20.13.5.1 Medical Applications -- Skin Lesions, Wound Healing

Chapter Contents (Back)
Dermatology. Skin Lesions.

Bon, F.X., Briand, E., Guichard, S., Couturaud, B., Revol, M., Servant, J.M., Dubertret, L.,
Quantitative and kinetic evolution of wound healing through image analysis,
MedImg(19), No. 7, July 2000, pp. 767-772.
IEEE Top Reference. 0110
BibRef

Hansen, G.L., Sparrow, E.M., Kokate, J.Y., Leland, K.J., Iaizzo, P.A.,
Wound status evaluation using color image processing,
MedImg(16), No. 1, February 1997, pp. 78-86.
IEEE Top Reference. 0205
BibRef

Qi, J.Y.[Jin-Yi], Huesman, R.H.,
Theoretical study of lesion detectability of MAP reconstruction using computer observers,
MedImg(20), No. 8, August 2001, pp. 815-822.
IEEE Top Reference. 0110

See also theoretical study of the contrast recovery and variance of MAP reconstructions from PET data, A. BibRef

Thirion, J.P., Calmon, G.,
Deformation analysis to detect and quantify active lesions in three-dimensional medical image sequences,
MedImg(18), No. 5, May 1999, pp. 429-441.
IEEE Top Reference. 0110
BibRef

Grana, C., Pellacani, G., Cucchiara, R., Seidenari, S.,
A new algorithm for border description of polarized light surface microscopic images of pigmented skin lesions,
MedImg(22), No. 8, August 2003, pp. 959-964.
IEEE Abstract. 0308

See also Comments on A New Algorithm for Border Description of Polarized Light Surface Microscopic Images of Pigmented Skin Lesions. BibRef

Cheng, H.D., Cui, M.[Muyi],
Mass lesion detection with a fuzzy neural network,
PR(37), No. 6, June 2004, pp. 1189-1200.
Elsevier DOI 0405
BibRef

Zhang, Y., Goldgof, D.B., Sarkar, S., Tsap, L.V.,
A Modeling Approach for Burn Scar Assessment Using Natural Features and Elastic Property,
MedImg(23), No. 10, October 2004, pp. 1325-1329.
IEEE Abstract. 0410
BibRef

Burroni, M., Alparone, L., Argenti, F.,
Comments on 'A New Algorithm for Border Description of Polarized Light Surface Microscopic Images of Pigmented Skin Lesions',
MedImg(25), No. 12, December 2006, pp. 1655-1656.
IEEE DOI 0701

See also new algorithm for border description of polarized light surface microscopic images of pigmented skin lesions, A. BibRef

Gomez, D.D.[David Delgado], Butakoff, C.[Constantine], Ersboll, B.[Bjarne], Carstensen, J.M.[Jens Michael],
Automatic change detection and quantification of dermatological diseases with an application to psoriasis images,
PRL(28), No. 9, 1 July 2007, pp. 1012-1018.
Elsevier DOI 0704
Change detection; Skin lesion; Psoriasis; Image analysis BibRef

Gomez, D.D.[David Delgado], Carstensen, J.M.[Jens Michael], Ersboll, B.[Bjarne], Skov, L.[Lone], Bang, B.[Bo],
Building an Image-Based System to Automatically Score Psoriasis,
SCIA03(557-564).
Springer DOI 0310
BibRef

Maletti, G.[Gabriela], Ersbøll, B.[Bjarne],
Illumination Correction from Psoriasis Image Data,
SCIA03(549-556).
Springer DOI 0310
BibRef

Yuan, X.J.[Xiao-Jing], Situ, N.[Ning], Zouridakis, G.[George],
A narrow band graph partitioning method for skin lesion segmentation,
PR(42), No. 6, June 2009, pp. 1017-1028.
Elsevier DOI 0902
Automatic skin lesion segmentation; Melanoma detection; Border detection; Active contour; Snake; Narrow band energy BibRef

Treuillet, S.[Sylvie], Albouy, B.[Benjamin], Lucas, Y.[Yves],
Three-Dimensional Assessment of Skin Wounds Using a Standard Digital Camera,
MedImg(28), No. 5, May 2009, pp. 752-762.
IEEE DOI 0905
BibRef

Wannous, H.[Hazem], Lucas, Y.[Yves], Treuillet, S.[Sylvie], Albouy, B.[Benjamin],
A complete 3D wound assessment tool for accurate tissue classification and measurement,
ICIP08(2928-2931).
IEEE DOI 0810
BibRef
And:
Fusion of Multi-view Tissue Classification Based on Wound 3D Model,
ACIVS08(xx-yy).
Springer DOI 0810
BibRef

Veredas, F., Mesa, H., Morente, L.,
Binary Tissue Classification on Wound Images With Neural Networks and Bayesian Classifiers,
MedImg(29), No. 2, February 2010, pp. 410-427.
IEEE DOI 1002
BibRef

Wannous, H., Lucas, Y., Treuillet, S.,
Enhanced Assessment of the Wound-Healing Process by Accurate Multiview Tissue Classification,
MedImg(30), No. 2, February 2011, pp. 315-326.
IEEE DOI 1102
BibRef

Hani, A.F.M., Eltegani, N.M., Arshad, L., Hussein, S.H., Jamil, A., Gill, P.,
Wound model reconstruction from three-dimensional skin surface imaging using the convex hull approximation method,
IET-IPR(6), No. 5, 2012, pp. 521-533.
DOI Link 1210
BibRef

Pereyra, M., Dobigeon, N., Batatia, H., Tourneret, J.,
Segmentation of Skin Lesions in 2-D and 3-D Ultrasound Images Using a Spatially Coherent Generalized Rayleigh Mixture Model,
MedImg(31), No. 8, August 2012, pp. 1509-1520.
IEEE DOI 1208
BibRef

Shepherd, T., Prince, S.J.D., Alexander, D.C.,
Interactive Lesion Segmentation with Shape Priors From Offline and Online Learning,
MedImg(31), No. 9, September 2012, pp. 1698-1712.
IEEE DOI 1209
BibRef

Xie, F.Y.[Feng-Ying], Bovik, A.C.[Alan C.],
Automatic segmentation of dermoscopy images using self-generating neural networks seeded by genetic algorithm,
PR(46), No. 3, March 2013, pp. 1012-1019.
Elsevier DOI 1212
Dermoscopy images; Self-generating neural network; Image clustering; Automatic segmentation; Generic algorithms BibRef

Deng, Z.L.[Zi-Lin], Fan, H.D.[Hai-Di], Xie, F.Y.[Feng-Ying], Cui, Y.[Yong], Liu, J.[Jie],
Segmentation of Dermoscopy Images Based on Fully Convolutional Neural Network,
ICIP17(1732-1736)
IEEE DOI 1803
Convolutional neural networks, Feature extraction, Image analysis, Image segmentation, Lesions, Robustness, Skin, Lesion segmentation BibRef

Lu, J., Kazmierczak, E., Manton, J.H., Sinclair, R.,
Automatic Segmentation of Scaling in 2-D Psoriasis Skin Images,
MedImg(32), No. 4, April 2013, pp. 719-730.
IEEE DOI 1304
BibRef

Sadeghi, M., Lee, T.K., McLean, D., Lui, H., Atkins, M.S.,
Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions,
MedImg(32), No. 5, May 2013, pp. 849-861.
IEEE DOI 1305
BibRef

Vionnet, L., Gateau, J., Schwarz, M., Buehler, A., Ermolayev, V., Ntziachristos, V.,
24-MHz Scanner for Optoacoustic Imaging of Skin and Burn,
MedImg(33), No. 2, February 2014, pp. 535-545.
IEEE DOI 1403
biomedical optical imaging BibRef

Korotkov, K., Quintana, J., Puig, S., Malvehy, J., Garcia, R.,
A New Total Body Scanning System for Automatic Change Detection in Multiple Pigmented Skin Lesions,
MedImg(34), No. 1, January 2015, pp. 317-338.
IEEE DOI 1502
biomedical optical imaging BibRef

Kasmi, R., Mokrani, K.,
Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule,
IET-IPR(10), No. 6, 2016, pp. 448-455.
DOI Link 1606
biomedical optical imaging BibRef

Zortea, M.[Maciel], Flores, E.[Eliezer], Scharcanski, J.[Jacob],
A simple weighted thresholding method for the segmentation of pigmented skin lesions in macroscopic images,
PR(64), No. 1, 2017, pp. 92-104.
Elsevier DOI 1701
Segmentation BibRef

Shrivastava, S.[Shubhangi], Raj, A.N.J.[Alex Noel Joseph],
A vision-based non-contact area and volume estimation of irregular structures towards applications in wound measurement,
IJCVR(7), No. 5, 2017, pp. 489-501.
DOI Link 1709
BibRef

Pezeshk, A., Petrick, N., Chen, W., Sahiner, B.,
Seamless Lesion Insertion for Data Augmentation in CAD Training,
MedImg(36), No. 4, April 2017, pp. 1005-1015.
IEEE DOI 1704
Biomedical imaging BibRef

Jaisakthi, S.M.[Seetharani Murugaiyan], Mirunalini, P.[Palaniappan], Aravindan, C.[Chandrabose],
Automated skin lesion segmentation of dermoscopic images using GrabCut and k-means algorithms,
IET-CV(12), No. 8, December 2018, pp. 1088-1095.
DOI Link 1812
BibRef

Creswell, A.[Antonia], Pouplin, A.[Alison], Bharath, A.A.[Anil A.],
Denoising adversarial autoencoders: classifying skin lesions using limited labelled training data,
IET-CV(12), No. 8, December 2018, pp. 1105-1111.
DOI Link 1812
BibRef

Guo, K.[Kehua], He, Y.[Yan], Kui, X.Y.[Xiao-Yan], Sehdev, P.[Paramjit], Chi, T.[Tao], Zhang, R.[Ruifang], Li, J.[Jialun],
LLTO: Towards efficient lesion localization based on template occlusion strategy in intelligent diagnosis,
PRL(116), 2018, pp. 225-232.
Elsevier DOI 1812
BibRef

Huang, L.[Lin], Zhao, Y.G.[Yi-Gong], Yang, T.J.[Tie-Jun],
Skin lesion segmentation using object scale-oriented fully convolutional neural networks,
SIViP(13), No. 3, April 2019, pp. 431-438.
WWW Link. 1904
BibRef

Sevik, U.[Ugur], Karakullukçu, E.[Erdinç], Berber, T.[Tolga], Akbas, Y.[Yesim], Türkyilmaz, S.[Serdar],
Automatic classification of skin burn colour images using texture-based feature extraction,
IET-IPR(13), No. 11, 19 September 2019, pp. 2018-2028.
DOI Link 1909
BibRef

Ren, S.[Sheng], Jain, D.K.[Deepak Kumar], Guo, K.[Kehua], Xu, T.[Tao], Chi, T.[Tao],
Towards efficient medical lesion image super-resolution based on deep residual networks,
SP:IC(75), 2019, pp. 1-10.
Elsevier DOI 1906
Super-resolution, Deep residual networks, Medical diagnosis BibRef

Yang, T.J.[Tie-Jun], Chen, Y.W.[Yao-Wen], Lu, J.[Jiewei], Fan, Z.[Zhun],
Sampling with level set for pigmented skin lesion segmentation,
SIViP(13), No. 4, June 2019, pp. 813-821.
Springer DOI 1906
BibRef

Nitkunanantharajah, S., Zahnd, G., Olivo, M., Navab, N., Mohajerani, P., Ntziachristos, V.,
Skin Surface Detection in 3D Optoacoustic Mesoscopy Based on Dynamic Programming,
MedImg(39), No. 2, February 2020, pp. 458-467.
IEEE DOI 2002
Skin, Imaging, Image segmentation, surface segmentation BibRef

Serte, S.[Sertan], Demirel, H.[Hasan],
Wavelet-based deep learning for skin lesion classification,
IET-IPR(14), No. 4, 27 March 2020, pp. 720-726.
DOI Link 2003
BibRef

Filko, D.[Damir], Cupec, R.[Robert], Nyarko, E.K.[Emmanuel Karlo],
Wound measurement by RGB-D camera,
MVA(29), No. 4, May 2018, pp. 633-654.
WWW Link. 1805
BibRef

Li, C.[Chao], Brost, V.[Vincent], Benezeth, Y.[Yannick], Marzani, F.[Franck], Yang, F.[Fan],
Design and evaluation of a parallel and optimized light-tissue interaction-based method for fast skin lesion assessment,
RealTimeIP(15), No. 2, August 2018, pp. 407-420.
Springer DOI 1808
BibRef

Ashour, A.S.[Amira S.], Hawas, A.R.[Ahmed Refaat], Guo, Y.H.[Yan-Hui], Wahba, M.A.[Maram A.],
A novel optimized neutrosophic k-means using genetic algorithm for skin lesion detection in dermoscopy images,
SIViP(12), No. 7, October 2018, pp. 1311-1318.
Springer DOI 1809
BibRef

Rundo, F.[Francesco], Conoci, S.[Sabrina], Banna, G.L.[Giuseppe L.], Ortis, A.[Alessandro], Stanco, F.[Filippo], Battiato, S.[Sebastiano],
Evaluation of Levenberg-Marquardt neural networks and stacked autoencoders clustering for skin lesion analysis, screening and follow-up,
IET-CV(12), No. 7, October 2018, pp. 957-962.
DOI Link 1809
BibRef

Xie, Y., Zhang, J., Xia, Y., Shen, C.,
A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification,
MedImg(39), No. 7, July 2020, pp. 2482-2493.
IEEE DOI 2007
Lesions, Image segmentation, Skin, Task analysis, Feature extraction, Decoding, Computational modeling, Skin lesion segmentation, dermoscopy BibRef

de A. Rodrigues, D.[Douglas], Ivo, R.F.[Roberto F.], Satapathy, S.C.[Suresh Chandra], Wang, S.[Shuihua], Hemanth, J.[Jude], Filho, P.P.R.[Pedro P. Rebouças],
A new approach for classification skin lesion based on transfer learning, deep learning, and IoT system,
PRL(136), 2020, pp. 8-15.
Elsevier DOI 2008
Skin lesion, Transfer learning, CNN BibRef

Nikesh, P., Raju, G.,
Directional Vector-Based Skin Lesion Segmentation: A Novel Approach to Skin Segmentation,
IJIG(20), No. 3, July 2020, pp. 2050021.
DOI Link 2008
BibRef

Jiji, G.W.[G. Wiselin], Rajesh, A., Raj, P.J.D.[P. Johnson Durai],
Decision Support Techniques for Dermatology Using Case-Based Reasoning,
IJIG(20), No. 3, July 2020, pp. 2050024.
DOI Link 2008
BibRef

Chakkaravarthy, A.P.[A. Prabhu], Chandrasekar, A.,
Anatomical region segmentation method from dermoscopic images of pigmented skin lesions,
IJIST(30), No. 3, 2020, pp. 636-652.
DOI Link 2008
canny edge detection, color segmentation, Gaussian filter, image gradient, morphological dilation, salt and pepper noise, watershed segmentation BibRef

Vasconcelos, C.N.[Cristina Nader], Vasconcelos, B.N.[Bárbara Nader],
Experiments using deep learning for dermoscopy image analysis,
PRL(139), 2020, pp. 95-103.
Elsevier DOI 2011
Skin lesion classification, Dermoscopy image analysis, Lesion dermoscopic feature extraction and classification, Deep learning BibRef

Barata, C.[Catarina], Celebi, M.E.[M. Emre], Marques, J.S.[Jorge S.],
Explainable skin lesion diagnosis using taxonomies,
PR(110), 2021, pp. 107413.
Elsevier DOI 2011
Hierarchical deep learning, Explainability, Channel attention, Spatial attention, Safety-critical CADS, Skin cancer BibRef

Wu, H., Pan, J., Li, Z., Wen, Z., Qin, J.,
Automated Skin Lesion Segmentation Via an Adaptive Dual Attention Module,
MedImg(40), No. 1, January 2021, pp. 357-370.
IEEE DOI 2012
Lesions, Skin, Image segmentation, Feature extraction, Melanoma, Task analysis, Shape, Skin lesion segmentation, deep learning BibRef

Xie, Y., Zhang, J., Lu, H., Shen, C., Xia, Y.,
SESV: Accurate Medical Image Segmentation by Predicting and Correcting Errors,
MedImg(40), No. 1, January 2021, pp. 286-296.
IEEE DOI 2012
Image segmentation, Lesions, Medical diagnostic imaging, Skin, Glands, Retina, Medical image segmentation, correction learning BibRef

Cai, J., Harrison, A.P., Zheng, Y., Yan, K., Huo, Y., Xiao, J., Yang, L., Lu, L.,
Lesion-Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale,
MedImg(40), No. 1, January 2021, pp. 59-70.
IEEE DOI 2012
Lesions, Proposals, Computed tomography, Training, Detectors, Biomedical imaging, pseudo 3D IoU BibRef

Kosgiker, G.M.[Gouse Mohiuddin], Deshpande, A.[Anupama], Kauser, A.[Anjum],
SegCaps: An efficient SegCaps network-based skin lesion segmentation in dermoscopic images,
IJIST(31), No. 2, 2021, pp. 874-894.
DOI Link 2105
deep learning, dermoscopic images, multi-level preprocessing, SegCaps model, skin lesion segmentation BibRef

Saha, M.[Manas], Naskar, M.K.[Mrinal Kanti], Chatterji, B.N.,
Human skin ringworm detection using wavelet and curvelet transforms: A Comparative Study,
IJCVR(11), No. 3, 2021, pp. 245-263.
DOI Link 2106
BibRef

Li, W.[Wei], Joseph Raj, A.N.[Alex Noel], Tjahjadi, T.[Tardi], Zhuang, Z.M.[Zhe-Min],
Digital hair removal by deep learning for skin lesion segmentation,
PR(117), 2021, pp. 107994.
Elsevier DOI 2106
Dermoscopy, Digital hair removal, Skin lesion segmentation, Deep learning BibRef

Liu, Y.P.[Yi-Peng], Wang, Z.M.[Zi-Ming], Li, Z.Q.[Zhan-Qing], Li, J.[Jing], Li, T.[Ting], Chen, P.[Peng], Liang, R.H.[Rong-Hua],
Multiscale ensemble of convolutional neural networks for skin lesion classification,
IET-IPR(15), No. 10, 2021, pp. 2309-2318.
DOI Link 2108
BibRef

Bagheri, F.[Fatemeh], Tarokh, M.J.[Mohammad Jafar], Ziaratban, M.[Majid],
Skin lesion segmentation based on mask RCNN, Multi Atrous Full-CNN, and a geodesic method,
IJIST(31), No. 3, 2021, pp. 1609-1624.
DOI Link 2108
geodesic, MAFCNN, Mask R-CNN, semantic segmentation, skin lesion BibRef

Melbin, K., Raj, Y.J.V.[Y. Jacob Vetha],
Automated detection and classification of skin diseases using diverse features and improved gray wolf-based multiple-layer perceptron neural network,
IJIST(31), No. 3, 2021, pp. 1317-1333.
DOI Link 2108
dataset and classification, features, morphological operation, skin disease BibRef

Li, Y.[Yan], Murthy, R.S.[Raksha Sreeramachandra], Zhu, Y.[Yirui], Zhang, F.[Fengyi], Tang, J.N.[Jia-Ning], Mehrabi, J.N.[Joseph N.], Kelly, K.M.[Kristen M.], Chen, Z.[Zhongping],
1.7-Micron Optical Coherence Tomography Angiography for Characterization of Skin Lesions: A Feasibility Study,
MedImg(40), No. 9, September 2021, pp. 2507-2512.
IEEE DOI 2109
Imaging, Lesions, Visualization, Green products, Angiography, Spatial resolution, clinical diagnosis BibRef

Wang, X.H.[Xiao-Hong], Jiang, X.D.[Xu-Dong], Ding, H.H.[Heng-Hui], Zhao, Y.Q.[Yu-Qian], Liu, J.[Jun],
Knowledge-aware deep framework for collaborative skin lesion segmentation and melanoma recognition,
PR(120), 2021, pp. 108075.
Elsevier DOI 2109
Melanoma diagnosis, Knowledge-aware deep framework, Lesion-based pooling and shape extraction, Recursive mutual learning BibRef


Mirikharaji, Z.[Zahra], Abhishek, K.[Kumar], Izadi, S.[Saeed], Hamarneh, G.[Ghassan],
D-LEMA: Deep Learning Ensembles from Multiple Annotations - Application to Skin Lesion Segmentation,
ISIC21(1837-1846)
IEEE DOI 2109
Training, Image segmentation, Uncertainty, Annotations, Computational modeling, Training data, Predictive models BibRef

Bissoto, A.[Alceu], Valle, E.[Eduardo], Avila, S.[Sandra],
GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review,
ISIC21(1847-1856)
IEEE DOI 2109
Training, Biomedical equipment, Medical services, Generative adversarial networks, Skin BibRef

Mohseni, M.[Mohammadreza], Yap, J.[Jordan], Yolland, W.[William], Koochek, A.[Arash], Atkins, M.S.[M. Stella],
Can self-training identify suspicious ugly duckling lesions?,
ISIC21(1829-1836)
IEEE DOI 2109
Deep learning, Sensitivity, Malignant tumors, Medical services, Manuals, Feature extraction BibRef

Groh, M.[Matthew], Harris, C.[Caleb], Soenksen, L.[Luis], Lau, F.[Felix], Han, R.[Rachel], Kim, A.[Aerin], Koochek, A.[Arash], Badri, O.[Omar],
Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset,
ISIC21(1820-1828)
IEEE DOI 2109
Deep learning, Training, Image segmentation, Dermatology, Neural networks, Training data BibRef

Reimers, C.[Christian], Penzel, N.[Niklas], Bodesheim, P.[Paul], Runge, J.[Jakob], Denzler, J.[Joachim],
Conditional dependence tests reveal the usage of ABCD rule features and bias variables in automatic skin lesion classification,
ISIC21(1810-1819)
IEEE DOI 2109
Training, Image color analysis, Shape, Supervised learning, Melanoma, Skin, Classification algorithms BibRef

Oota, S.R.[Subba Reddy], Rowtula, V.[Vijay], Mohammed, S.[Shahid], Galitz, J.[Jeffrey], Liu, M.[Minghsun], Gupta, M.[Manish],
HealTech - A System for Predicting Patient Hospitalization Risk and Wound Progression in Old Patients,
WACV21(2462-2471)
IEEE DOI 2106
Analytical models, Neural networks, Evolutionary computation, Wounds, Predictive models BibRef

Allegretti, S.[Stefano], Bolelli, F.[Federico], Pollastri, F.[Federico], Longhitano, S.[Sabrina], Pellacani, G.[Giovanni], Grana, C.[Costantino],
Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval,
ICPR21(8053-8060)
IEEE DOI 2105
Image retrieval, Medical services, Feature extraction, Skin, Pattern recognition, Lesions, Convolutional neural networks BibRef

Carcagnì, P.[Pierluigi], Leo, M.[Marco], Celeste, G.[Giuseppe], Distante, C.[Cosimo], Cuna, A.[Andrea],
A Systematic Investigation on Deep Architectures for Automatic Skin Lesions Classification,
ICPR21(8639-8646)
IEEE DOI 2105
Training, Systematics, Sociology, Pipelines, Skin, Data models, Lesions BibRef

Gallucci, A.[Alessio], Znamenskiy, D.[Dmitry], Pezzotti, N.[Nicola], Petkovic, M.[Milan],
Don't Tear Your Hair Out: Analysis of the Impact of Skin Hair on the Diagnosis of Microscopic Skin Lesions,
AIHA20(406-416).
Springer DOI 2103
BibRef

Kumar, A., Hamarneh, G., Drew, M.S.,
Illumination-based Transformations Improve Skin Lesion Segmentation in Dermoscopic Images,
ISIC20(3132-3141)
IEEE DOI 2008
Skin, Lesions, Image color analysis, Image segmentation, Gray-scale, Lighting, Semantics BibRef

Ribeiro, V., Avila, S., Valle, E.,
Less is More: Sample Selection and Label Conditioning Improve Skin Lesion Segmentation,
ISIC20(3182-3191)
IEEE DOI 2008
Image segmentation, Lesions, Training, Machine learning, Skin, Task analysis, Data models BibRef

Bissoto, A., Valle, E., Avila, S.,
Debiasing Skin Lesion Datasets and Models? Not So Fast,
ISIC20(3192-3201)
IEEE DOI 2008
Lesions, Correlation, Skin, Task analysis, Training, Feature extraction, Data models BibRef

Barata, C., Santiago, C.,
How Important Is Each Dermoscopy Image?,
ISIC20(3202-3210)
IEEE DOI 2008
Training, Lesions, Skin, Task analysis, Neural networks, Computer architecture, Image analysis BibRef

Combalia, M., Hueto, F., Puig, S., Malvehy, J., Vilaplana, V.,
Uncertainty Estimation in Deep Neural Networks for Dermoscopic Image Classification,
ISIC20(3211-3220)
IEEE DOI 2008
Uncertainty, Lesions, Skin, Neural networks, Monte Carlo methods, Training, Task analysis BibRef

Mahajan, K., Sharma, M., Vig, L.,
Meta-DermDiagnosis: Few-Shot Skin Disease Identification using Meta-Learning,
ISIC20(3142-3151)
IEEE DOI 2008
Skin, Diseases, Lesions, Task analysis, Training, Biomedical imaging, Adaptation models BibRef

Andrade, C.[Catarina], Teixeira, L.F.[Luís F.], Vasconcelos, M.J.M.[Maria João M.], Rosado, L.[Luís],
Deep Learning Models for Segmentation of Mobile-acquired Dermatological Images,
ICIAR20(II:228-237 Open Access).
Springer DOI 2007
BibRef

Bekmirzaev, S., Oh, S., Yo, S.,
RethNet: Object-by-Object Learning for Detecting Facial Skin Problems,
VRMI19(425-433)
IEEE DOI 2004
computer vision, face recognition, feature extraction, image segmentation, learning (artificial intelligence), fine grained object categorization BibRef

Wu, X., Wen, N., Liang, J., Lai, Y., She, D., Cheng, M., Yang, J.,
Joint Acne Image Grading and Counting via Label Distribution Learning,
ICCV19(10641-10650)
IEEE DOI 2004
Code, Dermatology.
WWW Link. diseases, learning (artificial intelligence), medical image processing, skin, joint acne image grading, Training BibRef

Gavrilov, D.A., Shchelkunov, N.N., Melerzanov, A.V.,
Deep Learning Based Skin Lesions Diagnosis,
PTVSBB19(81-85).
DOI Link 1912
BibRef

Tu, W., Liu, X., Hu, W., Pan, Z., Xu, X., Li, B.,
Segmentation of Lesion in Dermoscopy Images Using Dense-Residual Network with Adversarial Learning,
ICIP19(1430-1434)
IEEE DOI 1910
dermoscopic image, skin lesion, convolutional neural networks, adversarial learning, Dense-Residual block BibRef

Wang, X., Ding, H., Jiang, X.,
Dermoscopic Image Segmentation Through the Enhanced High-Level Parsing and Class Weighted Loss,
ICIP19(245-249)
IEEE DOI 1910
Skin lesion segmentation, fully convolutional neural network, enhanced high-level parsing, class weighed loss BibRef

Ferreira, B.[Bárbara], Barata, C.[Catarina], Marques, J.S.[Jorge S.],
What Is the Role of Annotations in the Detection of Dermoscopic Structures?,
IbPRIA19(II:3-11).
Springer DOI 1910
BibRef

Franco-Ceballos, R.[Ricardo], Torres-Madronero, M.C.[Maria C.], Galeano-Zea, J.[July], Murillo, J.[Javier], Zarzycki, A.[Artur], Garzon, J.[Johnson], Robledo, S.M.[Sara M.],
Spectral Band Subset Selection for Discrimination of Healthy Skin and Cutaneous Leishmanial Ulcers,
IbPRIA19(I:398-408).
Springer DOI 1910
BibRef

Carcagnì, P.[Pierluigi], Leo, M.[Marco], Cuna, A.[Andrea], Mazzeo, P.L.[Pier Luigi], Spagnolo, P.[Paolo], Celeste, G.[Giuseppe], Distante, C.[Cosimo],
Classification of Skin Lesions by Combining Multilevel Learnings in a DenseNet Architecture,
CIAP19(I:335-344).
Springer DOI 1909
BibRef

Bonechi, S.[Simone], Bianchini, M.[Monica], Bongini, P.[Pietro], Ciano, G.[Giorgio], Giacomini, G.[Giorgia], Rosai, R.[Riccardo], Tognetti, L.[Linda], Rossi, A.[Alberto], Andreini, P.[Paolo],
Fusion of Visual and Anamnestic Data for the Classification of Skin Lesions with Deep Learning,
NTIAP19(211-219).
Springer DOI 1909
BibRef

Piantadosi, G.[Gabriele], Bovenzi, G.[Giampaolo], Argenziano, G.[Giuseppe], Moscarella, E.[Elvira], Parmeggiani, D.[Domenico], Docimo, L.[Ludovico], Sansone, C.[Carlo],
Skin Lesions Classification: A Radiomics Approach with Deep CNN,
NTIAP19(252-259).
Springer DOI 1909
BibRef

Adegun, A.[Adekanmi], Viriri, S.[Serestina],
Deep Learning Model for Skin Lesion Segmentation: Fully Convolutional Network,
ICIAR19(II:232-242).
Springer DOI 1909
BibRef

Canalini, L.[Laura], Pollastri, F.[Federico], Bolelli, F.[Federico], Cancilla, M.[Michele], Allegretti, S.[Stefano], Grana, C.[Costantino],
Skin Lesion Segmentation Ensemble with Diverse Training Strategies,
CAIP19(I:89-101).
Springer DOI 1909
BibRef

Czovny, R.K., Bellon, O.R.P., Silva, L., Costa, H.S.G.,
Minutia Matching using 3D Pore Clouds,
ICPR18(3138-3143)
IEEE DOI 1812
Dermis, Epidermis, Databases, Biomedical imaging, Measurement uncertainty BibRef

Yang, J., Sun, X., Liang, J., Rosin, P.L.,
Clinical Skin Lesion Diagnosis Using Representations Inspired by Dermatologist Criteria,
CVPR18(1258-1266)
IEEE DOI 1812
Skin, Lesions, Diseases, Image color analysis, Medical diagnostic imaging, Shape BibRef

Li, H., He, X., Yu, Z., Zhou, F., Cheng, J., Huang, L., Wang, T., Lei, B.,
Skin Lesion Segmentation via Dense Connected Deconvolutional Network,
ICPR18(671-675)
IEEE DOI 1812
Lesions, Skin, Decoding, Training, Convolution, Imaging, Image restoration, Skin lesion segmentation, Dermoscopy image, Chained residual pooling BibRef

Luo, W., Yang, M.,
Fast Skin Lesion Segmentation via Fully Convolutional Network with Residual Architecture and CRF,
ICPR18(1438-1443)
IEEE DOI 1812
Lesions, Image segmentation, Convolution, Skin, Kernel, Pipelines, Training, Melanoma, Fully Convolutional Network, Conditional Random Field BibRef

Filali, Y., Ennouni, A., Sabri, M.A., Aarab, A.,
A study of lesion skin segmentation, features selection and classification approaches,
ISCV18(1-7)
IEEE DOI 1807
cancer, feature extraction, feature selection, image classification, image colour analysis, image segmentation, machine-learning BibRef

Zeng, G.D.[Guo-Dong], Zheng, G.[Guoyan],
Multi-scale Fully Convolutional DenseNets for Automated Skin Lesion Segmentation in Dermoscopy Images,
ICIAR18(513-521).
Springer DOI 1807
BibRef

Mahdiraji, S.A., Baleghi, Y., Sakhaei, S.M.,
Skin lesion images classification using new color pigmented boundary descriptors,
IPRIA17(102-107)
IEEE DOI 1712
cameras, cancer, feature extraction, image classification, image colour analysis, image texture, medical image processing, Skin lesion BibRef

Rundo, F.[Francesco], Conoci, S.[Sabrina], Banna, G.L.[Giuseppe L.], Stanco, F.[Filippo], Battiato, S.[Sebastiano],
Bio-Inspired Feed-Forward System for Skin Lesion Analysis, Screening and Follow-Up,
CIAP17(II:399-409).
Springer DOI 1711
BibRef

Balducci, F.[Fabrizio], Grana, C.[Costantino],
Pixel Classification Methods to Detect Skin Lesions on Dermoscopic Medical Images,
CIAP17(II:444-455).
Springer DOI 1711
BibRef

Al-abayechi, A.A.A.[Alaa Ahmed Abbas], Jalab, H.A.[Hamid A.], Ibrahim, R.W.[Rabha W.], Hasan, A.M.[Ali M.],
Image Enhancement Based on Fractional Poisson for Segmentation of Skin Lesions Using the Watershed Transform,
IVIC17(249-259).
Springer DOI 1711
BibRef

Bozkurt, A., Gale, T., Kose, K., Alessi-Fox, C., Brooks, D.H., Rajadhyaksha, M., Dy, J.G.,
Delineation of Skin Strata in Reflectance Confocal Microscopy Images with Recurrent Convolutional Networks,
Microscopy17(777-785)
IEEE DOI 1709
Dermis, Epidermis, Feature extraction, Imaging, Training BibRef

Hajdu, A., Harangi, B., Besenczi, R., Lázár, I., Emri, G., Hajdu, L., Tijdeman, R.,
Measuring regularity of network patterns by grid approximations using the LLL algorithm,
ICPR16(1524-1529)
IEEE DOI 1705
Approximation algorithms, Lesions, Measurement uncertainty, Noise level, Pattern recognition, Pigments, Skin BibRef

Kaur, P., Dana, K.J., Cula, G.O., Mack, M.C.,
Hybrid deep learning for Reflectance Confocal Microscopy skin images,
ICPR16(1466-1471)
IEEE DOI 1705
Epidermis, Histograms, Image recognition, Libraries, Machine learning, Neural, networks BibRef

Pal, A., Chaturvedi, A., Garain, U., Chandra, A., Chatterjee, R.,
Severity grading of psoriatic plaques using deep CNN based multi-task learning,
ICPR16(1478-1483)
IEEE DOI 1705
Computer architecture, Convolution, Diseases, Drugs, Estimation, Kernel, Skin BibRef

Liao, H.[Haofu], Li, Y.[Yuncheng], Luo, J.B.[Jie-Bo],
Skin disease classification versus skin lesion characterization: Achieving robust diagnosis using multi-label deep neural networks,
ICPR16(355-360)
IEEE DOI 1705
Dermatology, Diseases, Lesions, Malignant tumors, Skin, Training, Visualization, convolutional neural networks, skin disease classification, skin, lesion, characterization BibRef

Jafari, M.H., Karimi, N., Nasr-Esfahani, E., Samavi, S., Soroushmehr, S.M.R., Ward, K., Najarian, K.,
Skin lesion segmentation in clinical images using deep learning,
ICPR16(337-342)
IEEE DOI 1705
Feature extraction, Image segmentation, Lesions, Lighting, Machine learning, Malignant tumors, Skin, Melanoma, convolutional neural network, deep learning, medical image segmentation, skin, cancer BibRef

Faraz, K.[Khuram], Blondel, W.[Walter], Amouroux, M.[Marine], Daul, C.[Christian],
Towards skin image mosaicing,
IPTA16(1-6)
IEEE DOI 1703
Tele-dermatology. feature extraction BibRef

Majtner, T., Yildirim-Yayilgan, S., Hardeberg, J.Y.,
Combining deep learning and hand-crafted features for skin lesion classification,
IPTA16(1-6)
IEEE DOI 1703
biomedical optical imaging BibRef

Haji Rassouliha, A., Kmiecik, B., Taberner, A.J., Nash, M.P., Nielsen, P.M.F.,
A Low-cost, hand-held stereoscopic device for measuring dynamic deformations of skin in vivo,
ICVNZ15(1-6)
IEEE DOI 1701
deformation BibRef

Kawahara, J.[Jeremy], Hamarneh, G.[Ghassan],
Multi-resolution-Tract CNN with Hybrid Pretrained and Skin-Lesion Trained Layers,
MLMI16(164-171).
Springer DOI 1611
BibRef

Bozorgtabar, B.[Behzad], Abedini, M.[Mani], Garnavi, R.[Rahil],
Sparse Coding Based Skin Lesion Segmentation Using Dynamic Rule-Based Refinement,
MLMI16(254-261).
Springer DOI 1611
BibRef

Kropidlowski, K.[Karol], Kociolek, M.[Marcin], Strzelecki, M.[Michal], Czubinski, D.[Dariusz],
Blue Whitish Veil, Atypical Vascular Pattern and Regression Structures Detection in Skin Lesions Images,
ICCVG16(418-428).
Springer DOI 1611
BibRef

Sun, X.X.[Xiao-Xiao], Yang, J.F.[Ju-Feng], Sun, M.[Ming], Wang, K.[Kai],
A Benchmark for Automatic Visual Classification of Clinical Skin Disease Images,
ECCV16(VI: 206-222).
Springer DOI 1611
BibRef

Bulan, O., Artan, Y.,
Wheal detection from skin prick test images using normalized-cuts and region selection,
ICIP16(1250-1253)
IEEE DOI 1610
Calibration BibRef

Schneider, D., Hecht, A.,
Photogrammetric 3d Acquisition And Analysis Of Medicamentous Induced Pilomotor Reflex (goose Bumps),
ISPRS16(B5: 903-908).
DOI Link 1610
BibRef

Gonzalez-Castro, V., Debayle, J., Wazaefi, Y., Rahim, M., Gaudy, C., Grob, J.J., Fertil, B.,
Automatic classification of skin lesions using geometrical measurements of adaptive neighborhoods and local binary patterns,
ICIP15(1722-1726)
IEEE DOI 1512
General adaptive neighborhoods BibRef

Santos, A.[Anderson], Pedrini, H.[Helio],
Human Skin Segmentation Improved by Saliency Detection,
CAIP15(II:146-157).
Springer DOI 1511
BibRef

Rizzi, M.[Maria], d'Aloia, M.[Matteo], Cice, G.[Gianpaolo],
Computer Aided Evaluation (CAE) of Morphologic Changes in Pigmented Skin Lesions,
ISCA15(250-257).
Springer DOI 1511
BibRef

Santos, A.[Anderson], Pedrini, H.[Helio],
Human Skin Segmentation Improved by Texture Energy Under Superpixels,
CIARP15(35-42).
Springer DOI 1511
BibRef

Kaur, P.[Parneet], Dana, K.J.[Kristin J.], Cula, G.O.[Gabriela Oana],
From photography to microbiology: Eigenbiome models for skin appearance,
BioImage15(1-10)
IEEE DOI 1510
Artificial neural networks BibRef

Gupta, M.D.[Mithun Das], Srinivasa, S.[Srinidhi], Madhukara, J., Antony, M.[Meryl],
KL divergence based agglomerative clustering for automated Vitiligo grading,
CVPR15(2700-2709)
IEEE DOI 1510
BibRef

Koehoorn, J.[Joost], Sobiecki, A.C.[André C.], Boda, D.[Daniel], Diaconeasa, A.[Adriana], Doshi, S.[Susan], Paisey, S.[Stephen], Jalba, A.[Andrei], Telea, A.[Alexandru],
Automated Digital Hair Removal by Threshold Decomposition and Morphological Analysis,
ISMM15(15-26).
Springer DOI 1506
BibRef

Wazaefi, Y., Paris, S., Fertil, B.,
Contribution of a classifier of skin lesions to the dermatologist's decision,
IPTA12(207-211)
IEEE DOI 1503
cancer BibRef

Toth, J.[Janos], Szakacs, L.[Laszlo], Hajdu, A.[Andras],
Finding the optimal parameter setting for an ensemble-based lesion detector,
ICIP14(3532-3536)
IEEE DOI 1502
Databases BibRef

Lezoray, O., Revenu, M., Desvignes, M.,
Graph-based skin lesion segmentation of multispectral dermoscopic images,
ICIP14(897-901)
IEEE DOI 1502
Clustering algorithms BibRef

Vasconcelos, M.J.M.[Maria João M.], Rosado, L.[Luís], Ferreira, M.[Márcia],
Principal Axes-Based Asymmetry Assessment Methodology for Skin Lesion Image Analysis,
ISVC14(II: 21-31).
Springer DOI 1501
BibRef

Satat, G., Barsi, C., Raskar, R.,
Skin perfusion photography,
ICCP14(1-8)
IEEE DOI 1411
biomedical optical imaging BibRef

Nasonova, A.[Alexandra], Nasonov, A.[Andrey], Krylov, A.[Andrey], Pechenko, I.[Ivan], Umnov, A.[Alexey], Makhneva, N.[Natalia],
Image Warping in Dermatological Image Hair Removal,
ICIAR14(II: 159-166).
Springer DOI 1410
BibRef

Somoza, E.[Eduardo], Cula, G.O.[Gabriela Oana], Correa, C.[Catherine], Hirsch, J.B.[Julie B.],
Automatic Localization of Skin Layers in Reflectance Confocal Microscopy,
ICIAR14(II: 141-150).
Springer DOI 1410
BibRef

Abbas, Q.[Qaisar], Fondón, I.[Irene], Sarmiento, A.[Auxiliadora], Celebi, M.E.[M. Emre],
An Improved Segmentation Method for Non-melanoma Skin Lesions Using Active Contour Model,
ICIAR14(II: 193-200).
Springer DOI 1410
BibRef

Ma, Z.[Zhen], Tavares, J.M.R.S.[João Manuel R. S.],
Segmentation of Skin Lesions Using Level Set Method,
CompIMAGE14(228-233).
Springer DOI 1407
BibRef

Jaworek-Korjakowska, J.[Joanna], Tadeusiewicz, R.[Ryszard],
Assessment of dots and globules in dermoscopic color images as one of the 7-point check list criteria,
ICIP13(1456-1460)
IEEE DOI 1402
dots and globules, feature extraction, hair removal, melanoma;skin lesion BibRef

Pereira, C.[Carla], Veiga, D.[Diana], Mahdjoub, J.[Jason], Guessoum, Z.[Zahia], Gonçalves, L.[Luís],
Small Red Lesions Detection Using a MAS Approach,
ICIAR13(521-529).
Springer DOI 1307
BibRef

He, Y.D.[Ying-Ding], Xie, F.Y.[Feng-Ying],
Automatic Skin Lesion Segmentation Based on Texture Analysis and Supervised Learning,
ACCV12(II:330-341).
Springer DOI 1304
BibRef

Razeghi, O.[Orod], Qiu, G.P.[Guo-Ping], Williams, H.[Hywel], Thomas, K.[Kim],
Computer Aided Skin Lesion Diagnosis with Humans in the Loop,
MLMI12(266-274).
Springer DOI 1211
BibRef

Oyola, J.[Julián], Arroyo, V.[Virginia], Ruedin, A.[Ana], Acevedo, D.[Daniel],
Detection of Chickenpox Vesicles in Digital Images of Skin Lesions,
CIARP12(583-590).
Springer DOI 1209
BibRef

Güçin, M., Patias, P., Altan, M.O.,
Detection and Evaluation of Skin Disorders By One of Photogrammetric Image Analysis Methods,
ISPRS12(XXXIX-B3:537-542).
DOI Link 1209
BibRef

Amelard, R.[Robert], Wong, A.[Alexander], Clausi, D.A.[David A.],
Extracting High-Level Intuitive Features (HLIF) for Classifying Skin Lesions Using Standard Camera Images,
CRV12(396-403).
IEEE DOI 1207
BibRef

Ramli, R., Malik, A.S., Hani, A.F.M., Yap, F.B.,
Segmentation of Acne Vulgaris Lesions,
DICTA11(335-339).
IEEE DOI 1205
BibRef

Cavalcanti, P.G., Yari, Y., Scharcanski, J.,
Pigmented skin lesion segmentation on macroscopic images,
IVCNZ10(1-7).
IEEE DOI 1203
BibRef

Sirakov, N.M.[Nikolay Metodiev], Mete, M.[Mutlu], Chakrader, N.S.[Nara Surendra],
Automatic boundary detection and symmetry calculation in dermoscopy images of skin lesions,
ICIP11(1605-1608).
IEEE DOI 1201
BibRef

Prigent, S.[Sylvain], Zugaj, D.[Didier], Descombes, X.[Xavier], Martel, P.[Philippe], Zerubia, J.B.[Josiane B.],
Estimation of an optimal spectral band combination to evaluate skin disease treatment efficacy using multi-spectral images,
ICIP11(2801-2804).
IEEE DOI 1201
BibRef

Khak Abi, S.[Sina], Lee, T.K.[Tim K.], Atkins, M.S.[M. Stella],
Tree Structured Model of Skin Lesion Growth Pattern via Color Based Cluster Analysis,
MLMI11(291-299).
Springer DOI 1109
BibRef

Babu, M.N.[M. Naresh], Madasu, V.K.[Vamsi K.], Hanmandlu, M., Vasikarla, S.,
Histo-pathological image analysis using OS-FCM and level sets,
AIPR10(1-8).
IEEE DOI 1010
Orientation Sensitive Fuzzy C-means algorithm (OS-FCM) Skin cancer, melanoma, analysis BibRef

Placidi, G.[Giuseppe], Cifone, M.G.[Maria Grazia], Cinque, B.[Benedetta], Franchi, D.[Danilo], Giuliani, M.[Maurizio], La Torre, C.[Cristina], Macchiarelli, G.[Guido], Maglione, M.[Marta], Maurizi, A.[Alfredo], Miconi, G.[Gianfranca], Sotgiu, A.[Antonello],
Numerical Methods for the Semi-automatic Analysis of Multimodal Wound Healing Images,
CompIMAGE10(151-162).
Springer DOI 1006
BibRef

Subramaniam, N.[Nitya], Saman, G.[Gule], Hancock, E.R.[Edwin R.],
Detection of skin lesions using diffuse polarisation,
ICIP10(3021-3024).
IEEE DOI 1009
BibRef

Zacher, A.[Andrzej],
Utilization of Multi-spectral Images in Photodynamic Diagnosis,
ICCVG10(II: 367-375).
Springer DOI 1009
BibRef

Zacher, A.[Andrzej],
The Spectral Analysis of Human Skin Tissue Using Multi-spectral Images,
ICCVG10(II: 376-384).
Springer DOI 1009
BibRef

George, Y., Aldeen, M.[Mohammad], Garnavi, R.[Rahil],
A Pixel-Based Skin Segmentation in Psoriasis Images Using Committee of Machine Learning Classifiers,
DICTA17(1-8)
IEEE DOI 1804
diseases, feature extraction, image classification, image colour analysis, image segmentation, Training BibRef

George, Y., Aldeen, M.[Mohammad], Garnavi, R.[Rahil],
Skin Hair Removal for 2D Psoriasis Images,
DICTA15(1-8)
IEEE DOI 1603
diseases BibRef

Garnavi, R.[Rahil], Aldeen, M.[Mohammad], Finch, S.[Sue], Varigos, G.[George],
Global versus Hybrid Thresholding for Border Detection in Dermoscopy Images,
ICISP10(531-540).
Springer DOI 1006
BibRef

Hani, A.F.M.[Ahmad Fadzil M.], Eltegani, N.M.[Nejood M.], Hussein, S.H.[Suraiya H.], Jamil, A.[Adawiyah], Gill, P.[Priya],
Assessment of Ulcer Wounds Size Using 3D Skin Surface Imaging,
IVIC09(243-253).
Springer DOI 0911
BibRef

Nayak, R.[Rohit], Kumarand, P.[Pramod], Galigekere, R.R.[Ramesh R.],
Towards a comprehensive assessment of wound-composition using color-image processing,
ICIP09(4185-4188).
IEEE DOI 0911
BibRef

Jha, A.K.[Abhinav K.], Kupinski, M.A.[Matthew A.], Rodriguez, J.J.[Jeffrey J.], Stephen, R.M.[Renu M.], Stopeck, A.T.[Alison T.],
ADC estimation of lesions in diffusion-weighted MR images: A Maximum-Likelihood Approach,
Southwest10(209-212).
IEEE DOI 1005
BibRef

Fadzil, M.H.A.[M.H. Ahmad], Fitriyah, H.[Hurriyatul], Prakasa, E.[Esa], Nugroho, H.[Hermawan], Hussein, S.H., Affandi, A.M.[Azura Mohammed],
Thickness Characterization of 3D Skin Surface Images Using Reference Line Construction Approach,
IVIC09(448-454).
Springer DOI 0911
3d surface analysis for skin lesions. BibRef

Clawson, K.M., Morrow, P.J., Scotney, B.W., McKenna, D.J., Dolan, O.M.,
Analysis of Pigmented Skin Lesion Border Irregularity Using the Harmonic Wavelet Transform,
IMVIP09(18-23).
IEEE DOI 0909
BibRef

Celebi, M.E.[M. Emre], Hwang, S.[Sae], Iyatomi, H.[Hitoshi], Schaefer, G.[Gerald],
Robust border detection in dermoscopy images using threshold fusion,
ICIP10(2541-2544).
IEEE DOI 1009
BibRef

Iyatomi, H.[Hitoshi], Celebi, M.E.[M. Emre], Schaefer, G.[Gerald], Tanaka, M.[Masaru],
Automated color normalization for dermoscopy images,
ICIP10(4357-4360).
IEEE DOI 1009
BibRef

Celebi, M.E.[M. Emre], Iyatomi, H.[Hitoshi], Schaefer, G.[Gerald],
Contrast enhancement in dermoscopy images by maximizing a histogram bimodality measure,
ICIP09(2601-2604).
IEEE DOI 0911
BibRef

Schaefer, G.[Gerald], Rajab, M.I.[Maher I.], Celebi, M.E.[M. Emre], Iyatomi, H.[Hitoshi],
Skin lesion extraction in dermoscopic images based on colour enhancement and iterative segmentation,
ICIP09(3361-3364).
IEEE DOI 0911
BibRef

Celebi, M.E.[M. Emre], Iyatomi, H.[Hitoshi], Schaefer, G.[Gerald], Stoecker, W.V.[William V.],
Localization of Lesions in Dermoscopy Images Using Ensembles of Thresholding Methods,
PSIVT09(1094-1103).
Springer DOI 0901
BibRef

Madasu, V.K.[Vamsi K.], Lovell, B.C.[Brian C.],
Blotch Detection in Pigmented Skin Lesions Using Fuzzy Co-clustering and Texture Segmentation,
DICTA09(25-31).
IEEE DOI 0912
BibRef

Capdehourat, G.[Germán], Corez, A.[Andrés], Bazzano, A.[Anabella], Musé, P.[Pablo],
Pigmented Skin Lesions Classification Using Dermatoscopic Images,
CIARP09(537-544).
Springer DOI 0911
BibRef

Ma, L.[Li], Guo, A.Z.[An-Zhe], Zou, S.F.[Shao-Fang], Xu, W.D.[Wei-Dong],
Irregularity and Asymmetry Analysis of Skin Lesions Based on Multi-Scale Local Fractal Distributions,
CISP09(1-5).
IEEE DOI 0910
BibRef

Madan, S.K.[Siddharth K.], Dana, K.J.[Kristin J.], Cula, O.G.[Oana G.],
Quasiconvex alignment of multimodal skin images for quantitative dermatology,
MMBIA09(117-124).
IEEE DOI 0906
BibRef

Mirzaalian, H.[Hengameh], Lee, T.K.[Tim K.], Hamarneh, G.[Ghassan],
Learning features for streak detection in dermoscopic color images using localized radial flux of principal intensity curvature,
MMBIA12(97-101).
IEEE DOI 1203
BibRef
Earlier: A1, A3, A2:
A graph-based approach to skin mole matching incorporating template-normalized coordinates,
CVPR09(2152-2159).
IEEE DOI 0906
BibRef

Situ, N.[Ning], Yuan, X.J.[Xiao-Jing], Zouridakis, G.[George], Mullani, N.[Nizar],
Automatic Segmentation of Skin Lesion Images using Evolutionary Strategy,
ICIP07(VI: 277-280).
IEEE DOI 0709
BibRef

Clawson, K.M., Morrow, P.J., Scotney, B.W., McKenna, D.J., Dolan, O.M.,
Determination of Optimal Axes for Skin Lesion Asymmetry Quantification,
ICIP07(II: 453-456).
IEEE DOI 0709
BibRef
And:
Computerised Skin Lesion Surface Analysis for Pigment Asymmetry Quantification,
IMVIP07(75-82).
IEEE DOI 0709
BibRef

Odeh, S.M.[Suhail M.], Ros, E.[Eduardo], Rojas, I.[Ignacio], Palomares, J.M.[Jose M.],
Skin Lesion Diagnosis Using Fluorescence Images,
ICIAR06(II: 648-659).
Springer DOI 0610
BibRef

Kolesnik, M.[Marina], Fexa, A.[Ales],
Multi-dimensional Color Histograms for Segmentation of Wounds in Images,
ICIAR05(1014-1022).
Springer DOI 0509
BibRef

Schmid, P.[Philippe],
Lesion Detection in Dermatoscopic Images Using Anisotropic Diffusion and Morphological Flooding,
ICIP99(III:449-453).
IEEE DOI BibRef 9900

Fischer, S.[Stefan], Schmid, P.[Philippe], Guillod, J.[Joel],
Analysis of Skin Lesions with Pigmented Networks,
ICIP96(I: 323-326).
IEEE DOI BibRef 9600

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications -- Endoscopy .


Last update:Nov 1, 2021 at 09:26:50