Development of intelligent systems based on Bayesian regularization network and neuro-fuzzy models for mass detection in mammograms: A comparative analysis.
Journal:
Computer methods and programs in biomedicine
Published Date:
Nov 6, 2015
Abstract
Female breast cancer is the second most common cancer in the world. Several efforts in artificial intelligence have been made to help improving the diagnostic accuracy at earlier stages. However, the identification of breast abnormalities, like masses, on mammographic images is not a trivial task, especially for dense breasts. In this paper we describe our novel mass detection process that includes three successive steps of enhancement, characterization and classification. The proposed enhancement system is based mainly on the analysis of the breast texture. First of all, a filtering step with morphological operators and soft thresholding is achieved. Then, we remove from the filtered breast region, all the details that may interfere with the eventual masses, including pectoral muscle and galactophorous tree. The pixels belonging to this tree will be interpolated and replaced by the average of the neighborhood. In the characterization process, measurement of the Gaussian density in the wavelet domain allows the segmentation of the masses. Finally, a comparative classification mechanism based on the Bayesian regularization back-propagation networks and ANFIS techniques is proposed. The tests were conducted on the MIAS database. The results showed the robustness of the proposed enhancement method.
Authors
Keywords
Algorithms
Artificial Intelligence
Bayes Theorem
Breast
Breast Neoplasms
Databases, Factual
Diagnosis, Computer-Assisted
Female
Fuzzy Logic
Humans
Mammography
Models, Statistical
Nerve Net
Normal Distribution
Pattern Recognition, Automated
Pectoralis Muscles
Radiographic Image Interpretation, Computer-Assisted
Reproducibility of Results