An integrated approach for detection of masses and macro calcification in mammogram images using dexterous variant median fuzzy c-means algorithm

Kowsalya, S.; Priyaa, D. Shanmuga

doi:10.1109/isco.2016.7727146

Cited by 1 publication

(2 citation statements)

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“…The mass densities of all the four second-level bins are found (refer to Table 1), and the range of the mass density of the malignant microcalcification related to the RoI lies between 2.7 and 3.1 g/cm 3 . Mapping the range of the reflection coefficient (0.9 to 1) to the mass density (2.7 to 3.1) is performed (refer to Fig.…”

Section: (Vii) Mapping Between Reflection Coefficient and Mass Densitymentioning

confidence: 99%

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Pattern Recognition and Size Prediction of Microcalcification Based on Physical Characteristics by Using Digital Mammogram Images

et al. 2018

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Breast cancer is one of the life-threatening cancers occurring in women. In recent years, from the surveys provided by various medical organizations, it has become clear that the mortality rate of females is increasing owing to the late detection of breast cancer. Therefore, an automated algorithm is needed to identify the early occurrence of microcalcification, which would assist radiologists and physicians in reducing the false predictions via image processing techniques. In this work, we propose a new algorithm to detect the pattern of a microcalcification by calculating its physical characteristics. The considered physical characteristics are the reflection coefficient and mass density of the binned digital mammogram image. The calculation of physical characteristics doubly confirms the presence of malignant microcalcification. Subsequently, by interpolating the physical characteristics via thresholding and mapping techniques, a three-dimensional (3D) projection of the region of interest (RoI) is obtained in terms of the distance in millimeter. The size of a microcalcification is determined using this 3D-projected view. This algorithm is verified with 100 abnormal mammogram images showing microcalcification and 10 normal mammogram images. In addition to the size calculation, the proposed algorithm acts as a good classifier that is used to classify the considered input image as normal or abnormal with the help of only two physical characteristics. This proposed algorithm exhibits a classification accuracy of 99%.

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Section: (Vii) Mapping Between Reflection Coefficient and Mass Densitymentioning

confidence: 99%

“…From the perspective of a physician, the major abnormalities related to breast are masses and calcifications. Masses [2,3] are not much vulnerable compared with calcifications. Calcification can be further classified as macro and microcalcification.…”

Section: Introductionmentioning

confidence: 98%