In an attempt to find features to distinguish chromosomes from overlapping chromosomes or from nuclei or small image segmentation artifacts ("dusts"), the area and the convex-hull area of isolated particles were computed after segmentation of a metaphasic chromosome image.
Image segmentation and convex-hull area computation were performed with scripts written previously. The features were saved in csv files.
miniKar produced an other csv file where the particle category (label) was saved.
This is an opportunity to handle the data with the pandas library and to see from this limited data set if the two features may be good candidates to classify at least particles in one of the three categories (single, overlapping, nuclei). A features file and the labels file were loaded, merged in one data frame. The features were filtered according to the label (the particle category) and then were displayed in a scatter plot:
|green:single chromosomes; red overlapping chromosomes; blue:nuclei|
The normalized area is the 1000*area of a particle divided by the image size (1024x1536). From this scatter plot, it's clear that at least an other feature will be necessary to separate the single chromosomes from the overlapping chromosomes. This may be due to the chromosomes bending.
A surprising thing, the ratio should be such: area/convexhull <1 and it is not true for all the particles.
import KarIO import os,csv import pylab import pandas #make a configurator object config=KarIO.ClassifConf() #build the the name feature file ##list all the feature files ##répertoire courant : os.listdir(os.getcwd()) featurespath=os.path.join(os.getcwd(),"Results","features") labelpath=os.path.join(os.getcwd(),"Results","labels") #print featurespath ##open features csv files featuresFilesList=os.listdir(featurespath) labelsFilesList=os.listdir(labelpath) ## #let's find a pair of feature file/label file #open with pandas #two files match if they come from the same metaphase featfile=config.user+'-'+config.slide+'-'+config.metaphases_list+'-'+config.counterstain+'.csv' labelfile='shapeCateg-'+featfile fea=pandas.read_csv(os.path.join(featurespath,featfile),header=None,names=['particle','ratio','area']) lab=pandas.read_csv(os.path.join(labelpath,labelfile),header=None,names=['particle','type']) #merge columns:features and label data=fea.join(lab) single=data[data['type']=='single'] touching=data[data['type']=='touching'] nuclei=data[data['type']=='nuclei'] dusts=data[data['type']=='dusts'] ##ploting with pylab #different colors according to the category fig=pylab.figure() ax = fig.add_subplot(111) ax.scatter(single['ratio'],single['area'],c='green',marker='o') ax.scatter(touching['ratio'],touching['area'],c='red',marker='o') ax.scatter(nuclei['ratio'],nuclei['area'],c='blue',marker='o') pylab.show()