How to rotate faster chromosomes vertically (a little bit)

It is possible to orientate chromosomes vertically with a morphological  "rose des directions" using successive erosions. The larger the particle is, the longer the operation is. However, the iterations can be reduced by submitting not the particle itself, but a Top-Hat version of the image, or the particle skeleton or the particle contour yielding similar results with a reduced cost.

The first rose-des-directions was obtained with the original particle, I didn't compute the total amount of erosion, but finding the principal orientation around 120° required around 50 successive erosions, whereas only 16 erosions are necessary if the particle skeleton is analysed, 30 erosions with the Top-Hat filtered image or 20 with the contour.

With a more complex image, the "rose des directions" detects the orientation of the larger chromosome.

chromosomes, overlapping chromosomes and nuclei

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[2]+'-'+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()

making a scatter plot with tags: playing with the iris data set

I need to display a scatterplot and to distinguish the points according to a category. I start with the iris dataset available in scikit-learn:

# -*- coding: utf-8 -*-
"""
"""
import pylab
from scikits.learn import datasets
iris = datasets.load_iris()
X=iris.data
Y=iris.target
print X
fig=pylab.figure()
ax = fig.add_subplot(111, aspect='equal')
print type(iris)
print iris.data.shape
print X[0:4,1]
print X[0:4,2]
print Y[0:4]
ax.scatter(X[:,0],X[:,1],c=Y[:])
pylab.show()

The Y column contains the labels stored as numerical values used to select the a color.

A pig chromosomes gallery: a modified makeMosaic function

I fixed a bug in the makeMosaic function in the script used to build a chromosomes gallery. Instead of removing, too small or to large particles from a list, now the makeMosaic function build a new list of filtered particles (chromosomes). The script was modified to display chromosomes in inverse DAPI with

pylab.set_cmap(pylab.cm.Greys)

Inverse DAPI pig chromosomes

Original pig DAPI stained metaphase