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Medical Image Classification by Mathematical Morphology Operators

SP-ASC 2010 São Paulo Advanced School of Computing. 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 Plata Mar del Plata, Buenos Aires Argentina.

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Medical Image Classification by Mathematical Morphology Operators

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  1. SP-ASC 2010São Paulo Advanced School of Computing Medical Image Classification byMathematical Morphology Operators Dra. Mariela Azul Gonzalez Director: Dra. Virginia Ballarin Co-Director: Dr. Marcel Brun Universidad Nacional de Mar del Plata Mar del Plata, Buenos Aires Argentina

  2. Phd Thesis: Bone Marrow Biopsies Segmentation

  3. Conventional Image Processing Techniques Bone Marrow Image Watershed Transform Contour Tracing Region Growing Thereshold Thereshold

  4. Watershed Lines basins Markers Watershed Transform Flooding Algorithm

  5. Proposed algorithm for marker definition

  6. Final results of other bone marrow biopsies.

  7. 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.

  8. 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.

  9. 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.

  10. Granulometric Function

  11. Proposed Method 1° - To obtain Granulometric Functions with different structuring elements, 2° - To compute its moments and compare them, 3° - To analyze morphological characteristics of objects of interest.

  12. Area: NGF and its derivative

  13. Area Mean Value Area

  14. Preliminary Results: Mean Value (round SE) vs. Diameter Mean Value Diameter (µm)

  15. Preliminary Results: Mean Values (linear SE) vs. Orientation Mean Value SE orientation

  16. 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

  17. Thanks to SP-ASC 2010, the organizate committee, speakers and students Mar del Plata, Buenos Aires, Argentina

  18. 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.

  19. Medical Image Classification byMathematical Morphology Operators Mariela Azul Gonzalez mazulgonzalez@fi.mdp.edu.ar Directora: Virginia Ballarin Co-Director: Marcel Brun SP-ASC 2010São Paulo Advanced School of Computing Universidad Nacional de Mar del Plata Buenos Aires Argentina

  20. Morphologicaloperatorsforbinaryimages • Erosion: • Dilation:

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