HSL Color Space Based Skin Lesion Segmentation Using Fuzzy-Based Techniques

2020 ◽  
pp. 903-911
Author(s):  
P. Ganesan ◽  
B. S. Sathish ◽  
L. M. I. Leo Joseph
Keyword(s):  
Skin Lesion ◽  
Color Space ◽  
Lesion Segmentation

scholarly journals HAIR ARTIFACT REMOVAL AND SKIN LESION SEGMENTATION OF DERMOSCOPY IMAGES

2018 ◽  
Vol 11 (15) ◽  
pp. 36 ◽  
Author(s):  
Julie Ann Salido ◽  
Conrado Ruiz Jr
Keyword(s):  
Skin Lesion ◽  
Detection Rate ◽  
Median Filter ◽  
Research Work ◽  
Color Space ◽  
Reference Image ◽  
Skin Area ◽  
Lesion Segmentation ◽  
Artifact Removal ◽  
Morphological Opening

Objective: The objective of this research is to perform automatic hair artifact removal and skin lesion segmentation on dermoscopy images.Methods: Dermoscopy images are images from the examination of the skin lesion using a dermatoscope. There are different types of skin lesion artifacts, structures, or objects that are present in dermoscopy images. This is a pertinent problem that can inhibit the proper examination and accurately segment the skin lesion from the surrounding skin area. Artifacts, such as hair strands, introduce additional features that can also cause problems during classification. Our process starts with hair removal using a median filter on each color space of RGB, a bottom hat filter, a binary conversion, a dilation and morphological opening, and then the removal of small connected pixels. The detected hair regions are then filled up using harmonic inpainting. Then, skin lesion segmentation is performed using a binary conversion, a dilation, a perimeter detection and morphological opening, and then the removal of small connected pixels.Results: Experiments were carried out on the PH2 dermoscopy images. The border of the lesion was quantified for evaluation by four statistical metrics with the lesions identified by the PH2 as the reference image, resulting with a true detection rate (TDR) of 82.31 and a false detection rate of 5.69.Conclusions: The results obtained in the research work on hair artifacts removal and skin lesion segmentation provides acceptable results in terms of TDR and low false-positive rates.

A color space study for skin lesion segmentation

2013 ◽  
Author(s):  
Humaira Nisar ◽  
Yau Kwang Ch'ng ◽  
Tsyr Yee Chew ◽  
Vooi Voon Yap ◽  
Kim Ho Yeap ◽  
...  
Keyword(s):  
Skin Lesion ◽  
Color Space ◽  
Lesion Segmentation

scholarly journals FAC-Net: Feedback Attention Network Based on Context Encoder Network for Skin Lesion Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5172
Author(s):  
Yuying Dong ◽  
Liejun Wang ◽  
Shuli Cheng ◽  
Yongming Li
Keyword(s):  
Skin Lesion ◽  
Real Life ◽  
Good Deal ◽  
Experimental Tests ◽  
Skin Lesions ◽  
Lesion Segmentation ◽  
Attention Network ◽  
Effective Feedback ◽  
Public Datasets ◽  
Learning Architectures

Considerable research and surveys indicate that skin lesions are an early symptom of skin cancer. Segmentation of skin lesions is still a hot research topic. Dermatological datasets in skin lesion segmentation tasks generated a large number of parameters when data augmented, limiting the application of smart assisted medicine in real life. Hence, this paper proposes an effective feedback attention network (FAC-Net). The network is equipped with the feedback fusion block (FFB) and the attention mechanism block (AMB), through the combination of these two modules, we can obtain richer and more specific feature mapping without data enhancement. Numerous experimental tests were given by us on public datasets (ISIC2018, ISBI2017, ISBI2016), and a good deal of metrics like the Jaccard index (JA) and Dice coefficient (DC) were used to evaluate the results of segmentation. On the ISIC2018 dataset, we obtained results for DC equal to 91.19% and JA equal to 83.99%, compared with the based network. The results of these two main metrics were improved by more than 1%. In addition, the metrics were also improved in the other two datasets. It can be demonstrated through experiments that without any enhancements of the datasets, our lightweight model can achieve better segmentation performance than most deep learning architectures.

Skin lesion segmentation from dermoscopic images by using Mask R-CNN, Retina-Deeplab, and graph-based methods

2021 ◽  
Vol 67 ◽  
pp. 102533
Author(s):  
Fatemeh Bagheri ◽  
Mohammad Jafar Tarokh ◽  
Majid Ziaratban
Keyword(s):  
Skin Lesion ◽  
Lesion Segmentation

scholarly journals Lightweight encoder-decoder model for automatic skin lesion segmentation

2021 ◽  
pp. 100640
Author(s):  
Adi Wibowo ◽  
Satriawan Rasyid Purnama ◽  
Panji Wisnu Wirawan ◽  
Hanif Rasyidi
Keyword(s):  
Skin Lesion ◽  
Lesion Segmentation
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