<br>HI, xi<br> May be you could try a similar way like this<br><a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1352428&tag=1">http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1352428&tag=1</a><br>
<br>1. you always define 2 classes. the background and foreground (only for nonzero pixels)<br>2. compute mean value for each class.<br>3. perform k Means classification.<br>4. remove background (set to 0). test whether foreground satisfied your condition(like variance < threshold)<br>
5. if 4 not satisfied, go to 2,3,4.<br><br><br>regards<br><br>wanlin<br><br><br><div class="gmail_quote">On Tue, Oct 26, 2010 at 10:04 AM, Xi Liang <span dir="ltr"><<a href="mailto:xliang.cad@gmail.com">xliang.cad@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;"><br>
I tried KMeans by splitting the subtraction images into two/three/four<br>
clusters.<br>
<br>
The initial means is set to be (0, MaxIntensity) where the interval is equal<br>
to MaxIntensity/#Clusters. I tested k=2,3,4,5 means method, 3-means method<br>
shows best segmenation in my case by using the cluster with the highest<br>
intensity mean as tumor mask.<br>
<br>
Kind regards,<br>
<br>
Xi<br>
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