• Sameer Ahmad Khan Department of Computer and Information Sciences, Universiti Teknologi PETRONAS
  • Suet Peng Yong Department of Computer and Information Sciences, Universiti Teknologi PETRONAS
  • Uzair Iqbal Janjua Department of Computer and Information Sciences, Universiti Teknologi PETRONAS




Modality classification, features, local, global, evaluation.


Medical images are increasing at an alarming rate. This increasing number of images affects the interpreting capacity of radiologists. In order to reduce the burden of radiologists, automatic categorization of medical images based on modality is the need of the hour. Because image modality is an important and fundamental image characteristic. The important factor in the automatic medical image categorization based on modality are the features used for categorization purpose, because nice treatment on these subtleties can lead to good results. Many descriptors have been proposed in the literature for medical image categorization. It is unclear which descriptor encodes the content information efficiently. The descriptors that are calculated from these medical images should be descriptive, distinctive and robust to various transformations. The stability of these descriptors are evaluated under various transformations and are then analyzed for their discriminatory ability for the task of classification. In this study the criteria of transformations, repeatability, matching and classification accuracy on the basis of precision recall is used to evaluate the performance of these descriptors. The experimental results illustrates that among global descriptors local features patches histogram and among local descriptors SIFT encodes the content information quite efficiently.


R. Mar´ee, P. Geurts, and L. Wehenkel. 2007. Random Subwindows And Extremely Randomized Trees For Image Classification In Cell Biology. BMC Cell Biology. 8: S2.

M. O. Gueld, D. Keysers, T. Deselaers, M. Leisten, H. Schubert, H. Ney, et al. 2004. Comparison Of Global Features For Categorization Of Medical Images. Medical Imaging 2004. 211-222.

M. Awedh. 2015. Medical Image Classification Using Multi—Vocabulary.

A. T. Pinhas and H. K. Greenspan. 2004. A Continuous And Probabilistic Framework For Medical Image Representation And Categorization. Medical Imaging. 230-238.

J. Goldberger, S. Gordon, and H. Greenspan. 2006. Unsupervised image-set clustering using an information theoretic framework. Image Processing, IEEE Transactions 15: 449-458.

H. Greenspan and A. T. Pinhas. 2007. Medical Image Categorization And Retrieval For PACS Using The GMM-KL Framework. Information Technology in Biomedicine, IEEE Transactions. 11: 190-202.

A. Mueen, R. Zainuddin, and M. S. Baba. 2008. Automatic Multilevel Medicalimage Annotation And Retrieval,†Journal Of Digital Imaging. 21: 290-295.

A. Mojsilovic and J. Gomes. 2002. Semantic Based Categorization, Browsing And Retrieval In Medical Image Databases. In Image Processing. Proceedings. 2002 International Conference. III-145-III-148.

G. Tian, H. Fu, and D. D. Feng. 2008. Automatic Medical Image Categorization And Annotation Using LBP and MPEG-7 Edge Histograms. Information Technology and Applications in Biomedicine. ITAB 2008. International Conference. 51-53.

U. Avni, H. Greenspan, M. Sharon, E. Konen, and J. Goldberger. 2009. X-Ray Image Categorization And Retrieval Using Patch-Based Visualwords Representation. Biomedical Imaging: From Nano to Macro. ISBI’09. IEEE International Symposium. 350-353.

S. H. Kim, J. H. Lee, B. Ko, and J. Y. Nam. 2010. X-Ray Image Classification Using Random Forests With Local Binary Patterns Machine Learning and Cybernetics

(ICMLC), 2010 International Conference on, 2010, pp. 3190-3194.

J. Wang, Y. Li, Y. Zhang, C. Wang, H. Xie, G. Chen, et al. 2011. Bag Of Features Based Medical Image Retrieval Via Multiple Assignment And Visual Words Weighting. IEEE Transactions On Medical Imaging. 30: 1996-2011.

A. Mueen, M. S. Baha, and R. Zainuddin. 2007. Multilevel Feature Extraction and X-ray Image Classification. Journal of Applied Sciences, 1224-1229.

F. Florea, H. M¨uller, A. Rogozan, A. Geissbuhler, and S. Darmoni. 2006. Medical Image Categorization With MedIC and MedGIFT. Proc Med Inform Europe (MIE 2006). 3-11.

O. A. Penatti, E. Valle, and R. d. S. Torres. 2012. Comparative Study Of Global Color And Texture Descriptors For Web Image Retrieval. Journal of Visual Communication and Image Representation. 23: 359-380.

R. M. Haralick, K. Shanmugam, and I. H. Dinstein. 1973. Textural Features For Image Classification Systems. Man and Cybernetics, IEEE Transactions. 610-621.

M. Roy. 2014. Classification of Ultrasonography Images of Human Fatty and Normal Livers using GLCM Textural Features.

I. Kitanovski, K. Trojacanec, I. Dimitrovski, and S. Loskovska. 2012. Modality Classification Using Texture Features. ICT Innovations 2011, ed: Springer. 189-198.

Y. Y. Gao, R. Fu, Y. Kuang, and Q. W. Lv. 2012. Classification and Retrieval of Abdominal Medical Image Based on Gray Level Concurrence Matrix. Chinese Medical Equipment Journal. 3: 006.

M. R. Zare, A. Mueen, and W. C. Seng. 2014. Automatic Medical X-ray Image Classification using Annotation. Journal Of Digital Imaging. 27: 77-89.

S. Jafarpour, Z. Sedghi, and M. C. Amirani. 2012. A Robust Brain MRI Classification with GLCM Features. International Journal of Computer Applications. 37.

A. Shahbahrami, T. A. Pham, and K. Bertels. 2012. Parallel implementation of Gray Level Co-occurrence Matrices And Haralick Texture Features On Cell Architecture. The Journal of Supercomputing. 59: 1455-1477.

D. Zhang, M. M. Islam, and G. Lu. 2012. A Review On Automatic Image Annotation Techniques. Pattern Recognition. 45: 346-362.

B. Julesz. 1981. Textons, The Elements Of Texture Perception, And Their Interactions. Nature, 290: 91-97.

T. Leung and J. Malik. 2001. Representing And Recognizing The Visual Appearance Of Materials Using Three-Dimensional Textons. International Journal of Computer Vision, 43: 29-44.

M. Sadeghi, T. K. Lee, D. McLean, H. Lui, and M. S. Atkins. 2012. Global Pattern Analysis And Classification Of Dermoscopic Images Using Textons. SPIE Medical Imaging. 83144X-83144X-6.

F. Riaz, M. Areia, F. B. Silva, M. Dinis-Ribeiro, P. P. Nunes, and M. Coimbra. 2011. Gabor Textons For Classification Of Gastroenterology Images. Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium. 117-120.

M. J. Gangeh, L. Sørensen, S. B. Shaker, M. S. Kamel, M. De Bruijne, and M. Loog. 2010. A Texton-Based Approach For The Classification Of Lung Parenchyma In CT Images. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2010, ed: Springer. 595-602.

F. Riaz, F. B. Silva, M. D. Ribeiro, and M. T. Coimbra. 2012. Invariant Gabor Texture Descriptors For Classification Of Gastroenterology Images. Biomedical Engineering, IEEE Transactions. 59: 2893-2904.

M. Varma and A. Zisserman. 2005. A Statistical Approach To Texture Classification From Single Images. International Journal of Computer Vision. 62: 61-81.

Z. Guo, Z. Zhang, X. Li, Q. Li, and J. You. 2014. Texture Classification by Texton: Statistical versus Binary. Plos One. 9: e88073.

T. Ojala, M. Pietikainen, and T. Maenpaa. 2002. Multiresolution Gray-Scale And Rotation Invariant Texture Classification With Local Binary Patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions. 24: 971-987.

M. Tuceryan and A. K. Jain1998. Texture analysis. The Handbook Of Pattern Recognition And Computer Vision. 2: 207-248.

M. Pietik¨ainen, A. Hadid, G. Zhao, and T. Ahonen2011. Local Binary Patterns For Still Images. Computer Vision Using Local Binary Patterns, ed: Springer. 13-47.

B. C. Ko, S. H. Kim, and J.-Y. Nam. 2011. X-ray Image Classification Using Random Forests With Local Wavelet-Based CS-Local Binary Patterns. Journal Of Digital Imaging. 24: 1141-1151.

J. D. Deng. 2009. Improving Feature Extraction For Automatic Medical Image Categorization. Image and Vision Computing New Zealand, 2009. IVCNZ’09. 24th International Conference. 379-384.

F. Ghofrani, M. Helfroush, H. Danyali, and K. Karimi. 2012. Medical X-ray Image Classification Using Gabor-Based CS-Local Binary Patterns. Int Conf Electron Biomed Eng Appl (ICEBEA). 8.

M. Heikkil¨a, M. Pietik¨ainen, and C. Schmid. 2009. Description Of Interest Regions With Local Binary Patterns. Pattern Recognition.42: 425-436.

C. K. Chui. 1992. An Introduction To Wavelets. 1: Academic Press.

M. Vetterli and C. Herley. 1992. Wavelets And Filter Banks: Theory And Design. Signal Processing, IEEE Transactions. 40: 2207-2232.

S. Lahmiri and M. Boukadoum. 2013. Hybrid Discrete Wavelet Transform And Gabor Filter Banks Processing For Features Extraction From Biomedical Images. Journal of Medical Engineering 2013.

M. KocioÅ‚ek, A. Materka, M. Strzelecki, and P. Szczypi´nski. 2001. Discrete Wavelet Transform-Derived Features For Digital Image Texture Analysis. International Conference on Signals and Electronic Systems, Å´od´z- Poland. 99-104.

J. K. Kamarainen, V. Kyrki, and H. Kalviainen. 2006. Invariance Properties Of Gabor Filter-Based Features-Overview And Applications. Image Processing, IEEE Transactions 15: 1088-1099.

Y.-h. Liu, M. Muftah, T. Das, L. Bai, K. Robson, and D. Auer. 2012. Classification of MR Tumor Images Based On Gabor Wavelet Analysis. Journal of Medical and Biological Engineering, 32: 22-28.

I. Buciu and A. Gacsadi. 2009. Gabor Wavelet Based Features For Medical Image Analysis And Classification. Applied Sciences in Biomedical and Communication Technologies, 2009. ISABEL 2009. 2nd International Symposium. 1-4.

T. Tuytelaars and K. Mikolajczyk. 2008. Local Invariant Feature Detectors: A Survey. Foundations and Trends. Computer Graphics and Vision. 3: 177-280.

H. I. Kim, S. Shin, W. Wang, and S. I. Jeon. 2013. SVM-Based Harris Corner Detection For Breast Mammogram Image Normal/Abnormal Classification. Proceedings of the 2013 Research in Adaptive and Convergent Systems. 187-191.

D. G. Lowe. 1999. Object Recognition From Local Scale-Invariant Features. Computer Vision. The Proceedings Of The Seventh IEEE International Conference. 1150-1157.

I. Dimitrovski, D. Kocev, I. Kitanovski, S. Loskovska, and S. Dˇzeroski. 2014. Improved Medical Image Modality Classification Using A Combination Of Visual And Textual Features. Computerized Medical Imaging and Graphics.

S. Manivannan, R. Wang, and E. Trucco. 2014. Inter-Cluster Features For Medical Image Classification. Medical Image Computing and Computer- Assisted Intervention–MICCAI 2014, ed: Springer. 345-352.

G. Tian, H. Fu, and D. D. Feng. 2008. Automatic Medical Image Categorization And Annotation Using LBP and MPEG-7 edge histograms. Information Technology and Applications in Biomedicine, 2008. ITAB 2008. International Conference. 51-53.

S.-H. Kim, J.-H. Lee, B. Ko, and J.-Y. Nam. 2010. X-ray Image Classification Using Random Forests With Local Binary Patterns. in Machine Learning and Cybernetics (ICMLC), 2010 International Conference. 3190-3194.

H. Miller, J. Kalpathy-Cramer, I. Eggel, S. Bedrick and E. K. Charles Jr. W. 2010. Overview Of The CLEF 2010 Medical Image Retrieval Track. The Working Notes of CLEF.

K. Mikolajczyk and C. Schmid. 2005. A Performance Evaluation Of Local Descriptors. Pattern Analysis and Machine Intelligence, IEEE Transactions. 27: 1615-1630.




How to Cite

A COMPARATIVE EVALUATION OF FEATURES FOR MEDICAL IMAGE MODALITY CLASSIFICATION. (2016). Jurnal Teknologi, 78(8-2). https://doi.org/10.11113/jt.v78.9550