Int J Comput Assist Radiol Surg - Breast masses detection using phase portrait analysis and fuzzy inference systems.

Tópicos

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Resumo

RPOSE: Breast masses exhibit variability in margins, shapes, and dimensions, so their detection is a difficult task in mammographic computer-aided diagnosis. Mass detection is usually a two-step procedure: mass identification and false-positive reduction. A new method to automatically detect mass lesions in mammographic images with tuning according to the breast tissue density was developed and tested.METHODS: A modified phase portrait analysis method was introduced, based on the eigenvalue condition number and an eigenvalue intensity map. The method uses an iterative and tissue density-adaptive segmentation procedure with extraction of geometric features. False-positive reduction is accomplished using a fuzzy inference-based classifier. A leave-one-image-out cross-validation procedure was implemented, and stepwise regression analysis was used to automatically extract an optimal set of features. Testing and validation were performed on two different data sets containing at least one malignant mass D1 (388 images) and D2 (674 images), and a third data set N1 (50 images) was used consisting of normal controls. These three data sets were taken from the Digital Database for Screening Mammography.RESULTS: For sensitivities of 0.9, 0.85, 0.80, and 0.75, the best results on cancer images exhibit an False-Positive per Image (FPpI) equal to 0.6, 0.45, 0.35, and 0.3, respectively, using a Bayes Linear Discriminant Analysis (LDA) classifier and an FPpI of 0.85, 0.7, 0.55, and 0.45 using a fuzzy inference system (FIS) for false-positive reduction. When the algorithm is tested on normal images, an FPpI equal to 0.4, 0.3, 0.25, and 0.2 was observed using LDA and 0.3, 0.25, 0.2, and 0.15 using the FIS.CONCLUSION: A preclinical study of an automatic breast mass detection algorithm provided promising results in terms of sensitivity and low false-positive rate. Further development and clinical testing are justified based on the results.

Resumo Limpo

rpose breast mass exhibit variabl margin shape dimens detect difficult task mammograph computeraid diagnosi mass detect usual twostep procedur mass identif falseposit reduct new method automat detect mass lesion mammograph imag tune accord breast tissu densiti develop testedmethod modifi phase portrait analysi method introduc base eigenvalu condit number eigenvalu intens map method use iter tissu densityadapt segment procedur extract geometr featur falseposit reduct accomplish use fuzzi inferencebas classifi leaveoneimageout crossvalid procedur implement stepwis regress analysi use automat extract optim set featur test valid perform two differ data set contain least one malign mass d imag d imag third data set n imag use consist normal control three data set taken digit databas screen mammographyresult sensit best result cancer imag exhibit falseposit per imag fppi equal respect use bay linear discrimin analysi lda classifi fppi use fuzzi infer system fis falseposit reduct algorithm test normal imag fppi equal observ use lda use fisconclus preclin studi automat breast mass detect algorithm provid promis result term sensit low falseposit rate develop clinic test justifi base result

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