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Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods

M. Emre Celebi, Quan Wen, Sae Hwang, Hitoshi Iyatomi, Gerald Schaefer
2012 Skin research and technology  

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2013 pre-print arXiv:1312.7345v1 fatcat:foyabrivjzav3mbjrrrakfhfvu
In this paper, we present an automated method for detecting lesion borders in dermoscopy images using ensembles of thresholding methods.  ...  Border detection is often the first step in this analysis. In many cases, the lesion can be roughly separated from the background skin using a thresholding method applied to the blue channel.  ...  Acknowledgments This publication was made possible by grants from the Louisiana Board of Regents (LEQSF2008-11-RD-A-12), US National Science Foundation (0959583, 1117457), and National Natural Science  ... 
doi:10.1111/j.1600-0846.2012.00636.x pmid:22676490 fatcat:cy5xdlz27vb2vpqkihwv7koiqa
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M. Emre Celebi, Quan Wen, Sae Hwang, Hitoshi Iyatomi, Gerald Schaefer. "Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods." Skin research and technology 19.1 (2012) e252-e258

Approximate lesion localization in dermoscopy images

M. Emre Celebi, Hitoshi Iyatomi, Gerald Schaefer, William V. Stoecker
2009 Skin research and technology  

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2010 pre-print
arXiv:1009.1362v1 fatcat:v3vf76b3kjd3dnc76lc53gz2ia
In this method, first the black frame around the image is removed using an iterative algorithm. The approximate location of the lesion is then determined using an ensemble of thresholding algorithms.  ...  Methods: In this article, we present an approximate lesion localization method that serves as a preprocessing step for detecting borders in dermoscopy images.  ...  Acknowledgments This work was supported by grants from Louisiana Board of Regents (LEQSF2008-11-RD-A-12) and NIH (SBIR #2R44 CA-101639-02A2).  ... 
doi:10.1111/j.1600-0846.2009.00357.x pmid:19624428 pmcid:PMC3152314 fatcat:h7kmxwistvaqpb46wn6fvbclmi
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M. Emre Celebi, Hitoshi Iyatomi, Gerald Schaefer, William V. Stoecker. "Approximate lesion localization in dermoscopy images." Skin research and technology 15.3 (2009) 314-322

Localization of Lesions in Dermoscopy Images Using Ensembles of Thresholding Methods [chapter]

M. Emre Celebi, Hitoshi Iyatomi, Gerald Schaefer, William V. Stoecker
2009 Lecture Notes in Computer Science  

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In this method, first the black frame around the image is removed using an iterative algorithm. The approximate location of the lesion is then determined using an ensemble of thresholding algorithms.  ...  In this article, we present an approximate lesion localization method that serves as a preprocessing step for detecting borders in dermoscopy images.  ...  Automated border detection is often the first step in the automated analysis of dermoscopy images [7, 8, 9] . It is crucial for the image analysis for two main reasons.  ... 
doi:10.1007/978-3-540-92957-4_95 fatcat:xauam5hddbbhro2bazgyrgkiuy
Citation

M. Emre Celebi, Hitoshi Iyatomi, Gerald Schaefer, William V. Stoecker. "Localization of Lesions in Dermoscopy Images Using Ensembles of Thresholding Methods." Lecture Notes in Computer Science (2009) 1094-1103

Contrast enhancement in dermoscopy images by maximizing a histogram bimodality measure

M. Emre Celebi, Hitoshi Iyatomi, Gerald Schaefer
2009 2009 16th IEEE International Conference on Image Processing (ICIP)  

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Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions.  ...  two regions using Otsu's thresholding method.  ...  [13] proposed a method for approximate lesion localization using an ensemble of thresholding algorithms. In this paper, we present an effective method to enhance the contrast in dermoscopy images.  ... 
doi:10.1109/icip.2009.5413990 dblp:conf/icip/CelebiIS09 fatcat:23nosj3jwrcuppuye4qqqkzjpa
Citation

M. Emre Celebi, Hitoshi Iyatomi, Gerald Schaefer. "Contrast enhancement in dermoscopy images by maximizing a histogram bimodality measure." 2009 16th IEEE International Conference on Image Processing (ICIP) (2009) 2601-2604

Classification of Melanoma on Dermoscopy Images using SVM Classifier

Ashtami K. M
2018 International Journal for Research in Applied Science and Engineering Technology  

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The algorithm contains the following steps: first, lesions extracted using k-means method; then second, features are extracted; and third, lesion classification by using a classifier based on a Support  ...  Lesions occur that are overlarge to be entirely contained among the dermoscopy image.  ...  Fig. 1 . 1 Dermoscopy images of incomplete lesion objects. Fig. 2 . 2 Segmentation instances on dermoscopy image (yellow line: k-means). Generation of two lesion regions.  ... 
doi:10.22214/ijraset.2018.4440 fatcat:cmjr6xd6tnegfba634l3amdj3y
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Citation

Ashtami K. M. "Classification of Melanoma on Dermoscopy Images using SVM Classifier." International Journal for Research in Applied Science and Engineering Technology 6.4 (2018) 2631-2638

Effective Classification Techniques for Melanoma Classification on Dermoscopy Images

Ms. Ashtami K M
2018 International Journal for Research in Applied Science and Engineering Technology  

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Dermoscopy technique is one of the major imaging modalities used in the diagnosis of the pigmented skin lesions.  ...  Classifying the melanocytic tumors as benign or malignant by the analysis of dermoscopy images using Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifier.  ...  ARTIFACTS REMOVING AND DETECTION OF BOUNDARY The purpose of artifacts removal stage is that the hair removal. Due to the presence of hairs dermoscopy image analysis is greatly difficult.  ... 
doi:10.22214/ijraset.2018.7089 fatcat:5i43tgpagvec3ieq6npqclqdaa
Open Access
Citation

Ms. Ashtami K M. "Effective Classification Techniques for Melanoma Classification on Dermoscopy Images." International Journal for Research in Applied Science and Engineering Technology 6.7 (2018) 521-526

FUSION OF STRUCTURAL AND TEXTURAL FEATURES FOR MELANOMA AND SKIN DISEASE RECOGNITION USING IMAGE PROCESSING

Vanaja C, Pragadeesh M, Rathesh R
2022 IJIREEICE  

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Dermoscopy is a non-invasive skin imaging technique of acquiring a magnified and illuminated image of a region of skin for increased clarity of the spots on the skin.  ...  In the first stage, the image of the skin disease is subject to various kinds of pre-processing techniques followed by feature extraction.  ...  Automatic Skin Cancer Image Lesion Classification To improve the accuracy of the model Detection in Dermoscopy and deploy the model to the mobile end Images Based on Ensemble or the web end for people  ... 
doi:10.17148/ijireeice.2022.10229 fatcat:ffirznddirbptgh2ib2dg3p6ru
Citation

Vanaja C, Pragadeesh M, Rathesh R. "FUSION OF STRUCTURAL AND TEXTURAL FEATURES FOR MELANOMA AND SKIN DISEASE RECOGNITION USING IMAGE PROCESSING." IJIREEICE 10.2 (2022)

Classification of Dermoscopy Images for Early Detection of Skin Cancer – A Review

Ebrahim Mohammed, Mukti E.
2019 International Journal of Computer Applications  

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A number of methods have been researched to use automated and computerized system for skin diseases image processing.  ...  Various dermoscopy image processing techniques have been reviewed to explore the possible solution to skin diseases and to select an appropriate method for early detection7 of skin diseases.  ...  Lesiton detects the boundary lesion. 100 images of dermoscopy are used for analytical purposes.  ... 
doi:10.5120/ijca2019918986 fatcat:jhcnpokx5fffbiw7m3qmcmocgy
Citation

Ebrahim Mohammed, Mukti E.. "Classification of Dermoscopy Images for Early Detection of Skin Cancer – A Review." International Journal of Computer Applications 178.17 (2019) 37-43

Comparative Analyses of Classifiers for Diagnosis of Skin Cancer using Dermoscopic Images

P . Kavimathi
2016 Indian Journal of Science and Technology  

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An efficient image analysis module has been developed with efficient algorithm to detect the skin lesions. In the analysis system classification plays an important role in identification of defect.  ...  In the proposed system different types of classifiers such as Support Vector Machine, ensemble classifier, probabilistic neural network and adaptive neuro-fuzzy inference system classifiers are used in  ...  Keywords: Classification, Ensemble, Image Segmentation, Neural Network, Neuro-Fuzzy, Skin Cancer Abbas proposed an effective and simple method to detect the borders of tumors in color images.  ... 
doi:10.17485/ijst/2016/v9i43/103824 fatcat:vitsyqpkzrfrfiujkekm4qtesi
Citation

P . Kavimathi. "Comparative Analyses of Classifiers for Diagnosis of Skin Cancer using Dermoscopic Images." Indian Journal of Science and Technology 9.43 (2016)

Computational methods for the image segmentation of pigmented skin lesions: A review

Roberta B. Oliveira, Mercedes E. Filho, Zhen Ma, João P. Papa, Aledir S. Pereira, João Manuel R.S. Tavares
2016 Computer Methods and Programs in Biomedicine  

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A B S T R A C T Background and objectives: Because skin cancer affects millions of people worldwide, computational methods for the segmentation of pigmented skin lesions in images have been developed in  ...  In addition, several of the reviewed techniques are applied to macroscopic and dermoscopy images in order to exemplify their results.  ...  Acknowledgements The first author would like to thank the "Conselho Nacional de Desenvolvimento Científico e Tecnológico" (CNPq), in Brazil, for her PhD grant.  ... 
doi:10.1016/j.cmpb.2016.03.032 pmid:27265054 fatcat:llydo4vyzzailpzcdemg3givpq
Citation

Roberta B. Oliveira, Mercedes E. Filho, Zhen Ma, João P. Papa, Aledir S. Pereira, João Manuel R.S. Tavares. "Computational methods for the image segmentation of pigmented skin lesions: A review." Computer Methods and Programs in Biomedicine (2016) 127-141

Melanoma Classification Using Machine Learning Techniques [chapter]

J. Jacinth Poornima, J. Anitha, Asha Priya Henry, D. Jude Hemanth
2023 Frontiers in Artificial Intelligence and Applications  

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The main objective of this paper is to classify the Melanoma and Non-melanoma using dermoscopy images from the Med Node Dataset.  ...  The images are enhanced by bottomhat filter, and then segmentation is done by two methods 1. Morphological Operations, 2. Otsu Thresholding.  ...  There are two method's used for the segmentation of lesion. Otsu's Method Lesion segmentation from the enlarged picture is accomplished using the Otsu Thresholding method.  ... 
doi:10.3233/faia220712 fatcat:nhftdvwj6bhwvdjjmmhhqqbheu
Open Access
Citation

J. Jacinth Poornima, J. Anitha, Asha Priya Henry, D. Jude Hemanth. "Melanoma Classification Using Machine Learning Techniques." Frontiers in Artificial Intelligence and Applications (2023)

A Review of Prevalent Methods for Automatic Skin Lesion Diagnosis

Damilola A Okuboyejo, Oludayo O Olugbara
2018 Open Dermatology Journal  

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Current and state-ofthe-art skin lesion diagnostic methods for assisting the diagnosis of melanocytic lesions are reviewed in Section 5.  ...  Methods: A comparative study was carried out on selected literature from different accessible digital libraries of skin lesion research, especially cancerous moles in regard to the convention used, assumptions  ...  different automatic border detection methods for lesion images.  ... 
doi:10.2174/187437220181201014 fatcat:73fywmwnwbfydea535vgmekram
Citation

Damilola A Okuboyejo, Oludayo O Olugbara. "A Review of Prevalent Methods for Automatic Skin Lesion Diagnosis." Open Dermatology Journal 12.1 (2018) 14-53

Artificial Intelligence in Cutaneous Oncology

Yu Seong Chu, Hong Gi An, Byung Ho Oh, Sejung Yang
2020 Frontiers in Medicine  

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In addition, the universal use of dermoscopy, which allows for non-invasive inspection of the upper dermal level of skin lesions with a usual 10-fold magnification, adds to the image storage and analysis  ...  This paper presents a comprehensive review of the clinical applications of artificial intelligence and a discussion on how it can be implemented in the field of cutaneous oncology.  ...  (decision tree) n = 659 dermoscopy images 299 (BCC) 360 (Non-BCC) Kefel et al. (44) Automatic method for detection of pink blush (common feature in BCC) Manually created borders vs.  ... 
doi:10.3389/fmed.2020.00318 pmid:32754606 pmcid:PMC7366843 fatcat:557xllh2dzf5hbnrrmcdbg2kyi
DOAJ
Citation

Yu Seong Chu, Hong Gi An, Byung Ho Oh, Sejung Yang. "Artificial Intelligence in Cutaneous Oncology." Frontiers in Medicine (2020) 318

Inferring Skin Lesion Segmentation with Fully Connected CRFs based on Multiple Deep Convolutional Neural Networks

Yuming Qiu, Jingyong Cai, Xiaolin Qin, Ju Zhang
2020 IEEE Access  

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Therefore, lesion border detection or lesion segmentation is extremely important in analyzing dermoscopy images that are the major imaging modality in the diagnosis of pigmented skin lesions [1] , [5  ...  the local information in fuzzy dermoscopy images and be able to find more accurate lesion border.  ...  His research interests include computer algorithms and application of artificial intelligence in medical sciences.  ... 
doi:10.1109/access.2020.3014787 fatcat:nkbbl5z3jje6tkaksbae35u5ca
DOAJ
Citation

Yuming Qiu, Jingyong Cai, Xiaolin Qin, Ju Zhang. "Inferring Skin Lesion Segmentation with Fully Connected CRFs based on Multiple Deep Convolutional Neural Networks." IEEE Access (2020) 1-1

Classification of Melanoma Lesions Using Wavelet-Based Texture Analysis

Rahil Garnavi, Mohammad Aldeen, James Bailey
2010 2010 International Conference on Digital Image Computing: Techniques and Applications  

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images, which resulted in an accuracy of 88.24% and ROC area of 0.918.  ...  The method applies tree-structured wavelet transform on different color channels of red, green, blue and luminance of dermoscopy images, and employs various statistical measures and ratios on wavelet coefficients  ...  Emre Celebi from Department of Computer Science, Louisiana State University in Shreveport, USA, for assisting with the data.  ... 
doi:10.1109/dicta.2010.22 dblp:conf/dicta/GarnaviAB10 fatcat:ntlmlbyj5ffvbpw7t5zvidf4ku
Citation

Rahil Garnavi, Mohammad Aldeen, James Bailey. "Classification of Melanoma Lesions Using Wavelet-Based Texture Analysis." 2010 International Conference on Digital Image Computing: Techniques and Applications (2010) 75-81

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