Shape-Based Tumor Retrieval in Mammograms Using Relevance-Feedback Techniques

Tzikopoulos, Stylianos; Georgiou, Harris V.; Mavroforakis, Michael E.

doi:10.1007/978-3-642-15819-3_33

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…15,30,35,[37][38][39][40][41] Regardless, several studies have shown that shape factors perform better in discriminating between benign masses and malignant tumors than other measures related to breast density, contrast, texture, and edge sharpness. For these reasons, CAD systems and algorithms need to use combinations of several features derived from various images or regions related to breast masses; it is common to use many measures representing not only shape but also texture, density, and edge sharpness.…”

Section: Shape Characteristics Of Breast Massesmentioning

confidence: 99%

Fractal analysis and classification of breast masses using the power spectra of signatures of contours

Rangayyan

Oloumi

2012

J. Electron. Imaging

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The contours of benign masses and malignant tumors in mammograms and other types of breast images differ substantially in their shape and complexity; the former are usually round and smooth, whereas the latter are typically spiculated and irregular. We demonstrate the usefulness of fractal analysis via a frequency domain approach applied to one-dimensional signatures of the twodimensional contours of breast masses in mammograms. The 1∕f model related to fractional Brownian motion was applied via power spectral analysis of signatures to estimate the fractal dimension. Experiments with a dataset of 111 contours, including those of 65 benign masses and 46 malignant tumors, indicated a high classification performance of 0.8962 in terms of the area under the receiveroperating characteristic curve. The method should be useful in computer-aided diagnosis of breast cancer.

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