Computer Aid Diagnostic in Mammogram Image Using SUSAN Algorithm and Hierarchical Watershed Transform
This work is directed toward a conception of a computer aid diagnosis (CAD) system to detect suspicious area in digital mammogram and classify them into normal and abnormal. Original image is preprocessed to separate the breast region from it’s background with pectoral muscle suppression to reduce false positive rate.
The suspicious regions are extracted using a modified SUSAN algorithm, followed by a function that extract dense regions, then an hierarchical watershed transforms applied to detect edges of suspicious regions.
For detected edges Fourier Descriptors are computed and stored as feature vector. A support vector machine is used to classify suspicious regions into normal or abnormal. The proposed system is tested on Mini-Mias database.
KeywordsMammogram image Preprocessing Segmentation Modified Hierarchical watershed transform Fourier descriptor Support Vector Machine (SVM)
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- 1.Computer-aided diagnostics of screening mammography using content-based image retrieval (2012)Google Scholar
- 2.Alam, N., Mohammed, J.I.: Pectoral muscle elimination on mammogram using k-means clustering approach. International Journal of Computer Vision and Signal Processing (2014)Google Scholar
- 3.Beucher, S.: Watershed, hierarchical segmentation and waterfall algorithm. In: Computational Imaging and Vision. Springer, Netherlands (1994)Google Scholar
- 4.Sampaio, W.B., Diniz, E.M., Silva, A.C., De Paiva, A.C., Gattass, M.: Detection of masses in mammogram images using CNN, geostatistic functions and SVM. Computers in Biology and Medicine (2011)Google Scholar
- 5.Boss, R.S.C., Thangavel, K., Daniel, D.A.P.: Automatic mammogram image breast region extraction and removal of pectoral muscle. International Journal of Scientific & Engineering Research (2013)Google Scholar
- 6.Camilus, K.S., Govindan, V.K., Sathidevi, P.S.: Pectoral muscle identification in mammograms. Journal of Appied Cinical Medical Physics (2011)Google Scholar
- 7.Fatehia, B.G., Mawia, A.H.: Classification of breast tissue as normal or abnormal based on texture analysis of digital mammogram. Journal of Medical Imaging and Health Informatics (2014)Google Scholar
- 8.Gao, C., Zhu, H., Guo, Y.: Analysis and improvement of susan algorithm. Signal Processing (2012)Google Scholar
- 9.Jen, C., Yu, S.: Automatic detection of abnormal mammograms in mammographic images. Expert Systems with Applications (2015)Google Scholar
- 10.Bandyopadhyay, S.K.: Detection of abnormal masses in mammogram images. International Journal of Computer Science and Information Technologies (2010)Google Scholar
- 11.Mirzaalian, H., Ahmadzadeh, M.R., Sadri, S., Jafari, M.: Pre-processing algorithms on digital mammograms. In: Conference on Machine Vision Applications (2007)Google Scholar
- 13.Sharma, S., Khanna, P.: Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM. Journal of Digit Imaging (2015)Google Scholar
- 14.Smith, S., Brady, J.: Susan-a new approach to low level image processing. International Journal of Computer Vision (1997)Google Scholar
- 15.Vapnik, V.: Statistical Learning Theory. Wiley (1998)Google Scholar
- 16.Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Inteligence (1991)Google Scholar
- 17.Zhang, D., Lu, G.: A comparative study of fourier descriptors for shape representation and retrieval. In: 5th Asian Conference on Computer Vision (ACCV), pp. 646–651. Springer (2002)Google Scholar
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