Affiliations: [a] Department of Electronics and Communication Engineering, Anna University, Chennai, India | [b] Department of Radiology & Imaging Sciences, Sri Ramachandra Medical Centre, Chennai, India
Correspondence:
[*]
Corresponding author: K.S. Thivya, Department of Electronics and Communication Engineering, Anna University, Chennai, India. Tel.: +91 9566159360; E-mail:grassdew@rediffmail.com
Abstract: Breast cancer is the second threatening tumor among the women. The effective
way of reducing breast cancer is its early detection which helps to improve
the diagnosing process. Digital mammography plays a significant role in
mammogram screening at earlier stage of breast carcinoma. Even though, it is
very difficult to find accurate abnormality in prevalent screening by
radiologists. But the possibility of precise breast cancer screening is
encouraged by
predicting
the accurate type of abnormality through Computer
Aided Diagnosis (CAD) systems. The two most important indicators of breast
malignancy are microcalcifications and masses. In this study, framelet
transform, a multiresolutional analysis is investigated for the
classification of the above mentioned two indicators. The statistical and
co-occurrence features are extracted from the framelet decomposed mammograms
with different resolution levels and support vector machine is employed for
classification with k-fold cross validation. This system achieves 94.82%
and 100% accuracy in normal/abnormal classification (stage I) and
benign/malignant classification (stage II) of mass classification system and
98.57% and 100% for microcalcification system when using the MIAS
database.
Keywords: Framelet, Haralick features, breast cancer, mammography, microcalcification and mass
