Update app.py

app.py

@@ -125,20 +125,32 @@ class DicomAnalyzer:
             if self.current_image is None:
                 return None, "No image loaded"
 
-            # Get raw image data before any processing
-            raw_image = self.current_image.copy()
-            
-            # Get clicked coordinates and adjust for zoom/pan
+            # Get raw image data
+            raw_image = self.current_image
+
+            # Get image dimensions
+            height, width = raw_image.shape[:2]
+
+            # Calculate coordinates
             clicked_x = evt.index[0]
             clicked_y = evt.index[1]
-            
-            # Convert to original image coordinates
-            x = int((clicked_x + self.pan_x) / self.zoom_factor)
-            y = int((clicked_y + self.pan_y) / self.zoom_factor)
 
-            # Create mask for ROI using raw image dimensions
+            # Adjust coordinates for ImageJ compatibility
+            x = int(clicked_x)
+            y = int(clicked_y)
+
+            if self.zoom_factor != 1.0:
+                # Adjust for zoom and pan
+                x = int((clicked_x + self.pan_x) / self.zoom_factor)
+                y = int((clicked_y + self.pan_y) / self.zoom_factor)
+
+            # Ensure coordinates are within bounds
+            x = max(0, min(x, width-1))
+            y = max(0, min(y, height-1))
+
+            # Create circular mask
             mask = np.zeros_like(raw_image, dtype=np.uint8)
-            y_indices, x_indices = np.ogrid[:raw_image.shape[0], :raw_image.shape[1]]
+            y_indices, x_indices = np.ogrid[:height, :width]
             radius = self.circle_diameter / 2
             distance_from_center = np.sqrt(
                 (x_indices - x)**2 + (y_indices - y)**2
@@ -148,17 +160,18 @@ class DicomAnalyzer:
             # Get ROI pixels from raw image
             roi_pixels = raw_image[mask == 1]
 
-            # Calculate statistics from raw pixel values
+            # Calculate statistics using raw values
             pixel_spacing = float(self.dicom_data.PixelSpacing[0])
             area_pixels = np.sum(mask)
             area_mm2 = area_pixels * (pixel_spacing ** 2)
             
-            # Calculate statistics without any normalization
+            # Use raw pixel values for statistics
             mean = np.mean(roi_pixels)
             stddev = np.std(roi_pixels)
             min_val = np.min(roi_pixels)
             max_val = np.max(roi_pixels)
 
+            # Store results
             result = {
                 'Area (mm²)': f"{area_mm2:.3f}",
                 'Mean': f"{mean:.3f}",
@@ -170,9 +183,14 @@ class DicomAnalyzer:
             
             self.results.append(result)
             self.marks.append((x, y, self.circle_diameter))
-            print(f"ROI analyzed at point ({x}, {y})")
-            print(f"Mean: {mean:.3f}, StdDev: {stddev:.3f}")
-            print(f"Min: {min_val:.3f}, Max: {max_val:.3f}")
+            
+            # Debug information
+            print(f"Clicked at: ({clicked_x}, {clicked_y})")
+            print(f"Adjusted to: ({x}, {y})")
+            print(f"Zoom factor: {self.zoom_factor}")
+            print(f"Pan: ({self.pan_x}, {self.pan_y})")
+            print(f"ROI Statistics: Mean={mean:.3f}, StdDev={stddev:.3f}")
+            print(f"Min={min_val:.3f}, Max={max_val:.3f}")
 
             return self.update_display(), self.format_results()
         except Exception as e: