diff --git a/utils/data_processing/data_processing.py b/utils/data_processing/data_processing.py
index 7a6f5ad..04ef41b 100644
--- a/utils/data_processing/data_processing.py
+++ b/utils/data_processing/data_processing.py
@@ -107,7 +107,7 @@ def get_discrete_data(obj:ScannedObject, ndigits:int, delta_z:float=1, update_pr
         progress += 1
     return data
 
-def get_advanced_data(discrete_data:dict, update_progress_bar= progressbar_placeholder)->dict:
+def get_advanced_data(discrete_data:dict, V_scan = 0, update_progress_bar= progressbar_placeholder)->dict:
     """
     Calculates morphological indicators from the given discrete data
 
@@ -122,20 +122,20 @@ def get_advanced_data(discrete_data:dict, update_progress_bar= progressbar_place
         - Tortuosite
         - Volume en mm3
         - Surface en mm2
-        - Moyenne des rayons moyens
-        - Ecart-type des rayons moyens
-        - Sigma r tot
+        - Moyenne des rayons moyens 〈R〉
+        - Ecart-type des rayons moyens σ_〈R〉
+        - σ_〈R〉^tot
         - MI_l
         - MI_p
+        - Rayon hydraulique R_h
     """
-    
+    all_R = discrete_data["Rayon moyen (en mm)"]
     # Tortusity
     l = 0
     L = 0
     vertices = list(zip(discrete_data["X moy (en mm)"],
                         discrete_data["Y moy (en mm)"],
                         discrete_data["Z moy (en mm)"]))
-    
     for index in range(len(vertices)-1):
         l += de.get_distance_between_two_vertices(vertices[index], vertices[index+1])
     L = de.get_distance_between_two_vertices(vertices[0], vertices[-1])
@@ -144,26 +144,44 @@ def get_advanced_data(discrete_data:dict, update_progress_bar= progressbar_place
     
     # Volume and surface
     H = discrete_data["Z moy (en mm)"][-1] - discrete_data["Z moy (en mm)"][0]
-    R = de.get_mean([np.power(r,2) for r in discrete_data["Rayon moyen (en mm)"]])
-    V = np.pi * R * H
-    S = 2 * np.pi * R * H
+    R2_mean = de.get_mean([np.power(r,2) for r in all_R])
+    V = np.pi * R2_mean * H
+    S = 2 * np.pi * R2_mean * H
     update_progress_bar(30)
     
     # Morphological indicators
-    R_mean = de.get_mean(discrete_data["Rayon moyen (en mm)"])
-    R_mean_std = de.get_standard_deviation(discrete_data["Rayon moyen (en mm)"])
+    R_mean = de.get_mean(all_R)
+    R_mean_std = de.get_standard_deviation(all_R)
     mean_sigma_r_squared = de.get_mean([np.power(r,2) for r in discrete_data["Rayon ecart type (en mm)"]])
     sigma_r_tot = np.sqrt(np.power(R_mean_std,2) + mean_sigma_r_squared )
     MI_l = R_mean_std/R_mean
     MI_p = np.sqrt(mean_sigma_r_squared)/R_mean
+
+    R_max = max(all_R)
+    MI_mR = R_max/R_mean
+    MI_mH = R_max/H
+    #MI_mr_in = R_max/R_in
+    # TODO understand what is R_in
+    R_V_scan = np.sqrt(V_scan/np.pi*H)
+    S_V_scan = 2 * np.sqrt(np.pi * H * V_scan)
+    R_h = R2_mean/ R_mean
+    HI = R_mean * R_V_scan / R2_mean
     update_progress_bar(100)
     return {
         "Tortuosité":T,
         "Volume en mm3":V,
         "Surface en mm2":S,
-        "Moyenne des rayons moyens":R_mean,
-        "Ecart-type des rayons moyens":R_mean_std,
-        "Sigma r tot":sigma_r_tot,
+        "Moyenne des rayons moyens 〈R〉":R_mean,
+        "Ecart-type des rayons moyens σ_〈R〉":R_mean_std,
+        "σ_〈R〉^tot":sigma_r_tot,
         "MI_l":MI_l,
-        "MI_p":MI_p
+        "MI_p":MI_p,
+        "MI_mR":MI_mR,
+        "MI_mH":MI_mH,
+        # "MI_mr_in":MI_mr_in,   
+        "V_scan":V_scan,
+        "R_V_scan":R_V_scan,
+        "S_V_scan":S_V_scan,
+        "Rayon hydraulique R_h":R_h,
+        "HI":HI
     }