Automated Brain Tissue Classification by Multisignal Wavelet Decomposition and Independent Component Analysis

<table>Performance measure variations for Daubechies wavelets with different orders “<svg height="11.3125" id="M70" style="vertical-align:-0.1092pt" version="1.1" viewbox="0 0 14.9875 11.3125" width="14.9875" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
<g transform="matrix(.017,-0,0,-.017,.062,11.113)"><path d="M857 650l-6 -28q-44 -4 -61.5 -16.5t-29.5 -48.5q-11 -32 -37 -166l-78 -399h-29l-351 537h-4l-56 -276q-24 -120 -24 -164q0 -35 17.5 -46t75.5 -15l-6 -28h-245l7 28q41 2 62 14t31 44q10 30 41 171l53 245q8 44 6.5 60.5t-14.5 33.5q-10 15 -27 19.5t-64 6.5l6 28h153
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</svg>”. (a) Tanimoto Index (b) Error rate.</table>

International Scholarly Research Notices

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Figure 6

Figure 6: Automated Brain Tissue Classification by Multisignal Wavelet Decomposition and Independent Component Analysis 