On Sun, Mar 9, 2014 at 11:05 PM, Eldad Afik <eldad.afik@gmail.com> wrote:

Dear Devs, 

In the process of tracking objects under the microscope I was confronted with an image analysis problem, for which I could not find a satisfactory solution in איק standard scientific packages (nor in the  literature survey I made). 

At the time I started this, scikit-image did not include a circle Hough transform and OpenCV's was not good enough.

I ended up developing an algorithm myself, which can be regarded as an off-spring of the circle Hough transform. 

I summarised the work in a manuscript titled:

 "Robust and highly performant ring detection algorithm for 3d particle tracking using 2d microscope imaging" (a pre-print is available at arXiv:1310.1371).

NB, it was written minded at potential users and applications rather than  computer scientists, which I am not.

I would like to make the code publicly available.

I believe it may be beneficial to the scikit-image community and users as a practical alternative to the current Standard Circle Hough transform.

I contacted Stéfan van der Walt, who expressed interest in the algorithm.

I would be grateful to have your feedback, any input would be helpful!

With kind thanks to all contributors, 

Eldad

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Eldad Afik

Physics of Complex Systems

Weizmann Institute of Science

Some technical notes:

A typical example of the images I need to analyse can be found in the manuscript arXiv:1310.1371 (Fig. 1a as well as S2a). 

The results of the software I wrote exhibit a detection rate which exceeds 94% and have only 1% false-detection, providing sub-pixel accuracy.

The conceptual steps in making the algorithm robust are sketched in Fig. 2.

The main steps to make it efficient, both in execution-time as well as memory consumption, are outlines on page 8; more technical details in this respect can be found in the "Supporting Information" Text section.

The "methods" section of the manuscript provides some details regarding the benchmarking.

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