Conventional Segmentation Methods Research Articles

A Novel Super Voxel-based 3D Segmentation Method for Irritable Bowel Syndrome

The main aim of this research paper is to implement a model-driven machine learning based adaptive 3D Segmentation Scheme for detecting the IBS (Irritable bowel syndrome) disease. This algorithm taking into account by endoscopy driven visual images for the purpose of machine analyzing and convert that 2D RGB coordinates into 3D RGB coordinates for improving the accuracy of the segmentation. In previous segmentation schemes, the IBS images are obtained by the use of ultrasound imaginary technique, but the main issue of the imaginary was the noise present in the images. We are overcoming this issue by applying the endoscopy images. Adaptive smoothing technique used in pre-processing stages with neighboring pixel reference. The feature data extraction stages estimate the shape and color and region-based features for segmentation. The proposed scheme performance with our 50 image Database shows that the results accuracy of proposed system outperforms multiple conventional segmentation methods.

Coal Cleat/Fracture Segmentation Using Convolutional Neural Networks

Cleats/fractures play a vital role in gas extraction from coal bed methane reservoirs. As such, fractures are identified in microcomputed tomography images by segmentation in detailed fracture characterization studies. Conventional segmentation methods include thresholding-based, region growing, and hybrid methods. In digital images, these methods are unable to differentiate fractures from other coal constituents with a similar intensity on the gray scale. However, a supervised segmentation is not a wise approach, since it requires a considerable amount of time. Recently, machine learning—more specifically, the convolutional neural networks (CNN)—has demonstrated excellent performance in machine vision applications. This study uses CNN-based methods to segment cleats/fractures of two types of coal samples: homogenous (with typical cleat system) and heterogeneous (with complex background and irregular fracture system). Two CNNs (2D and 3D) with similar architectures are applied to classify the central pixel (or voxel) of the input image as either fracture or nonfracture classes. In the case of the homogenous coal, the existing fractures in one 2D image are manually segmented and used for the training, validation, and test dataset. Results indicate that the 3D CNN is more robust and accurate than the 2D CNN, as the former extracts the features in 3D space. For the complex and heterogeneous sample, five 2D images are segmented for a more representative training dataset. The extracted fracture system using the 3D CNN displays an accuracy of 96.7%. A comparison with the conventional methods in both cases showed that the CNN-based approach not only detects the fracture system more efficiently but also generates fractures with a correct aperture size. These results reveal that CNN-based methods successfully discern fractures from other constituents of coal with similar grayscale intensity.

Dilated-Inception Net: Multi-Scale Feature Aggregation for Cardiac Right Ventricle Segmentation.

Segmentation of cardiac ventricle from magnetic resonance images is significant for cardiac disease diagnosis, progression assessment, and monitoring cardiac conditions. Manual segmentation is so time consuming, tedious, and subjective that automated segmentation methods are highly desired in practice. However, conventional segmentation methods performed poorly in cardiac ventricle, especially in the right ventricle. Compared with the left ventricle, whose shape is a simple thick-walled circle, the structure of the right ventricle is more complex due to ambiguous boundary, irregular cavity, and variable crescent shape. Hence, effective feature extractors and segmentation models are preferred. In this paper, we propose a dilated-inception net (DIN) to extract and aggregate multi-scale features for right ventricle segmentation. The DIN outperforms many state-of-the-art models on the benchmark database of right ventricle segmentation challenge. In addition, the experimental results indicate that the proposed model has potential to reach expert-level performance in right ventricular epicardium segmentation. More importantly, DIN behaves similarly to clinical expert with high correlation coefficients in four clinical cardiac indices. Therefore, the proposed DIN is promising for automated cardiac right ventricle segmentation in clinical applications.

Pixel-wise segmentation of severely pathologic retinal pigment epithelium and choroidal stroma using multi-contrast Jones matrix optical coherence tomography

Tissue segmentation of retinal optical coherence tomography (OCT) is widely used in ophthalmic diagnosis. However, its performance in severe pathologic cases is still insufficient. We propose a pixel-wise segmentation method that uses the multi-contrast measurement capability of Jones matrix OCT (JM-OCT). This method is applicable to both normal and pathologic retinal pigment epithelium (RPE) and choroidal stroma. In this method, "features," which are sensitive to specific tissues of interest, are synthesized by combining the multi-contrast images of JM-OCT, including attenuation coefficient, degree-of-polarization-uniformity, and OCT angiography. The tissue segmentation is done by simple thresholding of the feature. Compared with conventional segmentation methods for pathologic maculae, the proposed method is less computationally intensive. The segmentation method was validated by applying it to images from normal and severely pathologic cases. The segmentation results enabled the development of several types of en face visualizations, including melano-layer thickness maps, RPE elevation maps, choroidal thickness maps, and choroidal stromal attenuation coefficient maps. These facilitate close examination of macular pathology. The melano-layer thickness map is very similar to a near infrared fundus autofluorescence image, so the map can be used to identify the source of a hyper-autofluorescent signal.

Automatic segmentation of gallbladder using bio-inspired algorithm based on a spider web construction model

In medical image processing, an accurate segmentation and classification are very important and this field still needs an effective computer-based algorithm for accomplishing the task. In gallbladder segmentation, only few automatic segmentation methodologies have been presented. The accuracy is very important in medical image processing for accurate diagnosis of the disease. In this paper, by exploiting the basic web building behavior of spiders, we developed a bio-inspired algorithm based on the spider web construction process for image segmentation process in medical images. The aim of this paper is to detect the shape of the gallbladder and to segment the gallstones and polyps located inside the gallbladder using a computer-based algorithm. It is necessary to apply a suitable preprocessing method in order to eliminate the irregularities presenting in the ultrasound scan images. In the preprocessing stage, histogram equalization and DooG filter are applied to enhance the contrast of the image. After that, the proposed spider web algorithm is applied in the segmentation process. The performance metrics of the proposed method are evaluated by implementing the proposed method to test the input dataset of 60 patients and it is compared with the results obtained from conventional segmentation methods. The values of DSC, OF, OV and PE for images with no lesions are 0.873167, 0.8389, 0.81705 and 0.81452.

Voxel-based segmentation of 3D point clouds from construction sites using a probabilistic connectivity model

A construction site is the place of constructing buildings or infrastructure, showing very dynamic behaviors in changes and including plenty of complex objects. For recognizing building structures and other objects (e.g., workers and equipment) from 3D measurements (e.g., point clouds), segmentation is normally required. Here, we propose a voxel- and probabilistic model-based method (VPM) for point cloud segmentation, which is designed for automatically and adaptively partitioning the 3D scene. To deal with outliers and uneven points density, we organize the entire point cloud firstly by 3D cubes using an octree-based voxel structure. Then, the normal vector and centroid of the points within each voxel are calculated as the attribute of voxel. The geometric cues between voxels, including proximity, smoothness, closure, and continuity, are calculated on the basis of the attributes of voxels. Unlike conventional segmentation methods which only consider the relations between two points or voxels, the pairwise connectivity between two voxels is modeled and estimated by a probabilistic formulation on the basis of all the voxels in their local vicinities. Whether two voxels are connected or not is determined by the posterior probability deduced from the likelihood density and prior. Finally, all the connected voxels are clustered into individual segments, having meaningful geometric consistence. Our proposed method is tested by using both laser scanned and photogrammetric point clouds of different scenes. Qualitative and quantitative results reveal that our method can outperform representative segmentation algorithms, i.e., point- and voxel-based region growing, difference of normal based clustering, and LCCP, for our applications, having overall F1-measures better than 0.7 and 0.6 using different kinds of point clouds from both laser scanner and RGB images, respectively.

Comparison of novel multi-level Otsu (MO-PET) and conventional PET segmentation methods for measuring FDG metabolic tumor volume in patients with soft tissue sarcoma

BackgroundWe have previously developed a novel and highly consistent PET segmentation algorithm using a multi-level Otsu method (MO-PET). The aim of this study was to evaluate the reliability of MO-PET compared to conventional PET segmentation methods for measuring 18F-FDG (FDG) PET metabolic tumor volume (MTV) in patients with soft tissue sarcoma (STS). Clinical and imaging data were obtained from the Cancer Imaging Archive. Forty-eight STS patients with FDG PET/CT and MR prior to therapy were analyzed. MTV of the tumor using MO-PET was compared to other conventional methods (absolute SUV threshold values of 2.0, 2.5, or 3.0 and percentage of tumor SUVmax values of 30, 40, 50, or 60%) and gradient-based method (PET Edge™). The reference volume was defined as an MR-based gross tumor volume (GTV). Spearman, intra-class correlation, and Bland-Altman analysis were performed to evaluate the correlation and agreement of MTV to GTV.ResultsMTVs obtained using each conventional SUV parameter, PET Edge™, and MO-PET were highly correlated with the GTV in Spearman and intra-class correlation analysis (p < 0.05). MO-PET and PET Edge™ showed high intra-class correlation coefficient of MTV to GTV (0.93 and 0.84, respectively). The Bland-Altman bias results showed the highest agreement for MTV using MO-PET with GTV (26.0 ± 489.6 cm3) compared to other methods (SUV 2.0 with − 69.3 ± 765.8, 30% SUVmax with − 255.0 ± 876.6, and PET Edge™ with − 26.46 ± 668.82 cm3).ConclusionsPET MTV segmented with MO-PET showed higher correlation and agreement with GTV in comparison to conventional percentage SUVmax and absolute SUV threshold-based PET segmentation methods. MO-PET is comparable to PET Edge™. MO-PET is a reliable and consistent method for measuring tumor MTV.

Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method.

We propose a single network trained by pixel-to-label deep learning to address the general issue of automatic multiple organ segmentation in three-dimensional (3D) computed tomography (CT) images. Our method can be described as a voxel-wise multiple-class classification scheme for automatically assigning labels to each pixel/voxel in a 2D/3D CT image. We simplify the segmentation algorithms of anatomical structures (including multiple organs) in a CT image (generally in 3D) to a majority voting scheme over the semantic segmentation of multiple 2D slices drawn from different viewpoints with redundancy. The proposed method inherits the spirit of fully convolutional networks (FCNs) that consist of "convolution" and "deconvolution" layers for 2D semantic image segmentation, and expands the core structure with 3D-2D-3D transformations to adapt to 3D CT image segmentation. All parameters in the proposed network are trained pixel-to-label from a small number of CT cases with human annotations as the ground truth. The proposed network naturally fulfills the requirements of multiple organ segmentations in CT cases of different sizes that cover arbitrary scan regions without any adjustment. The proposed network was trained and validated using the simultaneous segmentation of 19 anatomical structures in the human torso, including 17 major organs and two special regions (lumen and content inside of stomach). Some of these structures have never been reported in previous research on CT segmentation. A database consisting of 240 (95% for training and 5% for testing) 3D CT scans, together with their manually annotated ground-truth segmentations, was used in our experiments. The results show that the 19 structures of interest were segmented with acceptable accuracy (88.1% and 87.9% voxels in the training and testing datasets, respectively, were labeled correctly) against the ground truth. We propose a single network based on pixel-to-label deep learning to address the challenging issue of anatomical structure segmentation in 3D CT cases. The novelty of this work is the policy of deep learning of the different 2D sectional appearances of 3D anatomical structures for CT cases and the majority voting of the 3D segmentation results from multiple crossed 2D sections to achieve availability and reliability with better efficiency, generality, and flexibility than conventional segmentation methods, which must be guided by human expertise.

Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities

The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks. Convolutional neural networks (CNN) have had huge successes in computer vision, but they lack the natural ability to incorporate the anatomical location in their decision making process, hindering success in some medical image analysis tasks. In this paper, to integrate the anatomical location information into the network, we propose several deep CNN architectures that consider multi-scale patches or take explicit location features while training. We apply and compare the proposed architectures for segmentation of white matter hyperintensities in brain MR images on a large dataset. As a result, we observe that the CNNs that incorporate location information substantially outperform a conventional segmentation method with handcrafted features as well as CNNs that do not integrate location information. On a test set of 50 scans, the best configuration of our networks obtained a Dice score of 0.792, compared to 0.805 for an independent human observer. Performance levels of the machine and the independent human observer were not statistically significantly different (p-value = 0.06).

Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images.

The segmentation of skin lesions in dermoscopic images is a fundamental step in automated computer-aided diagnosis of melanoma. Conventional segmentation methods, however, have difficulties when the lesion borders are indistinct and when contrast between the lesion and the surrounding skin is low. They also perform poorly when there is a heterogeneous background or a lesion that touches the image boundaries; this then results in under- and oversegmentation of the skin lesion. We suggest that saliency detection using the reconstruction errors derived from a sparse representation model coupled with a novel background detection can more accurately discriminate the lesion from surrounding regions. We further propose a Bayesian framework that better delineates the shape and boundaries of the lesion. We also evaluated our approach on two public datasets comprising 1100 dermoscopic images and compared it to other conventional and state-of-the-art unsupervised (i.e., no training required) lesion segmentation methods, as well as the state-of-the-art unsupervised saliency detection methods. Our results show that our approach is more accurate and robust in segmenting lesions compared to other methods. We also discuss the general extension of our framework as a saliency optimization algorithm for lesion segmentation.

Segmentation Method of Breast Masses on Ultrasonographic Images Using Level Set Method Based on Statistical Model

It is important to segment mass region accurately in a computer-aided diagnosis (CADx) scheme for evaluating the likelihood of malignancy of the mass on ultrasonographic breast image. The purpose of this study was to develop a novel level set method for segmentation of breast mass on ultrasonographic image. Our database consisted of 151 ultrasonographic images with 70 malignant and 81 benign breast masses. In a novel level set method, an energy function was defined with region-based, edge-based, and regularizing terms. The region-based term analyzed global information, whereas the edge-based term analyzed local information. The regularizing term also controlled the length of the boundary curve. The region of breast mass was segmented so that the energy based on those terms was minimized. With our proposed method, true positive (TP) ratio, false positive (FP) ratio, jaccard similarity (JS), and Dice similarity coefficient (DSC) were 92.2%, 9.1%, 84.2%, and 91.3%, respectively. These results tended to be substantially higher than those with two conventional segmentation methods. Our proposed method based on the novel level set method was shown to segment mass region accurately on ultrasonographic breast image.

Spatial Fuzzy C Means and Expectation Maximization Algorithms with Bias Correction for Segmentation of MR Brain Images.

The Fuzzy C Means (FCM) and Expectation Maximization (EM) algorithms are the most prevalent methods for automatic segmentation of MR brain images into three classes Gray Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF). The major difficulties associated with these conventional methods for MR brain image segmentation are the Intensity Non-uniformity (INU) and noise. In this paper, EM and FCM with spatial information and bias correction are proposed to overcome these effects. The spatial information is incorporated by convolving the posterior probability during E-Step of the EM algorithm with mean filter. Also, a method of pixel re-labeling is included to improve the segmentation accuracy. The proposed method is validated by extensive experiments on both simulated and real brain images from standard database. Quantitative and qualitative results depict that the method is superior to the conventional methods by around 25% and over the state-of-the art method by 8%.

ApesNet: a pixel‐wise efficient segmentation network for embedded devices

Road scene understanding and semantic segmentation is an on-going issue for computer vision. A precise segmentation can help a machine learning model understand the real world more accurately. In addition, a well-designed efficient model can be used on source limited devices. The authors aim to implement an efficient high-level, scene understanding model in an embedded device with finite power and resources. Toward this goal, the authors propose ApesNet, an efficient pixel-wise segmentation network which understands road scenes in near real-time and has achieved promising accuracy. The key findings in the authors’ experiments are significantly lower the classification time and achieving a high accuracy compared with other conventional segmentation methods. The model is characterised by an efficient training and a sufficient fast testing. Experimentally, the authors use two road scene benchmarks, CamVid and Cityscapes to show the advantages of ApesNet. The authors’ compare the proposed architecture's accuracy and time performance with SegNet-Basic, a deep convolutional encoder–decoder architecture. ApesNet is 37% smaller than SegNet-Basic in terms of model size. With this advantage, the combining encoding and decoding time for each image is 2.5 times faster than SegNet-Basic.

Automated segmentation and area estimation of neural foramina with boundary regression model

Accurate segmentation and area estimation of neural foramina from both CT and MR images are essential to clinical diagnosis of neural foramina stenosis. Existing clinical routine, relying on physician's purely manual segmentation, becomes very tedious, laborious, and inefficient. Automated segmentation is highly desirable but faces big challenges from diverse boundary, local weak/no boundary, and intra/inter-modality intensity inhomogeneity. In this paper, a novel boundary regression segmentation framework is proposed for fully automated and multi-modal segmentation of neural foramina. It creatively formulates the segmentation task as a boundary regression problem which models a highly nonlinear mapping function from substantially diverse neural foramina images directly to desired object boundaries. By leveraging a seamless combination of multiple output support vector regression (MSVR) and multiple kernel learning (MKL), the proposed framework enables the domain knowledge learning in a holistic fashion which successfully handles the extreme diversity posing a tremendous challenge to conventional segmentation methods. The performance evaluation was conducted on a dataset including 912 MR images and 306 CT images collected from 152 subjects. Experimental results show that the proposed automated segmentation framework is highly consistent with physician with average DSI (dice similarity index) as high as 0.9005 (CT), 0.8984 (MR), 0.8935 (MR+CT) and BD (boundary distance) as low as 0.6393mm (CT), 0.6586mm (MR), 0.6881mm (MR+CT). Based on this accurate automated segmentation, the estimated areas, highly correlated to their independent ground truth, have been achieved with correlation coefficient: 0.9154 (CT) and 0.8789 (MR). Hence, the proposed approach enables an efficient, accurate and convenient tool for clinical diagnosis of neural foramina stenosis.

A prior-knowledge-based threshold segmentation method of forward-looking sonar images for underwater linear object detection

Raw sonar images may not be used for underwater detection or recognition directly because disturbances such as the grating-lobe and multi-path disturbance affect the gray-level distribution of sonar images and cause phantom echoes. To search for a more robust segmentation method with a reasonable computational cost, a prior-knowledge-based threshold segmentation method of underwater linear object detection is discussed. The possibility of guiding the segmentation threshold evolution of forward-looking sonar images using prior knowledge is verified by experiment. During the threshold evolution, the collinear relation of two lines that correspond to double peaks in the voting space of the edged image is used as the criterion of termination. The interaction is reflected in the sense that the Hough transform contributes to the basis of the collinear relation of lines, while the binary image generated from the current threshold provides the resource of the Hough transform. The experimental results show that the proposed method could maintain a good tradeoff between the segmentation quality and the computational time in comparison with conventional segmentation methods. The proposed method redounds to a further process for unsupervised underwater visual understanding.

SU‐C‐207B‐03: A Geometrical Constrained Chan‐Vese Based Tumor Segmentation Scheme for PET

Purpose:Accurate segmentation of tumor in PET is challenging when part of tumor is connected with normal organs/tissues with no difference in intensity. Conventional segmentation methods, such as thresholding or region growing, cannot generate satisfactory results in this case. We proposed a geometrical constrained Chan‐Vese based scheme to segment tumor in PET for this special case by considering the similarity between two adjacent slices.Methods:The proposed scheme performs segmentation in a slice‐by‐slice fashion where an accurate segmentation of one slice is used as the guidance for segmentation of rest slices. For a slice that the tumor is not directly connected to organs/tissues with similar intensity values, a conventional clustering‐based segmentation method under user's guidance is used to obtain an exact tumor contour. This is set as the initial contour and the Chan‐Vese algorithm is applied for segmenting the tumor in the next adjacent slice by adding constraints of tumor size, position and shape information. This procedure is repeated until the last slice of PET containing tumor. The proposed geometrical constrained Chan‐Vese based algorithm was implemented in Matlab and its performance was tested on several cervical cancer patients where cervix and bladder are connected with similar activity values. The positive predictive values (PPV) are calculated to characterize the segmentation accuracy of the proposed scheme.Results:Tumors were accurately segmented by the proposed method even when they are connected with bladder in the image with no difference in intensity. The average PPVs were 0.9571±0.0355 and 0.9894±0.0271 for 17 slices and 11 slices of PET from two patients, respectively.Conclusion:We have developed a new scheme to segment tumor in PET images for the special case that the tumor is quite similar to or connected to normal organs/tissues in the image. The proposed scheme can provide a reliable way for segmenting tumors.

Segmentation of PLS path models by iterative reweighted regressions

Uncovering unobserved heterogeneity is a requirement to obtain valid results when using structural equation modeling (SEM). Conventional segmentation methods usually fail in an SEM context because they account for the indicator data, but not for the latent variables and their relationships in the structural model. This research introduces a new segmentation approach to variance-based SEM using partial least squares path modeling (PLS). The iterative reweighted regressions segmentation method for PLS (PLS-IRRS) effectively identifies and treats unobserved heterogeneity in data sets. Compared to existing alternatives, PLS-IRRS is multiple times faster while delivering results of the same quality. Researchers should therefore routinely use PLS-IRRS to address the critical issue of unobserved heterogeneity in PLS.

Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation

Background and objectiveSignal segmentation and spike detection are two important biomedical signal processing applications. Often, non-stationary signals must be segmented into piece-wise stationary epochs or spikes need to be found among a background of noise before being further analyzed. Permutation entropy (PE) has been proposed to evaluate the irregularity of a time series. PE is conceptually simple, structurally robust to artifacts, and computationally fast. It has been extensively used in many applications, but it has two key shortcomings. First, when a signal is symbolized using the Bandt–Pompe procedure, only the order of the amplitude values is considered and information regarding the amplitudes is discarded. Second, in the PE, the effect of equal amplitude values in each embedded vector is not addressed. To address these issues, we propose a new entropy measure based on PE: the amplitude-aware permutation entropy (AAPE). MethodsAAPE is sensitive to the changes in the amplitude, in addition to the frequency, of the signals thanks to it being more flexible than the classical PE in the quantification of the signal motifs. To demonstrate how the AAPE method can enhance the quality of the signal segmentation and spike detection, a set of synthetic and realistic synthetic neuronal signals, electroencephalograms and neuronal data are processed. We compare the performance of AAPE in these problems against state-of-the-art approaches and evaluate the significance of the differences with a repeated ANOVA with post hoc Tukey's test. ResultsIn signal segmentation, the accuracy of AAPE-based method is higher than conventional segmentation methods. AAPE also leads to more robust results in the presence of noise. The spike detection results show that AAPE can detect spikes well, even when presented with single-sample spikes, unlike PE. For multi-sample spikes, the changes in AAPE are larger than in PE. ConclusionWe introduce a new entropy metric, AAPE, that enables us to consider amplitude information in the formulation of PE. The AAPE algorithm can be used in almost every irregularity-based application in various signal and image processing fields. We also made freely available the Matlab code of the AAPE.

Multi-attribute market segmentation for the major customers of an Iranian steel-making company using value proposition elements

Mass market strategy does not have a big chance for success in a competitive market. Therefore, by segmenting a heterogeneous market in to some smaller and homogenous markets in which customers have similar characteristics, it is expected that the resources can be more efficiently utilised. The purpose of this study was a mathematical modelling of market segmentation of an Iranian steel-making company using value proposition elements. The model mentioned was used for the analysis of data related to six value proposition elements from 129 major customers of the company. This model was solved using GAMS software and the optimal number of segments was 9. In this study, the results obtained were compared with those achieved by the conventional segmentation methods such as K-means and SOM and two-step clustering. Further, for the validation of the mathematical model used, discriminant analysis of research data was done after segmentation and the success percentage of the ranking rule was found to be 95.3%. Also, the similarity criterion was computed for each potentially new customer.

7T-Guided Learning Framework for Improving the Segmentation of 3T MR Images.

The emerging era of ultra-high-field MRI using 7T MRI scanners dramatically improved sensitivity, image resolution, and tissue contrast when compared to 3T MRI scanners in examining various anatomical structures. The advantages of these high-resolution MR images include higher segmentation accuracy of MRI brain tissues. However, currently, accessibility to 7T MRI scanners remains much more limited than 3T MRI scanners due to technological and economical constraints. Hence, we propose in this work the first learning-based model that improves the segmentation of an input 3T MR image with any conventional segmentation method, through the reconstruction of a higher-quality 7T-like MR image, without actually acquiring an ultra-high-field 7T MRI. Our proposed framework comprises two main steps. First, we estimate a non-linear mapping from 3T MRI to 7T MRI space, using random forest regression model with novel weighting and ensembling schemes, to reconstruct initial 7T-like MR images. Second, we use a group sparse representation with a new pre-selection approach to further refine the 7T-like MR image reconstruction. We evaluated our 7T MRI reconstruction results along with their segmentation results using 13 subjects acquired with both 3T and 7T MR images. For tissue segmentation, we applied two widely used segmentation methods (FAST and SPM) to perform the experiments. Our results showed (1) the improvement of WM, GM and CSF brain tissues segmentation results when guided by reconstructed 7T-like images compared to 3T MR images, and (2) the outperformance of the proposed 7T MRI reconstruction method when compared to other state-of-the-art methods.