--- a
+++ b/vessel_segment.py
@@ -0,0 +1,110 @@
+"""
+This file is for plumonary vessels enhancement.
+"""
+import numpy as np
+import SimpleITK as sitk
+from scipy import ndimage
+import cv2
+
+from itertools import izip_longest
+
+class VesselSegment:
+    """
+    This class is desigened for lung vasculature enhancement, including the methods of:
+                1. downsampling
+                2. thresholding
+                3. 3D region growing
+                4. filtering small struture
+    """
+
+    def __init__(self, original, closing):
+        """
+        :param original: the orignal image in ITK formate
+        :param closing: the Closing result image
+        :param thval: threshold value (default: 130HU)
+        :param filter_vol: filter value for removing small struture after region growing (default: 2ml)
+        """
+        self.original_img = original
+        self.closing_img = sitk.GetArrayFromImage(closing)
+        self.img = None
+        self.thval = None
+        self.filter_vol = None
+        self.temp_img = None
+
+    def erosion(self, lunglabel=[201, 202]):
+        """
+        Shrik the region of lung...
+        """
+        temp = np.zeros_like(self.closing_img)
+        temp[self.closing_img == lunglabel[0]] = 1
+        temp[self.closing_img == lunglabel[1]] = 1
+        Erode_filter = sitk.BinaryErodeImageFilter()
+        Erode_filter.SetKernelRadius ( 2 ).SetForegroundValue ( 1 )
+        self.closing_img = sitk.GetArrayFromImage( Erode_filter.Execute ( sitk.GetImageFromArray(temp) ) )
+
+    def generate_lung_mask(self, offset = 0):
+        """
+        Generate lung mask
+        :return: None
+        """
+        self.img = sitk.GetArrayFromImage(self.original_img).copy() - offset
+        self.img[self.closing_img == 0] = 0
+
+    def downsampling(self):
+        """
+        Downsample the input image from [-1024, 0] to [0, 255] for reducing memory requirement.
+                              / max(0, min(254, (Vorig+1024)/4), v belongs to Lung region
+                        Vds =
+                              \ 255,                             otherwise
+        :return: None
+        """
+        temp = (self.img + 1024) / 4
+        temp[temp > 254] = 254
+        temp[temp < 0] = 0
+        self.temp_img = temp
+
+    def thresholding(self, thval=130):
+        """
+        Threshold the image with given thresholding value.
+        :return: None
+        """
+        self.thval = thval
+        self.temp_img[self.temp_img < thval] = thval
+
+    def max_filter(self, filter_size=5):
+        """
+        Implement maximum filter.
+        :param filter_size: filter size
+        :return: None
+        """
+        temp = self.temp_img.copy()
+        temp[temp >= 254] = 0
+        temp[temp <= self.thval] = 0
+        self.temp_img = ndimage.filters.maximum_filter(temp, size=filter_size)
+
+    def filtering(self, min_size=10, max_size=5000):
+        """
+        Remove the small structure which volumn is less than given value (2ml)
+        :param min_size:
+        :return: None
+        """
+        self.filter_vol = min_size
+        z, y, x = self.temp_img.shape
+        count_labels = []
+        for i in range(z):
+            dist_transform = cv2.distanceTransform(np.uint8(self.temp_img[i, :, :]), cv2.DIST_L2, 5)
+            ret, sure_fg = cv2.threshold(dist_transform, 0.7 * dist_transform.max(), 255, 0)
+            sure_fg = np.uint8(sure_fg)
+            # Marker labelling
+            ret, markers = cv2.connectedComponents(sure_fg)
+            # Add one to all labels so that sure background is not 0, but 1
+            markers += 1
+            count_labels = np.asarray([x + y for x, y in izip_longest(count_labels,
+                                                                      np.bincount(markers.flatten()),
+                                                                      fillvalue=0)])
+        labels = np.arange(0, len(count_labels))
+        labels[count_labels < min_size] = 0
+        labels[count_labels > max_size] = 0
+        labels = np.asarray(list(set(labels)))
+        for label in labels:
+            self.temp_img[self.temp_img == label] = 0
\ No newline at end of file