Electric field theory based approach to search-direction line definition in image segmentation: application to optimal femur-tibia cartilage segmentation in knee-joint 3-D MR

Yin, Yin; Sonka, Milan

doi:10.1117/12.844573

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…Solutions to these problem have been proposed as non-intersecting graph columns constructed using electric field theory 15 and training cost function parameters from known samples. 15,16 The numerical results of the segmentation validation showed that the graph search and hybrid method achieved very similar accuracy. However, the benefits of the hybrid method such as topology error correction and feasibility of the resulting surfaces are not fully reflected in these measures.…”

Section: Discussionmentioning

confidence: 73%

Left-ventricle segmentation in real-time 3D echocardiography using a hybrid active shape model and optimal graph search approach

et al. 2010

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Quantitative analysis of the left ventricular shape and motion patterns associated with left ventricular mechanical dyssynchrony (LVMD) is essential for diagnosis and treatment planning in congestive heart failure. Real-time 3D echocardiography (RT3DE) used for LVMD analysis is frequently limited by heavy speckle noise or partially incomplete data, thus a segmentation method utilizing learned global shape knowledge is beneficial. In this study, the endocardial surface of the left ventricle (LV) is segmented using a hybrid approach combining active shape model (ASM) with optimal graph search. The latter is used to achieve landmark refinement in the ASM framework. Optimal graph search translates the 3D segmentation into the detection of a minimum-cost closed set in a graph and can produce a globally optimal result. Various information -gradient, intensity distributions, and regional-property terms -are used to define the costs for the graph search. The developed method was tested on 44 RT3DE datasets acquired from 26 LVMD patients. The segmentation accuracy was assessed by surface positioning error and volume overlap measured for the whole LV as well as 16 standard LV regions. The segmentation produced very good results that were not achievable using ASM or graph search alone.

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Section: Discussionmentioning

confidence: 73%

Left-ventricle segmentation in real-time 3D echocardiography using a hybrid active shape model and optimal graph search approach

et al. 2010

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“…In the future, for the 2-D vessel pixel classification, the texture information around the NCO might be helpful for more accurately identifying the NCO and blood vessels. For the graph construction, using an arc-weighted graph [8] and considering the graph column crossing problem of vessel bifurcations [9] would also help improve the algorithm performance.…”

Section: Discussionmentioning

confidence: 99%

Automated Segmentation of 3-D Spectral OCT Retinal Blood Vessels by Neural Canal Opening False Positive Suppression

Niemeijer

Abrà

et al. 2010

Lecture Notes in Computer Science

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We present a method for automatically segmenting the blood vessels in optic nerve head (ONH) centered spectral-domain optical coherence tomography (SD-OCT) volumes, with a focus on the ability to segment the vessels in the region near the neural canal opening (NCO). The algorithm first pre-segments the NCO using a graph-theoretic approach. Oriented Gabor wavelets rotated around the center of the NCO are applied to extract features in a 2-D vessel-aimed projection image. Corresponding oriented NCO-based templates are utilized to help suppress the false positive tendency near the NCO boundary. The vessels are identified in a vessel-aimed projection image using a pixel classification algorithm. Based on the 2-D vessel profiles, 3-D vessel segmentation is performed by a triangular-mesh-based graph search approach in the SD-OCT volume. The segmentation method is trained on 5 and is tested on 10 randomly chosen independent ONH-centered SD-OCT volumes from 15 subjects with glaucoma. Using ROC analysis, for the 2-D vessel segmentation, we demonstrate an improvement over the closest previous work with an area under the curve (AUC) of 0.81 (0.72 for previously reported approach) for the region around the NCO and 0.84 for the region outside the NCO (0.81 for previously reported approach).

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“…The graph-based approaches, such as the "graph cut" approach for finding a single surface by Boykov et al [30], the graph search approach for finding a single surface by Wu and Chen [10], the extension of the graph search approach for finding multiple surfaces by Li et al [11] using edge information, the extension of graph search approach for finding multiple surfaces using additionally regional information by Garvin et al [12,31], etc. [32,33], have been very attractive due to the ability of finding the globally optimal solution.…”

Section: Prior Approaches Of Clinical Disc Margin Segmentation In Sd-mentioning

confidence: 99%

Multimodal 3-D segmentation of optic nerve head structures from spectral domain Oct volumes and color fundus photographs

Hu¹

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Standard structural methods for the assessment of glaucoma, such as the planimetry of stereo color photographs of the optic disc, involve a subjective component either by the patient or examiner and suffer from poor reproducibility. Spectral-domain optical coherence tomography (SD-OCT) provides a 3-D, cross-sectional, microscale depiction of biological tissues. Given the wealth of volumetric information at microscale resolution available with SD-OCT volumes of the back of the eye, it is likely that better parameters can be obtained for measuring glaucoma changes that move beyond what is possible using fundus photography. The neural canal opening (NCO) is a 3-D single anatomic structure in SD-OCT volumes. It is proposed as a basis for a stable reference plane from which various optic nerve morphometric parameters can be derived. The overall aim of this Ph.D. project is to develop a framework to segment the 3-D NCO and retinal vessels using information from SD-OCT volumes and fundus photographs to aid in the management of glaucoma. Based on the mutual positional relationship of the NCO and vessels, a multimodal 3-D scale-learning-based framework is developed to iteratively identify these structures in SD-OCT volumes by incorporating each other's pre-identified positional information. The algorithm first applies a 3-D wavelet-transform-learning-based layer segmentation and pre-segments the NCO using a graph-theoretic approach. To improve the NCO segmentation, the vessels are identified either using an OCT vessel segmentation approach by incorporating the pre-segmented NCO positional informa-SD-OCT data of human scans; go to our funding support of NIH grant EY018853, RPB, Department of Veterans Affairs, Marlene S. and Leonard A. Hadley Glaucoma Research Fund, and AGS Mid-Career Physician Scientist Award etc. Deep thanks go to my parents, brother, sister, and all the family in China for their understanding for me to pursuit the Ph.D. study; go to my husband for his encouragement; and more importantly, go to my intelligent and cute son for his wonderful mental support.

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Electric field theory based approach to search-direction line definition in image segmentation: application to optimal femur-tibia cartilage segmentation in knee-joint 3-D MR

Cited by 5 publications

References 8 publications

Left-ventricle segmentation in real-time 3D echocardiography using a hybrid active shape model and optimal graph search approach

Left-ventricle segmentation in real-time 3D echocardiography using a hybrid active shape model and optimal graph search approach

Automated Segmentation of 3-D Spectral OCT Retinal Blood Vessels by Neural Canal Opening False Positive Suppression

Multimodal 3-D segmentation of optic nerve head structures from spectral domain Oct volumes and color fundus photographs

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