Random Walker Method Research Articles

An Automatic Random Walker Algorithm for Segmentation of Ground Glass Opacity Pulmonary Nodules.

Automatic and accurate segmentation of ground glass opacity (GGO) nodules still remains challenging due to inhomogeneous interiors, irregular shapes, and blurred boundaries from different patients. Despite successful applications in the image processing domains, the random walk has some limitations for segmentation of GGO pulmonary nodules. In this paper, an improved random walker method is proposed for the segmentation of GGO nodules. To calculate a new affinity matrix, intensity, spatial, and texture features are incorporated. It strengthens discriminative power between two adjacent nodes on the graph. To address the problem of robustness in seed acquisition, the geodesic distance is introduced and a novel local search strategy is presented to automatically acquire reliable seeds. For segmentation, a label constraint term is introduced to the energy function of original random walker, which alleviates the accumulation of errors caused by the initial seeds acquisition. Massive experiments conducted on Lung Images Dataset Consortium (LIDC) demonstrate that the proposed method achieves visually satisfactory results without user interactions. Both qualitative and quantitative evaluations also demonstrate that the proposed method obtains better performance compared with conventional random walker method and state-of-the-art segmentation methods in terms of the overlap score and F-measure.

Effective Framework for Pulmonary Nodule Classification from CT Images Using the Modified Gradient Boosting Method

Lung carcinoma, which is commonly known as lung cancer, is one of the most common cancers throughout the world. Mostly, it is not diagnosed until it has spread, and it is very difficult to treat. Hence, early diagnosis of benign and malignant pulmonary nodules can help in the risk assessment of lung cancer for patients, and with proper treatment can save their lives. In this study, a framework for the classification of pulmonary nodules from Computerized Tomography (CT) images using the machine learning-based modified gradient boosting method is proposed. Initially, the obtained CT scan images are preprocessed for better image quality. Next, a random walker method is used to segment the lung nodule boundaries based on seeds provided by the user. After that, the intensity and texture features are extracted using the Local Binary Pattern (LBP) filter and the coefficients of the Riesz wavelet transform. Finally, the proposed modified gradient boost classifier model is trained and tested using the extracted features to classify nodules as either benign or malignant. The proposed framework is verified and validated using the Lung Image Database Consortium (LIDC-IDRI) dataset. From the performance analysis, it was observed that the proposed method achieves a precision, recall, F1 score, and validation accuracy of 0.957, 0.91, 0.941, and 95.67%, respectively. The performance of the proposed method is compared with existing models and is found to be superior. It was found that the proposed classifier is able to efficiently classify pulmonary nodules as either benign or malignant.

Multilayer Global Spectral–Spatial Attention Network for Wetland Hyperspectral Image Classification

Coastal wetland monitoring plays an important role in the protection and restoration of ecosystems in this world. UAV-hyperspectral imaging, as an emerging technique for Earth observation and space exploration, provides the huge potential ability to identify different wetland species. In this work, a multilayer global spectral–spatial attention network (MGSSAN) is proposed for mapping coastal wetlands, which mainly consists of two major steps. First, a two-branch convolutional neural network (CNN) framework with residual connection is developed to obtain an initial classification probability map, in which one branch is used to capture the spectral information, the other branch is used to extract spatial information, and a global spectral–spatial attention module is designed to guide networks focusing on those features that are more discriminative. Second, an extended random walker method is utilized to optimize the initial classification probabilities, so as to yield the final map. Experiments performed on three wetland HSI datasets created by ourselves verify that the proposed method can obtain superior performance with respect to several state-of-the-art hyperspectral image classification methods.

Hierarchical Random Walker Segmentation for Large Volumetric Biomedical Images.

The random walker method for image segmentation is a popular tool for semi-automatic image segmentation, especially in the biomedical field. However, its linear asymptotic run time and memory requirements make application to 3D datasets of increasing sizes impractical. We propose a hierarchical framework that, to the best of our knowledge, is the first attempt to overcome these restrictions for the random walker algorithm and achieves sublinear run time and constant memory complexity. The goal of this framework is- rather than improving the segmentation quality compared to the baseline method- to make interactive segmentation on out-of-core datasets possible. The method is evaluated quantitatively on synthetic data and the CT-ORG dataset where the expected improvements in algorithm run time while maintaining high segmentation quality are confirmed. The incremental (i.e., interaction update) run time is demonstrated to be in seconds on a standard PC even for volumes of hundreds of gigabytes in size. In a small case study the applicability to large real world from current biomedical research is demonstrated. An implementation of the presented method is publicly available in version 5.2 of the widely used volume rendering and processing software Voreen (https://www.uni-muenster.de/Voreen/).

Multi-view iterative random walker for automated salvageable tissue delineation in ischemic stroke from multi-sequence MRI

Background and objectiveNon-invasive and robust identification of salvageable tissue (penumbra) is crucial for interventional stroke therapy. Besides identifying stroke injury as a whole, the ability to automatically differentiate core and penumbra tissues, using both diffusion and perfusion magnetic resonance imaging (MRI) sequences is essential for ischemic stroke treatment. MethodA fully automated and novel one-shot multi-view iterative random walker (MIRW) method with an automated injury seed point detection is developed for lesion delineation. MIRW utilizes the heirarchical decomposition of multi-sequence MRI physical properties of the underlying tissue within the lesion to maximize the inter-class variations of the volumetric histogram to estimate the probable seed points. These estimates are further utilized to conglomerate the lesion estimations iteratively from axial, coronal and sagittal MRI volumes for a computationally efficient segmentation and quantification of salvageable and necrotic tissues from multi-sequence MRI. ResultsComprehensive experimental analysis of MIRW is performed on three challenging adult(sub-)acute ischemic stroke datasets using performance measures like precision, sensitivity, specificity and Dice similarity score (DSC), which are computed with respect to the manual ground-truth. Comparison with existing methodsMIRW method resulted in a high DSC of 83.5% in a very less computational time of 98.23 s/volume, which is a significant improvement on the ISLES benchmark dataset for penumbra detection, compared to the state-of-the-art techniques. ConclusionQuantitative measures demonstrate the promising potential of MIRW for computational analysis of adult stroke and quantifying penumbra in stroke patients which is essential for selecting the good candidates for recanalization.

A Curvature-Enhanced Random Walker Segmentation Method for Detailed Capture of 3D Cell Surface Membranes.

High-resolution 3D microscopy is a fast advancing field and requires new techniques in image analysis to handle these new datasets. In this work, we focus on detailed 3D segmentation of Dictyostelium cells undergoing macropinocytosis captured on an iSPIM microscope. We propose a novel random walker-based method with a curvature-based enhancement term, with the aim of capturing fine protrusions, such as filopodia and deep invaginations, such as macropinocytotic cups, on the cell surface. We tested our method on both real and synthetic 3D image volumes, demonstrating that the inclusion of the curvature enhancement term can improve the segmentation of the aforementioned features. We show that our method performs better than other state of the art segmentation methods in 3D images of Dictyostelium cells, and performs competitively against CNN-based methods in two Cell Tracking Challenge datasets, demonstrating the ability to obtain accurate segmentations without the requirement of large training datasets. We also present an automated seeding method for microscopy data, which, combined with the curvature-enhanced random walker method, enables the segmentation of large time series with minimal input from the experimenter.

Hyperspectral Anomaly Detection With Morphological Random Walker

This work proposes a new morphological random walker (MRW) method for hyperspectral anomaly detection. The proposed method introduces a morphology-based objective function into a random walker (RW) algorithm, sufficiently exploiting spatial morphological property and spatial similarity of HSIs for detection. The MRW method comprises two major stages. Firstly, we employ the extended morphological profiles (EMPs) and different operations to extract the spatial morphological property of HSIs. Second, according to the morphological property, we construct a morphology-based objective function. This function is incorporated into the RW-based optimization model, encoding the spatial similarity of HSIs in a weighted graph. Two factors determine the class of test pixels, including the spatial morphological information learned by EMPs, and the spatial correlation among adjoining pixels modeled by the weighted graph. Since the two factors are well considered in the MRW method, the proposed method illustrates outstanding detection performances for several widely used real HSIs.

AUTOMATIC DETERMINATION OF SEEDS FOR RANDOM WALKER BY SEEDED WATERSHED TRANSFORM FOR TUNA IMAGE SEGMENTATION

Tuna fish image classification is an important part to sort out the type and quality of the tuna based upon the shape. The image of tuna should have good segmentation results before entering the classification stage. It has uneven lighting and complex texture resulting in inappropriate segmentation. This research proposed method of automatic determination seeded random walker in the watershed region for tuna image segmentation. Random walker is a noise-resistant segmentation method that requires two types of seeds defined by the user, the seed pixels for background and seed pixels for the object. We evaluated the proposed method on 30 images of tuna using relative foreground area error (RAE), misclassification error (ME), and modified Hausdroff distances (MHD) evaluation methods with values of 4.38%, 1.34% and 1.11%, respectively. This suggests that the seeded random walker method is more effective than exiting methods for tuna image segmentation.

Automatic liver segmentation on Computed Tomography using random walkers for treatment planning.

Segmentation of the liver from Computed Tomography (CT) volumes plays an important role during the choice of treatment strategies for liver diseases. Despite lots of attention, liver segmentation remains a challenging task due to the lack of visible edges on most boundaries of the liver coupled with high variability of both intensity patterns and anatomical appearances with all these difficulties becoming more prominent in pathological livers. To achieve a more accurate segmentation, a random walker based framework is proposed that can segment contrast-enhanced livers CT images with great accuracy and speed. Based on the location of the right lung lobe, the liver dome is automatically detected thus eliminating the need for manual initialization. The computational requirements are further minimized utilizing rib-caged area segmentation, the liver is then extracted by utilizing random walker method. The proposed method was able to achieve one of the highest accuracies reported in the literature against a mixed healthy and pathological liver dataset compared to other segmentation methods with an overlap error of 4.47 % and dice similarity coefficient of 0.94 while it showed exceptional accuracy on segmenting the pathological livers with an overlap error of 5.95 % and dice similarity coefficient of 0.91.

Permeability estimation of porous media by using an improved capillary bundle model based on micro-CT derived pore geometries

The purpose of this work is to develop a permeability estimation method for porous media. This method is based on an improved capillary bundle model by introducing some pore geometries. We firstly carried out micro-CT scans to extract the 3D digital model of porous media. Then we applied a maximum ball extraction method to the digital model to obtain the topological and geometrical pore parameters such as the pore radius, the throat radius and length and the average coordination number. We also applied a random walker method to calculate the tortuosity factors of porous media. We improved the capillary bundle model by introducing the pore geometries and tortuosity factors. Finally, we calculated the absolute permeabilities of four kinds of porous media formed of glass beads and compared the results with experiments and several other models to verify the improved model. We found that the calculated permeabilities using this improved capillary bundle model show better agreement with the measured permeabilities than the other methods.

3-D Ultrasound Segmentation of the Placenta Using the Random Walker Algorithm: Reliability and Agreement

Volumetric segmentation of the placenta using 3-D ultrasound is currently performed clinically to investigate correlation between organ volume and fetal outcome or pathology. Previously, interpolative or semi-automatic contour-based methodologies were used to provide volumetric results. We describe the validation of an original random walker (RW)-based algorithm against manual segmentation and an existing semi-automated method, virtual organ computer-aided analysis (VOCAL), using initialization time, inter- and intra-observer variability of volumetric measurements and quantification accuracy (with respect to manual segmentation) as metrics of success. Both semi-automatic methods require initialization. Therefore, the first experiment compared initialization times. Initialization was timed by one observer using 20 subjects. This revealed significant differences (p < 0.001) in time taken to initialize the VOCAL method compared with the RW method. In the second experiment, 10 subjects were used to analyze intra-/inter-observer variability between two observers. Bland–Altman plots were used to analyze variability combined with intra- and inter-observer variability measured by intra-class correlation coefficients, which were reported for all three methods. Intra-class correlation coefficient values for intra-observer variability were higher for the RW method than for VOCAL, and both were similar to manual segmentation. Inter-observer variability was 0.94 (0.88, 0.97), 0.91 (0.81, 0.95) and 0.80 (0.61, 0.90) for manual, RW and VOCAL, respectively. Finally, a third observer with no prior ultrasound experience was introduced and volumetric differences from manual segmentation were reported. Dice similarity coefficients for observers 1, 2 and 3 were respectively 0.84 ± 0.12, 0.94 ± 0.08 and 0.84 ± 0.11, and the mean was 0.87 ± 0.13. The RW algorithm was found to provide results concordant with those for manual segmentation and to outperform VOCAL in aspects of observer reliability. The training of an additional untrained observer was investigated, and results revealed that with the appropriate initialization protocol, results for observers with varying levels of experience were concordant. We found that with appropriate training, the RW method can be used for fast, repeatable 3-D measurement of placental volume.

Hybrid of Fuzzy Logic and Random Walker Method for Medical Image Segmentation

The procedure of partitioning an image into various segments to reform an image into somewhat that is more significant and easier to analyze, defined as image segmentation. In real world applications, noisy images exits and there could be some measurement errors too. These factors affect the quality of segmentation, which is of major concern in medical fields where decisions about patients' treatment are based on information extracted from radiological images. Several algorithms and techniques have developed for image segmentation and have their own advantages and disadvantages. Random walker method is a supervised segmentation method and it requires that it should be more efficient in producing effective segmentation results in case of medical images which are complex images. In the present paper, we are going to incorporate the advantages of fuzzy logic with a random walker to make resulting segmentation better in texture and quality. For this, we will use fuzzy rules to approximate boundaries in images which will improve segmentation results.

Supervised Visual System for Recognition of Erythema Migrans, an Early Skin Manifestation of Lyme Borreliosis

Lyme borreliosis is the most common human tick-borne infectious disease in the northern hemisphere, occurring predominantly in temperate regions of North America, Europe and Asia. The disease’s most frequent manifestation is erythema migrans, a skin lesion that appears within days to weeks of a tick bite. Early recognition of the lesion is important since it enables proper management and thus prevention of later consequences of the disease which can hamper normal life. In this article, a novel visual system for recognition of erythema migrans is presented based on new technology of smartphones. For detecting erythema migrans edge, we compared three different methods: GrowCut, Maximal Similarity Based Region Merging and Random Walker segmentation method. We have found that the results obtained with GrowCut method are better than those obtained with Random Walker method. Also the GrowCut method, improved with our new figure draw (FD1) marker yields comparable results to those obtained with Maximal Similarity Based Region Merging method. Several classification algorithms Naive Bayes, Support Vector Machine, Adaboost, Random forest and Neural network were compared and used for classification of skin lesions into ellipse, the most common shape of erythema migrans and erythema migrans class.

A THEORETICAL STUDY ON PARTIALLY AUTOMATED METHOD FOR (PROSTATE) CANCER PINPOINT USING MAGNETIC RESONANCE IMAGING (MRI)

A Partially Automated method for (Prostate) Cancer pinpoint using Multi-parametric magnetic resonance imaging has been proposed in this paper, which can be used in guidin g surgery. A Random Walker (RW) algorithm has been analyzed with seed initialization to perform (Prostate) cancer pinpoin t using Magnetic Resonance Imaging (MRI). Segmentat ion can be done by using Random Walker (RW) algorithm which has to be considered to be a fastest method. Random Walker (R W) method can be used with multi-parametric magnetic resonance imagi ng (MRI) and then by using Support Vector Machine ( SVM) method, we can determine the seed points in a partially automated manner. By using this method, more weights to the i mage can be assigned in order to produce improved segmentation process. The proposed method can also give high specificity rate without reducing the sensitivity which is better than earlier methods an d fisher sign test can be also used to find the statistical differences.

3D time series analysis of cell shape using Laplacian approaches

BackgroundFundamental cellular processes such as cell movement, division or food uptake critically depend on cells being able to change shape. Fast acquisition of three-dimensional image time series has now become possible, but we lack efficient tools for analysing shape deformations in order to understand the real three-dimensional nature of shape changes.ResultsWe present a framework for 3D+time cell shape analysis. The main contribution is three-fold: First, we develop a fast, automatic random walker method for cell segmentation. Second, a novel topology fixing method is proposed to fix segmented binary volumes without spherical topology. Third, we show that algorithms used for each individual step of the analysis pipeline (cell segmentation, topology fixing, spherical parameterization, and shape representation) are closely related to the Laplacian operator. The framework is applied to the shape analysis of neutrophil cells.ConclusionsThe method we propose for cell segmentation is faster than the traditional random walker method or the level set method, and performs better on 3D time-series of neutrophil cells, which are comparatively noisy as stacks have to be acquired fast enough to account for cell motion. Our method for topology fixing outperforms the tools provided by SPHARM-MAT and SPHARM-PDM in terms of their successful fixing rates. The different tasks in the presented pipeline for 3D+time shape analysis of cells can be solved using Laplacian approaches, opening the possibility of eventually combining individual steps in order to speed up computations.

Prostate Cancer Localization Using Multiparametric MRI based on Semisupervised Techniques With Automated Seed Initialization

In this paper, we propose a novel and efficient semisupervised technique for automated prostate cancer localization using multiparametric magnetic resonance imaging (MRI). This method can be used in guiding biopsy, surgery, and therapy. We systematically present a new segmentation technique by developing a multiparametric graph based random walker (RW) algorithm with automated seed initialization to perform prostate cancer segmentation using multiparametric MRI. RW algorithm has proved to be accurate and fast in segmentation applications; however it requires a set of (user provided) seed points in order to perform segmentation. In this study, we first developed a novel RW method, which can be used with multiparametric MR images and then devised alternative methods that can determine seed points in an automated manner using discriminative classifiers such as support vector machines (SVM). Proposed RW method with automated seed initialization is able to produce improved segmentation results by assigning more weights to the images with more discriminative power.We applied the proposed method to a multiparametric dataset obtained from biopsy confirmed prostate cancer patients. Proposed method produces a sensitivity/ specificity rate of 0.76 and 0.86, respectively. Both visual, quantitative as well as statistical results are presented to show the significant performance improvements. Fisher sign test is used to demonstrate the statistical significance of our results by achieving p-values less than 0.05. This method outperforms available RW and SVM based methods by achieving a high specificity rate while not reducing sensitivity.

X-ray microtomography shows pore structure and tortuosity in alkali-activated binders

Durability of alkali-activated binders is of vital importance in their commercial application, and depends strongly on microstructure and pore network characteristics. X-ray microtomography (μCT) offers, for the first time, direct insight into microstructural and pore structure characteristics in three dimensions. Here, μCT is performed on a set of sodium metasilicate-activated fly ash/slag blends, using a synchrotron beamline instrument. Segmentation of the samples into pore and solid regions is then conducted, and pore tortuosity is calculated by a random walker method. Segmented porosity and diffusion tortuosity are correlated, and vary as a function of slag content (slag addition reduces porosity and increases tortuosity), and sample age (extended curing gives lower porosity and higher tortuosity). This is particularly notable for samples with ≥50% slag content, where a space-filling calcium (alumino)silicate hydrate gel provides porosity reductions which are not observed for the sodium aluminosilicate (‘geopolymer’) gels which do not chemically bind water of hydration.

Segmentation of Stochastic Images With a Stochastic Random Walker Method

We present an extension of the random walker segmentation to images with uncertain gray values. Such gray-value uncertainty may result from noise or other imaging artifacts or more general from measurement errors in the image acquisition process. The purpose is to quantify the influence of the gray-value uncertainty onto the result when using random walker segmentation. In random walker segmentation, a weighted graph is built from the image, where the edge weights depend on the image gradient between the pixels. For given seed regions, the probability is evaluated for a random walk on this graph starting at a pixel to end in one of the seed regions. Here, we extend this method to images with uncertain gray values. To this end, we consider the pixel values to be random variables (RVs), thus introducing the notion of stochastic images. We end up with stochastic weights for the graph in random walker segmentation and a stochastic partial differential equation (PDE) that has to be solved. We discretize the RVs and the stochastic PDE by the method of generalized polynomial chaos, combining the recent developments in numerical methods for the discretization of stochastic PDEs and an interactive segmentation algorithm. The resulting algorithm allows for the detection of regions where the segmentation result is highly influenced by the uncertain pixel values. Thus, it gives a reliability estimate for the resulting segmentation, and it furthermore allows determining the probability density function of the segmented object volume.

Resource searching in an unstructured P2P network based on Cloning Random Walker assisted by Dominating Set

Peer-to-Peer (P2P) networks have been developed for resource sharing for years. In a P2P network, the first and foremost challenge is resource searching to find out target objects (i.e., data, file, etc.) scattered in the P2P network. The searching efficiency highly depends on the popularity of the target object. However, the popularity is too variable to be estimated since a P2P network is dynamic. A good searching scheme in such a variable environment with a better trade-off among long searching time, a large amount of duplicated query messages and a low successful search hit rate is hard to be determined. Many efficient methods have been proposed to resolve this issue. Most of them may only be beneficial for searching a popular object. In this paper, we propose a Cloning Random Walker assisted by Dominating Set (CloneRW+DS) method to adaptively locate the target object no matter the target object is popular or unpopular in a P2P network. Our theoretical analysis shows that our proposed method consumes a moderate amount of query messages between the message amount in the Random Walker method and the one in the flooding method. Additionally, our simulation results also depict that our proposed scheme can perform a better trade-off among the search delay, message overhead per hit and success rate while conducting resource searching in a P2P network.

An improved image graph for semi-automatic segmentation

The problem of semi-automatic segmentation has attracted much interest over the last few years. The Random Walker algorithm [1] has proven to be quite a popular solution to this problem, as it is able to deal with several components and models the image using a convenient graph structure. We propose two improvements to the image graph used by the Random Walker method. First, we propose a new way of computing the edge weights. Traditionally, such weights are based on the similarity between two neighbouring pixels, using their greyscale intensities or colours. We substitute a new definition of weights based on the probability distributions of colours. This definition is much more robust than traditional measures, as it allows for textured objects, and objects that are composed of multiple perceptual components. Second, the traditional graph has a vertex set which is the set of pixels and edges between each pair of neighbouring pixels. We substitute a smaller, irregular graph based on Mean Shift oversegmentation. This new graph is typically several orders of magnitude smaller than the original image graph, which can lead to a major savings in computing time. We show results demonstrating the substantial improvement achieved when using the proposed image graph.