Dear ITK users community,<br><br>I have been using ITK library to implement an algorithm of mass detection in mammography based on gaussian mixture modelling of texture as part of my master thesis project.<br><br>At first we tried to used the library objects itk::Statistics::ExpectationMaximizationMixtureModelEstimator<br>
with the itk::Statistics::ListSample sample interface as explained in the software guide. We tried to pass by means of the sample interface the measurement vectors to the EM algorithm. For our purpouse those vectors are the gray levels of the pixels from the local neighborhoods of each target pixel. We get those sample vectors by means of shaped neightborhood iterator itk::ShapedNeighborhoodIterator without problems.<br>
<br>Our problem is then of memory allocation. We tried to allocate every mv vector by vector inside the sample object by means of its smart pointer as:<br><br>sample->PushBack(mv) ;<br><br>but soon our system ran out of memory, as expected since mammograms are about 12 MPixels and the mv size is 145. <br>
<br>We decided to implement by ourselves the method using image iterators, linear algebra and gaussian density functions. After some programming and debuging we have got a working prototype, but we would like to ask for ideas to work around the mentioned problem. Is anybody supporting the EM implementation in ITK or has anybody solved a similar issue before?. Any advice is appreciated.<br>
<br>We really would like to compare our prototype's output with the ITK tested implementation. Also let us know, if it is appropriate, how can we contribute to the development of ITK.<br><br>Thank you very much,<br><br>
Sincerely,<br><br>-- <br>Nicolás Gallego Ortiz<br><a href="mailto:nicgallego@ieee.org">nicgallego@ieee.org</a><br>Departamento de Ingeniería Electrónica y Telecomunicaciones<br>Universidad de Antioquia<br>Medellín, Colombia<br>
<br>