In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.
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I.J. Image, Graphics and Signal Processing, 2013, 8, 29-36
Bindu, G; Dr.Mathew, K T(Cochin University of Science & Technology, January , 2007)
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Abstract:
Breast cancer is the most common non - skin malignancy in women and a leading cause of female morality. A potentially important strategy for reducing this menace is the detection at an early stage . The invention of non-invasive and non-ionizing microwave technique, to reveal the internal structure of biological objects was a break through in the field of medical diagnostics. Electrical properties of biological tissues and their interaction with electromagmetic waves have direct impact on human life. This thesis focuses on theoretical and experimental investigations of active microwave imaging techniques for breast cancer detection.
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Department of Electronics, Cochin University of Science and Technology
Kannan, Balakrishnan; Anil, Kumar; Sindhumol, S(IEEE, April , 2013)
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Abstract:
Multispectral analysis is a promising approach in
tissue classification and abnormality detection from Magnetic
Resonance (MR) images. But instability in accuracy and
reproducibility of the classification results from conventional
techniques keeps it far from clinical applications. Recent studies
proposed Independent Component Analysis (ICA) as an effective
method for source signals separation from multispectral MR data.
However, it often fails to extract the local features like small
abnormalities, especially from dependent real data. A multisignal
wavelet analysis prior to ICA is proposed in this work to resolve
these issues. Best de-correlated detail coefficients are combined
with input images to give better classification results.
Performance improvement of the proposed method over
conventional ICA is effectively demonstrated by segmentation
and classification using k-means clustering. Experimental results
from synthetic and real data strongly confirm the positive effect
of the new method with an improved Tanimoto index/Sensitivity
values, 0.884/93.605, for reproduced small white matter lesions
Description:
International conference on Communication and Signal Processing, April 3-5, 2013, India