TY - JOUR
T1 - Three-Dimensional Segmentation of the Ex-Vivo Anterior Lamina Cribrosa from Second-Harmonic Imaging Microscopy
AU - Ram, Sundaresh
AU - Danford, Forest
AU - Howerton, Stephen
AU - Rodríguez, Jeffrey J.
AU - Geest, Jonathan P.Vande
N1 - Funding Information:
Manuscript received November 6, 2016; revised February 11, 2017; accepted February 17, 2017. Date of publication February 23, 2017; date of current version June 18, 2018. This work was supported by the National Institutes of health under Grant NIH-1R01EY020890 (to J. P. Vande Geest) and an NIH-sponsored shared device (NIH/NCRR S10RR023737). (Corresponding author: Jonathan P. Vande Geest.) S. Ram is with the School of Electrical and Computer Engineering, and the School of Biomedical Engineering, Cornell University.
Publisher Copyright:
© 1964-2012 IEEE.
PY - 2018/7
Y1 - 2018/7
N2 - The lamina cribrosa (LC) is a connective tissue in the posterior eye with a complex mesh-like trabecular microstructure, through which all the retinal ganglion cell axons and central retinal vessels pass. Recent studies have demonstrated that changes in the structure of the LC correlate with glaucomatous damage. Thus, accurate segmentation and reconstruction of the LC is of utmost importance. This paper presents a new automated method for segmenting the microstructure of the anterior LC in the images obtained via multiphoton microscopy using a combination of ideas. In order to reduce noise, we first smooth the input image using a 4-D collaborative filtering scheme. Next, we enhance the beam-like trabecular microstructure of the LC using wavelet multiresolution analysis. The enhanced LC microstructure is then automatically extracted using a combination of histogram thresholding and graph-cut binarization. Finally, we use morphological area opening as a postprocessing step to remove the small and unconnected 3-D regions in the binarized images. The performance of the proposed method is evaluated using mutual overlap accuracy, Tanimoto index, F-score, and Rand index. Quantitative and qualitative results show that the proposed algorithm provides improved segmentation accuracy and computational efficiency compared to the other recent algorithms.
AB - The lamina cribrosa (LC) is a connective tissue in the posterior eye with a complex mesh-like trabecular microstructure, through which all the retinal ganglion cell axons and central retinal vessels pass. Recent studies have demonstrated that changes in the structure of the LC correlate with glaucomatous damage. Thus, accurate segmentation and reconstruction of the LC is of utmost importance. This paper presents a new automated method for segmenting the microstructure of the anterior LC in the images obtained via multiphoton microscopy using a combination of ideas. In order to reduce noise, we first smooth the input image using a 4-D collaborative filtering scheme. Next, we enhance the beam-like trabecular microstructure of the LC using wavelet multiresolution analysis. The enhanced LC microstructure is then automatically extracted using a combination of histogram thresholding and graph-cut binarization. Finally, we use morphological area opening as a postprocessing step to remove the small and unconnected 3-D regions in the binarized images. The performance of the proposed method is evaluated using mutual overlap accuracy, Tanimoto index, F-score, and Rand index. Quantitative and qualitative results show that the proposed algorithm provides improved segmentation accuracy and computational efficiency compared to the other recent algorithms.
KW - Graph cut segmentation
KW - histogram thresholding
KW - lamina cribrosa
KW - volumetric data denoising
KW - wavelet
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U2 - 10.1109/TBME.2017.2674521
DO - 10.1109/TBME.2017.2674521
M3 - Article
C2 - 28252388
AN - SCOPUS:85049067798
VL - 65
SP - 1617
EP - 1629
JO - IRE transactions on medical electronics
JF - IRE transactions on medical electronics
SN - 0018-9294
IS - 7
ER -