Hierarchical classification architectures applied to Magnetic Resonance Images

The main goal of the paper is to explore hierarchical classification. The investigation is performed on the dataset of Magnetic Resonance Images (MRI) which is hierarchically organized. Generalized top-down hierarchical classification architecture is proposed in the paper. Additionally, two specific cases of the generalized architecture are explored: three-stage hierarchical architecture based on SVM and three-stage hierarchical architecture based on ANN. From the performed experiments, it is concluded that the SVM based scheme outperforms the ANN based scheme. Moreover, the gain of the investigation conducted in this paper becomes bigger with the possibilities given by the proposed generalized architecture for further investigations.

[1]  Susan T. Dumais,et al.  Hierarchical classification of Web content , 2000, SIGIR '00.

[2]  Samy Bengio,et al.  Torch: a modular machine learning software library , 2002 .

[3]  Ee-Peng Lim,et al.  Hierarchical text classification and evaluation , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[4]  Susan Gauch,et al.  Hierarchical Text Classification , .

[5]  Maurice Bruynooghe,et al.  Hierarchical multi-classification , 2002, KDD 2002.

[6]  Alex A. Freitas,et al.  A survey of hierarchical classification across different application domains , 2010, Data Mining and Knowledge Discovery.

[7]  David Taniar,et al.  Research and Trends in Data Mining Technologies and Applications , 2007 .

[8]  Dejan Gjorgjevikj,et al.  HIERARCHICAL CLASSIFICATION OF MAGNETIC RESONANCE IMAGES , 2010 .

[9]  Chuin-Mu Wang,et al.  Classification for Breast MRI Using Support Vector Machine , 2008, 2008 IEEE 8th International Conference on Computer and Information Technology Workshops.

[10]  Dejan Gjorgjevikj,et al.  Classification of magnetic resonance images , 2010, Proceedings of the ITI 2010, 32nd International Conference on Information Technology Interfaces.

[11]  Dejan Gjorgjevikj,et al.  Towards Improving Magnetic Resonance Image Classification , 2010 .

[12]  Antoine Geissbühler,et al.  A Review of Content{Based Image Retrieval Systems in Medical Applications { Clinical Bene(cid:12)ts and Future Directions , 2022 .

[13]  Yu-Chung N. Cheng,et al.  Magnetic Resonance Imaging: Physical Principles and Sequence Design , 1999 .

[14]  Maria A. Zuluaga,et al.  Fuzzy classification of brain MRI using a priori knowledge: weighted fuzzy C-means , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[15]  Ee-Peng Lim,et al.  Performance measurement framework for hierarchical text classification , 2003, J. Assoc. Inf. Sci. Technol..

[16]  D. Selvathi,et al.  Brain MRI Slices Classification Using Least Squares Support Vector Machine , 2007 .

[17]  Y. M. Latha.  Content Based Color Image Retrieval via Wavelet Transforms , 2007 .

[18]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[19]  B. S. Manjunath,et al.  Introduction to MPEG-7: Multimedia Content Description Interface , 2002 .

[20]  Yi Gao,et al.  Tissue tracking: applications for brain MRI classification , 2007, SPIE Medical Imaging.