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Evaluating Lesion Segmentation on Breast Sonography as Related to Lesion Type
Author(s) -
Pons Gerard,
Martí Joan,
Martí Robert,
Ganau Sergi,
Vilanova Joan Carles,
Noble J. Alison
Publication year - 2013
Publication title -
journal of ultrasound in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.574
H-Index - 91
eISSN - 1550-9613
pISSN - 0278-4297
DOI - 10.7863/ultra.32.9.1659
Subject(s) - medicine , lesion , segmentation , radiology , markov random field , mammography , artificial intelligence , modality (human–computer interaction) , distortion (music) , pattern recognition (psychology) , image segmentation , pathology , computer science , breast cancer , amplifier , computer network , cancer , bandwidth (computing)
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.