Node-Based Graph Convolutional Network With SLIC Method for Breast Cancer Ultrasound Images Classification by Kien Trang, Fung Fung Ting, Bao Quoc Vuong, Chee-Ming Ting

“…This research presents a novel node-based Graph Convolutional Network (GCN) approach for the classification of breast cancer from ultrasound images. Utilizing the Simple Linear Iterative Clustering (SLIC) algorithm, superpixels are segmented and treated as nodes and connected based on color similarity and spatial proximity. …”
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Automated Image-Based Wound Area Assessment in Outpatient Clinics Using Computer-Aided Methods: A Development and Validation Study by Kuan-Chen Li, Ying-Han Lee, Yu-Hsien Lin

“…K-means clustering is a machine learning algorithm that segments the wound region by grouping pixels in an image according to their color similarity. …”
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A comparative analysis of imaging-based algorithms for detecting focal cortical dysplasia type II in children by Jan Šanda, Zuzana Holubová, David Kala, Kateřina Jiránková, Martin Kudr, Tomáš Masák, Anežka Bělohlávková, Pavel Kršek, Jakub Otáhal, Martin Kynčl

“…MRI data from 23 surgical pediatric patients with histologically confirmed FCD type II were retrospectively analyzed. Three imaging-based detection algorithms were applied to T1-weighted images, each targeting key structural features: cortical thickness, gray matter intensity (extension), and gray–white matter junction blurring. …”
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TIRDH: A Novel Three-Shadow-Image Reversible Data Hiding Algorithm Using Weight and Modulo by Li-Chian Chin, Yu-Chen Li, Hung-Mo Hsieh, Chung-Ming Wang

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Independently Identifying Noise Clusters in 2D LiDAR Scanning with Clustering Algorithms by Chiew Wei Wen, Chuan Hsian Pu

“…The research proposes a novel method for independently identifying and filtering noise clusters in 2-Dimensional (2D) LiDAR scans based on 2 distinct clustering algorithms of K-Means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). …”
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Detecting Positive and Negative Changes From SAR Images by an Evolutionary Multi-Objective Approach by Shuang Liang, Hao Li, Yun Zhu, Maoguo Gong

“…In order to reduce the corruption of speckle noise present in the multitemporal SAR images, a fuzzy cluster validity index is established to exploit local spatial and gray level information. …”
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Autonomous novel class discovery for vision-based recognition in non-interactive environments by Xuelin Zhang, Feng Liu, Xuelian Cheng, Siyuan Yan, Zhibin Liao, Zongyuan Ge

“…We consider a practical problem setting that aims to allow robots to automatically discover novel classes with only labelled known class samples in hand, defined as open-set clustering (OSC). To address the OSC problem, we propose a framework combining three approaches: 1) using selfsupervised vision transformers to mitigate the discard of information needed for clustering unknown classes; 2) adaptive weighting for image patches to prioritize patches with richer textures; and 3) incorporating a temperature scaling strategy to generate more separable feature embeddings for clustering. …”
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K-means Method Based on the Approximate Backbone and the Shuffled Frog Leaping Algorithm and Its Application in Fundus Medical Record Images by SUN Ying;ZHANG Yi;DING Weiping;JU Hengrong;REN Longjie

“…Finally, this study applies the improved clustering algorithm to the medical fundus medical records images, which has a better effect on the vascular cutting.…”
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Detecting Arctic Icebergs in Sea Ice in L-Band SAR Images Using a Multiscale CFAR Algorithm by Laust Faerch, Wolfgang Dierking, Nick Hughes, Anthony P. Doulgeris

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Positron emission tomography imaging biomarker and artificial intelligence for the characterization of solitary pulmonary nodule by Ashish Kumar Jha, Ashish Kumar Jha, Umeshkumar Baburao Sherkhane, Umeshkumar Baburao Sherkhane, Nilendu C. Purandare, Nilendu C. Purandare, Leonard Wee, Andre Dekker, Venkatesh Rangarajan, Venkatesh Rangarajan

“…A total of 163 patients who underwent PET/CT imaging were included in this study. A total of 1,098 features were extracted from PET images using PyRadiomics. …”
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Explainable Two-Layer Mode Machine Learning Method for Hyperspectral Image Classification by Wenjia Chen, Junwei Cheng, Song Yang, Li Sun

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Decoding Lung Cancer Radiogenomics: A Custom Clustering/Classification Methodology to Simultaneously Identify Important Imaging Features and Relevant Genes by Destie Provenzano, John P. Lichtenberger, Sharad Goyal, Yuan James Rao

“…The algorithm was run as follows: (1) genetic clusters were initialized using random clusters, binary matrix factorization, or k-means; (2) image classification was run on CT data for these genetic clusters; (3) misclassified subjects were re-classified based on the image classification algorithm; and (4) the algorithm was run until an accuracy of 90% or no improvement after 10 runs. …”
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Mapping Burned Forest Areas in Western Yunnan, China, Using Multi-Source Optical Imagery Integrated with Simple Non-Iterative Clustering Segmentation and Random Forest Algorithms i... by Yue Chen, Weili Kou, Wenna Miao, Xiong Yin, Jiayue Gao, Weiyu Zhuang

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Early Diagnosis of Alzheimer’s Disease Using Adaptive Neuro K-Means Clustering Technique by Karan Kumar, Shweta Agrawal, Isha Suwalka, Celestine Iwendi, Cresantus N. Biamba

“…This study proposes a novel framework for early AD diagnosis using T1-weighted Magnetic Resonance Imaging (MRI). The approach integrates the Adaptive Moving Self-Organizing Map (AMSOM), a neural network technique for unsupervised training and tissue segmentation, with K-means clustering and Principal Component Analysis (PCA) for feature selection. …”
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Deep unsupervised clustering for prostate auto-segmentation with and without hydrogel spacer by Hengrui Zhao, Biling Wang, Michael Dohopolski, Ti Bai, Steve Jiang, Dan Nguyen

“…However, this substantially affects the computed tomography image appearance, which downstream reduced the contouring accuracy of auto-segmentation algorithms. …”
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Infrared Thermography-Based Insulator Fault Classification via Unsupervised Clustering and Semi-Supervised Learning by Usman Shafique, Syed Muhammad Alam, Umar Rashid, Wahab Javed, Haris Anwaar, Malik Shah Zeb, Talha Ahmad, Uzair Imtiaz, Frederic Nzanywayingoma

“…The trained algorithm utilizes the extracted characteristics to effectively identify and classify several fault types in thermal images of insulators. …”
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Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python by Polina Lemenkova

“…Image processing using Machine Learning (ML) and Artificial Neural Network (ANN) methods was investigated by employing the algorithms of Geographic Resources Analysis Support System (GRASS) Geographic Information System GIS with embedded Scikit-Learn library of Python language. …”
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Clustering-Based Thermography for Detecting Multiple Substances Under Large-Scale Floating Covers by Yue Ma, Benjamin Steven Vien, Thomas Kuen, Wing Kong Chiu

“…Cooling constants are used to reconstruct thermal images, and clustering algorithms are explored to segment and identify different material states beneath the covers. …”
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Change Detection in Multitemporal High Spatial Resolution Remote-Sensing Images Based on Saliency Detection and Spatial Intuitionistic Fuzzy C-Means Clustering by Liang Huang, Qiuzhi Peng, Xueqin Yu

“…Firstly, the cluster-based saliency cue method is used to obtain the saliency maps of two temporal remote-sensing images; then, the saliency difference is obtained by subtracting the saliency maps of two temporal remote-sensing images; finally, the SIFCM clustering algorithm is used to classify the saliency difference image to obtain the change regions and unchange regions. …”
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A control-driven transition strategy for enhanced multi-level threshold image segmentation optimization by Laith Abualigah, Mohammad H. Almomani, Saleh Ali Alomari, Raed Abu Zitar, Vaclav Snasel, Kashif Saleem, Aseel Smerat, Absalom E. Ezugwu

“…This work proposes an image segmentation approach based on a multi-threshold segmentation method and the enhanced Flood Algorithm combined with the Non-Monopolize search (named Improved IFLANO). …”
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