Prostate segmentation by sparse representation based classification

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

Volumn 7512 LNCS, Issue , 2012, Pages 451-458

  Gao, Yaozong ^a,b   Liao, Shu ^b   Shen, Dinggang ^b  

^a University of North Carolina   (United States)

^b UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE SEGMENTATION; ITERATIVE METHODS; MEDICAL COMPUTING; MEDICAL IMAGING; PATIENT TREATMENT; REGRESSION ANALYSIS; RIGIDITY; UROLOGY;

CLASSIFICATION PERFORMANCE; DIFFERENT TREATMENTS; IMAGE GUIDED RADIOTHERAPY; LINEAR REGRESSION MODELS; PROSTATE SEGMENTATION; SOFT CLASSIFICATION; SPARSE REPRESENTATION; SPARSE REPRESENTATION BASED CLASSIFICATIONS;

COMPUTERIZED TOMOGRAPHY;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; HUMAN; IMAGE QUALITY; MALE; METHODOLOGY; PROSTATE TUMOR; RADIOGRAPHY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; MALE; PATTERN RECOGNITION, AUTOMATED; PROSTATIC NEOPLASMS; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; TOMOGRAPHY, X-RAY COMPUTED;

MLCS; MLOWN;

EID: 84872929584     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33454-2_56     Document Type: Conference Paper

References (10)

1. Chen, S., Lovelock, D.M., Radke, R.J.: Segmenting the prostate and rectum in CT imagery using anatomical constraints. Med. Image Anal. 15, 1–11 (2011)
2. Feng, Q., Foskey, M., Chen, W., Shen, D.: Segmenting CT prostate images using population and patient-specific statistics for radiotherapy. Med. Phys. 37, 4121–4132 (2010)
3. Davis, B.C., Foskey, M., Rosenman, J., Goyal, L., Chang, S., Joshi, S.: Automatic Segmentation of Intra-treatment CT Images for Adaptive Radiation Therapy of the Prostate. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 442–450. Springer, Heidelberg (2005)
4. Liao, S., Shen, D.: A Learning Based Hierarchical Framework for Automatic Prostate Localization in CT Images. In: Madabhushi, A., Dowling, J., Huisman, H., Barratt, D. (eds.) Prostate Cancer Imaging 2011. LNCS, vol. 6963, pp. 1–9. Springer, Heidelberg (2011)
5. Li, W., Liao, S., Feng, Q., Chen, W., Shen, D.: Learning Image Context for Segmentation of Prostate in CT-Guided Radiotherapy. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 570–578. Springer, Heidelberg (2011)
6. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust Face Recognition via Sparse Representation. PAMI 31(2), 210–227 (2009)
7. Tropp, J.A.: Greed is good: algorithmic results for sparse approximation. IEEE Transactions on Information Theory 50(10), 2231–2242 (2004)
8. Aharon, M., Elad, M., Bruckstein, A.: K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation. IEEE Transactions on Signal Processing 54(11), 4311–4322 (2006)
9. Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)
10. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)