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Applying the principles of mathematic morphology to image and data analysis

Idaho National Laboratory

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Technology Marketing Summary

Computers and automated systems have accelerated productivity and improved quality and reliability for nearly everything in our modern world, and are destined to take on increasing roles as time moves on. One major limiting factor for automated systems is their inability to categorize and recognize objects, particularly under changing lighting or other conditions. Examples of how this could be useful include automatically detecting manufacturing defects, analyzing changes between two images (e.g. medical scans), noise filtering in radio frequency communications, and extracting weak signals or images from various sources.

Current approaches to making “smart” systems generally build custom solutions for every problem with very specific outcomes. Examples include self-driving car systems and facial recognition software, which have very specific features and approaches built in that usually do not translate to other applications very well. Other examples, such as automatic defect detection, require tightly controlled lighting and often require the object being inspected to be in the same position to be able to identify problems. Generally, these automated systems can, when conditions match the programmed expectations, identify that there is a problem, but have very limited ability when measurement conditions are dynamic or the situation changes in unanticipated ways.


Researchers at INL have developed an analysis system known as MorphoHawk for automatic feature detection and classification across a host of applications in changing environmental conditions. In general, MorphoHawk can be trained to identify features of interest, and will then group features in a scene (e.g. image, signal, etc) and categorize them according to the rules it was conditioned with. After it has categorized an image or other multi-dimentional data set, it can compare the features it has identified with subsequent data sets, allowing it to detect changes (e.g. manufacturing quality control) or detect the introduction of new features (e.g. a tumor in medical scans or a person entering a scene monitored by a camera). It has shown that it can discern between an object and its shadow, meaning it can handle differences in registration and light conditions in dynamic environments.  This is possible because MorphoHawk algorithms characterize and compare morphological features, rather than conducting a binary analysis (e.g. light vs. dark).

MorphoHawk has shown utility as a signal filtering tool to differentiate between noise and meaningful data in analysis of digital images and electronic signals, resulting in sharp, cleaned images and clearly extracting the message of the signals while removing the noise. MorphoHawk can be applied to analyze images for manufacturing defects, enhancing the capability of existing inspection systems. Feature extraction is another unique capability of MorphoHawk.  For example, metal surface topology can be separated into effects of rolling and grinding, allowing discrepancies to be assigned to the appropriate process. It has even been used to identify a facture path in materials and examine structural changes in battery electrodes to predict battery lifetime.

  • Trainable object identification and analysis
  • Ability to correctly identify and categorize objects in different environmental conditions
  • Ability to identify noise or artifacts and clean up images and signals
  • Ability to track changes over time
  • Ability to separate different types of changes to help identify root causes of problems
Applications and Industries
  • Manufacturing (e.g. automated defect detection, quality control on processes, etc)
  • Wireless communications (e.g. message extraction from noisy signal)
  • Medical (e.g. comparison of medical images to detect changes)
  • Image processing (e.g. sharpening filters)
  • Tracking (bar code reading)
  • Surveillance / security (e.g. automatically detecting what type of vehicle enters a scene)
  • Counterfeit detection
  • Object classification for autonomous vehicles and aircraft
  • Other applications where automated identification and classification is required
More Information

The MorphoHawk methodology has been demonstrated in multiple specific applications.  Additional development will be required to adapt the software / methodology to function in new scenarios of interest.

MorphoHawk introductory video:

Publication: “Application of Morphological Synthesis for Understanding Electrode Microstructure Evolution as a Function of Applied Charge/Discharge Cycles,” Applied Physics A, 122(10), 894 (2016).


Patents and Patent Applications
ID Number
Title and Abstract
Primary Lab
Patent 9,342,876
Methods, apparatuses, and computer-readable media for projectional morphological analysis of N-dimensional signals
Embodiments discussed herein in the form of methods, systems, and computer-readable media deal with the application of advanced "projectional" morphological algorithms for solving a broad range of problems. In a method of performing projectional morphological analysis, an N-dimensional input signal is supplied. At least one N-dimensional form indicative of at least one feature in the N-dimensional input signal is identified. The N-dimensional input signal is filtered relative to the at least one N-dimensional form and an N-dimensional output signal is generated indicating results of the filtering at least as differences in the N-dimensional input signal relative to the at least one N-dimensional form.
Idaho National Laboratory 05/17/2016
Technology Status
Technology IDDevelopment StageAvailabilityPublishedLast Updated
BA-481Prototype - Further development required to implement in specific applicationsAvailable11/21/201711/07/2017

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