Dr. Carole Lartizien Profile

Dr. Carole Lartizien

at CREATIS

SPIE Involvement:

Conference Program Committee | Author

Proceedings Article | 4 April 2022 Presentation + Paper

Learning to segment prostate cancer by aggressiveness from scribbles in bi-parametric MRI

Audrey Duran, Gaspard Dussert, Carole Lartizien

Proceedings Volume 12032, 120320Q (2022) https://doi.org/10.1117/12.2607502

KEYWORDS: Prostate, Image segmentation, Data modeling, Magnetic resonance imaging, Scanners, Prostate cancer, Biopsy, Imaging systems

Read Abstract +

Proceedings Article | 9 March 2011 Paper

Computer-aided diagnosis for prostate cancer detection in the peripheral zone via multisequence MRI

Emilie Niaf, Olivier Rouvière, Carole Lartizien

Proceedings Volume 7963, 79633P (2011) https://doi.org/10.1117/12.877231

KEYWORDS: Magnetic resonance imaging, Tissues, Feature extraction, Cancer, Feature selection, Prostate cancer, Databases, Computer aided diagnosis and therapy, Diffusion weighted imaging, Gold

Read Abstract +

Proceedings Article | 9 March 2011 Paper

Computer-aided tumor detection stemmed from the fuzzification of the Dempster-Shafer theory

Obioma Udobata, Chantal Revol-Muller, Carole Lartizien

Proceedings Volume 7963, 79633M (2011) https://doi.org/10.1117/12.877226

KEYWORDS: Tumors, Positron emission tomography, Image fusion, Fuzzy logic, Lung, Computer aided diagnosis and therapy, 3D image processing, Databases, Chromium, Computer aided design

Read Abstract +

Proceedings Article | 8 March 2007 Paper

Combining a wavelet transform with a channelized Hotelling observer for tumor detection in 3D PET oncology imaging

Carole Lartizien, Sandrine Tomei, Voichita Maxim, Christophe Odet

Proceedings Volume 6515, 651519 (2007) https://doi.org/10.1117/12.706753

KEYWORDS: 3D image processing, 3D modeling, Positron emission tomography, Signal to noise ratio, Wavelets, 3D acquisition, Liver, Visualization, Oncology, Databases

Read Abstract +

This study evaluates new observer models for 3D whole-body Positron Emission Tomography (PET) imaging based on a wavelet sub-band decomposition and compares them with the classical constant-Q CHO model. Our final goal is to develop an original method that performs guided detection of abnormal activity foci in PET oncology imaging based on these new observer models. This computer-aided diagnostic method would highly benefit to clinicians for diagnostic purpose and to biologists for massive screening of rodents populations in molecular imaging. Method: We have previously shown good correlation of the channelized Hotelling observer (CHO) using a constant-Q model with human observer performance for 3D PET oncology imaging. We propose an alternate method based on combining a CHO observer with a wavelet sub-band decomposition of the image and we compare it to the standard CHO implementation. This method performs an undecimated transform using a biorthogonal B-spline 4/4 wavelet basis to extract the features set for input to the Hotelling observer. This work is based on simulated 3D PET images of an extended MCAT phantom with randomly located lesions. We compare three evaluation criteria: classification performance using the signal-to-noise ratio (SNR), computation efficiency and visual quality of the derived 3D maps of the decision variable &lgr;. The SNR is estimated on a series of test images for a variable number of training images for both observers. Results: Results show that the maximum SNR is higher with the constant-Q CHO observer, especially for targets located in the liver, and that it is reached with a smaller number of training images. However, preliminary analysis indicates that the visual quality of the 3D maps of the decision variable &lgr; is higher with the wavelet-based CHO and the computation time to derive a 3D &lgr;-map is about 350 times shorter than for the standard CHO. This suggests that the wavelet-CHO observer is a good candidate for use in our guided detection method.

Proceedings Article | 20 April 2005 Paper

Effect of patient thickness and scan duration on lesion detectability in PET oncology imaging

Phillip Cheng, Paul Kinahan, Claude Comtat, Carole Lartizien, Thomas Lewellen

Proceedings Volume 5745, (2005) https://doi.org/10.1117/12.595428

KEYWORDS: Target detection, Positron emission tomography, Signal attenuation, Image quality, Abdomen, Scanners, Oncology, Spherical lenses, Image filtering, Target acquisition

Read Abstract +

Proceedings Article | 22 May 2003 Paper

Performance comparisons of planar and volumetric observers for lesion detection in PET scanning

Jae-Seung Kim, Paul Kinahan, Carole Lartizien, Claude Comtat, Tom Lewellen

Proceedings Volume 5034, (2003) https://doi.org/10.1117/12.480329

KEYWORDS: 3D modeling, 3D acquisition, Signal to noise ratio, Positron emission tomography, Performance modeling, 3D image processing, Smoothing, Data modeling, Target detection, Optical spheres

Read Abstract +

Proceedings Article | 14 April 2000 Paper

Evaluating image reconstruction methods for tumor detection performance in whole-body PET oncology imaging

Carole Lartizien, Paul Kinahan, Claude Comtat, Michael Lin, Richard Swensson, Regine Trebossen, Bernard Bendriem

Proceedings Volume 3981, (2000) https://doi.org/10.1117/12.383125

KEYWORDS: Reconstruction algorithms, Positron emission tomography, Liver, Lung, Tissues, Image restoration, Expectation maximization algorithms, 3D image reconstruction, Tumors, Oncology

Read Abstract +

Showing 5 of 7 publications

Conference Committee Involvement (4)

Image Processing

17 February 2025 | San Diego, California, United States

Image Processing

19 February 2024 | San Diego, California, United States

Image Processing

20 February 2023 | San Diego, California, United States

Image Processing

20 February 2022 | San Diego, California, United States

Keywords/Phrases

Search In:

Publication Years