In intervertebral disk distraction [2]. Traction may improve conduction

In current scenario back pain is a leading
cause of work absenteeism. Another area of concerns is the cervical spine
trauma which causes majority of spinal lesions. Many of these problems are the
consequence of an abnormal spinal motion. In past a Greek method was applied for
placing the displaced bones at their original positions. This method was known
as automatic method as no machines were used and force was applied manually.
Then with the advancement automatic traction therapy was replaced by mechanical
traction. The Skin traction was used at a great extent during the Civil War for
fractured femurs (thigh bone). It was commonly known as the “American Method
for treating bones fractures and dislocation”.

Now a day’s Decompression Traction Therapy
becomes an effective treatment for spinal traction treatment. It is less
expensive than surgery. Decompression Traction Therapy is the new therapeutic
device for treatment of painful nerve compression and disc herniation

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Mainly we can perform traction either in
continuous manner or an intermittent manner. Usually static traction is used
for cervical spondylosis and osteoarthritis or degenerative disc disease DDD
and intermittent traction is common for Facet joint Dysfunction and joint hypo
mobility. Both of the intermittent and the continuous cervical traction had a
significant effect on neck and arm pain reduction, a significant improvement in
nerve function and a significant increase in neck mobility 1
Cervical traction is a method in which a distracting force is administered to
the neck so as to separate the cervical segments and relieve compression of
nerve roots by intervertebral disk distraction 2. Traction may
improve conduction by improving blood flow to cervical nerve roots. Cervical
traction has a significant biomechanical effect on spinal structures, which can
be demonstrated by CT evaluation before and after traction. 3 One practical device
to extract spine motion in real-time is the digital video fluoroscopy (DVF). 4


Advances in
medical image processing have led to increase in large image collections.
Hence, techniques that are computer assisted are becoming more promising
approach in medical image analysis. The feasibility of computer assisted
techniques for the segmentation of vertebral bodies in spine X-ray images has
been of great interest 24, 25 to biomedical researchers. Reliable extraction
of vertebrae boundaries is a prerequisite for subsequent pathology validation
and Content-Based Image Retrieval (CBIR) research. However, fully automated
segmentation of spine X-ray images is a very challenging problem.


 A smart approach is discussed for cervical
traction which is a combination of image Processing and Physics theory. A
fundamental issue faced in the design of image analysis techniques is the
identification and characterization of the image space. In plain words, one
ends up having first to answer the question “What do we mean
mathematically by images?” A pertinent example of this point can be found
in the variational approach to image de-noising, the goal of which is to
estimate the original version of an image from a given degraded one. The
variation approach to this problem seeks to exhibit the “restored”
image as the minimize of a functional defined over the space of all images. The
first task is clearly to decide which space of functions to take images from.
For instance, it is easy to see why Sobolev spaces are ill suited for this
purpose: their elements cannot have discontinuities across co-dimension one
surfaces, and yet any successful model of images should allow for them. Such
discontinuities need to be allowed because one of the most important features
of images, namely “edges” (places where one object in the scene ends
and another begins) correspond squarely to this type of behavior. The space of
functions of bounded variation therefore provides the more appropriate setting.
(Another example comes from the variational approach to the image segmentation
problem, where the correct space of functions for minimizing one of the most
successful models in this context, the Mumford-Shah segmentation model, turns
out to be a subset of functions of bounded variation, known as special
functions of bounded variation) 32. Roughly the various approaches to image analysis is divided into
three categories: (A) Statistical representations (B) Spectral and wavelet representations,
and (C) Scale-space representations.

Statistical approaches treat images as
samples from random fields, which are often modeled by Markov/Gibbs fields or
via statistical learning from an image database. This approach was pioneered in
the 80’s by Grenander (Brown) and the Gemans (Brown and John Hopkins.

 Spectral and wavelet representations are the
mathematical foundation for JPEG Internet image coding protocols. It has uses
beyond image compression. In fields like computer vision and medical imaging,
there is great demand for efficient and accurate image processors.



Making of                                                                                                        



Fig: Designing of Image Processor

task is how to design an image processor that performs efficiently and well?
Typical tasks would be: denoising, edge detection, intensity enhancement, and
compression and decompression. In addition to these relatively low-level tasks,
there are mid- and high-level tasks like image segmentation, and pattern
identification and recognition.

a suitable model is constructed for the given task. Currently, the developed processing models
(Fig) are using tools like Bayesian 34, 35 decision, inverse problems,
variational optimization, etc. The need of these approaches arise mainly in the
 fields of statistical mechanics, nonlinear
PDEs, differential geometry and topology, the calculus of variations, ,
harmonic and functional analysis, and numerical analysis. Image denoising is
one of the major problem faced during analysis of image. Numerous methods has
been proposed for image denoising problem, from using transformations and
statistical methods to using PDEs. For transformations, typically spectral transformation
are used and recently Curvlet or Ridgelet transformation are used. Another problem
in Image reconstruction is inpainting. The Digital inpainting problem, is
related to disocclusion problem. There are various approaches for the image
segmentation problem: using snake methods or region growing and merging
techniques or global optimization approaches using energy functional or
Bayesian approaches 33, 34.

When one
has a processing model, the next stage is its analysis, keeping in mind the end
goal to answer inquiries like presence and uniqueness, dependability, properties
of solutions etc. Many image processing model are nonlinear and they requires
new mathematical insights, they require rigorous mathematical analysis. As
there are many transformation algorithms present ,the major question arises is
which among the many techniques proposed for the same task is superior, what
effect the various parameters that appear in a method have on its behavior, and
under what conditions a given technique can be expected to perform well.

what is an effective algorithm for implementing the image analysis model? One
can certainly create, implement and plan schemes without performing the type of
numerical analyses described above. But one runs the risk of developing an
algorithm that performs ineffectively in the presence of noise. Also, the
history of scientific computing shows that the breakthroughs that led to
massive speed-up would have been unimaginable without a profound understanding
of the mathematics. Mostly the mathematical calculations can be converted into
efficient and robust algorithms. The methods currently being used in image
processing come from almost all branches of scientific computing including fast
fourier and wavelet transforms, multigrid algorithm, dynamic programming,
combinatorial optimization, computational PDEs, NLA, and Monte-Carlo

We can
use the important step of image preprocessing i.e image segmentation to analyze
how mathematics has contributed to image processing in the stages discussed
above: modeling, analysis, and implementation 36. The goal of segmentation is
to divide up the image domain into as smaller segments as possible, so that
image features (such as gray-scale intensity, or color) either slowly varying
in each piece or approximately constant. This procedure helps differentiate
between parts of the image domain occupied by distinct objects in the scene. It
is a challenging problem that is intimately connected with edge detection.