Multi-Channel Mapping Image Segmentation Method Based on LDA

2020 ◽  
pp. 2154012
Author(s):  
Qiu-Xia Hu ◽  
Jie Tian ◽  
Dong-Jian He
Keyword(s):  
Plant Disease ◽  
Experimental Result ◽  
Cluster Center ◽  
Segmentation Method ◽  
Linear Discriminant ◽  
Linear Discrimination ◽  
Initial Cluster ◽  
Segmentation Accuracy ◽  
Eigen Values ◽  
Better Than

In order to improve the segmentation accuracy of plant lesion images, multi-channels segmentation algorithm of plant disease image was proposed based on linear discriminant analysis (LDA) method’s mapping and K-means’ clustering. Firstly, six color channels from RGB model and HSV model were obtained, and six channels of all pixels were laid out to six columns. Then one of these channels was regarded as label and the others were regarded as sample features. These data were grouped for linear discrimination analysis, and the mapping values of the other five channels were applied to the eigen vector space according to the first three big eigen values. Secondly, the mapping value was used as the input data for K-means and the points with minimum and maximum pixel values were used as the initial cluster center, which overcame the randomness for selecting the initial cluster center in K-means. And the segmented pixels were changed into background and foreground, so that the proposed segmentation method became the clustering of two classes for background and foreground. Finally, the experimental result showed that the segmentation effect of the proposed LDA mapping-based method is better than those of K-means, ExR and CIVE methods.

DEGENERATIVE DISC SEGMENTATION AND DIAGNOSIS TECHNOLOGY USING IMPORTANT FEATURES FROM MRI OF SPINE IN IMAGES

2014 ◽  
Vol 26 (04) ◽  
pp. 1440008
Author(s):  
Ming-Chi Wu ◽  
Yu-Liang Kuo ◽  
Chen-Wei Chen ◽  
Cheng-An Fang ◽  
Chiun-Li Chin ◽  
...  
Keyword(s):  
Threshold Value ◽  
Experimental Result ◽  
Average Height ◽  
Common People ◽  
Automated Method ◽  
Segmentation Accuracy ◽  
Spine Diseases ◽  
Medical Reports ◽  
Standard Models ◽  
Better Than

In this paper, we focus on the medical imaging segmentation techniques which are used in the study of spine diseases. In the medical reports, it is shown that common people worry more about the spine diseases caused by the disc degeneration. Because of the complex composition of the spine, which includes the spine bones, cartilage, fat, water and soft tissue, it is hard to correctly and easily find out the position of each cartilage in the spine images. This above problem always causes over-segmentation or unability to extract the cartilages. Thus, we propose an accurate and automated method to detect the abnormal disc. We combine two standard models with the threshold value to accurately identify the cartilage. Among the processing, we also solve the noising problems of spine image through morphological methods, removing the noncartilage areas using our proposed method, and find out the average height of the cartilages. Therefore, we can easily determine whether the disc is degenerated or not. In the experimental result, the segmentation accuracy of the extracted region by the proposed approach is evaluated by two criterions. The first criterion is statistical evaluation indices of image segmentation. It is evaluated by professional physician's manual segmentation, and the results show that our proposed method is easily implemented and has high accuracy, with the highest rate reaching 99.88%. The second criterion is a comparison evaluation index evaluated by our proposed system and other existence system. From this result, we know that our proposed system is better than other existence system.

Image Segmentation Method Based on Improved Genetic Algorithm and Fuzzy Clustering

2010 ◽  
Vol 143-144 ◽  
pp. 379-383 ◽  
Author(s):  
Jing Zhang ◽  
Xiang Zhang ◽  
Jie Zhang
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Cluster Center ◽  
Adaptive Genetic Algorithm ◽  
Segmentation Method ◽  
Improved Genetic Algorithm ◽  
Advantages And Disadvantages ◽  
Initial Cluster ◽  
Important Means

Image segmentation is an important means of the implementation of image analysis. The existing segmentation methods have their own advantages and disadvantages in segmentation time and segmentation effect. Image segmentation based on fuzzy clustering and genetic algorithm is studied. An adaptive genetic algorithm is improved, the crossover rate and mutation rate are optimized, and a new adaptive operator is adopted to achieve a non-linear adaptive adjustment. A new combined image segmentation means is presented, in which the genetic algorithm is adopted to optimize the initial cluster center and then the fuzzy clustering is used for image segmentation. The practice proves that this image segmentation method and algorithm is superior to the traditional one, which improves the segmentation performance and the segmentation effect.

scholarly journals Cloud and Snow Segmentation in Satellite Images Using an Encoder–Decoder Deep Convolutional Neural Networks

2021 ◽  
Vol 10 (7) ◽  
pp. 462
Author(s):  
Kai Zheng ◽  
Jiansheng Li ◽  
Lei Ding ◽  
Jianfeng Yang ◽  
Xucheng Zhang ◽  
...  
Keyword(s):  
Satellite Images ◽  
Segmentation Method ◽  
Deep Convolutional Neural Networks ◽  
Segmentation Accuracy ◽  
Decoder Architecture ◽  
Spatial Pyramid Pooling ◽  
The Relationship ◽  
Th 1 ◽  
Spatial Pyramid ◽  
Better Than

The segmentation of cloud and snow in satellite images is a key step for subsequent image analysis, interpretation, and other applications. In this paper, a cloud and snow segmentation method based on a deep convolutional neural network (DCNN) with enhanced encoder–decoder architecture—ED-CNN—is proposed. In this method, the atrous spatial pyramid pooling (ASPP) module is used to enhance the encoder, while the decoder is enhanced with the fusion of features from different stages of the encoder, which improves the segmentation accuracy. Comparative experiments show that the proposed method is superior to DeepLabV3+ with Xception and ResNet50. Additionally, a rough-labeled dataset containing 23,520 images and fine-labeled data consisting of 310 images from the TH-1 satellite are created, where we studied the relationship between the quality and quantity of labels and the performance of cloud and snow segmentation. Through experiments on the same network with different datasets, we found that the cloud and snow segmentation performance is related more closely to the quantity of labels rather than their quality. Namely, under the same labeling consumption, using rough-labeled images only performs better than rough-labeled images plus 10% fine-labeled images.

scholarly journals IKM-NCS: A Novel Clustering Scheme Based on Improved K-Means Algorithm

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Clustering Algorithm ◽  
User Behavior ◽  
Abnormal Behavior ◽  
Cluster Center ◽  
Initial Sample ◽  
Initial Cluster ◽  
Weight Calculation ◽  
Data Points ◽  
Abnormal Behaviors ◽  
Better Than

Aiming at the problems of distorted center selection and slow iteration convergence in traditional clustering analysis algorithm, a novel clustering scheme based on improved k-means algorithm is proposed. In this paper, based on the analysis of all user behavior sets contained in the initial sample, a weight calculation method for abnormal behaviors and an eigenvalue extraction method for abnormal behavior set are proposed and a set of abnormal behaviors is constructed for each user according to the behavior data generated by abnormal users. Then, on the basis of the traditional k-means clustering algorithm, an improved algorithm is proposed. By calculating the compactness of all data points and selecting the initial cluster center among the data points with high and low compactness, the clustering performance is enhanced. Finally, the eigenvalues of the abnormal behavior set are used as the input of the algorithm to output the clustering results of the abnormal behavior. Experimental results show that the clustering performance of this algorithm is better than the traditional clustering algorithm, and can effectively improve the clustering performance of abnormal behavior

Segmentation In Medical Resonance images to extract the cancerous nodule for early diagnosis on cancer

2012 ◽  
Vol 3 (2) ◽  
pp. 253-255
Author(s):  
Raman Brar
Keyword(s):  
Human Lung ◽  
Research Work ◽  
Vital Role ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Background Segmentation ◽  
Lung Mri

Image segmentation plays a vital role in several medical imaging programs by assisting the delineation of physiological structures along with other parts. The objective of this research work is to segmentize human lung MRI (Medical resonance Imaging) images for early detection of cancer.Watershed Transform Technique is implemented as the Segmentation method in this work. Some comparative experiments using both directly applied watershed algorithm and after marking foreground and computed background segmentation methods show the improved lung segmentation accuracy in some image cases.

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

Identification of Essential Proteins in Yeast Using Mean Weighted Average and Recursive Feature Elimination

2019 ◽  
Vol 12 (1) ◽  
pp. 5-10 ◽  
Author(s):  
Sivagnanam Rajamanickam Mani Sekhar ◽  
Siddesh Gaddadevara Matt ◽  
Sunilkumar S. Manvi ◽  
Srinivasa Krishnarajanagar Gopalalyengar
Keyword(s):  
Drug Design ◽  
Weighted Average ◽  
Living Organism ◽  
Experimental Result ◽  
Recursive Feature Elimination ◽  
Protein Interaction Data ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
Result Show ◽  
Better Than

Background: Essential proteins are significant for drug design, cell development, and for living organism survival. A different method has been developed to predict essential proteins by using topological feature, and biological features. Objective: Still it is a challenging task to predict essential proteins effectively and timely, as the availability of protein protein interaction data depends on network correctness. Methods: In the proposed solution, two approaches Mean Weighted Average and Recursive Feature Elimination is been used to predict essential proteins and compared to select the best one. In Mean Weighted Average consecutive slot data to be taken into aggregated count, to get the nearest value which considered as prescription for the best proteins for the slot, where as in Recursive Feature Elimination method whole data is spilt into different slots and essential protein for each slot is determined. Results: The result shows that the accuracy using Recursive Feature Elimination is at-least nine percentages superior when compared to Mean Weighted Average and Betweenness centrality. Conclusion: Essential proteins are made of genes which are essential for living being survival and drug design. Different approaches have been proposed to anticipate essential proteins using either experimental or computation methods. The experimental result show that the proposed work performs better than other approaches.

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

scholarly journals A Method of Fast Segmentation for Banana Stalk Exploited Lightweight Multi-Feature Fusion Deep Neural Network

Machines ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 66
Author(s):  
Tianci Chen ◽  
Rihong Zhang ◽  
Lixue Zhu ◽  
Shiang Zhang ◽  
Xiaomin Li
Keyword(s):  
Neural Network ◽  
Execution Time ◽  
Deep Neural Network ◽  
Feature Fusion ◽  
Frame Rate ◽  
Light Conditions ◽  
Segmentation Method ◽  
Segmentation Accuracy ◽  
Automatic Picking ◽  
Changing Light

In an orchard environment with a complex background and changing light conditions, the banana stalk, fruit, branches, and leaves are very similar in color. The fast and accurate detection and segmentation of a banana stalk are crucial to realize the automatic picking using a banana picking robot. In this paper, a banana stalk segmentation method based on a lightweight multi-feature fusion deep neural network (MFN) is proposed. The proposed network is mainly composed of encoding and decoding networks, in which the sandglass bottleneck design is adopted to alleviate the information a loss in high dimension. In the decoding network, a different sized dilated convolution kernel is used for convolution operation to make the extracted banana stalk features denser. The proposed network is verified by experiments. In the experiments, the detection precision, segmentation accuracy, number of parameters, operation efficiency, and average execution time are used as evaluation metrics, and the proposed network is compared with Resnet_Segnet, Mobilenet_Segnet, and a few other networks. The experimental results show that compared to other networks, the number of network parameters of the proposed network is significantly reduced, the running frame rate is improved, and the average execution time is shortened.

scholarly journals Numerical Study of Pressure Attenuation Effect on Tunnel Structures Subjected to Blast Loads

2021 ◽  
Vol 11 (12) ◽  
pp. 5646
Author(s):  
Cheng-Wei Hung ◽  
Ying-Kuan Tsai ◽  
Tai-An Chen ◽  
Hsin-Hung Lai ◽  
Pin-Wen Wu
Keyword(s):  
Numerical Simulation ◽  
Orifice Plate ◽  
Experimental Result ◽  
Scale Model ◽  
Pressure Reduction ◽  
Expansion Chamber ◽  
Pressure Relief ◽  
Attenuation Effect ◽  
The One ◽  
Better Than

This study used experimental and numerical simulation methods to discuss the attenuation mechanism of a blast inside a tunnel for different forms of a tunnel pressure reduction module under the condition of a tunnel near-field explosion. In terms of the experiment, a small-scale model was used for the explosion experiments of a tunnel pressure reduction module (expansion chamber, one-pressure relief orifice plate, double-pressure relief orifice plate). In the numerical simulation, the pressure transfer effect was evaluated using the ALE fluid–solid coupling and mapping technique. The findings showed that the pressure attenuation model changed the tunnel section to diffuse, reduce, or detour the pressure transfer, indicating the blast attenuation effect. In terms of the effect of blast attenuation, the double-pressure relief orifice plate was better than the one-pressure relief orifice plate, and the single-pressure relief orifice plate was better than the expansion chamber. The expansion chamber attenuated the blast by 30%, the one-pressure relief orifice plate attenuated the blast by 51%, and the double-pressure relief orifice plate attenuated the blast by 82%. The blast attenuation trend of the numerical simulation result generally matched that of the experimental result. The results of this study can provide a reference for future protective designs and reinforce the U.S. Force regulations.

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