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<h1>mmdfila
<br>
<span class="subtitle">Detect Filarial Worms.
</span>
</h1>
<div class="descr">
<H2>Description</H2>
<div class="H2">
<p>
This procedure extracts the filarial worms from a complex microscopic image by eliminating progressively the other objects present in the image. The selection of the structures begins in the gray-scale level and, after a threshold, continues based on the object geometrical properties.
</p>
</div>
</div>
<div class="script">
<H2>Demo Script</H2>
<div class="H2">
<div class="slide">
<H3>Reading</H3>
<div class="H3">
<p>
<p>
A microscopic gray-scale image, with two filarial worms, is read.
</p>
<div class="example">
<div class="listing">
<pre class="user">>>> a = mmreadgray('danaus.tif');</pre>
<pre class="computer"></pre>
<pre class="user">>>> mmshow(a);</pre>
<pre class="computer"></pre>
</div>
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<td><img width="256" src="../images/img_mmdfila_001.jpg"></td>
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<td><a href="../images/img_mmdfila_001.jpg">a</a></td>
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</p>
</div>
</div>
<div class="slide">
<H3>Background regularization.</H3>
<div class="H3">
<p>
<p>
The Close by Reconstruction Top-Hat operator is applied to regularize the image background.
</p>
<div class="example">
<div class="listing">
<pre class="user">>>> b = mmcloserecth(a,mmsebox(5));</pre>
<pre class="computer"></pre>
<pre class="user">>>> mmshow(b);</pre>
<pre class="computer"></pre>
</div>
<table class="images">
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<td><img width="256" src="../images/img_mmdfila_002.jpg"></td>
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<td><a href="../images/img_mmdfila_002.jpg">b</a></td>
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</p>
</div>
</div>
<div class="slide">
<H3>Removing narrow objects</H3>
<div class="H3">
<p>
<p>
The gray-scale opening by the elementary cross is applied to remove narrow objects.
</p>
<div class="example">
<div class="listing">
<pre class="user">>>> c = mmopen(b);</pre>
<pre class="computer"></pre>
<pre class="user">>>> mmshow(c);</pre>
<pre class="computer"></pre>
</div>
<table class="images">
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<td><img width="256" src="../images/img_mmdfila_003.jpg"></td>
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<td><a href="../images/img_mmdfila_003.jpg">c</a></td>
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</p>
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</div>
<div class="slide">
<H3>Removing small objects</H3>
<div class="H3">
<p>
<p>
The gray-scale area open operator is applied to remove small objects.
</p>
<div class="example">
<div class="listing">
<pre class="user">>>> d = mmareaopen(c,200);</pre>
<pre class="computer"></pre>
<pre class="user">>>> mmshow(d);</pre>
<pre class="computer"></pre>
</div>
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<td><img width="256" src="../images/img_mmdfila_004.jpg"></td>
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<td><a href="../images/img_mmdfila_004.jpg">d</a></td>
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</p>
</div>
</div>
<div class="slide">
<H3>Thresholding</H3>
<div class="H3">
<p>
<p>
The threshold operator is applied to extract a reduced set of structures that include the two worms present in the image.
</p>
<div class="example">
<div class="listing">
<pre class="user">>>> e = mmthreshad(d,50);</pre>
<pre class="computer">Warning: Converting input image from int32 to uint8.</pre>
<pre class="user">>>> mmshow(e);</pre>
<pre class="computer"></pre>
</div>
<table class="images">
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<tr class="image" valign="bottom">
<td><img width="256" src="../images/img_mmdfila_005.jpg"></td>
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<tr class="title" valign="baseline">
<td><a href="../images/img_mmdfila_005.jpg">e</a></td>
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</p>
</div>
</div>
<div class="slide">
<H3>Skeleton</H3>
<div class="H3">
<p>
<p>
The objective of the sequence of transformations, that begin with the homotopic skeleton, is to eliminateg the structures that are not worms. The information used for the filtering is that the worms are longer than any other structure found.
</p>
<div class="example">
<div class="listing">
<pre class="user">>>> f = mmthin(e);</pre>
<pre class="computer"></pre>
<pre class="user">>>> mmshow(f);</pre>
<pre class="computer"></pre>
</div>
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<td><img width="256" src="../images/img_mmdfila_006.jpg"></td>
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<td><a href="../images/img_mmdfila_006.jpg">f</a></td>
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</p>
</div>
</div>
<div class="slide">
<H3>Elimination of short structures</H3>
<div class="H3">
<p>
<p>
The first 12 points of the skeleton branches, counting from their extremities, are eliminated. The structures that were not eliminated will be the markers for extracting the two worms.
</p>
<div class="example">
<div class="listing">
<pre class="user">>>> g = mmthin(f,mmendpoints(), 12);</pre>
<pre class="computer"></pre>
<pre class="user">>>> mmshow(g);</pre>
<pre class="computer"></pre>
</div>
<table class="images">
<tbody align="center">
<tr class="image" valign="bottom">
<td><img width="256" src="../images/img_mmdfila_007.jpg"></td>
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<td><a href="../images/img_mmdfila_007.jpg">g</a></td>
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</p>
</div>
</div>
<div class="slide">
<H3>Extraction of the worms from their markers</H3>
<div class="H3">
<p>
<p>
The binary reconstruction operator is applied to reconstruct the binary image produced by the threshold from the marker image.
</p>
<div class="example">
<div class="listing">
<pre class="user">>>> h = mminfrec(g,e);</pre>
<pre class="computer"></pre>
<pre class="user">>>> mmshow(h);</pre>
<pre class="computer"></pre>
</div>
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<td><img width="256" src="../images/img_mmdfila_008.jpg"></td>
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<td><a href="../images/img_mmdfila_008.jpg">h</a></td>
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</p>
</div>
</div>
<div class="slide">
<H3>Final display</H3>
<div class="H3">
<p>
<p>
The structures extracted are overlaid to the input gray-scale image.
</p>
<div class="example">
<div class="listing">
<pre class="user">>>> mmshow(a,h);</pre>
<pre class="computer"></pre>
</div>
<table class="images">
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<tr class="image" valign="bottom">
<td><img width="256" src="../images/img_mmdfila_009.jpg"></td>
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<tr class="title" valign="baseline">
<td><a href="../images/img_mmdfila_009.jpg">a,h</a></td>
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</table>
</div>
</p>
</div>
</div>
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<td class="copyright">Copyright (c) 2003, Roberto A. Lotufo, UNICAMP-University of Campinas; Rubens C. Machado, CenPRA-Renato Archer Research Center.</td>
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distrib
>
Mandriva
>
2010.0
>
i586
>
media
>
contrib-release
>
by-pkgid
>
4a71d9984febeb5a206904a5a379841a
>
files
>
622
