Identity statement area
Reference TypeConference Proceedings
Last Update2006: administrator
Metadata Last Update2020: administrator
Citation KeyRodrigues:2006:NoEnCA
TitleNon-Extensive Entropy for CAD Systems of Breast Cancer Images
Date8-11 Oct. 2006
Access Date2020, Dec. 05
Number of Files1
Size223 KiB
Context area
AuthorRodrigues, Paulo SÚrgio Silva
AffiliationNational Laboratory for Scientific Computing
EditorOliveira Neto, Manuel Menezes de
Carceroni, Rodrigo Lima
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 19 (SIBGRAPI)
Conference LocationManaus
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2008-07-17 14:11:05 :: paulo -> administrator ::
2009-08-13 20:38:17 :: administrator -> banon ::
2010-08-28 20:02:26 :: banon -> administrator ::
2020-02-19 03:17:50 :: administrator -> :: 2006
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Content TypeExternal Contribution
Tertiary TypeFull Paper
KeywordsCAD Tsallis Entropy Medical Image Analysis Breast Tumor.
AbstractRecent statistics show that breast cancer is a major cause of death among women in all of the world. Hence, early diagnostic with Computer Aided Diagnosis (CAD) systems is a very important tool. This task is not easy due to poor ultrasound resolution and large amount of patient data size. Then, initial image segmentation is one of the most important and challenging task. Among several methods for medical image segmentation, the use of entropy for maximization the information between the foreground and background is a well known and applied technique. But, the traditional Shannon entropy fails to describe some physical systems with characteristics such as long-range and longtime interactions. Then, a new kind of entropy, called nonextensive entropy, has been proposed in the literature for generalizing the Shannon entropy. In this paper, we propose the use of non-extensive entropy, also called q-entropy, applied in a CAD system for breast cancer classification in ultrasound of mammographic exams. Our proposal combines the non-extensive entropy, a level set formulation and a Support Vector Machine framework to achieve better performance than the current literature offers. In order to validate our proposal, we have tested our automatic protocol in a data base of 250 breast ultrasound images (100 benign and 150 malignant). With a cross-validation protocol, we demonstrate systems accuracy, sensitivity, specificity, positive predictive value and negative predictive value as: 95%, 97%, 94%, 92% and 98%, respectively, in terms of ROC (Receiver Operating Characteristic) curves and Az areas.
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