def optic_disk_detect_3(img):
'''
Method that seems to work well with DIARETDB1 database.
'''
hsi = rgb_to_hsi(img)
intensity = hsi[:, :, 2].copy()
#plt.axis('off'); show_image(intensity)
#i_sp = add_salt_and_pepper(intensity, 0.005)
i_med = mh.median_filter(intensity) # use Wiener filter instead?
i_clahe = skimage.exposure.equalize_adapthist(i_med)
#plt.axis('off'); show_image(i_clahe)
seeds = (i_clahe > 0.85)
seeds = skimage.morphology.remove_small_objects(seeds, min_size=300, connectivity=2)
#plt.axis('off'); show_image(seeds)
optic_disk_map = region_growing_1(i_clahe, radius=3, tol1=0.1, tol2=0.2, tol3=0.2, seeds=seeds)
optic_disk_map += 1
#plt.axis('off'); show_image(optic_disk_map)
_cl_areas = cl_areas(optic_disk_map)
print _cl_areas
optic_disk_map = leave_segments_by_mask(optic_disk_map,
(8000 < _cl_areas) & (_cl_areas < 30000))
#optic_disk_map = skimage.morphology.remove_small_objects(optic_disk_map, min_size=500, connectivity=2)
optic_disk_map = mh.close_holes(mh.close(optic_disk_map, Bc=np.ones((10, 10))))
#optic_disk_map = skimage.morphology.remove_small_objects(optic_disk_map, min_size=5000, connectivity=2)
if np.all(optic_disk_map == 0):
print 'Disk not found'
return optic_disk_map
def exudate_detect_2(img):
# Partially following H. Li et al.
img_luv = skimage.color.rgb2luv(img)
img_luv = normalize_image(img_luv)
img_luv2 = skimage.exposure.equalize_adapthist(img_luv)
img_luv2 = normalize_image(img_luv2)
img_luv3 = sp.signal.wiener(img_luv2, noise=10.0)
img_luv3 = normalize_image(img_luv3)
edges = skimage.feature.canny(img_luv3[:, :, 1], sigma=2.0)
tol1, tol2, tol3 = 100, 40, 100
img_obj_map = region_growing_1(img_luv3[:, :, 1] * 255, radius=3, edges=edges,
tol1=tol1, tol2=tol2, tol3=tol3)
segm_to_leave = list(np.where((cl_circularity(img_obj_map) > 0.05) & \
(cl_areas(img_obj_map) > 10) & \
(cl_areas(img_obj_map) < 1000) & \
(cl_eccentricity(img_obj_map) > 0.1))[0])
#(cl_mean_intensity(img_obj_map, img_luv3[:, :, 1]) < 20))[0])
exud1 = leave_segments(img_obj_map, segm_to_leave, \
replace_value=-1)
return exud1
def hemorrhages_detect_2(img):
moat = moat_operator(img, sigma=5.0)
#show_image(moat); plt.axis('off'); plt.savefig('../presentation/pics/hemorrhages/1.png')
m_clahe = skimage.exposure.equalize_adapthist(moat)
m_med = mh.median_filter(m_clahe, Bc=np.ones((5, 5)))
#show_image(m_med); plt.axis('off'); plt.savefig('../presentation/pics/hemorrhages/2.png')
threshold_global_otsu = skimage.filters.threshold_otsu(m_med)
print 'Otsu threshold:', threshold_global_otsu
segmented = m_med <= 0.9 * threshold_global_otsu
#show_image(segmented); plt.axis('off'); plt.savefig('../presentation/pics/hemorrhages/3.png')
segm_label = skimage.measure.label(segmented)
segm_eccen = np.array(map(attrgetter('eccentricity'), skimage.measure.regionprops(segm_label + 1)))
segm_area = cl_areas(segm_label)
#segm_circ = cf.cl_circularity(segm_label)
segm_filtered = leave_segments_by_mask(segm_label, (segm_eccen < 0.85) & (10 < segm_area) & (segm_area < 30000),
replace_value=0) != 0
return segm_filtered
def hemorrhages_get_features_5(img, moat, fundus_mask):
m_clahe = skimage.exposure.equalize_adapthist(moat)
m_med = mh.median_filter(m_clahe, Bc=np.ones((5, 5)))
#seeds_1 = m_med < threshold
#minima = pf.local_minima(m_med, stride_width=5, neighbd_rad=20, mask=fundus_mask)
#minima &= fundus_mask & (m_med < threshold)
#show_image(seeds_2, fig_size=(7, 7))
#label = region_growing_1(m_med, radius=1, tol1=0.01, tol2=0.01, tol3=0.01, seeds=minima) + 1
threshold_global_otsu = skimage.filters.threshold_otsu(m_med)
cand = m_med <= 0.95 * threshold_global_otsu
# erase vessels
label = skimage.measure.label(cand)
n_clusters = label.max() + 1
features_df = pd.DataFrame(columns=['circularity', 'eccentricity',
'std intensity', 'mean red', 'mean green'],
index=np.arange(n_clusters))
areas = cl_areas(label)
features_df['area'] = areas.astype(np.float64)
features_df['circularity'] = cl_circularity(label)
#features_df['eccentricity'] = cl_eccentricity(label)
features_df['eccentricity'] = map(attrgetter('eccentricity'),
skimage.measure.regionprops(label + 1))
intensity = img.sum(axis=2) / 3.0
i_clahe = skimage.exposure.equalize_adapthist(intensity)
r_clahe = skimage.exposure.equalize_adapthist(img[:, :, 0])
g_clahe = skimage.exposure.equalize_adapthist(img[:, :, 1])
# perform median filtering on i_clahe and r_clahe?
for i in xrange(n_clusters):
cl_map = (label == i)
if cl_map.sum() == 0:
features_df.ix[i, 'std intensity'] = 0
features_df.ix[i, 'mean red'] = 0
features_df.ix[i, 'mean green'] = 0
else:
features_df.ix[i, 'std intensity'] = i_clahe[cl_map].std()
features_df.ix[i, 'mean red'] = r_clahe[cl_map].mean()
features_df.ix[i, 'mean green'] = g_clahe[cl_map].mean()
features_df = features_df.loc[(areas != 0) & \
(areas < 80000), :]
return label, features_df
def hemorrhages_detect(img):
moat = moat_operator(img, sigma=5.0)
show_image(moat); plt.axis('off'); plt.savefig('../presentation/pics/hemorrhages/1.png')
m_clahe = skimage.exposure.equalize_adapthist(moat)
m_med = mh.median_filter(m_clahe, Bc=np.ones((5, 5)))
show_image(m_med); plt.axis('off'); plt.savefig('../presentation/pics/hemorrhages/2.png')
threshold_global_otsu = skimage.filters.threshold_otsu(m_med)
print 'Otsu threshold:', threshold_global_otsu
segmented = m_med <= 0.9 * threshold_global_otsu
show_image(segmented); plt.axis('off'); plt.savefig('../presentation/pics/hemorrhages/3.png')
#vessels = od.detect_vessels_3(img)
vessels = detect_vessels_3(m_med, one_channel_img=True, gabor_perc=88.0)
vessels = skimage.morphology.remove_small_objects(vessels, min_size=150)
segmented[mh.dilate(vessels, Bc=np.ones((10, 10)))] = False
show_image(segmented); plt.axis('off'); plt.savefig('../presentation/pics/hemorrhages/4.png')
segm_label = skimage.measure.label(segmented)
segm_eccen = np.array(map(attrgetter('eccentricity'), skimage.measure.regionprops(segm_label + 1)))
segm_area = cl_areas(segm_label)
#segm_circ = cf.cl_circularity(segm_label)
segm_filtered = leave_segments_by_mask(segm_label, (segm_eccen < 0.85) & (segm_area < 30000),
replace_value=0) != 0
return segm_filtered
def hemorrhages_get_features_4(img, fundus_mask, threshold=0.25):
green = img[:, :, 1]
g_clahe = skimage.exposure.equalize_adapthist(green)
g_med = mh.median_filter(g_clahe, Bc=np.ones((5, 5)))
#cand = g_med < threshold
#label = skimage.measure.label(cand)
seeds_1 = g_med < threshold
#seeds_2 = pf.local_minima(g_med, stride_width=5, neighbd_rad=4, mask=seeds_1 & fundus_mask)
#show_image(seeds_2, fig_size=(7, 7))
#label = region_growing_1(g_med, radius=1, tol1=0.05, tol2=0.05, tol3=0.05, seeds=seeds_2) + 1
label = skimage.measure.label(seeds_1)
n_clusters = label.max() + 1
features_df = pd.DataFrame(columns=['area', 'circularity', 'eccentricity',
'std intensity', 'mean red', 'mean green'],
index=np.arange(n_clusters))
features_df['area'] = cl_areas(label)
features_df['circularity'] = cl_circularity(label)
features_df['eccentricity'] = cl_eccentricity(label)
intensity = img.sum(axis=2) / 3.0
i_clahe = skimage.exposure.equalize_adapthist(intensity)
r_clahe = skimage.exposure.equalize_adapthist(img[:, :, 0])
# perform median filtering on i_clahe and r_clahe?
for i in xrange(n_clusters):
cl_map = (label == i)
if cl_map.sum() == 0:
features_df.ix[i, 'std intensity'] = 0
features_df.ix[i, 'mean red'] = 0
features_df.ix[i, 'mean green'] = 0
else:
features_df.ix[i, 'std intensity'] = i_clahe[cl_map].std()
features_df.ix[i, 'mean red'] = r_clahe[cl_map].mean()
features_df.ix[i, 'mean green'] = g_clahe[cl_map].mean()
features_df = features_df.loc[(features_df.area != 0) & \
(features_df.area < 80000), :]
return label, features_df
def hemorrhages_ground_truth(label, true):
covering_level = cl_mean_intensity(label, true.astype(np.float64))
areas = cl_areas(label)
covering_level = covering_level[(areas != 0) & (areas < 80000)]
return covering_level