak
Author_Institution :
Bilgisayar Muhendisligi Bolumu, Hacettepe Univ., Ankara, Turkey
Abstract :
Recognition of lung nodules and classification of them as benign and malignent are very important in diagnosis of lung cancer. Present methods on nodule classification generally concentrate on defining nodule as either benign or malignent but do not consider radiographic descriptors that play important role on classification of small-sized lung nodules. In this paper, features extracted from nodule images to denote radiographic descriptors are studied. With the results from classification and dimension reduction approaches, which images features truly denote radiographic descriptors is analyzed.
Keywords :
cancer; diagnostic radiography; feature extraction; image classification; lung; medical image processing; dimension reduction; feature extraction; lung cancer diagnosis; lung nodule classification; lung nodule recognition; radiographic descriptor; Biomedical imaging; Cancer; Computed tomography; Educational institutions; Lungs; Radiology; Support vector machines; classification; dimension reduction; image processing; small pulmonary nodules;
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2013 21st
Conference_Location :
Haspolat
Print_ISBN :
978-1-4673-5562-9
Electronic_ISBN :
978-1-4673-5561-2
DOI :
10.1109/SIU.2013.6531396