isolating objects from image stack presented by: md. amjad hossain and raja naresh
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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
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