A novel approach to segment and classify regional lymph nodes on computed tomography images

Computational and Mathematical Methods in Medicine

Volumn 2012, Issue , 2012, Pages

  Cai, Hongmin ^a   Cui, Chunyan ^b   Tian, Haiying ^b   Zhang, Min ^b   Li, Li ^b  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^b SUN YAT SEN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DIAGNOSIS; DISEASES; FORECASTING; IMAGE CLASSIFICATION; IMAGE SEGMENTATION; NONLINEAR EQUATIONS; SUPPORT VECTOR MACHINES;

COMPUTED TOMOGRAPHY IMAGES; DIAGNOSIS AND PROGNOSIS; DIAGNOSTIC PERFORMANCE; MORPHOLOGICAL INFORMATION; MORPHOLOGICAL MEASUREMENTS; NONLINEAR DIFFUSION EQUATIONS; PREDICTION PERFORMANCE; SUPPORT VECTOR MACHINE MODELS;

COMPUTERIZED TOMOGRAPHY;

ARTICLE; COMPUTER ASSISTED TOMOGRAPHY; IMAGE ANALYSIS; IMAGE ENHANCEMENT; IMAGE PROCESSING; IMAGE QUALITY; LYMPH NODE; SUPPORT VECTOR MACHINE; ALGORITHM; COMPUTER SIMULATION; DIFFUSION; HUMAN; LYMPH NODE METASTASIS; METHODOLOGY; NEOPLASM; NONLINEAR SYSTEM; NORMAL DISTRIBUTION; PATHOLOGY; PROGNOSIS; RECEIVER OPERATING CHARACTERISTIC; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THEORETICAL MODEL;

ALGORITHMS; COMPUTER SIMULATION; DIFFUSION; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LYMPH NODES; LYMPHATIC METASTASIS; MODELS, THEORETICAL; NEOPLASMS; NONLINEAR DYNAMICS; NORMAL DISTRIBUTION; PROGNOSIS; REPRODUCIBILITY OF RESULTS; ROC CURVE; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINES; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84870199023     PISSN: 1748670X     EISSN: 17486718     Source Type: Journal    
DOI: 10.1155/2012/145926     Document Type: Article

References (30)

1. Rosenberg R., Friederichs J., Schuster T., Gertler R., Maak M., Becker K., Grebner A., Ulm K., Höer H., Nekarda H., Siewert J. R., Prognosis of patients with colorectal cancer is associated with lymph node ratio a single-center analysis of 3026 patients over a 25-year time period. Annals of Surgery 2008 248 6 968 978 2-s2.0-58149386920 10.1097/SLA.0b013e318190eddc
2. Brown G., Richards C. J., Bourne M. W., Newcombe R. G., Radcliffe A. G., Dallimore N. S., Williams G. T., Morphologic predictors of lymph node status in rectal cancer with use of high-spatial-resolution MR imaging with histopathologic comparison. Radiology 2003 227 2 371 377 2-s2.0-0037406971 10.1148/radiol.2272011747 (Pubitemid 36514749)
3. Kim J. H., Beets G. L., Kim M. J., Kessels A. G. H., Beets-Tan R. G. H., High-resolution MR imaging for nodal staging in rectal cancer: are there any criteria in addition to the size? European Journal of Radiology 2004 52 1 78 83 2-s2.0-4544382535 10.1016/j.ejrad.2003.12.005 (Pubitemid 39233657)
4. Lahaye M. J., Beets G. L., Engelen S. M. E., Kessels A. G. H., De Bruïne A. P., Kwee H. W. S., Van Engelshoven J. M. A., Van De Velde C. J. H., Beets-Tan R. G. H., Locally advanced rectal cancer: MR imaging for restaging after neoadjuvant radiation therapy with concomitant chemotherapy part II. What are the criteria to predict involved lymph nodes? Radiology 2009 252 1 81 91 2-s2.0-67650081524 10.1148/radiol.2521081364
5. Takemura A., Shimizu A., Hamamoto K., Discrimination of breast tumors in ultrasonic images using an ensemble classifier based on the adaboost algorithm with feature selection. IEEE Transactions on Medical Imaging 2010 29 3 598 609 2-s2.0-77749291907 10.1109/TMI.2009.2022630
6. Kim Y. S., Kim J. H., Yoon S. M., Choi E. K., Ahn S. D., Lee S. W., Kim J. C., Yu C. S., Kim H. C., Kim T. W., Chang H. M., Lymph node ratio as a prognostic factor in patients with stage III rectal cancer treated with total mesorectal excision followed by chemoradiotherapy. International Journal of Radiation Oncology Biology Physics 2009 74 3 796 802 2-s2.0-67049107496 10.1016/j.ijrobp.2008.08.065
7. Moug S. J., Saldanha J. D., McGregor J. R., Balsitis M., Diament R. H., Positive lymph node retrieval ratio optimises patient staging in colorectal cancer. British Journal of Cancer 2009 100 10 1530 1533 2-s2.0-67349127552 10.1038/sj.bjc.6605049
8. Vapnik V. N., Statistical Learning Theory 1998 Wiley-Interscience
9. Rudin L. I., Osher S., Fatemi E., Nonlinear total variation based noise removal algorithms. Physica D 1992 60 1-4 259 268 2-s2.0-44049111982
10. Chan T., Shen J., Vese L., Variational PDE models in image processing. Notices of the American Mathematical Society 2003 50 14 26
11. Perona P., Malik J., Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 1990 12 7 629 639 2-s2.0-0025465145 10.1109/34.56205 (Pubitemid 20726016)
12. Barash D., A fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002 24 6 844 847 2-s2.0-0036603206 10.1109/TPAMI.2002.1008390 (Pubitemid 34669596)
13. Kass M., Witkin A., Terzopoulos D., Snakes: active contour models. International Journal of Computer Vision 1987 1 321 332
14. Xu C., Prince J. L., Snakes, shapes, and gradient vector flow. IEEE Transactions on Image Processing 1998 7 3 359 369 2-s2.0-0032028944 (Pubitemid 128745354)
15. Jacob M., Blu T., Unser M., Efficient energies and algorithms for parametric snakes. IEEE Transactions on Image Processing 2004 13 9 1231 1244 2-s2.0-4344607611 10.1109/TIP.2004.832919
16. Ray N., Acton S. T., Motion gradient vector flow: an external force for tracking rolling leukocytes with shape and size constrained active contours. IEEE Transactions on Medical Imaging 2004 23 12 1466 1478 2-s2.0-10044237712 10.1109/TMI.2004.835603
17. Cai H., Xu X., Lu J., Lichtman J. W., Yung S. P., Wong S. T. C., Repulsive force based snake model to segment and track neuronal axons in 3D microscopy image stacks. NeuroImage 2006 32 4 1608 1620 2-s2.0-33748327078 10.1016/j.neuroimage.2006.05.036 (Pubitemid 44332286)
18. Xie X., Mirmehdi M., RAGS: region-aided geometric snake. IEEE Transactions on Image Processing 2004 13 5 640 652 2-s2.0-2442474070 10.1109/TIP.2004.826124
19. Duan K. B., Rajapakse J. C., Wang H., Azuaje F., Multiple SVM-RFE for gene selection in cancer classification with expression data. IEEE Transactions on Nanobioscience 2005 4 3 228 234 2-s2.0-26644466101 10.1109/TNB.2005.853657 (Pubitemid 41441412)
20. Furey T. S., Cristianini N., Duffy N., Bednarski D. W., Schummer M., Haussler D., Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000 16 10 906 914 2-s2.0-0033636139
21. Guler I., Ubeyli E., Multiclass support vector machines for EEG-signals classification. IEEE Transactions on Information Technology in Biomedicine 2007 11 117 126 (Pubitemid 46523131)
22. Oskoei M., Hu H., Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Transactions on Biomedical Engineering 2008 55 1956 1965 (Pubitemid 352013532)
23. Osowski S., Siroic R., Markiewicz T., Siwek K., Application of support vector machine and genetic algorithm for improved blood cell recognition. IEEE Transactions on Instrumentation and Measurement 2009 58 7 2159 2168 2-s2.0-67649631501 10.1109/TIM.2008.2006726
24. Rojo-Álvarez J. L., Bermejo J., Juárez-Caballero V. M., Yotti R., Cortina C., García-Fernández M. A., Antoranz J. C., Support vector analysis of color-doppler images: a new approach for estimating indices of left ventricular function. IEEE Transactions on Medical Imaging 2006 25 8 1037 1043 2-s2.0-33746895160 10.1109/TMI.2006.875437 (Pubitemid 44192175)
25. Wei L., Yang Y., Nishikawa R. M., Jiang Y., A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications. IEEE Transactions on Medical Imaging 2005 24 2 371 380 2-s2.0-19044383038 10.1109/TMI.2004.842457
26. Wei L., Yang Y., Nishikawa R. M., Wernick M. N., Edwards A., Relevance vector machine for automatic detection a of clustered microcalcifications. IEEE Transactions on Medical Imaging 2005 24 10 1278 1285 2-s2.0-26844433062 10.1109/TMI.2005.855435 (Pubitemid 41463566)
27. Kampouraki A., Manis G., Nikou C., Heartbeat time series classification with support vector machines. IEEE Transactions on Information Technology in Biomedicine 2009 13 4 512 518 2-s2.0-67749108627 10.1109/TITB.2008.2003323
28. Moradi M., Abolmaesumi P., Siemens D. R., Sauerbrei E. E., Boag A. H., Mousavi P., Augmenting detection of prostate cancer in transrectal ultrasound images using SVM and RF time series. IEEE Transactions on Biomedical Engineering 2009 56 9 2214 2224 2-s2.0-67149119060 10.1109/TBME.2008.2009766
29. Selvathi D., Prakash R. S. R., Selvi S. T., Performance evaluation of kernel based techniques for brain MRI data classification. 2 International Conference on Computational Intelligence and Multimedia Applications (ICCIMA '07) December 2007 456 460 2-s2.0-41149098155 10.1109/ICCIMA.2007.346
30. Erickson M. L., Dissimilarity and the classification of male singing voices. Journal of Voice 2008 22 3 290 299 (Pubitemid 351609315)