Intelligent Image Processing On Medical Application

Digital image processing plays a vital role in the medical domain. Image processing is used to restore noisy or degraded images, extract features, analyze shapes and textures, volumetric and quantitative evaluation of structures and so on. Nowadays, the use of image processing is gaining interest for the surface area and volume determination. Most widely used methods for visualizing internal anatomical features of human body are computed tomography (CT scan) and magnetic resonance imaging (MRI). Both of these produce two-dimensional image slice of the body. Volume assessment of the bones can be achieved from an image acquired from a MRI scan.

Volume assessment usually requires precise and accurate identification of the extent of the bone via the process of image segmentation. Image Processing Toolbox of Matlab provides a comprehensive set of algorithms and graphical tools to perform various processes such as enhancement, segmentation, restoration, transformations and so on in a digital image. The Matlab’s Image Processing Toolbox will be very useful when being applied to this problem of volumetric analysis of the three bones, namely, femur, tibia and patella. LITERATURE REVIEW Estimating the volume of the bones will surely consume some effort.

Numerous methods are available to evaluate the volume of a particular area from a MRI scan image. The tedious part is to assess the best one and apply that to the quantitative analysis. Thus the best image segmentation and enhancement techniques have to be applied as a pre-requisite for the volume determination process. METHODOLOGY To calculate the volume of the parts namely, femur, tibia and patella a number of steps has to be carried out. After data acquisition the digital image has to be subjected to the image segmentation process in order to clearly define the boundary of the bone under consideration.

Following the segmentation process the volume of the particular bone under investigation can be determined using the widely recognized divide and conquer method. Since the structure of the femur, tibia and patella bones does not pertain to any regular shapes, it is better if the volume is calculated by slicing the bone structure under study, into smaller units and then determining the volumes of the individual parts and consequently obtaining the entire bone volume. DATA ANALYSIS MRI scanning uses large and powerful magnets and radio waves to form images.

MRI is sensitive to differences in chemical content, usually water. Hence, two tissues that are about the same density, but slightly differ in water content can be seen as different in MRI. Thus, generally in MRI bone (with little water content) will appear in light color whereas the muscles, cartilages and the other things that are high in water content will appear dark in color. This enables to easily and clearly distinguish between the bone segments by subjecting the digital image thus acquired from the MRI scan to image segmentation process. RESULTS

The suggested approach to volumetric evaluation enables to determine the volume of the femur, tibia and patella bones in a considerably accurate manner. Since, MRI is sensitive to differences in chemical content; the image segmentation process becomes very easier. DISCUSSION In the medical field, an image created imaging technology, whether it an X-ray computed tomography (CT), digital subtraction angiography (DSA) or a magnetic resonance imaging (MRI) is converted to a digital image and magnified by means of a variable density image processing apparatus and is broken down into constituent graphic elements.

The ability to produce more sophisticated images has increased as a consequence of the availability of the increasing computing power. Thus, by compiling a number of neighboring slices, three-dimensional images of individual structures within the body can be produced. By stacking all the outlines of the slices on top of one another and filling in the spaces between the slices, the computer forms a continuous surface and produces a three-dimensional image. These slices can be used to determine the volume of the bone structure in question. MRI scan produces two-dimensional slices of the part under study.

After image acquisition, gamma correction and power-law transformation are applied for general-purpose contrast manipulation. This enhances the image by upholding the contrast of the digital image, thus providing a clear-cut image of the part. Image segmentation algorithms of Matlab are used to determine the region boundaries in an image. Many image segmentation approaches are available in the Image Processing Toolbox of Matlab, including automatic thresholding, edge-based methods, and morphology-based methods such as the watershed transform, etc.

Any one of these methods can be used to determine the boundary of the femur, tibia and patella bones of a MRI scan image. Following this, the morphological operators are applied to the image. These morphological operators of Matlab facilitates to detect edges, enhance contrast, remove noise, segment an image into regions, thin regions, or perform skeletonization on regions. The area of irregularly shaped parts, in this case, femur, tibia and patella can be determined by tracing the structures onto a paper. As an initiative, cut out the structure thus traced and find its mass with the help of an analytical balance.

And as a matter of fact, the area of the paper can be determined with no trouble. Being fairly uniform the mass of the paper should be proportional to its area. Thus from these proportions, the area of the structure can be evaluated very simply as follows: Here the values of all the variables are known, except the area of the structure and that can be determined without difficulty. Also, if the thickness of the image slice is known, then the volume of the structure under consideration can be evaluated by multiplying the area of the bone structure and the thickness of the image slice as follows:

Volume of the bone structure = (Area of the bone) x (Thickness of the image slice) By augmenting the volumes of all the individual slices the volume of the three-dimensional bone structure may be determined. CONCLUSION Applying the thus stated technique, the femur, tibia and patella volumes can be determined efficiently. This seems an efficient and easiest way to calculate the volume of not only the bone structures, but also for any kind of things such as cartilage, fat, cancer cells or whatever it is.

Thus, the above provided methodology to estimate the volume of the femur, tibia and patella bones based on the Image Processing Toolbox of Matlab is the best approach as far as I have researched through. REFERENCES 1. Digital Image Processing using Matlab – Rafael Gozalez & Richard E. Woods. 2. Image Processing, Analysis and Machine Vision – Milan Sonka, Vaclav Hlavac and Roger Boyle. 3. Anatomy in Diagnostic Imaging – Fleckenstein, Peter and Jorgen Tranum-Jenson.

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