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Medical Image Classification by Mathematical Morphology Operators Dra. Mariela Azul Gonzalez Director: Dra. Virginia Ballarin Co-Director: Dr. Marcel Brun.

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Presentation on theme: "Medical Image Classification by Mathematical Morphology Operators Dra. Mariela Azul Gonzalez Director: Dra. Virginia Ballarin Co-Director: Dr. Marcel Brun."— Presentation transcript:

1 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 SP-ASC 2010SP-ASC 2010 São Paulo Advanced School of Computing

2 Phd Thesis: Bone Marrow Biopsies Segmentation

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

4 Watershed Transform Flooding Algorithm Markers basins Watershed Lines

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

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

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

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 by Mathematical Morphology Operators Mariela Azul Gonzalez Directora: Virginia Ballarin Co-Director: Marcel Brun Universidad Nacional de Mar del Plata Buenos Aires Argentina SP-ASC 2010SP-ASC 2010 São Paulo Advanced School of Computing

20 Erosion: Dilation: Morphological operators for binary images


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