Cell-Segmentation | Automated identification of cell boundaries | Computer Vision library

by iitmcvg Python Version: Current License: No License

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kandi X-RAY | Cell-Segmentation Summary

Cell-Segmentation is a Python library typically used in Artificial Intelligence, Computer Vision, OpenCV applications. Cell-Segmentation has no bugs, it has no vulnerabilities and it has low support. However Cell-Segmentation build file is not available. You can download it from GitHub.

This project aims at performing automated identification of cell boundaries from the pathological video data. We are given the video file cells.avi as input. The problem statement can be found here.

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Cell-Segmentation has a low active ecosystem.

It has 18 star(s) with 9 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 3 have been closed. On average issues are closed in 115 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Cell-Segmentation is current.

Quality

Cell-Segmentation has 0 bugs and 0 code smells.

Security

Cell-Segmentation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Cell-Segmentation code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Cell-Segmentation does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

Cell-Segmentation releases are not available. You will need to build from source code and install.

Cell-Segmentation has no build file. You will be need to create the build yourself to build the component from source.

Cell-Segmentation saves you 42 person hours of effort in developing the same functionality from scratch.

It has 112 lines of code, 0 functions and 3 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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Cell-Segmentation Key Features

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Cell-Segmentation Examples and Code Snippets

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Community Discussions

Trending Discussions on Cell-Segmentation

Erosion without losing regions

QUESTION

Erosion without losing regions

Asked 2017-May-04 at 22:20

I have an image containing cells. I can't provide it, but it is similar to the image used as an example here: http://blogs.mathworks.com/steve/2006/06/02/cell-segmentation/ but without the characteristic nuclei.

I have done some processing and am now left with a pretty good segmentation, but some cells are close to each other and I need to split them. Most of them consist of more or less overlapping ellipses.

I am certain that a few iterations of simple erosion will split almost all of those regions. But some of the other cells are so small, they will disappear before the others split. Therefore I need an algorithm that erodes the image, allowing region splitting, but does not delete the last pixel of a region.

I want to use watershed afterwards to segment the cells.

I guess I could implement this on my own by searching for cennected regions and then tracking that I don't lose any or something like that, but the implementation seems messy even in my head and I think there must be an easier way. So my question is basically, what's the name of this so I can google an implementation? Or if there is no off-the-shelf solution, what's an elegant way of implementing this without dozens of iterations and for loops etc.

(Language is python)

...

ANSWER

Answered 2017-May-04 at 22:20

It's a classical problem, and if the overlap between cells is too important, let's say 40% or more, then there is not a good solution.

However, if the overlap is not important, here is the solution:

You start from the segmentation you have, let's call it S
You computer the ultimate eroded UE(S). It will give you the center of each cell. It will give you something like the red points on this image. In this image, they use a distance map, an ultimate eroded will be more stable. If there are still many red points per cell, then a dilation of the UE(S) will fix your problem like this example.
You invert Inv(S) or compute the voronoi diagram Voi(S) in order to have a marker in the background.
Watershed on the gradient image of S, using the UE(S) as inner marker (perfect because you have one point by cell) and Inv(S) or Voi(S) as background/outer marker.

You will get something like this example.

Source https://stackoverflow.com/questions/43789484

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Cell-Segmentation

You can download it from GitHub.
You can use Cell-Segmentation like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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