TY - GEN
T1 - 3D small structure detection in medical image using texture analysis
AU - Gao, Fei
AU - Zhang, Min
AU - Wu, Teresa
AU - Bennett, Kevin M.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/13
Y1 - 2016/10/13
N2 - Small structure segmentation from medical images is a challenging problem yet has important applications. Examples are labeling cell, lesion and glomeruli for disease diagnosis, just to name a few. Though extensive research has proposed various detectors for this type of problem, most are 2D detectors. Recently, we have developed a Hessian based 3D detector to segment small structures from medical images (e.g., MRI). In our detector, two 3D geometrical features: regional blobness and flatness, in conjunction with the intensity features are fully utilized to serve the segmentation purpose. The objective of this research is to further improve the 3D detector with additions of texture features. Medical images contain rich information which can be presented as texture, the local characteristics pattern of image intensity. We hypothesize the Hessian based detector extended with the 3D texture features is expected to have improved performance in segmenting small structures. To thoroughly evaluate the contributions from the textual features, 25 synthetic images and 6 real world rat MR images are studied. It is observed the combination of intensity, blobness, and two texture features: intensity standard deviation and entropy improves performance in synthetic dataset by about 19% in F-score, and performs as well as other detectors on rat MR images.
AB - Small structure segmentation from medical images is a challenging problem yet has important applications. Examples are labeling cell, lesion and glomeruli for disease diagnosis, just to name a few. Though extensive research has proposed various detectors for this type of problem, most are 2D detectors. Recently, we have developed a Hessian based 3D detector to segment small structures from medical images (e.g., MRI). In our detector, two 3D geometrical features: regional blobness and flatness, in conjunction with the intensity features are fully utilized to serve the segmentation purpose. The objective of this research is to further improve the 3D detector with additions of texture features. Medical images contain rich information which can be presented as texture, the local characteristics pattern of image intensity. We hypothesize the Hessian based detector extended with the 3D texture features is expected to have improved performance in segmenting small structures. To thoroughly evaluate the contributions from the textual features, 25 synthetic images and 6 real world rat MR images are studied. It is observed the combination of intensity, blobness, and two texture features: intensity standard deviation and entropy improves performance in synthetic dataset by about 19% in F-score, and performs as well as other detectors on rat MR images.
UR - http://www.scopus.com/inward/record.url?scp=85009062934&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2016.7592201
DO - 10.1109/EMBC.2016.7592201
M3 - Conference contribution
C2 - 28269719
AN - SCOPUS:85009062934
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 6433
EP - 6436
BT - 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Y2 - 16 August 2016 through 20 August 2016
ER -