Adaptive fuzzy-SIFT rule-based registration for 3D cardiac motion estimation

3D Speckle tracking techniques are used to quantify cardiac deformation in 3D echocardiographic images. Elastic image registration methods are successful in solving 3D speckle tracking problems. However, a suitable solution should be exploited to deal with the poor spatio-temporal resolution in the echocardiographic images. That is why the registration problem may encounter some challenges in representing accurate features and defining suitable geometric transformation. The strong modeling ability of a fuzzy rule-based inference system can aid the challenge in geometric modeling. This paper, thus, aims to solve the 3D speckle tracking problem in a new scheme through a fuzzy modeling procedure. The algorithm begins to work by extracting a well-suited local feature descriptor, scale- invariant feature transform (SIFT). Then, the relevant features are aligned with sets of fuzzy rules the optimum parameters of which are adaptively learned in the hybrid learning process of adaptive-neuro fuzzy inference system (ANFIS) structure. Applying the adaptive fuzzy method on STRAUS synthetic dataset yields an acceptable tracking error below 1 mm. Further, strain analysis indicates the capacity of the proposed method in discriminating pathological diagnosis from a healthy one.

[1]  Gang Wang,et al.  Fuzzy correspondences guided Gaussian mixture model for point set registration , 2017, Knowl. Based Syst..

[2]  Eigil Samset,et al.  Robust spatio-temporal registration of 4D cardiac ultrasound sequences , 2016, SPIE Medical Imaging.

[3]  Ezhilmathi Nagarathinam,et al.  Image registration‐based brain tumor detection and segmentation using ANFIS classification approach , 2019, Int. J. Imaging Syst. Technol..

[4]  P. Thirumurugan,et al.  Brain tumor detection and diagnosis using ANFIS classifier , 2016, Int. J. Imaging Syst. Technol..

[5]  Daniel L. Rubin,et al.  Volumetric Image Registration From Invariant Keypoints , 2017, IEEE Transactions on Image Processing.

[6]  Ananya Das,et al.  A novel systematic approach to diagnose brain tumor using integrated type-II fuzzy logic and ANFIS (adaptive neuro-fuzzy inference system) model , 2019, Soft Computing.

[7]  Bernhard Freudenthaler,et al.  Fuzzy Membership Functional Analysis for Nonparametric Deep Models of Image Features , 2020, IEEE Transactions on Fuzzy Systems.

[8]  Hamido Fujita,et al.  Data-Driven Models for Objective Grading Improvement of Parkinson’s Disease , 2020, Annals of Biomedical Engineering.

[9]  Maxime Sermesant,et al.  Regional Analysis of Left Ventricle Function Using a Cardiac-Specific Polyaffine Motion Model , 2013, FIMH.

[10]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[11]  Seung-Bok Choi,et al.  Recurrent Mechanism and Impulse Noise Filter for Establishing ANFIS , 2018, IEEE Transactions on Fuzzy Systems.

[12]  Majid Ghasemi,et al.  AFDL: a new adaptive fuzzy dictionary learning for medical image classification , 2020, Pattern Anal. Appl..

[13]  Ning Ning,et al.  Predicting a Stock Portfolio with the Multivariate Bayesian Structural Time Series Model: Do News or Emotions Matter? , 2019 .

[14]  Radu-Emil Precup,et al.  Evolving fuzzy models for myoelectric-based control of a prosthetic hand , 2016, 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[15]  K. Manivannan,et al.  Fusion based Glioma brain tumor detection and segmentation using ANFIS classification , 2018, Comput. Methods Programs Biomed..

[16]  Myo Taeg Lim,et al.  Arbitration algorithm of FIR filter and optical flow based on ANFIS for visual object tracking , 2015 .

[17]  Pooja,et al.  Wavelet and Curvelet Transformation based Image Fusion with ANFIS and SVM , 2015 .

[18]  Ying Li,et al.  Handwritten Chinese character recognition using fuzzy image alignment , 2016, Soft Comput..

[19]  Adnan Rashdi,et al.  A Speckle Noise Removal Method , 2018, Circuits Syst. Signal Process..

[20]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[21]  Gang Wang,et al.  Fuzzy Correspondences and Kernel Density Estimation for Contaminated Point Set Registration , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[22]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[23]  Zhaohong Deng,et al.  An Adaptive Fuzzy-Inference-Rule-Based Flexible Model for Automatic Elastic Image Registration , 2009, IEEE Transactions on Fuzzy Systems.

[24]  Huafeng Liu,et al.  Volumetric Modeling Electromechanics of the Heart , 2011, STACOM.

[25]  Zahra Hossein-Nejad,et al.  An adaptive image registration method based on SIFT features and RANSAC transform , 2017, Comput. Electr. Eng..

[26]  Siddhant Jain,et al.  Fuzzy match index for scale-invariant feature transform (SIFT) features with application to face recognition with weak supervision , 2015, IET Image Process..

[27]  William J. Emery,et al.  SAR Image Content Retrieval Based on Fuzzy Similarity and Relevance Feedback , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[28]  Piotr Zarychta.  Application of Fuzzy Image Concept to Medical Images Matching , 2018, ITIB.

[29]  Hamido Fujita,et al.  Neural-fuzzy with representative sets for prediction of student performance , 2018, Applied Intelligence.

[30]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, CVPR 2004.

[31]  Mohammad Mehdi Arefi,et al.  Nonlinear system identification based on Takagi-Sugeno fuzzy modeling and unscented Kalman filter. , 2018, ISA transactions.

[32]  Martino Alessandrini,et al.  Challenge on Endocardial Three-dimensional Ultrasound Segmentation (CETUS) , 2014, The MIDAS Journal.

[33]  Hao Yang,et al.  Lung motion estimation by robust point matching and spatiotemporal tracking for 4D CT , 2016, Comput. Biol. Medicine.

[34]  Susana M. Vieira,et al.  Takagi–Sugeno Fuzzy Modeling Using Mixed Fuzzy Clustering , 2017, IEEE Transactions on Fuzzy Systems.

[35]  Scott Sorensen,et al.  Robust Shape Registration using Fuzzy Correspondences , 2017, ArXiv.

[36]  Erik Valdemar Cuevas Jiménez,et al.  Nonlinear system identification based on ANFIS-Hammerstein model using Gravitational search algorithm , 2017, Applied Intelligence.

[37]  R. Radha,et al.  A medical analytical system using intelligent fuzzy level set brain image segmentation based on improved quantum particle swarm optimization , 2020, Microprocess. Microsystems.

[38]  Seyed Hamid Zahiri,et al.  Variable Length IPO and its application in concurrent design and train of ANFIS systems , 2018, Applied Intelligence.

[39]  Dimitar Filev,et al.  Generation of Fuzzy Rules by Mountain Clustering , 1994, J. Intell. Fuzzy Syst..

[40]  Saeid Rastegar,et al.  Online identification of Takagi–Sugeno fuzzy models based on self-adaptive hierarchical particle swarm optimization algorithm , 2017 .

[41]  Abhirup Datta,et al.  A hybrid fuzzy filtering - fuzzy thresholding technique for region of interest detection in noisy images , 2019, Applied Intelligence.

[42]  Lutz Kolbe,et al.  Fuzzy theoretic model based analysis of image features , 2019, Inf. Sci..

[43]  Ali Fatemi,et al.  Studying the Origin of Reverberation Clutter in Echocardiography: In Vitro Experiments and In Vivo Demonstrations. , 2019, Ultrasound in medicine & biology.

[44]  Chen Wang,et al.  Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography , 2016, IEEE Transactions on Medical Imaging.

[45]  Hervé Delingette,et al.  Detailed Evaluation of Five 3D Speckle Tracking Algorithms Using Synthetic Echocardiographic Recordings , 2016, IEEE Transactions on Medical Imaging.

[46]  Ajith Abraham,et al.  A Novel Type-2 Fuzzy C-Means Clustering for Brain MR Image Segmentation , 2020, IEEE Transactions on Cybernetics.

[47]  Gerhard-Wilhelm Weber,et al.  Introduction to the Special Issue on Fuzzy Analytics and Stochastic Methods in Neurosciences , 2020, IEEE Trans. Fuzzy Syst..

[48]  Sahil Raheja,et al.  Edge detection based on type-1 fuzzy logic and guided smoothening , 2019, Evolving Systems.

[49]  Norbert Stoll,et al.  Fuzzy Membership Descriptors for Images , 2016, IEEE Transactions on Fuzzy Systems.

[50]  M. De Craene,et al.  A Pipeline for the Generation of Realistic 3D Synthetic Echocardiographic Sequences: Methodology and Open-Access Database , 2015, IEEE Transactions on Medical Imaging.