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modified_chen_main.py
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import os
import cv2
import MAFOD as mf
import argparse
import numpy as np
import scipy as sc
from os import listdir
from skimage import io
import filterfunctions as FF
from skimage import img_as_uint
from os.path import isfile, join
from matplotlib import pyplot as plt
octParams = dict()
rpe_scan_list = list()
nrpe_scan_list = list()
def init_meta_info():
useChenDefault = False
metaFile = open('OCT_info.txt','r')
lines=metaFile.readlines()
for l in lines:
l = l.rstrip()
l = l.strip()
params=l.split('=')
if(len(params)==2):
paramName = params[0].strip()
val = params[1].strip()
if(paramName=='useDefaultFromChen'):
useChenDefault = True if val.lower()=='yes' else False
else:
octParams[paramName] = float(val)
if(useChenDefault):
# Defaults w.r.t. Chen et al. paper
octParams['sizeInZ']=6 # Size in mm
octParams['sizeInX']=6 # Size in mm
octParams['sizeInY']=2 # Size in mm
octParams['widthOverHeightCoeff']=6
octParams['intensity']=4
octParams['smoothingSigma']=0.1
def show_image( image, block = True ):
plt.imshow( image, cmap = plt.get_cmap('gray'))
plt.show(block)
def show_images( images, r, c, titles = [], d = 0 , save_path = "" , block = True):
i = 1
for img in images:
ax = plt.subplot( r, c, i )
ax.xaxis.set_visible( False )
ax.yaxis.set_visible( False )
if( len(titles) != 0 ):
ax.set_title( titles[i-1] )
if( len(img.shape) > 2 ):
plt.imshow( img )
else:
plt.imshow( img , cmap = plt.get_cmap('gray'))
i += 1
if( save_path != "" ):
plt.savefig(save_path+".png")
plt.close()
else:
plt.show(block)
def compute_heights(cca,mask ):
max_hs = np.zeros( cca.shape )
bg_lbl = get_label_of_background_component( cca ,mask)
labels=np.unique(cca)
for l in labels:
if( l != bg_lbl ):
y, x = np.where( cca == l )
h = np.max(y) - np.min(y) + 1
max_hs[cca == l] = h
return max_hs
def find_rel_maxima( arr ):
val = []
pre = -1
for a in arr:
if( a != pre ):
val.append(a)
pre = a
val = np.asarray(val)
return val[sc.signal.argrelextrema(val, np.greater)]
def compute_component_sum_local_max_height( cca,mask ):
labels = np.unique( cca )
max_hs = np.zeros( cca.shape )
bg_lbl = get_label_of_background_component( cca,mask )
heights = compute_heights( cca,mask )
for l in labels:
if( l != bg_lbl ):
region = cca == l
masked_heights = region * heights
col_h = np.max( masked_heights, axis = 0 )
local_maxima = find_rel_maxima( col_h )
if( len(local_maxima) == 0 ):
local_maxima = np.asarray([np.max(masked_heights)])
max_hs[region] = np.sum(local_maxima)
return max_hs
def compute_component_max_height( cca,mask ):
labels = np.unique( cca )
max_hs = np.zeros( cca.shape )
bg_lbl = get_label_of_background_component( cca ,mask)
heights = compute_heights( cca ,mask)
for l in labels:
if( l != bg_lbl ):
region = cca == l
max_hs[region] = np.max( region * heights )
return max_hs
def filter_drusen_by_size( dmask, slice_num=-1 ):
drusen_mask = np.copy( dmask )
return drusen_mask
cca, num_drusen = sc.ndimage.measurements.label( drusen_mask )
# Find the size of each component
filtered_mask = np.ones( drusen_mask.shape )
h = compute_heights( cca,drusen_mask )
filtered_mask[np.where( h <= h_threshold )] = 0.0
h = compute_component_max_height( cca,drusen_mask )
filtered_mask[np.where( h <= max_h_t )] = 0.0
cca, num_drusen = sc.ndimage.measurements.label( filtered_mask )
# Find the ratio of height over component width and maximum hight of each component
w_o_h, height = compute_width_height_ratio_height_local_max( cca,filtered_mask )
filtered_mask = np.ones( drusen_mask.shape ).astype('float')
filtered_mask[np.where(w_o_h > w_over_h_ratio_threshold)] = 0.0
filtered_mask[np.where(w_o_h == 0.0)] = 0.0
return filtered_mask
def remove_drusen_with_1slice_size( projection_mask ):
mask = np.copy( projection_mask )
cca, numL = sc.ndimage.measurements.label( mask )
bgL = get_label_of_background_component( cca, mask )
for l in np.unique( cca ):
if( l != bgL ):
y, x = np.where( cca == l )
if(len(np.unique(y)) == 1 ):
mask[y,x] = 0.0
return mask
def read_b_scans( path , img_type = "None",toID=False,returnIds=False):
d2 = [f for f in listdir(path) if isfile(join(path, f))]
rawstack = list()
ind = list()
rawStackDict=dict()
rawSize=()
for fi in range(len(d2)):
filename = path+os.sep+d2[fi]
ftype = d2[fi].split('-')[-1]
if(ftype==img_type or img_type=="None"):
ind.append(int(d2[fi].split('-')[0]))
raw = io.imread(filename)
if(len(raw.shape)==3):
raw=raw.astype('float')
raw=(0.2989*raw[:,:,0])+(0.5870*raw[:,:,1])+(0.1140*raw[:,:,2])
if(np.mean(raw)>170):
raw=255.-raw
if(toID):
raw=convert_label_uint_to_id(raw)
rawSize = raw.shape
rawStackDict[ind[-1]]=raw
rawstack=np.empty((rawSize[0],rawSize[1],len(ind)))
keys=rawStackDict.keys()
keys = sorted(keys)
i=0
for k in keys:
rawstack[:,:,i]=rawStackDict[k]
i+=1
if(returnIds):
return rawstack,keys
return rawstack
def get_label_of_background_component( labels,mask ):
bgInd=labels[np.where(mask==0)]
if(len(bgInd)>0):
return np.unique(bgInd)[0]
size = np.bincount(labels.ravel())
largest_comp_ind = size.argmax()
return largest_comp_ind
def compute_component_width(cca,mask ):
labels = np.unique( cca )
max_ws = np.zeros( cca.shape )
bg_lbl = get_label_of_background_component( cca ,mask)
for l in labels:
if( l != bg_lbl ):
y, x = np.where( cca == l )
w = np.max(x) - np.min(x) +1
max_ws[cca == l] = w
return max_ws
def compute_width_height_ratio_height_local_max(cca,mask ):
mx_h = compute_heights( cca,mask )
mx_w = compute_component_width( cca,mask )
forDiv=np.copy(mx_h)
forDiv[mx_h==0]=1
return mx_w.astype('float')/(forDiv.astype('float')), mx_h
def produce_drusen_projection_image_chen( b_scans ):
height,width,depth=b_scans.shape
b_scans = b_scans.astype('float')
masks=np.zeros(b_scans.shape)
baseLines=np.zeros(b_scans.shape)
projection = np.zeros((b_scans.shape[2], b_scans.shape[1]))
projection2 = np.zeros((b_scans.shape[2], b_scans.shape[1]))
total_y_max = 0
max_i = 0
img_max = np.zeros(b_scans[:,:,0].shape)
for i in range(b_scans.shape[2]):
b_scan = b_scans[:,:,i]
b_scan = (b_scan - np.min(b_scan))/(np.max(b_scan)-np.min(b_scan)) if len(np.unique(b_scan))>1 else np.ones(b_scan.shape)
rpe = rpe_scan_list[i]
nrpe = nrpe_scan_list[i]
mask = draw_lines_on_mask( rpe, nrpe, b_scan.shape )
area_mask = find_area_btw_RPE_normal_RPE( mask )
baseLines[:,:,i]=(mask>=2).astype(int)
area_mask=filter_drusen_by_size(area_mask)
masks[:,:,i]=np.copy(area_mask)
y_diff = np.sum(area_mask, axis=0)
y_max = np.max(y_diff)
if( total_y_max < y_max ):
rpe = rpe.astype('int')
nrpe = nrpe.astype('int')
img_max = np.copy(b_scan)
img_max[nrpe[:,1],nrpe[:,0]] = 0.5
img_max[rpe[:,1],rpe[:,0]] = 1.0
kk = nrpe[:,1]-y_max
img_max[kk.astype('int'),nrpe[:,0]] = 1.0
total_y_max = y_max
for i in range(b_scans.shape[2]):
b_scan = b_scans[:,:,i]
b_scan = (b_scan - np.min(b_scan))/(np.max(b_scan)-np.min(b_scan)) if len(np.unique(b_scan))>1 else np.ones(b_scan.shape)
n_bscan = np.copy(b_scan)
rpe = rpe_scan_list[i]
nrpe = nrpe_scan_list[i]
rpe = fill_inner_gaps2(rpe)
rpe = rpe.astype('int')
nrpe = nrpe.astype('int')
nrpe[np.where(nrpe[:,1]<0),1]=0
nrpe[np.where(nrpe[:,1]>=height),1]=height-1
drpe = dict(rpe)
dnrpe = dict(nrpe)
upper_y = np.copy(nrpe)
y_max = total_y_max
upper_y[:,1] = (upper_y[:,1] - y_max)
upper_y[np.where(upper_y[:,1]<0),1]=0
durpe = dict(upper_y)
for ix in range(b_scan.shape[1]):
if( (ix in drpe.keys()) and (ix in dnrpe.keys())):
n_bscan[drpe[ix]:dnrpe[ix]+1,ix] = np.max(b_scan[durpe[ix]:dnrpe[ix]+1,ix])
projection[i,ix] = np.sum(n_bscan[durpe[ix]:dnrpe[ix]+1,ix])
projection2[i,ix] = np.sum(n_bscan[durpe[ix]:drpe[ix]+1,ix])*0.5 + np.sum(n_bscan[drpe[ix]:dnrpe[ix]+1,ix])
n_bscan[upper_y[:,1].astype('int'),nrpe[:,0].astype('int')] = 1
n_bscan[rpe[:,1].astype('int'),rpe[:,0]] = 1
n_bscan[nrpe[:,1].astype('int'),nrpe[:,0].astype('int')] = 0.5
return projection.astype('float'), masks,baseLines
def get_outer_boundary( mask, size ):
a = sc.ndimage.binary_dilation(mask, iterations = size)
return a.astype('float') - mask.astype('float')
def remove_non_bright_spots( projection_img, drusen_mask, threshold = 4 ):
projection_img = (projection_img.astype('float')-np.min(projection_img))/\
(np.max(projection_img)-np.min(projection_img))* 255.0
cca, num_lbls = sc.ndimage.measurements.label( drusen_mask )
bg_lbl = get_label_of_background_component( cca,drusen_mask )
labels = np.unique( cca )
res_m = np.zeros( drusen_mask.shape, dtype='float' )
for l in labels:
if( l != bg_lbl ):
reg_mask = cca == l
vreg = projection_img[reg_mask>0.0]
vreg = vreg[vreg>0.0]
boundary = get_outer_boundary( reg_mask , size = 2 )
vbnd = projection_img[boundary>0.0]
vbnd = vbnd[vbnd>0.0]
reg_mean = np.mean( vreg )
bnd_mean = np.mean( vbnd )
# If the mean intensity of the spot is larger by a threshold than the
# region then keep this spot, otherwise remove it
if( (reg_mean - bnd_mean) > threshold ):
res_m[cca==l] = 1.0
return res_m
#==============================================================================
# Modified Chen Method
#==============================================================================
def seg_modified_chen( inputImage, useMAFOD, debug=False ):
if(useMAFOD):
filter1=mf.MAFOD_filter(inputImage,octParams['sizeInY']/float(inputImage.shape[0]),\
octParams['sizeInX']/float(inputImage.shape[1]),\
int(octParams['stoppingTime']),int(octParams['numberOfCycles']),\
octParams['lambda'])
else:
filter1=FF.FilterBilateral(inputImage)
threshold=FF.computeHistogrammThreshold(filter1,octParams['sizeInY'])
RNFL=FF.getTopRNFL(filter1,threshold,False)
if(debug):
#show top RNFL on input
tmp=np.copy(filter1)
tmp=tmp.astype(float)
tmp=tmp/np.max(tmp)
rgbTmp=np.empty((filter1.shape[0],filter1.shape[1],3))
rgbTmp[:,:,0]=tmp
rgbTmp[:,:,1]=tmp
rgbTmp[:,:,2]=tmp
rgbTmp[RNFL[:,1],RNFL[:,0],0]=1.
rgbTmp[RNFL[:,1],RNFL[:,0],1]=0.
rgbTmp[RNFL[:,1],RNFL[:,0],2]=0.
rgbTmp[RNFL[:,1]+1,RNFL[:,0],0]=1.
rgbTmp[RNFL[:,1]+1,RNFL[:,0],1]=0.
rgbTmp[RNFL[:,1]+1,RNFL[:,0],2]=0.
rgbTmp[RNFL[:,1]-1,RNFL[:,0],0]=1.
rgbTmp[RNFL[:,1]-1,RNFL[:,0],1]=0.
rgbTmp[RNFL[:,1]-1,RNFL[:,0],2]=0.
if( len(RNFL) ==0):
return [],[]
mask=FF.thresholdImage(filter1,threshold)
bandRadius=int(0.04/float(octParams['sizeInY']/float(inputImage.shape[0]))) # Must be 40um
mask2=FF.removeTopRNFL(filter1,mask,RNFL,bandRadius=bandRadius)
FF.extractRegionOfInteresst(filter1,mask2,bw=bandRadius);
centerLine1=FF.getCenterLine(mask2)
centerLine2,mask3=FF.segmentLines_new(filter1,centerLine1,debug)
if(False):
tmp=np.copy(mask3)
tmp=tmp.astype(float)
tmp=tmp/np.max(tmp)
cl=centerLine2.astype(int)
cl=connect_all(cl)
rgbTmp=np.empty((filter1.shape[0],filter1.shape[1],3))
rgbTmp[:,:,0]=tmp
rgbTmp[:,:,1]=tmp
rgbTmp[:,:,2]=tmp
rgbTmp[cl[:,1],cl[:,0],0]=1.
rgbTmp[cl[:,1],cl[:,0],1]=0.
rgbTmp[cl[:,1],cl[:,0],2]=0.
rgbTmp[cl[:,1]+1,cl[:,0],0]=1.
rgbTmp[cl[:,1]+1,cl[:,0],1]=0.
rgbTmp[cl[:,1]+1,cl[:,0],2]=0.
rgbTmp[cl[:,1]-1,cl[:,0],0]=1.
rgbTmp[cl[:,1]-1,cl[:,0],1]=0.
rgbTmp[cl[:,1]-1,cl[:,0],2]=0.
# show_images([rgbTmp],1,1)
itterations=5;
dursenDiff=5;
idealRPE=FF.normal_RPE_estimation(filter1,centerLine2,itterations,dursenDiff)
if(True):
tmp=np.copy(filter1)
tmp=tmp.astype(float)
tmp=tmp/np.max(tmp)
cl=centerLine2.astype(int)
cl=fill_inner_gaps2(cl)
cl=connect_all(cl)
rgbTmp=np.empty((filter1.shape[0],filter1.shape[1],3))
rgbTmp[:,:,0]=tmp
rgbTmp[:,:,1]=tmp
rgbTmp[:,:,2]=tmp
rgbTmp[cl[:,1],cl[:,0],0]=1.
rgbTmp[cl[:,1],cl[:,0],1]=0.
rgbTmp[cl[:,1],cl[:,0],2]=0.
rgbTmp[cl[:,1]+1,cl[:,0],0]=1.
rgbTmp[cl[:,1]+1,cl[:,0],1]=0.
rgbTmp[cl[:,1]+1,cl[:,0],2]=0.
rgbTmp[cl[:,1]-1,cl[:,0],0]=1.
rgbTmp[cl[:,1]-1,cl[:,0],1]=0.
rgbTmp[cl[:,1]-1,cl[:,0],2]=0.
cl2=idealRPE.astype(int)
cl2=fill_inner_gaps2(cl2)
cl2=connect_all(cl2)
rgbTmp[cl2[:,1],cl2[:,0],0]=1.
rgbTmp[cl2[:,1],cl2[:,0],1]=1.
rgbTmp[cl2[:,1],cl2[:,0],2]=0.
rgbTmp[cl2[:,1]+1,cl2[:,0],0]=1.
rgbTmp[cl2[:,1]+1,cl2[:,0],1]=1.
rgbTmp[cl2[:,1]+1,cl2[:,0],2]=0.
rgbTmp[cl2[:,1]-1,cl2[:,0],0]=1.
rgbTmp[cl2[:,1]-1,cl2[:,0],1]=1.
rgbTmp[cl2[:,1]-1,cl2[:,0],2]=0.
rgbTmp=np.empty((filter1.shape[0],filter1.shape[1],3))
rgbTmp[:,:,0]=tmp
rgbTmp[:,:,1]=tmp
rgbTmp[:,:,2]=tmp
rgbTmp[cl[:,1],cl[:,0],0]=1.
rgbTmp[cl[:,1],cl[:,0],1]=0.
rgbTmp[cl[:,1],cl[:,0],2]=0.
rgbTmp[cl2[:,1],cl2[:,0],0]=1.
rgbTmp[cl2[:,1],cl2[:,0],1]=1.
rgbTmp[cl2[:,1],cl2[:,0],2]=0.
mask = draw_lines_on_mask(cl, cl2, filter1.shape)
area_mask = find_area_btw_RPE_normal_RPE( mask )
yy,xx=np.where(area_mask>0)
rgbTmp[yy,xx,0]=1.
rgbTmp[yy,xx,1]=rgbTmp[yy,xx,1]*0.8
rgbTmp[yy,xx,2]=rgbTmp[yy,xx,1]*0.8
return centerLine2, idealRPE
def save_masks(masks,refPath,savePath,img_type="None"):
d2 = [f for f in listdir(refPath) if isfile(join(refPath, f))]
ind = list()
for fi in range(len(d2)):
ftype = d2[fi].split('-')[-1]
if(ftype==img_type or img_type=="None"):
ind.append(int(d2[fi].split('-')[0]))
ind = np.asarray(ind)
sin = ind[np.argsort(ind)]
if(len(masks.shape)==3):
for i in range(masks.shape[2]):
fname=str(sin[i])+'-binmask.png'
# print "Saving......",i
lsav=masks[:,:,i]/float(np.max(masks[:,:,i])) if np.max(masks[:,:,i])!=0 else masks[:,:,i]
lsav=(lsav*255).astype("uint8")
cv2.imwrite(savePath+os.sep+fname,lsav)
else:
for i in range(masks.shape[3]):
fname=str(sin[i])+'-binmask.png'
lsav=masks[:,:,:,i]/float(np.max(masks[:,:,:,i])) if np.max(masks[:,:,:,i])!=0 else masks[:,:,:,i]
lsav=(lsav*255).astype("uint8")
cv2.imwrite(savePath+os.sep+fname,lsav)
def get_label_from_projection_image_chen(projected_labels, labels):
lbls = np.copy(labels)
for i in range( labels.shape[2] ):
valid_drusens = np.tile(projected_labels[i, :], labels.shape[0]).\
reshape(labels.shape[0],labels.shape[1])
l_area = lbls[:,:,i]
lbls[:,:,i] = l_area * valid_drusens
return lbls
def save_b_scans(bscans,savePath):
create_directory(savePath)
for i in range(bscans.shape[2]):
io.imsave(savePath+str(i+39)+"-Input.tif",bscans[:,:,i].astype("uint8"))
exit()
def segment_drusen_using_chen_modified_chen_method(path,savePath, method,layerPath="",drusenPath="",enfacePath=""):
print("Eliminating false positives...")
bs = read_b_scans( path, "Input.tif")
postProcessedMasks=run_chen_modified_chen_method(bs,method=method,\
layerPath=layerPath,drusenPath=drusenPath,\
enfacePath=enfacePath)
create_directory(savePath)
save_masks(postProcessedMasks, refPath=path,savePath=savePath,img_type="Input.tif")
enfaceDrusenMask=(np.sum(postProcessedMasks,axis=0)>0).astype(int).T
io.imsave(enfacePath+os.sep+"enface-drusen-afterFPE.png",(enfaceDrusenMask*255).astype("uint8"))
print("Done.")
def remove_false_positives_from_gt(drusenPath,savePath,enfacePath=""):
gt=read_b_scans(drusenPath,"drusen.png")
masks=(gt>0).astype(int)
projection=read_enface_from_image(enfacePath)
projection=projection.astype(float)
projection /= np.max(projection) if np.max(projection) != 0.0 else 1.0
beforeMask=(np.sum(masks,axis=0)>0).astype(int)
enFaceMask=remove_false_positives_gt( projection, gt)
filteredMasks=get_label_from_projection_image_chen(enFaceMask, masks)
# Add smoothing
postProcessedMasks=smooth_drusen_gt(filteredMasks,sigma=octParams['smoothingSigma'])
afterMask=(np.sum(filteredMasks,axis=0)>0).astype(int).T
create_directory(savePath)
save_masks(postProcessedMasks, refPath=drusenPath,savePath=savePath,img_type="drusen.png")
def draw_lines_on_mask(rpe , nrpe, shape ):
mask = np.zeros(shape)
if(rpe is None):
rpe=[]
if(nrpe is None):
nrpe=[]
if(len(rpe)==0):
return mask
rpearr = np.asarray(rpe)
nrpearr = np.asarray(nrpe)
checkRpe = np.abs(list(rpearr))
checkNrpe = np.abs(list(nrpearr))
if( np.array_equal(rpearr,checkRpe) and np.array_equal(nrpearr,checkNrpe)):
mask[rpe[:,1].astype('int'),rpe[:,0].astype('int')] += 1.0
mask[nrpe[:,1].astype('int'),nrpe[:,0].astype('int')] += 2.0
return mask
def find_area_btw_RPE_normal_RPE( mask ):
area_mask = np.zeros(mask.shape)
for i in range( mask.shape[1] ):
col = mask[:,i]
v1 = np.where(col==1.0)
v2 = np.where(col==2.0)
v3 = np.where(col==3.0)
v1 = np.min(v1[0]) if len(v1[0]) > 0 else -1
v2 = np.max(v2[0]) if len(v2[0]) > 0 else -1
v3 = np.min(v3[0]) if len(v3[0]) > 0 else -1
if( v1 >= 0 and v2 >= 0 ):
area_mask[v1:v2,i] = 1
return area_mask
def seg_chen( inputImage, useMAFOD,debug=False):
if(useMAFOD):
filter1=mf.MAFOD_filter(inputImage,octParams['sizeInY']/float(inputImage.shape[0]),\
octParams['sizeInX']/float(inputImage.shape[1]),\
int(octParams['stoppingTime']),int(octParams['numberOfCycles']),\
octParams['lambda'])
else:
filter1=FF.FilterBilateral(inputImage)
threshold=FF.computeHistogrammThreshold(filter1,octParams['sizeInY'])
RNFL=FF.getTopRNFL(filter1,threshold,False)
if(debug):
#show top RNFL on input
tmp=np.copy(filter1)
tmp=tmp.astype(float)
tmp=tmp/np.max(tmp)
rgbTmp=np.empty((filter1.shape[0],filter1.shape[1],3))
rgbTmp[:,:,0]=tmp
rgbTmp[:,:,1]=tmp
rgbTmp[:,:,2]=tmp
rgbTmp[RNFL[:,1],RNFL[:,0],0]=1.
rgbTmp[RNFL[:,1],RNFL[:,0],1]=0.
rgbTmp[RNFL[:,1],RNFL[:,0],2]=0.
rgbTmp[RNFL[:,1]+1,RNFL[:,0],0]=1.
rgbTmp[RNFL[:,1]+1,RNFL[:,0],1]=0.
rgbTmp[RNFL[:,1]+1,RNFL[:,0],2]=0.
rgbTmp[RNFL[:,1]-1,RNFL[:,0],0]=1.
rgbTmp[RNFL[:,1]-1,RNFL[:,0],1]=0.
rgbTmp[RNFL[:,1]-1,RNFL[:,0],2]=0.
# show_images([rgbTmp],1,1)
mask=FF.thresholdImage(filter1,threshold)
if( len(RNFL) ==0):
return [],[]
mask[RNFL[:,1],RNFL[:,0]]=255
bandRadius=int(0.04/float(octParams['sizeInY']/float(inputImage.shape[0]))) # Must be 40um
mask2=FF.removeTopRNFL(filter1,mask,RNFL,bandRadius)
centerLine1=FF.getCenterLine(mask2)
if(False):
tmp=np.copy(mask2)
tmp=tmp.astype(float)
tmp=tmp/np.max(tmp)
cl=centerLine1.astype(int)
cl=fill_inner_gaps2(cl)
rgbTmp=np.empty((filter1.shape[0],filter1.shape[1],3))
rgbTmp[:,:,0]=tmp
rgbTmp[:,:,1]=tmp
rgbTmp[:,:,2]=tmp
rgbTmp[cl[:,1],cl[:,0],0]=1.
rgbTmp[cl[:,1],cl[:,0],1]=0.
rgbTmp[cl[:,1],cl[:,0],2]=0.
rgbTmp[cl[:,1]+1,cl[:,0],0]=1.
rgbTmp[cl[:,1]+1,cl[:,0],1]=0.
rgbTmp[cl[:,1]+1,cl[:,0],2]=0.
rgbTmp[cl[:,1]-1,cl[:,0],0]=1.
rgbTmp[cl[:,1]-1,cl[:,0],1]=0.
rgbTmp[cl[:,1]-1,cl[:,0],2]=0.
# show_images([rgbTmp],1,1)
itterations=5;
dursenDiff=5;
idealRPE=FF.normal_RPE_estimation(filter1,centerLine1,itterations,dursenDiff)
if(True):
tmp=np.copy(inputImage)
tmp=tmp.astype(float)
tmp=tmp/np.max(tmp)
cl=centerLine1.astype(int)
cl=fill_inner_gaps2(cl)
cl=connect_all(cl)
rgbTmp=np.empty((filter1.shape[0],filter1.shape[1],3))
rgbTmp[:,:,0]=tmp
rgbTmp[:,:,1]=tmp
rgbTmp[:,:,2]=tmp
rgbTmp[cl[:,1],cl[:,0],0]=1.
rgbTmp[cl[:,1],cl[:,0],1]=0.
rgbTmp[cl[:,1],cl[:,0],2]=0.
rgbTmp[cl[:,1]+1,cl[:,0],0]=1.
rgbTmp[cl[:,1]+1,cl[:,0],1]=0.
rgbTmp[cl[:,1]+1,cl[:,0],2]=0.
rgbTmp[cl[:,1]-1,cl[:,0],0]=1.
rgbTmp[cl[:,1]-1,cl[:,0],1]=0.
rgbTmp[cl[:,1]-1,cl[:,0],2]=0.
cl2=idealRPE.astype(int)
cl2=fill_inner_gaps2(cl2)
cl2=connect_all(cl2)
rgbTmp[cl2[:,1],cl2[:,0],0]=1.
rgbTmp[cl2[:,1],cl2[:,0],1]=1.
rgbTmp[cl2[:,1],cl2[:,0],2]=0.
rgbTmp[cl2[:,1]+1,cl2[:,0],0]=1.
rgbTmp[cl2[:,1]+1,cl2[:,0],1]=1.
rgbTmp[cl2[:,1]+1,cl2[:,0],2]=0.
rgbTmp[cl2[:,1]-1,cl2[:,0],0]=1.
rgbTmp[cl2[:,1]-1,cl2[:,0],1]=1.
rgbTmp[cl2[:,1]-1,cl2[:,0],2]=0.
# show_images([rgbTmp],1,1)
rgbTmp=np.empty((filter1.shape[0],filter1.shape[1],3))
rgbTmp[:,:,0]=tmp
rgbTmp[:,:,1]=tmp
rgbTmp[:,:,2]=tmp
rgbTmp[cl[:,1],cl[:,0],0]=1.
rgbTmp[cl[:,1],cl[:,0],1]=0.
rgbTmp[cl[:,1],cl[:,0],2]=0.
rgbTmp[cl2[:,1],cl2[:,0],0]=1.
rgbTmp[cl2[:,1],cl2[:,0],1]=1.
rgbTmp[cl2[:,1],cl2[:,0],2]=0.
mask = draw_lines_on_mask(cl, cl2, filter1.shape)
area_mask = find_area_btw_RPE_normal_RPE( mask )
yy,xx=np.where(area_mask>0)
rgbTmp[yy,xx,0]=1.
rgbTmp[yy,xx,1]=rgbTmp[yy,xx,1]*0.8
rgbTmp[yy,xx,2]=rgbTmp[yy,xx,1]*0.8
# show_images([rgbTmp],1,1)
return centerLine1, idealRPE
def fill_inner_gaps( layer ):
d_layer = dict(layer)
prev = -1
if( len(d_layer.keys())>0):
maxId=int(np.max(np.asarray(list(d_layer.keys()))))
for i in range(maxId):
if( not i in d_layer.keys() and prev!=-1):
d_layer[i] = prev
if( i in d_layer.keys() ):
prev = d_layer[i]
return np.asarray([list(d_layer.keys()),list(d_layer.values())]).T
else:
return np.asarray([])
def connect_all( layer ):
d_layer = dict(layer)
connectedLayer=[]
if( len(d_layer.keys())>0):
maxId=int(np.max(np.asarray(list(d_layer.keys()))))
minId=int(np.min(np.asarray(list(d_layer.keys()))))
for i in range(minId,maxId+1):
if( i in d_layer.keys() ):
currY=d_layer[i]
connectedLayer.append([i,currY])
if((i+1) in d_layer.keys()):
nextY=d_layer[i+1]
for j in range(abs(nextY-currY)):
if(nextY>currY):
connectedLayer.append([i,currY+j])
else:
connectedLayer.append([i,currY-j])
return np.asarray(connectedLayer)
else:
return np.asarray([])
def fill_inner_gaps2( layer ):
d_layer = dict(layer)
prev = -1
minY=int(np.min(np.asarray(list(d_layer.values()))))
maxY=int(np.max(np.asarray(list(d_layer.values()))))
if( len(d_layer.keys())>0):
maxId=int(np.max(np.asarray(list(d_layer.keys()))))
minId=int(np.min(np.asarray(list(d_layer.keys()))))
for i in range(minId,maxId+1):
if( not i in d_layer.keys() ):
endP=-1
startingP=prev
startingI=i-1
while(endP==-1):
i=i+1
if( i in d_layer.keys()):
endP=d_layer[i]
endI=i
m=(endP-startingP)/(endI-startingI)
for j in range(startingI,endI+1):
d_layer[j] = max(minY,min(maxY,int(m*(j-startingI)+startingP)))
if( not i in d_layer.keys() and prev!=-1):
d_layer[i] = prev
if( i in d_layer.keys() ):
prev = d_layer[i]
return np.asarray([list(d_layer.keys()),list(d_layer.values())]).T
else:
return np.asarray([])
def find_drusen_in_stacked_slices_chen( b_scans ):
hmask = np.zeros((b_scans.shape[2], b_scans.shape[1]))
for i in range(b_scans.shape[2]):
b_scan = b_scans[:,:,i]
rpe = rpe_scan_list[i]
nrpe = nrpe_scan_list[i]
mask = draw_lines_on_mask(rpe, nrpe, b_scan.shape)
area_mask = find_area_btw_RPE_normal_RPE( mask )
hmask[i,:] = np.sum(area_mask, axis=0)
return hmask
def remove_false_positives_chen( projection_image, b_scans):
height_mask = find_drusen_in_stacked_slices_chen( b_scans )
height_mask[height_mask<=1]=0.
mask = (height_mask>0.0).astype('int')
cca, num_drusen = sc.ndimage.measurements.label( mask )
bgLbl=get_label_of_background_component(cca,mask)
woh,heights=compute_width_height_ratio_height_local_max(cca,mask)
diffBScanLocation=octParams['sizeInZ']/float(b_scans.shape[2]-1)
isHighRes = diffBScanLocation < 0.1 # Diff less than 100 um
wohT=octParams['widthOverHeightCoeff']*((octParams['sizeInZ']/float(b_scans.shape[2]))/(octParams['sizeInX']/float(b_scans.shape[1])))
mask3=None
if( isHighRes ):
# Remove drusen that appear in 1 slice
cca2=np.copy(cca)
mask2=np.copy(mask)
cca2[np.where(heights==1)]=bgLbl
mask2[np.where(heights==1)]=0
# Remove thin drusen based on w/h
cca2[np.where(woh>wohT)]=bgLbl
mask2[np.where(woh>wohT)]=0
# Remove based on brightness
mask3 = (mask2>0.0).astype('float')
mask3 = remove_non_bright_spots(projection_image,mask2,octParams['intensity'])
else:
cca2=np.copy(cca)
mask2=np.copy(mask)
# Remove thin drusen based on w/h
cca2[np.where(woh>(wohT))]=bgLbl
mask2[np.where(woh>(wohT))]=0
# Remove based on brightness
mask3 = (mask2>0.0).astype('float')
mask3 = remove_non_bright_spots(projection_image,mask2,octParams['intensity'])
return mask3
def remove_false_positives_gt( projection_image, gt):
height_mask = np.sum(gt,axis=0).T
mask = (height_mask>0.0).astype('int')
cca, num_drusen = sc.ndimage.measurements.label( mask )
bgLbl=get_label_of_background_component(cca,mask)
woh,heights=compute_width_height_ratio_height_local_max(cca,mask)
diffBScanLocation=octParams['sizeInZ']/float(gt.shape[2]-1)
isHighRes = diffBScanLocation < 0.1 # Diff less than 100 um
wohT=octParams['widthOverHeightCoeff']*((octParams['sizeInZ']/float(gt.shape[2]))/(octParams['sizeInX']/float(gt.shape[1])))
mask3=None
if( isHighRes ):
# Remove drusen that appear in 1 slice
cca2=np.copy(cca)
mask2=np.copy(mask)
cca2[np.where(heights==1)]=bgLbl
mask2[np.where(heights==1)]=0
# Remove thin drusen based on w/h
cca2[np.where(woh>wohT)]=bgLbl
mask2[np.where(woh>wohT)]=0
# Remove based on brightness
mask3 = (mask2>0.0).astype('float')
mask3 = remove_non_bright_spots(projection_image,mask2,octParams['intensity'])
else:
cca2=np.copy(cca)
mask2=np.copy(mask)
# Remove thin drusen based on w/h
cca2[np.where(woh>(wohT))]=bgLbl
mask2[np.where(woh>(wohT))]=0
# Remove based on brightness
mask3 = (mask2>0.0).astype('float')
mask3 = remove_non_bright_spots(projection_image,mask2,octParams['intensity'])
return mask3
def delete_rpe_nrpe_lists( ):
del rpe_scan_list[:]
del nrpe_scan_list[:]
def initialize_rpe_nrpe_lists( b_scans,method='chen',useMAFOD=False):
debug=False
for i in range(b_scans.shape[2]):
print("Segmenting Drusen in B-scan:#",i)
if(method=='chen'):
rpe, nrpe = seg_chen(b_scans[:,:,i],useMAFOD)
elif(method=='modifiedChen'):
rpe, nrpe = seg_modified_chen(b_scans[:,:,i],useMAFOD)
if(len(rpe)==0):
xr=np.arange(b_scans.shape[1])
yr=np.ones(xr.shape)
rpe=np.zeros((b_scans.shape[1],2))
rpe[:,0]=xr
rpe[:,1]=yr
nrpe=np.copy(rpe)
nrpe[np.where(nrpe[:,1]>=b_scans.shape[0]),1]=b_scans.shape[0]-1
rpe_scan_list.append(rpe)
nrpe_scan_list.append(nrpe)
if(debug):
mm = draw_lines_on_mask(rpe , nrpe, shape=b_scans[:,:,0].shape )
# show_images([b_scans[:,:,i],mm],1,2)
def smooth_drusen(masks,baseLines,sigma=0.):
smoothedDrusen=sc.ndimage.gaussian_filter(masks,sigma)
smoothedDrusen=(smoothedDrusen>0).astype(int)
filterMask=np.zeros((masks.shape[0],masks.shape[1]))
for i in range(smoothedDrusen.shape[2]):
nrpe=nrpe_scan_list[i]
nrpe = nrpe.astype('int')
filterMask=baseLines[:,:,i]
#Flip
filterMask=np.flipud(filterMask)
filterMask=(np.cumsum(filterMask,axis=0)>0).astype('int')
filterMask=np.flipud(filterMask)
smoothedDrusen[:,:,i]=smoothedDrusen[:,:,i]*filterMask
filterMask.fill(0)
return smoothedDrusen
def smooth_drusen_gt(masks,sigma=0.):
smoothedDrusen=sc.ndimage.gaussian_filter(masks,sigma)
smoothedDrusen=(smoothedDrusen>0).astype(int)
return smoothedDrusen
def remove_dangling_components(filteredMasks):
for i in range(filteredMasks.shape[2]):
cy,cx=np.where(filteredMasks[:,:,i]>0)
nextInd=min(i+1,filteredMasks.shape[2]-1)
prevInd=max(i-1,0)
#currentCount
c=np.sum((filteredMasks[:,:,i]>0).astype(int))
if(c==0): # Drusen free B-scan
continue
sumNext=np.sum((filteredMasks[cy,cx,nextInd]>0).astype(int))
sumPrev=np.sum((filteredMasks[cy,cx,prevInd]>0).astype(int))
if(sumNext==0 and sumPrev==0): # If the current drusen are dangling, remove them
filteredMasks[:,:,i].fill(0.)
return filteredMasks
def run_chen_modified_chen_method(b_scans,method,savePath="",layerPath="",\
drusenPath="",enfacePath=""):
if(layerPath!=""):
baseLines=read_rpe_nrpe_from_image(layerPath)
masks=read_drusen_from_image(drusenPath)
projection=read_enface_from_image(enfacePath)
else:
initialize_rpe_nrpe_lists(b_scans,method)
projection,masks,baseLines = produce_drusen_projection_image_chen( b_scans )
projection /= np.max(projection) if np.max(projection) != 0.0 else 1.0
enFaceMask=remove_false_positives_chen( projection, b_scans)
filteredMasks=get_label_from_projection_image_chen(enFaceMask, masks)
# Add smoothing
filteredMasks=smooth_drusen(filteredMasks,baseLines,sigma=octParams['smoothingSigma'])
delete_rpe_nrpe_lists()
enFaceMask=(np.sum(filteredMasks,axis=0)>0).astype(int).T
return filteredMasks
def save_rpe_nrpe_drusen(layerPath,druPath,enfacePath,refPath,method,useMAFOD):
bScans = read_b_scans( refPath, "Input.tif")
#save_b_scans(bScans,savePath="C:\\Users\\shekoufeh\\Desktop\\Manuscript\\ChenGit\\DrusenSegmentation-ModifiedChen\\volume2\\")
initialize_rpe_nrpe_lists(bScans,method,useMAFOD)
hmask = np.zeros((bScans.shape[2], bScans.shape[1]))
rpeNrpe=np.empty(bScans.shape)
drusen=np.empty(bScans.shape)
for i in range(bScans.shape[2]):
b_scan = bScans[:,:,i]
rpe = rpe_scan_list[i]
nrpe = nrpe_scan_list[i]
rpe = fill_inner_gaps2(rpe)
nrpe = fill_inner_gaps2(nrpe)
mask = draw_lines_on_mask(rpe, nrpe, b_scan.shape)
rpeNrpe[:,:,i]=mask
area_mask = find_area_btw_RPE_normal_RPE( mask )
drusen[:,:,i]=area_mask
hmask[i,:] = np.sum(area_mask, axis=0)
print("Saving...")
create_directory(layerPath)
save_masks(rpeNrpe, refPath=refPath,savePath=layerPath,img_type="Input.tif")
create_directory(druPath)
save_masks(drusen, refPath=refPath,savePath=druPath,img_type="Input.tif")
# Create projectoion image
projection,masks,baseLines = produce_drusen_projection_image_chen( bScans )
projection /= np.max(projection) if np.max(projection) != 0.0 else 1.0
create_directory(enfacePath)
io.imsave(enfacePath+os.sep+"enface.png",(projection*255).astype("uint8"))
enfaceDrusenMask=(np.sum(masks,axis=0)>0).astype(int).T
io.imsave(enfacePath+os.sep+"enface-drusen-withoutFPE.png",(enfaceDrusenMask*255).astype("uint8"))
print("Done.")
def read_rpe_nrpe_from_image(readPath):
layers=read_b_scans(readPath,"binmask.png")
baseLines=np.empty(layers.shape)
for i in range(layers.shape[2]):
layers[:,:,i]=convert_label_uint_to_id(layers[:,:,i])
rpeImg=(layers[:,:,i]==1).astype(int)
rpeImg[np.where(layers[:,:,i]==3)]=1
nrpeImg=(layers[:,:,i]>1).astype(int)
baseLines[:,:,i]=nrpeImg
yrpe,xrpe=np.where(rpeImg>0)
rpe=np.asarray(list(zip(xrpe,yrpe)))
ynrpe,xnrpe=np.where(nrpeImg>0)
nrpe=np.asarray(list(zip(xnrpe,ynrpe)))
if(len(rpe)==0):
xr=np.arange(layers.shape[1])
yr=np.ones(xr.shape)
rpe=np.zeros((layers.shape[1],2))
rpe[:,0]=xr
rpe[:,1]=yr
nrpe=np.copy(rpe)
rpe_scan_list.append(rpe)
nrpe_scan_list.append(nrpe)
return baseLines
def read_drusen_from_image(readPath):
drusen=read_b_scans(readPath,"binmask.png")
drusen=(drusen).astype(int)
return drusen
def read_enface_from_image(readPath):
return io.imread(readPath+os.sep+"enface.png")
#========================================= end of Chen related method functions
def create_directory(path):
"""
Check if the directory exists. If not, create it.
Input:
path: the directory to create
Output:
None.
"""
if not os.path.exists(path):
os.makedirs(path)