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Assessing the combined performance of texture and morphological parameters in distinguishing breast tumors in ultrasound images
Author(s) -
Alvarenga André Victor,
Infantosi Antonio Fernando C.,
Pereira Wagner C. A.,
Azevedo Carolina M.
Publication year - 2012
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 - 10.1118/1.4766268
Subject(s) - texture (cosmology) , linear discriminant analysis , pattern recognition (psychology) , artificial intelligence , ultrasound , receiver operating characteristic , residual , predictive value , mathematics , image texture , computer science , radiology , statistics , medicine , image processing , image (mathematics) , algorithm
Purpose: This work aims to investigate the combination of morphological and texture parameters in distinguishing between malignant and benign breast tumors in ultrasound images. Methods: Linear discriminant analysis was applied to sets of up to five parameters, and then the performances were assessed using the area A z (± standard error) under the receiver operator characteristic curve, accuracy ( Ac ), sensitivity ( Se ), specificity ( Sp ), positive predictive value, and negative predictive value. Results: The most relevant individual parameter was the normalized residual value ( nrv ), calculated from the convex polygon technique. The best performance among all studied combinations was achieved by two morphological and three texture parameters ( nrv , con , std , R , and asm i ), which correctly distinguished nearly 85% of the breast tumors. Conclusions: This result indicates that the combination of morphological and texture parameters may be useful to assist physicians in the diagnostic process, especially if it is associated with an automatic classification tool.