Fuzzy C Mean Thresholding based Level Set for Automated Segmentation of Skin Lesions

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Journal of Signal and Information Processing

ISSN Print: 2159-4465
ISSN Online: 2159-4481
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"Fuzzy C Mean Thresholding based Level Set for Automated Segmentation of Skin Lesions"
written by Ammara Masood, Adel Ali Al-Jumaily,
published by Journal of Signal and Information Processing, Vol.4 No.3B, 2013

has been cited by the following article(s):

Google Scholar
CrossRef

[1]	The Fast and Accurate Approach to Detection and Segmentation of Melanoma Skin Cancer using Fine-tuned Yolov3 and SegNet Based on Deep Transfer Learning
	arXiv preprint arXiv:2210.05167, 2022

[2]	A comparative analysis of melanoma detection methods based on computer aided diagnose system
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[3]	Deep Learning-Based Melanoma Detection with Optimized Features via Hybrid Algorithm
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[4]	Image Processing and Analysis for Decision Making Applied to Melanoma
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[5]	A Segmentation of Melanocytic Skin Lesions in Dermoscopic and Standard Images Using a Hybrid Two-Stage Approach
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[6]	A brain extraction algorithm for infant T2 weighted magnetic resonance images based on fuzzy c-means thresholding
	Scientific Reports, 2021

[7]	MATLAB image processing tool-based GUI for high-throughput image segmentation and analysis to study structure and morphology of skin H&E stained sections
	2020

[8]	Directional Vector-Based Skin Lesion Segmentation—A Novel Approach to Skin Segmentation
	2020

[9]	Melanoma lesion detection and segmentation using YOLOv4-DarkNet and active contour
	2020

[10]	Engineering and Technology for Healthcare
	2020

[11]	Automated Diagnosis of Skin Cancer for Healthcare: Highlights and Procedures
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[12]	A Comparative Study of Meningioma Tumors Segmentation Methods from MR Images
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[13]	Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion
	2019

[14]	Detection and diagnosis of dilated cardiomyopathy from the left ventricular parameters in echocardiogram sequences
	2019

[15]	Supervised Saliency Map Driven Segmentation of Lesions in Dermoscopic Images
	2018

[16]	New FCM Segmentation Approach Based on Multi-Resolution Analysis
	International Journal of Fuzzy System Applications (IJFSA), 2018

[17]	Segmentation of Melanoma Skin Lesions Using Anisotropic Diffusion and Adaptive Thresholding
	ICBET 2018 Proceedings of the 2018 8th International Conference on Biomedical Engineering and Technology, 2018

[18]	Detection of Fonts and Characters with Hybrid Graphic-Text Plate Numbers
	2018

[19]	Orientation Sensitive Fuzzy C Means Based Fast Level Set Evolution for Segmentation of Histopathological Images to Detect Skin Cancer
	2018

[20]	Implementation of Fuzzy Thresholding for Segmentation of Images
	International Journal of Computer Applications, 2017

[21]	Αυτόματη Ανίχνευση Αθηρωματικής Πλάκας Σε Εικόνες B-Mode Υπερήχων Μέσω Ανάλυσης Υφής
	2017

[22]	Segmentation of Brain Tumour Based on Clustering Technique: Performance Analysis
	Journal of Intelligent Systems, 2017

[23]	Supervised Saliency Map Driven Segmentation of the Lesions in Dermoscopic Images
	IEEE JBHI; Special Issue on “Skin Lesion Image Analysis for Melanoma Detection” , 2017

[24]	Detection and diagnosis of dilated cardiomyopathy and hypertrophic cardiomyopathy using image processing techniques
	Engineering Science and Technology, an International Journal, 2016

[25]	Proposed Threshold Algorithm for Accurate Segmentation for Skin Lesion
	2016

[26]	Estudo comparativo de técnicas para segmentação e classificação de imagens de lesões de pele
	Repositório Institucional UNESP, 2016

[27]	Developing improved algorithms for detection and analysis of skin cancer
	2016

[28]	Enhancement of dermoscopic images and feature extraction for classification of skin lesions
	ProQuest Dissertations Publishing, 2015

[29]	GMM guided automated Level Set algorithm for PET image segmentation
	World Congress on Medical Physics and Biomedical Engineering, June 7-12, 2015, Toronto, Canada, 2015

[30]	Self-supervised learning model for skin cancer diagnosis
	2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), 2015

[31]	A local fuzzy thresholding methodology for multiregion image segmentation
	Knowledge-Based Systems, 2015

[32]	SA-SVM based automated diagnostic system for skin cancer
	Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 2015

[33]	Automatic skin cancer detection system
	2014

[34]	Texture Analysis Based Automated Decision Support System for Classification of Skin Cancer Using SA-SVM
	Neural Information Processing.Springer, 2014

[35]	Integrating soft and hard threshold selection algorithms for accurate segmentation of skin lesion
	Biomedical Engineering (MECBME), 2014 Middle East Conference on. IEEE, 2014

[36]	Development of Automated Diagnostic System for Skin Cancer: Performance Analysis of Neural Network Learning Algorithms for Classification
	Artificial Neural Networks and Machine Learning–ICANN 2014. Springer International Publishing, 2014

[37]	A BINARY LEVEL SET METHOD BASED ON K-MEANS FOR CONTOUR TRACKING ON SKIN CANCER IMAGES
	Proceeding (818) Biomedical Engineering / 817: Robotics Applications, 2014

[38]	A Hybrid Dermoscopic Images Segmentation Scheme Using Fast FCM, DWT2 and YUV
	IJEIR, 2014

[39]	A Hybrid Dermoscopic Images Segmentation Scheme Using Fast FCM DWT2 and YUV
	2014

[40]	Automated segmentation of skin lesions: Modified Fuzzy C mean thresholding based level set method
	Multi Topic Conference (INMIC), 2013 16th International. IEEE, 2013

[41]	Computer aided diagnostic support system for skin cancer: A review of techniques and algorithms
	International journal of biomedical imaging, 2013

[1]	Smart Trends in Computing and Communications Lecture Notes in Networks and Systems, 2023 DOI:10.1007/978-981-99-0838-7_9

[2]	DI-UNet: dual-branch interactive U-Net for skin cancer image segmentation Journal of Cancer Research and Clinical Oncology, 2023 DOI:10.1007/s00432-023-05319-4

[3]	Deep Learning-Based Melanoma Detection with Optimized Features via Hybrid Algorithm International Journal of Image and Graphics, 2023 DOI:10.1142/S0219467823500560

[4]	Smart Trends in Computing and Communications Lecture Notes in Networks and Systems, 2023 DOI:10.1007/978-981-99-0838-7_9

[5]	Engineering and Technology for Healthcare 2021 DOI:10.1002/9781119644316.ch10

[6]	Engineering and Technology for Healthcare 2021 DOI:10.1002/9781119644316.ch10

[7]	A Segmentation of Melanocytic Skin Lesions in Dermoscopic and Standard Images Using a Hybrid Two-Stage Approach BioMed Research International, 2021 DOI:10.1155/2021/5562801

[8]	Advanced Methods for Human Biometrics Smart Sensors, Measurement and Instrumentation, 2021 DOI:10.1007/978-3-030-81982-8_12

[9]	MATLAB image processing tool-based GUI for high-throughput image segmentation and analysis to study structure and morphology of skin H&E stained sections IOP Conference Series: Materials Science and Engineering, 2020 DOI:10.1088/1757-899X/737/1/012232

[10]	A Comparative Study of Meningioma Tumors Segmentation Methods from MR Images 2020 3rd International Conference on Signal Processing and Information Security (ICSPIS), 2020 DOI:10.1109/ICSPIS51252.2020.9340134

[11]	Melanoma Lesion Detection and Segmentation Using YOLOv4-DarkNet and Active Contour IEEE Access, 2020 DOI:10.1109/ACCESS.2020.3035345

[12]	Directional Vector-Based Skin Lesion Segmentation — A Novel Approach to Skin Segmentation International Journal of Image and Graphics, 2020 DOI:10.1142/S0219467820500217

[13]	Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion Microscopy Research and Technique, 2019 DOI:10.1002/jemt.23220

[14]	Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion Microscopy Research and Technique, 2019 DOI:10.1002/jemt.23220

[15]	Detection of Fonts and Characters with Hybrid Graphic-Text Plate Numbers TENCON 2018 - 2018 IEEE Region 10 Conference, 2018 DOI:10.1109/TENCON.2018.8650097

[16]	New FCM Segmentation Approach Based on Multi-Resolution Analysis International Journal of Fuzzy System Applications, 2018 DOI:10.4018/IJFSA.2018100105

[17]	Segmentation of Melanoma Skin Lesions Using Anisotropic Diffusion and Adaptive Thresholding Proceedings of the 2018 8th International Conference on Biomedical Engineering and Technology, 2018 DOI:10.1145/3208955.3208961

[18]	Segmentation of Melanoma Skin Lesions Using Anisotropic Diffusion and Adaptive Thresholding Proceedings of the 2018 8th International Conference on Biomedical Engineering and Technology - ICBET '18, 2018 DOI:10.1145/3208955.3208961

[19]	Oncology 2017 DOI:10.4018/978-1-5225-0549-5.ch009

[20]	Segmentation of Brain Tumour Based on Clustering Technique: Performance Analysis Journal of Intelligent Systems, 2017 DOI:10.1515/jisys-2017-0027

[21]	SA-SVM based automated diagnostic system for skin cancer Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 2015 DOI:10.1117/12.2179094

[22]	A local fuzzy thresholding methodology for multiregion image segmentation Knowledge-Based Systems, 2015 DOI:10.1016/j.knosys.2015.02.029

[23]	Self-supervised learning model for skin cancer diagnosis 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), 2015 DOI:10.1109/NER.2015.7146798

[24]	Integrating soft and hard threshold selection algorithms for accurate segmentation of skin lesion 2nd Middle East Conference on Biomedical Engineering, 2014 DOI:10.1109/MECBME.2014.6783212

[25]	Neural Information Processing Lecture Notes in Computer Science, 2014 DOI:10.1007/978-3-319-12640-1_13

[26]	Artificial Neural Networks and Machine Learning – ICANN 2014 Lecture Notes in Computer Science, 2014 DOI:10.1007/978-3-319-11179-7_105

[27]	Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms International Journal of Biomedical Imaging, 2013 DOI:10.1155/2013/323268

[28]	Automated segmentation of skin lesions: Modified Fuzzy C mean thresholding based level set method INMIC, 2013 DOI:10.1109/INMIC.2013.6731350

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

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