Hybrid Monte Carlo-diffusion method for light propagation in tissue with a low-scattering region

The heterogeneity of the tissues in a head, especially the low-scattering cerebrospinal fluid (CSF) layer surrounding the brain has previously been shown to strongly affect light propagation in the brain. The radiosity-diffusion method, in which the light propagation in the CSF layer is assumed to obey the radiosity theory, has been employed to predict the light propagation in head models. Although the CSF layer is assumed to be a nonscattering region in the radiosity-diffusion method, fine arachnoid trabeculae cause faint scattering in the CSF layer in real heads. A novel approach, the hybrid Monte Carlo-diffusion method, is proposed to calculate the head models, including the low-scattering region in which the light propagation does not obey neither the diffusion approximation nor the radiosity theory. The light propagation in the high-scattering region is calculated by means of the diffusion approximation solved by the finite-element method and that in the low-scattering region is predicted by the Monte Carlo method. The intensity and mean time of flight of the detected light for the head model with a low-scattering CSF layer calculated by the hybrid method agreed well with those by the Monte Carlo method, whereas the results calculated by means of the diffusion approximation included considerable error caused by the effect of the CSF layer. In the hybrid method, the time-consuming Monte Carlo calculation is employed only for the thin CSF layer, and hence, the computation time of the hybrid method is dramatically shorter than that of the Monte Carlo method.     

Near-infrared light propagation in an adult head model. I. Modeling of low-level scattering in the cerebrospinal fluid layer

Adequate modeling of light propagation in a human head is important for quantitative near-infrared spectroscopy and optical imaging. The presence of a nonscattering cerebrospinal fluid (CSF) that surrounds the brain has been previously shown to have a strong effect on light propagation in the head. However, in reality, a small amount of scattering is caused by the arachnoid trabeculae in the CSF layer. In this study, light propagation in an adult head model with discrete scatterers distributed within the CSF layer has been predicted by Monte Carlo simulation to investigate the effect of the small amount of scattering caused by the arachnoid trabeculae in the CSF layer. This low scattering in the CSF layer is found to have little effect on the mean optical path length, a parameter that can be directly measured by a time-resolved experiment. However, the partial optical path length in brain tissue that relates the sensitivity of the detected signal to absorption changes in the brain is strongly affected by the presence of scattering within the CSF layer. The sensitivity of the near-infrared signal to hemoglobin changes induced by brain activation is improved by the effect of a low-scattering CSF layer.     

Practical and adequate approach to modeling light propagation in an adult head with low-scattering regions by use of diffusion theory

A practical and adequate approach to modeling light propagation in an adult head with a low-scattering cerebrospinal fluid (CSF) region by use of diffusion theory was investigated. The diffusion approximation does not hold in a nonscattering or low-scattering regions. The hybrid radiosity-diffusion method was adopted to model the light propagation in the head with a nonscattering region. In the hybrid method the geometry of the nonscattering region is acquired as a priori information. In reality, low-level scattering occurs in the CSF region and may reduce the error caused by the diffusion approximation. The partial optical path length and the spatial sensitivity profile calculated by the finite-element method agree well with those calculated by the Monte Carlo method in the case in which the transport scattering coefficient of the CSF layer is greater than 0.3 mm(-1). Because it is feasible to assume that the transport scattering coefficient of a CSF layer is 0.3 mm(-1), it is practical to adopt diffusion theory to the modeling of light propagation in an adult head as an alternative to the hybrid method.     

Theoretical and experimental investigation of near-infrared light propagation in a model of the adult head

Near-infrared light propagation in various models of the adult head is analyzed by both time-of-flight measurements and mathematical prediction. The models consist of three- or four-layered slabs, the latter incorporating a clear cerebrospinal fluid (CSF) layer. The most sophisticated model also incorporates slots that imitate sulci on the brain surface. For each model, the experimentally measured mean optical path length as a function of source-detector spacing agrees well with predictions from either a Monte Carlo model or a finite-element method based on diffusion theory or a hybrid radiosity-diffusion theory. Light propagation in the adult head is shown to be highly affected by the presence of the clear CSF layer, and both the optical path length and the spatial sensitivity profile of the models with a CSF layer are quite different from those without the CSF layer. However, the geometry of the sulci and the boundary between the gray and the white matter have little effect on the detected light distribution.     

Near-infrared light propagation in an adult head model. II. Effect of superficial tissue thickness on the sensitivity of the near-infrared spectroscopy signal

It is important for near-infrared spectroscopy (NIRS) and imaging to estimate the sensitivity of the detected signal to the change in hemoglobin that results from brain activation and the volume of tissue interrogated for a specific source-detector fiber spacing. In this study light propagation in adult head models is predicted by Monte Carlo simulation to investigate the effect of the superficial tissue thickness on the partial optical path length in the brain and on the spatial sensitivity profile. In the case of source-detector spacing of 30 mm, the partial optical path length depends mainly on the depth of the inner skull surface whereas the spatial sensitivity profile is significantly affected by the thickness of the cerebrospinal fluid layer. The mean optical path length that can be measured by time-resolved experiments increases when the skull thickness increases whereas the partial mean optical path length in the brain decreases when the skull thickness increases. These results indicate that it is not appropriate to use the mean optical path length as an alternative to the partial optical path length to compensate the NIRS signal for the difference in sensitivity caused by variation of the superficial tissue thickness.     

Near-infrared spectroscopy of the adult head: effect of scattering and absorbing obstructions in the cerebrospinal fluid layer on light distribution in the tissue

Previous modeling of near-infrared (NIR) light distribution in models of the adult head incorporating a clear nonscattering cerebrospinal fluid (CSF) layer have shown the latter to have a profound effect on the resulting photon measurement density function (PMDF). In particular, the presence of the CSF limits the PMDF largely to the outer cortical gray matter with little signal contribution from the deeper white matter. In practice, the CSF is not a simple unobstructed clear layer but contains light-scattering membranes and is crossed by various blood vessels. Using a radiosity-diffusion finite-element model, we investigated the effect on the PMDF of introducing intrusions within the clear layer. The results show that the presence of such obstructions does not significantly increase the light penetration into the brain tissue, except immediately adjacent to the obstruction and that its presence also increases the light sampling of the adjacent skull tissues, which would lead to additional contamination of the NIR spectroscopy signal by the surface tissue layers.     

Simulation study of magnetic resonance imaging-guided cortically constrained diffuse optical tomography of human brain function

Diffuse optical imaging can measure brain activity noninvasively in humans through the scalp and skull by measuring the light intensity modulation arising from localized-activity-induced absorption changes within the cortex. Spatial resolution and localization accuracy are currently limited by measurement geometry to approximately 3 cm in the plane parallel to the scalp. Depth resolution is a more significant challenge owing to the limited angle tomography permitted by reflectance-only measurements. We combine previously established concepts for improving image quality and demonstrate, through simulation studies, their application for improving the image quality of adult human brain function. We show in a three-dimensional human head model that localization accuracy is significantly improved by the addition of measurements that provide overlapping samples of brain tissue. However, the reconstructed absorption contrast is significantly underestimated because its depth is underestimated. We show that the absorption contrast amplitude accuracy can be significantly improved by providing a cortical spatial constraint in the image reconstruction to obtain a better depth localization. The cortical constraint makes physiological sense since the brain-activity-induced absorption changes are occurring in the cortex and not in the scalp, skull, and cerebral spinal fluid. This spatial constraint is provided by segmentation of coregistered structural magnetic resonance imaging (MRI). However, the absorption contrast deep within the cortex is reconstructed superficially, resulting in an underestimation of the absorption contrast. The synthesis of techniques described here indicates that multimodality imaging of brain function with diffuse optical imaging and MRI has the potential to provide more quantitative estimates of the total and deoxyhemoglobin response to brain activation, which is currently not provided by either method independently. However, issues of depth resolution within the cortex remain to be resolved.     

Brightness preserving optimized weighted bi-histogram equalization algorithm and its application to MR brain image segmentation

Medical image segmentation is crucial for neuroscience research and computer-aided diagnosis. However, intensity inhomogeneity and existence of noise in magnetic resonance images lead to incorrect segmentation. In this article, an effective method called enhanced fuzzy level set algorithm is presented to segment the white matter, gray matter, and cerebrospinal fluid automatically in contrast-enhanced brain images. In this method, first, exposure threshold is computed to divide the input histogram into two sub-histograms of different gray levels. The input histogram is clipped using a mean gray level to control the excessive enhancement rate. Then, these two sub-histograms are modified and equalized independently to get a better contrast enhanced image. Finally, an enhanced fuzzy level set algorithm is employed to facilitate image segmentation. The extensive experimental results proved the outstanding performance of the proposed algorithm compared with other existing methods. The results conform its effectiveness for MR brain image segmentation.     

Hierarchical fuzzy segmentation of brain MR images

In brain magnetic resonance (MR) images, image segmentation and 3D visualization are very useful tools for the diagnosis of abnormalities. Segmentation of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is the basic process for 3D visualization of brain MR images. Of the many algorithms, the fuzzy c‐means (FCM) technique has been widely used for segmentation of brain MR images. However, the FCM technique does not yield sufficient results under radio frequency (RF) nonuniformity. We propose a hierarchical FCM (HFCM), which provides good segmentation results under RF nonuniformity and does not require any parameter setting. We also generate Talairach templates of the brain that are deformed to 3D brain MR images. Using the deformed templates, only the cerebrum region is extracted from the 3D brain MR images. Then, the proposed HFCM partitions the cerebrum region into WM, GM, and CSF. © 2003 Wiley Periodicals, Inc. Int J Imaging Syst Technol 13, 115–125, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10035     

An efficient approach for brain image (tissue) compression based on the position of the brain tumor

Medical image processing plays an important role in brain tissue detection and segmentation. In this paper, a computer aided detection of brain tissue compression based on the estimation of the location of the brain tumor. The proposed system detects and segments the brain tissues and brain tumor using mathematical morphological operations. Further, the brain tissue with tumor is compressed using lossless compression technique and the brain tissue without tumor is compressed using lossy compression technique. The proposed method achieves 96.46% sensitivity, 99.20% specificity and 98.73% accuracy for the segmentation of white matter regions from the brain. The proposed method achieves 98.16% sensitivity, 99.36% specificity and 98.78% accuracy for the segmentation of cerebrospinal fluid (CSF) regions from the brain and also achieves 93.07% sensitivity, 98.79% specificity and 97.63% accuracy for the segmentation of grey matter regions from the brain. This paper focus the brain tissue compression based on the location of brain tumor. The grey matter of the brain is applied to lossless compression due to the presence of the tumor in grey matter of the brain. The proposed system achieves 29.23% of compression ratio for compressing the grey matter of the brain region. The white matter and CSF regions of the brain are applied to lossy compression due to the non‐presence of the tumor. The proposed system achieves 39.13% of compression ratio for compressing the white matter and also achieves 37.5% of compression ratio for compressing the CSF tissue. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 237–242, 2016     

Brain tumor detection and diagnosis using ANFIS classifier

In this article, the segmented brain tumor region is diagnosed into mild, moderate, and severe case based on the presence of tumor cells in the brain components such as Gray Matter (GM), White Matter (WM), and cerebrospinal fluid (CSF). The modified spatial fuzzy c mean algorithm is used to segment brain tissues. The feature Local binary pattern is extracted from segmented tissues, which is trained and classified by ANFIS Classifier. The performance of the proposed brain tissues segmentation system is analyzed in terms of sensitivity, specificity, and accuracy with respect to manually segmented ground truth images. The severity of brain tumor is diagnosed into mild case if the segmented brain tumor is present in the grey matter. The severity of brain tumor is diagnosed into moderate case if the segmented brain tumor is present in the WM. The severity of brain tumor is diagnosed into severe case if the segmented brain tumor is present in the CSF region. The immediate surgery is required for severe case and medical treatment is preferred for mild and moderate case.     

Acoustic microscopy: a study of contrast in fresh tissue

The scanning laser acoustic microscope (SLAM), which exhibits a resolution of about 15 μm in biological materials, was operated at a frequency of 100 MHz to evaluate the source of image contrast. Specifically, the SLAM's quantitative capabilities yielded the ultrasonic propagation properties of attenuation coefficient and propagation speed of rat brain tissue (white versus gray matter) and these properties mere correlated with tissue constituents and image contrast. The SLAM's image contrast between the two brain layers in both fresh and fixed specimens was analyzed subjectively by an experienced microscopist. It was determined that ethanol fixation decreased the image contrast between the brain layers. Additionally, the propagation speed was the least affected property in the fresh tissue specimens yet increased in both brain layers after fixation whereas the attenuation coefficient of white matter in unfixed brain tissue was higher than that of gray matter. These results indicate that the SLAM's acoustic image contrast is a direct reflection of the difference in attenuation coefficient whereas the propagation speed is not a significant contributor to the image contrast     

Fully automatic method for segmentation of brain tumor from multimodal magnetic resonance images using wavelet transformation and clustering technique

Fully automatic brain tumor segmentation is one of the critical tasks in magnetic resonance imaging (MRI) images. This proposed work is aimed to develop an automatic method for brain tumor segmentation process by wavelet transformation and clustering technique. The proposed method using discrete wavelet transform (DWT) for pre‐ and post‐processing, fuzzy c‐means (FCM) for brain tissues segmentation. Initially, MRI images are preprocessed by DWT to sharpen the images and enhance the tumor region. It assists to quicken the FCM clustering technique and classified into four major classes: gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and background (BG). Then check the abnormality detection using Fuzzy symmetric measure for GM, WM, and CSF classes. Finally, DWT method is applied in segmented abnormal region of images respectively and extracts the tumor portion. The proposed method used 30 multimodal MRI training datasets from BraTS2012 database. Several quantitative measures were calculated and compared with the existing. The proposed method yielded the mean value of similarity index as 0.73 for complete tumor, 0.53 for core tumor, and 0.35 for enhancing tumor. The proposed method gives better results than the existing challenging methods over the publicly available training dataset from MICCAI multimodal brain tumor segmentation challenge and a minimum processing time for tumor segmentation. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 305–314, 2016     

A comprehensive review: Segmentation of MRI images—brain tumor

Segmentation of tumors in human brain aims to classify different abnormal tissues (necrotic core, edema, active cells) from normal tissues (cerebrospinal fluid, gray matter, white matter) of the brain. In existence, detection of abnormal tissues is easy for studying brain tumor, but reproducibility, characterization of abnormalities and accuracy are complicated in the process of segmentation. The magnetic resonance imaging (MRI)‐based segmentation of tumors in brain images is more enhancing and attracting in current years of research studies. It is due to non‐invasive examination and good contrast prone to soft tissues of images obtained from MRI modality. Medical approval of different segmentation techniques depends on the benchmark and simplicity of the method. This article incorporates both fully‐automatic and semi‐automatic methods for segmentation. The outlook study of this article is to provide the summary of most significant segmentation methods of tumors in brain using MRI. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 295–304, 2016     

Optical tomography of a realistic neonatal head phantom

We have begun clinical trials of optical tomography of the neonatal brain. To validate this research, we have built and imaged an anatomically realistic, tissue-equivalent neonatal head phantom that is hollow, allowing contrasting objects to be placed inside it. Images were reconstructed by use of two finite-element meshes, one generated from a computed tomography image of the phantom and the other spherical. The phantom was filled with a liquid of the same optical properties as the outer region, and two perturbations were placed inside. These were successfully imaged with good separation between the absorption and scatter coefficients. The phantom was then refilled with a liquid of increased absorption compared with the background to simulate the brain, and the absolute properties of the two regions were found. These were used as a priori information for the complete reconstruction. Both perturbations were visible, superimposed on the increased absorption of the central region. The head-shaped mesh performed slightly better than the spherical mesh, particularly when the absorption of the central region of the phantom was increased.     

Brain segmentation in magnetic resonance human head scans using multi-seeded region growing

This paper presents a skull stripping method to segment the brain from MRI human head scans using multi-seeded region growing technique. The proposed method has two stages. In Stage-1, the brain in the middle slice is segmented, the brains in the remaining slices are segmented in Stage-2. In each stage, the proposed method is required to identify the rough brain mask. The fine brain region in the rough brain mask is segmented using multi-seeded region growing approach. The proposed method uses multiple seed points which are selected automatically based on the intensity profile of grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) of the brain image. The proposed brain segmentation method using multi-seeded region growing (BSMRG) was validated using 100 volumes of T1, T2 and PD-weighted MR brain images obtained from Internet Brain Segmentation Repository (IBSR), LONI and Whole Brain Atlas (WBA). The best Dice (D) value of 0·971 and Jaccard (J) value of 0·944 were recorded by the proposed BSMRG method on IBSR dataset. For LONI dataset, the best values of D?=?0·979 and J?=?0·960 were obtained for the sagittal oriented images by the proposed method. The performance consistency of the proposed method was tested on the brain images of all types and orientation and have and produced better and stable results than the existing methods Brain Extraction Tool (BET), Brain Surface Extraction (BSE), Watershed Algorithm (WAT), Hybrid Watershed Algorithm (HWA) and Skull Stripping using Graph Cuts (GCUT).     

Modeling of diffuse-diffuse photon coupling via a nonscattering region: a comparative study

It is well established that diffusion approximation is valid for light propagation in highly scattering media, but it breaks down in nonscattering regions. The previous methods that manipulate nonscattering regions are essentially boundary-to-boundary coupling (BBC) methods through a nonscattering void region based on the radiosity theory. We present a boundary-to-interior coupling (BIC) method. BIC is based on the fact that the collimated pencil beam incident on the medium can be replaced by an isotropic point source positioned at one reduced scattering length inside the medium from an illuminated point. A similar replacement is possible for the nondiffuse lights that enter the diffuse medium through the void, and it is formulated as the BIC method. We implemented both coupling methods using the finite element method (FEM) and tested for the circle with a void gap and for a four-layer adult head model. For mean time of flight, the BIC shows better agreement with Monte Carlo (MC) simulation results than BBC. For intensity, BIC shows a comparable match with MC data compared with that of BBC. The effect of absorption of the clear layer in the adult head model was investigated. Both mean time and intensity decrease as absorption of the clear layer increases.     

Residual UNet with Dual Attention—An ensemble residual UNet with dual attention for multi-modal and multi-class brain MRI segmentation

In this paper, we have proposed a variant of UNet for brain magnetic resonance imaging (MRI) segmentation. The proposed model, termed as Residual UNet with Dual Attention (RUDA), addresses the two significant challenges of UNet: extraction of the complex features with unclear boundaries and the problem of over-segmentation due to the redundancy caused by the skip connection usage. RUDA is constituted upon the residual blocks for extracting the complex structures. It Introduces attention into the skip connections to avoid redundancy and thereby the chance of over-segmentation. Our model segments brain MRI into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) regions, which are considered crucial informative substructures for diagnosing neurological disorders such as Alzheimer's. It has been implemented in an ensemble manner to accommodate the multi-sequence (T1-weighted, IR, and T2-FLAIR) scans. The empirical analysis shows that with an accuracy of 93.80%, RUDA outperforms the two baseline models: UNet (91.37%), ResUNet (91.44%).     

Automated brain tumour segmentation techniques— A review

Automatic segmentation of brain tumour is the process of separating abnormal tissues from normal tissues, such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The process of segmentation is still challenging due to the diversity of shape, location, and size of the tumour segmentation. The metabolic process, psychological process, and detailed information of the images, are obtained using positron emission tomography (PET) image, Computer Tomography (CT) image and Magnetic Resonance Image (MRI). Multimodal imaging techniques (such as PET/CT and PET/MRI) that combine the information from many imaging techniques contribute more for accurate brain tumour segmentation. In this article, a comprehensive overview of recent automatic brain tumour segmentation techniques of MRI, PET, CT, and multimodal imaging techniques has been provided. The methods, techniques, their working principle, advantages, their limitations, and their future challenges are discussed in this article. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 66–77, 2017     

Experimental validation of Monte Carlo and finite-element methods for the estimation of the optical path length in inhomogeneous tissue

To validate models of light propagation in biological tissue, experiments to measure the mean time of flight have been carried out on several solid cylindrical layered phantoms. The optical properties of the inner cylinders of the phantoms were close to those of adult brain white matter, whereas a range of scattering or absorption coefficients was chosen for the outer layer. Experimental results for the mean optical path length have been compared with the predictions of both an exact Monte Carlo (MC) model and a diffusion equation, with two differing boundary conditions implemented in a finite-element method (PEM). The MC and experimental results are in good agreement despite poor statistics for large fiber spacings, whereas good agreement with the FEM prediction requires a careful choice of proper boundary conditions.