Adaptation of the Daugman-Downing texture demodulation to highlight circumscribed mass lesions on mammograms

Kwok, S.M.; Chandrasekhar, Ramachandran; Attikiouzel, Y.

doi:10.1109/icdsp.2002.1027915

Cited by 2 publications

(1 citation statement)

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“…17,20,28,34 Gradient information is another commonly used feature, which is used for finding spicules, [21][22][23] or to refine the boundary of the mass, in which case, it is usually combined with graylevel information. [29][30][31] Although some authors have used texture information in the detection step, [35][36][37][38] this information is more commonly used for mass classification purposes ͑in false positive reduction algorithms or to determine if the mass is malignant or benign͒. Finally, shape information is the least used feature, probably due to the fact that masses can be present in a large range of shapes ͑and sizes͒.…”

Section: Introductionmentioning

confidence: 99%

Eigendetection of masses considering false positive reduction and breast density information

Freixenet¹,

Oliver²,

Martí³

et al. 2008

Medical Physics

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The purpose of this article is to present a novel algorithm for the detection of masses in mammographic computer-aided diagnosis systems. Four key points provide the novelty of our approach: (1) the use of eigenanalysis for describing variation in mass shape and size; (2) a Bayesian detection methodology providing a mathematical sound framework, flexible enough to include additional information; (3) the use of a two-dimensional principal components analysis approach to facilitate false positive reduction; and (4) the incorporation of breast density information, a parameter correlated with the performance of most mass detection algorithms and which is not considered in existing approaches. To study the performance of the system two experiments were carried out. The first is related to the ability of the system to detect masses, and thus, free-response receiver operating characteristic analysis was used, showing that the method is able to give high accuracy at a high specificity (80% detection at 1.40 false positives per image). Second, the ability of the system to highlight the pixels belonging to a mass is studied using receiver operating characteristic analysis, resulting in A(z) = 0.89 +/- 0.04. In addition, the robustness of the approach is demonstrated in an experiment where we used the Digital Database for Screening Mammography database for training and the Mammographic Image Analysis Society database for testing the algorithm.

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