Pradnya Kulkarni¹, Andrew Stranieri¹, Siddhivinayak Kulkarni¹, Julien Ugon¹ and Manish Mittal², ¹Federation University, Australia and ²Lakeimaging, Australia

Various texture, shape, boundary features have been used previously to classify regions of interest in radiological mammograms into normal and abnormal categories. Although, bag-of-phrases or n-gram model has been effective in text representation for classification or retrieval of text, these approaches have not been widely explored for medical image processing. Our purpose is to represent regions of interest using an n-gram model, then deploy the n-gram features into a back-propagation trained neural network for classifying regions of interest into normal and abnormal categories. Experiments on the benchmark miniMIAS database show that the n-gram features can be effectively used for classification of mammograms into normal and abnormal categories in this way. Very promising results were obtained on fatty background tissue with 83.33% classification accuracy.

N gram, Bag of Phrases, Neural Network, Mammograms, Image Processing