Statistical image segmentation for the detection of skin lesion borders in UV fluorescence excitation

2016 ◽  
Author(s):  
Antonio Ortega-Martinez ◽  
Juan Pablo Padilla-Martinez ◽  
Walfre Franco
Keyword(s):  
Image Segmentation ◽  
Skin Lesion ◽  
Fluorescence Excitation ◽  
Uv Fluorescence

Simplification of neural networks for skin lesion image segmentation using color channel pruning

2020 ◽  
Vol 82 ◽  
pp. 101729
Author(s):  
M. Hajabdollahi ◽  
R. Esfandiarpoor ◽  
P. Khadivi ◽  
S.M.R. Soroushmehr ◽  
N. Karimi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Skin Lesion ◽  
Color Channel

scholarly journals Dermoscopic skin lesion image segmentation based on Local Binary Pattern Clustering: Comparative study

2020 ◽  
Vol 59 ◽  
pp. 101924 ◽  
Author(s):  
Pedro M.M. Pereira ◽  
Rui Fonseca-Pinto ◽  
Rui Pedro Paiva ◽  
Pedro A.A. Assuncao ◽  
Luis M.N. Tavora ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Skin Lesion ◽  
Comparative Study ◽  
Local Binary Pattern ◽  
Pattern Clustering

scholarly journals Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Oludayo O. Olugbara ◽  
Tunmike B. Taiwo ◽  
Delene Heukelman
Keyword(s):  
Image Segmentation ◽  
Skin Lesion ◽  
Morphological Analysis ◽  
Color Difference ◽  
Cancer Disease ◽  
Essential Requirement ◽  
Segmentation Algorithms ◽  
Death Cases ◽  
Aided Diagnosis ◽  
Melanoma Skin

The prevalence of melanoma skin cancer disease is rapidly increasing as recorded death cases of its patients continue to annually escalate. Reliable segmentation of skin lesion is one essential requirement of an efficient noninvasive computer aided diagnosis tool for accelerating the identification process of melanoma. This paper presents a new algorithm based on perceptual color difference saliency along with binary morphological analysis for segmentation of melanoma skin lesion in dermoscopic images. The new algorithm is compared with existing image segmentation algorithms on benchmark dermoscopic images acquired from public corpora. Results of both qualitative and quantitative evaluations of the new algorithm are encouraging as the algorithm performs excellently in comparison with the existing image segmentation algorithms.

scholarly journals UV fluorescence excitation imaging of healing of wounds in skin: Evaluation of wound closure in organ culture model

2016 ◽  
Vol 48 (7) ◽  
pp. 678-685 ◽  
Author(s):  
Ying Wang ◽  
Enoch Gutierrez‐Herrera ◽  
Antonio Ortega‐Martinez ◽  
Richard Rox Anderson ◽  
Walfre Franco
Keyword(s):  
Organ Culture ◽  
Wound Closure ◽  
Fluorescence Excitation ◽  
Uv Fluorescence ◽  
Culture Model ◽  
Healing Of Wounds

scholarly journals Deep Hybrid Convolutional Neural Network for Segmentation of Melanoma Skin Lesion

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Cheng-Hong Yang ◽  
Jai-Hong Ren ◽  
Hsiu-Chen Huang ◽  
Li-Yeh Chuang ◽  
Po-Yin Chang
Keyword(s):  
Image Segmentation ◽  
Skin Cancer ◽  
Skin Lesion ◽  
Survival Rates ◽  
Skin Lesions ◽  
Manual Segmentation ◽  
Cure Rate ◽  
Dice Coefficient ◽  
Skin Imaging ◽  
Low Efficiency

Melanoma is a type of skin cancer that often leads to poor prognostic responses and survival rates. Melanoma usually develops in the limbs, including in fingers, palms, and the margins of the nails. When melanoma is detected early, surgical treatment may achieve a higher cure rate. The early diagnosis of melanoma depends on the manual segmentation of suspected lesions. However, manual segmentation can lead to problems, including misclassification and low efficiency. Therefore, it is essential to devise a method for automatic image segmentation that overcomes the aforementioned issues. In this study, an improved algorithm is proposed, termed EfficientUNet++, which is developed from the U-Net model. In EfficientUNet++, the pretrained EfficientNet model is added to the UNet++ model to accelerate segmentation process, leading to more reliable and precise results in skin cancer image segmentation. Two skin lesion datasets were used to compare the performance of the proposed EfficientUNet++ algorithm with other common models. In the PH2 dataset, EfficientUNet++ achieved a better Dice coefficient (93% vs. 76%–91%), Intersection over Union (IoU, 96% vs. 74%–95%), and loss value (30% vs. 44%–32%) compared with other models. In the International Skin Imaging Collaboration dataset, EfficientUNet++ obtained a similar Dice coefficient (96% vs. 94%–96%) but a better IoU (94% vs. 89%–93%) and loss value (11% vs. 13%–11%) than other models. In conclusion, the EfficientUNet++ model efficiently detects skin lesions by improving composite coefficients and structurally expanding the size of the convolution network. Moreover, the use of residual units deepens the network to further improve performance.

scholarly journals Skin Lesion Segmentation using Deep Hypercolumn Descriptors

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Dhanesh Ramachandram ◽  
Graham W. Taylor
Keyword(s):  
Image Segmentation ◽  
Skin Lesion ◽  
State Of The Art ◽  
Jaccard Index ◽  
Segmentation Method ◽  
Lesion Segmentation ◽  
Malignant Skin

We present a image segmentation method based on deep hypercolumndescriptors which produces state-of-the-art results for thesegmentation of several classes of benign and malignant skin lesions.We achieve a Jaccard index of 0.792 on the 2017 ISIC SkinLesion Segmentation Challenge dataset.

Energy Transfer from NADPH to Protochlorophyllide in Isolated Protochlorophyllide Holochrome as Determined by UV-Fluorescence Excitation Spectroscopy at 77 K

1993 ◽  
Vol 48 (3-4) ◽  
pp. 402-406 ◽  
Author(s):  
Nikolai N. Lebedev ◽  
Ether Dujardin
Keyword(s):  
Energy Transfer ◽  
Low Temperature ◽  
Excitation Spectrum ◽  
Intermediate State ◽  
Redox State ◽  
Surrounding Medium ◽  
Excitation Spectra ◽  
Fluorescence Excitation ◽  
Uv Fluorescence

The low -temperature fluorescence excitation analysis of different protochlorophyllide (PChlide) forms has been extended to the UV part of the spectrum . A new band at about 360 nm was detected in excitation spectra of active PChlide forms bound to isolated protochlorophyllide holochrome. This band is very similar to the absorbance of NADPH in this region and its intensity depends on the redox state of the surrounding medium. On illumination at low temperature the intensity of the band decreases considerably with out any corresponding changes in the redox state of “ free” NADPH in the surrounding medium. A new intermediate state exhibiting a mixed excitation spectrum between PChlide and chlorophyllide (Chlide) was detected in the course of PChlide photoconversion.

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