Aim:
Perform gray level image segmentation on the basis of the gray level histogram, but without using the "traditional" gray level thresholding approach.
Method:
The method lies within the context of fuzzy methods.
First, the gray level histogram is computed.
Then, the histogram is smoothed in such a way that M modes are produced (M is the number of classes the segmentation will produce. It is fixed by the user.
On the basis of these M modes, grades of membership to the M classes are computed for each gray level.
M images, representing the grades of membership of every pixel to the M classes, are computed.
Probabilistic relaxation is applied to these images.
Finally, a defuzzification of the relaxed grades of membership produces the final segmented image.
The rationale for this method has been published in:
BONNET N., CUTRONA J. and HERBIN M.
A �no-threshold� histogram-based image segmentation technique.
Pattern Recognition (2002) 35(10), 2319-2322.
DOWNLOAD
V2 works on 3D images as well as 2D images.
Also: intra-class or (intra+inter-class) relaxation can be performed.
User interface (V2):
Number of classes: chosen by the user
Choice of a relaxation method: intra-class or (intra+inter-class) relaxation
Number of relaxation iterations: chosen by the user
Show the memberships; show the distances to modes; show the histograms::
clicked ==> yes; unclicked (default value) ==> no
Illustration:
Perform gray level image segmentation on the basis of the gray level histogram, but without using the "traditional" gray level thresholding approach.
Method:
The method lies within the context of fuzzy methods.
First, the gray level histogram is computed.
Then, the histogram is smoothed in such a way that M modes are produced (M is the number of classes the segmentation will produce. It is fixed by the user.
On the basis of these M modes, grades of membership to the M classes are computed for each gray level.
M images, representing the grades of membership of every pixel to the M classes, are computed.
Probabilistic relaxation is applied to these images.
Finally, a defuzzification of the relaxed grades of membership produces the final segmented image.
The rationale for this method has been published in:
BONNET N., CUTRONA J. and HERBIN M.
A �no-threshold� histogram-based image segmentation technique.
Pattern Recognition (2002) 35(10), 2319-2322.
DOWNLOAD
V2 works on 3D images as well as 2D images.
Also: intra-class or (intra+inter-class) relaxation can be performed.
User interface (V2):
Number of classes: chosen by the user
Choice of a relaxation method: intra-class or (intra+inter-class) relaxation
Number of relaxation iterations: chosen by the user
Show the memberships; show the distances to modes; show the histograms::
clicked ==> yes; unclicked (default value) ==> no
Illustration:
