An efficient MRF embedded level set method for image segmentation

Multi-strategy learning-based particle swarm optimization algorithm for COVID-19 threshold segmentation

With advancements in science and technology, the depth of human research on COVID-19 is increasing, making the investigation of medical images a focal point. Image segmentation, a crucial step preceding image processing, holds significance in the realm of medical image analysis. Traditional threshold image segmentation proves to be less efficient, posing challenges in selecting an appropriate threshold value. In response to these issues, this paper introduces Inner-based multi-strategy particle swarm optimization (IPSOsono) for conducting numerical experiments and enhancing threshold image segmentation in COVID-19 medical images. A novel dynamic oscillatory weight, derived from the PSO variant for single-objective numerical optimization (PSOsono) is incorporated. Simultaneously, the historical optimal positions of individuals in the particle swarm undergo random updates, diminishing the likelihood of algorithm stagnation and local optima. Moreover, an inner selection learning mechanism is proposed in the update of optimal positions, dynamically refining the global optimal solution. In the CEC 2013 benchmark test, PSOsono demonstrates a certain advantage in optimization capability compared to algorithms proposed in recent years, proving the effectiveness and feasibility of PSOsono. In the Minimum Cross Entropy threshold segmentation experiments for COVID-19, PSOsono exhibits a more prominent segmentation capability compared to other algorithms, showing good generalization across 6 CT images and further validating the practicality of the algorithm.

Kidney segmentation from DCE-MRI converging level set methods, fuzzy clustering and Markov random field modeling

Early diagnosis of transplanted kidney function requires precise Kidney segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging images as a preliminary step. In this regard, this paper aims to propose an automated and accurate DCE-MRI kidney segmentation method integrating fuzzy c-means (FCM) clustering and Markov random field modeling into a level set formulation. The fuzzy memberships, kidney's shape prior model, and spatial interactions modeled using a second-order MRF guide the LS contour evolution towards the target kidney. Several experiments on real medical data of 45 subjects have shown that the proposed method can achieve high and consistent segmentation accuracy regardless of where the LS contour was initialized. It achieves an accuracy of 0.956 ± 0.019 in Dice similarity coefficient (DSC) and 1.15 ± 1.46 in 95% percentile of Hausdorff distance (HD95). Our quantitative comparisons confirm the superiority of the proposed method over several LS methods with an average improvement of more than 0.63 in terms of HD95. It also offers HD95 improvements of 9.62 and 3.94 over two deep neural networks based on the U-Net model. The accuracy improvements are experimentally found to be more profound on low-contrast images as well as DCE-MRI images with high noise levels.

Image Segmentation Technology Based on Attention Mechanism and ENet

With the development of today's society, medical technology is becoming more and more important in people's daily diagnosis and treatment and the number of computed tomography (CT) images and MRI images is also increasing. It is difficult to meet today's needs for segmentation and recognition of medical images by manpower alone. Therefore, the use of computer technology for automatic segmentation has received extensive attention from researchers. We design a tooth CT image segmentation method combining attention mechanism and ENet. First, dilated convolution is used with the spatial information path, with a small downsampling factor to preserve the resolution of the image. Second, an attention mechanism is added to the segmentation network based on CT image features to improve the accuracy of segmentation. Then, the designed feature fusion module obtains the segmentation result of the tooth CT image. It was verified on tooth CT image dataset published by West China Hospital, and the average intersection ratio and accuracy were used as the metric. The results show that, on the dataset of West China Hospital, Mean Intersection over Union (MIOU) and accuracy are 83.47% and 95.28%, respectively, which are 3.3% and 8.09% higher than the traditional model. Compared with the multiple watershed algorithm, the Chan-Vese segmentation algorithm, and the graph cut segmentation algorithm, our algorithm increases the calculation time by 56.52%, 91.52%, and 62.96%, respectively. It can be seen that our algorithm has obvious advantages in MIOU, accuracy, and calculation time.

A hybrid level set model for image segmentation

Active contour models driven by local binary fitting energy can segment images with inhomogeneous intensity, while being prone to falling into a local minima. However, the segmentation result largely depends on the location of the initial contour. We propose an active contour model with global and local image information. The local information of the model is obtained by bilateral filters, which can also enhance the edge information while smoothing the image. The local fitting centers are calculated before the contour evolution, which can alleviate the iterative process and achieve fast image segmentation. The global information of the model is obtained by simplifying the C-V model, which can assist contour evolution, thereby increasing accuracy. Experimental results show that our algorithm is insensitive to the initial contour position, and has higher precision and speed.

A Novel Active Contour Model for Noisy Image Segmentation based on Adaptive Fractional Order Differentiation

The images used in various practices are often disturbed by noise, such as Gaussian noise, speckled noise, and salt and pepper noise. Images with noise are one of the challenges for segmentation, since the noise may cause inaccurate segmented results. To cope with the effect of noise on images during segmentation, a novel active contour model is proposed in this paper. The newly proposed model consists of fitting term, regularization term and penalty term. The fitting term is designed using a Gaussian kernel function and fractional order differentiation with an adaptively defined fractional order, which applies different orders to different pixels. The regularization term is applied to maintain the smoothness of curves. In order to ensure stable evolution of curves, a penalty term is added into the proposed model. Comparison experiments are conducted to show the effectiveness and efficiency of the proposed model.

Anisotropic Diffusion Combined with Nonconvex Functional for Noise Image Segmentation

Aiming at the problems of low segmentation accuracy of noise image, poor noise immunity of the existing models and poor adaptability to complex noise environment, a noise image segmentation algorithm using anisotropic diffusion and nonconvex functional was proposed. First, focusing on the “staircase effect”, a nonconvex functional was introduced into the energy functional model for smooth denoising. Second, the validity and accuracy of the model were established by proving that there was no global minimum in the solution space of the nonconvex energy functional model; the improved model was then used to obtain a smooth and clear image edge while maintaining the edge integrity. Third, the smooth image obtained from the nonconvex energy functional model was combined with the level set model to obtain the anisotropic diffusion gray level set model. The optimal outline of the target was obtained by calculating the minimum value of the energy functional. Finally, an anisotropic diffusion equation with nonconvex energy functional model was built in this algorithm to segment noise image accurately and quickly. A series of comparative experiments on the proposed algorithm and similar algorithms were conducted. The results showed that the proposed algorithm had strong noise resistance and provided precise segmentation for noise image.

Segmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference

BACKGROUND

Image segmentation is an important part of computer-aided diagnosis (CAD), the segmentation of small ground glass opacity (GGO) pulmonary nodules is beneficial for the early detection of lung cancer. For the segmentation of small GGO pulmonary nodules, an integrated active contour model based on Markov random field energy and Bayesian probability difference (IACM_MRFEBPD) is proposed in this paper.

METHODS

First, the Markov random field (MRF) is constructed on the computed tomography (CT) images, then the MRF energy is calculated. The MRF energy is used to construct the region term. It can not only enhance the contrast between pulmonary nodule and the background region, but also solve the problem of intensity inhomogeneity using local spatial correlation information between neighboring pixels in the image. Second, the Gaussian mixture model is used to establish the probability model of the image, and the model parameters are estimated by the expectation maximization (EM) algorithm. So the Bayesian posterior probability difference of each pixel can be calculated. The probability difference is used to construct the boundary detection term, which is 0 at the boundary. Therefore, the blurred boundary problem can be solved. Finally, under the framework of the level set, the integrated active contour model is constructed.

RESULTS

To verify the effectiveness of the proposed method, the public data of the lung image database consortium and image database resource initiative (LIDC-IDRI) and the clinical data of the Affiliated Jiangmen Hospital of Sun Yat-sen University are used to perform experiments, and the intersection over union (IOU) score is used to evaluate the segmentation methods. Compared with other methods, the proposed method achieves the best results with the highest average IOU of 0.7444, 0.7503, and 0.7450 for LIDC-IDRI test set, clinical test set, and all test sets, respectively.

CONCLUSIONS

The experiment results show that the proposed method can segment various small GGO pulmonary nodules more accurately and robustly, which is helpful for the accurate evaluation of medical imaging.

Segmentation of MR Brain Images Through Hidden Markov Random Field and Hybrid Metaheuristic Algorithm

Image segmentation is one of the most critical tasks in Magnetic Resonance (MR) images analysis. Since the performance of most current image segmentation methods is suffered by noise and intensity non-uniformity artifact (INU), a precise and artifact resistant method is desired. In this work, we propose a new segmentation method combining a new Hidden Markov Random Field (HMRF) model and a novel hybrid metaheuristic method based on Cuckoo search (CS) and Particle swarm optimization algorithms (PSO). The new model uses adaptive parameters to allow balancing between the segmented components of the model. In addition, to improve the quality of searching solutions in the Maximum a posteriori (MAP) estimation of the HMRF model, the hybrid metaheuristic algorithm is introduced. This algorithm takes into account both the advantages of CS and PSO algorithms in searching ability by cooperating them with the same population in a parallel way and with a solution selection mechanism. Since CS and PSO are performing exploration and exploitation in the search space, respectively, hybridizing them in an intelligent way can provide better solutions in terms of quality. Furthermore, initialization of the population is carefully taken into account to improve the performance of the proposed method. The whole algorithm is evaluated on benchmark images including both the simulated and real MR brain images. Experimental results show that the proposed method can achieve satisfactory performance for images with noise and intensity inhomogeneity, and provides better results than its considered competitors.

Infrared image segmentation based on multi-information fused fuzzy clustering method for electrical equipment

Serious noise pollution and background interference bring great difficulties to infrared image segmentation of electronic equipment. A novel infrared image segmentation method based on multi-information fused fuzzy clustering method is proposed in this article. Firstly, saliency detection is performed on the infrared image to obtain the saliency map, which determines the initial clustering center and enhances the contrast of the original infrared image. Secondly, the weighting exponent in the objective function is adjusted adaptively. Then local and global spatial constraints are added to the objective function of the fuzzy clustering method, which can reduce the noise and background interference. Finally, the Markov constrained field is calculated according to the initial segmentation result. After that the joint field of fuzzy clustering field and the Markov random field is constructed to obtain the optimized segmentation result. The algorithm is evaluated on the infrared images of electrical equipment, and the experimental results show that the proposed method is robust to noise and complicated background. Compared with other methods, the proposed method improves the average segmentation accuracy and T measure by about 10% and 13%.

Variational Textured Dirichlet Process Mixture Model with Pairwise Constraint for Unsupervised Classification of Polarimetric SAR Images

This paper proposes an unsupervised classification method for multilook polarimetric synthetic aperture radar (Pol-SAR) data. The proposed method simultaneously deals with the heterogeneity and incorporates the local correlation in PolSAR images. Specifically, within the probabilistic framework of the Dirichlet process mixture model (DPMM), an observed PolSAR data point is described by the multiplication of a Wishartdistributed component and a class-dependent random variable (i.e., the textual variable). This modeling scheme leads to the proposed textured DPMM (tDPMM), which possesses more flexibility in characterizing PolSAR data in heterogeneous areas and from high-resolution images due to the introduction of the classdependent texture variable. The proposed tDPMM is learned by solving an optimization problem to achieve its Bayesian inference. With the knowledge of this optimization-based learning, the local correlation is incorporated through the pairwise constraint, which integrates an appropriate penalty term into the objective function so as to encourage the neighboring pixels to fall into the same category and to alleviate the "salt-and-pepper" classification appearance.We develop the learning algorithm with all the closed-form updates. The performance of the proposed method is evaluated with both low-resolution and high-resolution PolSAR images, which involve homogeneous, heterogeneous, and extremely heterogeneous areas. The experimental results reveal that the class-dependent texture variable is beneficial to PolSAR image classification and the pairwise constraint can effectively incorporate the local correlation in PolSAR images.

Small Object Sensitive Segmentation of Urban Street Scene with Spatial Adjacency Between Object Classes

Recent advancements in deep learning have shown exciting promise in the urban street scene segmentation. However, many objects, such as poles and sign symbols, are relatively small and they usually cannot be accurately segmented since the larger objects usually contribute more to the segmentation loss. In this paper, we propose a new boundary-based metric that measures the level of spatial adjacency between each pair of object classes and find that this metric is robust against object size induced biases. We develop a new method to enforce this metric into the segmentation loss. We propose a network, which starts with a segmentation network, followed by a new encoder to compute the proposed boundary-based metric, and then trains this network in an end-to-end fashion. In deployment, we only use the trained segmentation network, without the encoder, to segment new unseen images. Experimentally, we evaluate the proposed method using CamVid and CityScapes datasets and achieve a favorable overall performance improvement and a substantial improvement in segmenting small objects.

Superpixel-Based Foreground Extraction With Fast Adaptive Trimaps

Extracting the foreground from a given complex image is an important and challenging problem. Although there have been many methods to perform foreground extraction, most of them are time-consuming, and the trimaps used in the matting step are labeled manually. In this paper, we propose a fast interactive foreground extraction method based on the superpixel GrabCut and image matting. Specifically, we first extract superpixels from a given image and apply GrabCut on them to obtain a raw mask. Due to that the resulting mask border is hard and toothing, we further propose fast and adaptive trimaps (FATs), and construct an FATs-based shared matting for computing a refined mask. Finally, by interactive processing, we can obtain the final foreground. Experimental results on the BSDS500 and alphamatting datasets demonstrate that our proposed method is faster than five representative methods, and performs better than the interactive representative methods in terms of the three evaluation criteria: 1) mean square error; 2) sum of absolute difference; and 3) execution time.

Patch Alignment Manifold Matting

Image matting is generally modeled as a space transform from the color space to the alpha space. By estimating the alpha factor of the model, the foreground of an image can be extracted. However, there is some dimensional information redundancy in the alpha space. It usually leads to the misjudgments of some pixels near the boundary between the foreground and the background. In this paper, a manifold matting framework named Patch Alignment Manifold Matting is proposed for image matting. In particular, we first propose a part modeling of color space in the local image patch. We then perform whole alignment optimization for approximating the alpha results using subspace reconstructing error. Furthermore, we utilize Nesterov's algorithm to solve the optimization problem. Finally, we apply some manifold learning methods in the framework, and obtain several image matting methods, such as named ISOMAP matting and its derived Cascade ISOMAP matting. The experimental results reveal that the manifold matting framework and its two examples are effective when compared with several representative matting methods.

Carotid plaque segmentation from three-dimensional ultrasound images by direct three-dimensional sparse field level-set optimization

Total plaque volume (TPV) measured from 3D carotid ultrasound has been shown to be able to predict cardiovascular events and is sensitive in detecting treatment effects. Manual plaque segmentation was performed in previous studies to quantify TPV, but is tedious, requires long training times and is prone to observer variability. This article introduces the first 3D direct volume-based level-set algorithm to segment plaques from 3D carotid ultrasound images. The plaque surfaces were first initialized based on the lumen and outer wall boundaries generated by a previously described semi-automatic algorithm and then deformed by a direct three-dimensional sparse field level-set algorithm, which enforced the longitudinal continuity of the segmented plaque surfaces. This is a marked advantage as compared to a previously proposed 2D slice-by-slice plaque segmentation method. In plaque boundary initialization, the previous technique performed a search on lines connecting corresponding point pairs of the outer wall and lumen boundaries. A limitation of this initialization strategy was that an inaccurate initial plaque boundary would be generated if the plaque was not enclosed entirely by the wall and lumen boundaries. A mechanism is proposed to extend the search range in order to capture the entire plaque if the outer wall boundary lies on a weak edge in the 3D ultrasound image. The proposed method was compared with the previously described 2D slice-by-slice plaque segmentation method in 26 three-dimensional carotid ultrasound images containing 27 plaques with volumes ranging from 12.5 to 450.0 mm³. The manually segmented plaque boundaries serve as the surrogate gold standard. Segmentation accuracy was quantified by volume-, area- and distance-based metrics, including absolute plaque volume difference (|ΔPV|), Dice similarity coefficient (DSC), mean and maximum absolute distance (MAD and MAXD). The proposed direct 3D plaque segmentation algorithm was associated with a significantly lower |ΔPV|, MAD and MAXD, and a significantly higher DSC compared to the previously described slice-by-slice algorithm (|ΔPV|:p=0.012, DSC: p=2.1×10^-4, MAD: p=1.3×10^-4, MAXD: p=5.2×10^-4). The proposed 3D volume-based algorithm required 72±22 s to segment a plaque, which is 40% lower than the 2D slice-by-slice algorithm (114±18 s). The proposed automatic plaque segmentation method generates accurate and reproducible boundaries efficiently and will allow for streamlining plaque quantification based on 3D ultrasound images.

Oil Spill Detection in Terma-Side-Looking Airborne Radar Images Using Image Features and Region Segmentation

This work presents a method for oil-spill detection on Spanish coasts using aerial Side-Looking Airborne Radar (SLAR) images, which are captured using a Terma sensor. The proposed method uses grayscale image processing techniques to identify the dark spots that represent oil slicks on the sea. The approach is based on two steps. First, the noise regions caused by aircraft movements are detected and labeled in order to avoid the detection of false-positives. Second, a segmentation process guided by a map saliency technique is used to detect image regions that represent oil slicks. The results show that the proposed method is an improvement on the previous approaches for this task when employing SLAR images.

Computer-based radiological longitudinal evaluation of meningiomas following stereotactic radiosurgery

PURPOSE

Stereotactic radiosurgery (SRS) is a common treatment for intracranial meningiomas. SRS is planned on a pre-therapy gadolinium-enhanced T1-weighted MRI scan (Gd-T1w MRI) in which the meningioma contours have been delineated. Post-SRS therapy serial Gd-T1w MRI scans are then acquired for longitudinal treatment evaluation. Accurate tumor volume change quantification is required for treatment efficacy evaluation and for treatment continuation.

METHOD

We present a new algorithm for the automatic segmentation and volumetric assessment of meningioma in post-therapy Gd-T1w MRI scans. The inputs are the pre- and post-therapy Gd-T1w MRI scans and the meningioma delineation in the pre-therapy scan. The output is the meningioma delineations and volumes in the post-therapy scan. The algorithm uses the pre-therapy scan and its meningioma delineation to initialize an extended Chan-Vese active contour method and as a strong patient-specific intensity and shape prior for the post-therapy scan meningioma segmentation. The algorithm is automatic, obviates the need for independent tumor localization and segmentation initialization, and incorporates the same tumor delineation criteria in both the pre- and post-therapy scans.

RESULTS

Our experimental results on retrospective pre- and post-therapy scans with a total of 32 meningiomas with volume ranges 0.4-26.5 cm[Formula: see text] yield a Dice coefficient of [Formula: see text]% with respect to ground-truth delineations in post-therapy scans created by two clinicians. These results indicate a high correspondence to the ground-truth delineations.

CONCLUSION

Our algorithm yields more reliable and accurate tumor volume change measurements than other stand-alone segmentation methods. It may be a useful tool for quantitative meningioma prognosis evaluation after SRS.

Brain MR image segmentation based on an improved active contour model

It is often a difficult task to accurately segment brain magnetic resonance (MR) images with intensity in-homogeneity and noise. This paper introduces a novel level set method for simultaneous brain MR image segmentation and intensity inhomogeneity correction. To reduce the effect of noise, novel anisotropic spatial information, which can preserve more details of edges and corners, is proposed by incorporating the inner relationships among the neighbor pixels. Then the proposed energy function uses the multivariate Student's t-distribution to fit the distribution of the intensities of each tissue. Furthermore, the proposed model utilizes Hidden Markov random fields to model the spatial correlation between neigh-boring pixels/voxels. The means of the multivariate Student's t-distribution can be adaptively estimated by multiplying a bias field to reduce the effect of intensity inhomogeneity. In the end, we reconstructed the energy function to be convex and calculated it by using the Split Bregman method, which allows our framework for random initialization, thereby allowing fully automated applications. Our method can obtain the final result in less than 1 second for 2D image with size 256 × 256 and less than 300 seconds for 3D image with size 256 × 256 × 171. The proposed method was compared to other state-of-the-art segmentation methods using both synthetic and clinical brain MR images and increased the accuracies of the results more than 3%.

The Recognition of the Point Symbols in the Scanned Topographic Maps

It is difficult to separate the point symbols from the scanned topographic maps accurately, which brings challenges for the recognition of the point symbols. In this paper, based on the framework of generalized Hough transform (GHT), we propose a new algorithm, which is named shear line segment GHT (SLS-GHT), to recognize the point symbols directly in the scanned topographic maps. SLS-GHT combines the line segment GHT (LS-GHT) and the shear transformation. On the one hand, LS-GHT is proposed to represent the features of the point symbols more completely. Its R-table has double level indices, the first one is the color information of the point symbols, and the other is the slope of the line segment connected a pair of the skeleton points. On the other hand, the shear transformation is introduced to increase the directional features of the point symbols; it can make up for the directional limitation of LS-GHT indirectly. In this way, the point symbols are detected in a series of the sheared maps by LS-GHT, and the final optimal coordinates of the setpoints are gotten from a series of the recognition results. SLS-GHT detects the point symbols directly in the scanned topographic maps, totally different from the traditional pattern of extraction before recognition. Moreover, several experiments demonstrate that the proposed method allows improved recognition in complex scenes than the existing methods.

A Robust and Fast Method for Sidescan Sonar Image Segmentation Using Nonlocal Despeckling and Active Contour Model

Sidescan sonar image segmentation is a very important issue in underwater object detection and recognition. In this paper, a robust and fast method for sidescan sonar image segmentation is proposed, which deals with both speckle noise and intensity inhomogeneity that may cause considerable difficulties in image segmentation. The proposed method integrates the nonlocal means-based speckle filtering (NLMSF), coarse segmentation using k -means clustering, and fine segmentation using an improved region-scalable fitting (RSF) model. The NLMSF is used before the segmentation to effectively remove speckle noise while preserving meaningful details such as edges and fine features, which can make the segmentation easier and more accurate. After despeckling, a coarse segmentation is obtained by using k -means clustering, which can reduce the number of iterations. In the fine segmentation, to better deal with possible intensity inhomogeneity, an edge-driven constraint is combined with the RSF model, which can not only accelerate the convergence speed but also avoid trapping into local minima. The proposed method has been successfully applied to both noisy and inhomogeneous sonar images. Experimental and comparative results on real and synthetic sonar images demonstrate that the proposed method is robust against noise and intensity inhomogeneity, and is also fast and accurate.

Connecting Markov random fields and active contour models: application to gland segmentation and classification

We introduce a Markov random field (MRF)-driven region-based active contour model (MaRACel) for histological image segmentation. This Bayesian segmentation method combines a region-based active contour (RAC) with an MRF. State-of-the-art RAC models assume that every spatial location in the image is statistically independent, thereby ignoring valuable contextual information among spatial locations. To address this shortcoming, we incorporate an MRF prior into energy term of the RAC. This requires a formulation of the Markov prior consistent with the continuous variational framework characteristic of active contours; consequently, we introduce a continuous analog to the discrete Potts model. Based on the automated segmentation boundary of glands by MaRACel model, explicit shape descriptors are then employed to distinguish prostate glands belonging to Gleason patterns 3 (G3) and 4 (G4). To demonstrate the effectiveness of MaRACel, we compare its performance to the popular models proposed by Chan and Vese (CV) and Rousson and Deriche (RD) with respect to the following tasks: (1) the segmentation of prostatic acini (glands) and (2) the differentiation of G3 and G4 glands. On almost 600 prostate biopsy needle images, MaRACel was shown to have higher average dice coefficients, overlap ratios, sensitivities, specificities, and positive predictive values both in terms of segmentation accuracy and ability to discriminate between G3 and G4 glands compared to the CV and RD models.

Automatic Choroidal Layer Segmentation Using Markov Random Field and Level Set Method

The choroid is an important vascular layer that supplies oxygen and nourishment to the retina. The changes in thickness of the choroid have been hypothesized to relate to a number of retinal diseases in the pathophysiology. In this paper, an automatic method is proposed for segmenting the choroidal layer from macular images by using the level set framework. The three-dimensional nonlinear anisotropic diffusion filter is used to remove all the optical coherence tomography (OCT) imaging artifacts including the speckle noise and to enhance the contrast. The distance regularization and edge constraint terms are embedded into the level set method to avoid the irregular and small regions and keep information about the boundary between the choroid and sclera. Besides, the Markov random field method models the region term into the framework by correlating the single-pixel likelihood function with neighborhood information to compensate for the inhomogeneous texture and avoid the leakage due to the shadows cast by the blood vessels during imaging process. The effectiveness of this method is demonstrated by comparing against other segmentation methods on a dataset with manually labeled ground truth. The results show that our method can successfully and accurately estimate the posterior choroidal boundary.

Tumor or abnormality identification from magnetic resonance images using statistical region fusion based segmentation

In this article, a statistical fusion based segmentation technique is proposed to identify different abnormality in magnetic resonance images (MRI). The proposed scheme follows seed selection, region growing-merging and fusion of multiple image segments. In this process initially, an image is divided into a number of blocks and for each block we compute the phase component of the Fourier transform. The phase component of each block reflects the gray level variation among the block but contains a large correlation among them. Hence a singular value decomposition (SVD) technique is adhered to generate a singular value of each block. Then a thresholding procedure is applied on these singular values to identify edgy and smooth regions and some seed points are selected for segmentation. By considering each seed point we perform a binary segmentation of the complete MRI and hence with all seed points we get an equal number of binary images. A parcel based statistical fusion process is used to fuse all the binary images into multiple segments. Effectiveness of the proposed scheme is tested on identifying different abnormalities: prostatic carcinoma detection, tuberculous granulomas identification and intracranial neoplasm or brain tumor detection. The proposed technique is established by comparing its results against seven state-of-the-art techniques with six performance evaluation measures.

Shape-Constrained Sparse and Low-Rank Decomposition for Auroral Substorm Detection

An auroral substorm is an important geophysical phenomenon that reflects the interaction between the solar wind and the Earth's magnetosphere. Detecting substorms is of practical significance in order to prevent disruption to communication and global positioning systems. However, existing detection methods can be inaccurate or require time-consuming manual analysis and are therefore impractical for large-scale data sets. In this paper, we propose an automatic auroral substorm detection method based on a shape-constrained sparse and low-rank decomposition (SCSLD) framework. Our method automatically detects real substorm onsets in large-scale aurora sequences, which overcomes the limitations of manual detection. To reduce noise interference inherent in current SLD methods, we introduce a shape constraint to force the noise to be assigned to the low-rank part (stationary background), thus ensuring the accuracy of the sparse part (moving object) and improving the performance. Experiments conducted on aurora sequences in solar cycle 23 (1996-2008) show that the proposed SCSLD method achieves good performance for motion analysis of aurora sequences. Moreover, the obtained results are highly consistent with manual analysis, suggesting that the proposed automatic method is useful and effective in practice.

Polar Embedding for Aurora Image Retrieval

Exploring the multimedia techniques to assist scientists for their research is an interesting and meaningful topic. In this paper, we focus on the large-scale aurora image retrieval by leveraging the bag-of-visual words (BoVW) framework. To refine the unsuitable representation and improve the retrieval performance, the BoVW model is modified by embedding the polar information. The superiority of the proposed polar embedding method lies in two aspects. On the one hand, the polar meshing scheme is conducted to determine the interest points, which is more suitable for images captured by circular fisheye lens. Especially for the aurora image, the extracted polar scale-invariant feature transform (polar-SIFT) feature can also reflect the geomagnetic longitude and latitude, and thus facilitates the further data analysis. On the other hand, a binary polar deep local binary pattern (polar-DLBP) descriptor is proposed to enhance the discriminative power of visual words. Together with the 64-bit polar-SIFT code obtained via Hamming embedding, the multifeature index is performed to reduce the impact of false positive matches. Extensive experiments are conducted on the large-scale aurora image data set. The experimental result indicates that the proposed method improves the retrieval accuracy significantly with acceptable efficiency and memory cost. In addition, the effectiveness of the polar-SIFT scheme and polar-DLBP integration are separately demonstrated.

Boundary detection using double-opponency and spatial sparseness constraint

Brightness and color are two basic visual features integrated by the human visual system (HVS) to gain a better understanding of color natural scenes. Aiming to combine these two cues to maximize the reliability of boundary detection in natural scenes, we propose a new framework based on the color-opponent mechanisms of a certain type of color-sensitive double-opponent (DO) cells in the primary visual cortex (V1) of HVS. This type of DO cells has oriented receptive field with both chromatically and spatially opponent structure. The proposed framework is a feedforward hierarchical model, which has direct counterpart to the color-opponent mechanisms involved in from the retina to V1. In addition, we employ the spatial sparseness constraint (SSC) of neural responses to further suppress the unwanted edges of texture elements. Experimental results show that the DO cells we modeled can flexibly capture both the structured chromatic and achromatic boundaries of salient objects in complex scenes when the cone inputs to DO cells are unbalanced. Meanwhile, the SSC operator further improves the performance by suppressing redundant texture edges. With competitive contour detection accuracy, the proposed model has the additional advantage of quite simple implementation with low computational cost.