Performance evaluation of the wave atom algorithm to classify mammographic images

The most common type of cancer seen in women is breast cancer. To enable recovery from this severe disease, monitoring and early detection must be provided, and related precautions must be taken as a first step. During diagnosis, some cases may be overlooked due to fatigue and eyestrain, because the determination of abnormalities is a repetitive procedure. In this study, a computer-aided diagnosis (CAD) system, using the wave atom transform (WAT) algorithm and support vector machine (SVM), is proposed to evaluate mammography images. During the process, the region of interest (ROI) is defined before applying the method. The system includes a feature extraction approach based on the WAT algorithm. In terms of classification, the process has 2 main stages: the classification of normal/abnormal regions and malignant/benign ones. The proposed system also uses principle component analysis (PCA) for further dimensional reduction and feature selection. A dataset from the Mammographic Image Analysis Society database is employed for testing and measuring the performance of the proposed system. The best success rates in this work are obtained using the coefficients at scales of 1, 2, and 3, by employing SVM with PCA. The maximum classification success rate to define the regions of interest as normal/abnormal is 100%. The success rate of malignant/benign classification is also achieved as 100% in the tests. According to the results, it is observed that these features ensure important support for more comprehensive clinical investigations and the results are very encouraging when mammograms are categorized via WAT, PCA, and SVM.

Anahtar Kelimeler:

Mammography, CAD system, wave atom transform, SVM, ROC analysis

Performance evaluation of the wave atom algorithm to classify mammographic images

Keywords:

Mammography, CAD system, wave atom transform, SVM, ROC analysis,

PDF

___

a b c 0.8 0.9 1 0.1 0.2 0.3 0.4 0.5 1 - Specificity 1
Figure 6. ROC curves for benign and malignant classification with wave atom coefficients and PCA. According to the
maximum values of sensitivity and specificity: a) for a scale of 2, b) for a scale of 3, c) for a scale of 4.
great advantage for normal/abnormal classification. Using the WAT and SVM with PCA method, an accuracy
rate of 100% is achieved for normal/abnormal classification. For malignant/benign classification, an accuracy
rate of 100% is also achieved using the WAT and SVM methods. From these results, it is observed that such
features provide important support for more detailed clinical investigations and the results are very encouraging
when mammograms are classified with WAT, PCA, and SVM.
N.R. Pal, B. Bhowmick, S.K. Patel, S. Pal, J. Das, “A multi-stage neural network aided system for detection of
micro-calcifications in digitized mammograms”, Neurocomputing, Vol. 71, pp. 2625–2634, 2008.
J.C. Fu, S.K. Lee, S.T.C. Wong, J.Y. Yeh, A.H. Wang, H.K. Wu, “Image segmentation, feature selection and
pattern classification for mammographic microcalcifications”, Computerized Medical Imaging and Graphics, Vol.
29, pp. 419–29, 2005. R.M. Rangayyan, F.J. Ayres, J.E.L. Desautels, “A review of computer aided diagnosis of breast cancer: toward the detection of subtle signs”, Journal of the Franklin Institute, Vol. 344, pp. 312–348, 2007. M.L. Giger, N. Krassemeijer, S.G. Armato, “Computer aided diagnosis in medical imaging”, IEEE Transactions on Medical Imaging, Vol. 20, pp. 1205–1208, 2001.
R.J. Ferrari, R.M. Rangayyan, J.E.L. Desautels, A.F. Frere, “Analysis of asymmetry in mammograms via directional
filtering with Gabor wavelets”, IEEE Transactions on Medical Imaging, Vol. 20, pp. 953–964, 2001.
L. Bocchi, G. Coppini, J. Nori, G. Valli, “Detection of single and clustered microcalcifications in mammograms
using fractals models and neural networks”, Medical Engineering & Physics, Vol. 26, pp. 303–312, 2004.
I. Christoyianni, A. Koutras, E. Dermatas, G. Kokkinakis, “Computer aided diagnosis of breast cancer in digitized
mammograms”, Computerized Medical Imaging and Graphics, Vol. 26, pp. 309–319, 2002.
L.F.A. Campos, A.C. Silva, A.K. Barros, “Diagnosis of breast cancer in digital mammograms using independent
component analysis and neural networks”, Proceedings of the 10th Iberoamerican Congress on Progress in Pattern
Recognition, Image Analysis and Applications, Vol. 3773, pp. 460–469, 2005.
D.D. Costa, L.F.A. Campos, A.K. Barros, “Classification of breast tissue in mammograms using efficient coding”,
Biomedical Engineering on Line, Vol. 10, pp. 50, 2011.
C.Y. Wang, C.G. Wu, Y.C. Liang, X.C. Guo, “Diagnosis of breast cancer tumor based on ICA and LS-SVM”, Proceedings of the 5th International Conference on Machine Learning and Cybernetics, pp. 2565–2570, 2006.
K. Polat, S. G¨une¸s, “Breast cancer diagnosis using least square support vector machine”, Digital Signal Processing, Vol. 17, pp. 694–701, 2007.
S.G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 7, pp. 674–693, 1989.
R. Mousa, Q. Munib, A. Moussa, “Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural”,
Expert Systems with Applications, Vol. 28, pp. 713–723, 2005.
K.K. Rajkumar, G. Raju, “A comparative study on classification of mammogram images using different wavelet transformations”, International Journal of Machine Intelligence, Vol. 3, pp. 310–317, 2011.
G. Boccignone, A. Chianese, A. Picariello, “Computer aided detection of microcalcifications in digital mammo
grams”, Computers in Biology and Medicine, Vol. 30, pp. 267–286, 2000.
M.N. Gurcan, Y. Yardımcı, A.E. C¸ etin, R. Ansari, “Automated detection and enhancement of microcalcifications in mammograms using nonlinear subband decomposition”, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 4, pp. 3069, 1997.
M.N. Gurcan, Y. Yardımcı, A.E. C¸ etin, R. Ansari, “Detection of microcalcifications in mammograms using higher order statistics”, IEEE Signal Processing Letters, Vol. 4, pp. 213–216, 1997.
A.M. Bagci, A.E. Cetin, “Detection of microcalcifications in mammograms using local maxima and adaptive wavelet
transform analysis”, IEEE Electronics Letters, Vol. 38, pp. 1311–1313, 2002.
F. Moayedi, Z. Azimifar, R. Boostani, S. Katebi, “Contourlet-based mammography mass classification”, Lecture
Notes in Computer Science, Vol. 4633, pp. 923–934, 2007.
E.J. Cand`es, D.L. Donoho, “Curvelets, multiresolution representation, and scaling laws”, SPIE Wavelet Applications in Signal and Image Processing VIII, Vol. 4119, 2000.
F.E. Ali, I.M. El-Dokany, A.A. Saad, F.E. Abd El-Samie, “Curvelet fusion of MR and CT images”, Progress in
Electromagnetics Research, Vol. 3, pp. 215–224, 2008.
N.T. Binh, N.C. Thanh, “Object detection of speckle image base on curvelet transform”, ARPN Journal of Engineering and Applied Sciences, Vol. 2, pp. 14–16, 2007.
M.M. Eltoukhy, I. Faye, B.B. Samir, “A comparison of wavelet and curvelet for breast cancer diagnosis in digital
L. Demanet, L.X. Ying, “Wave atoms and sparsity of oscillatory patterns”, Applied and Computational Harmonic
Analysis, Vol. 23, pp. 368–387, 2007.
Mammographic Image Analysis Society Database Web Page, 2012. Available at: http://peipa.essex.ac.uk/info/mias.html.