Problem Statement
The healthcare industry requires efficient and accurate methods to analyze histology images for research and diagnostic purposes. Manual counting of hepatocytes is time-consuming and prone to human error.
Description
Developed a Python script, HistologyFilter.py, to automate the identification and counting of hepatocytes in histology images.
Implemented Solution
Utilized image processing techniques to enhance, segment, and detect circular hepatocytes. Key steps included:
- Feature extraction to identify darker, circular hepatocytes.
- Image enhancement and blurring using convolution.
- Segmentation via adaptive thresholding to isolate hepatocytes from the background.
- Detection using Hough Transform to pinpoint hepatocytes.
Technologies Used
Python, OpenCV, Convolution, Edge Detection, Image Enhancement, Segmentation, Hough Transform.
Results
Successfully automated the detection and counting of hepatocytes, significantly reducing the analysis time. The output image highlights hepatocytes with green circles and provides a count, as seen in the attached image.
Client Feedback
“The automated system has greatly improved our efficiency in histology image analysis, ensuring accurate and rapid results.”