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Calcifications can typically be seen on X-rays. Calcification plaques are the most complete barrier for the actual organ detection or segmentation from CT scan images. When bloodstream fails to get rid of excess calcium, it can end up in the: arteries of the heart, brain (cranial calcification), breasts, kidneys (as a part of kidney stones, or calcium deposits in the kidneys). A variety of factors can lead to calcification. In many cases, it is a normal part of aging or the result of an injury. Other factors may include: (a) infection of the breast, brain, or kidneys, (b) disorders of calcium metabolism, such as osteoporosis or hypercalcemia (too much calcium in the blood), (c) genetic or autoimmune disorders that affect the skeletal system and connective tissues. X-rays are the most common diagnostic tools used to detect calcification. These tests utilize electromagnetic radiation to record images of internal organs. A type of X-ray called a mammogram is used to see calcium deposits in the breast tissue.
A novel mammogram image segmentation algorithm that makes use of Scale Invariant Feature Transform (SIFT) to compute the key point in the suspicious area of the mammograms automatically (Guan, Zhang, Chen, & Todd-Pokropek, 2008). Fuzzify the original image of a mammogram in order to make the difference between the backgrounds and object more than the region of interest is enhanced and simultaneously suppressed the tissues along with background (Mohanalin, Kalra, & Kumar, 2008). A method is depicted for automatic detection of clustered micro calcifications (both malignant and benign) in digitized mammograms on the basis of UIQI (universal quality index) (Murty, Sudheer, & Reddy, 2011). Introduce computer aided two separate techniques for mass and micro-calcification segmentation in digital mammograms using several steps of operations (Hanmandlu, Vineel, Madasu, & Vasikarla, 2008). The computer aided micro-calcification detection based on regular wavelets, multiplexed wavelet transform technique and wavelet domain hidden Markov tree model (Lemaur, Drouiche, & DeConinck, 2003; Mini, Devassia, & Thomas, 2004; Nakayama, Uchiyama, Yamamoto, Watanabe, & Namba, 2006; Regentova, Zhang, Zheng, & Veni, 2007). The image filtering methods are described on computer-aided detection of micro-calcification clusters in mammograms and the performance of those methods (Eddaoudi & Regragui, 2011; El-Naqa, & Yang, 2005; Jing, Yang, & Nishikawa, 2011; Kabbadj, Regragui, & Himmi, 2012; Lemaur et al., 2003; Tang, Rangayyan, Xu, El-Naqa, & Yang, 2009; Zhang et al., 2013). Machine learning approaches for detection of micro-calcifications (Bocchi, Coppini, Nori, & Valli, 2004; Gurcan et al., 2002; Jiang, Yao, & Wason, 2007; Peng, Yao, & Jiang, 2006).