Simplified false-positive reduction in computer-aided detection scheme of clustered microcalcifications in digital breast tomosynthesis

Jeong, Ji-Wook; Chae, Seung‐Hoon; Lee, Sooyeul; Chae, Eun Young; Kim, Hak Hee; Choi, Young-Wook

doi:10.1117/12.2081511

Cited by 2 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There have been a number of studies regarding the development of CADe techniques for the detection of masses in DBT [ 11 , 14 – 17 ]. Compared with the detection of masses, preliminary researches regarding the detection of MCCs on DBT have been reported [ 12 , 18 – 23 ].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, a simple and efficient FP reduction scheme coupled with a detection algorithm for MCCs in a reconstructed DBT volume using 3D objectness- and SNR-enhanced images is suggested [ 13 , 23 ]. For a dataset of two-view DBTs of 69 breasts with or without MCCs, a view-based sensitivity of 83.3% was achieved at 2.47 FPs per DBT volume.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Three-Dimensional Computer-Aided Detection of Microcalcification Clusters in Digital Breast Tomosynthesis

Jeong

Chae

et al. 2016

BioMed Research International

Self Cite

View full text Add to dashboard Cite

We propose computer-aided detection (CADe) algorithm for microcalcification (MC) clusters in reconstructed digital breast tomosynthesis (DBT) images. The algorithm consists of prescreening, MC detection, clustering, and false-positive (FP) reduction steps. The DBT images containing the MC-like objects were enhanced by a multiscale Hessian-based three-dimensional (3D) objectness response function and a connected-component segmentation method was applied to extract the cluster seed objects as potential clustering centers of MCs. Secondly, a signal-to-noise ratio (SNR) enhanced image was also generated to detect the individual MC candidates and prescreen the MC-like objects. Each cluster seed candidate was prescreened by counting neighboring individual MC candidates nearby the cluster seed object according to several microcalcification clustering criteria. As a second step, we introduced bounding boxes for the accepted seed candidate, clustered all the overlapping cubes, and examined. After the FP reduction step, the average number of FPs per case was estimated to be 2.47 per DBT volume with a sensitivity of 83.3%.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Computer-Aided Detection of Microcalcification Clusters in Digital Breast Tomosynthesis

Jeong

Chae

et al. 2016

BioMed Research International

Self Cite

View full text Add to dashboard Cite

show abstract

Simplified computer-aided detection scheme of microcalcification clusters in digital breast tomosynthesis images

Jeong

Chae

et al. 2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

View full text Add to dashboard Cite

A computer-aided detection (CADe) algorithm for clustered microcalcifications (MCs) in reconstructed digital breast tomosynthesis (DBT) images is suggested. The MC-like objects were enhanced by a Hessian-based 3D calcification response function, and a signal-to-noise ratio (SNR) enhanced image was also generated to screen the MC clustering seed objects. A connected component segmentation method was used to detect the cluster seed objects, which were considered as potential clustering centers of MCs. Bounding cubes for the accepted clustering seed candidate were generated and the overlapping cubes were combined and examined. After the MC clustering and false-positive (FP) reduction step, the average number of FPs was estimated to be 0.87 per DBT volume with a sensitivity of 90.5%.

show abstract

Simplified false-positive reduction in computer-aided detection scheme of clustered microcalcifications in digital breast tomosynthesis

Cited by 2 publications

References 7 publications

Three-Dimensional Computer-Aided Detection of Microcalcification Clusters in Digital Breast Tomosynthesis

Three-Dimensional Computer-Aided Detection of Microcalcification Clusters in Digital Breast Tomosynthesis

Simplified computer-aided detection scheme of microcalcification clusters in digital breast tomosynthesis images

Contact Info

Product

Resources

About