Instance camera focus prediction for crystal agglomeration classification

TL;DR

This paper introduces a novel method combining camera focus prediction and instance segmentation to improve crystal agglomeration classification accuracy from microscopic images, addressing depth and focus challenges.

Contribution

The study proposes an instance camera focus prediction network integrated with segmentation for more accurate crystal agglomeration analysis, outperforming baseline models.

Findings

Higher agglomeration classification accuracy

Improved segmentation performance

Better focus level alignment with visual observations

Abstract

Agglomeration refers to the process of crystal clustering due to interparticle forces. Crystal agglomeration analysis from microscopic images is challenging due to the inherent limitations of two-dimensional imaging. Overlapping crystals may appear connected even when located at different depth layers. Because optical microscopes have a shallow depth of field, crystals that are in-focus and out-of-focus in the same image typically reside on different depth layers and do not constitute true agglomeration. To address this, we first quantified camera focus with an instance camera focus prediction network to predict 2 class focus level that aligns better with visual observations than traditional image processing focus measures. Then an instance segmentation model is combined with the predicted focus level for agglomeration classification. Our proposed method has a higher agglomeration classification and segmentation accuracy than the baseline models on ammonium perchlorate crystal and sugar crystal dataset.