Enhanced needle localization in ultrasound using beam steering and learning-based segmentation

Highlights • Beam steering is used to enhance the appearance of needles in ultrasound images. • Needle segmentation is challenging, even when using beam steering-based needle enhancement. • Needle segmentation is posed as a machine learning problem. A boosted classifier using a bank of log-Gabor wav...

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Bibliographic Details
Published inComputerized medical imaging and graphics Vol. 41; pp. 46 - 54
Main Authors Hatt, Charles R, Ng, Gary, Parthasarathy, Vijay
Format Journal Article
LanguageEnglish
Published United States Elsevier Ltd 01.04.2015
Subjects
Algorithms
Animals
Chickens
Classification
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
In Vitro Techniques
Injections - instrumentation
Injections - methods
Internal Medicine
Machine Learning
Needle guidance
Needles
Nerve Block - methods
Other
Pattern Recognition, Automated - methods
Regional anesthesia
Reproducibility of Results
Segmentation
Sensitivity and Specificity
Swine
Ultrasonography, Interventional - methods
Ultrasound
linear pre-segmentation method from
FI
intensity-based segmentation method
Segmentation
targeting error
R T
Needle guidance
proposed boosted classifier based segmentation method using T features
IS
filter-based segmentation method
FS
TE
beam-steered ultrasound image with enhanced needle appearance
Regional anesthesia
fused image combining needle enhanced image and B-mode image
Classification
BCS- T
NLSR
NI
rectified classification confidence using T features
ULS
Ultrasound
needle localization success rate
RT
BCS-T
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Summary:Highlights • Beam steering is used to enhance the appearance of needles in ultrasound images. • Needle segmentation is challenging, even when using beam steering-based needle enhancement. • Needle segmentation is posed as a machine learning problem. A boosted classifier using a bank of log-Gabor wavelets is proposed for needle pixel classification. The classifier is trained on ex vivo and clinical nerve block datasets. • As a result of improved segmentation performance compared to previous methods, needle localization accuracy and robustness are increased using the proposed method.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0895-6111
1879-0771
DOI:10.1016/j.compmedimag.2014.06.016