Measuring Intensity

Biomedical Image Analysis in Python

Stephen Bailey

Instructor

Measuring intensity

We have the following labels for a single volume of the cardiac time series:

Left ventricle
Central portion

heart-image-2d with labels

Functions

`scipy.ndimage.measurements`

ndi.mean()

ndi.median()

ndi.sum()

ndi.maximum()

ndi.standard_deviation()

ndi.variance()

Functions applied over all dimensions, optionally at specific labels.

Custom functions:

ndi.labeled_comprehension()

Calling measurement functions

import imageio
import scipy.ndimage as ndi
vol=imageio.volread('SCD-3d.npz')
label=imageio.volread('labels.npz')

# All pixels
ndi.mean(vol)

3.7892

# Labeled pixels
ndi.mean(vol, label)

89.2342

# Label 1
ndi.mean(vol, label, index=1)

163.2930

# Labels 1 and 2
ndi.mean(vol, label, index=[1,2])

[163.2930, 60.2847]

Object histograms

hist=ndi.histogram(vol, min=0, max=255, bins=256)

obj_hists=ndi.histogram(vol, 0, 255, 256, 
                        labels, index=[1, 2])
len(obj_hists)

Object histograms

plt.plot(obj_hists[0], 
   label='Left ventricle')
plt.plot(obj_hists[1], 
   label='Other labelled pixels')
plt.legend()
plt.show()

histograms-plot

Histograms containing multiple tissue types will have several peaks
Histograms for well-segmented tissue often resemble a normal distribution

Let's practice!

Biomedical Image Analysis in Python