Earticle

초록

영어

Multispectral approach to brain MRI analysis has shown great advance recently in pathology and tissue analysis. However, poor performance of the feature extraction and classification techniques involved in it discourages radiologists to use it in clinical applications. Transform based feature extraction methods like Independent Component Analysis (ICA) and its variants have contributed a lot in this research field. But these global transforms often fails in extraction of local features like small lesions from clinical cases and noisy data. Feature extraction part of the recently introduced Multiresolution Independent Component Analysis (MICA) algorithm in microarray classification is proposed in this work to resolve this issue. Effectiveness of the algorithm in MRI analysis is demonstrated by training and classification with Support Vector Machines (SVM). Both synthetic and real abnormal data from T1-weighted, T2-weighted, proton density, fluid-attenuated inversion recovery and diffusion weighted MRI sequences are considered for detailed evaluation of the method. Tanimoto index, sensitivity, specificity and accuracy of the classified results are measured and analyzed for brain abnormalities, affected white matter and gray matter tissues in all cases including noisy environment. A detailed comparative study of classification using MICA and ICA is also carried out to confirm the positive effect of the proposed method. MICA based SVM is found to yield very good results in anomaly detection, around 2.5 times improvement in classification accuracy is observed for abnormal data analysis.

목차

Abstract
 1. Introduction
 2. Materials and Methods
  2.1. Database
  2.2. Multisignal Wavelet Analysis
  2.3. Proposed Multi-resolution Independent Component Analysis in MRI
  2.4. Classification Using SVM
  2.5. Quantitative Measures
 3. Experimental Results and Discussions
  3.1. Synthetic Image Analysis
  3.2. Clinical Image Analysis
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• S. Sindhumol [ Artificial Intelligence Lab, Department of Computer Applications, Cochin University of Science and Technology ]
• Anilkumar [ Institute of Radiology and Imaging Sciences, Indira Gandhi Co-operative Hospital ]
• Kannan Balakrishnan [ Artificial Intelligence Lab, Department of Computer Applications, Cochin University of Science and Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605