Authors:
Soumaya Trabelsi Ben Ameur
1
;
Florence Cloppet
2
;
Dorra Sellami Masmoudi
3
and
Laurent Wendling
2
Affiliations:
1
Paris Descartes University and National Engineering School of Sfax (ENIS), France
;
2
Paris Descartes University, France
;
3
National Engineering School of Sfax (ENIS), Tunisia
Keyword(s):
Breast Cancer, Computer Aided Diagnosis, Mammography, Ultrasound, MRI, Dual-energy Contrast-Enhanced Digital Mammography, Choquet Integral.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Classification
;
Clustering
;
Computational Intelligence
;
Feature Selection and Extraction
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Object Recognition
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Software Engineering
;
Theory and Methods
Abstract:
This paper focuses on breast cancer of the mammary gland. Both basic segmentation steps and usual features
are recalled. Then textural and morphological information are combined to improve the overall performance
of breast MRI in a computer-aided system. A model of selection based on Choquet integral is provided. Such
model is suitable when handling with a weak amount of data even ambiguous in some extent. Achieved results
compared to well-known classification methods show the interest of our approach.