Improving the classification accuracy of the classic RF method by intelligent feature selection and weighted voting of trees with application to medical image segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7009 LNCS, Issue , 2011, Pages 184-192

  Yaqub, Mohammad ^a,b   Javaid, M Kassim ^b   Cooper, Cyrus ^b   Noble, J Alison ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

3D fetal ultrasound segmentation; brain MRI segmentation; feature selection; machine learning; Random forests

Indexed keywords

3D FETAL ULTRASOUND SEGMENTATION; 3D OBJECT; ADULT BRAIN; BRAIN MRI SEGMENTATION; CLASSIFICATION ACCURACY; DATA SETS; IMAGING MODALITY; MACHINE-LEARNING; MEDICAL IMAGE SEGMENTATION; PROBABILISTIC DECISIONS; RANDOM FORESTS; SEGMENTATION ACCURACY; WEIGHTED VOTING;

DECISION TREES; FEATURE EXTRACTION; IMAGE SEGMENTATION; LEARNING SYSTEMS; PLANT EXTRACTS; THREE DIMENSIONAL; ULTRASONICS;

MEDICAL IMAGING;

EID: 80053932755     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-24319-6_23     Document Type: Conference Paper

References (19)

1. Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E.: Regression forests for efficient anatomy detection and localization in CT studies. In: Menze, B., Langs, G., Tu, Z., Criminisi, A., et al. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 106-117. Springer, Heidelberg (2011)
2. Lempitsky, V., Verhoek, M., Noble, J.A., Blake, A.: Random forest classification for automatic delineation of myocardium in real-time 3D echocardiography. In: Ayache, N., Delingette, H., Sermesant, M., et al. (eds.) FIMH 2009. LNCS, vol. 5528, pp. 447-456. Springer, Heidelberg (2009)
3. Yi, Z., Criminisi, A., Shotton, J., Blake, A.: Discriminative, semantic segmentation of brain tissue in MR images. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C., et al. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 558-565. Springer, Heidelberg (2009)
4. Geremia, E., Menze, B.H., Clatz, O., Konukoglu, E., Criminisi, A., Ayache, N.: Spatial Decision Forests for MS Lesion Segmentation in Multi-Channel MR Images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 111-118. Springer, Heidelberg (2010)
5. Yaqub, M., et al.: Weighted Voting in 3D Random Forest Segmentation. In: MIUA (2010)
6. Criminisi, A., et al.: Decision Forests with Long-Range Spatial Context for Organ Localization in CT Volumes. In: MICCAI-PMMIA (2009)
7. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123-140 (1996)
8. Breiman, L.: Random Forests. Machine Learning 45(1), 5-32 (2001)
9. Lepetit, V., Fua, P.: Keypoint recognition using randomized trees. PAMI 28(9), 1465-1479 (2006)
10. Shotton, J., et al.: Semantic Texton Forests for Image Categorization and Segmentation. In: CVPR (2008)
11. Oren, M., et al.: Pedestrian detection using wavelet templates. In: CVPR, pp. 193-199 (1997)
12. Geurts, P., et al.: Extremely randomized trees. Mach. Learn. 63(1), 3-42 (2006)
13. Mahon, P.A., et al.: The use of 3D ultrasound to investigate fetal bone development. Norsk Epidemiologi. 19(1) (2009)
14. Gale, C., et al.: Maternal vitamin D status during pregnancy and child outcomes. EJCN 62, 68-77 (2008)
15. Tustison, N.J., et al.: N4ITK: Improved N3 Bias Correction. IEEE TMI 29(6), 1310-1320 (2010)
16. Thomas, J., Jouve, P.-E., Nicoloyannis, N.: Optimisation and evaluation of random forests for imbalanced datasets. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G., et al. (eds.) ISMIS 2006. LNCS (LNAI), vol. 4203, pp. 622-631. Springer, Heidelberg (2006) (Pubitemid 44618707)
17. Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: CVPR, pp. 886-893 (2005)
18. Rajpoot, K., et al.: Local-phase based 3D boundary detection using monogenic signal and its application to real-time 3-D echocardiography images. In: ISBI (2009)
19. Tu, Z., et al.: Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models. IEEE TMI, 495-508 (2008)