Segmenting and validating brain tissue definitions in the presence of varying tissue contrast

Bansal, Ravi; Hao, Xuejun; Peterson, Bradley S.

doi:10.1016/j.mri.2016.08.002

Cited by 2 publications

(1 citation statement)

References 63 publications

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“…In the brain diagnostic system, brain tissue segmentation is essential to identify many brain disorders. Segmenting brain tissues into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) helps to identify the neurodegenerative diseases like Alzheimer, alcoholism, bipolar disorder, Huntington disease, multiple Sclerosis, and tumors…”

Section: Introductionmentioning

confidence: 99%

Rapid brain tissue segmentation process by modified FCM algorithm with CUDA enabled GPU machine

Kalaiselvi

Sriramakrishnan

2018

Int J Imaging Syst Tech

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The proposed work introduces a modified method of fuzzy c means (FCM) algorithm using bias field correction and partial supervision techniques. The proposed method is named as bias corrected partial supervision FCM (BCPSFCM). The modified membership function takes the advantage of available knowledge from labeled patterns with the bias field correction. The experiment is tested on internet brain segmentation repository with their gold standard. The performance of the method is compared with three existing methods and 12 state of the art methods using dice coefficient, sensitivity, specificity, and accuracy. Accuracy of the proposed method reached upto 98%, 98%, and 99% of GM, WM, and CSF segmentation but required additional computation power from graphics processing unit (GPU). Further parallel BCPSFCM is proposed with the help of compute unified device architecture enabled GPU machine and the processing time is reduced up to 49 times than the serial implementation.

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Section: Introductionmentioning

confidence: 99%

Rapid brain tissue segmentation process by modified FCM algorithm with CUDA enabled GPU machine

Kalaiselvi

Sriramakrishnan

2018

Int J Imaging Syst Tech

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Features of the normal choriocapillaris with OCT-angiography: Density estimation and textural properties

Montesano

Allegrini

Colombo³

et al. 2017

PLoS ONE

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The main objective of our work is to perform an in depth analysis of the structural features of normal choriocapillaris imaged with OCT Angiography. Specifically, we provide an optimal radius for a circular Region of Interest (ROI) to obtain a stable estimate of the subfoveal choriocapillaris density and characterize its textural properties using Markov Random Fields. On each binarized image of the choriocapillaris OCT Angiography we performed simulated measurements of the subfoveal choriocapillaris densities with circular Regions of Interest (ROIs) of different radii and with small random displacements from the center of the Foveal Avascular Zone (FAZ). We then calculated the variability of the density measure with different ROI radii. We then characterized the textural features of choriocapillaris binary images by estimating the parameters of an Ising model. For each image we calculated the Optimal Radius (OR) as the minimum ROI radius required to obtain a standard deviation in the simulation below 0.01. The density measured with the individual OR was 0.52 ± 0.07 (mean ± STD). Similar density values (0.51 ± 0.07) were obtained using a fixed ROI radius of 450 μm. The Ising model yielded two parameter estimates (β = 0.34 ± 0.03; γ = 0.003 ± 0.012; mean ± STD), characterizing pixel clustering and white pixel density respectively. Using the estimated parameters to synthetize new random textures via simulation we obtained a good reproduction of the original choriocapillaris structural features and density. In conclusion, we developed an extensive characterization of the normal subfoveal choriocapillaris that might be used for flow analysis and applied to the investigation pathological alterations.

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Segmenting and validating brain tissue definitions in the presence of varying tissue contrast

Cited by 2 publications

References 63 publications

Rapid brain tissue segmentation process by modified FCM algorithm with CUDA enabled GPU machine

Rapid brain tissue segmentation process by modified FCM algorithm with CUDA enabled GPU machine

Features of the normal choriocapillaris with OCT-angiography: Density estimation and textural properties

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