The New Image Proccessing Technologies in Medicine

The use of image proccessing on medical field offers great promise and potential for advances in the field of science and medicine, one example of this is detection of brain tumor in MRI images and CT scan images have been vigorous exploration area in research. Images Segmentation is performed on the input images. This facilitate easier analysis of the image thereby leading to better tumor detection efficiency.

Image processing is an intensive computation power that is required to achieve high accuracy and real-time performance.

Images may use different formats or configurations for the processing of raw data. For systems to work, they need the data to be homogeneous with the same format and configuration.

Images must be converted from RGB to Grayscale to make further processing of data easier. Grayscale is the simplest form of an image. Threshold and segmentation methods are the most commonly used in image processing technique to get the needed parameters by counting the pixel. Threshold segmentation is one of the simplest segmentation methods.

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The input grayscale image is converted into a binary format. The technique is based on a threshold value which will convert grayscale image into a binary image.

Morphological technique in image processing follows the goal of eliminating imperfections by targeting the form and structure of the image. Binarization of an image is one of the techniques of morphological image processing to separate the background from the foreground of an image. Image de-noising is a process in digital image processing directing at the removal of noise, which may corrupt an image during its attainment or transmission.

Segmentation is one of the important pars in image processing to lessen the data for easy analysis. Thresholding is the easiest way of segmentation. It is completed thru threshold values which are gained from the histogram of the edges the original image, the threshold values are obtained from the edge detected image. If the edge detection is accurate the threshold will also be accurate. Segmentation through thresholding has smaller amount of calculations compared to other methods.

Image segmentation is the basic prerequisite of any computer vision application; people are generally concerned only in certain parts of the image. Image segmentation results in non-overlapping objects labeled with different region numbers. Threshold based image segmentation techniques discriminate regions on the basis of intensity value difference between pixels. Preprocessing of the images is used to reduce the influence of the texture and to increase the contrast between the road pavement and the crack.