c)Hough transform to detect ellipses. In this part I would check two references:
Yuen, H. K., Illingworth, J., & Kittler, J. (1989). Detecting partially occluded ellipses using the Hough transform. Image and Vision Computing, 7(1), 31-37.
Chia, A. Y., Rajan, D., Leung, M. K., & Rahardja, S. (2008, October). A split and merge based ellipse detector. In Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on (pp. 3212-3215). IEEE.
A lot of noise has been distorted by jpeg compression artifacts. Hopefully you have better original images. If you have, I would first try to get rid of some of that noise; maybe a de-speckle filter.
Thresholding will not work because on the left there are parts outside the cells that are darker then the right side of the cells.
The Hough transform will give you the best fitting ellipses:
- It will find more than one per cell
- It will be very noisy as well
- It will NOT give you the exact shapes
Since some boundaries can hardly be seen by the human eye (where cells overlap), and maybe three or more overlapping cells might occur in other images, I think the segmentation cannot be done using 'simple' image processing techniques.
That previous post is about counting the number of objects. Mahsa wants to recover their shapes as well, which is much harder to do, especially when one can not (or hardly) see the boundary. It will need some modeling or machine learning to accomplish that.
However, if you can do it using simple image processing techniques, post your code here please!
@Lambert: Once has to do post processing of the above result to get middle cell which is overlapped with other two cells. There is a lot of inter-observer variability. So an expert validated ground truth is also required to know the exact shape, size and location of cells otherwise it is difficult to design an segmentation algorithm & judge its result. For better visualisation of cell boundaries, you may see the mask and other segmentation results from following link:
@Chanukya: you are absolutely right about the observer variability of the ground truth. Still I am not convinced that post-processing (using 'ordinary' image processing techniques without user interaction) will give you the shape of the middle cell.
How would you do it?
I think you need to do some modeling.
Or -if a rough estimate of the shape is good enough- you might convert the detected known boundary of the troublesome cell to vertices and line segments, and extrapolate it by minimizing the curvature (angular deficit) at all the newly introduced vertices.
Given the results, I'm not sure that modifying program to be adapted to this problem is an easy process. Especially considering features that segmentation algorithms work based on them. Actually developing an algorithm based on this kind of images is a lot easier than modifying an already developed coed. Though using previous knowledge can ease the process.
And idea mentioned by @Lambert Zijp that "I think you need to do some modeling."
It's right and somehow inevitable to do so. and given the conditions algorithm will be iterative.
initial shapes are extracted with a few parameters such as center and radius and then by moving pixels real shape must be extracted.
The Mayo Biomedical Imaging Resource (BIR) conducts research into and development of image analysis, visualization, and measurement capabilities and software tools for biomedical imaging applications. The design goal for these tools includes full interactivity, yet some tools are both compute bound and time sensitive. Therefore, effective use of th...
Automatic biomedical image processing has enjoyed increased popularity of late, primarily because it can be used to enhance images to measure and count accurately and quickly in various types of applications. Preliminary background and basic terminology commonly used in biomedical image processing will be reviewed. Among these are sources and forms...
Two new techniques are described and illustrated. The first is a variation of the pyramidal algorithm. In this technique a set of filtering operations is used to enhance the image. The technique described here is simple and requires less computational power. The second describes a new pre-compensation algorithm based on the ocular point spread func...