plt.savefig(os.path.join(out_dir_noAdapt, "%03.0d_hist.png"%i))
plt.close()
def cut_off_values_upper_lower_percentile(image, percentile_lower=0.2, percentile_upper=99.8):
cut_off_lower = np.percentile(image[image!=image[0,0,0]].ravel(), percentile_lower)
cut_off_upper = np.percentile(image[image!=image[0,0,0]].ravel(), percentile_upper)
image[image < cut_off_lower] = cut_off_lower
image[image > cut_off_upper] = cut_off_upper
return image
percentile_target = [0., 100.]
for i in range(999):
print i
t1_img, t1km_img, flair_img, adc_img, cbv_img, _, seg_combined = load_patient_with_t1(i, [1., 0.5, 0.5], 'new')
if t1_img is not None:
np.save(os.path.join(out_dir_resampled, "%03.0d_t1.npy" % i), t1_img)
np.save(os.path.join(out_dir_resampled, "%03.0d_t1km.npy" % i), t1km_img)
np.save(os.path.join(out_dir_resampled, "%03.0d_flair.npy" % i), flair_img)
np.save(os.path.join(out_dir_resampled, "%03.0d_adc.npy" % i), adc_img)
np.save(os.path.join(out_dir_resampled, "%03.0d_cbv.npy" % i), cbv_img)
np.save(os.path.join(out_dir_resampled, "%03.0d_seg.npy" % i), seg_combined)
for i in range(119, 150):
if not os.path.isfile(os.path.join(out_dir_resampled, "%03.0d_t1.npy" % i)):
continue
t1_img = np.load(os.path.join(out_dir_resampled, "%03.0d_t1.npy" % i))
t1km_img = np.load(os.path.join(out_dir_resampled, "%03.0d_t1km.npy" % i))
all_min_vals_t1c = []
all_min_vals_flair = []
all_min_vals_adc = []
all_min_vals_cbv = []
all_std_t1 = []
all_std_t1c = []
all_std_flair = []
all_std_adc = []
all_std_cbv = []
all_shapes = []
for k in training_patients:
print k
t1_img, t1c_img, flair_img, adc_img, cbv_img, t1km_downsampled, seg = load_patient_with_t1(k, [1,1,1], 'new')
if t1_img is None:
continue
pool = Pool(5)
t1_img, t1c_img, flair_img, adc_img, cbv_img = pool.map(cut_off_values_upper_lower_percentile, (t1_img, t1c_img, flair_img, adc_img, cbv_img))
pool.close()
pool.join()
brain_mask = seg!=0
all_means_t1.append(t1_img[brain_mask].mean())
all_means_t1c.append(t1c_img[brain_mask].mean())
all_means_flair.append(flair_img[brain_mask].mean())
all_means_adc.append(adc_img[brain_mask].mean())
all_means_cbv.append(cbv_img[brain_mask].mean())
percentile_lower=0.2,
percentile_upper=99.8):
cut_off_lower = np.percentile(image[image != image[0, 0, 0]].ravel(),
percentile_lower)
cut_off_upper = np.percentile(image[image != image[0, 0, 0]].ravel(),
percentile_upper)
image[image < cut_off_lower] = cut_off_lower
image[image > cut_off_upper] = cut_off_upper
return image
percentile_target = [0., 100.]
for i in range(999):
print i
t1_img, t1km_img, flair_img, adc_img, cbv_img, _, seg_combined = load_patient_with_t1(
i, [1., 0.5, 0.5], 'new')
if t1_img is not None:
np.save(os.path.join(out_dir_resampled, "%03.0d_t1.npy" % i), t1_img)
np.save(os.path.join(out_dir_resampled, "%03.0d_t1km.npy" % i),
t1km_img)
np.save(os.path.join(out_dir_resampled, "%03.0d_flair.npy" % i),
flair_img)
np.save(os.path.join(out_dir_resampled, "%03.0d_adc.npy" % i), adc_img)
np.save(os.path.join(out_dir_resampled, "%03.0d_cbv.npy" % i), cbv_img)
np.save(os.path.join(out_dir_resampled, "%03.0d_seg.npy" % i),
seg_combined)
for i in range(119, 150):
if not os.path.isfile(os.path.join(out_dir_resampled,
"%03.0d_t1.npy" % i)):
continue
