Mammogram images thresholding based on between-class variance using a mixture of gamma distributions

With one million new cases in the world every year, breast cancer is the most common malignancy in women and it has been proved that an early diagnosis of the disease can help to strongly enhance the expectancy of survival. Mammography is the most effective imaging method for detecting no-palpable early-stage breast cancer. Image processing techniques has been used for processing the mammogram image. Image thresholding is an important concept, both in the area of objects segmentation and recognition. It has been widely used due to the simplicity of implementation and speed of time execution. Many thresholding techniques have been proposed in the literature. The aim of this paper is to provide formula and their implementation to threshold images using Between-Class Variance with a Mixture of Gamma Distributions. The algorithms will be described by given their steps, and applications. Experimental results are presented to show good results on segmentation of mammogram image.