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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...

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Related Experiment Video

Updated: Nov 4, 2025

Assessment of Bone Fracture Healing Using Micro-Computed Tomography

Assessment of Bone Fracture Healing Using Micro-Computed Tomography

Published on: December 9, 2022

Automatic quantification package (Hone Graph) for phantom-based image quality assessment in bone SPECT: computerized

Hajime Ichikawa^1,2, Kazunori Kawakami³, Masahisa Onoguchi⁴

¹Department of Radiology, Toyohashi Municipal Hospital, 50 Aza Hachiken Nishi, Aotake-Cho, Toyohashi, Aichi, 4418570, Japan.

Annals of Nuclear Medicine

|May 24, 2021

Summary

This summary is machine-generated.

A new software system automatically assesses image quality in bone SPECT using quantitative indexes. This system demonstrates high accuracy and reproducibility in classifying lesion detectability, improving bone SPECT analysis.

Keywords:

Bone scintigraphy Detectability Image analysis Percentage of detectability equivalence volume Single-photon emission computed tomography

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Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

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Published on: April 8, 2015

Area of Science:

Medical Imaging
Nuclear Medicine Technology
Quantitative Image Analysis

Background:

A custom thoracic bone scintigraphy phantom (SIM² bone phantom) was previously developed to evaluate bone single-photon emission computed tomography (SPECT) image quality.
Assessing bone SPECT image quality and lesion detectability is crucial for accurate diagnosis.

Purpose of the Study:

To develop an automated assessment system for bone SPECT imaging technology.
To validate the accuracy and reproducibility of this automated system.

Main Methods:

Utilized the SIM² bone phantom with simulated lesions and radioactivity.
Performed dynamic SPECT acquisitions and ordered subset expectation maximization reconstructions.
Developed software using statistical parametric mapping to calculate quantitative indexes and a detectability score (DS).
Employed decision tree analysis for automated DS classification based on quantitative indexes.

Main Results:

Quantitative indexes showed consistent trends during automated analysis.
The developed software accurately classified lesion detectability scores (DS).
Achieved 91.7% agreement and a kappa coefficient of 0.93 with the gold standard in validation.

Conclusions:

The software successfully automates lesion detectability classification in bone SPECT.
The system demonstrates excellent reproducibility and accuracy for quantitative analysis.
This automated approach can enhance the efficiency and reliability of bone SPECT image quality assessment.