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

syndromes.”

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.

Transform

Used

Making of

Model

Fig: Designing of Image Processor

Major

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.

Initially

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.

Finally,

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

simulations.

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.