Microcalcification border characterization

Docusse, Tiago Alexandre; Pereira, Aledir Silveira; Marranghello, Norian

doi:10.1109/memb.2009.934583

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…A method to classify borders of microcalcifications as smooth and rugged by using the wavelet transform up to fourth level is illustrated in [7]. This method consists of four sections, the image is normalized by early processing, and its resolution is changed.…”

Section: Introductionmentioning

confidence: 99%

Haralick fetaures based mammogram classification system

Ohmshankar¹,

Paul²

2014

Second International Conference on Current Trends in Engineering and Technology - ICCTET 2014

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The second cause of the death among women arises due to breast cancer that affects the breast tissues. The efficient prognosis way of breast cancer is processed with the aid of mammogram images. The proposed mammogram classification system improves the diagnosis and early detection of breast cancer by using mammogram images. It helps radiologists to diagnose cancer accurately. MIAS database images are used for the evaluation. Thirteen Haralick texture features such as correlation, contrast, entropy, homogeneity and energy are extracted. The robust k-nearest neighbor (KNN) is used as classifier, and it classifies the mammogram images into two categories, which are normal and abnormal.The proposed approach provides satisfactory classification accuracy of over 92%.

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Section: Introductionmentioning

confidence: 99%