Evaluation of Spatial Dependence Matrices on Multiscale Instantaneous Amplitude for Mammogram Classification

Breast cancer is the most common cancer in women. Mammography is the only breast cancer screening method that has proven to be effective. Mammographic breast density is increasingly assessed towards the development of more personalized screening routines. This work presents the estimation of spatial dependence (SD) or otherwise called cooccurrence matrices on the Instantaneous Amplitude (IA) evaluated for different frequency scales using Amplitude-Modulation Frequency-Modulation (AM-FM) methods. Texture has been shown to be an important feature for mammographic image analysis. This multiscale texture analysis method captures both spatial and statistical information and is thus used to quantify image characteristics for breast density classification. AM-FM demodulation is used to estimate the IA at different frequency scales using multiscale Dominant Analysis. Following normalized SD matrices are evaluated on the IA estimates for each scale, for the segmented breast region, providing IA amplitude co-occurrence relative frequencies. These are used to represent the relative variations in the breast tissue, characteristic to the different breast density classes. Classification of a new mammogram into one of the density categories is achieved using the k-nearest neighbor method and the Euclidean distance metric. The method is evaluated using the Breast Imaging Reporting and Data System density classification on the Medical Image Analysis Society mammographic database and the results are presented and compared to other methods in the literature. The incorporation of IA spatial dependencies allows for breast density classification accuracy reaching over 82.5%. This classification accuracy is better using IA SD matrices when compared to IA histograms, warranting further investigation. Keywords— Amplitude-Modulation Frequency Modulation, Spatial Dependence Matrices, Image Classification, Texture,