What is it about?
Providing better subtype discrimination of histopathological brain tumors through exploiting cell nuclei fractal characteristics.
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Why is it important?
A different approach via measuring the fractal dimension for tree structured wavelet decomposition demonstrated its performance in distinguishing grade I histopathological meningioma images with an improved accuracy as compared to conventional energy based decomposition. The Best Basis Selection based on Fractal Dimension characteristics (BBSFD) relies on revealing texture structure complexity which would better characterising the information situated in the middle and high frequency bands. Also, the appropriate decomposition level would be detected when no more significant difference in-between the subbands exist, saving unnecessary computational operations.
Perspectives
Histopathological tissue heterogeneity can be better characterised using the underlying cell nuclei texture fractal properties.
Dr Omar S Al-Kadi
University of Jordan
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This page is a summary of: A fractal dimension based optimal wavelet packet analysis technique for classification of meningioma brain tumours, November 2009, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/icip.2009.5414534.
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