Update app.py

app.py

@@ -143,24 +143,22 @@ class DicomAnalyzer:
             y = int(round(y))
             
             # Get image dimensions
-            height, width = self.current_image.shape[:2]
+            height, width = self.original_image.shape[:2]
             
             # Create mask exactly as ImageJ does
             Y, X = np.ogrid[:height, :width]
-            center_x = x
-            center_y = y
             
-            # ImageJ uses a specific radius calculation
-            radius = self.circle_diameter / 2.0
+            # ImageJ's circle creation method
+            radius = (self.circle_diameter - 1) / 2.0
             
-            # Create the mask using ImageJ's method
-            dx = X - center_x
-            dy = Y - center_y
-            dist_squared = dx * dx + dy * dy
-            mask = dist_squared <= (radius * radius)
+            # Calculate distances using ImageJ's method
+            dx = X - x
+            dy = Y - y
+            dist_squared = dx*dx + dy*dy
+            mask = dist_squared <= radius*radius
             
-            # Get ROI pixels
-            roi_pixels = self.current_image[mask]
+            # Get ROI pixels using original image values
+            roi_pixels = self.original_image[mask]
             
             if len(roi_pixels) == 0:
                 return self.display_image, "Error: No pixels selected"
@@ -168,11 +166,11 @@ class DicomAnalyzer:
             # Get pixel spacing (mm/pixel)
             pixel_spacing = float(self.dicom_data.PixelSpacing[0])
             
-            # Calculate statistics exactly as ImageJ does
+            # Calculate area (this is correct)
             n_pixels = np.sum(mask)
             area = n_pixels * (pixel_spacing ** 2)
             
-            # Use ImageJ's statistical calculations
+            # Calculate statistics using original pixel values
             mean_value = np.mean(roi_pixels)
             std_dev = np.std(roi_pixels, ddof=1)  # ImageJ uses n-1
             min_val = np.min(roi_pixels)
@@ -225,7 +223,7 @@ 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/2) * self.zoom_factor)
+                zoomed_radius = int(((diameter - 1) / 2) * self.zoom_factor)  # ImageJ radius
                 
                 # Draw main circle
                 cv2.circle(zoomed_bgr,