Fast computation of multiscale morphological operations for local contrast enhancement

被引:3
作者
Ye Derong [1 ]
Zhao Yuanyuan [1 ]
Li Dongguo [1 ]
机构
[1] Capital Univ Med Sci, Dept Biomed Engn, Beijing, Peoples R China
来源
2005 27TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-7 | 2005年
关键词
D O I
10.1109/IEMBS.2005.1617128
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Poor local contrast is a nettlesome problem in biomedical image analysis. The multiscale method based on mathematical morphology has been successfully used in local contrast enhancement. However, the computational time is high. In this paper, the existing method is much improved by using few feature levels in an efficient way. The new algorithm is tested by experiments. Compared with the previous method, the results show that the proposed method is more effective, faster and less sensitive to noise. It is a fast and simple method for eliminating the non-uniformity of the intensities in images.
引用
收藏
页码:3090 / 3092
页数:3
相关论文
共 10 条
[1]  
[Anonymous], IMAGE ANAL USING MAT
[2]  
DORST L, 1982, P 6 INT C PATT REC M
[4]  
MEYER F, 1978, QUANTITATIVE ANAL MI
[5]   ADAPTIVE NEIGHBORHOOD EXTENDED CONTRAST ENHANCEMENT AND ITS MODIFICATIONS [J].
MUKHERJEE, D ;
CHATTERJI, BN .
GRAPHICAL MODELS AND IMAGE PROCESSING, 1995, 57 (03) :254-265
[6]   A multiscale morphological approach to local contrast enhancement [J].
Mukhopadhyay, S ;
Chanda, B .
SIGNAL PROCESSING, 2000, 80 (04) :685-696
[7]   REAL-TIME ADAPTIVE CONTRAST ENHANCEMENT [J].
NARENDRA, PM ;
FITCH, RC .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1981, 3 (06) :655-661
[8]   GRAYSCALE MORPHOLOGY [J].
STERNBERG, SR .
COMPUTER VISION GRAPHICS AND IMAGE PROCESSING, 1986, 35 (03) :333-355
[9]   Automatic morphology-based brain segmentation (MBRASE) from MRI-T1 data [J].
Stokking, R ;
Vincken, KL ;
Viergever, MA .
NEUROIMAGE, 2000, 12 (06) :726-738
[10]  
YE DR, IN PRESS BEIJING BIO