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Local community detection (or local clustering) is of fundamental importance in large network analysis. Random walk based methods have been routinely used in this task. Most existing random walk methods are based on the single-walker model. However, without any guidance, a single-walker may not be adequate to effectively capture the local cluster. In this paper, we study a multi-walker chain (MWC)...
Symmetric non-negative matrix factorization (SymNMF) has important applications in data analytics problems such as document clustering, community detection and image segmentation. In this paper, we propose a novel nonconvex variable splitting method for solving SymNMF. Different from the existing works, we prove that the algorithm converges to the set of Karush-Kuhn-Tucker (KKT) points of the nonconvex...
Superpixel segmentation refers to represent an image by small regions composed of pixels with the similar characteristics, which can carry more perceptual and semantic meaning than their simple pixel grid counterparts. Therefore, it is very important to distribute superpixels with different size over an image for describing image details. In this paper, a new superpixel generation method based on...
Segmentation of dynamic PET images is often needed to extract the time activity curve (TAC) of regions. While clustering methods have been proposed to segment the PET sequence, they are generally either sensitive to initial conditions or favor convex shaped clusters. Recently, we have proposed a deterministic and automatic spectral clustering method (AD-KSC) of PET images. It has the advantage of...
Brain-tumor segmentation method is an important clinical requirement for the brain-tumor diagnosis and the radiotherapy planning. But the number of clusters is very difficult to define for high diversity in the appearance of tumor tissue among the different patients and the ambiguous boundaries about the lesions. In our study, the nonparametric mixture of Dirichlet process (MDP) model is used to segment...
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields such as satellite, remote sensing, object identification, face tracking and most importantly medical applications. Here in this paper, we here supposed to propose a novel image segmentation using iterative partitioning mean shift clustering algorithm, which overcomes the drawbacks of conventional...
Image Segmentation is essential and challenging to visualize the tissue of human for analyzing the MR images. In brain MR images, the boundary of tumor tissue is highly irregular. Deformable models and Region based methods are extensively used for medical image segmentation, to locate the boundary of the tumor. Problems associated with non-linear distribution of real data, User interaction and poor...
Clustering is an important facet of explorative data mining and finds extensive use in several fields. In this paper, we propose an extension of the classical Fuzzy C-Means clustering algorithm. The proposed algorithm, abbreviated as VFC, adopts a multi-dimensional membership vector for each data point instead of the traditional, scalar membership value defined in the original algorithm. The membership...
Co-segmentation of 3D shapes has been receiving increasing attention, and treated as clustering problem in a descriptor space by a few unsupervised approaches to achieve proper co-segmentation of shapes with large variability. However, most of the existing algorithms are performed on segment level and heavily dependent on the per-object segmentation. Accordingly, we propose a co-segmentation method...
A novel clustering algorithm called Immune Memetic Clustering Algorithm (IMCA) is proposed in the paper. IMCA combines Immune Clone Selection and Memetic algorithm; Two populations are used in the evolutionary process. Clone reproduction and selection, Memetic mutation, crossover, individual learning and selection are adopted to evolve the two populations. After watershed proceeding, extracting the...
C-means clustering algorithms have proven effective for image segmentation, but are limited by the following aspects: 1) the determination of a priori number of clusters. If the number of clusters can be incorrectly determined, a good-quality segmented image cannot be assured; 2) the poor real-time performances due to great time-consuming, and 3) the poor typicality of each cluster represented by...
This paper deals with the implementation of Simple Algorithm for detection of range and shape of tumor in brain MR images. Tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and they have different Characteristics and different treatment. As it is known, brain tumor is inherently serious and life-threatening because of its character in the limited space...
Image segmentation is typically used to locate objects and boundaries in images. The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image. K-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean...
A novel image segmentation method based on modified fuzzy c-means (FCM) is proposed in this paper. Using the neighborhood pixels selectively, a spatial constraint punishment term is added in the objective function of standard FCM to improve the robust to noise, raise the convergent rate and reduce computing time. Experiments on standard image segmentation prove the availability of the improvement.
A mode seeking algorithm not only can automatically find mode of density of a given data but also can be used for data clustering. However, finding the mode of all data points produce redundant computations. In this paper, a simultaneous mode seeking and clustering which is called Generalized Transport Mean Shift (GTMS) algorithm was proposed. An idea of transportation was used for remedying the problem...
Gaussian Mixture Models (GMMs) have interesting properties that make them useful for many different image applications because they have powerful probabilistic statistical theory basis. However, the application of GMMs to medical image segmentation faces some difficulties. First, many typical model selection criterions become invalid when they estimate the number of components of medical images. Second,...
Artificial neural networks (ANN) and fuzzy systems are the widely preferred artificial intelligence techniques for biological computational applications. While ANN is less accurate than fuzzy logic systems, fuzzy theory needs expertise knowledge to guarantee high accuracy. Since both the methodologies possess certain advantages and disadvantages, it is primarily important to compare and contrast these...
This paper introduces a new formula for the objective function of the famous fuzzy C-means algorithm. Two weighted terms are added to the objective function to reflect any available information about the class center and class pixels distribution throughout the datasets. The algorithm is evaluated for the task of the segmentation of medical MRI brain volume. The results show that the algorithm has...
TurSOM is a novel self-organizing map algorithm with the capability of connection reorganization, not just neuron reorganization. This behavior facilitates the ability to map distinct patterns in a given input space. Multiple networks exist, and operate independently. This work presents an application driven approach, based on the theoretical and empirical work of previous TurSOM experiments. TurSOM...
Recognition and mining (RM) applications are an emerging class of computing workloads that will be commonly executed on future multi-core and many-core computing platforms. The explosive growth of input data and the use of more sophisticated algorithms in RM applications will ensure, for the foreseeable future, a significant gap between the computational needs of RM applications and the capabilities...
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