Introduction

Figure 1

Bacteria colony

Figure 2

Colonies counted

Figure 3

Bacteria colony

Image Basics

Figure 1

Original size image

Figure 2

Enlarged image area

Figure 3

Image of 8

Figure 4

Image of 0

Figure 5

Cartesian coordinate system

Figure 6

Image coordinate system

Figure 7

Left-hand coordinate system

Figure 8

Image of 5

Figure 9

Image of three colours

Figure 10

Image in greyscale

Figure 11

Image of checkerboard

Figure 12

Image of red channel

Figure 13

Image of green channel

Figure 14

Image of blue channel

Figure 15

RGB colour table

Figure 16

Original image

Figure 17

Enlarged, uncompressed

Figure 18

Enlarged, compressed

Figure 19

Uncompressed histogram

Working with skimage

Figure 1

Root cluster image

Figure 2

Thresholded root image

Figure 3

Su-Do-Ku puzzle

Figure 4

Modified Su-Do-Ku puzzle

Figure 5

Whiteboard image

Figure 6

Whiteboard coordinates

Figure 7

"Erased" whiteboard

Drawing and Bitwise Operations

Figure 1

Maize seedlings

Figure 2

Here is what our constructed mask looks like: Maize image mask

Figure 3

Sample shapes

Figure 4

Applied mask

Figure 5

Remote control image

Figure 6

Remote control masked

Figure 7

96-well plate

Figure 8

Masked 96-well plate

Creating Histograms

Figure 1

We will start with grayscale images, and then move on to colour images. We will use this image of a plant seedling as an example: Plant seedling

Figure 2

Plant seedling

Figure 3

Plant seedling histogram

Figure 4

Grayscale histogram of masked area

Figure 5

Colour histogram

Figure 6

Well plate image

Figure 7

Masked well plate

Figure 8

Well plate histogram

Blurring Images

Figure 1

Cat image

Figure 2

Cat eye pixels

Figure 3

A Gaussian function maps random variables into a normal distribution or “Bell Curve”. Gaussian function

Figure 4

2D Gaussian function

Figure 5

2D Gaussian function

Figure 6

Image corner pixels

Figure 7

Image multiplication

Figure 8

Blur demo animation

Figure 9

Original image

Figure 10

Original image

Figure 11

Rectangular kernel blurred image

Thresholding

Figure 1

{alt=‘Image with geometric shapes on white background’ .image-with-shadow}

Figure 2

{alt=‘Grayscale image of the geometric shapes’ .image-with-shadow}

Figure 3

Grayscale histogram of the geometric shapes image

Figure 4

Binary mask of the geometric shapes created by thresholding

Figure 5

Selected shapes after applying binary mask

Figure 6

Another image with geometric shapes on white background

Figure 7

Grayscale histogram of the second geometric shapes image

Figure 8

Binary mask created by thresholding the second geometric shapes image

Figure 9

Selected shapes after applying binary mask to the second geometric shapes image

Figure 10

Image of a maize root

Figure 11

Grayscale histogram of the maize root image

Figure 12

Binary mask of the maize root system

Figure 13

Masked selection of the maize root system

Figure 14

Four images of maize roots

Figure 15

Binary masks of the four maize root images

Figure 16

Improved binary masks of the four maize root images

Figure 17

Image of bacteria colonies in a petri dish

Figure 18

Grayscale histogram of the bacteria colonies image

Figure 19

Binary mask of the bacteria colonies image

Connected Component Analysis

Figure 1

{alt=‘Original shapes image’ .image-with-shadow}

Figure 2

Mask created by thresholding

Figure 3

Labeled objects

Figure 4

shapes-01.jpg mask detail

Figure 5

Histogram of object areas

Figure 6

Objects filtered by area

Figure 7

Objects colored by area

Capstone Challenge

Figure 1

Colony image 1

Figure 2

Colony image 2

Figure 3

Colony image 3

Figure 4

Sample morphometric output

Figure 5

Colony image 1

Figure 6

Gray Colonies

Figure 7

Histogram image

Figure 8

Colony mask image

Figure 9

Sample morphometric output

Figure 10

Colony 1 outputColony 2 outputColony 3 output