Brief Introduction to Image Segmentation

    Image segmentation is a technical process which can divide an image into regions with certain and special characteristics and extract the objectives interested from them. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) 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 fall under a category where they have some or the other thing common in them.

    Segmentation in Image Processing is being used in the medical industry for efficient and faster diagnosis, detecting diseases, tumors, and cell and tissue patterns from various medical imagery generated from radiography, MRI, endoscopy, thermography, ultrasonography, etc. Also image segmentation has a massive application area in robotics, like RPA, self-driving cars, etc. Security images can be processed to detect harmful objects, threats, people and incidents using image segmentation. 

    Segmentation techniques are either contextual or non-contextual. The latter take no account of spatial relationships between features in an image and group pixels together on the basis of some global attribute, e.g. grey level or colour. Contextual techniques additionally exploit these relationships, e.g. group together pixels with similar grey levels and close spatial locations. Lets look at the main two techniques of image segmentation. 

1. Threshold based segmentation 

     This  is perhaps the most basic and yet powerful technique to identify the required objects in an image. Based on the intensity, the pixels in an image get divided by comparing the pixel’s intensity with a threshold value. The threshold method proves to be advantageous when the objects in the image in question are assumed to be having more intensity than the background (and unwanted components) of the image. At its simpler level, the threshold value T is considered to be a constant. But that approach may be futile considering the amount of noise (unwanted information) that the image contains. So, we can either keep it constant or change it dynamically based on the image properties and thus obtain better results. Based on that, thresholding is of the following types:

• Simple Thresholding - This technique replaces the pixels in an image with either black or white. If the intensity of a pixel (i,j) at position (i,j) is less than the threshold (T), then we replace that with black and if it is more, then we replace that pixel with white. This is a binary approach to thresholding.

• Adaptive Thresholding - Imaging factors such as uneven illumination can effect the global thresholding process and the results may be unsatisfactory. In such situations the image can be split into smaller squares and examine the variances of grey levels within squares. If the variance within a square exceed 100 that image segment (square) should be thresholded separately using the iterative process. All other image segments (squares) should be considered as a single region and perform segmentation using a single threshold.

2. Region Based Segmentation

    The region based segmentation methods involve the algorithm creating segments by dividing the image into various components having similar characteristics. These components, simply put, are nothing but a set of pixels. Region-based image segmentation techniques initially search for some seed points – either smaller parts or considerably bigger chunks in the input image. Next, certain approaches are employed, either to add more pixels to the seed points or further diminish or shrink the seed point to smaller segments and merge with other smaller seed points. Hence, there are two basic techniques based on this method.

• Region Growing - It’s a bottom to up method where we begin with a smaller set of pixel and start accumulating or iteratively merging it based on certain pre-determined similarity constraints. Region growth algorithm starts with choosing an arbitrary seed pixel in the image and compare it with its neighboring pixels. If there is a match or similarity in neighboring pixels, then they are added to the initial seed pixel, thus increasing the size of the region. When we reach the saturation and hereby, the growth of that region cannot proceed further, the algorithm now chooses another seed pixel, which necessarily does not belong to any region(s) that currently exists and start the process again. Region growth, also a pixel based algorithm like thresholding but the major difference is thresholding extracts a large region based out of similar pixels, from anywhere in the image whereas region-growth extracts only the adjacent pixels. Region growing techniques are preferable for noisy images, where it is highly difficult to detect the edges.

• Region Splitting and Merging - The splitting and merging based segmentation methods use two basic techniques done together in conjunction – region splitting and region merging – for segmenting an image. Splitting involves iteratively dividing an image into regions having similar characteristics and merging employs combining the adjacent regions that are somewhat similar to each other. A region split, unlike the region growth, considers the entire input image as the area of business interest. Then, it would try matching a known set of parameters or pre-defined similarity constraints and picks up all the pixel areas matching the criteria. This is a divide and conquers method as opposed to the region growth algorithm. Quad Tree is a useful data structure which can be used in this operation.

There are many other methods of segmentation like Multi-scale segmentation, Watershed transformation, Model-based segmentation, Dual clustering method etc.