Mammographic mass detection by stochastic modeling and a multimodular neural network

Huai Li; Shih-Chung Benedict Lo; Yue Joseph Wang; Matthew T. Freedman M.D.; Seong Ki Mun

doi:10.1117/12.274134

25 April 1997 Mammographic mass detection by stochastic modeling and a multimodular neural network

Huai Li, Shih-Chung Benedict Lo, Yue Joseph Wang, Matthew T. Freedman M.D., Seong Ki Mun

Proceedings Volume 3034, Medical Imaging 1997: Image Processing; (1997) https://doi.org/10.1117/12.274134
Event: Medical Imaging 1997, 1997, Newport Beach, CA, United States

Abstract

In this paper, we have developed a combined method utilizing morphological operations, a finite generalized Gaussian mixture (FGGM) modeling, and a contextual Bayesian relaxation labeling technique (CBRL) to enhance and extract suspicious masses. A feature space is constructed based on multiple feature extraction from the regions of interest (ROIs). Finally, a multi-modular neural network (MMNN) is employed to distinguish true masses from non-masses. We have applied these methods to test our mammogram database. The true masses in the database were identified by a radiologist with biopsy reports. The results demonstrated that all the areas of suspicious masses in mammograms were extracted in the prescan step using the proposed segmentation procedure. We found that 6 - 15 suspected masses per mammogram were detected and required further evaluation. We also found that the MMNN can reduce the number of suspicious masses with a sensitivity of 84% at 1 - 2 false positive (FP) per mammogram based on the database containing 46 mammograms (23 of them have biopsy proven masses). In conclusion, the experimental results indicate that morphological filtering combined with FGGM model-based segmentation is an effective way to extract mammographic suspicious mass patterns. Compared with conventional neural networks, the probabilistic MMNN can lead to a more efficient learning algorithm and can provide more understanding in the analysis of the distribution patterns of multiple features extracted from the suspicious masses.

Citation Download Citation

Huai Li, Shih-Chung Benedict Lo, Yue Joseph Wang, Matthew T. Freedman M.D., and Seong Ki Mun "Mammographic mass detection by stochastic modeling and a multimodular neural network", Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); https://doi.org/10.1117/12.274134

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