Improving mass detection using combined feature representations from projection views and reconstructed volume of DBT and boosting based classification with feature selection.

Abstract:

:In digital breast tomosynthesis (DBT), image characteristics of projection views and reconstructed volume are different and both have the advantage of detecting breast masses, e.g. reconstructed volume mitigates a tissue overlap, while projection views have less reconstruction blur artifacts. In this paper, an improved mass detection is proposed by using combined feature representations from projection views and reconstructed volume in the DBT. To take advantage of complementary effects on different image characteristics of both data, combined feature representations are extracted from both projection views and reconstructed volume concurrently. An indirect region-of-interest segmentation in projection views, which projects volume-of-interest in reconstructed volume into the corresponding projection views, is proposed to extract combined feature representations. In addition, a boosting based classification with feature selection has been employed for selecting effective feature representations among a large number of combined feature representations, and for reducing false positives. Experiments have been conducted on a clinical data set that contains malignant masses. Experimental results demonstrate that the proposed mass detection can achieve high sensitivity with a small number of false positives. In addition, the experimental results demonstrate that the selected feature representations for classifying masses complementarily come from both projection views and reconstructed volume.

journal_name

Phys Med Biol

authors

Kim DH,Kim ST,Ro YM

doi

10.1088/0031-9155/60/22/8809

subject

Has Abstract

pub_date

2015-11-21 00:00:00

pages

8809-32

issue

22

eissn

0031-9155

issn

1361-6560

journal_volume

60

pub_type

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