Parallel-beam X-ray CT datasets of apples with internal defects and label balancing for machine learning

TL;DR

This paper introduces three comprehensive X-ray CT datasets of apples with internal defects, designed for developing and testing machine learning methods in image reconstruction, segmentation, and defect detection, while addressing label bias issues.

Contribution

The paper provides real 3D X-ray CT datasets with defect labels and proposes a novel optimization approach to eliminate label bias for machine learning applications.

Findings

Datasets include noiseless and noisy simulations based on real data.

A new method for eliminating label bias in datasets is demonstrated.

Datasets support various tasks like reconstruction, segmentation, and defect detection.

Abstract

We present three parallel-beam tomographic datasets of 94 apples with internal defects along with defect label files. The datasets are prepared for development and testing of data-driven, learning-based image reconstruction, segmentation and post-processing methods. The three versions are a noiseless simulation; simulation with added Gaussian noise, and with scattering noise. The datasets are based on real 3D X-ray CT data and their subsequent volume reconstructions. The ground truth images, based on the volume reconstructions, are also available through this project. Apples contain various defects, which naturally introduce a label bias. We tackle this by formulating the bias as an optimization problem. In addition, we demonstrate solving this problem with two methods: a simple heuristic algorithm and through mixed integer quadratic programming. This ensures the datasets can be split into test, training or validation subsets with the label bias eliminated. Therefore the datasets can be used for image reconstruction, segmentation, automatic defect detection, and testing the effects of (as well as applying new methodologies for removing) label bias in machine learning.