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Muscles of the Anterior Neck

Muscles of the Anterior Neck

The anterior neck muscles are the group of muscles covering the front part of the neck. These muscles are classified into three subgroups. The first one is the superficial muscles, the most visible muscles in the front of the neck. It includes the platysma and sternocleidomastoid. The second group is the suprahyoid muscles, located above the hyoid bone. This group comprises the digastric, mylohyoid, geniohyoid, and stylohyoid. Lastly, the infrahyoid muscles are found below the hyoid bone and...

Muscles that Move the Head

Muscles that Move the Head

The muscles that move the head are a dynamic and complex group of structures that work together to facilitate a wide range of head movements, including rotation, flexion, extension, and lateral bending.
The bilateral sternocleidomastoid, or SCM, and the suprahyoid and infrahyoid muscles are significant head flexors. The SCM muscles originate at the sternum and clavicle and attach to the mastoid process of the temporal bone. The SCM contracts bilaterally to bend the head forward, whereas...

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Related Experiment Video

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Fast Computation of Neck-Like Features.

Hayam Abdelrahman, Yiying Tong

IEEE Transactions on Visualization and Computer Graphics

|October 4, 2022

Summary

This summary is machine-generated.

This study introduces a novel topology-aware geometric approach to precisely identify tight loops around neck features on 3D surfaces. This method enhances shape analysis and robotics applications by accurately detecting narrow surface regions.

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Area of Science:

Computer Graphics
Computational Geometry
Geometric Modeling

Background:

Identifying neck-like features on surfaces is vital for applications like segmentation, shape analysis, path planning, and robotics.
Existing topological methods struggle with genus-0 shapes and can produce imprecise loops.
3D geometry-aware topological approaches are often complex and may yield geometrically wide loops.

Purpose of the Study:

To develop a "topology-aware geometric approach" for computing the tightest loops around neck features on surfaces, including genus-0 shapes.
To offer criteria for measuring the significance of neck features.
To accelerate the detection process through mesh simplification.

Main Methods:

Utilizing a volumetric representation of the input surface.
Calculating the distance function to the boundary as a Morse function, identifying neck features via critical points (type-2 saddles).
Directly creating cutting planes through neck features and tightening loops to geodesic representations.

Main Results:

The proposed method accurately computes tightest loops around neck features, overcoming limitations of purely topological approaches.
Criteria for assessing neck feature significance based on Morse function critical point evolution are provided.
Mesh simplification is employed to speed up detection without sacrificing loop quality.

Conclusions:

The topology-aware geometric approach offers an efficient and accurate solution for detecting neck features on diverse surfaces.
This method improves upon existing techniques by providing tighter, more geometrically relevant loops.
The approach has significant implications for various fields requiring precise surface feature identification.