Landmark-driven, atlas-based segmentation of mouse brain tissue images containing gene expression data

Lecture Notes in Computer Science

Volumn 3216, Issue PART 1, 2004, Pages 192-199

  Kakadiaris, Ioannis A ^a   Bello, Musodiq ^a   Arunachalam, Shiva ^a   Kang, Wei ^a   Ju, Tao ^b   Warren, Joe ^b   Carson, James ^c   Chiu, Wan ^c   Thaller, Christina ^c   Eichele, Gregor ^d  

^a University of Houston   (United States)

^b Rice University   (United States)

^c BAYLOR COLLEGE OF MEDICINE   (United States)

^d MAX PLANCK INSTITUTE FOR BIOPHYSICAL CHEMISTRY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN; CELLS; COMPUTATIONAL COMPLEXITY; DEFORMATION; GENES; GENETIC ENGINEERING; IMAGE ANALYSIS; MEDICAL IMAGING; NUCLEIC ACID SEQUENCES; TISSUE; IMAGE SEGMENTATION; MAMMALS;

ANATOMICAL FEATURES; BRAIN TISSUES; GENE EXPRESSION; MOUSE BRAIN; ANATOMICAL LANDMARKS; ANATOMICAL MODELING; ATLAS-BASED SEGMENTATION; AUTOMATED METHODS; GENE ACTIVITIES; GENE EXPRESSION DATA; GENE EXPRESSION PATTERNS; MAMMALIAN BRAIN;

IMAGE SEGMENTATION; GENE EXPRESSION;

EID: 20344380926     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-30135-6_24     Document Type: Conference Paper

References (13)

1. Herzig, U., Cadenas, C., Sieckmann, F., Sierralta, W., Thaller, C., Visel, A., Eichele, G.: Development of high-throughput tools to unravel the complexity of gene expression patterns in the mammalian brain. In Bock, G., Goode, J., eds.: Complexity in Biological Information Processing. Novartis Foundation Symposium 239. John Wiley & Sons, Chicester (2001) 129-149
2. Carson, J., Thaller, C., Eichele, G.: A transcriptome atlas of the mouse brain at cellular resolution. Current Opinion in Neurobiology 12 (2002) 562-5
3. Visel, A., Thaller, C., Eichele, G.: Genepaint.org: an atlas of gene expression patterns in the mouse embryo. Nucleic Acids Research 32 (2004) D552-556
4. Carson, J.: Quantitative annotation and analysis of gene expression patterns with an atlas of the mouse brain. PhD thesis, Baylor College of Medicine (2004)
5. Ju, T., Warren, J., Eichele, G., Thaller, C., Chiu, W., Carson, J.: A geometric database for gene expression data. In Kobbelt, L., Schröader, P., Hoppe, H., eds.: Eurographics Symposium on Geometry Processing, Aachen, Germany (2003) 166-176
6. Warren, J., Weimer, H.: Subdivision Methods for Geometric Design: A Constructive Approach. Morgan Kaufmann Publishers, San Francisco, CA (2002)
7. Jain, A.K., Duin, R.P.W, Mao, J.: Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (2000) 4-37
8. Plutowski, M., White, H.: Selecting concise training sets from clean data. IEEE Transactions on Neural Networks 4 (1993) 305-318
9. Duda, R.O., Hart, P.E., Stork, D.O.: Pattern Classification. John Wiley & Sons Inc. (2001)
10. Vapnik,V.N.: The Nature of Statistical Learning. Springer (1998)
11. Freund, Y., Schapire, R.E.: An introduction to boosting. Journal of Japanese Society for Artificial Intelligence 14 (1999) 771-780
12. Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge, MA (2002)
13. Hsu, C., Chang, C., Lin, C.: A practical guide to support vector classification. Technical report, National Taiwan University, Taipei 106, Taiwan (2003)