Electronic Thesis and Dissertation Repository

Thesis Format

Monograph

Degree

Master of Arts

Program

Anthropology

Supervisor

Nelson, Andrew J.

Abstract

Using multi-resolution CT techniques, this project examined the visibility of identifiable skeletal traits in mummified avian bundles from ancient Egypt and the specificity with which avian taxa can be identified with digital 3D scans. Six mummified birds were scanned and processed with a deep learning segmentation algorithm. Three raptors were successfully identified as Falco tinnunculus, a species associated with the Egyptian goddess Isis. Analyses revealed that low-resolution (~110-80 μm) micro-CTs are sufficient for visualizing the bird skeleton (specifically the accessory pygostyle bones and distal wing sesamoid bones), while high-resolution (~30-20 μm) is necessary only for minute cranial details (the scleral ossicle). Clinical CT scans did not provide sufficient resolution to visualize skeletal traits; long bone measurements were found to be accurate with these scans. This research demonstrates the effectiveness of 3D imaging techniques for studying mummified birds and contributes to the growing body of research on ancient Egyptian animal mummies.

Summary for Lay Audience

Egyptian animal mummies have long fascinated researchers and archaeologists, but studying them typically involves unwrapping and damaging the bundles. This thesis uses non-destructive imaging methods to examine six mummified bird bundles (four raptors and two ibises) from ancient Egypt. The goal was to identify the bird species inside using their skeletons and propose interpretations of the bundles' roles in ancient Egyptian religion.

Three imaging techniques were used to assess how different resolutions would impact the ability to identify the birds from their skeletons. Clinical CT scans, which are common and easily accessible, provided the lowest resolution images. A dental cone-beam CT scanner gave medium-resolution images, while a micro-CT scanner offered high-resolution scans. A deep learning algorithm (an advanced form of machine learning) was trained and used to digitally separate the skeletons from the mummified bundles.

Using the lowest (~110-80µm) and highest (~30-20µm) resolution micro-CT scans, three of the raptors were successfully identified as belonging to the species Falco tinnunculus (common kestrel) by examining their whole skeletons and cranial features, respectively. This species identification can then be used to interpret the importance of certain bird species. For example, the common kestrel was associated with the goddess Isis, revered for her powers of fertility and healing. Identifying bird skeletons allows for their interpretation in their original context and enhances our understanding of ancient Egyptian beliefs.

Although clinical and dental cone-beam CT scans did not produce clear enough 3D images for species identification, clinical CTs provided accurate skeletal measurements, and the dental cone-beam CT showed good contrast resolution, indicating potential for future studies of bird mummies.

This thesis demonstrates the effectiveness of non-destructive digital imaging techniques for studying mummified birds from ancient Egypt. While micro-CT analysis is ideal, they are expensive, time-consuming, and produce very large data sets (~10 KB) that require significant processing power. Thus, even though clinical and cone-beam CT scans may not produce the best 3D images, they still provide valuable information that can aid in narrowing down a species identification of a mummified bird.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Download

Share

COinS