1. Petroudi S., Constantinou I., Tziakouri C., Pattichis M. and Pattichis C., Investigation of AM-FM Methods for Mammographic Breast Density Classification. 13th International Conference on BioInformatics and BioEngineering (BIBE 2013). Chania, Greece, 2013.
Breasts are composed of a mixture of fibrous and glandular tissue as well as adipose tissue and breast density describes the prevalence of fibroglandular tissue as it appears on a mammogram. Over the past few years, evaluation and reporting of breast density as it appears on mammograms has received a lot of attention because it impacts one's risk of developing breast cancer but also the capability of detecting breast cancer on mammograms. In addition, mammography fails in the identification of breast cancer in almost half of the women with dense breasts. Different image analysis methods have been investigated for automatic breast density classification. The presented method investigates the use of Amplitude-Modulation Frequency-Modulation (AM-FM) multi-scale feature sets for characterization of breast density as the first step in the development of a density specific Computer Aided Detection System. AM-FM decompositions use different scales and bandpass filters to extract the instantaneous frequencies (IF), instantaneous amplitude (IA) and instantaneous phase (IP) components from an image. Normalized histograms of the maximum IA across all frequencies and scales are used to model the different breast density classes. Classification of a new mammogram into one of the breast density classes is achieved using the k-nearest neighbor method with k=5 and the euclidean distance metric. The method is evaluated on the Medical Image Analysis Society (MIAS) mammographic database and the results are presented. The presented method allows breast density classification accuracy reaching over 84%. Future work will involve a new AM-FM methodology approach based on adaptive filterbank design and performance index decision.
2. Constantinou I., Pattichis M., Tziakouri C., Pattichis C. and Petroudi S., AM-FM Multiscale Instantaneous Amplitude Evaluation for Mammographic Density Classification. 18th Annual Conference in Medical Image Understanding and Analysis (MIUA 2014). London, UK, 2014.
Breast cancer is the most common cancer in women and the number of incidences keeps rising. Mammographic breast density has been recognized as a most important breast cancer risk and can also mask abnormalities. Information regarding mammographic breast density may be used for planning individualized breast cancer screening and treatment. Thus breast density is increasingly assessed as the limitations of a one-fits-all method of screening and Computer Aided Detection become more apparent. The presented work investigates the use of Amplitude-Modulation Frequency-Modulation models for the evaluation of multiscale Instantaneous Amplitude (IA) features for the characterization of breast density. The IA evaluated at different frequency scales is used to capture the relative variations in the breast tissue characteristic to the different breast density classes. Normalized histograms of the IA across different frequency scales - evaluated using multiscale Dominant Component Analysis - are used to model the different breast density classes. To classify a new mammogram k-nearest neighbors with Euclidean distance are used. The method is evaluated using the Breast Imaging Reporting and Data System on the Medical Image Analysis Society mammographic database and the results are presented and compared to other methods in the literature. The presented method allows breast density classification accuracy reaching over 80%.
3. Petroudi S., Constantinou I., Pattichis M., Tziakouri C. and Pattichis C., Evaluation of Spatial Dependence Matrices on Multiscale Instantaneous Amplitude for Mammogram Classification. 6th European Conference of the International Federation for Medical and Biological Engineering (MBEC 2014). Dubrovnik, Croatia, 2014.
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 co-occurrence 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 multi-scale 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. To classify a new mammogram into one of the density categories, the k-nearest neighbor method and the Euclidean distance metric are used. 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.
4. Petroudi S., Constantinou I., Pattichis M., Tziakouri C. and Pattichis C., Evaluation of Co-occurrence Texture Features on AM-FM Instantaneous Amplitude for Breast Density Classification. 8th European Conference on Medical Physics (ECMP2014). Athens, Greece, 2014.
This work is supported by the Cyprus Research Promotion Foundation's Grant ΤΠΕ/ΟΡΙΖΟ/311(ΒΙΕ)/29 and is co-funded by the Republic of Cyprus and the European Regional Development Fund.