The week's pick
Random Articles
Reseach Article
An Effective System for Content MRI Brain Image Retrieval using Angular Radial Transform
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 117 - Number 24 |
| Year of Publication: 2015 |
| Authors: Abderrahim Khatabi, Amal Tmiri, Ahmed Serhir |
10.5120/20705-3607
|
Abderrahim Khatabi, Amal Tmiri, Ahmed Serhir . An Effective System for Content MRI Brain Image Retrieval using Angular Radial Transform. International Journal of Computer Applications. 117, 24 ( May 2015), 29-32. DOI=10.5120/20705-3607
Abstract
Nowadays, the growth of huge amount of medical image has become one of the most important clinical diagnosis components, Furthermore, there is an urgent need a system of Content Based Image Retrieval (CBIR) to obtain essential information such as type of image and extracting features of the image, such as color, shape and texture. This system is based on the image content that retrieves similar pathology involving magnetic resonance (MR) images of the medical database to assist the radiologist in the diagnosis of brain tumor; the shape recovery is the one of the top performers. Content-based image retrieval can also be used to locate brain tumors in medical images in large databases. In this paper, we propose a new method to build a new research system by the content of MRI images (CBIR) based on image characteristics, such as shape using the ART descriptor (angular radial transform) that is applied to reveal the characteristics of MR images. ART descriptor (angular radial transform) of shape is based Region adopted in MPEG-7 has the properties invariant to scale, rotation and robustness to noise, thanks to its properties we have used in this system. After the segmentation process, extracting the visual features of shape by calculating the coefficients of the ART and forming a second feature vector to be input to a support vector machine (SVM) for determining the presence tumor or not tumor followed by KNN (K-nearest neighbor) that retrieves the most similar images in the database. To provide faster image search.
References
- H. Boussen, H. Bouzaiene, J. Ben Hassouna, A. Gamoudi, F. Benna, and K. Rahal, "Inflammatory breast cancer in Tunisia: reassessment of ,incidence and clinicopathological features". Semin Oncol. 2008, 35, pp. 17–24.
- Mina Rafi Nazari and Emad Fatemizadeh, 'A CBIR System for Human Brain Magnetic Resonance Image', International Journal of Computer Applications (0975 – 8887) Volume 7– No. 14, October 2010 Indexing.
- R. Guruvasuki, A. Josephine Pushpa Arasi, "MRI Brain Image retrieval using Multi Support Vector Machine Classifier",
- Hatice Cinar Akakin and Metin N. Gurcan, "Content-Based Microscopic Image Retrieval System for Multi-Image Queries", IEEE Transaction on Information Technology in Biomedicine, Vol. 16, No. 4, pp 758-768, 2012.
- M. Bober, "MPEG-7 visual shape descriptors," IEEE Trans. Circuits Syst. Video Technol. , vol. 11, pp. 716–719, June. 2001.
- Mokhtarian, F. , S. Abbasi and J. Kittler, ``Efficient and Robust Retrieval by Shape Content through Curvature Scale Space,'' Proc. International Workshop on Image Databases and Multimedia Search, pp. 35-42, Amsterdam, the Netherlands, 1996.
- M. -S. Choi and W. -Y. Kim, "The description and retrieval of a sequence of moving objects using a shape variation map," Pattern Recognition Letters, Vol. 25, pp. 1369–1375, Sep. 2004.
- S. Hwang and W. Kim, "Fast and Efficient Method for Computing ART," IEEE Transaction on Image processing, vol. 15, pp. 112-117, Jan. 2006.
- Nello Cristianini and John Shawe-Taylor, "An Introduction to Support Vector Machines and Other Kernel-based Learning Methods", Cambridge University Press, 2000. Kharat, K. D. , P. P. Kulkarni and M. B. Nagori, 2012. Brain tumor classification using neural network based methods. Int. J. Comput. Sci. Inform. , 1:
- Amanatiadis, A. , Kaburlasos, V. G. , Gasteratos, A. , & Papadakis, S. E. (2011). Evaluation of shape descriptors for shape-based image retrieval. Image Processing, 5, 493–499.
- Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289-1305.
- The Moving Picture Experts Group (MPEG), http://www. chiariglione. org/mpeg, 2009. 12. 01.
- Amanatiadis, A. , Kaburlasos, V. G. , Gasteratos, A. , & Papadakis, S. E. (2011). Evaluation of shape descriptors for shape-based image retrieval. Image Processing, 5, 493–499.
- Pooja, C. S. (2012). An effective image retrieval system using region and contour based features. In IJCA proceedings on international conference on recent advances and future trends in information technology (pp. 7–12).
- S. Haykin, Neural Networks – A Comprehensive Foundation, Prentice Hall Inc, 1999.
- M. Hearst, "Support vector machines", IEEE Intelligence Systems, pp. 18 - 28, July/August, 1998.
- J. P. Lewis, Tutorial on SVM, CGIT Lab, USC, 2004.
- S. Haykin, Neural Networks – A Comprehensive Foundation, Prentice Hall Inc, 1999
- Burges B. ~Scholkopf, editor, "Advances in Kernel Methods--Support Vector Learning". MIT press, 1998.
- Pooja, C. S. (2012). An effective image retrieval system using region and contour based features. In IJCA proceedings on international conference on recent advances and future trends in information technology (pp. 7–12).
- Hatice Cinar Akakin and Metin N. Gurcan, "Content-Based Microscopic Image Retrieval System for Multi-Image Queries", IEEE Transaction on Information Technology in Biomedicine, Vol. 16, No. 4, pp 758-768, 2012.
- Mohanpriya S. , Vadivel M, "Automatic Retrieval of MRI Brain Image using Multiqueries System", IEEE Conference, pp 1099-1103, 2013.
- Neha Bhuptani and Bijal Talati, An Efficient Image Retrieval Technique using Shape Context Feature. International Journal of Computer Applications (0975 – 8887) Volume 98– No. 1, July 2014.
Index Terms
Keywords