Key Terms Associated with the Understanding of Segmentation Imaging and Analysis (SIA)

Segmentation-Imaging-and-Analysis

Image processing is a very fast growing industry in the current context of digital gaming, AR VR interactions, and video photography editing. Without the use of Segmentation Imaging techniques, it is impossible to decipher the real value of clustering, automation, and machine learning based image processing capabilities for various critical tasks undertaken across industries.

In our last article on SIA, we described the contemporary techniques used in image processing and how these apply to industrial practices. In this article, we will dig deeper into the SIA platforms, and explore the most important keywords that are often associated with the segmentation image and analysis.

Let’s start.

Digital Image Processing:

Digital image processing is the superset of the SIA technique. DIP is used as an extension of computing techniques built specifically for the pre-processing, enhancement and display, and extraction of image information using mathematical algorithms. Digital processing is perhaps the most advanced form of any digital enhancement capabilities, providing better results than analog signal processing. With the advent of modern day GPUs and MOSFETs, we are witnessing a wide scale adoption of digital image processing in the areas of pixel enhancement.

Active Contour Model

In the world of 3D computerized image processing, Active Contour Models play a vital role. It’s defined as the branch of Segmentation Image Analysis that specifically focuses on 2D slicing of “target pixels” for superior segmentation and processing, applied in areas where energy functions and contour / boundaries are often subjected to deformities and curves generated from 2D images. 

Medical image processing is a highly revered data science specialization. Millions of dollars are being invested to develop medical image analysis techniques that can provide accurately scaled images using active contours. Active Contour Models can be categorized into 3 sub-fields: Gradient Vector Flow, Balloon, and Geometric Models. Each of these techniques is used extensively in recreating medical images from brain CT images, PET/ SPECT, cardiography, neural signals, and MRI scans.

Snakes 

In the world of Pythons and Anacondas, we can always expect new algorithms for image processing. Do you agree?

Well, meet the “Snakes” model in digital image processing.

Snakes modeling is derived from the Active Contour Models that I just explained above. It is used to refine 2D images, often finding applications in Computer Vision techniques associated with advanced capabilities such as Object tracking, Contour recognition, Curs expansion, Facial recognition, Stereo-matching / stereo tracking, motion tracking, and gesture control, and edge detection. 

Gradient Vector Flow (GVF) snake model and Diffusion snake model are very popular techniques used in segmentation technology development.

Geometric Active Contour or Geodesic Active Contour (GAC)

GAC is part of the family of Segmentation Image analysis. It derives its origin from the Active Contour Models that we described above, but braches apart from the rest of the models by virtue of its ability to modify smooth curves in the Euclidean plane perpendicular to the tangents on the curve. The motion points flow along the curvature of the target pixels, and therefore, give a sense of geometric flow along the curve, making it easy for Machine Learning image processing algorithms to detect large sized objects and their edge stability.

There are countless projects involving GACs in the medical image analysis industry.

Watersheds

This is my favorite keyword associated with the Image processing and analysis techniques that I have come across as a data scientist in recent years. The Watershed segmentation technique is a mathematical morphology algorithm that was first introduced and explained in 1978 by Digabel and Lantuejoul. Vincent and Soille carried out many breakthrough experiments with Watersheds to apply them to the topographic interpretation of landscapes, contours, and curves. It further developed into 3d imaging analysis using watershed “seeds” to develop advanced images that have brighter pixels resolution. Its application is widely accepted in the cancer detection algorithms that require watershed image segmentation to deliver high-quality lymph nodes image, separating the target region from surrounding and background.

Top industries apart from medical image analysis include LiDAR images particularly important in the domains of military surveillance; deep sea mining, oceanography, astronomy and space research, and drone-based image photography; and re-engineering of old video games.

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