An Empirical Optimization to Logistic Classification Model

Please use this identifier to cite or link to this publication:
Recently, the scientific community has been proposing several automatic algorithms to biomedical image segmentation procedure, being an interesting and helpful approach to assist both technicians and radiologists in this time-consuming and subjective task. One of these interesting and widely used image segmentation method could be the voxel intensity-based algorithms, e.g. image histogram threshold methods, which have been intensively improved in the past decades. Recently, an interesting approach that gained focus is the logistic classification (LC) for object detection in biomedical images. Even though the general concept behind the LC method is fairly known, the proper method's optimization still commonly adjusted by hand which naturally adds a level of uncertainty and subjectivity in the general segmentation performance. Therefore, an empirical LC optimization is presented, offering a ITK class that performs the LC parameters optimization based on empirical input data analysis. It is worth mentioning that the LogisticContrastEnhancementImageFilter class showed here is also applied on others computational problems, being briefly explained in this document.
The source code for this publication has not been tested per author's request.
There is no review at this time. Be the first to review this publication!

Quick Comments

Download All
Download Paper , View Paper
Download Source code
Source code repository

Statistics more
Global rating: starstarstarstarstar
Review rating: starstarstarstarstar [review]
Code rating:
Paper Quality: plus minus

Information more
Categories: Mixture of densities, Parameter Techniques
Keywords: Classification, Unsupervised learning
Tracking Number: 405574/2017-7
Toolkits: ITK, CMake
Export citation:


Recommended Publications more
Computing Bone Morphometric Feature Maps from 3-Dimensional Images Computing Bone Morphometric Feature Maps from 3-Dimensional Images
by Vimort J., McCormick M., Paniagua B.
MR Brain Segmentation using Decision Trees MR Brain Segmentation using Decision Trees
by Jog A., Roy S., Prince J.L., Carass A.

View license
Loading license...

Send a message to the author
Powered by Midas