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

Phd Thesis: Bone Marrow Biopsies Segmentation

Conventional Image Processing Techniques

Bone Marrow Image

Thereshold Contour Tracing Region GrowingWatershed Transform

Thereshold

Watershed Transform Flooding Algorithm

Markers

basins

Watershed Lines

Proposed algorithm for marker definition

Final results of other bone marrow biopsies.

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.

( ) ( )( ) 1

( ) ( )

N

N

A E A E

A EG

A

( )( ) 1

( )

A EG

AEEAxEA )()(

Granulometric Function

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.

Area: NGF and its derivative

Area

Area

Mean Value

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

Mean Value

Diameter (µm)

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

SE orientation

Mean Value

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 Gonzalezmazulgonzalez@fi.mdp.edu.ar

Directora: Virginia Ballarin

Co-Director: Marcel Brun

Universidad Nacional de Mar del PlataBuenos Aires

Argentina

SP-ASC 2010 São Paulo Advanced School of Computing

Erosion:

Dilation:

: ( ) { : }

A B x B x Asiendo B x b x b B

( ) A B x B x A

Morphological operators for binary images