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Classification of mammographic tissue using shape and texture features

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2 Author(s)
Enderwick, C.Y. ; Rutgers Univ., Piscataway, NJ, USA ; Micheli-Tzanakou, E.

The authors have performed a pilot study on the classification of regions of interest (ROI) containing normal tissue, biopsy-proven malignant masses, and biopsy-proven microcalcification (MCC) clusters using a mix of shape and texture features. Shape features included size, translation, and rotation invariant moments. Texture features included fractal-based features and spatial gray level dependence (SGLD) matrix features. The entropy was also computed for each ROI. The type of classifier used was a neural network based on the ALOPEX training algorithm. Best results using a database of 40 normal, 32 mass, and 20 MCC ROIs for training and 5 normal, 5 mass, and 4 MCC ROIs for testing were obtained using texture features and a binary tree neural network which splits the classification of the three types of tissue into two steps. Classification between normal ROIs and abnormal (mass+MCC) ROIs reached 95% training and 100% testing and classification of mass and MCC ROIs reached 98% training and 100% testing

Published in:

Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE  (Volume:2 )

Date of Conference:

30 Oct-2 Nov 1997