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This study investigates a novel technique of tissues segmentation of high-grade (HG) glioma. Segmentation of tumor and edema for treatment planning is crucial. Anisotropic diffusion filter removes the noise and preserves the tumor tissues in MRI images. K-mean clustering algorithm clusters the brain tissues in normal and tumor tissues. The healthy tissues surround tumor tissues. Hierarchical centroid...
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)...
This paper presents an automatic detection system capable of detecting an automobile dashboard with high accuracy. Since the structure of an automobile dashboard is quite different from general instruments, commonly used algorithms for instrument detection can hardly meet the accuracy and robustness. In this paper, a novel approach is presented to detect an automobile dashboard. The contour retrieving...
Image segmentation has always been an important research direction in the field of images processing, however, due to the long cycle of algorithm, the image segmentation techniques have never been widely applied. According to the problem above, a image segmentation algorithm of Gaussian Mixture Model (GMM) based on Map/Reduce is proposed to improve the real-time performance. Firstly, the architecture...
Brain tumour diagnosis is usually a vital use of medical image processing, where clustering technique commonly used with medical application especially regarding brain tumour diagnosis with magnetic resonance imaging (MRI). In this MRI has been considered because it provides accurate visualization of anatomical structure of tissues. The conventional mean shift technique utilizes radially symmetric...
Glaucoma is an eye disease that causes irreversible vision loss. Retinography is done manually by the ophthalmologist and is the cheapest, least invasive and most effective way to diagnose glaucoma. The ratio between the diameter of the outer part of the Optic Disc (OD) and the cup (internal part) called CDR (cup-to-disc ratio) is an important indicator of glaucoma presence in patients. This paper...
Infrared imaging technology has gained more attention and become an interesting method in electrical maintenance. This paper proposes a novel method for current transformer recognition and location in infrared images. K-means algorithm is used in current transformer segmentation while an expansion-corrosion algorithm is applied to denoise and eliminate small pieces of equipment such as power lines...
Exploitation and using of clean and renewable energy to improve the domestic energy structure is important for building smart city. Photovoltaic (PV) power generation is a good choose, and modules inspection is an important direction of PV power generation. The manual inspection is a common way but is not feasible for large-scale PV systems in practice due to low efficiency, high error rate and long...
Inspired by recent successful deep learning methods, this paper presents a new approach for polarimetric synthetic aperture radar (PolSAR) image classification. It combines both advantages of pixel-based and object-based methods. An improved simple linear iterative clustering (SLIC) superpixel segmentation algorithm is used to obtain spatial information in the PolSAR image. Then, a Deep Belief Network...
Detection of repetitive patterns in images is subject of several research papers. The majority of them deals with detection of lattice patterns of repetitive elements. However, there are many situations, when element's repetition doesn't follow any particular pattern. In this paper we focus on the following two objectives. Firstly, our algorithm detects repetitive elements regardless of their relative...
In the paper, a rough spatial kernelized fuzzy c-means clustering (RSKFCM) based medical image segmentation algorithm is proposed. This technique is a combination of rough set and spatial kernelized fuzzy c-means clustering (SKFCM). SKFCM is failed to remove the indistinct knowledge that is associated with each data set during the process of its assignment to a particular cluster. The rough set is...
In modern remote sensing procedures, one of the most important issues is to distinguish specific types of land coverage. Discrimination between different land coverages especially in metropolitan surveying is so important that the in front civilization projects are basically dependent to them. In this paper, an innovative image processing strategy is employed for distinguishing green lands from other...
A new color image segmentation of noisy images based on spatial information with the Generalized Dirichlet mixture model is presented. The methodology uses Markov Random Field distribution with a novel factor that is induced in mixture model. The model is learned using Expectation Maximization (EM) algorithm based on Newton-Raphson approach. The obtained results using real images are more encouraging...
The segmentation of multispectral images is considered as a key step in image processing for biomedical applications. Performing this step using the appropriate methodology is a real issue that being investigated by the research community. In this paper, we propose a new algorithm to perform automatic segmentation based on k-means methodology within an automatic generation of the optimal value of...
The presence of outliers, noise, corrupt pieces of data and great quantity of samples in a multispectral image, makes the segmentation analysis work tedious. The fuzzy clustering approach, specially, is susceptible to inhomogeneity of characteristics. Furthermore, many algorithms such us FCM, PFCM, FCC, FWCM and modification aim to solve these problems by integrating spacial information. This process...
Automatic segmentation in Ziehl-Neelsen Stained Tissue Slide Images is to help identify whether the blood cells that have been exposed to tuberculosis. In an image segmentation in the detection of TB disease are still many obstacles and requires in many time. in this study perform segmentation is useful to help detect the germs of TB disease in the blood cells and segmentation, there are several ways...
Data clustering methods have been used extensively for image segmentation in the past decade. In our previous work, we had established that combining the traditional clustering algorithms with a meta-heuristic like Firefly Algorithm improves the stability of the output as well as the speed of convergence. In this paper, we have replaced the Euclidean distance formula with kernels. We have combined...
In a large-scale indoor environment, a mobile robot needs a proper internal representation of the surrounding environment to carry out its tasks. The metric (grid-based) map and topological map are two common internal representations in robotic realm. In order to take advantage of the two kinds of environmental representations, this paper aims to construct a topological map of an indoor environment...
This paper studies the segmentation methods by analyzing the threshold method of rubbings. Firstly, OTSU and fast 2 dimensional OTSU threshold segmentation algorithms are presented, and the segmentation effect of a given image is analyzed. The limitation of OTSU, and fast 2 dimensional OTSU segmentation algorithm are explained. Then the segmentation algorithm is given in conjunction with the histogram,...
We present an improved version of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Unlike SLIC, our algorithm is non-iterative, enforces connectivity from the start, requires lesser memory, and is faster. Relying on the superpixel boundaries obtained using our algorithm, we also present a polygonal partitioning algorithm. We demonstrate that our superpixels as well as the polygonal...
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