Image Features Extraction Package

This package allows the fast extraction and classification of features from a set of images.

Tutorial

This Python package allows the fast extraction and classification of features from a set of images. The resulting data frame can be used as training and testing set for machine learning classifier.

This package was originally developed to extract measurements of single cell nuclei from microscopy images (see figure above). The package can be used to extract features from any set of images for a variety of applications. Below it is shown a map of Boston used for city density and demographic models.

Features extraction for spatial classification of images

The image below shows a possible workflow for image feature extraction: two sets of images with different classification labels are used to produce two data sets for training and testing a classifier

An example of Collection-object and Iterator implementation

The object 'Image' includes the function Voronoi(), which returns the object Voronoi of my package Voronoi_Features. The Voronoi object can be used to measure the voronoi tassels of each image regions. It includes >30 measurements. Below an example of voronoi diagrams from the image shown above

Image features extraction for city density and demographic analysis modelling

Create the Images root object and laod the images contained in the folder

% matplotlib inline
import matplotlib.pyplot as plt

import image_features_extraction.Images as fe


IMGS = fe.Images('../images/CITY')

IMG = IMGS.item(0)


print(IMG.file_name())


fig, ax = plt.subplots(figsize=(20, 20))

ax.imshow(IMGS.item(0).get_image_segmentation())

../images/CITY/Boston_Center.tif





<matplotlib.image.AxesImage at 0x11f3e2400>

features = IMG.features(['label', 'area','perimeter', 'centroid', 'moments'])

df2 = features.get_dataframe()

df2.head()

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	id	label	area	perimeter	centroid_x	centroid_y	moments
0	0	44	4	4.000000	2.500000	122.500000	[[4.0, 2.0, 2.0, 2.0], [2.0, 1.0, 1.0, 1.0], [...
1	1	45	6	5.207107	4.333333	3.833333	[[6.0, 8.0, 14.0, 26.0], [5.0, 8.0, 14.0, 26.0...
2	2	46	64	36.556349	7.718750	34.015625	[[64.0, 302.0, 1862.0, 13058.0], [385.0, 1857....
3	3	47	29	23.520815	6.517241	146.689655	[[29.0, 102.0, 476.0, 2580.0], [78.0, 305.0, 1...
4	4	48	165	62.355339	10.121212	460.951515	[[165.0, 1175.0, 10225.0, 99551.0], [1807.0, 1...

# SHOW THE FOUND CENTROIDS

fig, ax = plt.subplots(figsize=(20, 20))

plt.plot(df2.centroid_x,df2.centroid_y,'.r' )

[<matplotlib.lines.Line2D at 0x119b1ea58>]

h = plt.hist(df2.area,100)

Image features extraction for cellular spatial analysis

Images show cell nuclei

% matplotlib inline
import matplotlib.pyplot as plt

import image_features_extraction.Images as fe


IMGS = fe.Images('../images/CA/1')


# the iterator at work ...
for IMG in IMGS:
    print(IMG.file_name())

../images/CA/1/ORG_8bit.tif
../images/CA/1/ORG_bin.tif

fig, ax = plt.subplots(figsize=(20, 20))

ax.imshow(IMGS.item(1).get_image_segmentation())

<matplotlib.image.AxesImage at 0x11ab282b0>

An example of measurement and visualization of a property, e.g., area

IMG = IMGS.item(1)


REGS = IMG.regions()


areas = REGS.prop_values('area')


plt.plot(areas)
plt.ylabel('region area (px^2)')

<matplotlib.text.Text at 0x11f38b048>

h = plt.hist(df2.area,100)

VORONOI FEATURES

vor = IMG.Voronoi()

vor = IMG.Voronoi()
IMG_VOR = vor.get_voronoi_map()
fig = plt.figure(figsize=(20,20))
plt.imshow(IMG_VOR, cmap=plt.get_cmap('jet'))

<matplotlib.image.AxesImage at 0x11d228e48>

i1 = IMGS.item(0).get_image_segmentation()
i2 = vor.get_voronoi_map()

i3 = i1[:,:,0] + i2/1000
fig = plt.figure(figsize=(yinch,xinch))
plt.imshow(i3, cmap=plt.get_cmap('Reds'))

<matplotlib.image.AxesImage at 0x11ebbd6d8>

Feature from the image only

features1 = IMG.features(['area','perimeter','centroid','bbox', 'eccentricity'])
features1.get_dataframe().head()

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	id	area	perimeter	centroid_x	centroid_y	bbox	eccentricity
0	0	4	4.000000	2.500000	122.500000	(2, 122, 4, 124)	0.000000
1	1	6	5.207107	4.333333	3.833333	(3, 3, 6, 6)	0.738294
2	2	64	36.556349	7.718750	34.015625	(3, 28, 14, 39)	0.410105
3	3	29	23.520815	6.517241	146.689655	(3, 144, 11, 151)	0.736301
4	4	165	62.355339	10.121212	460.951515	(3, 450, 19, 471)	0.718935

Features from the voronoi diagram only

features2 = vor.features(['area','perimeter','centroid','bbox', 'eccentricity'])
features2.get_dataframe().head()

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	id	voro_area	voro_perimeter	voro_centroid	voro_bbox	voro_eccentricity
0	24	314	71.112698	(13.9203821656, 407.257961783)	(2, 395, 25, 416)	0.502220
1	33	365	78.526912	(18.2, 481.273972603)	(2, 473, 32, 491)	0.861947
2	71	343	94.911688	(17.8717201166, 723.320699708)	(3, 706, 30, 740)	0.955651
3	32	161	50.662951	(15.7701863354, 450.565217391)	(5, 445, 24, 460)	0.738073
4	46	160	50.591883	(15.8625, 516.75)	(5, 511, 24, 524)	0.782348

Merge features from the image + the voronoi diagram

features3 = features1.merge(features2, how_in='inner')
features3.get_dataframe().head()

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	id	area	perimeter	centroid_x	centroid_y	bbox	eccentricity	voro_area	voro_perimeter	voro_centroid	voro_bbox	voro_eccentricity
0	8	147	95.041631	18.843537	151.149660	(5, 146, 34, 157)	0.967212	257	67.355339	(22.2762645914, 152.482490272)	(12, 143, 36, 162)	0.799861
1	15	485	279.260931	25.649485	170.092784	(8, 155, 40, 188)	0.618654	447	80.325902	(29.0604026846, 169.451901566)	(17, 157, 42, 185)	0.558628
2	17	114	69.562446	20.061404	747.701754	(8, 739, 33, 753)	0.960308	73	31.798990	(20.1369863014, 748.931506849)	(14, 744, 26, 754)	0.530465
3	18	106	48.556349	17.990566	119.075472	(9, 114, 28, 125)	0.810733	151	48.763456	(18.2185430464, 117.688741722)	(10, 109, 25, 124)	0.756768
4	21	2	0.000000	9.500000	395.000000	(9, 395, 11, 396)	1.000000	63	33.349242	(10.0158730159, 392.698412698)	(6, 387, 15, 400)	0.742086

Add class name and value

features3.set_class_name('class')
features3.set_class_value('test_class_val')

features3.get_dataframe(include_class=True).head()

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	id	area	perimeter	centroid_x	centroid_y	bbox	eccentricity	voro_area	voro_perimeter	voro_centroid	voro_bbox	voro_eccentricity	class
0	8	147	95.041631	18.843537	151.149660	(5, 146, 34, 157)	0.967212	257	67.355339	(22.2762645914, 152.482490272)	(12, 143, 36, 162)	0.799861	test_class_val
1	15	485	279.260931	25.649485	170.092784	(8, 155, 40, 188)	0.618654	447	80.325902	(29.0604026846, 169.451901566)	(17, 157, 42, 185)	0.558628	test_class_val
2	17	114	69.562446	20.061404	747.701754	(8, 739, 33, 753)	0.960308	73	31.798990	(20.1369863014, 748.931506849)	(14, 744, 26, 754)	0.530465	test_class_val
3	18	106	48.556349	17.990566	119.075472	(9, 114, 28, 125)	0.810733	151	48.763456	(18.2185430464, 117.688741722)	(10, 109, 25, 124)	0.756768	test_class_val
4	21	2	0.000000	9.500000	395.000000	(9, 395, 11, 396)	1.000000	63	33.349242	(10.0158730159, 392.698412698)	(6, 387, 15, 400)	0.742086	test_class_val

To measure intensity from image regions

The example below shows how to associate a grayscale image to a binary one for intensity measurement. The package uses intenally a very simple segmentation algorithm based on an Otsu Thresholding method for segmentation of binary images. The goal of the package is not to segment images but to measure their segmented features. The correct way to use this package is by using as input pre-segmented binary images and if intensity measurement are needed to associate the original grayscale image.

IMG = IMGS.item(1)

IMG.set_image_intensity(IMGS.item(0))

features = IMG.features(['label', 'area','perimeter', 'centroid', 'moments','mean_intensity'])

df = features.get_dataframe()

df.head()

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	id	label	area	perimeter	centroid_x	centroid_y	moments	mean_intensity
0	0	22	64	28.278175	5.468750	584.375000	[[64.0, 286.0, 1630.0, 10366.0], [280.0, 1223....	170.078125
1	1	23	86	33.556349	6.418605	621.546512	[[86.0, 466.0, 3268.0, 25726.0], [391.0, 2067....	139.127907
2	2	24	100	35.556349	5.720000	1290.330000	[[100.0, 472.0, 2988.0, 21442.0], [533.0, 2238...	99.360000
3	3	25	50	24.142136	5.600000	23.040000	[[50.0, 180.0, 846.0, 4458.0], [202.0, 699.0, ...	181.940000
4	4	26	80	31.556349	7.325000	99.462500	[[80.0, 426.0, 2894.0, 21846.0], [357.0, 1969....	157.675000

Plot area vs perimeter and area histogram

plt.plot(df.area, df.mean_intensity, '.b')
plt.xlabel('area')
plt.ylabel('mean_intensity')

<matplotlib.text.Text at 0x114a69908>

An example of how save measured features

This package includes the class Features for data managment layer, which is used to separate the business from the data layer and allow easy scalability of the data layer.

import image_features_extraction.Images as fe


IMGS = fe.Images('../images/EDGE')

storage_name = '../images/DB1.csv'
class_value = 1

for IMG in IMGS:
    print(IMG.file_name())

    REGS = IMG.regions()

    FEATURES = REGS.features(['area','perimeter', 'extent', 'equivalent_diameter', 'eccentricity'], class_value=class_value)

    FEATURES.save(storage_name, type_storage='file', do_append=True)

../images/EDGE/ca_1.tif
../images/EDGE/ca_2.tif
../images/EDGE/ca_3.tif

Pytest: Units test

!py.test

�[1m============================= test session starts ==============================�[0m
platform darwin -- Python 3.5.3, pytest-3.1.3, py-1.4.34, pluggy-0.4.0
rootdir: /Users/remi/Google Drive/INSIGHT PRJ/PRJ/Image-Features-Extraction, inifile:
collected 0 items �[0m�[1m
�[0m
�[1m�[33m========================= no tests ran in 0.01 seconds =========================�[0m

rempic/Image-Features-Extraction

Image Features Extraction Package

Package documentation

Tutorial

Features extraction for spatial classification of images

An example of Collection-object and Iterator implementation

Image features extraction for city density and demographic analysis modelling

Image features extraction for cellular spatial analysis

An example of measurement and visualization of a property, e.g., area

VORONOI FEATURES

Feature from the image only

Features from the voronoi diagram only

Merge features from the image + the voronoi diagram

Add class name and value

To measure intensity from image regions

Plot area vs perimeter and area histogram

An example of how save measured features

Pytest: Units test