title: Evaluating lesion segmentation on breast sonography as related to lesion type.
abstract: Breast sonography currently provides a complementary diagnosis when other modalities are not conclusive. However, lesion segmentation on sonography is still a challenging problem due to the presence of artifacts. To solve these problems, Markov random fields and maximum a posteriori-based methods are used to estimate a distortion field while identifying regions of similar intensity inhomogeneity. In this study, different initialization approaches were exhaustively evaluated using a database of 212 B-mode breast sonograms and considering the lesion types. Finally, conclusions about the relationship between the segmentation results and lesions types are described.
authors: Department of Computer Architecture and Technology, University of Girona, Girona, Spain. gpons@eia.udg.edu
authors: Gerard
authors: G
authors: Pons
authors: Joan
authors: J
authors: Martí
authors: Robert
authors: R
authors: Martí
authors: Sergi
authors: S
authors: Ganau
authors: Joan Carles
authors: JC
authors: Vilanova
authors: J Alison
authors: JA
authors: Noble
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000453:epidemiology*; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015995:Prevalence; D012307:Risk Factors; D012680:Sensitivity and Specificity; D013030:Spain / Q000453:epidemiology; D016217:Ultrasonography, Mammary / Q000379:methods* / Q000706:statistics & numerical data*
keywords: Markov random fields; algorithm evaluation; breast lesion segmentation; maximum a posteriori; sonography