Update README.md

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.