Isolating Objects From Image Stack

Presented By:Md. Amjad Hossain and Raja Naresh

Objectives

- Constracting objects from the image sequence constructed by slicing the objects.

Objectives

- Constructing objects from the image sequence obtained by slicing the objects.

A Problem Instance

Isolating a single Axon from the stack of images.

• Identify each part of same axon in each image of the stack.

• Merge all parts to get the 3D form of the axon.

A Problem Instance

Isolating a single Axon from the stack of images.

• Identify if the axon is splitting from one image to another image.

A Problem Instance

Isolating a single Axon from the stack of images.

• Identify if two or more axon’s parts are getting merged from one image to another image.

Region Growing algorithm

Region Growing (SeedPoint, Threshold).

1. insert SeedPoint into queue

2. Calculate low = SeedPoint<Threshold? 0 : Threshold

High = SeedPoint<Threshold? Threshold : 255

3. while(queue not empty)

if (neighbour >= low && neighbour <= high)

insert neighbour into queue.

include neighbour in the axon set.

- Used to identify a single part of axon within an image.

Algorithm

Scan through the mirroring coordinates in the second image of

the stack and

identify a pixel

between low and high calculated from threshold.

image 1

image 2

image 1

image 2

Pixel is not related to initial seed point

Identifying seed point and region growing for the consecutive image in the stack.

Algorithm

Splitting case.

Repeat the seed identification

process if a significant number

of pixels are still left.

The above process will allow

us to detect any splits occurring in the

consequent image slices in the stack.

Identifying seed point and region growing for the consecutive image in the stack.

Algorithm

Merging case.

-After getting all parts of the axon in an

image check whether those parts are

branching in the previous images.

- Spotting split in reverse stack is

basically merge.

Identifying seed point and region growing for the consecutive image in the stack.

Full object detection algorithm

Full object detection algorithm

Result-1 ( Isolating a single axon)To isolate object,1. Load image sequence2. Apply filtering (Gaussian Blur) 3. Select initial seed point

4. Apply the object detection algorithm

5. Apply thresholding to separate object pixels from other pixels.

6. Use 3D viewer to see the object

The steps are same as isolating single axon. Select multiple seed points (one on each axon) on the first image of the image stack.

Result-2 ( Isolating multiple axons)

Initial Image Stack 3D view Isolated axons

Result-3

Isolating all connected neurons from confocal image. Select one or multiple seed points

In this case, one seed point has been selected from 13 th image of the original image stack

Comparison with existing fast marching

- Fast marching in Fiji always convert original image to low quality image by applying a fixed filter. But the new algorithm can be applied to images of any quality.

- Although the Fast marching technique uses lower quality image, the process is extremely slow while our algorithm is very faster.

Conclusion and Future work

- We implemented very basic but crucial part for isolating objects from image stack very quickly.

- We have figure out algorithm for calculating surface area of the object detected but didn’t implemented as it is very trivial algorithm.

- As we didn’t know the actual voxel height so we didn’t go through implementation of calculating volume of the object.

The work can be extended to

- Calculating threshold value automatically.

- Calculate approximate surface area, volume of the objects detected