Image Processing with Python: Key Points

Introduction

Simple Python and 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.

Digital images are represented as rectangular arrays of square pixels.
Digital images use a left-hand coordinate system, with the origin in the upper left corner, the x-axis running to the right, and the y-axis running down. Some learners may prefer to think in terms of counting down rows for the y-axis and across columns for the x-axis. Thus, we will make an effort to allow for both approaches in our lesson presentation.
Most frequently, digital images use an additive RGB model, with eight bits for the red, green, and blue channels.
scikit-image images are stored as multi-dimensional NumPy arrays.
In scikit-image images, the red channel is specified first, then the green, then the blue, i.e., RGB.
Lossless compression retains all the details in an image, but lossy compression results in loss of some of the original image detail.
BMP images are uncompressed, meaning they have high quality but also that their file sizes are large.
JPEG images use lossy compression, meaning that their file sizes are smaller, but image quality may suffer.
TIFF images can be uncompressed or compressed with lossy or lossless compression.
Depending on the camera or sensor, various useful pieces of information may be stored in an image file, in the image metadata.

Images are read from disk with the iio.imread() function.
We create a window that automatically scales the displayed image with Matplotlib and calling imshow() on the global figure object.
Colour images can be transformed to grayscale using ski.color.rgb2gray() or, in many cases, be read as grayscale directly by passing the argument mode="L" to iio.imread().
We can resize images with the ski.transform.resize() function.
NumPy array commands, such as image[image < 128] = 0, can be used to manipulate the pixels of an image.
Array slicing can be used to extract sub-images or modify areas of images, e.g., clip = image[60:150, 135:480, :].
Metadata is not retained when images are loaded as NumPy arrays using iio.imread().

We can use the NumPy zeros() function to create a blank, black image.
We can draw on scikit-image images with functions such as ski.draw.rectangle(), ski.draw.disk(), ski.draw.line(), and more.
The drawing functions return indices to pixels that can be set directly.

In many cases, we can load images in grayscale by passing the mode="L" argument to the iio.imread() function.
We can create histograms of images with the np.histogram function.
We can display histograms using ax.plot() with the bin_edges and histogram values returned by np.histogram().
The plot can be customised using ax.set_xlabel(), ax.set_ylabel(), ax.set_xlim(), ax.set_ylim(), and ax.set_title().
We can separate the colour channels of an RGB image using slicing operations and create histograms for each colour channel separately.

Applying a low-pass blurring filter smooths edges and removes noise from an image.
Blurring is often used as a first step before we perform thresholding or edge detection.
The Gaussian blur can be applied to an image with the ski.filters.gaussian() function.
Larger sigma values may remove more noise, but they will also remove detail from an image.

Thresholding produces a binary image, where all pixels with intensities above (or below) a threshold value are turned on, while all other pixels are turned off.
The binary images produced by thresholding are held in two-dimensional NumPy arrays, since they have only one colour value channel. They are boolean, hence they contain the values 0 (off) and 1 (on).
Thresholding can be used to create masks that select only the interesting parts of an image, or as the first step before edge detection or finding contours.

We can use ski.measure.label to find and label connected objects in an image.
We can use ski.measure.regionprops to measure properties of labeled objects.
We can use ski.morphology.remove_small_objects to mask small objects and remove artifacts from an image.
We can display the labeled image to view the objects coloured by label.

Using thresholding, connected component analysis and other tools we can automatically segment images of bacterial colonies.
These methods are useful for many scientific problems, especially those involving morphometrics.

We can access open a Napari n-dimensional image viewer with viewer = napari.Viewer().
Image and Label Layers can be added to a viewer with viewer.add_image() and viewer.add_labels() respectively.
Many scikit-image functions such as ski.filters.gaussian(), ski.threshold.threshold_otsu(), ski.measure.label() and ski.measure.regionprops() work with 3D image data.
We can iterate through time-points to analyse timelapse movies.