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Classification of Bones

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The long...

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Updated: Jun 8, 2026

Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum

Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum

Published on: March 19, 2017

Long bone morphometrics for human from non-human discrimination.

Bree Saulsman¹, Charles E Oxnard, Daniel Franklin

¹Centre for Forensic Science, The University of Western Australia, 35 Stirling Highway, Crawley, Western Australia, Australia.

Forensic Science International

|September 15, 2010

Summary

This summary is machine-generated.

Forensic anthropologists can now use a new morphometric method to distinguish human from animal bones. This non-invasive technique accurately identifies human bone fragments, even from small diaphysis samples.

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Area of Science:

Forensic Anthropology
Bioarchaeology
Comparative Osteology

Background:

Distinguishing human from non-human skeletal remains is crucial in forensic anthropology.
Current methods (microscopic, immunological, DNA testing) can be time-consuming and expensive.
Bone fragments lacking distinct morphological markers pose a significant identification challenge.

Purpose of the Study:

To investigate an alternative morphometric approach for differentiating human long bones from those of common Australian quadrupedal and bipedal animals.
To develop a non-invasive method for identifying human bone fragments.

Main Methods:

Eight traditional measurements were taken on human (n=50) and animal (n≥10 per species) long bones (humeri, femora, tibiae).
Species included sheep, dog, pig, kangaroo, and emu.
Linear discriminant analysis (LDA) was used to analyze the morphometric data.

Main Results:

The morphometric approach achieved high accuracy (≥95% cross-validated) in classifying unknown bones as human or non-human.
The method remained accurate even when analyzing only fragments of the diaphysis (63-99% accuracy).
Sufficient variation exists in external bone structure to differentiate species.

Conclusions:

This morphometric technique offers a forensically useful, non-invasive method for distinguishing human from animal bone fragments.
The approach is effective even with limited skeletal material.
It provides a valuable alternative or complementary tool to existing identification methods.