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.”

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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.

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.

Typically, physics plays a vital role in
cultivating the knowledge of natural sciences and establishing the foundation of logistic and scientific
methods. The application of physics to medical sciences has been developed in
practice for centuries 5.
X-Ray, CT scan and MRI images of the spine provide a practical approach for
detecting and assessing vertebral abnormalities. For accurate vertebra
detection number of features of an image can be extracted and meaningful
information can be obtain from low level information in the image using
segmentation. Image segmentation is observed to be the maximum important part
in digital image processing. Segmentation is nothing but however a portion of
any photo and object is extracted and analyzed to get useful information. In
image segmentation, digital photo is splitted into a couple of set of pixels. Main objective of segmentation is to
partition the image into the meaningful areas 6.
The segmentation techniques used for medical images are specific to the type
body part and imaging modality to be used. Consistent algorithms are necessary for the explanation of
anatomical structures and other regions of interest (ROI).As the time grow
algorithmic complexity increases. As medical
images are complex and rarely have linear feature, segmentation of medical
images becomes a difficult task. There is no universal algorithm which can give
appropriate results for every image. While using segmentation algorithm
different artifacts and noise introduce by electronic system must be taken in
consideration 7.
A comparison of three generation medical image segmentation technique is
provided in literature 8.

Vertebrae localization is a difficult task
to perform manually. Model-based approach is used for vertebra detection,
identification and segmentation in the case of CT images 9.
Each cervical vertebra has a unique morphology, which make quantitative
analysis a challenging task. So developing an approach which is robust,
efficient and capable of robustly determining the location of the spine and the
contour of the vertebrae is essential. Author proposes fully automatic
framework for cervical spine mobility analysis on radiographs 10 which is based on
-fully automatic vertebra detection, vertebra segmentation and vertebra
mobility analysis.

Digital medical images have some degree of
noise it gets corrupted with noise during its acquisition from various sensors
and transmission process. Since
different types of images have different characteristics and different
applications have different requirements, the filtering algorithms should be
designed for each case properly. Mostly the medical images quality cannot
satisfy the medical analysis and the applied requirements. The denoising of
medical image plays an important role in the image processing, and it is the
basic of further analysis and computation. The image edge are easily
interrupted by noise, but the traditional image denoising smooth out the edges
of the reconstructed images which caused edge to be blurred, and made the
information lost. The
denoising algorithms based on partial differential of total variation is
proposed 11. Author ensures that
the algorithm works to obtain clear medical image and preserve the edge
information integrally at the same time.

Mostly, the
medical images quality cannot satisfy the medical analysis so to get best
quality image a set of textures and images is analyzed to determine the best
measure of image activity and it has showed that image activity measure has
powerful ability to capture the activities and differentiating between various
images 12.
To detect degenerative changes which occurs due to narrowing of  intervertebral disc space author introduce a
machine learning approach to detect and localize degenerative changes in X-ray
image analysis of cervical spine and author receive accuracy of 95%for a patient
data set of 103patients 13. Another author
discussed the same problem of detecting degenerative changes in particularly
C3-C6 vertebra of cervical. The result range from 82% to 94% ,author proposed segmentation
and clustering from where distance features are computed and mapped 14.

 

 To preserve edges and fine details while
effectively removing noise, both local gradient and variance are incorporated
into the diffusion model 15 16. A newer
technique is introduced for noise removal using fractional-order anisotropic
diffusion equations, which uses the discrete Fourier transform and an iterative
scheme in the frequency domain 17.
Image filtering is an effective means for improving image quality. Author
proposed local activity-tuned filtering frameworks for noise removal and image smoothing,
where the local activity measurement is given by the clipped and normalized
local variance or standard deviation. Local activity-tuned relative total
variation framework achieves good performance for image smoothing and
represents the image in different scale-space and it has been used for depth
image denoising 18.

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