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Supervised Saliency Map Driven Segmentation of Lesions in Dermoscopic Images
2018
IEEE journal of biomedical and health informatics
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In order to detect the lesion in the presence of these problems, we propose a supervised saliency detection method tailored for dermoscopic images based on the discriminative regional feature integration ...
DRFI method incorporates multi-level segmentation, regional contrast, property, background descriptors, and a random forest regressor to create saliency scores for each region in the image. ...
For the case of mDRFI, mid-level features are hand-crafted for the specific problem of dermoscopic image segmentation. ...
doi:10.1109/jbhi.2018.2839647
pmid:29994323
fatcat:63bke3p6kjbdnpocmip3ynu3ay
Guest Editorial Skin Lesion Image Analysis for Melanoma Detection
2019
IEEE journal of biomedical and health informatics
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In Supervised Saliency Map Driven Segmentation of Lesions in Dermoscopic Images, Jahanifar et al. investigate a three-stage saliency based approach. ...
In SDI+: A Novel Algorithm for Segmentation Dermoscopic Images, Guarracino and Maddalena present a three-stage segmentation algorithm. ...
doi:10.1109/jbhi.2019.2897338
fatcat:3cszhtjw6zc5thl4abzeaukq3q
Multilevel feature extraction for skin lesion segmentation in dermoscopic images
2012
Medical Imaging 2012: Computer-Aided Diagnosis
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The resulting per pixel feature vectors of length 13 are used in a supervised learning model for estimating parameters and segmenting the lesion. ...
This paper presents a novel approach in computer aided skin lesion segmentation of dermoscopic images. We apply spatial and color features in order to model the lesion growth pattern. ...
Statistical region merging (SRM) is another approach which is used to segment the skin lesion in dermoscopic images. 19 The combination of some of these methods in association with the supervised or ...
doi:10.1117/12.911664
dblp:conf/micad/KhakAbiWLA12
fatcat:g2jym44dcvaclisi5okwrzgxl4
iMSCGnet: Iterative Multi-scale Context-guided Segmentation of Skin Lesion in Dermoscopic Images
2020
IEEE Access
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INDEX TERMS Skin lesion segmentation, multi-scale context, attention, deep supervision. ...
Despite much effort has been devoted to skin lesion segmentation, the performance of existing methods is still not satisfactory enough for practical applications. ...
images for validation and 600 images for testing) datasets. ...
doi:10.1109/access.2020.2974512
fatcat:nwfoe4dthjbojpnbuwdh6wtmzi
Fuzzy C-Means Clustering with Histogram based Cluster Selection for Skin Lesion Segmentation using Non-Dermoscopic Images
2020
International Journal of Interactive Multimedia and Artificial Intelligence
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In this paper, lesion segmentation based on Fuzzy C-Means clustering using non-dermoscopic images has been proposed. ...
As the Value channel of HSV color image provides better and distinct histogram distribution based on the entropy, it has been used for segmentation purpose. ...
For non-dermoscopic images, red color channel image along with otsu thresholding has been used to segment the lesion region [15] . ...
doi:10.9781/ijimai.2020.01.001
fatcat:uic4tgxrvfdgnffp2xl3fad2ha
Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation
2022
Diagnostics
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Segmentation of skin lesions in dermoscopic images is an important requisite component of such a breakthrough innovation for an accurate melanoma diagnosis. ...
of the numerous leading supervised and unsupervised segmentation methods investigated in this study. ...
The scores recorded by the CHC-Otsu algorithm have shown the superiority of the algorithm when compared to other leading methods on this huge set of dermoscopic images. ...
doi:10.3390/diagnostics12020344
pmid:35204435
pmcid:PMC8871329
fatcat:5l34hkerc5c6bdt3ucjn3v2s64
Segmentation of Skin Lesions Using Level Set Method
[chapter]
2014
Lecture Notes in Computer Science
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Here, we propose a level set method to fulfill the segmentation of skin lesions presented in dermoscopic images. ...
The proposed algorithm is robust against the influences of noise, hair, and skin textures, and provides a flexible way for segmentation. ...
ACKNOWLEDGEMENT This work was done in the scope of the project "A novel framework for supervised mobile assessment and risk triage of skin lesions via non-invasive screening", with the reference PTDC/BBB-BMD ...
doi:10.1007/978-3-319-09994-1_20
fatcat:zqspm6b7vnhzpc6vvr3tr4bbka
Learning to Detect Blue-white Structures in Dermoscopy Images with Weak Supervision
[article]
2015
arXiv
pre-print
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This study provides an improvement on the scope of modelling for computerized image analysis of skin lesions, in particular in that it puts forward a framework for identification of dermoscopic local features ...
In this paper, we achieve this goal in a Multiple Instance Learning framework using only image-level labels of whether the feature is present or not. ...
In this dataset [2] , image-level labels encode only whether an image contains a dermoscopic feature or not, but the features themselves are not locally annotated. ...
arXiv:1506.09179v1
fatcat:uytzyhebdbebrefzrmrxoi2rj4
Contrast enhancement by searching discriminant color projections in dermoscopy images
2016
Revista Facultad de Ingeniería
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The method was tested using a set of 40 dermoscopy images, and a performance greater than 82% was obtained. ...
The use of color as a strategy for enhancing the contrast is useful for conducting feature extraction procedures in images with high illumination disorders; hence, in order to correct contrast problems ...
In [22] an evaluation of six methods for segmenting dermoscopic image is performed, including supervised and unsupervised methods, as adaptive thresholding, gradient vector flow, adaptive snake, level ...
doi:10.17533/udea.redin.n79a18
fatcat:7f5vnhu3tnetrfcfxq3g5xiyem
A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images
2018
Symmetry
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An indeterminate filter is then defined in the neutrosophic set for reducing the indeterminacy of the images. A neutrosophic c-means clustering algorithm is applied to segment the dermoscopic images. ...
First, the dermoscopic images are mapped into a neutrosophic set domain using the shearlet transform results for the images. ...
[13] proposed an automated skin lesion segmentation technique in dermoscopic images using a semi-supervised learning algorithm. ...
doi:10.3390/sym10040119
fatcat:c2pmt4aiafbyhifbb5qea5lzhu
A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering And Adaptive Region Growing In Dermoscopy Images
2018
Zenodo
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First, the dermoscopic images are mapped into a neutrosophic set domain using the shearlet transform results for the images. ...
This paper proposes novel skin lesion detection based on neutrosophic clustering and adaptive region growing algorithms applied to dermoscopic images, called NCARG. ...
[13] proposed an automated skin lesion segmentation technique in dermoscopic images using a semi-supervised learning algorithm. ...
doi:10.5281/zenodo.1410197
fatcat:jfodmn5h4rgyjkb7egol3i3n5m
Automatic Segmentation of Dermoscopic Images by Iterative Classification
2011
International Journal of Biomedical Imaging
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This paper presents a novel automatic approach to segmentation of skin lesions that is particularly suitable for analysis of dermoscopic images. ...
, increasing reliability and final accuracy, especially for those challenging images, where the contrast between the background skin and lesion is low. ...
Bremnes at the University Hospital of North Norway for the delineation of the borders of the the pigmented skin ...
doi:10.1155/2011/972648
pmid:21811493
pmcid:PMC3146984
fatcat:dcjfhmfpjrhdxoqitsog4rf454
A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification
[article]
2020
arXiv
pre-print
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In this paper, we propose the mutual bootstrapping deep convolutional neural networks (MB-DCNN) model for simultaneous skin lesion segmentation and classification. ...
segmentation. ...
In this experiment, we used 10697 dermoscopic images for the segmentation task and 21153 images for the classification task. ...
arXiv:1903.03313v4
fatcat:cv3ldlhts5gndpxb6ttmrlc3ya
Statistical Techniques Applied to the Automatic Diagnosis of Dermoscopic Images
2012
ACTA IMEKO
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Statistical techniques are introduced and adopted for border detection, feature extraction and classification as well as the resulting diagnostic score are described with reference to a large image set ...
structural dermoscopic criteria provided by the 7-Point Check. ...
attribute for the regions of each image belonging to the Training Sets. ...
doi:10.21014/acta_imeko.v1i1.7
fatcat:ie2ynzbklzbj5burizqrxejokq
Does a Previous Segmentation Improve the Automatic Detection of Basal Cell Carcinoma Using Deep Neural Networks?
2022
Applied Sciences
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Regarding the segmentation step, supervised deep learning-based methods outperform unsupervised ones. ...
The best segmentation results are obtained with a SegNet deep neural network. A 98% accuracy for distinguishing BCC from nevus and a 95% accuracy classifying BCC vs. all lesions have been obtained. ...
Supervised Methods Supervised methods need a training data set in order to fix the parameters of the classifier. ...
doi:10.3390/app12042092
fatcat:i7ccog5jnfhtddsdw2vksmrosa
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