From 44ade73ec55f4035743d023ba9a240d5176f5909 Mon Sep 17 00:00:00 2001 From: Ajinkya Kulkarni Date: Tue, 7 Nov 2023 09:57:57 +0100 Subject: [PATCH] Update README.md --- README.md | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/README.md b/README.md index cefa05f..9fa432d 100644 --- a/README.md +++ b/README.md @@ -8,3 +8,26 @@ A web application developed using Streamlit is available at [https://elispotanal ![alt text](https://github.com/ajinkya-kulkarni/PyElispotAnalysis/blob/main/AppImage1.png) ![alt text](https://github.com/ajinkya-kulkarni/PyElispotAnalysis/blob/main/AppImage2.png) ![alt text](https://github.com/ajinkya-kulkarni/PyElispotAnalysis/blob/main/AppImage3.png) + +### Overview +PyElispotAnalysis is a Streamlit web application that provides a user-friendly interface for the automated analysis of Elispot assay images. Developed with a focus on ease of use and accuracy, it allows for the rapid quantification of spots within an assay image, facilitating the assessment of immune responses. + +### Features +Image Upload: Users can upload Elispot assay images in various formats, including .tif, .tiff, .png, .jpg, and .jpeg. +Interactive Sliders: The app features interactive sliders for fine-tuning analysis parameters such as local window size, sensitivity for spot detection, and minimum and maximum spot area. +Automated Spot Detection: Utilizes adaptive thresholding and morphological operations to identify and count spots. +Visualization: Offers side-by-side visualization of the original and processed images with detected spots highlighted. +Histograms: Generates histograms for the distribution of spot sizes to provide insights into the range and density of the immune response. +Detailed Reporting: Produces a downloadable report with quantitative data on each detected spot, including area and diameter. +Responsive Design: Crafted to work on various devices with an intuitive layout that adapts to different screen sizes. + +### Workflow +Image Processing: Upon image upload, the app processes the image, converting it to grayscale and resizing it to suit the user interface. +Parameter Adjustment: Users can adjust analysis parameters using sliders to optimize spot detection according to their specific assay characteristics. +Spot Detection: The app applies adaptive thresholding and morphological operations to detect spots, which are then filtered based on size criteria set by the user. +Result Visualization: Detected spots are circled on the processed image, and the user can compare this with the original image using an interactive slider. +Data Analysis: The app generates a histogram of spot sizes and a detailed report, including a table with metrics for each spot. +Report Download: Users can download the processed image and a CSV file containing the detailed spot analysis. + +### Support and Contribution +Information on how to reach out for support, report bugs, or contribute to the project. Encourage contributions such as bug fixes, feature requests, and suggestions for improvement.