A Study on the Application of Fuzzy Information Seeded Region Growing in Brain MRI Tissue segmentation

被引：1

作者：

Wang, Chuin-Mu ^{[1
]}

Su, Shao-Wen ^{[2
]}

Kuo, Pei-Chi ^{[1
]}

Lin, Geng-Cheng ^{[3
]}

Da-Peng-Yang ^{[1
]}

机构：

[1] Natl Chin Yi Univ Technol, Dept Comp Sci & Informat Engn, Taichung, Taiwan

[2] Natl Chin Yi Univ Technol, Dept Appl English, Taichung, Taiwan

[3] Natl Cent Univ, Dept Comp Sci & Informat Engn, Taoyuan, Taiwan

来源：

2014 INTERNATIONAL SYMPOSIUM ON COMPUTER, CONSUMER AND CONTROL (IS3C 2014) | 2014年

关键词：

Seeded region growing; segmentations; Classification; MRI; IMAGE SEGMENTATION;

D O I：

10.1109/IS3C.2014.99

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Magnetic resonance imaging (MRI) is better than computed tomography (CT), because of its advantages of non-radiation and non-invasive. After long-term clinical trials, MRI has been proved to use in humans harmlessly, and it popular used in medical diagnosis. Although MR is highly sensitive, but it's provide abundant organization information. Therefore, how to transform the multi-spectral images which is easier to be used for doctor's clinical diagnosis. In this thesis, the fuzzy bi-directional edge detection method used to solve conventional SRG problem of growing order in the initial seeds stages. In order to overcome the problems of the different regions, but it's the same Euclidean distance for region growing and merging process stages. We present the peak detection method to improve it. The standard deviation target generation process (SDTGP) is applied to guarantee the regions merging process does not cause over or under segmentation.

引用

页码：356 / 359

页数：4

共 23 条

[11] Multispectral MR images segmentation based on fuzzy knowledge and modified seeded region growing [J].

Lin, Geng-Cheng ;

Wang, Wen-June ;

Kang, Chung-Chia ;

Wang, Chuin-Mu .

MAGNETIC RESONANCE IMAGING, 2012, 30 (02) :230-246

[12] Automated classification of multi-spectral MR images using Linear Discriminant Analysis [J].

Lin, Geng-Cheng ;

Wang, Wen-June ;

Wang, Chuin-Mu ;

Sun, Sheng-Yih .

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2010, 34 (04) :251-268

[13] An improved seeded region growing algorithm [J].

Mehnert, A ;

Jackway, P .

PATTERN RECOGNITION LETTERS, 1997, 18 (10) :1065-1071

[14]

METZ CE, 1986, INVEST RADIOL, V21, P720, DOI 10.1097/00004424-198609000-00009

[15] A REVIEW ON IMAGE SEGMENTATION TECHNIQUES [J].

PAL, NR ;

PAL, SK .

PATTERN RECOGNITION, 1993, 26 (09) :1277-1294

[16] INTEGRATING REGION GROWING AND EDGE-DETECTION [J].

PAVLIDIS, T ;

LIOW, YT .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1990, 12 (03) :225-233

[17]

Ruzon M. A., 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), P160, DOI 10.1109/CVPR.1999.784624

[18] Automatic seeded region growing for color image segmentation [J].

Shih, FY ;

Cheng, SX .

IMAGE AND VISION COMPUTING, 2005, 23 (10) :877-886

[19] Color image segmentation using histogram thresholding - Fuzzy C-means hybrid approach [J].

Tan, Khang Siang ;

Isa, Nor Ashidi Mat .

PATTERN RECOGNITION, 2011, 44 (01) :1-15

[20] Vector order statistics operators as color edge detectors [J].

Trahanias, PE ;

Venetsanopoulos, AN .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 1996, 26 (01) :135-143

← 1 2 3 →