Automatic brain extraction methods for T1 magnetic resonance images using region labeling and morphological operations

The high resolution magnetic resonance (MR) brain images contain some non-brain tissues such as skin, fat, muscle, neck, and eye balls compared to the functional images namely positron emission tomography (PET), single photon emission computed tomography (SPECT), and functional magnetic resonance imaging (fMRI) which usually contain relatively less non-brain tissues. The presence of these non-brain tissues is considered as a major obstacle for automatic brain image segmentation and analysis techniques. Therefore, quantitative morphometric studies of MR brain images often require a preliminary processing to isolate the brain from extra-cranial or non-brain tissues, commonly referred to as skull stripping. This paper describes the available methods on skull stripping and an exploratory review of recent literature on the existing skull stripping methods.

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“…Brain extraction method for T1-weighted MR brain images based on morphological operation and run-length scheme has also been proposed in [44].…”

Section: Morphology-based Methodsmentioning

confidence: 99%

Methods on Skull Stripping of MRI Head Scan Images—a Review

2015

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“…In MRI processing, Somasundaram and Kalaiselvi showed that more time is required to implement an algorithm for automatic segmentation of brain [10]. Eklund et al, gave a valuable survey on implementing various medical image processing algorithms like filtering, interpolation, segmentation, registration, noise removal and reconstruction [11].…”

Section: Related Workmentioning

confidence: 99%

Performance of Medical Image Processing Algorithms Implemented in CUDA running on GPU based Machine

Kalaiselvi¹,

Sriramakrishnan²,

Somasundaram³

2018

IJISA

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Abstract-This paper illustrates the design and performance evaluation of few algorithms used for analysing the medical image volumes on the massive parallel graphics processing unit (GPU) with compute unified device architecture (CUDA). These algorithms are selected from the general framework, devised for computer aided diagnostic (CAD) system. The CAD system used for analysing large medical image datasets are usually a pipeline processing that includes a variety of image processing operations. A MRI scanner captures the 3D human head into a series of 2D images. Considerable time spent in pre and post processing of these images. Noise filters, segmentation, image diffusion and enhancement are few such methods. The algorithms are chosen for study requires local information, available in few pixels or global information available in the entire image. These problems are best candidates for GPU implementation, since the parallelism is naturally provided by the proposed Per-Pixel Threading (PPT) or Per-Slice Threading (PST) operations. In this paper implement the algorithms for adaptive filtering, anisotropic diffusion, bilateral filtering, non-local means (NLM) filtering, K-Means segmentation and feature extraction in 1536 core's NVIDIA GPU and estimated the speed up gained. Our experiments show that the GPU based implementation achieved typical speedup values in the range of 3-338 times compared to conventional central processing unit (CPU) processor in PPT model and up to 30 times in PST model.

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“…To explore this further, additional numerical analysis was performed to characterize the similarity of the selected volumes by the assessors, as a predictor of reliability. In the literature, several measures were reported for quantitatively comparing the structural similarities between the two data sets (Somasundaram & Kalaiselvi, 2011). In this study, we employed Dice and Jaccard similarity indices (Dice, 1945;Jaccard, 1912), as they had the capacity to define the quality of spatial overlap between the two volumes.…”

Section: Methodsmentioning

confidence: 99%

Characterization of Tympanic Cavity Volume in Newborns using Computerized Tomography Scanning

et al. 2016

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Morphol., 34(1):189-196, 2016. SUMMARY:This study reports tympanic cavity (TC) volume in newborns, which was missing in the literature. Ex vivo histology and computerized tomography (CT) scans were performed on temporal bone and data were analyzed in part using software developed in house. CT images with a slice thickness of 0.5 mm were obtained from 5 newborn cadavers and analyzed independently by two expert researchers. The border of the TC was delineated manually and measurement of area of interest was calculated on masked images. Then, the area measurements from all sections were added to estimate the total volume. The agreements between the histological and CT findings were then compared for accuracy, repeatability and reliability. The Dice and Jaccard similarity coefficient measures were used as a statistical validation metric to evaluate the assessor's performance in manual volume segmentation. Good assessor agreement was observed with average Dice values above 0.8 indicating that consistent and reliable volume measurements were feasible. The proposed protocol was shown to be accurate in calculating the TC volume, and thus can be used for computer-assisted presurgical planning or for diagnosing structural alterations in TC.

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Automatic brain extraction methods for T1 magnetic resonance images using region labeling and morphological operations

Cited by 98 publications

References 20 publications

Methods on Skull Stripping of MRI Head Scan Images—a Review

Methods on Skull Stripping of MRI Head Scan Images—a Review

Performance of Medical Image Processing Algorithms Implemented in CUDA running on GPU based Machine

Characterization of Tympanic Cavity Volume in Newborns using Computerized Tomography Scanning

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