image quality improvement to widely used in medical images

Image improvement is useful for image processing to obtain results that are more in line with the expected image, for use in certain applications that are wrong in the medical field. Quality improvement is needed because it involves the image needed by the object of discussion of poor quality, for example the image increases noise when sending through the transmission line, the image is too bright / dark, the image is not sharp, blurry, and so on. Improve quality, repairs, repairs, noise reduction, and geometry improvements.

To make the image brighter or darker, we can change the brightness of the image. Image brightness can be improved by adding (or subtracting) a constant to (or from) each pixel in the image. As a result of this operation, the image histogram shifted.

Contrast states the distribution of light (lightness) and darkness (darkness) in an image. Images can be grouped into three contrast categories: low-contrast images (low contrast), good contrast images (good contrast or normal contrast), and high-contrast images (high contrast). These three categories are generally distinguished intuitively. Low-contrast images are characterized by most image compositions being bright or mostly dark. Whereas a good contrast image shows a wide range of gray values ​​without a predominant gray value. The image histogram shows a relatively uniform distribution of gray values. And high-contrast imagery, like good contrast images, has a wide range of gray values, but there is a wide area dominated by dark and light colors.

Noise is a disturbance caused by digital data storage received by the image data receiver that can interfere with image quality. If noise-containing images are directly processed and extracted, important features can cause accuracy problems. So the image should be cleaned of noise first, and then processed to extract important features. The method used depends on the type of noise, there are types of noise namely impulse noise, additive noise and multiplicative noise.

One of the digital image processing is geometric correction, this type of correction allows the user to adjust the image coordinate system to be analyzed. In this type of correction, the coordinate system used is usually adjusted to the usual coordinate system used. Image improvement by changing the geometric value, using the Method: Rotation, Translations, Scaling / Zooming / Reducing, and Skew.

The process of digital image processing, especially on MRI images depends on the presence of deficiencies in the image so that it will affect what methods will be used to improve the image. So that the image processing is very influential on the final image. So that the clearer or better the image will be useful to help doctors diagnose patients accurately.