Mammogram Research Paper

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AN OVERVIEW OF MAMMOGRAM NOISE AND DENOISING TECHNIQUES ABSTRACT Mammogram is a mammography exam used to aid in the diagnosis of breast diseases in women. This paper mainly focuses on the biomedical image processing area. Noise is an inevitable parameter that must be considered in the medical images. The main problem of mammogram is that like other medical data it is also affected with noise during the acquisition of the mammogram images. So it is a challengeable task for researchers to denoise the mammogram images while preserving the important features of the image. The main noises affecting the mammogram images are salt and pepper, guassian, speckle and poisson noise. In previous days, noises in the mammogram images are denoised by the linear…show more content…
Mammogram is an easy and affordable method for diagnosing the microcalcification and clinically hidden lump tissues in the breast at the early stage of cancer. Breast cancer can be defined as a malignant tumor that starts in the cells of the breast. A malignant tumor is a group of cancer cells that can grow into (invade) surrounding tissues or spread (metastasize) to distant areas of the body. The female breast is made up mainly of lobules (milk-producing glands), ducts (tiny tubes that carry the milk from the lobules to the nipple), and stroma (fatty tissue and connective tissue surrounding the ducts and lobules, blood vessels, and lymphatic vessels).Most breast cancers begin in the cells that line the ducts (ductal cancers) [3]. Sometimes the malignant tumor may begin in the cells of lobules, while a small number start in other tissues. Lobular cancers are begins in the…show more content…
This noise is due to the statistical behavior of electromagnetic waves such as x-rays, visible lights and gamma rays. In a mammogram image poisson noise is due to the change in the number of photons in the mammogram unit. As the name indicates this type of noise has a Poisson distribution and the probability distribution function given by[9] f(k,λ)=λke-λ /k! (4) k –number of occurrences of an event λ –Positive real number d) Gaussian noise:This noise model follow Gaussian distribution. That means each pixel in the noisy image is the sum of the random Gaussian distributed noise value and the true pixel value. This type of noise has a Gaussian distribution, which has a bell shaped probability distribution function given by P(x)=1/σ√2∏ e(x-µ)2/2σ2 (2) Where P(x) is the Gaussian distribution noise in image, μ is the mean and σ is the standard deviation respectively [4]. The Graphical representation of the probability distribution function is shown in Figure 1 Fig 4: PDF of Gaussian noise Fig 5:Gaussian noise image III DENOISING

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