1. Segmentation of CT angiography based on a combination of segmentation methods University of West Bohemia in Pilsen Czech republic Ing. Ivan Pirner Ing. Miroslav Jiřík Ing. Miloš Železný, Ph.D.

2. Contents: medical images segmentation techniques thresholding edge detection region growing process conclusion PRIA 2010, Saint Peterburg 8 th December2 of 16 Segmentation of CT angiography Ing. Ivan Pirner Sources of medical images: CT, MRI, USG, X-ray, PET, etc. Example: CT (picture source: http://www.jnch.nic.in)

3. Contents: medical images segmentation techniques thresholding edge detection region growing process conclusion PRIA 2010, Saint Peterburg 8 th December3 of 16 Segmentation of CT angiography Ing. Ivan Pirner Image properities: Image F(i,j) is a 2-dimensional array of pixels. Each pixel on the position i,j is characterized by its value – the density. The density is a non-negative integer value belonging to a known finite range, usually 8 or 16bit. Remark: Most of the medical images are grayscale. Methods used in this work may be generalized for color images.

4. Contents: medical images segmentation techniques thresholding edge detection region growing process conclusion PRIA 2010, Saint Peterburg 8 th December4 of 16 Segmentation of CT angiographyIng. Ivan Pirner Definition: Segmentation = labeling the pixels of an image in such way, that the labels have a strong correlation with real objects observed in the image. Purposes: removing unwanted regions of data counting regions measuring regions

5. Contents: medical images segmentation techniques thresholding edge detection region growing process conclusion PRIA 2010, Saint Peterburg 8 th December5 of 16 Segmentation of CT angiographyIng. Ivan Pirner Useful segmentation techniques: thresholding (threshold value?) edge-based methods edge image thresholding (threshold value?) region-based methods region growing (homogeneity rule?) graph cut segmentation energy minimization (model?, parameters?)

6. Contents: medical images segmentation techniques thresholding edge detection region growing process conclusion PRIA 2010, Saint Peterburg 8 th December6 of 16 Segmentation of CT angiographyIng. Ivan Pirner Thresholding: Thresholded image is a binary image G(i,j), where G(i,j) = 1 if F(i,j) > T and G(i,j) = 0otherwise i, j – spatial coordinates F(i,j) – original image pixels Conditions of use: Object to be segmented has other pixel values range than its background. The output segmentation needs often postprocessing, many dummy segments due to image noise. The threshold value must be chosen properly.

7. Contents: medical images segmentation techniques thresholding edge detection region growing process conclusion PRIA 2010, Saint Peterburg 8 th December7 of 16 Segmentation of CT angiographyIng. Ivan Pirner Example: left: original CT slice right: double thresholded image

8. Contents: medical images segmentation techniques thresholding edge detection region growing process conclusion PRIA 2010, Saint Peterburg 8 th December8 of 16 Segmentation of CT angiographyIng. Ivan Pirner Edge detection: The purpose is to find places in the image with significant discontinuities in the image function (big differences in values between neighborning pixels). There are many similar operators, which approximate the first derrivative of the image function. We used Sobel’s operator. first two of four Sobels’ operators (the basic mask is rotating)

9. Contents: medical images segmentation techniques thresholding edge detection region growing process conclusion PRIA 2010, Saint Peterburg 8 th December9 of 16 Segmentation of CT angiographyIng. Ivan Pirner Proceeding: The edge image is set as an output of a 2D-correlation between the mask and the original image. Results for different directions are summed and the output image then thresholded into a binary image. Conditions of use: The seeked region must be bordered by a “sharp” edge. The threshold value must be chosen properly.

10. Contents: medical images segmentation techniques thresholding edge detection region growing process conclusion PRIA 2010, Saint Peterburg 8 th December10 of 16 Segmentation of CT angiographyIng. Ivan Pirner Example: left: original CT slice right: edge image

11. Contents: medical images segmentation techniques thresholding edge detection region growing process conclusion PRIA 2010, Saint Peterburg 8 th December11 of 16 Segmentation of CT angiographyIng. Ivan Pirner Region growing: In this part we used the modified confidence connected algorithm: 1)Set a seed (1 or multiple points) and make it the current region. 2)Find all pixels neighboring upon the current region. 3)For all of this neighboring pixels decide, whether they fulfill the homogeneity criteria, if yes, append them to the current region. 4)If no points added in step 3, END, else GOTO 2. We chose as homogeneity criteria K(p) a double inequality: K(p) = 1 if p>T_min && p<T_max K(p) = 0otherwise p – tested pixel T_min – chosen minimum value T_max – chosen maximum value

12. Sketch: region growing (image source: http://www.cs.cf.ac.uk)http://www.cs.cf.ac.uk Conditions of use: Seeked region must be homogenous. The seed set must be chosen within the region. Contents: medical images segmentation techniques thresholding edge detection region growing process conclusion PRIA 2010, Saint Peterburg 8 th December12 of 16 Segmentation of CT angiographyIng. Ivan Pirner

13. Proceeding: Contents: medical images segmentation techniques thresholding edge detection region growing process conclusion PRIA 2010, Saint Peterburg 8 th December13 of 16 Segmentation of CT angiographyIng. Ivan Pirner original image edge imagesegmented bones+vessels bone image region growingedge detection morphological operations segmented vessels subtraction

14. Visualization of the 3D data: 3D model using volume rendering: Contents: medical images segmentation techniques thresholding edge detection region growing process conclusion PRIA 2010, Saint Peterburg 8 th December14 of 16 Segmentation of CT angiographyIng. Ivan Pirner

15. Conclusion: The CT angiography vessel segmentation may be made using “simple” methods when combining them together. Parameters of each of the used segmentation methods can be easily interpreted and either directly determined or experimentally measured. Future work: Graph cuts could bring more precise results, although we need to determine a proper model and estimate its parameters. Contents: medical images segmentation techniques thresholding edge detection region growing process conclusion PRIA 2010, Saint Peterburg 8 th December15 of 16 Segmentation of CT angiographyIng. Ivan Pirner

16. Thank you for your attention. Contents: medical images segmentation techniques thresholding edge detection region growing process conclusion PRIA 2010, Saint Peterburg 8 th December16 of 16 Segmentation of CT angiographyIng. Ivan Pirner