[Insight-users] About Image Segmentation StrategyUsing ITK---asking for suggestions

[Yu Long]

 Hi Luis,
  Thanks you for your detailed suggestions. They are all helpful to me.
Regards,
Yu
----- Original Message ----- 
> From: "Luis Ibanez" <luis.ibanez at kitware.com>
> To: "Yu Long" <yulong202 at 263.net>
> Cc: <insight-users at itk.org>
> Sent: Thursday, July 22, 2004 10:57 PM
> Subject: Re: [Insight-users] About Image Segmentation StrategyUsing ITK---asking for suggestions
> 
> 
> > 
> > Hi Yu,
> > 
> > 
> > 1) There are multiple possible strategies for segmenting
> >    gray matter. From the ones available in ITK you may
> >    want to try the following
> > 
> >           - Statistical Classification
> >           - Region Growing
> >           - LevelSets
> >           - Watersheds
> > 
> >    as you mention, it is likely that the right strategy
> >    for your data may involve a combinations of these
> >    techniques.
> > 
> >    Please be aware that Image Segmentation is *never* perfect.
> >    The reason is that the information provided by images is not
> >    sufficient for identifying organs, plus the fact that the
> >    anatomical defition of organs themselves is often ambigous.
> > 
> >    You cannot get "correct" segmentations, what you can
> >    expect to get are "segmentation that are good enough
> >    for a particular purpose". In that regard, you will have
> >    to define very well what is your purpose with segmenting
> >    the gray matter from this data set.
> > 
> > 
> > 2) You should read the Tutorials sessions
> > 
> >          http://www.itk.org/HTML/Tutorials.htm
> > 
> >    in particular the session on segmentation methods
> > 
> > http://www.itk.org/CourseWare/Training/SegmentationMethodsOverview.pdf
> > 
> >    and read the Chapter on Image Segmentation from the ITK
> >    Software Guide
> > 
> >          http://www.itk.org/ItkSoftwareGuide.pdf
> > 
> > 
> > 
> > 3) Slice-by-slice segmentaton is a bad approach. This is usually
> >    done as a desperate measure when the segmentation is guided
> >    by a human operator.  ITK provides N-Dimensional methods for
> >    image segmentation. You will probably find a combination that
> >    work properly in a native 3D setup.
> > 
> > 
> > 
> > 4) The human brain is not a very colorful structure, so you probably
> >    don't get much extra information by using color images.
> >    However, you can easily double check this by computing the Hue
> >    component of your RGB data and checking with an anatomist to
> >    see whether Hue turns out to be specific to the structure that
> >    you are trying to differentiate.
> > 
> > 
> > 
> > 5) ITK provides LevelSet methods that work on RGB images.
> > 
> >    In fact most of the structures shown on the cover of the
> >    ITK Sofware Guide book were extracted using the
> > 
> >       VectorThresholdSegmentationLevelSetImageFilter
> > 
> >    That includes the Gray Matter from the Visible Woman
> >    dataset.
> > 
> >    Note that those filters are not specific for RGB, instead
> >    they can be applied to any multi-component datasets.
> > 
> > 
> > 
> > 6) You will find useful to look at the validation study
> >    available in InsightApplications:
> > 
> >           InsightApplications/IBSRValidation
> > 
> >    You will find there methods such as
> > 
> >          - Atlas based segmentation
> >               - Demons deformable registration
> >               - FEM deformable registration
> > 
> >          - Statistical Classification.
> > 
> > 
> > 
> > 
> > 
> > 
> > Regards,
> > 
> > 
> >    Luis
> > 
> > 
> > ----------------
> > Yu Long wrote:
> > 
> > > Hi all,
> > > 
> > >      I have a sequence of human body images which comes from a Chinese 
> > > Virtual Human Project.( one of them is shown below). I want to segment 
> > > organs such as gray matter from this image sequence using ITK. But since 
> > > I have not enough segmentation experience using ITK, I'd like to ask for 
> > > some suggestions about segmentation methods using ITK. Any suggestion is 
> > > grateful.
> > > 
> > >      In my present plan, I want to use the ITK framework to integrate 
> > > several segmentation methods within ITK to form a hybrid segmentation 
> > > method. Is my plan feasible? Are there any recommended methods?
> > > 
> > >      The second question.  Shall I do a Slice-by-slice segmentation in 
> > > 2D space or directly segment in 3D space? I don't know which strategy is 
> > > better. As I know,　most methods in itk support 3d segmentation.
> > > 
> > >      The third question.  Shall I segment these images directly in color 
> > > space or turn them in to gray images first? Some segmentation methods 
> > > such as levelset methods do not support RGB images, right?
> > > 
> > >   
> > > 
> > > Thanks your suggestions in advance.
> > > 
> > >  
> > > 
> > > Regards,
> > > 
> > > YU Long
> > > 
> > >  
> > > 
> > > -------------------------------------------------------
> > > YU Long
> > > College of Hydropower and Information Engineering
> > > HuaZhong Univ. of Sci. & Tech,
> > > Wuhan China
> > > 430074
> > > Tel: +86 27 8754 4644
> > > 
> > > 
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