Update app.py

app.py

@@ -131,33 +131,35 @@ class DicomAnalyzer:
             clicked_x = evt.index[0]
             clicked_y = evt.index[1]
             
-            # Transform coordinates to match ImageJ exactly
+            # Transform coordinates
             x = clicked_x + self.pan_x
             y = clicked_y + self.pan_y
             if self.zoom_factor != 1.0:
                 x = x / self.zoom_factor
                 y = y / self.zoom_factor
             
-            # ImageJ uses integer coordinates
             x = int(round(x))
             y = int(round(y))
             
             # Get image dimensions
             height, width = self.original_image.shape[:2]
             
-            # Create mask exactly as ImageJ does
+            # Create mask with larger radius calculation
             Y, X = np.ogrid[:height, :width]
             
-            # ImageJ's circle creation method
-            radius = (self.circle_diameter - 1) / 2.0
+            # Increase effective radius to include boundary pixels
+            radius = (self.circle_diameter / 2.0) + 0.5  # Add 0.5 to include boundary pixels
+            r_squared = radius * radius
             
-            # Calculate distances using ImageJ's method
+            # Calculate distances with more inclusive boundary
             dx = X - x
             dy = Y - y
             dist_squared = dx*dx + dy*dy
-            mask = dist_squared <= radius*radius
             
-            # Get ROI pixels using original image values
+            # Include slightly more pixels in the mask
+            mask = dist_squared <= (r_squared + 0.25)  # Add small tolerance
+            
+            # Get ROI pixels
             roi_pixels = self.original_image[mask]
             
             if len(roi_pixels) == 0:
@@ -166,19 +168,17 @@ class DicomAnalyzer:
             # Get pixel spacing (mm/pixel)
             pixel_spacing = float(self.dicom_data.PixelSpacing[0])
             
-            # Calculate area (this is correct)
+            # Calculate statistics
             n_pixels = np.sum(mask)
             area = n_pixels * (pixel_spacing ** 2)
-            
-            # Calculate statistics using original pixel values
             mean_value = np.mean(roi_pixels)
-            std_dev = np.std(roi_pixels, ddof=1)  # ImageJ uses n-1
+            std_dev = np.std(roi_pixels, ddof=1)
             min_val = np.min(roi_pixels)
             max_val = np.max(roi_pixels)
 
-            # Print debug information
-            print(f"\nDetailed Analysis:")
-            print(f"Coordinates: ({x}, {y})")
+            print(f"\nEnhanced Analysis:")
+            print(f"Position: ({x}, {y})")
+            print(f"Effective Radius: {radius}")
             print(f"Pixel count: {n_pixels}")
             print(f"Area: {area:.3f} mm²")
             print(f"Mean: {mean_value:.3f}")
@@ -186,7 +186,6 @@ class DicomAnalyzer:
             print(f"Min: {min_val}")
             print(f"Max: {max_val}")
 
-            # Store results
             result = {
                 'Area (mm²)': f"{area:.3f}",
                 'Mean': f"{mean_value:.3f}",
@@ -223,7 +222,8 @@ class DicomAnalyzer:
             for x, y, diameter in self.marks:
                 zoomed_x = int(x * self.zoom_factor)
                 zoomed_y = int(y * self.zoom_factor)
-                zoomed_radius = int(((diameter - 1) / 2) * self.zoom_factor)  # ImageJ radius
+                # Use enhanced radius for display
+                zoomed_radius = int(((diameter/2.0 + 0.5) * self.zoom_factor))
                 
                 # Draw main circle
                 cv2.circle(zoomed_bgr,