Merge pull request #203 from datacarpentry/198-general-intro

A more general introduction

Author: tobyhodges

episodes/01-introduction.md

@@ -12,18 +12,53 @@ objectives:
 - "Recognize morphometric problems (those dealing with the number, size, or
 shape of the objects in an image)."
 keypoints:
-- "Simple Python and skimage (scikit-image) techniques can be used to solve genuine 
+- "Simple Python and skimage (scikit-image) techniques can be used to solve genuine
 image analysis problems."
 - "Morphometric problems involve the number, shape, and / or size of the
 objects in an image."
 ---
 
-We can use relatively simple image processing and computer vision techniques in
-Python, using the [skimage](https://scikit-image.org/) library. With careful
-experimental design, a digital camera or a flatbed scanner, in conjunction with
-some Python code, can be a powerful instrument in answering many different
-kinds of problems. Consider the following problem that might be
-of interest to a scientist.
+As computer systems have become faster and more powerful,
+and cameras and other imaging systems have become commonplace
+in many other areas of life,
+the need has grown for researchers to be able to
+process and analyse image data.
+Considering the large volumes of data that can be involved -
+high-resolution images that take up a lot of disk space/virtual memory,
+and/or collections of many images that must be processed together -
+and the time-consuming and error-prone nature of manual processing,
+it can be advantageous or even necessary for this processing and analysis
+to be automated as a computer program.
+
+This lesson introduces an open source toolkit for processing image data:
+the Python programming language
+and [the _scikit-image_ (`skimage`) library](https://scikit-image.org/).
+With careful experimental design, 
+Python code can be a powerful instrument in answering many different kinds of questions.
+
+
+## Uses of Image Processing in Research
+
+Automated processing can be used to analyse many different properties of an image,
+including the distribution and change in colours in the image,
+the number, size, position, orientation, and shape of objects in the image,
+and even - when combined with machine learning techniques for object recognition -
+the type of objects in the image.
+
+Some examples of image processing methods applied in research include:
+
+- [imaging a Black Hole](https://iopscience.iop.org/article/10.3847/2041-8213/ab0e85)
+- [estimating the population of Emperor Penguins](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3325796/)
+- [the global-scale analysis of marine plankton diversity](https://www.cell.com/cell/fulltext/S0092-8674(19)31124-9)
+- [segmentation of liver and vessels from CT images](https://doi.org/10.1016/j.cmpb.2017.12.008)
+
+With this lesson,
+we aim to provide a thorough grounding in the fundamental concepts and skills
+of working with image data in Python.
+Most of the examples used in this lesson focus on
+one particular class of image processing technique, _morphometrics_,
+but what you will learn can be used to solve a much wider range of problems.
+
 
 ## Morphometrics
 
@@ -71,7 +106,7 @@ image like this:
 
 As we move through this workshop, we will learn image analysis methods
 useful for many different scientific problems. These will be linked together
-and applied to a real problem in the final end-of-workshop 
+and applied to a real problem in the final end-of-workshop
 [capstone challenge]({{page.root}}/09-challenges/).
 
 Let's get started, by learning some basics about how images are represented and