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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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Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

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An Independent Active Contours Segmentation framework for bone micro-CT images.

Vasileios Ch Korfiatis¹, Simone Tassani², George K Matsopoulos¹

¹School of Electrical and Computer Engineering, National Technical University of Athens, Greece.

Computers in Biology and Medicine

|June 27, 2017

Summary

A new method, Independent Active Contours Segmentation (IACS), improves micro-CT image segmentation. This automated approach enhances accuracy and robustness for bone research and diagnostics.

Keywords:

Active Contours Image segmentation Micro-CT ROI extraction Trabecular bone

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A Sectioning, Coring, and Image Processing Guide for High-Throughput Cortical Bone Sample Procurement and Analysis for Synchrotron Micro-CT

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Published on: June 12, 2020

Area of Science:

Biomedical Imaging
Medical Image Analysis
Radiology

Background:

Micro-computed tomography (micro-CT) is crucial for preclinical research and bone pathology analysis.
Current micro-CT segmentation methods are often manual, subjective, and lack sophistication.
This leads to errors and inconsistent results in image analysis.

Purpose of the Study:

To introduce a novel, automated segmentation framework for micro-CT images.
To address the limitations of existing micro-CT segmentation techniques.
To improve the accuracy, reliability, and objectivity of micro-CT image analysis.

Main Methods:

Development of the Independent Active Contours Segmentation (IACS) framework.
IACS utilizes two modules: automatic Region of Interest (ROI) extraction and IAC Evolution.
Multiple Active Contours segment the ROI image, evolving simultaneously and independently.

Main Results:

IACS demonstrated superior performance compared to established methods (e.g., Otsu Thresholding, Region Growing, Chan-Vese AC).
The method showed enhanced object identification, accuracy, and robustness.
Performance was validated on phantom and real datasets, including under various noise conditions.

Conclusions:

IACS offers a significant advancement in micro-CT image segmentation.
The automated and robust nature of IACS facilitates more reliable bone research and diagnostics.
This technique can improve the evaluation of bone-related pathologies and treatment efficacy.