White Gaussian Noise Removal From Computed Tomography Images Using Python

Image processing one of the most important area of research as it is used to improve the quality of any images. It has vast application areas viz. law enforcement, entertainment, satellite imaging, remote sensing, weather forecasting, medical imaging etc. Medical imaging is one of the most emerging area in medical science and it helps the radiologists to understand any abnormalities or diseases in human body. There are different types of medical imaging techniques viz. Computed Tomography (CT) imaging, Ultra- Sound (US) imaging, X-ray imaging, Positron emission tomography (PET) imaging, Magnetic Resonance Imaging (MRI) etc. These all the imaging techniques are used for different purposes and each uses entirely different phenomena to capture the images. For each imaging techniques; expert radiologists are required who can examine the medical images and give the results accordingly. All the above mentioned medical images are used to examine internal organs of the body. However; almost all the imaging techniques suffer from a phenomenon called "Noise". In general noise is an unwanted signal which introduced in almost all the signals and deteriorates them. In general; images are two dimensional signals. All the aforementioned images suffer from different types are noise signals e.g. CT images are affected by Additive White Gaussian Noise (AWGN). Each imaging techniques use different frequency band hence noise introduced in the images are also different to each other. In this chapter; various types of imaging techniques and introduction of noise have been discussed. There are many techniques using which we can remove the noise of the signals. The process in which noise signals are suppressed from the images is called "Image De-noising".