https://github.com/nimavahdat/leukemia-cells-segmentation

The Leukemia Cells Segmentation project utilizes Genetic and Simulated Annealing algorithms to enhance medical image analysis. Precisely identifying and recoloring common colors in leukemia cell images.
https://github.com/nimavahdat/leukemia-cells-segmentation

Last synced: about 10 hours ago
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The Leukemia Cells Segmentation project utilizes Genetic and Simulated Annealing algorithms to enhance medical image analysis. Precisely identifying and recoloring common colors in leukemia cell images.

• Host: GitHub
• URL: https://github.com/nimavahdat/leukemia-cells-segmentation
• Owner: NimaVahdat
• License: mit
• Created: 2021-08-25T15:37:33.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-09-13T17:45:03.000Z (about 2 months ago)
• Last Synced: 2024-09-14T08:34:15.112Z (about 2 months ago)
• Language: Python
• Homepage:
• Size: 6.1 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Leukemia Cells Segmentation: Rediscovering Power in Image Analysis

## Project Overview

Step into a realm of rediscovery in image analysis with the Leukemia Cells Segmentation project. While the industry explores common and trending approaches, our initiative sets itself apart by utilizing potent Genetic and Simulated Annealing algorithms. This project rekindles the efficacy of a method often overlooked, aiming to elevate the segmentation of leukemia cell images by identifying and recoloring 3, 4, or 5 common colors within the images.



  



## Practical Rediscovery

In a landscape of evolving approaches, the Leukemia Cells Segmentation project offers:

* Revitalized Precision: The Genetic Algorithm resurfaces as a powerful tool for the accurate identification of common colors, enhancing the recognition of cell structures.

* Efficient Segmentation: Simulated Annealing, often forgotten in favor of newer trends, proves its worth by providing precise and reliable image segmentation.

* Adaptable Configurations: Choose between 3, 4, or 5 common colors, showcasing the flexibility of a method that stands the test of time.

## Relevance Across Applications

1. Biomedical Research: Rediscover efficiency in research with improved image analysis, fostering quicker insights.

2. Clinical Diagnostics: Uncover the potential of proven methodologies for enhanced diagnostic accuracy in medical imaging.

3. Industrial Imaging: Extend the utility to industrial settings, where a timeless approach brings precision to image analysis.