diff --git a/README.md b/README.md
index 52a7716..17787fa 100644
--- a/README.md
+++ b/README.md
@@ -2,15 +2,11 @@
 An implementation of 3D brain MRI super-resolution method by image gradient-tensor distance based patch clustering
 
 ### Conference Abstract
- - 3D Brain MRI Super-Resolution with Image Gradient Tensor Feature Clustering [[Poster]](https://anyscreeninc.com/PF/OHBM/2021/OHBM-Educational-Courses/pdf_poster_files/Seongsu_Park60785c7f217ee/Seongsu_Park.pdf)
+ - 3D Brain MRI Super-Resolution with Image Gradient Tensor Feature Clustering [[Poster]](https://github.com/Snailpong/SR_Tensor/files/7373293/ohbm.pdf)
   
  - Organization for Human Brain Mapping Annual Meeting, 2021
 
 
-### Journal Paper
- - Will be updated..
-
-
 ## Requirments
 
  - Both Linux and Windows are supported.
diff --git a/feature_model.py b/feature_model.py
index 5ddc85d..5d83eaa 100644
--- a/feature_model.py
+++ b/feature_model.py
@@ -19,7 +19,7 @@ def get_invarient_set(l1, l2, l3):
     l3 = math.sqrt(l3)
     trace = l1 + l2 + l3
     mean_la = trace / 3
-    fa = ((l1 - mean_la)**2 + (l2 - mean_la)**2 + (l3 - mean_la)**2)/ (l1**2 + l2**2 + l3**2)
+    fa = math.sqrt(((l1 - mean_la)**2 + (l2 - mean_la)**2 + (l3 - mean_la)**2) / (l1**2 + l2**2 + l3**2) / 2.)
     mode = (-l1-l2+2*l3)*(2*l1-l2-l3)*(-l1+2*l2-l3)/2/pow(l1**2+l2**2+l3**2-l1*l2-l1*l3-l2*l3, 1.5)
     return trace, fa, mode
 
diff --git a/preprocessing.py b/preprocessing.py
index 13ea6e0..140ea39 100644
--- a/preprocessing.py
+++ b/preprocessing.py
@@ -8,11 +8,11 @@
 
 
 def get_array_data(file, training):
-    raw_image = nib.load(file).get_fdata()
-    raw_array = np.array(raw_array, dtype=np.float32)
+    raw_image = nib.load(file)
+    raw_array = np.array(raw_image.get_fdata(), dtype=np.float32)
     raw_header = raw_image.header.copy()
 
-    clipped_image = clip_image(raw_image)
+    clipped_image = clip_image(raw_array)
     im = mod_crop(clipped_image, C.R)
     slice_area = crop_slice(im, C.PATCH_HALF, C.R)