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167 lines (134 loc) · 6.89 KB
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 20 11:02:35 2017
@author: keriabermudez
"""
import numpy as np
import cv2
from skimage.filters import threshold_otsu
from skimage.measure import regionprops
import mahotas as mh
import cell_segmentation as cellseg
from skimage import io
"""Class to measure foci in cells """
class Foci_class:
def __init__(self,group_name, scale,max_foci_coverage,min_cell_area,min_foci_area,path_results):
"""
group_name: String
The name of the group
scale: float
The pixel scale. For instance if one pixel is represents 0.099um then write 0.099
max_foci_coverage: float
Maximum foci coverage. For example if max_foci_coverage = 80, don't count foci if they cover 80 percent or more.
min_cell_area: int
Minimun cell area in pixels. Only analyze cells that are greater than the min_cell_area in number of pixels.
min_foci_area: int
Minimun foci area in pixels. Only analyze foci that are greater than the min_foci_area in number of pixels.
path_results: String
Where you want to save the images
"""
self.all_nuclei = {}
self.group_name = group_name
self.path_results =path_results
self.foci_areas = []
self.foci_sum_intensities = []
self.foci_mean_intensities = []
self.scale = scale
self.max_foci_coverage = max_foci_coverage
self.min_cell_area = min_cell_area
self.min_foci_area = min_foci_area
def __add_area(self, area):
self.foci_areas.append(area)
def __add_sum_intenisity(self,sum_int):
self.foci_sum_intensities.append(sum_int)
def __add_mean_intensity(self,mean):
self.foci_mean_intensities.append(mean)
def __areas_funct(labeled_image):
areas = []
for region in regionprops(labeled_image):
areas.append(region.area)
areas = np.array(areas)
return areas
def foci_results(self,labels_all,foci_intesity,name):
"""Function that analyzes the foci
labels_all: (N, M) ndarray
Where nuclei are labeled
foci_intesity: (N, M) ndarray
Intensity image of foci
name: String
Name of image
"""
font = cv2.FONT_HERSHEY_SIMPLEX
foci_mask = np.zeros(labels_all.shape, dtype = np.bool)
Foci_are_marked = np.zeros((foci_intesity.shape[0],foci_intesity.shape[1],3), dtype =np.uint8)
Foci_are_marked[:,:,1] = foci_intesity
for region in regionprops(labels_all,foci_intesity):
variables = {}
nucleus_mask = labels_all == region.label
if len(np.unique(foci_intesity[nucleus_mask])) == 1:
continue
th = threshold_otsu(foci_intesity[nucleus_mask])
th_mask = foci_intesity > th
(min_row, min_col, max_row, max_col) = region.bbox
inside_nucleus = np.logical_and(th_mask,nucleus_mask)
#labeled_foci,foci_number = mh.label(inside_nucleus, np.ones((3,3), bool))
##remove 4 pixels
inside_nucleus = cellseg.remove_regions(inside_nucleus,self.min_foci_area)
inside_nucleus = inside_nucleus > 0
labeled_foci,foci_number = mh.label(inside_nucleus, np.ones((3,3), bool))
#print(foci_number)
if foci_number == 1:
continue
foci_areas = []
foci_areas_scaled = []
foci_intensities = []
foci_mean_intensities = []
for foci in regionprops(labeled_foci,foci_intesity):
foci_areas.append(foci.area)
foci_areas_scaled.append(foci.area*self.scale**2)
foci_mean_intensities.append(foci.mean_intensity)
coords = foci.coords
intensities = 0
for coord in coords:
intensities += foci_intesity[coord[0],coord[1]]
foci_intensities.append(intensities)
self.__add_area(foci.area*self.scale**2)
self.__add_mean_intensity(foci.mean_intensity)
self.__add_sum_intenisity(foci_intensities)
nucleus_area_scaled = float(region.area) * self.scale**2
nuclear_mean_int = float(region.mean_intensity)
foci_intensities =np.array(foci_intensities,np.float32)
foci_areas_scaled = np.array(foci_areas_scaled)
foci_areas = np.array(foci_areas, np.float32)
total_foci_areas_scaled = foci_areas_scaled.sum()
foci_areas_perc = (foci_areas_scaled.sum()/nucleus_area_scaled)*100
total_foci_int = foci_intensities.sum()
if foci_areas_perc < self.max_foci_coverage and region.area > self.min_cell_area:# 80 and 10000
variables['File_Name'] = name
variables['Group'] = self.group_name
variables['Threshold'] = th
variables['Nucleus_Area_px='+str(self.scale)] = nucleus_area_scaled
variables['Nuclear_Mean_Int'] = nuclear_mean_int
variables['Foci_Number'] = foci_number
#Total
variables['Total_Foci_Areas_px='+str(self.scale)] = total_foci_areas_scaled
variables['Total_Foci_Sum_Int'] = total_foci_int
variables['Total_Foci_Mean_Int'] = total_foci_int/foci_areas.sum()
variables['Foci_Areas_%'] = foci_areas_perc
#Arrays
variables['Foci_Areas_Array_px='+str(self.scale)] = foci_areas_scaled
variables['Foci_Sum_Int_Array'] = foci_intensities
variables['Foci_Mean_Int_Array'] = foci_mean_intensities
self.all_nuclei[name+'_'+str(region.label)] = variables
foci_mask[inside_nucleus] = True
cv2.putText(Foci_are_marked,str(region.label),(min_col,min_row), font, 2,(255,255,255),2,cv2.LINE_AA)
else:
cv2.putText(Foci_are_marked,str(region.label),(min_col,min_row), font, 2,(0,0,255),2,cv2.LINE_AA)
all_labeled_foci,all_foci_number = mh.label(foci_mask, np.ones((3,3), bool))
im2, foci_contours, hierarchy = cv2.findContours(all_labeled_foci,cv2.RETR_FLOODFILL,cv2.CHAIN_APPROX_SIMPLE)
im2, nuclei_contours, hierarchy = cv2.findContours(np.int32(labels_all),cv2.RETR_FLOODFILL,cv2.CHAIN_APPROX_NONE)
cv2.drawContours(Foci_are_marked, foci_contours, -1, (255,0,0), 1)
cv2.drawContours(Foci_are_marked, nuclei_contours, -1, (0,255,255), 2)
Foci_are_marked[:,:,1] = foci_intesity
io.imsave(self.path_results+name+'_Marked_Foci.tif',Foci_are_marked)