Back
 JBiSE  Vol.9 No.6 , May 2016
A Method of Using Information Entropy of an Image as an Effective Feature for Com-puter-Aided Diagnostic Applications
Abstract: Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. In general, a CAD system employs a classifier to detect or distinguish between abnormal and normal tissues on images. In the phase of classification, a set of image features and/or texture features extracted from the images are commonly used. In this article, we investigated the characteristic of the output entropy of an image and demonstrated the usefulness of the output entropy acting as a texture feature in CAD systems. In order to validate the effectiveness and superiority of the output-entropy-based texture feature, two well-known texture features, i.e., mean and standard deviation were used for comparison. The database used in this study comprised 50 CT images obtained from 10 patients with pulmonary nodules, and 50 CT images obtained from 5 normal subjects. We used a support vector machine for classification. A leave-one-out method was employed for training and classification. Three combinations of texture features, i.e., mean and entropy, standard deviation and entropy, and standard deviation and mean were used as the inputs to the classifier. Three different regions of interest (ROI) sizes, i.e., 11 × 11, 9 × 9 and 7 × 7 pixels from the database were selected for computation of the feature values. Our experimental results show that the combination of entropy and standard deviation is significantly better than both the combination of mean and entropy and that of standard deviation and mean in the case of the ROI size of 11 × 11 pixels (p < 0.05). These results suggest that information entropy of an image can be used as an effective feature for CAD applications.
Cite this paper: Matsuyama, E. , Takahashi, N. , Watanabe, H. , Tsai, D. (2016) A Method of Using Information Entropy of an Image as an Effective Feature for Com-puter-Aided Diagnostic Applications. Journal of Biomedical Science and Engineering, 9, 315-322. doi: 10.4236/jbise.2016.96026.
References

[1]   Petrick, N., Sahiner, B., Armoto III, S.G., Bert, A., Correale, L. and Delsanto, S., et al. (2013) Evaluation of Computer-Aided Detection and Diagnosis Systems. Medical Physics, 40, 087001, 1-17.

[2]   Doi, K. (2007) Computer-Aided Diadnosis in Medical Imaging: Historical Review, Current Status and Future Potential. Computerized Medical Imaging and Graphics, 31, 198-211.
http://dx.doi.org/10.1016/j.compmedimag.2007.02.002

[3]   Pluim, J.P.W., Maintz, J.M.A. and Viergever, M.A. (2003) Mutual-Information-Based Registration of Medical Images: A Survey. IEEE Transaction Medical Imaging, 22, 986-1004.
http://dx.doi.org/10.1109/TMI.2003.815867

[4]   Tourassi, G.D., Vargas-Voracek, R., Catarious, Jr., D.M. and Floyd, Jr., C.E. (2003) Computer-Assisted Detection of Mammographic Masses: A Template Matching Scheme Based on Mutual Information. Medical Physics, 30, 2123-2130.
http://dx.doi.org/10.1118/1.1589494

[5]   Tourassi, G.D., Harrawood, B., Singh, S. and Lo, J.Y. (2007) Information-Theoretic CAD System in Mammography: Entropy-Based Indexing for Computational Efficiency and Robust Performance. Medical Physics, 34, 3193-3204.
http://dx.doi.org/10.1118/1.2751075

[6]   Last, M., Kandel, A. and Maimon, O. (2001) Information-Theoretic Algorithm for Feature Selection. Pattern Recognition Letters, 22, 799-811.
http://dx.doi.org/10.1016/S0167-8655(01)00019-8

[7]   Tourassi, G.D., Frederick, E.D., Markey, M.K. and Floyd, Jr., C.E. (2001) Application of the Mutual Information Criterion for Feature Selection in Computer-Aided Diagnosis. Medial Physics, 28, 2394-2402.
http://dx.doi.org/10.1118/1.1418724

[8]   Tsai, D.-Y., Lee, Y. and Matsuyama, E. (2008) In-formation Entropy Measure for Evaluation of Image Quality. Journal of Digital Imaging, 21, 338-347.
http://dx.doi.org/10.1007/s10278-007-9044-5

[9]   Matsuyama, E., Tsai, D.-Y. and Lee, Y. (2009) Mutual Information-Based Evaluation of Image Quality with Its Preliminary Application to Assessment of Medical Imaging Systems. Journal of Electronic Imaging, 18, 033011, 1-11.
http://dx.doi.org/10.1117/1.3206967

[10]   Matsuyama, E., Tsai, D.-Y., Lee, Y. and Kojima, K. (2010) Using Mutual Information to Evaluate Performance of Medical Imaging Systems. Health, 2, 279-285.
http://dx.doi.org/10.4236/health.2010.24040

[11]   Tsai, D.-Y. and Matsuyama, E. (2015) Recent Advances of Quality Assessment for Medical Imaging Systems and Medical Images. In: Deng, C., Ma, L., Lin, W. and Ngan, K.N., Eds., Visual Signal Quality Assessment, Springer, Cham Heidelberg, New York, Dordrecht, London, 157-183.
http://dx.doi.org/10.1007/978-3-319-10368-6_6

[12]   Wang, S., Burtt, K., Turkbey, B., Choyke, P. and Summers, R.M. (2014) Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A Technical Review of Current Research. BioMed Research International. 2014, Article ID: 789561.

[13]   Zhao, Q., Shi, C.-Z. and Luo, L.-P. (2014) Role of the Texture Features of Images in the Diagnosis of Solitary Pulmonary Nodules in Different Sizes. Chinese Journal of Cancer Research, 26, 451-458.

[14]   Tsai, D.-Y. and Tomita, M. (1995) A Computer-Aided System for Discrimination of Dilated Cardiomyopathy Using Echocardiographic Images. IEICE Transactions on Fundamental of Electronics, Communication and Computer Sciences, E78-A, 1649-1654.

[15]   Mabrouk, M., Karrar, A. and Sharawy, A. (2012) Computer Aided Detection of Large Lung Nodules Using Chest Computer Tomography Images. International Journal of Applied Information Systems, 3, 12-18.

[16]   Lee, Y., Hara, T., Fujita, H., Itoh, S. and Ishigaki, T. (2001) Automated Detection of Pulmonary Nodules in Helical CT Images Based on an Improved Template-Matching Technique. IEEE Transactions on Medical Imaging, 20, 595-604.
http://dx.doi.org/10.1109/42.932744

[17]   Vapnik, V.N. (1999) The Nature of Statistical Learning Theory: Statistics for Engineering and Information Science. 2nd Edition, Springer-Verlag, New York.

[18]   Cristianini, N. and Shawe-Taylor, J. (2000) An Introduction to Support Vector Machines and Other Kenel-Based Learning Methods. Cambridge University Press, Cambridge.
http://dx.doi.org/10.1017/CBO9780511801389

[19]   Takahashi, N., Lee, Y., Tsai, D.-Y., Matsuyama, E., Kinoshita, T. and Ishii, K. (2014) An Automatic Detection Method for the MCA Dot Sign of Acute Stroke in Unenhanced CT. Radiological Physics and Technology, 7, 79-88.
http://dx.doi.org/10.1007/s12194-013-0234-1

[20]   Dheeba, J., Singh, N.A. and Selvi, S.T. (2014) Computer-Aided Detection of Breast Cancer on Mammograms: A Swarm Intelligence Optimized Wavelet Neural Network Approach. Journal of Biomedical Informatics, 49, 45-52.
http://dx.doi.org/10.1016/j.jbi.2014.01.010

[21]   Hupse, R. and Karssemeijer. N. (2009) Use of Normal Tissue Context in Computer-Aided Detection of Masses in Mammograms. IEEE Transactions on Medical Imaging, 28, 2033-2041.
http://dx.doi.org/10.1109/TMI.2009.2028611

 
 
Top