Yusuf Artan, İmam Şamil Yetik · 2010
General purpose image segmentation is one of the important and challenging problems in image processing. Objective of image segmentation is to group regions with coherent cues such as intensity, texture, color and shape together. Most of the earlier studies on this issue are based on supervised and unsupervised learning methods. In this paper, we develop a semi-supervised image segmentation technique for images using filter bank responses as features. This study utilizes a graph based semi-supervised random walker algorithm to perform segmentation task. Filter bank response driven random walker algorithm has not been considered in the past. We present segmentation results using a variety of images to demonstrate the effectiveness of the proposed technique.
Cited by 6
Florian Maier, Andreas Wimmer, Grzegorz Soza et al. · Informatik aktuell · 2008
Cited by 11
Filip Malmberg, Camille Couprie, Leo Grady et al. · 2016
Many of the algorithms presented in the course are closely related. There have been attempts to investigate the theoretical relationship between the various methods. In this lecture, we will look at a theoretical framework that unifies many of the optimization methods that we have covered in this course. The framework is presented in the context of seeded image segmentation, but of course applies to other optimization problems as well. Power waterheds
Cited by 0
Motahare Soufi, Alireza Kamali‐Asl, Parham Geramifar et al. · Nuclear Medicine Communications · 2015
The proposed technique improves PET lesion delineation at different tumor sites. It depicts greater effectiveness in tumors with smaller size and/or low edge gradients, wherein most PET segmentation algorithms encounter serious challenges. Favorable execution time and accurate performance of the algorithm make it a great tool for clinical applications.
Cited by 4
Xiangxia Li, Bin Li, Hua Yin et al. · Journal of Healthcare Engineering · 2022
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.
Cited by 7Open access
Muhammad Mansoor, N. Nalini, Saumya Raj · 2023
To detect steatosis disease using region-based segmentation of GrowCut Algorithm and comparing with Random Walker Algorithm. Materials and Methods: It includes steatosis disease images, GrowCut algorithm and Random Walker Algorithm. The images were segmented using the GrowCut Algorithm and the accuracy of the segmentation was compared with the Random Walker algorithm. In the digital image processing lab, the segmentation procedure was carried out using MATLAB software, with a total sample size of 40 for the two groups and a sample quantity of 20. Results: It showed that the GrowCut Algorithm was more accurate than the Random Walker Algorithm for segmenting the steatosis disease images. Conclusion: The GrowCut Algorithm is a better method for segmenting the steatosis images than the Random Walker Algorithm. The GrowCut algorithm was more accurate than the Random Walker algorithm. As a result, the GrowCut Algorithm is suggested as a superior segmentation technique for pictures with steatosis.
Cited by 0
S. Subha, U. Kumaran · Multimedia Tools and Applications · 2024
Cited by 3
Gordon Stevenson, Sally Collins, Jane Ding et al. · Ultrasound in Medicine & Biology · 2015
Cited by 47
Ali Kemal Sinop, Leo Grady · 2007
In this work, we present a common framework for seeded image segmentation algorithms that yields two of the leading methods as special cases - The Graph Cuts and the Random Walker algorithms. The formulation of this common framework naturally suggests a new, third, algorithm that we develop here. Specifically, the former algorithms may be shown to minimize a certain energy with respect to either an 𝓁 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> or an 𝓁 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm. Here, we explore the segmentation algorithm defined by an 𝓁 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> norm, provide a method for the optimization and show that the resulting algorithm produces an accurate segmentation that demonstrates greater stability with respect to the number of seeds employed than either the Graph Cuts or Random Walker methods.
Cited by 280
Faezeh Fallah, Bin Yang, Fabian Bamberg · 2017
Automatic segmentation of distinct muscles is a crucial step for quantitative analysis of muscle's tissue properties. Magnetic resonance (MR) imaging provides a superior soft tissue contrast and noninvasive means for assessing muscular characteristics. However, automatic segmentation of muscles using common morphological MR imaging is very challenging as the intensities and textures of adjacent muscles are similar and the boundaries between them are mostly invisible or discontinuous. In this paper, we propose a novel fully automatic framework for 3D segmentation of muscles on water MR images. This framework generates the 3D average and probabilistic atlases of the targeted muscle to automatically define the labeled seeds, the edges weights, and the constraints of a constrained Random Walker algorithm. Also, the low-pass filtered atlas-derived muscle probability map is used to augment the intensities prior to the graph-based segmentation. This enables automatic localization of the targeted muscle and enforces dissimilarities between its intensities and the intensities of adjacent lean tissues. The proposed algorithm outperforms the original random Walker algorithm and the conventional multi-atlas registration for muscle segmentation and is less sensitive to errors in the manually segmented muscle masks used for training (atlas computation).
Cited by 5
Zhaohua Cui, Wenna Li, Gai Pan et al. · 2013
The random walker algorithm for image segmentation is an effective method. However, the traditional random walker algorithm (RW) is only determined by the adjacent node (pixels) intensity information in the feature space, and the algorithm does not take the spatial feature information of nodes (pixels) into consideration, which makes the segmentation results discrete in the spatial distribution. In this work, an improved random walker algorithm (SRW) has been proposed to improve the efficiency and accuracy of the extraction with complicate background. Firstly, spatial feature information has been employed to combine with the intensity information to measure weights between adjacent nodes (pixels). The freedom parameters have been then adjusted for the two features above to obtain the scale. Finally, the experimental result shows that the improved random walker algorithm (SRW) is effective in extracting objects contour with complicate background.
Cited by 2
A. V. Shukelovich, E. V. Snezhko, V. A. Kovalev et al. · SHILAP Revista de lepidopterología · 2016
A technique of marker set construction for muscle MRI segmentation using random walker approach is introduced. The possibility of clinician’s manual labor amount reduction and random walker algorithm optimization is studied.
Cited by 0Open access
Yin Cheng Qi, Ting Li, Ming Xiao Xi et al. · Applied Mechanics and Materials · 2014
The segmentation method of classical random walker needs to select seed points artificially, and may cause incomplete or inaccurate segmentation. In view of these problems, this paper presents an image positioning method of automatic random walker based on IFS (Intuitionistic Fuzzy Set). IFS edge detection method is used to get the edge information of image, and then the connected domains are found out in image edge using the morphology method. Select the central pixels of each connected domain as seed points for automatic random walker, and then segment the IFS edge image by random walker. Experimental results show that the proposed method overcomes the application limitation of semi-automatic random walker algorithm, and improves the accuracy of image positioning.
Cited by 0
Yong He, Yunlong Meng, Hui Gong et al. · PLoS ONE · 2014
Characterizing cytoarchitecture is crucial for understanding brain functions and neural diseases. In neuroanatomy, it is an important task to accurately extract cell populations' centroids and contours. Recent advances have permitted imaging at single cell resolution for an entire mouse brain using the Nissl staining method. However, it is difficult to precisely segment numerous cells, especially those cells touching each other. As presented herein, we have developed an automated three-dimensional detection and segmentation method applied to the Nissl staining data, with the following two key steps: 1) concave points clustering to determine the seed points of touching cells; and 2) random walker segmentation to obtain cell contours. Also, we have evaluated the performance of our proposed method with several mouse brain datasets, which were captured with the micro-optical sectioning tomography imaging system, and the datasets include closely touching cells. Comparing with traditional detection and segmentation methods, our approach shows promising detection accuracy and high robustness.
Cited by 21Open access
Javaheri, Pejvak, Lowman, Julian P. · Borealis · 2025
Supplemental dataset for the manuscript "A random walker algorithm for plate boundary detection in spherical mantle convection models and global geophysical datasets: application to Euler vector determination".
Cited by 1Open access
Yujian Zhang, Hechen Zhang · Highlights in Science Engineering and Technology · 2023
A random walk is known as a process that a random walker makes consecutive steps in space at equal intervals of time and the length and direction of each step is determined independently. It is an example of Markov processes, meaning that future movements of the random walker are independent of the past. The applications of random walks are quite popular in the field of mathematics, probability and computer science. Random walk related models can be used in different areas such as prediction, recommendation algorithm to recent supervised learning and networks. It is noticeable that there are few reviews about randoms for the beginners and how random walks are used nowadays in distinctive areas. Hence, the aim of the article is to provide a brief review of classical random walks, including basic concepts and models of the algorithm and then some applications in the field of computer science for the beginners to understand the significance and future of random walks.
Cited by 2Open access
M. Venkatesh Kumar, P. Kalamani, M. Chithambarathanu · 2015
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 guiding surgery. A Random Walker (RW) algorithm has been analyzed with seed initialization to perform (Prostate) cancer pinpoint using Magnetic Resonance Imaging (MRI). Segmentation can be done by using Random Walker (RW) algorithm which has to be considered to be a fastest method. Random Walker (RW) method can be used with multi-parametric magnetic resonance imaging (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 image 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 and fisher sign test can be also used to find the statistical differences.
Cited by 0
Manoj Kumar · International Journal of Research in Engineering and Technology · 2014
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 guiding surgery. A Random Walker (RW) algorithm has been analyzed with seed initialization to perform (Prostate) cancer pinpoint using Magnetic Resonance Imaging (MRI). Segmentation can be done by using Random Walker (RW) algorithm which has to be considered to be a fastest method. Random Walker (RW) method can be used with multi-parametric magnetic resonance imaging (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 image 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 and fisher sign test can be also used to find the statistical differences.
Cited by 0Open access
Hansang Kim, Youngbae Kim, Jae-Young Sim et al. · IEEE Transactions on Image Processing · 2015
A novel saliency detection algorithm for video sequences based on the random walk with restart (RWR) is proposed in this paper. We adopt RWR to detect spatially and temporally salient regions. More specifically, we first find a temporal saliency distribution using the features of motion distinctiveness, temporal consistency, and abrupt change. Among them, the motion distinctiveness is derived by comparing the motion profiles of image patches. Then, we employ the temporal saliency distribution as a restarting distribution of the random walker. In addition, we design the transition probability matrix for the walker using the spatial features of intensity, color, and compactness. Finally, we estimate the spatiotemporal saliency distribution by finding the steady-state distribution of the walker. The proposed algorithm detects foreground salient objects faithfully, while suppressing cluttered backgrounds effectively, by incorporating the spatial transition matrix and the temporal restarting distribution systematically. Experimental results on various video sequences demonstrate that the proposed algorithm outperforms conventional saliency detection algorithms qualitatively and quantitatively.
Cited by 151
Samuel Rota Bulò, Massimo Rabbi, Marcello Pelillo · 2011
In this paper we propose a novel approach to content-based image retrieval with relevance feedback, which is based on the random walker algorithm introduced in the context of interactive image segmentation. The idea is to treat the relevant and non-relevant images labeled by the user at every feedback round as “seed ” nodes for the random walker problem. The ranking score for each unlabeled image is computed as the probability that a random walker starting from that image will reach a relevant seed before encountering a non-relevant one. Our method is easy to implement, parameter-free and scales well to large datasets. Extensive experiments on different real datasets with several image similarity measures show the superiority of our method over different recent approaches.
Cited by 0