Merge branch 'master' of https://github.com/webvalley/sagittal-classi…

…fication

ImagePreprocessing.ipynb

@@ -41,8 +41,8 @@
    },
    "outputs": [],
    "source": [
-    "work_dir = './Data'\n",
-    "dcm_files = [pd.read_file(os.path.join(os.path.join(work_dir,file_path), file_path2)) for file_path in os.listdir(work_dir) for file_path2 in os.listdir(os.path.join(work_dir,file_path))]"
+    "work_dir = './Data/Raw'\n",
+    "dcm_files = [pd.read_file(os.path.join(work_dir,file_path)) for file_path in os.listdir(work_dir)]"
    ]
   },
   {
@@ -270,21 +270,21 @@
    },
    "outputs": [],
    "source": [
-    "folder_path = \"./Processed_Data\"\n",
+    "folder_path = \"./Data/Reg\"\n",
     "for counter, dcm in enumerate(dcm_files):\n",
     "    metacrop_arr = metacrop(dcm)\n",
-    "    blur_arr = gaussian_blur(metacrop_arr, 2)\n",
+    "    blur_arr = gaussian_blur(metacrop_arr, 15)\n",
     "    clahe_arr = clahe(blur_arr)\n",
-    "    canny_arr_2d = canny(clahe_arr[:,:,0], 1.5)\n",
-    "    canny_arr = conv_2d_3d(canny_arr_2d)\n",
+    "    #canny_arr_2d = canny(clahe_arr[:,:,0], 1.5)\n",
+    "    #canny_arr = conv_2d_3d(canny_arr_2d)\n",
     "    #denoise_arr = denoising(clahe_arr)\n",
-    "    flip_edge_arr = flip(canny_arr)\n",
+    "    #flip_edge_arr = flip(canny_arr)\n",
     "    flip_arr = flip(clahe_arr)\n",
     "    file_path = os.path.join(folder_path, str(counter))\n",
     "    np.save(file_path+\".npy\", clahe_arr)\n",
     "    np.save(file_path+\"rev.npy\", flip_arr)\n",
-    "    np.save(file_path+\"edge.npy\", canny_arr)\n",
-    "    np.save(file_path+\"edgerev.npy\", flip_edge_arr)"
+    "    #np.save(file_path+\"edge.npy\", canny_arr)\n",
+    "    #np.save(file_path+\"edgerev.npy\", flip_edge_arr)"
    ]
   },
   {
@@ -351,7 +351,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,

ImagePreprocessing.py

@@ -0,0 +1,194 @@
+import pydicom as pd
+import numpy as np
+import sklearn
+import matplotlib.pyplot as plt
+import sklearn.preprocessing as pp
+import skimage.filters as filters
+import skimage.feature as feature
+import skimage.restoration as restoration
+import skimage.exposure as exposure
+import os
+
+work_dir = './Data/Raw'
+dcm_files = [pd.read_file(os.path.join(work_dir,file_path)) for file_path in os.listdir(work_dir)]
+
+'''
+Cropping by Metadata
+
+Args: 
+    img: DCM file input
+Output: 
+    parr: Cropped pixel array
+'''
+def metacrop(file):
+    for key in file.dir():
+        value = getattr(file, key, "")
+        if(type(value) == pd.sequence.Sequence and key == "SequenceOfUltrasoundRegions"):
+            value = value[0]
+            break
+    x0, x1, y0, y1 = None, None, None, None
+    for key in value.dir():
+        if key == "RegionLocationMinX0":
+            x0 = getattr(value, key, "")
+        if key == "RegionLocationMaxX1":
+            x1 = getattr(value, key, "")
+        if key == "RegionLocationMinY0":
+            y0 = getattr(value, key, "")
+        if key == "RegionLocationMaxY1":
+            y1 = getattr(value, key, "")
+    print(x0, x1, y0, y1)
+    if(x0 == 0):
+        return colorcrop2(file.pixel_array)
+    return file.pixel_array[y0:y1,x0:x1]
+
+'''
+Cropping by Color
+
+Args: 
+    parr: Initial pixel array
+Output: 
+    parr: Cropped pixel array
+'''
+def colorcrop(pixel_arr):
+    midCol = pixel_arr.shape[1]//2
+    print(midCol)
+
+    # Left Side
+    flag = False
+    ind = -1
+    for i in range(midCol, 0, -1):
+        colArr = pixel_arr[:,i,0]
+        if(np.mean(colArr) == 0):
+            flag = True
+            ind = i
+            break
+    flag2 = False
+    ind2 = -1
+    for i in range(midCol, pixel_arr.shape[1]):
+        colArr = pixel_arr[:,i,0]
+        if(np.mean(colArr) == 0):
+            flag2 = True
+            ind2 = i
+            break
+    if(flag and flag2):
+        return pixel_arr[:,ind:ind2]
+    return pixel_arr
+
+'''
+Faster Cropping by Color
+
+Args: 
+    parr: Initial pixel array
+Output: 
+    parr: Cropped pixel array
+'''
+def colorcrop2(pixel_arr):
+    midCol = pixel_arr.shape[1]//2
+    x = pixel_arr[:,midCol:].sum(axis=0)[:,0]
+    distance = np.where(x == 0)[0][0]
+    ind = midCol - distance
+    ind2 = midCol + distance
+    print(ind, ind2)
+    return pixel_arr[:,ind:ind2]
+
+'''
+Returns flipped image (vertically)
+
+Args: 
+    parr: Initial pixel array
+Output: 
+    parr: Flipped pixel array
+'''
+def flip(img):
+    return np.fliplr(img)
+
+'''
+Gaussian Blur
+
+Args: 
+    parr: Initial pixel array
+    sigma: Hyperparameter σ
+Output: 
+    parr: Blurred pixel array
+'''
+def gaussian_blur(img, sig):
+    smooth_img = filters.gaussian(img, sigma = sig)
+    return smooth_img
+
+'''
+Sobel Edge Detection
+
+Args: 
+    parr: Initial pixel array
+Output: 
+    parr: Transformed pixel array
+'''
+def sobel(img):
+    return filters.sobel(img)
+
+'''
+Canny Edge Detection
+
+Args: 
+    parr: Initial pixel array
+Output: 
+    parr: Transformed pixel array
+'''
+def canny(img, sigma):
+    return feature.canny(img, sigma = sigma)
+
+'''
+Wavelet Denoising
+
+Args: 
+    parr: Initial pixel array
+Output: 
+    parr: Denoised pixel array
+'''
+def denoising(img):
+    denoise_img = restoration.denoise_wavelet(img)
+    return denoise_img
+
+'''
+Contrast-Limited Adaptive Histogram Equalization
+
+Args: 
+    parr: Initial pixel array
+Output: 
+    parr: Transformed pixel array
+'''
+def clahe(img):
+    return exposure.equalize_adapthist(img)
+
+'''
+Converting BW 2D array into BW 3D array
+
+Args: 
+    parr: Initial pixel array (2 dimensional)
+Output: 
+    parr: Transformed pixel array (3 dimensional)
+'''
+def conv_2d_3d(img):
+    arr = np.array(np.ones((img.shape[0], img.shape[1], 3), dtype=float))
+    print(arr.shape)
+    arr[:,:,0] = img
+    arr[:,:,1] = img
+    arr[:,:,2] = img
+    return arr
+
+folder_path = "./Data/Reg"
+for counter, dcm in enumerate(dcm_files):
+    metacrop_arr = metacrop(dcm)
+    blur_arr = gaussian_blur(metacrop_arr, 15)
+    clahe_arr = clahe(blur_arr)
+    #canny_arr_2d = canny(clahe_arr[:,:,0], 1.5)
+    #canny_arr = conv_2d_3d(canny_arr_2d)
+    #denoise_arr = denoising(clahe_arr)
+    #flip_edge_arr = flip(canny_arr)
+    flip_arr = flip(clahe_arr)
+    file_path = os.path.join(folder_path, str(counter))
+    np.save(file_path+".npy", clahe_arr)
+    np.save(file_path+"rev.npy", flip_arr)
+    #np.save(file_path+"edge.npy", canny_arr)
+    #np.save(file_path+"edgerev.npy", flip_edge_arr)
+