Medical Image Classification by
Mathematical Morphology Operators
Dra. Mariela Azul Gonzalez
Director: Dra. Virginia Ballarin
Co-Director: Dr. Marcel Brun
Universidad Nacional de Mar del PlataMar del Plata, Buenos Aires
Argentina
SP-ASC 2010 São Paulo Advanced School of Computing
Conventional Image Processing Techniques
Bone Marrow Image
Thereshold Contour Tracing Region GrowingWatershed Transform
Thereshold
Conclusion
•The classification by over-segmented regions has proved to be advantageous.
• It is less sensitive to the noise present in the medical images and reduces computational cost.
New Project
•The proper characterization and quantification of shape, size and direction of 2D Medical Images Components. •Future works oriented to process Medical Image 3D
2D Images Tissue Engineering Scaffolds and developing Neurons.
Granulometric Function
To obtain a Granulometric Function, first we applied openings with increasing structuring elements, Then we compute each area (or volume in gray level images). Those values are normalize to obtain a probabilistic distribution function. Finally we compute its moments to compare them in order to analyze morphological characteristics of objects of interest.
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Proposed Method
1° - To obtain Granulometric Functions withdifferent structuring elements,
2° - To compute its moments and compare them,
3° - To analyze morphological characteristicsof objects of interest.
Conclusion
The preliminary results shows there’s is an association between the NGF moments and components morphology (shape, size and orientation). Future studies are oriented to process a higher number of images
Thanks to SP-ASC 2010, the organizate committee, speakers and students
Mar del Plata, Buenos Aires, Argentina
Proposed algorithm for marker definition
a) Over-segmented regions were obtained through the application of the Watershed Transformation, using the
regional minima as markers
b) The region’s attributes were calculated. The average value was determined, along with the standard deviation of the
gray level values from the pixels belonging to each region.
c) The values of the attributes from each region were classified with several methods based on expert oriented
Clustering, Fuzzy Logic Inference Systems and Compensatory Fuzzy Logic Systems. The selected regions will be the Markers for a new application of the Watershed
Transform
e) Binarization.
f) Finally, openings with structuring elements of 3x3 pixels was carried through by unifying the adjacent regions and
eliminating the noise and irrelevant objects.
Medical Image Classification by
Mathematical Morphology Operators
Mariela Azul [email protected]
Directora: Virginia Ballarin
Co-Director: Marcel Brun
Universidad Nacional de Mar del PlataBuenos Aires
Argentina
SP-ASC 2010 São Paulo Advanced School of Computing