Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Facing Imbalanced Classes through Aggregation of Classifiers
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Markov Random Field Modeling in Image Analysis
Markov Random Field Modeling in Image Analysis
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Algorithms for detecting clusters of microcalcifications in mammograms
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
A tree-structured Markov random field model for Bayesian image segmentation
IEEE Transactions on Image Processing
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Objective: The aim of this paper is to describe a novel system for computer-aided detection of clusters of microcalcifications on digital mammograms. Methods and material: Mammograms are first segmented by means of a tree-structured Markov random field algorithm that extracts the elementary homogeneous regions of interest. An analysis of such regions is then performed by means of a two-stage, coarse-to-fine classification based on both heuristic rules and classifier combination. In this phase, we avoid taking a decision on the single microcalcifications and forward it to the successive phase of clustering realized through a sequential approach. Results: The system has been tested on a publicly available database of mammograms and compared with previous approaches. The obtained results show that the system is very effective, especially in terms of sensitivity. Conclusions: The proposed approach exhibits some remarkable advantages both in segmentation and classification phases. The segmentation phase employs an image model that reduces the computational burden, preserving the small details in the image through an adaptive local estimation of all model parameters. The classification stage combines the results of the classifiers focused on the single microcalcification and the cluster as a whole. Such an approach makes a detection system particularly effective and robust with respect to the large variations exhibited by the clusters of microcalcifications.