  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, $1.47 \pm 0.40$ 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.
