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Abstract
Knowledge of anatomy is an elementary step towards the understanding of the human body. First used by Alphonse Bertillon as an identification system, anthropometry refers to the measurements of human individuals. In orthopaedics, comparative analysis is widely used in the understanding of morphological variance due to races, sex and pathological conditions. The characterization of bone and joint geometry has also been a foundation of modern surgical implant design.
Traditional anthropometric studies rely on physical measurements by means of osteometric table. Recent advancements of 3-D imaging modalities and image processing techniques have empowered more fine-grained anthropometric characterization. The inspiration for the study is:
- the understanding of anatomy originating from the clinical domain have shown to contribute to undesirable inconsistency in the image processing domain.
- the difficulty of existing automated anthropometric methodology in handling pathological femur.
- the tedious amount of manual and subjective work involved with the increasing amount of high resolution imaging data.
The aim of the study is to:
- develop a consistent and robust methodology in accurate extraction of anthropometric parameters on the femur.
- increase the level of automation on the process of anthropometric parameter extraction.
With the bridging of anthropometry and the image processing disciplines, a robust methodology of anthropometric parameter extraction with high level of automation was developed, implemented and tested.
A dataset comprised of femoral CT scans of 19 healthy Australian, 10 healthy Japanese, 15 Japanese diagnosed with primary or secondary hip osteoarthritis and 20 adult sheep was utilized for testing. Intra-class correlation and Cronbach's α were extensively employed to evaluate the intra-rater, interrater and repeated scans consistency of the proposed methodology. High correlation values (mean > 0.95) were noted suggesting a high consistency of the methodology. All healthy and osteoarthritis human datasets were processed successfully. With the structural similarity between the sheep and human femur, the robustness was further demonstrated by accurate processing of the sheep dataset without the need of any modification of the underlying methodology. The methodology proposed is highly automated and requires very few user interactions in the parameter extraction stage.