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Classification of breast lesions using segmented quantitative ultrasound maps of homodyned K distribution parameters
Author(s) -
Byra Michał,
Nowicki Andrzej,
WróblewskaPiotrzkowska Hanna,
DobruchSobczak Katarzyna
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4962928
Subject(s) - kurtosis , receiver operating characteristic , pattern recognition (psychology) , artificial intelligence , segmentation , feature (linguistics) , parametric statistics , skewness , computer science , mathematics , statistics , linguistics , philosophy
Purpose Statistical modeling of an ultrasound backscattered echo envelope is used for tissue characterization. However, in the presence of complex structures within the analyzed area, estimation of parameters is disturbed and unreliable, e.g., in the case of breast tumor classification. In order to improve the differentiation of breast lesions, the authors proposed a method based on the segmentation of homodyned K distribution parameter maps. Regions within lesions of different scattering properties were extracted and analyzed. In order to improve the classification, the best‐performing features were selected from various regions and then combined. Methods A radio‐frequency data set consisting of 103 breast lesions was used in the authors’ analysis. Maps of homodyned K distribution parameters were created using an algorithm based on signal‐to‐noise ratio, kurtosis, and skewness of fractional‐order envelope moments. A Markov random field model was used to segment parametric maps. Features of different segments were extracted and evaluated based on bootstrapping and the receiver operating characteristic curve. To determine the best‐performing feature subset, the authors applied the joint mutual information criterion. Results It was found that there were individual features which performed better than the ones commonly used for lesion characterization, like the parameter obtained through averaging of values over the whole lesion. The authors selected and discussed the best‐performing features. Properties of different extracted regions were important and improved the distinction between benign and malignant tumors. The best performance was obtained by combining four features with the area under the receiver operating curve of 0.84. Conclusions The study showed that the analysis of internal changes in lesion parametric maps leads to a better classification of breast tumors. The authors recommend combining multiple features for characterization, instead of using only one parameter, especially in the case of heterogeneous lesions.