Neural networks for volumetric MR imaging of the brain

Gelenbe, Erol; Feng, Yi; Ranga, K.; Krishnan, Pranesh

doi:10.1109/nicrsp.1996.542760

Cited by 14 publications

(8 citation statements)

References 17 publications

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“…It has had applications in image processing [30,37,51] and network routing [24]. The model's capability to approximate arbitrary continuous and bounded functions [41] is the theoretical justification for the use of learning algorithms that store complex functional relationships within these models.…”

Section: This Special Issuementioning

confidence: 99%

Festschrift for Erol Gelenbe's 70th Birthday

Pekergin

2016

Prob. Eng. Inf. Sci.

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and Electronic Engineering (see http://san.ee.ic.ac.uk/Gelenbe2015). All but three of the fourteen papers presented here were written by his students and descendants. These papers are centered on probability models related to computer systems and networks, an area where Erol plays a pioneering role in which he remains very active with many innovations and new directions that he has introduced in the last ten or twelve years. We first briefly review Erol's work in these areas, and then discuss each of the contributions that appear here together with their links to Erol's own published research.

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Section: This Special Issuementioning

confidence: 99%

Festschrift for Erol Gelenbe's 70th Birthday

Pekergin

2016

Prob. Eng. Inf. Sci.

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“…When it comes to discrete and explicit segmentation, medical imaging research has used artificial neural networks as a means to assist in these tasks. 27,28 However, support vector machines 14,15,29 have demonstrated better results to date than neural networks ͑at a larger computational cost͒ when applied to pattern recognition including object identification, 30 text categorization, 31 and face detection. 32 Tzenget al 33 compared the use of neural networks and support vector machines when trying to construct an N-dimensional transfer function that uses additional variables such as variance and color ͑if present͒ in addition to scalar values and gradient information.…”

Section: Visualization and Analysis Softwarementioning

confidence: 99%

Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets

et al. 2007

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Abstract. Recent developments in Fourier domain-optical coherence tomography ͑Fd-OCT͒ have increased the acquisition speed of current ophthalmic Fd-OCT instruments sufficiently to allow the acquisition of volumetric data sets of human retinas in a clinical setting. The large size and three-dimensional ͑3D͒ nature of these data sets require that intelligent data processing, visualization, and analysis tools are used to take full advantage of the available information. Therefore, we have combined methods from volume visualization, and data analysis in support of better visualization and diagnosis of Fd-OCT retinal volumes. Custom-designed 3D visualization and analysis software is used to view retinal volumes reconstructed from registered B-scans. We use a support vector machine ͑SVM͒ to perform semiautomatic segmentation of retinal layers and structures for subsequent analysis including a comparison of measured layer thicknesses. We have modified the SVM to gracefully handle OCT speckle noise by treating it as a characteristic of the volumetric data. Our software has been tested successfully in clinical settings for its efficacy in assessing 3D retinal structures in healthy as well as diseased cases. Our tool facilitates diagnosis and treatment monitoring of retinal diseases.

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“…Machine learning algorithms such as artificial neural networks have been used in medical imaging research somewhat successfully [5,7]. However, SVMs [3,15,4] have yielded more reliable results for feature detection [2,21,16].…”

Section: Related Workmentioning

confidence: 99%

Segmentation of Three-dimensional Retinal Image Data

Fuller¹,

Zawadzki

Choi

et al. 2007

IEEE Trans. Visual. Comput. Graphics

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Abstract-We have combined methods from volume visualization and data analysis to support better diagnosis and treatment of human retinal diseases. Many diseases can be identified by abnormalities in the thicknesses of various retinal layers captured using optical coherence tomography (OCT). We used a support vector machine (SVM) to perform semi-automatic segmentation of retinal layers for subsequent analysis including a comparison of layer thicknesses to known healthy parameters. We have extended and generalized an older SVM approach to support better performance in a clinical setting through performance enhancements and graceful handling of inherent noise in OCT data by considering statistical characteristics at multiple levels of resolution. The addition of the multi-resolution hierarchy extends the SVM to have "global awareness." A feature, such as a retinal layer, can therefore be modeled within the SVM as a combination of statistical characteristics across all levels; thus capturing high-and low-frequency information. We have compared our semi-automatically generated segmentations to manually segmented layers for verification purposes. Our main goals were to provide a tool that could (i) be used in a clinical setting; (ii) operate on noisy OCT data; and (iii) isolate individual or multiple retinal layers in both healthy and disease cases that contain structural deformities.

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Neural networks for volumetric MR imaging of the brain

Cited by 14 publications

References 17 publications

Festschrift for Erol Gelenbe's 70th Birthday

Festschrift for Erol Gelenbe's 70th Birthday

Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets

Segmentation of Three-dimensional Retinal Image Data

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