1 / 21

Shaoting Zhang, Yiqiang Zhan, Yan Zhou, and Dimitris N. Metaxas

Efficient Sparse Shape Composition with its Applications in Biomedical Image Analysis: An Overview. Shaoting Zhang, Yiqiang Zhan, Yan Zhou, and Dimitris N. Metaxas. Outline. Introduction Our methods Experiments Future work. Introduction Motivations. Chest X-ray.

xanthus-clemons
Télécharger la présentation

Shaoting Zhang, Yiqiang Zhan, Yan Zhou, and Dimitris N. Metaxas

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Efficient Sparse Shape Composition with its Applications in Biomedical Image Analysis: An Overview Shaoting Zhang, Yiqiang Zhan, Yan Zhou, and Dimitris N. Metaxas

  2. Outline • Introduction • Our methods • Experiments • Future work

  3. IntroductionMotivations Chest X-ray Rat brain structure in MR Microscopy Lung CAD Liver in low-dose whole-body CT Segmentation (i.e., finding 2D/3D ROI) is a fundamental problem in medical image analysis. We use deformable models and shape prior. Shape representation is based on landmarks.

  4. IntroductionMotivations Segmentation system • Shape prior is important: • Automatic, accuracy, efficiency, robustness. • Handle weak or misleading appearance cues from image information. • Discover or preserve complex shape details.

  5. IntroductionChallenges – shape prior modeling • Good shape prior method needs to: • Handle gross errors of the input data. • Model complex shape variations. • Preserve local shape details.

  6. IntroductionRelevant work – shape prior methods • Handling gross errors. • RANSAC + ASM [M. Rogers, ECCV’02] • Robust Point Matching [J. Nahed, MICCAI’06] • Complex shape variation (multimodal distribution). • Mixture of Gaussians [T. F. Cootes, IVC’97] • Patient-specific shape [Y. Zhu, MICCAI’09] • Recovering local details. • Sparse PCA [K. Sjostrand, TMI’07] • Hierarchical ASM [D. Shen, TMI’03]

  7. Our ApproachShape prior using sparse shape composition ... ≈ • Our method is based on two observations: • An input shape can be approximately represented by a sparse linear combination of training shapes. • The given shape information may contain gross errors, but such errors are often sparse.

  8. Our ApproachShape prior using sparse shape composition Number of nonzero elements ... ... ≈ ≈ Global transformation operator Global transformation parameter Input y Weight x Aligned data matrix D Dense x Sparse x • Formulation: • Sparse linear combination:

  9. Our ApproachShape prior using sparse shape composition • Non-Gaussian errors:

  10. Our ApproachShape prior using sparse shape composition Zhang, Metaxas, et.al.: MedIA’11 • Why it works? • Robust: Explicitly modeling “e” with L0 norm constraint. Thus it can detect gross (sparse) errors. • General: No assumption of a parametric distribution model (e.g., a unimodal distribution assumption in ASM). Thus it can model complex shape statistics. • Lossless: It uses all training shapes. Thus it is able to recover detail information even if the detail is not statistically significant in training data.

  11. Experiments2D lung localization in X-ray • Setting: • Manually select landmarks for training purpose. • 200 training and 167 testing. • To locate the lung, detect landmarks, then predict a shape to fit them. • Sensitivity (P), Specificity (Q), Dice Similarity Coefficient.

  12. Experiments 2D lung localization in X-ray • Handling gross errors Detection PA ASM RASM NN TPS Sparse1 Sparse2 Procrustes analysis Active Shape Model Robust ASM Nearest Neighbors Thin-plate-spline Without modeling “e” Proposed method

  13. Experiments2D lung localization in X-ray • Multimodal distribution Detection PA ASM/RASM NN TPS Sparse1 Sparse2

  14. Experiments2D lung localization in X-ray • Recover local detail information Detection PA ASM/RASM NN TPS Sparse1 Sparse2

  15. Experiments2D lung localization in X-ray • Sparse shape components • ASM modes: ≈ + + 0.5760 0.2156 0.0982

  16. Experiments2D lung localization in X-ray • Mean values (µ) and standard deviations (σ). σ µ Left lung Right lung 1)PA, 2)ASM, 3)RASM, 4)NN, 5)TPS, 6)Sparse1, 7)Sparse2

  17. Future WorkDictionary Learning Dictionary size = 128 #Coefficients = 800 #Input samples = 800 ... ... ... ≈

  18. D Initialize D Sparse Coding Use MP or BP YT Dictionary Update Column-by-Column by SVD computation Future WorkDictionary Learning Use K-SVD to learn a compact dictionary Aharon, Elad, & Bruckstein (‘04) * The slides are from http://www.cs.technion.ac.il/~elad/talks/

  19. Future WorkOnline Update Dictionary ... ≈ ...

  20. Future WorkMesh Partitioning for Local Shape Priors Heart Lung Rib Abdomen Segmentation system Colon

  21. Thanks!Questions and comments

More Related