Joint 2D to 3D image registration workflow for comparing multiple slice photographs and CT scans of apple fruit with internal disorders

Postharvest Biology and Technology, vol. 211, pp. 112814

Abstract

A large percentage of apples are affected by internal disorders after long-term storage, which makes them unacceptable in the supply chain. CT imaging is a promising technique for in-line detection of these disorders. Therefore, it is crucial to understand how different disorders affect the image features that can be observed in CT scans. This paper presents a workflow for creating datasets of image pairs of photographs of apple slices and their corresponding CT slices. By having CT and photographic images of the same part of the apple, the complementary information in both images can be used to study the processes underlying internal disorders and how internal disorders can be measured in CT images. The workflow includes data acquisition, image segmentation, image registration, and validation methods. The image registration method aligns all available slices of an apple within a single optimization problem, assuming that the slices are parallel. This method outperformed optimizing the alignment separately for each slice. The workflow was applied to create a dataset of 1347 slice photographs and their corresponding CT slices. The dataset was acquired from 107 ‘Kanzi’ apples that had been stored in controlled atmosphere (CA) storage for 8 months. In this dataset, the distance between annotations in the slice photograph and the matching CT slice was, on average, 1.47 ± 0.40 mm. Our workflow allows collecting large datasets of accurately aligned photo-CT image pairs, which can help distinguish internal disorders with a similar appearance on CT. With slight modifications, a similar workflow can be applied to other fruits or MRI instead of CT scans.

Authors 7

1. Dirk Elias Schut corresponding

   Centrum Wiskunde & Informatica

   Affiliation as printed

   Computational Imaging Group, Centrum Wiskunde en Informatica (CWI), Science Park 123, 1098 XG Amsterdam, the Netherlands

2. Rachael Maree Wood

   Wageningen University & Research

   Affiliation as printed

   Horticulture and Product Physiology, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB Wageningen, the Netherlands

3. Anna Katharina Trull
   Affiliation as printed

   Greefa Machinebouw B.V., Langstraat 12, 4196 JB Tricht, the Netherlands

4. Rob E. Schouten

   Wageningen University & Research

   Affiliation as printed

   Wageningen Food and Biobased Research, Bornse Weilanden 9, 6708 WG Wageningen, the Netherlands

5. Robert van Liere

   Centrum Wiskunde & Informatica · Eindhoven University of Technology

   Affiliation as printed

   Computational Imaging Group, Centrum Wiskunde en Informatica (CWI), Science Park 123, 1098 XG Amsterdam, the Netherlands

   Visualization Group, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, the Netherlands

6. Tristan van Leeuwen

   Centrum Wiskunde & Informatica · Utrecht University

   Affiliation as printed

   Computational Imaging Group, Centrum Wiskunde en Informatica (CWI), Science Park 123, 1098 XG Amsterdam, the Netherlands

   Mathematisch Instituut, Utrecht University, Budapestlaan 6, 3584 CD Utrecht, the Netherlands

7. Kees Joost Batenburg Aachen Leiden Institute of Advanced Computer Science

   Leiden University · Centrum Wiskunde & Informatica

   Affiliation as printed

   Computational Imaging Group, Centrum Wiskunde en Informatica (CWI), Science Park 123, 1098 XG Amsterdam, the Netherlands

   Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Niels Bohrweg 1, 2333 CA Leiden, the Netherlands