Imaging

6/27/2023

https://bits.csb.pitt.edu/files/bioimaging.slides.html

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Images

Images are composed of pixels (pix - picture, el - element).

In a grayscale image the pixel contains the intensity. Depending on the image format this may range from 0-1, 0-255, or be any floating point number.

In a color image a pixel is (usually) a triple (red,green,blue) of color values where each color intensity ranges from 0-255 (24-bit color).

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A slightly irrelevant aside

Or, how to count with 16 fingers

It is very common for RGB color values to be represented in hexadecimal (e.g., HTML).

Hexadecimal number are usually indicated with a 0x or # prefix.

In hexadecimal, each digit can take one of 16 values (as opposed to 10). This means that each color can be represented with two digits (16*16 = 256)

https://www.youtube.com/watch?v=ffB0Je-xjKg

Hex	Dec
A	10
B	11
C	12
D	13
E	14
F	15

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Answers

print(0x1f, 0xfe)

31 254

<div style="color: #ff00ff">Hello</div>

Hello

PIL - The Python Image Library

documentation

The Python Imaging Library provides the ability to read, write an manipulate images in all the common formats. Some key modules:

• PIL.Image - primary Image object
• PIL.ImageChops - channel operations, for blending two images
• PIL.ImageEnhance - enhance your photos (sharpness, contrast, ...)
• PIL.ImageFilter - filters (blur, smooth, sharpen, ...)
• PIL.ImageOps - assorted operations (resizing, equalize, autocontrast, ...)
• PIL.ImageStat - statistics on image (min, max, ...)

History

Despite being a popular package, PIL development stopped in 2011 with no new releases since 2009.

Pillow is a fork of PIL that is fully backwards compatible and updated (and is what you are actually using rather than the original PIL).

https://python-pillow.org

Setup...

!wget http://bits.csb.pitt.edu/images/image1.tif

--2023-06-27 07:51:40--  http://bits.csb.pitt.edu/images/image1.tif
Resolving bits.csb.pitt.edu (bits.csb.pitt.edu)... 136.142.4.139
Connecting to bits.csb.pitt.edu (bits.csb.pitt.edu)|136.142.4.139|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 921814 (900K) [image/tiff]
Saving to: ‘image1.tif.2’

image1.tif.2        100%[===================>] 900.21K  --.-KB/s    in 0.06s   

2023-06-27 07:51:40 (15.4 MB/s) - ‘image1.tif.2’ saved [921814/921814]

import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
%matplotlib inline

from PIL import Image
im = Image.open('image1.tif')
im

Actin HSP27 DAPI

Image Methods

PIL refers to the different color channels as bands.

im.getbands()

('R', 'G', 'B')

You can split a color image into it's separate bands.

(r,g,b) = im.split()
r

Green

g

Blue

b

Image analysis is usually done on a single channel Why?

Converting Image Types

We can convert to grayscale ('L' - luminance)

grey = im.convert('L')
grey

Getting Pixels

im.size

(640, 480)

im.getpixel((320,240))

(159, 3, 18)

Getting all the pixels

Can get all the pixels in a flat list

list(im.getdata())

[(243, 3, 28),
 (238, 3, 27),
 (242, 3, 26),
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 (220, 3, 26),
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 (142, 1, 86),
 (144, 1, 85),
 (146, 1, 84),
 (151, 1, 85),
 (151, 1, 87),
 (146, 1, 85),
 (141, 1, 85),
 (141, 2, 84),
 (156, 1, 84),
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 (153, 1, 92),
 (151, 1, 96),
 (147, 1, 98),
 (151, 1, 96),
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 (150, 1, 87),
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 (151, 1, 80),
 (154, 1, 80),
 (166, 1, 78),
 (174, 1, 76),
 (175, 1, 72),
 (179, 1, 70),
 (179, 1, 68),
 (183, 1, 67),
 (182, 2, 66),
 (180, 1, 63),
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 (184, 1, 58),
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 (187, 2, 55),
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 (95, 1, 33),
 (95, 1, 33),
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 (89, 1, 30),
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 (83, 2, 19),
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 (180, 4, 26),
 (167, 4, 27),
 (158, 4, 28),
 (160, 4, 26),
 (151, 3, 27),
 (147, 3, 28),
 (147, 3, 28),
 (136, 4, 28),
 (140, 4, 28),
 (141, 3, 28),
 (136, 3, 28),
 (127, 3, 28),
 (122, 3, 29),
 (120, 3, 27),
 (115, 3, 29),
 (114, 3, 27),
 (112, 3, 29),
 (107, 3, 28),
 (102, 3, 29),
 (92, 4, 28),
 (92, 4, 27),
 (94, 3, 28),
 (91, 3, 28),
 (98, 4, 28),
 (85, 3, 27),
 (88, 3, 29),
 (85, 3, 27),
 (87, 4, 28),
 (92, 3, 26),
 (84, 4, 27),
 (92, 4, 27),
 (75, 3, 29),
 (81, 4, 29),
 (84, 4, 30),
 (78, 4, 28),
 (78, 4, 30),
 (77, 4, 30),
 (85, 4, 29),
 (75, 4, 30),
 (77, 4, 30),
 (79, 4, 29),
 (75, 4, 29),
 (74, 4, 30),
 (86, 4, 30),
 (76, 4, 29),
 (83, 4, 30),
 (83, 4, 30),
 (83, 4, 30),
 (83, 5, 31),
 (83, 4, 33),
 (90, 4, 33),
 (89, 4, 35),
 (82, 4, 34),
 (91, 4, 34),
 (90, 4, 33),
 (97, 4, 31),
 (93, 4, 33),
 (92, 5, 30),
 (94, 4, 31),
 (92, 4, 31),
 (102, 4, 31),
 (100, 5, 31),
 (99, 4, 29),
 (104, 4, 31),
 (106, 4, 32),
 (99, 4, 32),
 (110, 4, 31),
 (105, 4, 31),
 (109, 4, 32),
 (112, 4, 32),
 (105, 4, 32),
 (117, 4, 33),
 (111, 4, 33),
 (107, 4, 33),
 (114, 5, 35),
 (119, 4, 36),
 (106, 5, 37),
 (108, 5, 38),
 (118, 4, 38),
 (111, 4, 39),
 (112, 4, 42),
 (108, 5, 42),
 (110, 4, 44),
 (105, 4, 46),
 (106, 4, 47),
 (107, 4, 47),
 (105, 4, 48),
 (107, 4, 51),
 (103, 4, 51),
 (101, 4, 52),
 (108, 4, 53),
 (103, 4, 56),
 (105, 4, 60),
 (100, 4, 62),
 (98, 4, 62),
 (97, 4, 64),
 (98, 4, 63),
 (95, 4, 62),
 (100, 4, 60),
 (96, 4, 61),
 (91, 4, 64),
 (94, 4, 68),
 (94, 4, 71),
 (97, 4, 72),
 (89, 4, 74),
 (91, 4, 76),
 (89, 4, 73),
 (91, 3, 69),
 (81, 3, 69),
 (90, 3, 70),
 (89, 4, 67),
 (91, 4, 68),
 (91, 4, 67),
 (81, 4, 66),
 (80, 3, 65),
 (87, 4, 66),
 (87, 3, 66),
 (88, 3, 65),
 (86, 4, 63),
 (86, 3, 67),
 (85, 3, 69),
 (82, 3, 70),
 (84, 4, 71),
 (88, 4, 69),
 (75, 3, 67),
 (83, 3, 65),
 (80, 4, 65),
 (77, 3, 65),
 (87, 3, 64),
 (87, 3, 65),
 (87, 4, 64),
 (82, 3, 64),
 (81, 3, 64),
 (82, 3, 65),
 (83, 3, 67),
 (83, 3, 66),
 (92, 3, 67),
 (82, 3, 68),
 (84, 3, 62),
 (79, 3, 61),
 (88, 2, 61),
 (87, 3, 60),
 (83, 3, 57),
 (86, 3, 56),
 (89, 3, 53),
 (86, 3, 52),
 (83, 3, 51),
 (83, 3, 49),
 (79, 3, 44),
 (92, 3, 44),
 (87, 3, 40),
 (81, 3, 37),
 (88, 3, 38),
 (83, 3, 37),
 (85, 3, 36),
 (90, 4, 36),
 (88, 3, 36),
 (87, 3, 34),
 (82, 3, 35),
 (90, 2, 35),
 (89, 2, 33),
 (81, 3, 32),
 (81, 3, 33),
 (90, 3, 32),
 (89, 2, 32),
 (90, 2, 32),
 (88, 2, 33),
 (86, 3, 32),
 (93, 3, 32),
 (88, 2, 30),
 (89, 3, 31),
 (96, 3, 30),
 (91, 3, 30),
 (95, 3, 30),
 (103, 2, 30),
 (102, 2, 29),
 (109, 2, 30),
 (109, 3, 28),
 (109, 3, 28),
 (115, 3, 29),
 (112, 3, 27),
 (107, 3, 29),
 (106, 2, 29),
 (101, 3, 28),
 (109, 2, 29),
 (103, 3, 28),
 (100, 2, 28),
 (113, 2, 29),
 (111, 2, 28),
 (104, 2, 29),
 (115, 2, 29),
 (103, 2, 29),
 (107, 2, 29),
 (111, 2, 29),
 (108, 2, 30),
 (113, 2, 29),
 (115, 2, 30),
 (118, 2, 30),
 (115, 2, 30),
 (113, 2, 30),
 (122, 2, 31),
 (120, 2, 30),
 (113, 2, 30),
 (122, 2, 30),
 (123, 2, 32),
 (123, 2, 29),
 (124, 2, 29),
 (125, 2, 30),
 (121, 2, 28),
 (123, 2, 30),
 (125, 2, 29),
 (128, 2, 30),
 (137, 2, 30),
 (130, 2, 30),
 (130, 3, 29),
 (127, 2, 31),
 (129, 2, 31),
 (137, 2, 30),
 (143, 2, 30),
 (141, 2, 31),
 (138, 2, 31),
 (140, 2, 31),
 (140, 2, 31),
 (147, 2, 31),
 (149, 2, 33),
 (149, 2, 32),
 (153, 2, 32),
 (153, 2, 34),
 (159, 2, 33),
 (160, 2, 34),
 (170, 2, 35),
 (164, 2, 34),
 (181, 2, 35),
 (179, 2, 34),
 (177, 1, 35),
 (185, 2, 36),
 (184, 2, 37),
 (186, 2, 36),
 (195, 2, 36),
 (198, 2, 37),
 (198, 1, 37),
 (200, 2, 37),
 (204, 1, 39),
 (210, 1, 39),
 (210, 2, 40),
 (213, 1, 39),
 (200, 2, 41),
 (208, 2, 44),
 (220, 2, 42),
 (224, 2, 43),
 (212, 2, 44),
 (205, 1, 43),
 (211, 2, 43),
 (210, 2, 44),
 (209, 1, 45),
 (212, 1, 45),
 (214, 1, 47),
 (216, 1, 46),
 (206, 1, 46),
 (204, 2, 47),
 (192, 2, 49),
 (183, 2, 48),
 (174, 1, 51),
 (154, 1, 50),
 (156, 2, 54),
 (147, 1, 53),
 (143, 1, 52),
 (139, 1, 55),
 (139, 1, 56),
 (145, 1, 56),
 (135, 1, 58),
 (150, 1, 58),
 (136, 1, 57),
 (134, 2, 60),
 (141, 2, 62),
 (143, 1, 62),
 (143, 2, 64),
 (145, 1, 67),
 (145, 1, 66),
 (149, 1, 68),
 (149, 2, 66),
 (148, 1, 69),
 (141, 1, 69),
 (135, 1, 69),
 (139, 1, 73),
 (138, 1, 73),
 (143, 1, 74),
 (136, 2, 78),
 (150, 2, 76),
 (145, 1, 80),
 (137, 1, 79),
 (145, 1, 82),
 (135, 1, 80),
 (136, 1, 82),
 (139, 2, 83),
 (143, 1, 83),
 (144, 1, 80),
 (140, 2, 81),
 (151, 2, 84),
 (146, 1, 81),
 (147, 1, 82),
 (148, 1, 84),
 (154, 1, 82),
 (144, 1, 82),
 (152, 1, 83),
 (150, 2, 83),
 (154, 2, 83),
 (158, 1, 83),
 (158, 1, 82),
 (153, 1, 83),
 (152, 2, 85),
 (163, 1, 87),
 (160, 1, 87),
 (159, 1, 89),
 (162, 1, 90),
 (156, 1, 94),
 (158, 1, 96),
 (158, 1, 92),
 (166, 1, 88),
 (165, 1, 84),
 (168, 1, 79),
 (160, 1, 77),
 (168, 1, 78),
 (161, 1, 75),
 (176, 1, 74),
 (160, 1, 70),
 (177, 1, 67),
 (170, 1, 65),
 (176, 1, 63),
 (184, 1, 62),
 (172, 1, 60),
 (187, 1, 60),
 (189, 1, 59),
 (181, 1, 56),
 (187, 1, 54),
 (190, 1, 52),
 (184, 1, 51),
 (166, 1, 52),
 (161, 1, 53),
 (147, 1, 50),
 (138, 1, 48),
 (140, 1, 48),
 (134, 1, 48),
 (127, 1, 48),
 (129, 1, 48),
 (118, 1, 46),
 (129, 1, 43),
 (121, 1, 43),
 (107, 1, 42),
 (109, 1, 42),
 (110, 1, 42),
 (102, 1, 43),
 (103, 1, 40),
 (103, 1, 40),
 ...]

%%html
<div id="immaxhs" style="width: 500px"></div>
<script>
$('head').append('<link rel="stylesheet" href="http://bits.csb.pitt.edu/asker.js/themes/asker.default.css" />');

    var divid = '#immaxhs';
	jQuery(divid).asker({
	    id: divid,
	    question: "What is the maximum pixel value of the blue channel (band) for image1?",
		answers: ['127','192','223','255','256'],
        server: "http://bits.csb.pitt.edu/asker.js/example/asker.cgi",
		charter: chartmaker})
    
$(".jp-InputArea .o:contains(html)").closest('.jp-InputArea').hide();


</script>

Ways to answer

max(b.getdata())

192

from PIL import ImageStat
stat = ImageStat.Stat(b)
stat.extrema

[(6, 192)]

%%html
<div id="imcnths" style="width: 500px"></div>
<script>
$('head').append('<link rel="stylesheet" href="https://bits.csb.pitt.edu/asker.js/themes/asker.default.css" />');

    var divid = '#imcnths';
	jQuery(divid).asker({
	    id: divid,
	    question: "How many pixels of image1 have a red value of 255?",
		answers: ['0','105','1206','5178','65334'],
        server: "https://bits.csb.pitt.edu/asker.js/example/asker.cgi",
		charter: chartmaker})
    
$(".jp-InputArea .o:contains(html)").closest('.jp-InputArea').hide();


</script>

Histograms

bins=np.linspace(0,255,25)
plt.hist(r.getdata(),alpha=0.2,color='#ff0000',bins=bins)
plt.hist(g.getdata(),alpha=0.2,color='#00ff00',bins=bins)
plt.hist(b.getdata(),alpha=0.2,color='#0000ff',bins=bins)
plt.show()

More Histograms

plt.plot(r.histogram(),'r')
plt.plot(g.histogram(),'g')
plt.plot(b.histogram(),'b')
plt.ylim(ymax=24000)

(-6296.6, 24000.0)

What does the red peak at 255 mean?

Equalization

Equalization attempts to normalizes the image histogram to create a uniform distribution of grayscale values. This makes it easier to see differences, but distorts the meaning of the intensities (the transformation is non-linear).

from PIL import ImageOps
beq = ImageOps.equalize(b)
f = plt.figure(figsize=(12,6)); f.add_subplot(1, 2, 1)
plt.imshow(np.array(b),cmap = cm.Greys_r); f.add_subplot(1, 2, 2)
plt.imshow(np.array(beq),cmap = cm.Greys_r); 

Equalization

plt.plot(b.histogram(),'r')
plt.plot(beq.histogram(),'k')

[<matplotlib.lines.Line2D at 0x117d9c220>]

Autocontrast

Maximize (normalize) image contrast. This function calculates a histogram of the input image, removes a cutoff percent of the lightest and darkest pixels from the histogram, and remaps the image so that the darkest remaining pixel becomes black (0), and the lightest becomes white (255).

bcon = ImageOps.autocontrast(b,0.5)
f = plt.figure(figsize=(12,6)); f.add_subplot(1, 2, 1)
plt.imshow(np.array(b),cmap = cm.Greys_r); f.add_subplot(1, 2, 2)
plt.imshow(np.array(bcon),cmap = cm.Greys_r);

Autocontrast

plt.plot(bcon.histogram(),'k',linewidth=1);
plt.plot(b.histogram(),'r')

[<matplotlib.lines.Line2D at 0x117efaeb0>]

Thresholding

The point function can be used to apply a function to every pixel in the image and return the transformed image. This makes it easy to threshold images into a mask.

bthresh = beq.point(lambda x: x if x > 225 else 0)
f = plt.figure(figsize=(12,6)); f.add_subplot(1, 2, 1)
plt.imshow(np.array(beq),cmap = cm.Greys_r); f.add_subplot(1, 2, 2)
plt.imshow(np.array(bthresh),cmap = cm.Greys_r) ;

Looking at the thresholded image, any thoughts on how to count the number of cells?

Project

• Get the other three images from http://bits.csb.pitt.edu/images
• How much green is there in image1 (sum of values)?
• How much green is there that is co-located with blue > 40? As a percentage? For each image?
• What if we change the cutoff (40)?
• Plot the percentage of green co-located with blue for different values of the blue cutoff for each image.