scholarly journals SMBFT: A Modified Fuzzy c -Means Algorithm for Superpixel Generation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhen Yu ◽  
Cuihuan Tian ◽  
Shiyong Ji ◽  
Benzheng Wei ◽  
Yilong Yin
Keyword(s):  
Clustering Algorithm ◽  
Spatial Information ◽  
Fuzzy Theory ◽  
Image Data ◽  
Natural Images ◽  
Data Sets ◽  
Mr Images ◽  
Negative Effects ◽  
Maximum Probability ◽  
Segmentation Methods

Most traditional superpixel segmentation methods used binary logic to generate superpixels for natural images. When these methods are used for images with significantly fuzzy characteristics, the boundary pixels sometimes cannot be correctly classified. In order to solve this problem, this paper proposes a Superpixel Method Based on Fuzzy Theory (SMBFT), which uses fuzzy theory as a guide and traditional fuzzy c -means clustering algorithm as a baseline. This method can make full use of the advantage of the fuzzy clustering algorithm in dealing with the images with the fuzzy characteristics. Boundary pixels which have higher uncertainties can be correctly classified with maximum probability. The superpixel has homogeneous pixels. Meanwhile, the paper also uses the surrounding neighborhood pixels to constrain the spatial information, which effectively alleviates the negative effects of noise. The paper tests on the images from Berkeley database and brain MR images from the Brain web. In addition, this paper proposes a comprehensive criterion to measure the weights of two kinds of criterions in choosing superpixel methods for color images. An evaluation criterion for medical image data sets employs the internal entropy of superpixels which is inspired by the concept of entropy in the information theory. The experimental results show that this method has superiorities than traditional methods both on natural images and medical images.

scholarly journals Data Fusion Using a Multi-Sensor Sparse-Based Clustering Algorithm

2020 ◽  
Vol 12 (23) ◽  
pp. 4007
Author(s):  
Kasra Rafiezadeh Shahi ◽  
Pedram Ghamisi ◽  
Behnood Rasti ◽  
Robert Jackisch ◽  
Paul Scheunders ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Spatial Information ◽  
Clustering Algorithms ◽  
Hyperspectral Data ◽  
Sensor Data ◽  
Data Sets ◽  
Data Types ◽  
Data Set ◽  
Multiple Data Sets ◽  
Imaging Sensors

The increasing amount of information acquired by imaging sensors in Earth Sciences results in the availability of a multitude of complementary data (e.g., spectral, spatial, elevation) for monitoring of the Earth’s surface. Many studies were devoted to investigating the usage of multi-sensor data sets in the performance of supervised learning-based approaches at various tasks (i.e., classification and regression) while unsupervised learning-based approaches have received less attention. In this paper, we propose a new approach to fuse multiple data sets from imaging sensors using a multi-sensor sparse-based clustering algorithm (Multi-SSC). A technique for the extraction of spatial features (i.e., morphological profiles (MPs) and invariant attribute profiles (IAPs)) is applied to high spatial-resolution data to derive the spatial and contextual information. This information is then fused with spectrally rich data such as multi- or hyperspectral data. In order to fuse multi-sensor data sets a hierarchical sparse subspace clustering approach is employed. More specifically, a lasso-based binary algorithm is used to fuse the spectral and spatial information prior to automatic clustering. The proposed framework ensures that the generated clustering map is smooth and preserves the spatial structures of the scene. In order to evaluate the generalization capability of the proposed approach, we investigate its performance not only on diverse scenes but also on different sensors and data types. The first two data sets are geological data sets, which consist of hyperspectral and RGB data. The third data set is the well-known benchmark Trento data set, including hyperspectral and LiDAR data. Experimental results indicate that this novel multi-sensor clustering algorithm can provide an accurate clustering map compared to the state-of-the-art sparse subspace-based clustering algorithms.

scholarly journals Combination of Color and Focus Segmentation for Medical Images with Low Depth-of-Field

2018 ◽  
Vol 4 (1) ◽  
pp. 345-349
Author(s):  
Tamara Wirth ◽  
Ady Naber ◽  
Werner Nahm
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Medical Images ◽  
Image Data ◽  
Image Understanding ◽  
Surgical Instruments ◽  
Second Step ◽  
Depth Of Field ◽  
Segmentation Methods ◽  
Edge Based

AbstractImage segmentation plays an increasingly important role in image processing. It allows for various applications including the analysis of an image for automatic image understanding and the integration of complementary data. During vascular surgeries, the blood flow in the vessels has to be checked constantly, which could be facilitated by a segmentation of the affected vessels. The segmentation of medical images is still done manually, which depends on the surgeon’s experience and is time-consuming. As a result, there is a growing need for automatic image segmentation methods. We propose an unsupervised method to detect the regions of no interest (RONI) in intraoperative images with low depth-of-field (DOF). The proposed method is divided into three steps. First, a color segmentation using a clustering algorithm is performed. In a second step, we assume that the regions of interest (ROI) are in focus whereas the RONI are unfocused. This allows us to segment the image using an edge-based focus measure. Finally, we combine the focused edges with the color RONI to determine the final segmentation result. When tested on different intraoperative images of aneurysm clipping surgeries, the algorithm is able to segment most of the RONI not belonging to the pulsating vessel of interest. Surgical instruments like the metallic clips can also be excluded. Although the image data for the validation of the proposed method is limited to one intraoperative video, a proof of concept is demonstrated.

scholarly journals Implementation and evaluation of segmentation algorithms according to multimodal imaging in personalized medicine

2017 ◽  
Vol 3 (2) ◽  
pp. 207-210
Author(s):  
Manuel Stich ◽  
Jeannine Vogt ◽  
Michaela Lindner ◽  
Ralf Ringler
Keyword(s):  
Personalized Medicine ◽  
Multimodal Imaging ◽  
Medical Image ◽  
Current Trend ◽  
Image Data ◽  
Surrounding Tissue ◽  
Artificial Noise ◽  
Data Sets ◽  
Segmentation Methods ◽  
Medical Image Data

AbstractMultimodal imaging is gaining in importance in the field of personalized medicine and can be described as a current trend in medical imaging diagnostics. The segmentation, classification and analysis of tissue structures is essential. The goal of this study is the evaluation of established segmentation methods on different medical image data sets acquired with different diagnostic procedures. Established segmentation methods were implemented using the latest state of the art and applied to medical image data sets. In order to benchmark the segmentation performance quantitatively, medical image data sets were superimposed with artificial Gaussian noise, and the mis-segmentation as a function of the image SNR or CNR was compared to a gold standard. The evaluation of the image segmentation showed that the best results of pixel-based segmentation (< 3%) can be achieved with methods of machine learning, multithreshold and advanced level-set method - even at high artificial noise (SNR< 18). Finally, the complexity of the object geometry and the contrast of the ROI to the surrounding tissue must also be considered to select the best segmentation algorithm.

scholarly journals Spatial Modification in the Parameters of Mountain Image Clustering Algorithm

2019 ◽  
Vol 22 (1) ◽  
pp. 55-58
Author(s):  
Nahla Ibraheem Jabbar
Keyword(s):  
Clustering Algorithm ◽  
Spatial Information ◽  
Clustering Algorithms ◽  
Large Data ◽  
Large Data Sets ◽  
Image Clustering ◽  
Optimum Number ◽  
Data Sets ◽  
Number Of Clusters ◽  
Pixel Value

Our proposed method used to overcome the drawbacks of computing values parameters in the mountain algorithm to image clustering. All existing clustering algorithms are required values of parameters to starting the clustering process such as these algorithms have a big problem in computing parameters. One of the famous clustering is a mountain algorithm that gives expected number of clusters, we presented in this paper a new modification of mountain clustering called Spatial Modification in the Parameters of Mountain Image Clustering Algorithm. This modification in the spatial information of image by taking a window mask for each center pixel value to compute distance between pixel and neighborhood for estimation the values of parameters σ, β that gives a potential optimum number of clusters requiring in image segmentation process. Our experiments show ability the proposed algorithm in image brain segmentation with a quality in the large data sets

Automatic Location of the Talairach Cortical Landmarks from T2-Weighted MR Images

2011 ◽  
Vol 467-469 ◽  
pp. 629-634
Author(s):  
Yi Li Fu ◽  
Guang Cai Zhang ◽  
Qiu Yue Chang ◽  
Shu Guo Wang ◽  
Xian Wei Han
Keyword(s):  
Clustering Algorithm ◽  
Brain Atlas ◽  
Watershed Algorithm ◽  
Data Sets ◽  
Gray Level ◽  
Region Merging ◽  
Mr Images ◽  
Automatic Location ◽  
Fuzzy C Means Clustering ◽  
The Mean

For labeling the T2-weighted MR images using human brain atlas, it is prerequisite to the foundation of the Talairach space for T2W MR images, and the basic condition to found Talairach space is the location of Talairach cortical landmarks from T2W MR images. A method to locate the Talairach cortical landmarks from T2W MR images is proposed, it consists of three aspects: Firstly, determine the planes including the six cortical landmarks ; segment the planes based on fuzzy C-means clustering algorithm, gray level projection, watershed algorithm, region merging, thresholding, and morphologic operations; locate the cortical landmarks from the segmented planes. The algorithm has been validated quantitatively with 20 T2W MR images data sets. The mean errors of the Talairach cortical landmarks were below 1.00 mm. It took about 8 seconds for identifying them on P4 3.0 GHz. This fast, robust algorithm is potentially useful in clinic and for research.

User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

2020 ◽  
Vol 13 (5) ◽  
pp. 641-649
Author(s):  
Yuancheng Li ◽  
Yaqi Cui ◽  
Xiaolong Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Clustering Algorithm ◽  
Characteristic Curve ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Residential Areas ◽  
Processing Power ◽  
Learning Machine ◽  
Advanced Metering ◽  
Matlab Programming

Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. Methods: In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM. Results: Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data. Conclusion: The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.

scholarly journals Intelligent vehicle modeling design based on image processing

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142199334
Author(s):  
Guangchao Zhang ◽  
Junrong Liu
Keyword(s):  
Image Data ◽  
Front Face ◽  
Intelligent Vehicle ◽  
Surface Model ◽  
Data Sets ◽  
Face Modeling ◽  
Shape Coefficient ◽  
Automotive Market ◽  
Modeling Analysis ◽  
Family Analysis

With the urgent demand of consumers for diversified automobile modeling, simple, efficient, and intelligent automobile modeling analysis and modeling method is an urgent problem to be solved in current automobile modeling design. The purpose of this article is to analyze the modeling preference and trend of the current automobile market in time, which can assist the modeling design of new models of automobile main engine factories and strengthen their branding family. Intelligent rapid modeling shortens the current modeling design cycle, so that the product rapid iteration is to occupy an active position in the automotive market. In this article, aiming at the family analysis of automobile front face, the image database of automobile front face modeling analysis was created. The database included two data sets of vehicle signs and no vehicle signs, and the image data of vehicle front face modeling of most models of 22 domestic mainstream brands were collected. Then, this article adopts the image classification processing method in computer vision to conduct car brand classification training on the database. Based on ResNet-8 and other model architectures, it trains and classifies the intelligent vehicle brand classification database with and without vehicle label. Finally, based on the shape coefficient, a 3D wireframe model and a curved surface model are obtained. The experimental results show that the 3D curve model can be obtained based on a single image from any angle, which greatly shortens the modeling period by 92%.

Revisiting the K-nn algorithm for obstetric image segmentation

2021 ◽  
pp. 1-13
Author(s):  
P. Salgado ◽  
T.-P. Azevedo Perdicoúlis
Keyword(s):  
Magnetic Resonance Image ◽  
Medical Image ◽  
Clustering Algorithm ◽  
Ultrasound Image ◽  
Well Being ◽  
Health Conditions ◽  
Medical Applications ◽  
Training Set ◽  
Segmentation Methods ◽  
Segmentation Algorithms

Medical image techniques are used to examine and determine the well-being of the foetus during pregnancy. Digital image processing (DIP) is essential to extract valuable information embedded in most biomedical signals. After, intelligent segmentation methods, based on classifier algorithms, must be applied to identify structures and relevant features from previous data. The success of both is essential for helping doctors to identify adverse health conditions from the medical images. To obtain easy and reliable DIP methods for foetus images in real-time, at different gestational ages, aware pre-processing needs to be applied to the images. Thence, some data features are extracted that are meant to be used as input to the segmentation algorithms presented in this work. Due to the high dimension of the problems in question, assemblage of the data is also desired. The segmentation of the images is done by revisiting the K-nn algorithm that is a conventional nonparametric classifier. Besides its simplicity, its power to accomplish high classification results in medical applications has been demonstrated. In this work two versions of this algorithm are presented (i) an enhancement of the standard version by aggregating the data apriori and (ii) an iterative version of the same method where the training set (TS) is not static. The procedure is demonstrated in two experiments, where two images of different technologies were selected: a magnetic resonance image and an ultrasound image, respectively. The results were assessed by comparison with the K-means clustering algorithm, a well-known and robust method for this type of task. Both described versions showed results close to 100% matching with the ones obtained by the validation method, although the iterative version displays much higher reliability in the classification.

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