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Book ChapterDOI

Segmentation of Cochlear Nerve Based on Particle Swarm Optimization Method

01 Jan 2018-pp 203-210
TL;DR: A cochlear nerve segmentation approach based on modified particle swarm optimization (PSO) based on a constant adaptive inertia weight based on the kernel density estimation of the image histogram is estimated for fine-tuning the current search space to segment the co chlear nerve.
Abstract: Sensorineural hearing loss is a hearing impairment happens when there is damage to the inner ear or to the nerve pathways from the internal ear to the brain. Cochlear implants have been developed to help the patients with congenital or acquired hearing loss. The size of the cochlear nerve is a prerequisite for the successful outcome of cochlear implant surgery. Hence, an accurate segmentation of cochlear nerve is a critical assignment in computer-aided diagnosis and surgery planning of cochlear implants. This paper aims at developing a cochlear nerve segmentation approach based on modified particle swarm optimization (PSO). In the proposed approach, a constant adaptive inertia weight based on the kernel density estimation of the image histogram is estimated for fine-tuning the current search space to segment the cochlear nerve. The segmentation results are analyzed both qualitatively and quantitatively based on the performance measures, namely Jaccard index, Dice coefficient, sensitivity, specificity, and accuracy as well. These results indicate that the proposed algorithm performs better compared to standard PSO algorithm in preserving edge details and boundary shape. The proposed method is tested on different slices of eight patients undergone for magnetic resonance imaging in the assessment of giddiness/vertigo or fitness for the cochlear implant. The significance of this work is to segment the cochlear nerve from magnetic resonance (MR) images to assist the radiologists in their diagnosis and for successful cochlear implantation with the scope of developing speech and language, especially in children.
Citations
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Journal ArticleDOI
TL;DR: A Mask R-CNN approach driven with U-net to detect and segment the internal auditory canal and its nerves to aid the radiologists in making the right decisions as the localization and segmentation of IAC is accurate.
Abstract: Artificial intelligence (AI) in medical imaging is a burgeoning topic that involves the interpretation of complex image structures. The recent advancements in deep learning techniques increase the computational powers to extract vital features without human intervention. The automatic detection and segmentation of subtle tissue such as the internal auditory canal (IAC) and its nerves is a challenging task, and it can be improved using deep learning techniques. The main scope of this research is to present an automatic method to detect and segment the IAC and its nerves like the facial nerve, cochlear nerve, inferior vestibular nerve, and superior vestibular nerve. To address this issue, we propose a Mask R-CNN approach driven with U-net to detect and segment the IAC and its nerves. The Mask R-CNN with its backbone network of the RESNET50 model learns a background-based localization policy to produce an actual bounding box of the IAC. Furthermore, the U-net segments the structure related information of IAC and its nerves by learning its features. The proposed method was experimented on clinical datasets of 50 different patients including adults and children. The localization of IAC using Mask R-CNN was evaluated using Intersection of Union (IoU), and segmentation of IAC and its nerves was evaluated using Dice similarity coefficient. The localization result shows that mean IoU of RESNET50, RESNET101 are 0.79 and 0.74, respectively. The Dice similarity coefficient of IAC and its nerves using region growing, PSO and U-net method scored 92%, 94%, and 96%, respectively. The result shows that the proposed method outperform better in localization and segmentation of IAC and its nerves. Thus, AI aids the radiologists in making the right decisions as the localization and segmentation of IAC is accurate.

8 citations

References
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Proceedings ArticleDOI
06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.

35,104 citations

Journal ArticleDOI
TL;DR: An adaptive spatial fuzzy c-means clustering algorithm is presented in this paper for the segmentation of three-dimensional (3-D) magnetic resonance (MR) images that takes into account the spatial continuity constraints by using a dissimilarity index that allows spatial interactions between image voxels.
Abstract: An adaptive spatial fuzzy c-means clustering algorithm is presented in this paper for the segmentation of three-dimensional (3-D) magnetic resonance (MR) images. The input images may be corrupted by noise and intensity nonuniformity (INU) artifact. The proposed algorithm takes into account the spatial continuity constraints by using a dissimilarity index that allows spatial interactions between image voxels. The local spatial continuity constraint reduces the noise effect and the classification ambiguity. The INU artifact is formulated as a multiplicative bias field affecting the true MR imaging signal. By modeling the log bias field as a stack of smoothing B-spline surfaces, with continuity enforced across slices, the computation of the 3-D bias field reduces to that of finding the B-spline coefficients, which can be obtained using a computationally efficient two-stage algorithm. The efficacy of the proposed algorithm is demonstrated by extensive segmentation experiments using both simulated and real MR images and by comparison with other published algorithms.

347 citations

Journal ArticleDOI
TL;DR: An evaluation of convolution-based interpolation methods and rigid transformations for the specific task of applying geometrical transformations to medical images shows that spline interpolation is to be preferred over all other methods, both for its accuracy and its relatively low computational cost.

298 citations

Journal ArticleDOI
TL;DR: A new fast automated algorithm has been developed to segment the brain from T1‐weighted volume MR images using automated thresholding and morphological operations, which is fully three‐dimensional and therefore independent of scan orientation.
Abstract: A new fast automated algorithm has been developed to segment the brain from T-1-weighted volume MR images, The algorithm uses automated thresholding and morphological operations. It is fully three-dimensional and therefore independent of scan orientation. The validity and the performance of the algorithm were evaluated by comparing the automatically calculated brain volume with semi-automated measurements in 10 subjects, by calculating the brain volume from repeated scans in another 10 subjects, and by visual inspection. The mean and standard deviation of the difference between semi-automated and auto mated measurements were 0.56% and 2.8% of the mean brain volume, respectively, which is within inter-observer variability of the semi-automated method. The mean and standard deviation of the difference between the total volumes calculated from repeated scans were 0.40% and 1.2% of the mean brain volume, respectively. Good results were also obtained from a scan of abnormal brains. Magn Reson Med 42:127-135, 1999. (C) 1999 Wiley-Liss, Inc.

200 citations

Journal ArticleDOI
TL;DR: Wavelet packet analysis is a mathematical transformation that can be used to post‐process images, for example, to remove image noise (“denoising”), and complex denoising yielded sharper edges and better low‐intensity feature contrast.
Abstract: Wavelet packet analysis is a mathematical transformation that can be used to post-process images, for example, to remove image noise (“denoising”). At a very low signal-to-noise ratio (SNR <5), standard magnitude magnetic resonance images have skewed Rician noise statistics that degrade denoising performance. Since the quadrature images have approximately Gaussian noise, it was postulated that denoising would produce better contrast and sharper edges if performed before magnitude image formation. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge blurring effects of these two approaches were examined in synthetic, phantom, and human MR images. While magnitude and complex denoising both significantly improved SNR and CNR, complex denoising yielded sharper edges and better low-intensity feature contrast. Magn Reson Med 1999;41:631–635, 1999. © 1999 Wiley-Liss, Inc.

149 citations