Fuzzy C-means clustering-based multilayer perceptron neural network for liver CT images automatic segmentation

An Evolutionary Particle Swarm Optimization based on the Fractional Order Darwinian method for optimizing a Fast Fuzzy C-Means algorithm is proposed. This chapter aims at enhancing the performance of Fast Fuzzy C-Means, both in terms of the overall solution and speed. To that end, the concept of fractional calculus is used to control the convergence rate of particles, wherein each one of them represents a set of cluster centers. The proposed solution, denoted as FODPSO-FFCM, is applied on liver CT images, and compared with Fast Fuzzy C-Means and PSOFFCM, using Jaccard Index and Dice Coefficient. The computational efficiency is achieved by using the histogram of the image intensities during the clustering process instead of the raw image data. The experimental results based on the Analysis of Variance (ANOVA) technique and multiple pair-wise comparison show that the proposed algorithm is fast, accurate, and less time consuming.

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Semiautomatic Epicardial Fat Segmentation Based on Fuzzy c-Means Clustering and Geometric Ellipse Fitting

Journal of Healthcare Engineering ◽

10.1155/2017/5817970 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 6

Author(s):

Vladimir Zlokolica ◽

Lidija Krstanović ◽

Lazar Velicki ◽

Branislav Popović ◽

Marko Janev ◽

...

Keyword(s):

Automatic Segmentation ◽

User Interaction ◽

Ct Images ◽

Epicardial Fat ◽

Risk Level ◽

Good Correspondence ◽

Fuzzy C Means ◽

High Dependency ◽

Segmentation Approach ◽

Fuzzy C Means Clustering

Automatic segmentation of particular heart parts plays an important role in recognition tasks, which is utilized for diagnosis and treatment. One particularly important application is segmentation of epicardial fat (surrounds the heart), which is shown by various studies to indicate risk level for developing various cardiovascular diseases as well as to predict progression of certain diseases. Quantification of epicardial fat from CT images requires advance image segmentation methods. The problem of the state-of-the-art methods for epicardial fat segmentation is their high dependency on user interaction, resulting in low reproducibility of studies and time-consuming analysis. We propose in this paper a novel semiautomatic approach for segmentation and quantification of epicardial fat from 3D CT images. Our method is a semisupervised slice-by-slice segmentation approach based on local adaptive morphology and fuzzy c-means clustering. Additionally, we use a geometric ellipse prior to filter out undesired parts of the target cluster. The validation of the proposed methodology shows good correspondence between the segmentation results and the manual segmentation performed by physicians.

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Region-growing fully convolutional neural network interactive segmentation of liver CT images

Chinese Journal of Liquid Crystals and Displays ◽

10.37188/cjlcd.2020-0338 ◽

2021 ◽

Vol 36 (9) ◽

pp. 1294-1304

Author(s):

Li-juan ZHANG ◽

◽

Run ZHANG ◽

Dong-ming LI ◽

Yang LI ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Region Growing ◽

Ct Images ◽

Interactive Segmentation ◽

Liver Ct

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Automatic Segmentation of Kidney and Renal Tumor in CT Images Based on 3D Fully Convolutional Neural Network with Pyramid Pooling Module

2018 24th International Conference on Pattern Recognition (ICPR) ◽

10.1109/icpr.2018.8545143 ◽

2018 ◽

Cited By ~ 3

Author(s):

Guanyu Yang ◽

Guoqing Li ◽

Tan Pan ◽

Youyong Kong ◽

Jiasong Wu ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Renal Tumor ◽

Automatic Segmentation ◽

Ct Images

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Paraspinal Muscle Segmentation in CT Images Using GSM-Based Fuzzy C-Means Clustering

Journal of Computer and Communications ◽

10.4236/jcc.2014.29010 ◽

2014 ◽

Vol 02 (09) ◽

pp. 70-77 ◽

Cited By ~ 4

Author(s):

Yong Wei ◽

Xiuping Tao ◽

Bin Xu ◽

Arend P. Castelein

Keyword(s):

Ct Images ◽

Paraspinal Muscle ◽

Fuzzy C Means ◽

Fuzzy C Means Clustering ◽

Muscle Segmentation

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Design of Optimized Fuzzy C-Means Clustering based Radial Basis Function Neural Network for Partial Discharge Pattern Classifier: Comparative Study

Journal of Korean institute of intelligent systems ◽

10.5391/jkiis.2021.31.6.457 ◽

2021 ◽

Vol 31 (6) ◽

pp. 457-464

Author(s):

Byeong-Kuk Jeong ◽

Sung-Kwun Oh ◽

Young-Il Kim

Keyword(s):

Neural Network ◽

Comparative Study ◽

Radial Basis Function ◽

Basis Function ◽

Partial Discharge ◽

Discharge Pattern ◽

Fuzzy C Means ◽

Radial Basis ◽

Fuzzy C Means Clustering ◽

Pattern Classifier

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Automatic Segmentation and Classification of COVID-19 CT Image Using Deep Learning and Multi-Scale Recurrent Neural Network Based Classifier

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3850 ◽

2021 ◽

Vol 11 (10) ◽

pp. 2618-2625

Author(s):

R. T. Subhalakshmi ◽

S. Appavu Alias Balamurugan ◽

S. Sasikala

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Deep Learning ◽

Recurrent Neural Network ◽

Automatic Segmentation ◽

Ct Images ◽

Superior Performance ◽

Multi Scale ◽

Proposed Model ◽

Class Labels

In recent times, the COVID-19 epidemic turn out to be increased in an extreme manner, by the accessibility of an inadequate amount of rapid testing kits. Consequently, it is essential to develop the automated techniques for Covid-19 detection to recognize the existence of disease from the radiological images. The most ordinary symptoms of COVID-19 are sore throat, fever, and dry cough. Symptoms are able to progress to a rigorous type of pneumonia with serious impediment. As medical imaging is not recommended currently in Canada for crucial COVID-19 diagnosis, systems of computer-aided diagnosis might aid in early COVID-19 abnormalities detection and help out to observe the disease progression, reduce mortality rates potentially. In this approach, a deep learning based design for feature extraction and classification is employed for automatic COVID-19 diagnosis from computed tomography (CT) images. The proposed model operates on three main processes based pre-processing, feature extraction, and classification. The proposed design incorporates the fusion of deep features using GoogLe Net models. Finally, Multi-scale Recurrent Neural network (RNN) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the proposed model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity, specificity, and accuracy.

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