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Remote Sensing Image Retrieval Based on 3D-local ternary pattern(LTP)Features and Non-subsampled Shearlet Transform(NSST)Domain Statistical Features

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Computer Modeling in Engineering & Sciences 2022年第4期131卷 137-164页

作者： Hilly Gohain Baruah Vijay Kumar Nath Deepika Hazarika Department of Electronics and Communication Engineering Tezpur UniversityAssam784028India

With the increasing popularity of high-resolution remote sensing images,the remote sensing image retrieval(RSIR)has always been a topic of major issue.A combined,global non-subsampled shearlet transform(NSST)-domain statistical features(NSSTds)and local three dimensional local ternary pattern(3D-LTP)features,is proposed for high-resolution remote sensing *** model the NSST image coefficients of detail subbands using 2-state laplacian mixture(LM)distribution and its three parameters are estimated using Expectation-Maximization(EM)*** also calculate the statistical parameters such as subband kurtosis and skewness from detail subbands along with mean and standard deviation calculated from approximation subband,and concatenate all of them with the 2-state LM parameters to describe the global features of the *** various properties of NSST such as multiscale,localization and flexible directional sensitivity make it a suitable choice to provide an effective approximation of an *** order to extract the dense local features,a new 3D-LTP is proposed where dimension reduction is performed via selection of‘uniform’*** 3D-LTP is calculated from spatial RGB planes of the input *** proposed inter-channel 3D-LTP not only exploits the local texture information but the color information is captured ***,a fused feature representation(NSSTds-3DLTP)is proposed using new global(NSSTds)and local(3D-LTP)features to enhance the discriminativeness of *** retrieval performance of proposed NSSTds-3DLTP features are tested on three challenging remote sensing image datasets such as WHU-RS19,Aerial Image Dataset(AID)and patternNet in terms of mean average precision(MAP),average normalized modified retrieval rank(ANMRR)and precision-recall(P-R)*** experimental results are encouraging and the NSSTds-3DLTP features leads to superior retrieval performance compared to many well known existing descriptors such as Gabor RGB,Granulometry,loc

关键词： Remote sensing image retrieval laplacian mixture model local ternary pattern statistical modeling,KS test,texture,global features

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Ultrasound liver tumor segmentation using adaptively regularized kernel-based fuzzy C means with enhanced level set algorithm

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International Journal of Intelligent Computing and Cybernetics 2022年第3期15卷 438-453页

作者： Deepak S.Uplaonkar Virupakshappa Nagabhushan Patil Research Scholar in Electrical and Electronics Engineering Department Poojya Doddappa Appa College of EngineeringKalaburagiIndia Computer Science and Engineering Department Sharnbasva UniversityKalaburagiIndia Electrical and Electronics Engineering Department Poojya Doddappa Appa College of EngineeringKalaburagiIndia

Purpose-The purpose of this study is to develop a hybrid algorithm for segmenting tumor from ultrasound images of the ***/methodology/approach-After collecting the ultrasound images,contrast-limited adaptive histogram equalization approach(CLAHE)is applied as preprocessing,in order to enhance the visual quality of the images that helps in better ***,adaptively regularized kernel-based fuzzy C means(ARKFCM)is used to segment tumor from the enhanced image along with local ternary pattern combined with selective level set ***-The proposed segmentation algorithm precisely segments the tumor portions from the enhanced images with lower computation *** proposed segmentation algorithm is compared with the existing algorithms and ground truth values in terms of Jaccard coefficient,dice coefficient,precision,Matthews correlation coefficient,f-score and *** experimental analysis shows that the proposed algorithm achieved 99.18% of accuracy and 92.17% of f-score value,which is better than the existing *** implications-From the experimental analysis,the proposed ARKFCM with enhanced level set algorithm obtained better performance in ultrasound liver tumor segmentation related to graph-based ***,the proposed algorithm showed 3.11% improvement in dice coefficient compared to graph-based ***/value-The image preprocessing is carried out using CLAHE *** preprocessed image is segmented by employing selective level set model and local ternary pattern in ARKFCM *** this research,the proposed algorithm has advantages such as independence of clustering parameters,robustness in preserving the image details and optimal in finding the threshold value that effectively reduces the computational cost.

关键词： Adaptively regularized kernel-based fuzzy C means Contrast-limited adaptive histogram equalization Level set algorithm Liver tumor segmentation local ternary pattern

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