--- a
+++ b/segment_lung.py
@@ -0,0 +1,62 @@
+import numpy as np
+import nibabel as nib
+from scipy import ndimage
+from skimage import measure
+from scipy import stats
+
+def segment_lung(params, I, I_affine):
+	
+	#####################################################
+	# Intensity thresholding & Morphological operations
+	#####################################################
+
+	M = np.zeros(I.shape)
+	M [I > params['lungMinValue']] = 1
+	M [I > params['lungMaxValue']] = 0
+
+	struct_s = ndimage.generate_binary_structure(3, 1)
+	struct_m = ndimage.iterate_structure(struct_s, 2)
+	M = ndimage.binary_closing(M, structure = struct_s, iterations = 1)
+	M = ndimage.binary_opening(M, structure = struct_m, iterations = 1)
+
+	#####################################################
+	# Estimate lung filed of view
+	#####################################################
+
+	[m, n, p] = I.shape;
+	medx      = int(m/2)
+	medy      = int(n/2)
+	xrange1   = int(m/2*params['xRangeRatio1'])
+	xrange2   = int(m/2*params['xRangeRatio2'])
+	zrange1   = int(p*params['zRangeRatio1'])
+	zrange2   = int(p*params['zRangeRatio2'])
+
+	#####################################################
+	# Select largest connected components & save nii
+	#####################################################
+
+	M = measure.label(M)
+	label1 = M[medx - xrange2 : medx - xrange1, medy, zrange1 : zrange2]
+	label2 = M[medx + xrange1 : medx + xrange2, medy, zrange1 : zrange2]
+	label1 = stats.mode(label1[label1 > 0])[0][0]
+	label2 = stats.mode(label2[label2 > 0])[0][0]
+	M[M == label1] = -1
+	M[M == label2] = -1
+	M[M > 0] = 0
+	M = M*-1
+
+	M     = ndimage.binary_closing(M, structure = struct_m, iterations = 1)
+	M     = ndimage.binary_fill_holes(M)
+	Mlung = np.int8(M)
+	nib.Nifti1Image(Mlung,I_affine).to_filename('./result/sample_lungaw.nii.gz')
+	
+	return Mlung
+
+
+
+
+
+
+
+
+