# Optimization of Precipitate Segmentation Through Linear Genetic Programming of Image Processing

arXiv: https://arxiv.org/abs/2602.07310 · HTML: https://mlsanity.com/paper/2602.07310/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 7 Feb 2026.

## Builds on (most cited first, 6 of 6)

- [Segment Everything Everywhere All at Once](https://mlsanity.com/paper/2304.06718/) (2023)
- [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019)
- [Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentation](https://mlsanity.com/paper/2410.19836/) (2024)
- [A Recent Survey on the Applications of Genetic Programming in Image Processing](https://mlsanity.com/paper/1901.07387/) (2019)
- [Performance of Human Annotators in Object Detection and Segmentation of Remotely Sensed Data](https://mlsanity.com/paper/2409.10272/) (2024)
- [Bayesian SegNet for Semantic Segmentation with Improved Interpretation of Microstructural Evolution During Irradiation of Materials](https://mlsanity.com/paper/2502.14184/) (2025)

## Abstract (arXiv)

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.