A General System for Automatic Biomedical Image Segmentation Using Intensity Neighborhoods

<table class="table-group" id="tab2"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td align="left">  Statistical measure</td><td align="left"> GM (proposed system/[<a href="/journals/ijbi/2011/606857/#B2">19</a>])</td><td align="left"> WM (proposed system/[<a href="/journals/ijbi/2011/606857/#B2">19</a>])</td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td align="left"> Mean</td><td align="left"> 0.9053/0.8074</td><td align="left"> 0.8198/0.8868</td></tr><tr><td align="left"> Median</td><td align="left"> 0.9092/0.8009</td><td align="left"> 0.8382/0.8913</td></tr><tr><td align="left"> Standard deviation</td><td align="left"> 0.0304/0.0426</td><td align="left"> 0.0676/0.0179</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

Comparison: mean, median, and standard deviation for GM and WM tissues using different methods.

International Journal of Biomedical Imaging

tab2

Table 2

Table 2: A General System for Automatic Biomedical Image Segmentation Using Intensity Neighborhoods 