Image Segmentation: Unlocking Insights through Pixel Precision

By Fouad Sabry

What is Image Segmentation

In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.

How you will benefit

(I) Insights, and validations about the following topics:

Chapter 1: Image segmentation

Chapter 2: Edge detection

Chapter 3: Scale-invariant feature transform

Chapter 4: Thresholding (image processing)

Chapter 5: Otsu's method

Chapter 6: Corner detection

Chapter 7: Graph cuts in computer vision

Chapter 8: Mean shift

Chapter 9: Range segmentation

Chapter 10: Watershed (image processing)

(II) Answering the public top questions about image segmentation.

(III) Real world examples for the usage of image segmentation in many fields.

Who this book is for

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Image Segmentation.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: May 11, 2024

Book preview

Chapter 1: Image segmentation

Image segmentation, also known as image region or object segmentation, is a technique used in computer vision and image processing to divide a digital image into smaller, more manageable pieces (sets of pixels). Segmentation's purpose is to transform an image's representation into one that is more digestible and informative. Segmenting an image is a common technique for finding features like lines, curves, and other boundaries within a picture. Image segmentation, to be more specific, is the process of labeling each pixel in an image so that pixels with the same label share common properties.

Segmenting an image yields a collection of sub-images, or contours, that together make up the original image (see edge detection). All of the pixels in a certain area share a similarity in at least one computed property, like hue, saturation, or texture. Differential coloring between adjacent regions for the same feature (s).

Image segmentation has many real-world uses, including the following::

Image retrieval based on content

Machine vision

Diagnostic imaging techniques, such as computed tomography (CT) and magnetic resonance imaging (MRI) with volume rendering.

Diagnose cancer and other pathologies

Measure tissue volumes

Study of anatomy and diagnosis

Surgery planning

Virtual surgery simulation

In-operable guidance systems

Radiotherapy

Object detection

Pedestrian detection

Face detection

Brake light detection

Recognize things in space images (roads, forests, crops, etc.)

Recognition Tasks

Face recognition

Fingerprint recognition

Iris recognition

Traffic control systems

Video surveillance

Action detection and co-segmentation in videos

Numerous general-purpose algorithms and methods for image segmentation have been developed. These methods typically require domain-specific knowledge to be effectively applied to segmentation issues within a given domain.

Two types of segmentation methods exist.

Conventional techniques in computer vision

AI based techniques

Semantic segmentation is a method that assigns a class to each and every one of an object's pixels. Segmenting a figure into its constituent parts, such as its people, and its background, would result in two distinct objects.

Instance segmentation is a method that assigns each pixel to the correct object instance. It finds every individual object in the picture. Segmentation is used, for instance, when each individual part of a figure is treated as a separate entity.

Semantic and instance segmentation are brought together in panoptic segmentation. Similar to semantic segmentation, panoptic segmentation determines the pixel's class membership. Panoptic segmentation, in contrast to semantic segmentation, can tell apart members of the same class.

Thresholding is the simplest method of image segmentation. The clip-level (or threshold value) is the foundation of this technique for converting a grayscale image to a binary one.

Choosing the appropriate threshold is crucial to this approach (or values when multiple-levels are selected). The maximum entropy method, the balanced histogram thresholding method, Otsu's method (maximum variance), and the k-means clustering method are all widely used in business.

Thresholding techniques for computed tomography (CT) images have emerged as of late. The main idea is that the thresholds are derived from the radiographs rather than the (reconstructed) image, as in Otsu's method.

To divide an image into K groups, the K-means algorithm employs iterative techniques. Standard procedure is

Choose K nodes for a cluster at random or using a heuristic technique like K-means++.

Pixels should be assigned to clusters based on the shortest distance to the cluster centers.

Recalculate cluster centers by averaging cluster pixels.

To achieve convergence, steps 2 and 3 should be repeated (i.e. no pixels change clusters)

The distance between a single pixel and the center of a cluster is expressed as its squared or absolute distance. Color, brightness, texture, and position of individual pixels, or some combination thereof, often play a role in establishing the distinction. The value of K can be chosen empirically, arbitrarily, or heuristically. There is a guarantee of convergence for this algorithm, but it may not provide the best answer. The solution's efficacy is determined by the number of clusters given at the outset and the parameter K.

The Mean Shift algorithm is a method for grouping images into subsets whose number can't be determined in advance. The fact that this does not necessitate any prior knowledge of such a parameter makes it a superior general solution for a wider range of problems.

To perform segmentation, the motion-based method uses the image's motion information.

The concept is straightforward: just compare and contrast two images. If the target of study is in motion, the difference will be that target itself.

Kenney et al. [2] proposed interactive segmentation as an improvement on this idea. To create the motion signal required for motion-based segmentation, a robot is used to poke at objects.

Dov Katz [3, 4] and Oliver Brock [3, 4] proposed the interactive perception framework upon which interactive segmentation is based.

The motion-based rigid motion segmentation technique is another option.

According to the tenets of compression-based approaches, the best way to divide up data is into manageable chunks, with the smallest coding length being the optimal solution. Segmentation seeks out patterns in an image, and those patterns can be exploited to create compressed versions of the original. The technique characterizes each section by its boundary texture and contour. To determine the coding length of each of these parts, we use probability distribution functions as models:

The smooth contour of regions in natural images is exploited by the boundary encoding. Huffman coding uses this prior to encode the difference chain code of an image's contours. The shorter the coding length, the smoother the boundary.

Lossy compression is used to encode textures in a manner analogous to the MDL principle; however, the length of the data given the model is approximatively equal to the number of samples multiplied by the entropy of the model. A multivariate normal distribution is used to model the texture in each area, and the entropy of this distribution can be expressed in a closed form. The estimated entropy has upper bounds that are consistent with the true entropy of the data, which is an intriguing property of this model. This is due to the fact that the entropy of a normal distribution is the highest of any distribution for a given mean and covariance. Because of this, the algorithm's goal of minimization cannot exceed the actual coding length.

The number of bits needed to encode an image based on a given segmentation is calculated by this scheme. Therefore, the objective is to identify, among all possible image segmentations, the one that results in the shortest coding length. A straightforward agglomerative clustering approach will do the trick here. Coarseness of segmentation is set by lossy compression distortion, and the best setting can change from image to image. This parameter can be roughly estimated using an image's texture contrast as a guide. Stronger sensitivity and, therefore, lower quantization is required when, for example, the textures in an image are similar, as in camouflage images.

When compared to other image segmentation techniques, histogram-based methods excel due to the fact that they typically only need a single pass through the pixels. The method involves creating a histogram from the image's pixels and then using the histogram's peaks and valleys to pinpoint the clusters. The scale can be based on hue or brightness.

Recursively applying the histogram-seeking method to image clusters in order to divide them into smaller clusters is a refinement of this technique. Until no new clusters can be formed, this process is repeated with progressively smaller clusters.

It can be tricky to spot truly significant peaks and valleys in an image when using the histogram-seeking technique.

It is easy to extend histogram-based methods so that they work on multiple frames in a single pass.