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Related Concept Videos

Classification of Bones01:18

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...
Bone Remodeling01:40

Bone Remodeling

Bone remodeling is a continuous and balanced process of bone resorption by osteoclasts and bone formation by osteoblasts. In adults, it helps maintain bone mass and calcium homeostasis. While mechanical stress can stimulate turnover as part of the normal maintenance and reparative process, several hormones also regulate bone remodeling.
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
Bone Structure01:55

Bone Structure

Within the skeletal system, the structure of a bone, or osseous tissue, can be exemplified in a long bone, like the femur, where there are two types of osseous tissue: cortical and cancellous.
Bone Remodeling and Repair01:31

Bone Remodeling and Repair

Osteoclasts are cells responsible for bone resorption and remodeling. They originate from hematopoietic progenitor cells present in the bone marrow. Numerous progenitor cells fuse to form multinucleated cells, each with 10-20 nuclei. A single osteoclast has a diameter of 150 to 200 µM. These cells have ruffled borders that break down the underlying bone tissue and release minerals such as calcium into the blood in bone resorption. Osteoclasts cling to bones with their ruffled edges during bone...
Changes in the Appendicular Skeleton with Age01:09

Changes in the Appendicular Skeleton with Age

The upper and lower limb initially develops as a small bulge called a limb bud, which appears on the lateral side of the early embryo. The upper limb bud appears near the end of the fourth week of development, with the lower limb bud appearing shortly after.
Initially, the limb buds consist of a core of mesenchyme covered by a layer of ectoderm. The ectoderm at the end of the limb bud thickens to form a narrow crest called the apical ectodermal ridge. This ridge stimulates the underlying...

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Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

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Published on: November 28, 2025

Support vector machine classification based on correlation prototypes applied to bone age assessment.

M Harmsen, B Fischer, H Schramm

    IEEE Journal of Biomedical and Health Informatics
    |November 30, 2012
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a semi-automatic method for bone age assessment (BAA) using hand radiographs. The developed support vector machine (SVM) model achieved high accuracy, reducing the time-consuming nature of manual BAA.

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    Area of Science:

    • Radiology
    • Medical Imaging
    • Computer Science

    Background:

    • Bone age assessment (BAA) using hand radiographs is a common but labor-intensive task in radiology.
    • Accurate BAA is crucial for diagnosing growth disorders and determining skeletal maturity.

    Purpose of the Study:

    • To develop and evaluate a semi-automatic method for BAA on hand radiographs.
    • To improve the efficiency and accuracy of bone age determination.

    Main Methods:

    • Extraction of 14 epiphyseal regions from hand radiographs.
    • Image feature extraction using the IRMA framework.
    • Classification using a support vector machine (SVM) combined with cross-correlation to prototype images.

    Main Results:

    • The SVM model achieved a mean error of 0.83 years in age prediction.
    • Real-valued SVM demonstrated an accuracy of 96.16% for BAA within a two-year age range.
    • Comparison with k-nearest neighbor (kNN) showed superior performance of the SVM approach.

    Conclusions:

    • The proposed semi-automatic BAA method significantly enhances accuracy and efficiency compared to traditional methods.
    • The SVM-based approach offers a reliable tool for clinical application in pediatric radiology.
    • Further development could integrate this method into routine radiological workflows.