DOI: 10.5281/zenodo.8167284
title: Dataset of CT scans, slice photographs, and visual browning scores of 120 'Kanzi' apples

description: Summary
This dataset is a collection of CT scans, slice photographs, and visual browning scores of 120 'Kanzi' apples.
Description
Sample information
In 2022, 120 'Kanzi' apples that had been stored under CA conditions (4 °C, 1 kPa O2, 1.5 kPa CO2) for 8 months were obtained from FruitMasters, The Netherlands. The fruit was grown in orchards surrounding Geldermalsen, the Netherlands, and harvested at physiological maturity in 2021.
CT acquisition
The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].  A cone beam geometry with a circular trajectory was used to acquire 1440 projection images at an exposure time of 100ms, a tube peak voltage of 90kV, a current of 550uA, and 2 times binning, halving the detector resolution. Volumes were reconstructed with the FDK algorithm and a voxel size of 129.3um. Beam hardening correction was used from the FleXbox package [Kostenko 2020]. To make sure that the grey values could be compared between scans the spectral sensitivity of the scanner was first estimated for each scan individually and the average of these estimates was used for beam hardening correction on all CT scans. All apples were scanned with the stem side on top. Moreover, a line was drawn on all apples from the stem to the calyx. The apples were put in the CT scanner so that the line was facing the X-ray source.
The CT volumes are saved as .tiff stacks. All volumes have been cropped to remove the background.
Slicing and photograph acquisition
One day after CT scanning, the apples were sliced using a modified meat-slicing machine (CaterChef, house brand of EMGA, Mijdrecht, The Netherlands), which is illustrated in the file slicing_machine_labels.png. The sliding surface of the meat-slicing machine was replaced by a transparent acrylic sheet, and a camera was placed behind the slicing surface. While in the machine, each apple was kept in place by a suction cup so that it could not rotate during the slicing. All apples were sliced from the stem end to the calyx end, with a slice thickness of roughly 4mm. Every time before slicing, a picture was taken of the remaining part of the apple through the transparent sliding surface. To ensure that all apples were roughly aligned to the CT scans, the apples were oriented so that the line drawn earlier was on top.
The slice photographs are saved as .png files. All photographs have been cropped to remove the background and to center the apple in the image.
Visual browning scores
After each apple was sliced it was also visually inspected, and a score from one to ten was given to describe the amount of browning in the apple.
Related paper
When using this dataset please consider citing the following paper. It explains how the dataset was collected and used for the first time:
Dirk Elias Schut, Rachael Maree Wood, Anna Katharina Trull, Rob Schouten, Robert van Liere, Tristan van Leeuwen, Kees Joost Batenburg, "Detecting internal disorders in fruit by CT. Part 1: Joint 2D to 3D image registration workflow for comparing multiple slice photographs and CT scans of apple fruit", 2023, arXiv preprint arXiv:2310.01987
Research groupThis dataset was produced in a collaboration between the Computational Imaging group at Centrum Wiskunde & Informatica (CWI), and GREEFA.
https://www.cwi.nl/research/groups/computational-imaginghttps://www.greefa.com/nl/
Contact detailsdirk [dot] schut [at] cwi [dot] nl
AcknowledgmentsThis work was funded by the Dutch Research Council (NWO) through the UTOPIA project (ENWSS.2018.003). The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.