DocumentCode :
3426989
Title :
Probabilistic relational modelling of mammographic images
Author :
Ferreira, Nivea ; Lucas, Peter J F
Author_Institution :
Inst. for Comput. & Inf. Sci., Radboud Univ., Nijmegen, Netherlands
fYear :
2009
fDate :
2-5 Aug. 2009
Firstpage :
1
Lastpage :
8
Abstract :
Computer-aided detection (CAD) is used in medical science as a means of supporting a doctor´s observations and interpretations. While X-ray imaging techniques, such as mammography, yield a great deal of information, it is not always easy to evaluate detected mammographic regions as being suspicious for cancer, which results in a number of cancers to be misinterpreted or missed in an image. In this sense, CAD systems have as aim the increase of detection rates when analysing mammograms, by identifying features that are characteristic for breast cancer. In this research we aim at using the features extracted from mammographic images in order to analyse the development of suspicious lesions. Differently from other breast cancer models, the data modelling exploits object orientation. This allows not only for a natural description of domain entities and their intrinsic relations, but also the application of relational learning techniques, which handles our heterogeneous data instances both in terms of learning and inference.
Keywords :
biological organs; cancer; diagnostic radiography; feature extraction; learning (artificial intelligence); mammography; medical image processing; probability; tumours; wounds; X-ray imaging techniques; breast cancer; computer-aided detection; feature extraction; mammographic images; probabilistic relational modelling; relational learning techniques; Biomedical imaging; Breast cancer; Cancer detection; Data mining; Feature extraction; Image analysis; Mammography; X-ray detection; X-ray detectors; X-ray imaging;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer-Based Medical Systems, 2009. CBMS 2009. 22nd IEEE International Symposium on
Conference_Location :
Albuquerque, NM
ISSN :
1063-7125
Print_ISBN :
978-1-4244-4879-1
Electronic_ISBN :
1063-7125
Type :
conf
DOI :
10.1109/CBMS.2009.5255329
Filename :
5255329
Link To Document :
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