International Journal of Research in Signal Processing, Computing & Communication System Design

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Experimental Analysis of Medical Image Classification and Retrieval Techniques


Affiliations
1 ECE, Mahatma Gandhi Institute of Technology, Hyderabad, Telangana, India
2 Sri Devi Women’s Engineering College, Hyderabad, Telangana, India
     

   Subscribe/Renew Journal


Medical Image classification and similar image retrieval are the two important processes in diagnosis and automatic annotation. These help the doctors and radiologists in their decision making during decease identification and decision making. Image classification is usually done by checking its content similarity. Image content is its visual features referring to mathematical attributes. Similarity checking is done by using similarity or dissimilarity measures which are also known as distance metrics. As image attributes are wide in range, the similarity measure worked well for one feature set may not show the similar performance for other. For this reason in this paper we explored various existing similarity measures viz. Manhattan, Cosine, Chi-square and Cramer distances and their effect with respect to image intensity features and wavelet based texture features. We drawn certain conclusions on the performance of these distance metrics in classification and retrieval of IRMA data sets. Mean Average Precision and Average Recall Rates are used in analyzing retrieval performance for analyzing the medical image retrieval and classification task.

Keywords

Classifiers, Distance Metrics, Medical Images, Retrieval.
User
Subscription Login to verify subscription
Notifications
Font Size

  • R. M. Haralick, K. Shanmugam, and I. H. Dinstein, “Textural features for image classification,” Systems, Man and Cybernetics, IEEE Transactions, pp. 610-621, 6th Nov. 1973.

  • H. Tamura, S. Mori, and T. Yamawaki, “Textural features corresponding to visual perception,” Systems, Man and Cybernetics, IEEE Transactions, pp. 460-473, 8th June, 1978.

  • J. R. Smith, and S.-F. Chang, “Transform features for texture classification and discrimination in large image databases,” Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference, vol. 3. IEEE, 1994.

  • W. Y. Ma, and B. S. Manjunath, “A comparison of wavelet transform features for texture image annotation,” Second International Conference on Image Processing (ICIP’95), Washington, D.C., vol. 2, pp. 256-259, Nov. 1995.

  • E. Remias, G. Sheikholeslami, A. Zhang, and T. S. Mahmood, “Supporting content-based retrieval in large image database systems,” Multimedia Database Management Systems, Springer US, pp. 61-78, 1997.

  • H. D. Tagare, C. C. Jaffe, and J. Duncan, “Medical image databases,” Journal of the American Medical Informatics Association, vol. 4, no. 3, pp. 184-198, 1997.

  • J. Puzicha, J. M. Buhmann, Y. Rubner, and C. Tomasi, “Empirical evaluation of dissimilarity measures for color and texture,” In Computer Vision, Proceedings of the Seventh IEEE International Conference, vol. 2, pp. 1165-1172, 1999.

  • T. M. Lehmann, B. B. Wein, D. Keysers, M. Kohnen, and H. Schubert, “A monohierarchical multiaxial classification code for medical images in content-based retrieval,” In Biomedical Imaging, 2002. Proceedings. 2002 IEEE International Symposium on, pp. 313-316. Jul. 2002.

  • L. H. Y. Tang, R. Hanka, and H. H. S. Ip. “Histological image retrieval based on semantic content analysis,” IEEE Transactions on Information Technology in Biomedicine, vol. 7, no. 1, pp. 26-36, 2003.

  • M. P. Nalini, and B. L. Malleswari, “An empirical study and comparative analysis of Content Based Image Retrieval (CBIR) techniques with various similarity measures,” 3rd International Conference on Electrical, Electronics, Engineering Trends, Communication, Optimization and Sciences (EEECOS), pp. 373-379, June 2016.

  • M. P. Nalini, and B. L. Malleswari, “Review on content based image retrieval: From its origin to the new age,” International Journal of Research Studies in Science, Engineering and Technology, vol. 3, no. 2, pp. 18-41, Feb. 2016.

  • H. Cramer, “On the composition of elementary errors,” Scandinavian Actuarial Journal, no. 1, pp. 13-74, 1928.

  • R. E. Von Mises, “Wahrscheinlichkeit, statistik und wahrheit,” Julius Springer, 1928.

  • F. Long, H. Zhang, and D. D. Feng, “Fundamentals of content-based image retrieval,” Multimedia Information Retrieval and Management, pp. 1-26, Springer Berlin Heidelberg, 2003.

  • J. R. Smith, and S.-F. Chang, “Transform features for texture classifi cation and discrimination in large image databases,” In Image Processing, Proceedings. ICIP-94., IEEE International Conference, vol. 3, pp. 407-411, 1994.

  • M. N. Do, and M. Vetterli, “Wavelet-based texture retrieval using generalized gaussian density and kullback-leibler distance,” Image Processing, IEEE Transactions, vol. 11, no. 2, pp. 146-158, 2002.

  • W.-Y. Ma, and B. S. Manjunath, “A comparison of wavelet transform features for texture image annotation,” The International Conference on Image Processing, pp. 256-259, 1995.

  • T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray scale and rotation invariant texture classification with local binary patterns,” In Computer Vision-ECCV, pp. 404-420. Springer, 2000.

  • Y. An, M. Riaz, and J. Park, “CBIR based on adaptive segmentation of hsv color space,” In Computer Modelling and Simulation (UKSim), 12th International Conference, pp. 248-251, 2010.

  • Z. Dengsheng, and G. Lu, “Evaluation of similarity measurement for image retrieval,” Neural Networks and Signal Processing, Proceedings of the 2003 International Conference, vol. 2, 2003.

  • S. Siegel, “Nonparametric statistics for the behavioral sciences,” McGraw-Hill, 1956.

  • B. Leo, “Statistics with a view toward applications,” Boston: Houghton Mifflin Co., vol. 1, 1973.


Abstract Views: 246

PDF Views: 7




  • Experimental Analysis of Medical Image Classification and Retrieval Techniques

Abstract Views: 246  |  PDF Views: 7

Authors

P. Nalini
ECE, Mahatma Gandhi Institute of Technology, Hyderabad, Telangana, India
B. L. Malleswari
Sri Devi Women’s Engineering College, Hyderabad, Telangana, India

Abstract


Medical Image classification and similar image retrieval are the two important processes in diagnosis and automatic annotation. These help the doctors and radiologists in their decision making during decease identification and decision making. Image classification is usually done by checking its content similarity. Image content is its visual features referring to mathematical attributes. Similarity checking is done by using similarity or dissimilarity measures which are also known as distance metrics. As image attributes are wide in range, the similarity measure worked well for one feature set may not show the similar performance for other. For this reason in this paper we explored various existing similarity measures viz. Manhattan, Cosine, Chi-square and Cramer distances and their effect with respect to image intensity features and wavelet based texture features. We drawn certain conclusions on the performance of these distance metrics in classification and retrieval of IRMA data sets. Mean Average Precision and Average Recall Rates are used in analyzing retrieval performance for analyzing the medical image retrieval and classification task.

Keywords


Classifiers, Distance Metrics, Medical Images, Retrieval.

References