融合纹理信息的SLIC算法在医学图像中的研究

侯向丹; 李柏岑; 刘洪普; 杜佳卓; 郑梦敬; 于铁忠

doi:10.16383/j.aas.c180682

融合纹理信息的SLIC算法在医学图像中的研究

doi: 10.16383/j.aas.c180682

侯向丹^1,2,,
李柏岑^1,2,,
刘洪普^1,2,3, ,,
杜佳卓^1,2,,
郑梦敬^1,2,,
于铁忠^1,

1.
河北工业大学人工智能与数据科学学院天津 300401
2.
河北省大数据计算重点实验室天津 300401
3.
河北工业大学电气工程学院天津 300401

基金项目:

天津市自然科学基金 16JCYBJC15600

详细信息

作者简介:
侯向丹  河北工业大学人工智能与数据科学学院副教授.2007年获得河北工业大学电工理论与新技术专业博士学位.主要研究方向为数字图像处理, 智能算法.E-mail:hxd@scse.hebut.edu.cn

李柏岑  河北工业大学人工智能与数据科学学院硕士研究生.2015年获得黑龙江大学信息系统与信息管理专业学士学位.主要研究方向为数字图像处理.E-mail:zeroformove@gmail.com

杜佳卓  河北工业大学人工智能与数据科学学院硕士研究生.2017年获得唐山学院物联网工程专业学士学位.主要研究方向为数字图像处理.E-mail:zezzdjz0601@163.com

郑梦敬  河北工业大学人工智能与数据科学学院硕士研究生.2016年获得河北大学工商学院计算机科学与技术专业学士学位, 主要研究方向为数字图像处理.E-mail:zhengmengjing@cslc.com.cn

于铁忠  河北工业大学人工智能与数据科学学院正高级工程师.1991年获得国防科技大学硕士学位.主要研究方向为智能信息处理.E-mail:yutiezhong139@163.com

通讯作者:
刘洪普河北工业大学人工智能与数据科学学院讲师.2006年获得河北工业大学计算机应用技术专业硕士学位.主要研究方向为数字图像处理, 智能算法, 机器学习.本文通信作者.E-mail:liuii@scse.hebut.edu.cn

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出版历程
- 收稿日期: 2018-10-19
- 录用日期: 2019-02-13
- 刊出日期: 2019-05-20

SLICT: Computing Texture-Sensitive Superpixels in Medical Images

HOU Xiang-Dan^{1,2
,},
LI Bo-Cen^{1,2
,},
LIU Hong-Pu^{1,2,3
, ,},
DU Jia-Zhuo^{1,2
,},
ZHENG Meng-Jing^{1,2
,},
YU Tie-Zhong^1
,

1.
School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401
2.
Hebei Provincial Key Laboratory of Big Data Computing, Tianjin 300401
3.
School of Electrical Engineering, Hebei University of Technology, Tianjin 300401

Funds:

Natural Science Fundation of Tianjin 16JCYBJC15600

More Information

Author Bio:
Associate professor at the School of Artificial Intelligence, Hebei University of Technology. She received her Ph. D. degree in theory and new technology of electrical engineering from Hebei University of Technology in 2007. Her research interest covers digital image processing and intelligent algorithm

Master student at the School of Artificial Intelligence, Hebei University of Technology. He received his bachelor degree in information systems and information management from Heilongjiang University in 2015. His main research interest is digital image processing

Master student at the School of Artificial Intelligence, Hebei University of Technology. He received his bachelor degree in internet of things from Tangshan College in 2017. His main research interest is digital image processing

Master student at the School of Artificial Intelligence, Hebei University of Technology. She received her bachelor degree in computer science and technology from Industrial and Commercial College of Hebei University in 2016. Her main research interest is digital image processing

Professor of Engineering at the School of Artificial Intelligence, Hebei University of Technology. He received his master degree from National University of Defense Technology in 1991. His main research interest is intelligent information processing

Corresponding author: LIU Hong-Pu Lecturer at the School of Artificial Intelligence, Hebei University of Technology. He received his master degree in computer application technology from Hebei University of Technology in 2006. His research interests cover digital image processing, intelligent algorithm, and machine learning. Corresponding author of this paper

摘要

摘要: 随着超像素算法的发展，SLIC（Simple linear iterative clustering）由于时间复杂度低及良好的分割结果而被广泛关注.但是由于传统的SLIC算法并没有考虑到图像的纹理信息，故而对于纹理较复杂的图像分割效果略有不足.LBP（Local binary pattern）对于纹理的识别有着优秀的表现而且时间复杂度低，但是对于噪声的鲁棒性较差，并且会产生纹理偏移.因此，本文首先针对传统的LBP中存在的问题进行改进；然后将改进后的算法与SLIC结合，提出一种融合纹理信息的超像素算法——SLICT（Simple linear iterative clustering based on texture）.为验证分割效果，本文选取纹理较多的医学图像进行实验，采用心脏MRI数据库进行验证并与其他超像素算法进行对比.实验表明，SLICT在边缘召回率、欠分割错误率以及覆盖率上的综合表现优于其他算法.从分割结果上来看，SLICT不但能够更好地贴合图像边缘，而且对于连续区域的分割效果也较好，更适合纹理较复杂的图像.
- 纹理偏移 /
- SLIC /
- LBP /
- 医学图像 /
- 超像素
Abstract: With the development of superpixel algorithm, SLIC (Simple linear iterative clustering) is widely noticed because of its low time-complexity and remarkable results. However, traditional SLIC does not take texture information into consideration, which leads to unsatisfactory results on texture-complex images. LBP (Local binary pattern) algorithm has good performance on texture analysis. But it is not robust enough to texture noise, and it may also lead to texture deviation. So, we propose simple linear iterative clustering based on texture (SLICT) by improving the traditional LBP algorithm and combining it with SLIC. To verify our method's effectiveness, we use medical images which contain complex texture from cardiac MRI dataset and compare our algorithm with other superpixel algorithms. The experiment results show that SLICT has better performance than the other algorithms in boundary recall, under-segmentation error and coverage rate. Besides, image segmentation results show that SLICT can adhere to boundary more tightly and have better results on consistent area, which proves SLICT is more suitable to texture-complex images.
- Texture deviation /
- simple linear iterative clustering (SLIC) /
- local binary pattern (LBP) /
- medical images /
- superpixel
注释:

1) 本文责任编委刘成林

HTML全文

图 1 LBP算法中有无纹理偏移的效果对比

Fig. 1 The results of LBP algorithm with and without texture deviation

下载: 全尺寸图片幻灯片

图 2 心脏MRI分割结果

Fig. 2 The results of superpixel algorithms on MRI images

下载: 全尺寸图片幻灯片

图 3 心脏MRI数据库运行结果

Fig. 3 Expriment data on MRI dataset

下载: 全尺寸图片幻灯片

图 4 纹理特征图

Fig. 4 Texutre feature

下载: 全尺寸图片幻灯片

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