Why Medical Imaging?

The internal structure of the human body is not generally visible to the human eyes. However, by various imaging techniques images can be created through which the medical professionals can look into the body to diagnose abnormal conditions and guide the therapeutic procedures.

Noise in Medical Image: It is generally desirable that image brightness is to be uniform except where it changes to form an image. There is a variation in the brightness of a displayed image even when no image detail is present. This variation is usually random and has no particular pattern reducing the image quality specifically when the images are small and have relatively low contrast.

In many situations an artifact does not significantly affect object visibility and diagnostic accuracy. But artifacts can obscure a part of an image or may be interpreted as an anatomical feature.

MEHODOLOGY:

Noise Models :

Real images are often degraded by some random errors – This degradation is usually called noise. Noise can occur during image capture transmission or processing and may dependent on or independent of image content. Basically, there are two types of noise models: Noise in the spatial domain (described by the noise probability density function) and noise in the frequency domain described by various Fourier properties of the noise. Now here with we are discussing about the noise is independent of image co-ordinates.

Gaussian Noise:

Gaussian noise is popular noise approximation. A random variable with Gaussian (normal) distribution has its probability density is given by the Gaussian curve. The ID case the density function is P(x) = 1/ √2Π e-(z-μ) 2/22 Where μ is the mean and  is the standard deviation of random variable. Gaussian noise that occurs in many practical cases.

Additive Noise:

When an image is transmitted through some communication channel, a noise which is usually independent of the signal occurs. Similar noise arises in video camera. This signal independent degradation is called additive noise and can be described by the following models. F(x, y) = g(x, y) + v(x, y)

Multiplicative Noise:

 If the noise magnitude is much higher in comparison with the signal we can write

 F= g + gv = g (1+v) = gv

Quantization Noise:

It occurs when insuffient Quantization levels are used for ex 50 levels for monochrome image in this case false contour appear.

Impulsive Noise:

Impulsive noise means that an image is corrupted with individual noisy pixels whose brightness significantly differs from the neighborhood.

Salt and Pepper Noise:

It is another type of noise is used to describe saturated impulsive noise an image corrupted with white and/ or a black pixel is an example. Salt and Pepper noise can corrupt binary image.

Periodic Noise:

Periodic noise is an image arises typically from electrical and / or electromechanical inferences during the image acquisition. This is the only type of spatially dependent noise. Periodic noise typically handled in an image by filtering in the frequency domain. The model of periodic noise is a 2-D sinusoid with equation R(x, y) = Asin [2Πu0(x+Bx)/ 2Πv0(y+by)/N]