On the public citation graph
Automatic segmentation system for liver tumors based on the multilevel thresholding and electromagnetism optimization algorithm
0 signed · 50 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/igs.1016openalex-snapshot
Fully Automatic Anatomical, Pathological, and Functional Segmentation from CT Scans for Hepatic Surgery
inferredcites →https://doi.org/10.1002/ijc.25516openalex-snapshot
Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008
inferredcites →https://doi.org/10.1007/11428831_116openalex-snapshot
Automatic Hepatic Tumor Segmentation Using Statistical Optimal Threshold
inferredcites →https://doi.org/10.1007/978-3-319-13692-9_7openalex-snapshot
Metastatic Liver Tumor Segmentation Using Texture-Based Omni-Directional Deformable Surface Models
inferredcites →https://doi.org/10.1007/978-3-319-46723-8_48openalex-snapshot
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
inferredcites →https://doi.org/10.1007/s11548-016-1493-1openalex-snapshot
Scale-adaptive supervoxel-based random forests for liver tumor segmentation in dynamic contrast-enhanced CT scans
inferredcites →https://doi.org/10.1007/s11554-016-0578-yopenalex-snapshot
Accelerated liver tumor segmentation in four-phase computed tomography images
inferredcites →https://doi.org/10.1007/s11760-018-1314-9openalex-snapshot
A multiphase level set formulation for image segmentation using a MRF-based nonsymmetric Student’s-t mixture model
inferredcites →https://doi.org/10.1016/j.eswa.2018.07.031openalex-snapshot
Sensitive association rules hiding using electromagnetic field optimization algorithm
inferredcites →https://doi.org/10.1016/j.jhep.2018.12.001openalex-snapshot
The trends in incidence of primary liver cancer caused by specific etiologies: Results from the Global Burden of Disease Study 2016 and implications for liver cancer prevention
inferredcites →https://doi.org/10.1016/j.media.2009.09.002openalex-snapshot
Semi-automatic level set segmentation of liver tumors combining a spiral-scanning technique with supervised fuzzy pixel classification
inferredcites →https://doi.org/10.1016/j.media.2017.01.002openalex-snapshot
Adaptive local window for level set segmentation of CT and MRI liver lesions
inferredcites →https://doi.org/10.1016/j.neucom.2014.02.020openalex-snapshot
A Multilevel Thresholding algorithm using electromagnetism optimization
inferredcites →https://doi.org/10.1109/access.2019.2891673openalex-snapshot
Modified Grasshopper Algorithm-Based Multilevel Thresholding for Color Image Segmentation
inferredcites →https://doi.org/10.1109/access.2019.2903345openalex-snapshot
Multiverse Optimization Algorithm Based on Lévy Flight Improvement for Multithreshold Color Image Segmentation
inferredcites →https://doi.org/10.1109/cits.2017.8035318openalex-snapshot
Texture-based treatment prediction by automatic liver tumor segmentation on computed tomography
inferredcites →https://doi.org/10.1109/embc.2014.6944667openalex-snapshot
Random feature subspace ensemble based Extreme Learning Machine for liver tumor detection and segmentation
inferredcites →https://doi.org/10.1109/icpr.2010.618openalex-snapshot
Automatic Detection and Segmentation of Focal Liver Lesions in Contrast Enhanced CT Images
inferredcites →https://doi.org/10.1109/icsip.2014.24openalex-snapshot
Extracting the Liver and Tumor from Abdominal CT Images
inferredcites →https://doi.org/10.1109/iembs.2011.6090925openalex-snapshot
Optimal multiresolution 3D level-set method for liver segmentation incorporating local curvature constraints
inferredcites →https://doi.org/10.1109/tip.2014.2372615openalex-snapshot
An Efficient MRF Embedded Level Set Method for Image Segmentation
inferredcites →https://doi.org/10.1109/tmi.2012.2211887openalex-snapshot
Tumor Burden Analysis on Computed Tomography by Automated Liver and Tumor Segmentation
inferredcites →https://doi.org/10.1109/tmi.2018.2845918openalex-snapshot
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
inferredcites →https://doi.org/10.1155/2016/3219068openalex-snapshot
Liver Tumor Segmentation from MR Images Using 3D Fast Marching Algorithm and Single Hidden Layer Feedforward Neural Network
inferredcites →https://doi.org/10.13023/fphssr.0501.01openalex-snapshot
Update from the Editors, 2016
inferredcites →https://doi.org/10.1371/journal.pone.0191827openalex-snapshot
Hybrid two-stage active contour method with region and edge information for intensity inhomogeneous image segmentation
inferredcites →https://doi.org/10.1371/journal.pone.0193681openalex-snapshot
A serious game for children with Attention Deficit Hyperactivity Disorder: Who benefits the most?
inferredcites →https://doi.org/10.1371/journal.pone.0210411openalex-snapshot
A fast threshold segmentation method for froth image base on the pixel distribution characteristic
inferredcites →https://doi.org/10.14218/jcth.2017.00069openalex-snapshot
Differentiating Cystic Liver Lesions: A Review of Imaging Modalities, Diagnosis and Management
inferredcites →https://doi.org/10.3389/fpubh.2017.00307openalex-snapshot
Sensitivity, Specificity, and Predictive Values: Foundations, Pliabilities, and Pitfalls in Research and Practice
inferredcites →https://doi.org/10.3390/e21010001openalex-snapshot
Dynamics Analysis of a New Fractional-Order Hopfield Neural Network with Delay and Its Generalized Projective Synchronization
inferredcites →https://doi.org/10.3390/e21040398openalex-snapshot
A Chaotic Electromagnetic Field Optimization Algorithm Based on Fuzzy Entropy for Multilevel Thresholding Color Image Segmentation
inferredcites →https://doi.org/10.4236/jcc.2015.311023openalex-snapshot
Automatic Segmentation of Liver Tumor in CT Images with Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.5281/zenodo.1061401openalex-snapshot
Neural Network Based Texture Analysis Of Liver Tumor From Computed Tomography Images
inferredcites →https://doi.org/10.54294/msg94uopenalex-snapshot
Segmentation of Liver Metastases in CT Scans by Adaptive Thresholding and Morphological Processing
inferredcites →https://doi.org/10.9781/ijimai.2018.09.001openalex-snapshot
Multilevel Thresholding for Image Segmentation Using an Improved Electromagnetism Optimization Algorithm
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-981-99-6645-5_1openalex-snapshot
Biomedical Imaging Segmentation and Classification Framework Based on Soft Computing Techniques
inferred← citeshttps://doi.org/10.1007/s00521-025-11391-3openalex-snapshot
Improved grasshopper optimization and modified moth–flame optimization (IGH-MMFO) algorithm for identifying optimal threshold in image segmentation
inferred← citeshttps://doi.org/10.1007/s11042-023-15059-9openalex-snapshot
Segmentation of thermographies from electronic systems by using the global-best brain storm optimization algorithm
inferred← citeshttps://doi.org/10.1016/j.bspc.2023.104984openalex-snapshot
Application of Multilevel Thresholding and CNN for the Diagnosis of Skin Cancer Utilizing a Multi-Agent Fuzzy Buzzard Algorithm
inferred← citeshttps://doi.org/10.1038/s41598-024-60668-5openalex-snapshot
Deep learning-aided 3D proxy-bridged region-growing framework for multi-organ segmentation
inferred← citeshttps://doi.org/10.1063/5.0057980openalex-snapshot
Computerized segmentation of liver tumor using integrated fuzzy level set method
inferred← citeshttps://doi.org/10.1109/iccece51280.2021.9342530openalex-snapshot
RAU-Net: U-Net Model Based on Residual and Attention for Kidney and Kidney Tumor Segmentation
inferred← citeshttps://doi.org/10.1109/icev52951.2021.9632653openalex-snapshot
Automatic Segmentation of Lateral Ventricles in MRI of Pediatric Patients from 4 to 12 Months
inferred← citeshttps://doi.org/10.1109/icses52305.2021.9633879openalex-snapshot
Incremental feedback learning mechanism for highly efficient automatic image segmentation with feature coupling
inferred← citeshttps://doi.org/10.1109/mi-sta52233.2021.9464516openalex-snapshot
Improved Active Contour Model through Automatic Initialisation : Liver Segmentation
inferred← citeshttps://doi.org/10.14569/ijacsa.2020.0111149openalex-snapshot
Liver Tumor Segmentation using Superpixel based Fast Fuzzy C Means Clustering
inferred← citeshttps://doi.org/10.3390/biomimetics10090576openalex-snapshot
Liver Tumor Segmentation Based on Multi-Scale Deformable Feature Fusion and Global Context Awareness
inferred← citeshttps://doi.org/10.56726/irjmets38621openalex-snapshot
LIVER TUMOR DETECTION USING IMAGE PROCESSING TECHNIQUE: A SURVEY
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.