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| import streamlit as st | |
| import mahotas as mh | |
| import pandas as pd | |
| import plotly.express as px | |
| # Define a list of medical conditions | |
| conditions = [ | |
| {"name": "Depression", "test_for": "Patient Health Questionnaire-9 (PHQ-9)"}, | |
| {"name": "Anxiety", "test_for": "Generalized Anxiety Disorder-7 (GAD-7)"}, | |
| {"name": "Diabetes", "test_for": "Hemoglobin A1C test"}, | |
| {"name": "Hypertension", "test_for": "Blood pressure measurement"}, | |
| {"name": "Asthma", "test_for": "Pulmonary function test"}, | |
| {"name": "Cancer", "test_for": "Biopsy or imaging tests (e.g., CT scan, MRI)"}, | |
| {"name": "Arthritis", "test_for": "X-ray, MRI, or ultrasound"}, | |
| {"name": "Heart disease", "test_for": "Electrocardiogram (ECG)"}, | |
| {"name": "Obesity", "test_for": "Body mass index (BMI)"}, | |
| {"name": "Substance use disorder", "test_for": "Substance Abuse Subtle Screening Inventory (SASSI)"} | |
| ] | |
| # Define a function to process images using Mahotas | |
| def process_image(image): | |
| # Convert the image to grayscale | |
| grayscale_image = mh.colors.rgb2gray(image) | |
| # Apply a Gaussian filter to the image to reduce noise | |
| filtered_image = mh.gaussian_filter(grayscale_image, 4) | |
| # Threshold the image to create a binary image | |
| binary_image = filtered_image > mh.otsu(filtered_image) | |
| # Compute the connected components in the binary image | |
| labels, num_labels = mh.label(binary_image) | |
| # Compute the size of each connected component | |
| sizes = mh.labeled.labeled_size(labels) | |
| # Sort the sizes in descending order | |
| sorted_sizes = sorted(sizes, reverse=True) | |
| # Return the top 10 sizes | |
| return sorted_sizes[:10] | |
| # Define the Streamlit app | |
| def app(): | |
| # Add a title to the app | |
| st.title("Mahotas Demo") | |
| # Add a sidebar to the app | |
| st.sidebar.title("Medical Conditions") | |
| selected_condition = st.sidebar.selectbox("Select a condition", [c["name"] for c in conditions]) | |
| # Get the selected condition | |
| condition = next(c for c in conditions if c["name"] == selected_condition) | |
| # Display the selected condition | |
| st.header(condition["name"]) | |
| st.write("Test for:", condition["test_for"]) | |
| # Load an example medical image | |
| image = mh.imread("https://i.imgur.com/kPQoD8C.jpg") | |
| # Process the image using Mahotas | |
| sizes = process_image(image) | |
| # Display the top 10 connected component sizes | |
| df = pd.DataFrame({"Size": sizes}) | |
| st.write(df) | |
| # Create a sunburst chart using Plotly | |
| fig = px.sunburst( | |
| df, | |
| path=["Size"], | |
| values="Size", | |
| color="Size", | |
| color_continuous_scale="blues" | |
| ) | |
| st.plotly_chart(fig) | |
| if __name__ == "__main__": | |
| app() | |