Optimization of Precipitate Segmentation Through Linear Genetic Programming of Image Processing

TL;DR

This paper introduces a genetic programming-based method to optimize image segmentation of precipitates in micrographs, significantly improving speed and accuracy for alloy analysis.

Contribution

It develops an automated, interpretable image processing pipeline optimized via linear genetic programming for precipitate segmentation in micrographs.

Findings

Achieved 1.8% average pixel-by-pixel segmentation error.

Generated MATLAB code for human-interpretable image filtering pipelines.

Processed 3.6 MP images in about 2 seconds on average.

Abstract

Current analysis of additive manufactured niobium-based copper alloys relies on hand annotation due to varying contrast, noise, and image artifacts present in micrographs, slowing iteration speed in alloy development. We present a filtering and segmentation algorithm for detecting precipitates in FIB cross-section micrographs, optimized using linear genetic programming (LGP), which accounts for the various artifacts. To this end, the optimization environment uses a domain-specific language for image processing to iterate on solutions. Programs in this language are a list of image-filtering blocks with tunable parameters that sequentially process an input image, allowing for reliable generation and mutation by a genetic algorithm. Our environment produces optimized human-interpretable MATLAB code representing an image filtering pipeline. Under ideal conditions--a population size of 60 and a maximum program length of 5 blocks--our system was able to find a near-human accuracy solution with an average evaluation error of 1.8% when comparing segmentations pixel-by-pixel to a human baseline using an XOR error evaluation. Our automation work enabled faster iteration cycles and furthered exploration of the material composition and processing space: our optimized pipeline algorithm processes a 3.6 megapixel image in about 2 seconds on average. This ultimately enables convergence on strong, low-activation, precipitation hardened copper alloys for additive manufactured fusion reactor parts.