Reconstructing Geometrically Consistent Tree Structures from Noisy Images

Türetken, Engin; Blum, Christian; González, Germán; Fua, Pascal

doi:10.1007/978-3-642-15705-9_36

Cited by 5 publications

(6 citation statements)

References 12 publications

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“…The SCN shows the least results while the ANN and the CCN method shows better performance as compared with the other techniques. This shows that precision obtained in this work is comparable with those reported in the literature [6][7][8][9]. Fig.…”

Section: Tablesupporting

confidence: 82%

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A new method of vascular point detection using artificial neural network

Kader

Aibinu

Salami

2012

2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences

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Abstract-Vascular intersection is an important feature in retina fundus image (RFI). It can be used to monitor the progress of diabetes hence accurately determining vascular point is of utmost important. In this work a new method of vascular point detection using artificial neural network model has been proposed. The method uses a 5x5 window in order to detect the combination of bifurcation and crossover points in a retina fundus image. Simulated images have been used to train the artificial neural network and on convergence the network is used to test (RFI) from DRIVE database. Performance analysis of the system shows that ANN based technique achieves 100% accuracy on simulated images and minimum of 92% accuracy on RFI obtained from DRIVE database.

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Section: Tablesupporting

confidence: 82%

“…Some improvement has been done for efficient detection of bifurcation and crossover points in [7].Thus in this work, the bifurcation points were sampled so that the over counting pixels were resolved.…”

Section: Literature Reviewmentioning

confidence: 99%

A new method of vascular point detection using artificial neural network

Kader

Aibinu

Salami

2012

2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences

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“…Neurite tracing methods connect paths of maximum neuriteness voxels locally between sets of seed points to extract the global neurite structure. A common drawback of existing methods is their dependence on seed points [2]. Often, manual intervention is required to select the optimal seed points.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Model for Automatic Extraction of 3D Neuronal Morphology

Basu

Kulikova

Жижина

et al. 2013

Advanced Information Systems Engineering

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Tubular structures are frequently encountered in bio-medical images. The center-lines of these tubules provide an accurate representation of the topology of the structures. We introduce a stochastic Marked Point Process framework for fully automatic extraction of tubular structures requiring no user interaction or seed points for initialization. Our Marked Point Process model enables unsupervised network extraction by fitting a configuration of objects with globally optimal associated energy to the centreline of the arbors. For this purpose we propose special configurations of marked objects and an energy function well adapted for detection of 3D tubular branches. The optimization of the energy function is achieved by a stochastic, discrete-time multiple birth and death dynamics. Our method finds the centreline, local width and orientation of neuronal arbors and identifies critical nodes like bifurcations and terminals. The proposed model is tested on 3D light microscopy images from the DIADEM data set with promising results.

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“…González et al [2] has proposed a K-minimum spanning based approach for tracing filamentary structure. In the follow up research work E. Türetken et al [43,44,45,46] has proposed a quadratic mixed integer programming formulation for solving the K-MST problem. But none of their reserach work has addressed the problem of crossover.…”

Section: The Joint Winner Of the Challenge B Roysham's Team Has Propmentioning

confidence: 99%

“…Soares et al [52] adopt eighteen dimensional Gabor response feature to train two Gaussian Mixture Models (GMM), which are further employed to produce a binary probability map for a test image. The tracking-based methods [57,58,59,60,61,41,62,43,44,45,46], on the other hand, usually start with a seed and track the intended vessel based on local intensity or texture information. The authors of [58] divide the image into non-overlapping grid and considered each grid separately for seed finding, which are followed by the tracking procedure to uncover the vessel network structure.…”

Section: Literature Review Of Retinal Blood Vessel Tracingmentioning

confidence: 99%

Graph-based tracing of filamentary structured objects with applications in neuronal and retinal images

De¹

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I feel very fortunate that I have the opportunity to study as a graduate student in NTU for the past four years. My sincere gratitude goes to plenty of peoples who played very important roles on the completion of this thesis as well as my life. I would like to express my special appreciation and thanks to my supervisor Dr. Li Cheng, you have been a tremendous mentor for me. I would like to thank you for encouraging my research and for allowing me to grow as a research scientist. Your advice on both research as well as on my career have been priceless. I would also like to thank my NTU supervisor Assoc. Professor Dr. Lin Feng for his guidance about research work and thesis writing. I also want to thank you for handling the administrative matters from NTU side in a smooth and easy manner. I would also like to thank Dr. Manoranjan Dash and Dr. Vladimir Kuznetsov for accepting me as a PhD student. I would especially like to thank the administrative staff of BII Ms. Betty Kee, Christine and Norani and IT staff Mr. Robin, Mr. Ananthan for your constant support for the research activities in BII. A special thanks to my family. Words cannot express how grateful I am to my mother, father, mother-in law, father-in-law, sister, sister-in-law, uncle and aunty for all of the sacrifices that youve made on my behalf. Your prayer for me was what sustained me thus far. I would also like to thank all of my friends who supported me in writing, and incented me to strive towards my goal. This last word of acknowledgment I have saved for my dear wife Monalisa, who has been with me all these years and has made them the best years of my life.

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Reconstructing Geometrically Consistent Tree Structures from Noisy Images

Cited by 5 publications

References 12 publications

A new method of vascular point detection using artificial neural network

A new method of vascular point detection using artificial neural network

A Stochastic Model for Automatic Extraction of 3D Neuronal Morphology

Graph-based tracing of filamentary structured objects with applications in neuronal and retinal images

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