1. Image Processing Presentation-1 Nucleus classification By 1.Murali Kirshna 2.Rami Reddy 3.Sai Sandeep

2. Contents • Analyzing the 3d image • Problem • Idea of implementation • Work done so far

3. Analyzing the 3d Image 1.Basically the 3d image is stack of the images arranged frame by frame 2.So we can analyze the 3d image structure by studying the layer by layer which can give a better view of segmentation with at most accuracy.

4. Problem • Segmentation of the nuclei from 3d image. • Segment the nucleolus and heterochromatin from the extracted nucleus. • Calculating the volume, area and density of nucleolus and heterochromatin

5. Idea of Segmentation-Manual • The segmentation of the image is based on frame by frame. • Each frame is loaded and then segmentation of the each layer is extracted using the wand tool and then stored as array • When all the layer are merged together and then it would result in a well structured 3d segmented nucleus.

6. Segmented Image Layer By Layer

7. Segmented Image In 3D

8. Idea of Segmentation-Automated • Select the seed point and then based on that seed point the region is grown . • The region is achieved based on the threshold value . • Once the region is achieved then its separated and same process is repeated for the remaining layers.

9. Idea of Segmentation-Automated • All the separated regions together form the well defined 3d segmented stack.

10. Algorithm and steps followed • So after every iteration the output is moved to stack where all the segmented nucleus from each layer is present

11. Work done so far • We have done the work manually where we have segmented the layer by layer and then we are trying to automate the same. • We have selected a selected a seed point and then able to grow the region based on the threshold value for one layer • Next task is automate this process for the remaining layers of the image

14. Modules and sample code • Selecting the seed point • Add the segmented layers into a stack • Once the stack is formed then calculate the volume and density SAMPLE CODE: intnSeeds = (int)IJ.getNumber("Number of Regions/Seeds", 1); SeedGeneratorsg = new SeedGenerator(ip); sg.selectSeedsManually(imp, nSeeds);

15. References • Seeded Region Growing Features Extraction Algorithm by N. A. Mat-Isa, M. Y. Mashor, and N. H. Othman • R. Adams, and L. Bischof, “Seeded region growing,” IEEE Trans. Pattern Anal. Machine Intell., vol. 16, no. 6, pp. 641-647, June, 1994. • Seeded Region Growing (ImageJ Plugin)

16. Questions

17. Thank you