Fuzzy clustering recognition algorithm of medical image with multi‐resolution feature

Most current medical images are collected in the presence of interference by ignored interferences such as illumination, occlusion, etc. The recognition rate is low for multi‐resolution images in the case of color distortion. Therefore, a novel fuzzy clustering recognition algorithm of multi‐resolution medical image was proposed in this paper. First, medical images were analyzed from both acquisition time, shooting angle, resolution, natural light measurement, and background. Secondly, in order to avoid partial occlusion, region was recalculated by correlation between Fourier and Merlin transform. Moreover, Euclidean distance between samples were identified by standardized eigenvalues, where smallest distance obtained maximum degree of membership. Experiment results showed that the proposed method had higher recognition rate (accuracy 90.46% and sensitiveness 97.89%) and stronger anti‐interference than current methods.

[1]  Václav Snásel,et al.  Medical Image Retrieval Using Vector Quantization and Fuzzy S-tree , 2016, Journal of Medical Systems.

[2]  Max A. Viergever,et al.  Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities , 2016, MICCAI.

[3]  Wenwen Li,et al.  Dynamic threshold-setting for RF-powered cognitive radio networks in non-Gaussian noise , 2018, Phys. Commun..

[4]  Arun Kumar Sangaiah,et al.  Feature and Intensity Based Medical Image Registration Using Particle Swarm Optimization , 2017, Journal of Medical Systems.

[5]  Christoph Meinel,et al.  Deep Learning for Medical Image Analysis , 2018, Journal of Pathology Informatics.

[6]  Arun Kumar Sangaiah,et al.  Medical JPEG image steganography based on preserving inter-block dependencies , 2017, Comput. Electr. Eng..

[7]  C.-H. Fang.  The diagnostic accuracy of the medical image three-dimensional visualization system, MRCP, CT and US in hepatolithiasis: a comparative study , 2016 .

[8]  Lovedeep Gondara,et al.  Medical Image Denoising Using Convolutional Denoising Autoencoders , 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).

[9]  Zheng Pan,et al.  A NOVEL FAST FRACTAL IMAGE COMPRESSION METHOD BASED ON DISTANCE CLUSTERING IN HIGH DIMENSIONAL SPHERE SURFACE , 2017 .

[10]  Arun Kumar Sangaiah,et al.  Automatic histologically-closer classification of skin lesions , 2018, Comput. Medical Imaging Graph..

[11]  Andreas Gegenfurtner,et al.  The challenges of studying visual expertise in medical image diagnosis , 2017, Medical education.

[12]  Arun Kumar Sangaiah,et al.  Nucleosome Positioning With Fractal Entropy Increment of Diversity in Telemedicine , 2018, IEEE Access.

[13]  Nikos Chrisochoides,et al.  Accurate and fast deformable medical image registration for brain tumor resection using image-guided neurosurgery , 2016, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[14]  Payel Ghosh,et al.  Incorporating priors for medical image segmentation using a genetic algorithm , 2016, Neurocomputing.

[15]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[16]  Yi Chai,et al.  A novel dictionary learning approach for multi-modality medical image fusion , 2016, Neurocomputing.

[17]  Houbing Song,et al.  Digital image watermarking method based on DCT and fractal encoding , 2017, IET Image Process..

[18]  Sakshi Kaushal,et al.  The utility based non-linear fuzzy AHP optimization model for network selection in heterogeneous wireless networks , 2018, Appl. Soft Comput..

[19]  Johnny S. Wong,et al.  Design and implementation of an Internet-based medical image viewing system , 2003, J. Syst. Softw..

[20]  David Dagan Feng,et al.  An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification , 2017, IEEE Journal of Biomedical and Health Informatics.

[21]  Jiantao Zhou,et al.  Distribution of primary additional errors in fractal encoding method , 2014, Multimedia Tools and Applications.

[22]  Yu Xue,et al.  Partitioned-cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation , 2017, Appl. Soft Comput..

[23]  Ying Wang,et al.  Fuzzy clustering recognition algorithm of medical image with multi-resolution feature , 2020, Concurr. Comput. Pract. Exp..

[24]  Leonardo Yunda,et al.  An Open-Access Web-based Medical Image Atlas for Collaborative Medical Image Sharing, Processing, Web Semantic Searching and Analysis with Uses in Medical Training, Research and Second Opinion of Cases , 2014 .

[25]  Qidi Wu,et al.  The application of nonlocal total variation in image denoising for mobile transmission , 2017, Multimedia Tools and Applications.

[26]  Lili Guo,et al.  FRACTAL COMPLEXITY-BASED FEATURE EXTRACTION ALGORITHM OF COMMUNICATION SIGNALS , 2017 .

[27]  Shuai Liu,et al.  A Novel Distance Metric: Generalized Relative Entropy , 2017, Entropy.

[28]  Periyavattam Shanmugam Gomathi,et al.  Multimodal Medical Image Fusion in Non-Subsampled Contourlet Transform Domain , 2016 .