A comprehensive segmentation, registration, and cancer detection scheme on 3 tesla in vivo prostate DCE-MRI

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

Volumn 5241 LNCS, Issue PART 1, 2008, Pages 662-669

  Viswanath, Satish ^a   Bloch, B Nicolas ^b   Genega, Elisabeth ^b   Rofsky, Neil ^b   Lenkinski, Robert ^b   Chappelow, Jonathan ^a   Toth, Robert ^a   Madabhushi, Anant ^a  

^a RUTGERS UNIVERSITY   (United States)

^b BETH ISRAEL DEACONESS MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE SHAPE MODELS; BIOMEDICAL DATA; BREAST CANCER; CANCER DETECTION; CONSENSUS CLUSTERING; CONTRAST AGENT; DATA MANIFOLDS; DCE-MRI; DETECTION ACCURACY; DIMENSIONAL SUBSPACE; DYNAMIC CONTRAST ENHANCED MRI; DYNAMIC INTENSITY; GROUND TRUTH; HIGH RESOLUTION; HIGH-DIMENSIONAL; IN-VIVO; LOCALLY LINEAR EMBEDDING; MODEL FITTING; MULTI-ATTRIBUTES; MULTIMODAL REGISTRATION; NON-LINEAR; NONLINEAR DIMENSIONALITY REDUCTION; PROSTATE CANCERS; QUANTITATIVE EVALUATION; SPATIAL EXTENT; TIME POINTS; UNSUPERVISED DETECTION;

COMPUTER AIDED DESIGN; COMPUTER SCIENCE; FACE RECOGNITION; MAMMOGRAPHY; MEDICAL COMPUTING; MEDICAL IMAGING;

COMPUTER AIDED DIAGNOSIS;

CONTRAST MEDIUM;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; EVALUATION; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; MALE; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PROSTATE TUMOR; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CONTRAST MEDIA; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; PATTERN RECOGNITION, AUTOMATED; PROSTATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 58849119669     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85988-8_79     Document Type: Conference Paper

References (10)

1. Padhani, A., Gapinski, C., et al.: Dynamic Contrast Enhanced MRI of Prostate Cancer: Correlation with Morphology and Tumour Stage, Histological Grade and PSA. Clinical Radiology 55(2), 99-109 (2000)
2. Degani, H., Gusis, V., et al.: Mapping pathophysiological features of breast tumours by MRI at high spatial resolution. Nature Medicine 3(7), 780-782 (1997)
3. Vos, P., Hambrock, T., et al.: Computerized analysis of prostate lesions in the peripheral zone using dynamic contrast enhanced MRI. Medical Physics 35(3), 888-899 (2008)
4. Madabhushi, A., Udupa, J.: New Methods of MR Image Intensity Standardization via Generalized Scale. Medical Physics 33(9), 3426-3434 (2006)
5. Roweis, S., Saul, L.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290(5500), 2323-2326 (2000)
6. Varini, C., Degenhard, A., et al.: Visual exploratory analysis of DCE-MRI data in breast cancer by dimensional data reduction: a comparative study. Biomedical Signal Processing and Control 1(1), 56-63 (2006)
7. Toth, R., Tiwari, P., et al.: A multi-modal prostate segmentation scheme by combining spectral clustering and active shape models. In: SPIE Medical Imaging, pp. 69144S1-69144S12 (2008)
8. Chappelow, J., Madabhushi, A., et al.: A combined feature ensemble based mutual information scheme for robust inter-modal, inter-protocol image registration. In: International Symposium on Biomedical Imaging, pp. 644-647 (2007)
9. Fred, A., Jain, A.: Combining Multiple Clusterings Using Evidence Accumulation. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(6), 835-850 (2005)
10. Venna, J., Kaski, S.: Local multidimensional scaling. Neural Networks 19(6), 889-899 (2006)