Automatic segmentation of subcortical brain structures in MR images using information fusion

被引:86
作者
Barra, V [1 ]
Boire, JY [1 ]
机构
[1] Fac Med, ERIM, F-63001 Clermont Ferrand, France
关键词
brain structures; fusion; fuzzy logic; image segmentation;
D O I
10.1109/42.932740
中图分类号
TP39 [计算机的应用];
学科分类号
081203 [计算机应用技术]; 0835 [软件工程];
摘要
This paper reports a new automated method for the segmentation of internal cerebral structures using an information fusion technique. The information is provided both by images and expert knowledge, and consists in morphological, topological, and tissue constitution data. All this ambiguous, complementary and redundant information is managed using a three-step fusion scheme based on fuzzy logic. The information is first modeled into a common theoretical frame managing its imprecision and incertitude. The models are then fused and a decision is taken in order to reduce the imprecision and to increase the certainty in the location of the structures. The whole process is illustrated on the segmentation of thalamus, putamen, and head of the caudate nucleus from expert knowledge and magnetic resonance images, in a protocol involving 14 healthy volunteers. The quantitative validation is achieved by comparing computed, manually segmented structures and published data by means of indexes assessing the accuracy of volume estimation and spatial location. Results suggest a consistent volume estimation with respect to the expert quantification and published data, and a high spatial similarity of the segmented and computed structures. This method is generic and applicable to any structure that can be defined by expert knowledge and morphological images.
引用
收藏
页码:549 / 558
页数:10
相关论文
共 34 条
[1]
MULTIRESOLUTION ELASTIC MATCHING [J].
BAJCSY, R ;
KOVACIC, S .
COMPUTER VISION GRAPHICS AND IMAGE PROCESSING, 1989, 46 (01) :1-21
[2]
Barra V, 2000, JMRI-J MAGN RESON IM, V11, P267, DOI 10.1002/(SICI)1522-2586(200003)11:3<267::AID-JMRI5>3.0.CO
[3]
2-8
[4]
FUZZY MATHEMATICAL MORPHOLOGIES - A COMPARATIVE-STUDY [J].
BLOCH, I ;
MAITRE, H .
PATTERN RECOGNITION, 1995, 28 (09) :1341-1387
[5]
Information combination operators for data fusion: A comparative review with classification [J].
Bloch, I .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, 1996, 26 (01) :52-67
[6]
DISTANCE TRANSFORMATIONS IN ARBITRARY DIMENSIONS [J].
BORGEFORS, G .
COMPUTER VISION GRAPHICS AND IMAGE PROCESSING, 1984, 27 (03) :321-345
[7]
ESTIMATION OF CSF, WHITE AND GRAY-MATTER VOLUMES IN HYDROCEPHALIC CHILDREN USING FUZZY CLUSTERING OF MR-IMAGES [J].
BRANDT, ME ;
BOHAN, TP ;
KRAMER, LA ;
FLETCHER, JM .
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 1994, 18 (01) :25-34
[8]
A novel tool for rapid prototyping and development of simple 3D medical image processing applications on PCs. [J].
Colin, A ;
Boire, JY .
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 1997, 53 (02) :87-92
[9]
MODEL GENERATION AND MODEL-MATCHING OF REAL IMAGES BY A FUZZY APPROACH [J].
DELLEPIANE, S ;
VENTURI, G ;
VERNAZZA, G .
PATTERN RECOGNITION, 1992, 25 (02) :115-137
[10]
Segmentation and interpretation of MR brain images: An improved active shape model [J].
Duta, N ;
Sonka, M .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 1998, 17 (06) :1049-1062