Computer aided detection of mammographic mass using exact Gaussian–Hermite moments
Breast cancer is one of the common cancer deaths in women worldwide. Early detection is the key to reduce the mortality rate. Clinical trials have shown that computer aided systems (CAD) have improved the accuracy of breast cancer detection. This paper proposed a highly accurate CAD system based on extracting highly significant features using exact Gaussian–Hermite moments. The obtained feature vector is presented to K-NN, random forests and AdaBoost classifiers. The proposed system is evaluated using two different datasets namely IRMA and MIAS. The evaluation metrics of accuracy, TP, FP and area under ROC curve using 10-fold cross-validation are calculated. The results indicate the usefulness of the proposed exact Gaussian–Hermite moments features for distinguishing between normal and abnormal lesions and the superiority of the moments features compared with the conventional methods.
KeywordsComputer aided diagnose Gaussian–Hermite Breast cancer Health informatics
The IRMA dataset used in this study was used by courtesy of Thomas M. Deserno, Department of Medical Informatics, Aachen, Germany. In addition, we would like to thank Dr. Mohamed Tahoun and Dr. S. J. Gardezi for their discussion and invaluable comments.
- Abdelwahed NM, Eltoukhy MM, Wahed ME (2015) Computer aided system for breast cancer diagnosis in ultrasound images. J Ecol Health Environ 3(3):71–76Google Scholar
- American Cancer Society (2017) Cancer facts & figures 2017. American Cancer Society, AtlantaGoogle Scholar
- Darwish A, Hassanien AE, Elhoseny M, Sangaiah AK, Muhammad K (2017) The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-017-0659-1 Google Scholar
- Eltoukhy MM, Faye I (2013) An adaptive threshold method for mass detection in mammographic images. In: 2013 IEEE international conference on signal and image processing applications (ICSIPA). IEEE, Piscataway, pp 374–378Google Scholar
- Eltoukhy MM, Faye I, Samir BB (2010a) Curvelet based feature extraction method for breast cancer diagnosis in digital mammogram. In: 2010 International conference on intelligent and advanced systems (ICIAS). IEEE, Piscataway, pp 1–5Google Scholar
- MIAS (2017) http://www.wiau.man.ac.uk/services/MIAS/MIASweb.html. Visited Sept 2017
- Rahman SM, Reza MM, Hasani QZ (2013) Low-complexity iris recognition method using 2D Gauss–Hermite moments. In: 2013 8th International symposium on image and signal processing and analysis (ISPA). IEEE, Piscataway, pp 142–146Google Scholar
- Sajjad M, Nasir M, Muhammad K, Khan S, Jan Z, Sangaiah AK, … Baik SW (2017) Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities. Future Gener Comput SystGoogle Scholar
- Wu Y, Shen J (2005) Properties of orthogonal Gaussian–Hermite moments and their applications. EURASIP J Appl Signal Process 2005, 588–599Google Scholar