Image segmentation algorithm based on improved fuzzy clustering

Lei, Xiangxiao; Ouyang, Haibin

doi:10.1007/s10586-018-2128-9

Cited by 22 publications

(10 citation statements)

References 15 publications

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“…Dhanachandra et al [23] proposed clustering technique is based on the density estimation of the surrounding pixel values. Lei et al [24] proposed an image segmentation algorithm based on improved fuzzy clustering. Zhou et al [25] proposed an unsupervised segmentation framework based on a novel deep image clustering (DIC) model that consists of a feature transformation subnetwork (FTS) and a trainable deep clustering subnetwork (DCS) for unsupervised image clustering.…”

Section: D Point Data Processing Methodsmentioning

confidence: 99%

A novel approach for surface defectdetection of lithium battery based on improved K-Nearest Neighbor and Euclidean clustering segmentation

Liu¹,

Wu²,

Guo³

et al. 2023

Preprint

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Surface defects of lithium batteries seriously affect the product quality and may lead to safety risks. In order to accurately identify the surface defects of lithium battery, a novel defect detection approach is proposed based on improved K-Nearest Neighbor (KNN) and Euclidean clustering segmentation. Firstly, an improved Voxel density strategy for KNN is proposed to speed up the effect for point filtering. Then, the improved clustering segmentation strategy is applied to distinguish point clouds with defect features. The outline fitting algorithm based on least square method is applied to determine geometric features of each surface defects. Furthermore, experimental results show that the proposed surface defect detection method reaches 99.2% accuracy and 35.3ms average time consumption for data processing. Finally, an industrial application example of lithium battery production is demonstrated, which meets the requirements of industrial application.

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Section: D Point Data Processing Methodsmentioning

confidence: 99%

A novel approach for surface defectdetection of lithium battery based on improved K-Nearest Neighbor and Euclidean clustering segmentation

Liu¹,

Wu²,

Guo³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In order to improve the robustness of the FCM clustering algorithm, many researchers have introduced the kernel method into various clustering algorithms (YU et al, 2015;KANG et al, 2010;MA et al, 2007;GONG et al, 2013;ZHANG & LI, 2017;LEI & OUYANG, 2018;CHAIRA & RAY, 2005). The fundamental concept is to map each sample to the high-dimensional kernel space by using nonlinear mapping.…”

Section: Rural Engineeringmentioning

confidence: 99%

Mature pomegranate recognition methods in natural environments using machine vision

Lei

Ouyang

2019

Cienc. Rural

Self Cite

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The use of machine vision to recognize mature pomegranates in natural environments is of major significance in improving the applicability and work efficiency of picking robots. By analyzing the color characteristics of color images of mature pomegranates under different illumination conditions, the feasibility of the YCbCr color model for pomegranate image recognition under different illumination conditions was proven. First, the Cr component map of pomegranate image is selected and then the pomegranate fruit is segmented by the kernel fuzzy C-means clustering algorithm to obtain the pomegranate image. Contrast experiments of pomegranate image segmentation under different illumination conditions were then performed using the proposed kernel fuzzy C-means clustering algorithm, the fuzzy C-means clustering algorithm, the Otsu algorithm and the threshold segmentation algorithm. Results of the experiments verified the effectiveness and superiority of the proposed algorithm.

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“…Since it is pixel-based, clustering involves relatively simple algorithms, and its complexity is generally lower than that of region- or edge-based segmentation methods. The performance of clustering algorithms for image segmentation is susceptible to the features of objects in the image [ 16 ]. Furthermore, clustering is suitable for biomedical image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Periapical Radiograph Using Color Histogram and Machine Learning for Osteoporosis Detection

Widyaningrum

Sela

Pulungan

et al. 2023

International Journal of Dentistry

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Osteoporosis leads to the loss of cortical thickness, a decrease in bone mineral density (BMD), deterioration in the size of trabeculae, and an increased risk of fractures. Changes in trabecular bone due to osteoporosis can be observed on periapical radiographs, which are widely used in dental practice. This study proposes an automatic trabecular bone segmentation method for detecting osteoporosis using a color histogram and machine learning (ML), based on 120 regions of interest (ROI) on periapical radiographs, and divided into 60 training and 42 testing datasets. The diagnosis of osteoporosis is based on BMD as evaluated by dual X-ray absorptiometry. The proposed method comprises five stages: the obtaining of ROI images, conversion to grayscale, color histogram segmentation, extraction of pixel distribution, and performance evaluation of the ML classifier. For trabecular bone segmentation, we compare K-means and Fuzzy C-means. The distribution of pixels obtained from the K-means and Fuzzy C-means segmentation was used to detect osteoporosis using three ML methods: decision tree, naive Bayes, and multilayer perceptron. The testing dataset was used to obtain the results in this study. Based on the performance evaluation of the K-means and Fuzzy C-means segmentation methods combined with 3 ML, the osteoporosis detection method with the best diagnostic performance was K-means segmentation combined with a multilayer perceptron classifier, with accuracy, specificity, and sensitivity of 90.48%, 90.90%, and 90.00%, respectively. The high accuracy of this study indicates that the proposed method provides a significant contribution to the detection of osteoporosis in the field of medical and dental image analysis.

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Image segmentation algorithm based on improved fuzzy clustering

Cited by 22 publications

References 15 publications

A novel approach for surface defectdetection of lithium battery based on improved K-Nearest Neighbor and Euclidean clustering segmentation

A novel approach for surface defectdetection of lithium battery based on improved K-Nearest Neighbor and Euclidean clustering segmentation

Mature pomegranate recognition methods in natural environments using machine vision

Automatic Segmentation of Periapical Radiograph Using Color Histogram and Machine Learning for Osteoporosis Detection

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