IMAGE PROCESSING AVI MEDICAL IMAGE CONTROL

IMAGE PROCESSING AVI MEDICAL IMAGE CONTROL

THIS PROJECTS OR DONE BY 

JAY SRIRAM ENGINEERING COLLEGE, AVINASHIPALAYAM,DHARAPURAM, TIRUPPUR

GANESH.P

II-CSE 

ABSTRACT

The various Medical images acquired directly from various instruments are in the AVI format, which reduces the easy control of image display without conversion to medical image standard, that is the DICOM format. The purpose of this project is to develop software to handle online data acquisition from medical equipments like Ultra Sound machine, control the display rate, convert the AVI image acquired from the Medical equipment directly to DICOM image with patient’s detail’s got from the user, freeze the AVI image frame of interest, convert the freezed AVI frame to Bitmap image, convert this Bitmap image to DICOM image with patient’s details. This software is highly reliable, efficiently handles memory and very user friendly.

Medical equipments like Ultra Sound, CT etc… have images at their output in the AVI file format, which are acquired with the respective probes. These AVI images acquired are stored. The software captures this AVI image, displays them frame-by-frame in succession and converts them to DICOM image with required patient’s details obtained from the Specialist during conversion. The frame of interest can be freezed and converted to Bitmap image, which can also be viewed on a separate window with options to brighten, darken, change the color combination, invert the image and restore the image.  The converted DICOM image can be viewed on any Standard DICOM viewer. Mostly all the DICOM viewer will have provision to view the patient’s details entered during conversion.

OBJECTIVE

To help the doctor view a particular frame of interest captured from a medical equipment which is usually an AVI image and to enable the doctor to manipulate the frame for correct diagnosis and  provide efficient treatment.

                                    

MEDICAL IMAGING

From Ophthalmology and radiology to orthodontics, image processing touches the medical field in many ways. The ability to visualize and interactively manipulate three-dimensional objects derived from sets of two-dimensional MRI and CAT scan (now shortened to CT scan) slices has changed the way we deal with medicine. MRI stands for nuclear magnetic resonance imaging.

     DISADVANTAGE OF EXISTING SYSTEM

      There is no AVI viewer that facilitates the doctors to manipulate the medical image captured from the equipment. All AVI viewers available just displays the frames in predetermined time intervals and time of display of each frame cannot be controls as per the physicians requirement. Frame at a particular given time can be displayed but, it wont help the doctor capture the exact frame that is required to find out the exact defect.

    PROPOSED SYSTEM

This system will prove to be user friendly as this captures the medical AVI image, grabs the required header information, converts them to DICOM file format and stores it along with the patient’s details, physicians details, etc… so that any physician can diagnose the patient without any other further details. Moreover there are many DICOM viewer available with many image processing provision.

Steps To Control Image

Ø  Capture the image from an medical equipment which will normally be in AVI (Audio/Video Interleaved) format.

Ø  Analyze the header details of the AVI image.

Ø  Copy the required header details into the DICOM header format.

Ø  If the length of the header is greater than zero it is considered to be valid.

Ø  Find the start of frame in the AVI file, check for its length, if data is valid copy the frame into DICOM file else skip the frame.

Ø  View the DICOM file in appropriate DICOM viewer.

IMAGE FILTERING        

It is used to extract great amounts of information from our images – information to which we don’t have access normally

Ø  Edge

enhancement and sharpening filters will bring out details in objects that we would not otherwise have noticed.

Ø Averaging filters will smoothen the rough and jagged edges in our images, making them more appealing to the eye.

Ø Basic Statistical filter will remove much of the noise found in our CCD scanned images.

Ø Gradient analysis will help us visualize your image in a whole new light, greatly enhancing edges – allowing us to create interesting embossed image effects.

Ø Special filters can help us identify certain objects within an image.

Ø Low Pass filter passes on lower frequency components of an image, while attenuating or rejecting the higher frequency components.

Ø High Pass Filter is used to amplify the high-frequency details found in an image, while the integrity of low-frequency detail of the image remains.

       IMAGE PROCESSING:

            Images are a vital and integral part of every day life. On an individual, or person-to-person basis, images are used to reason, interpret, illustrate, represent, memorize, educate, communicate, evaluate, navigate, survey, entertain, etc. We do this continuously and almost entirely without conscious effort. As man builds machines to facilitate his ever more complex lifestyle, the only reason for NOT providing them with the ability to exploit or transparently convey such images is a weakness of available technology.

            Applied Image Processing, in its broadest and most literal interpretation, aims to address the goal of providing practical, reliable and affordable means to allow machines to cope with images while assisting man in his general endeavors.

By contrast, the term ‘image processing’ itself has become firmly associated with the much more limited objective of modifying images such that they are either:

a.                   Corrected for errors introduced during acquisition or transmission (‘restoration’); or

b.                  Enhanced to overcome the weakness of human visual system (‘enhancement’)

As such, the discipline of ‘pure’ image processing may be succinctly summarized as being concerned with

‘ a process which takes an image input and generates a modified image output ’

Clearly then, other disciplines must be allied to pure image processing in order to allow the stated goal to be achieved. ‘Pattern classification’, which may be defined simply as

‘ a process which takes a feature vector input and generates a class number output’

Confers the ability to identify or recognize objects and perform sorting and some inspection tasks. ‘Artificial intelligence’, which may be defined as

‘ a process which takes primitive data input and generates a description, or understanding or a behavior as an output’

            Confers a wide range of capability from description, in the form of simple measurement of parameters for inspection purpose, to a form of autonomy borne out of an ability to interpret the world through a visual sense.

Theses disciplines have been evolving steadily and independently ever since computer first became available, but only when they are all effectively harnessed together do machines acquire anything like the ability to exploit images in the way that humans do.

In particular, the marriage of one, or both, of the first two disciplines with artificial intelligence has given birth to the new, image specific disciplines, namely ‘image analysis’, ‘scene analysis’ and ‘image understanding’.

Image analysis is normally satisfied with quantifying data about objects which are known to exist within a scene, or determining their orientation, or recognizing them as one of a limited set of possible prototypes. As such it is largely concerned with the development of the 2-D applications, there is an undoubted need to extend this activity to the description of 3-D relationships between objects within a 2-D view of the real-world scene.       

Scene analysis was the original term coined to describe this extension of image analysis into the third dimension. Such work flourished in the 1960s and was concerned with the rigorous visual analysis of three-dimensional polyhedra (the so-called ‘blocks-world’), on the mistaken premise that it would be a trivial matter to extend these concepts to the analysis of natural scenes. The work was finally abandoned in the late 1970s when it was realized that the exploitation of application-dependent constraints was no way to research general-purpose vision systems.

Consequently, the term scene analysis fell into disuse only to be replaced by that of image understanding, which is more fundamentally based upon the physics of image formation and the operation of human visual system. It aims to allow machines to operate with ease in complex natural environments, which feature partially occluded objects or, ultimately, previously unseen objects.

A broad overview of the literature in the field of machine perception of images suggests the existence of two distinct ‘camps’ whose followers, while sharing common roots, set out to achieve fundamentally different objectives. We have chosen to label these camps as ‘computer vision’ and ‘machine vision’, and feel that they are essentially distinguished by their different approaches to the use of artificial intelligence and the degree to which it is employed. (‘Robot vision’ was also a popular alternative at one time, although it appears to be slowly falling into disuse, perhaps because of rather unfortunate science-fiction connotations.)

‘Computer vision’ is ultimately concerned with the goal of enabling machines to understand the world that they see, in real-time and without any form of human assistance. Thus, application-specific constraints are rejected wherever possible as the world is ‘interpreted on-line’. The complexity of this task is easily under-estimated by those who take human vision for granted, but it is fraught with many immensely difficult problems, and seriously hampered by inadequate processing power.

‘Machine vision’ on the other hand, is concerned with utilizing existing technology in the most effective way to endow a degree of autonomy in specific applications. The universal nature of the computer vision approach is sacrificed by deliberately exploiting application-specific constraints. Thus knowledge about the world is ‘pre-complied’, or engineered, into machine vision applications in order to provide cost-effective solutions to real-world problems.

DIGITAL IMAGE ACQUISITION:

            The general goal for image acquisition and processing is to bring pictures into the computer domain of the computer, where they can be displayed and then manipulated and altered for enhancement. Four processes are involved in image acquisition:

Ø    Input

Ø    Display

Ø    Manipulation

Ø    Output

‘The transformation of optical image ata into an array of numerical data which may be manipulation by a computer, so overall aim of machine vision may be achieved’

In order to achieve this aim three major issues must be tackled they are:

Ø    Representation

Ø    Transduction (or sensing)

Ø    Digitizing

ARITHMETIC OPERATIONS ON IMAGES:

            The arithmetic operations are absolutely essential for calibration and flattening of the image in certain applications, particularly in those applications that have a low signal. They are helpful tools for enhancing an image. The basic arithmetic operations on images are:

Ø    Addition

Ø    Subtraction

Ø    Multiplication

Ø    Division

GEOMETRIC TRANSFORMATIONS:

Many times, to combine images taken at different times or by different sources, we have to translate, rescale, and rotate the images. It is usually important that the images match spatially. Without proper registration of images before combination passes, most techniques for image enhancement will actually degrade the images, losing important or interesting information. The basic geometric transformations are:

Ø    Translation

Ø    Scaling/Zooming

Ø    Resampling

Ø    Rotation

Ø    Flipping

ADVANCED GEOMETRIC TRANSFORMATIONS:

            Have you ever wondered how those interesting special effects that you see in movies and commercials were made? How in an image can one person transform into another person or even an animal or another entity? The two advanced geometric transformations are:

Ø    Warping

Ø    Morphing

Warping is a digital technique of distorting an image hence also called geometric distortion. It has been used to create sophisticated special effects I movies and television shows and in recent times in a plethora of television commercials. They all use exotic computers and custom software.

            Morphing is an extension of warping and it is the complete and smooth transformation from one image to another. This technology, which traditionally has been prohibitively expensive, with a little effort, can now be done on the desktop computer very cheaply. Essentially, morphing involves two steps of warping, with a spline interpolation between the initial images and the resultant image. Morphing has you match key features such as the eyes, nose, mouth and other details on both the exact same graphic space. Finally, a weighted average is made of each step of transformation of the two wraps. For instance, to morph a truck into a train, the train is first warped into the same shape as the truck so that certain specific points, the windshields, headlights and grills match as closely as possible.

IMAGE PREPOCESSING:

            Image preprocessing seeks to modify and prepare the pixel values of a digitized image to produce a form that is more suitable for subsequent operations within the generic model. There are two major branches of image preprocessing, namely

Ø    Image Enhancements

Ø    Image Restoration

Image enhancement attempts to improve the quality of image or to emphasize particular aspects within the image. Such an objective usually implies a degree of a degree of subjective judgment about the resulting quality and will depend on the operation and the application in question. The results may produce an image, which is quite different from the original, and some aspects may have to be deliberately sacrificed in order to improve others.

The aim of image restoration is to recover the original image after ‘known’ effects such as geometric distortion within a camera system have degraded it or blur caused by poor optics or movement. In all cases a mathematical or statistical model of the degradation is required so that restorative action can be taken.

Both types of operation take the acquired image array as input and produce a modified image array as output, and they are thus representative of pure ‘image processing’. Many of the common images processing operations are essentially concerned with the application of linear filtering to the original image ‘signal’.

REFERENCE:

Ø  Anil K. Jain (1989) ‘Fundamentals of Digital Image Processing’, Prentice-Hall, Englewood Cliffs, N.J.

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