Segmentation (image processing) Totally Explained

Everything about Segmentation Image Processing totally explained

In computer vision, segmentation refers to the process of partitioning a digital image into multiple regions (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that's more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. The result of image segmentation is a set of regions that collectively cover the entire image, or a set of contours extracted from the image (see edge detection). Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic(s).

Locate tumors and other pathologies
Measure tissue volumes
Computer-guided surgery
Diagnosis
Treatment planning
Study of anatomical structure

Locate objects in satellite images (roads, forests, etc.)

Face recognition

Fingerprint recognition

Automatic traffic contolling systems

Machine vision Several general-purpose algorithms and techniques have been developed for image segmentation. Since there's no general solution to the image segmentation problem, these techniques often have to be combined with domain knowledge in order to effectively solve an image segmentation problem for a problem domain.

Clustering Methods

The K-means algorithm is an iterative technique that's used to partition an image into K clusters. The basic algorithm is:

Pick K cluster centers, either randomly or based on some heuristic

Assign each pixel in the image to the cluster that minimizes the variance between the pixel and the cluster center

Re-compute the cluster centers by averaging all of the pixels in the cluster

Repeat steps 2 and 3 until convergence is attained (for example no pixels change clusters) In this case, variance is the squared or absolute difference between a pixel and a cluster center. The difference is typically based on pixel color, intensity, texture, and location, or a weighted combination of these factors. K can be selected manually, randomly, or by a heuristic.
This algorithm is guaranteed to converge, but it may not return the optimal solution. The quality of the solution depends on the initial set of clusters and the value of K.

Histogram-Based Methods

Histogram-based methods are very efficient when compared to other image segmentation methods because they typically require only one pass through the pixels. In this technique, a histogram is computed from all of the pixels in the image, and the peaks and valleys in the histogram are used to locate the clusters in the image.
One disadvantage of the histogram-seeking method is that it may be difficult to identify significant peaks and valleys in the image. In this technique of image classification distance metric and integrated region matching are familiar.

Edge Detection Methods

Edge detection is a well-developed field on its own within image processing. Region boundaries and edges are closely related, since there's often a sharp adjustment in intensity at the region boundaries. Edge detection techniques have therefore been used as the base of another segmentation technique.
The edges identified by edge detection are often disconnected. To segment an object from an image however, one needs closed region boundaries. Discontinuities are bridged if the distance between the two edges is within some predetermined threshold.

Region Growing Methods

The first region growing method was the seeded region growing method. This method takes a set of seeds as input along with the image. The seeds mark each of the objects to be segmented. The regions are iteratively grown by comparing all unallocated neighbouring pixels to the regions. The difference between a pixel's intensity value and the region's mean,

delta

, is used as a measure of similarity. The pixel with the smallest difference measured this way is allocated to the respective region. This process continues until all pixels are allocated to a region.
Seeded region growing requires seeds as additional input. The segmentation results are dependent on the choice of seeds. Noise in the image can cause the seeds to be poorly placed. Unseeded region growing is a modified algorithm that doesn't require explicit seeds. It starts off with a single region

A_1

– the pixel chosen here doesn't significantly influence final segmentation. At each iteration it considers the neighbouring pixels in the same way as seeded region growing. It differs from seeded region growing in that if the minimum

delta

is less than a then a predefined threshold

T

then it's added to the respective region

A_j

. If not, then the pixel is considered significantly different from all current regions

A_i

and a new region

A_

is created with this pixel.
One variant of this technique, proposed by Haralick and Shapiro (1985),

Graph Partitioning Methods

The “normalized cuts” method was first proposed by Shi and Malik in 1997. In this method, the image being segmented is modelled as a weighted undirected graph. Each pixel is a node in the graph, and an edge is formed between every pair of pixels. The weight of an edge is a measure of the similarity between the pixels. The image is partitioned into disjoint sets (segments) by removing the edges connecting the segments. The optimal partitioning of the graph is the one that minimizes the weights of the edges that were removed (the “cut”). Shi’s algorithm seeks to minimize the “normalized cut”, which is the ratio of the “cut” to all of the edges in the set.

Watershed Transformation

The Watershed transformation considers the gradient magnitude of an image as a topographic surface. Pixels having the highest gradient magnitude intensities (GMIs) correspond to watershed lines, which represent the region boundaries. Water placed on any pixel enclosed by a common watershed line flows downhill to a common local intensity minima (LMI). Pixels draining to a common minimum form a catchment basin, which represent the regions.

Model based Segmentation

The central assumption of such an approach is that structures of interest/organs have a repetitive form of geometry. Therefore, one can seek for a probabilistic model towards explaining the variation of the shape of the organ and then when segmenting an image impose constraints using this model as prior. Such a task involves (i) registration of the training examples to a common pose, (ii) probabilistic representation of the variation of the registered samples, and (iii) statistical inference between the model and the image. State of the art methods in the literature for knowledge-based segmentation involve active shape and appearance models, active contours and deformable templates and level-set based methods.

Multi-scale Segmentation

Image segmentations are computed at multiple scales in scale-space and sometimes propagated from coarse to fine scales; see scale-space segmentation.
Segmentation criteria can be arbitrarily complex and may take into account global as well as local criteria. A common requirement is that each region must be connected in some sense.

Semi-automatic Segmentation

In this kind of segmentation, the user outlines the region of interest with the mouse clicks and algorithms are applied so that the path that best fits the edge of the image is shown.
Techniques like Livewire or Intelligent Scissors are used in this kind of segmentation.

Neural Networks Segmentation

Neural Network segmentation relies on processing small areas of an image using a neural network or a set of neural networks. After such processing the decision-making mechanism marks the areas of an image accordingly to the category recognized by the neural network.

Open Source Software

Several open source software packages are available for performing image segmentation

ITK

ITK-SNAP is a GUI tool that combines manual and semi-automatic segmentation with level sets.

GIMP

VXL

ImageMagick There are also free academic software packages:

GemIdentFurther Information

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