A color space study for skin lesion segmentation

被引：0

作者：

Nisar, Humaira ^{[1
]}

Ch'ng, Yau Kwang ^{[1
]}

Chew, Tsyr Yee ^{[1
]}

Yap, Vooi Voon ^{[1
]}

Yeap, Kim Ho ^{[1
]}

Tang, Jyh Jong ^{[2
]}

机构：

[1] Univ Tunku Abdul Rahman, Dept Elect Engn, Kampar, Malaysia

[2] Hosp Raja Permaisuri Bainum, Dept Dermatol, Ipoh, Malaysia

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON CIRCUITS AND SYSTEMS (ICCAS 2013) | 2013年

关键词：

color models; segmentation; image analysis; Eczema skin lesions; ATOPIC-DERMATITIS; INDEX;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The objective of this research is to identify the most suitable color model for segmentation of Eczema skin lesions. Eczema is a type of Atopic Dermatitis that is diagnosed by the dermatologists by visual inspection hence by subjective assessment. For segmentation, K-means clustering has been used. Four color spaces, i.e. HSI, CMY, YCbCr and CIELAB are used for comparisons. The final conclusion is made on the basis of the results for sensitivity, specificity and accuracy for each technique. It has been observed that the color channel "H" of HSI; followed by "a" of CIELab; gives better segmentation results.

引用

页码：172 / 176

页数：5

共 50 条

[41] Adaptive skin segmentation in color images
Phung, SL
Chai, D
Bouzerdoum, A
[J]. 2003 INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOL III, PROCEEDINGS, 2003, : 173 - 176
[42] Skin color constancy for illumination invariant skin segmentation
Gottumukkal, R
Asari, V
[J]. IMAGE AND VIDEO COMMUNICATIONS AND PROCESSING 2005, PTS 1 AND 2, 2005, 5685 : 969 - 976
[43] A Weighted Skin Probability Map for Skin Color Segmentation
Chakraborty, Biplab Ketan
Bhuyan, M. K.
Kumar, Sunil
[J]. PROCEEDINGS OF THE 2016 IEEE INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, SIGNAL PROCESSING AND NETWORKING (WISPNET), 2016, : 2133 - 2136
[44] Skin Color Enhancement Based on Favorite Skin Color in HSV Color Space
Zhang, Xiao-Ning
Jiang, Jue
Liang, Zhi-Hu
Liu, Chun-Liang
[J]. IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, 2010, 56 (03) : 1789 - 1793
[45] Residual refinement for interactive skin lesion segmentation
Dalei Jiang
Yin Wang
Feng Zhou
Hongtao Ma
Wenting Zhang
Weijia Fang
Peng Zhao
Zhou Tong
[J]. Journal of Biomedical Semantics, 12
[46] Rethinking Skin Lesion Segmentation in a Convolutional Classifier
Burdick, Jack
Marques, Oge
Weinthal, Janet
Furht, Borko
[J]. JOURNAL OF DIGITAL IMAGING, 2018, 31 (04) : 435 - 440
[47] Skin Lesion Segmentation with C-UNet
Wu, Junyan
Chen, Eric Z.
Rong, Ruichen
Li, Xiaoxiao
Xu, Dong
Jiang, Hongda
[J]. 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2019, : 2785 - 2788
[48] Efficient skin lesion segmentation with boundary distillation
Zhang, Zaifang
Lu, Boyang
[J]. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2024, 62 (09) : 2703 - 2716
[49] Automated seeding for ultrasound skin lesion segmentation
Marosan, Peter
Szalai, Klara
Csabai, Domonkos
Csany, Gergely
Horvath, Andras
Gyongy, Miklos
[J]. ULTRASONICS, 2021, 110
[50] Rethinking Skin Lesion Segmentation in a Convolutional Classifier
Jack Burdick
Oge Marques
Janet Weinthal
Borko Furht
[J]. Journal of Digital Imaging, 2018, 31 : 435 - 440

← 1 2 3 4 5 →