MMBIA 2012 Review

1. MMBIA 2012 Review Engin Dikici 20.01.2012

2. Automated Abdominal Fat Quantification and Food Residue Removal in CTAuthor: Makrogiannis et al. (NIH) The paper proposes a method to quantify fat as subcatenous and visceral in 2D abdomen CT images. Segmentation of especially the visceral fat using solely the Hounsfield Units (HU) is not reliable. First, the Fuzzy C-Means clustering is aplied to classify all pixels as air, muscle, fat and bone. Then, the subcatenous region is separated from the viscreal region using snakes algorithm (snakes is run at the C-means membership map instead of the direct pixel values). Finally, the visceral fat is region is further classified as visceral fat or food residues (apparently the most challenging part of the problem) using support vector machines. For the SVM, the set of features giving the texture information (coming from Gabor filter responses) are used. The method is tested using 144 CT images, with {2,3,4,5} fold cross validation.

3. A Coupled Segmentation and Registration Framework for Medical Image Analysis Using Robust Point Matching and Active Shape ModelAuthor: Lu et al. (Yale) The paper proposes a fast coupled framework to perform both segmentation and registration simultaneously. The method requires (1) the source and reference images, (2) segmented source organs. Then, it segments the reference organs and creates a non-rigid mapping field between the source and reference images. The segmentation module requires a point distribution model for the target organ. For the alignment of shapes in training dataset Procrustes Analysis is performed. The given shape is fit onto the reference image (fit onto intensity gradient) by using a modified Active Shape Models based. approach (ASM). The registration has 2 energy terms: (1) ASM energy to be sure that the deformations are in PDM model, (2) fitting quality onto the image information. This process only fits the control points (landmarks), the deformation of the rest of the mesh is found using thin-plate splines. The method is tested for the segmentation prostate and bladder in 40 3D CT images, and the left and right hippocampus in 15 3D fMRI images (leave 1 out). Takes approx 5min.

4. Pictorial Multi-atlas Segmentation of Brain MRIAuthor: Liu et al. (UCLA) Single-atlas segmentation works by finding the deformation field between the labeled atlas volume and target volume, and transferring the interesting landmarks to the target image. The use of a single atlas is problematic especially when the model variation is high. Alternatively, in multi-atlas methods, multiple atlases are registered to the target image and the resulting fields are weights fused with different weights. The paper proposes a method, each anatomical structure has its own atlas and inter-structure relations are also modeled (with spring-like connections). Hence, each structure is segmented with the local registrations and the global configuration is captured through the overlap of the propagated labels. First, the rigid affine transform between the target image and multiple atlases were found. Then, the transformations are turned into more complex form between the atlases and the target object, where the alignments are refined in multiple levels. The method is tested on 18 brain images with 84 annotated structures. High computational cost with promising results are reported.

5. A Fiber Tracking Method Guided by Volumetric Tract SegmentationAuthor: Ye et al. (Johns Hopkins) The paper proposes a method to track fibers in diffusion tensor images (DTI). It is a challenging problem since the fiber tracts may cross occasionally (look at corpus callosum and corticospinal tracks below). The novel approach utilizes a prior information coming from diffusion oriented track segmentation (DOTS), which produces a segmentation of white matter, to perform more robust tracking. DOTS is an atlas-based algorithm of white matter segmentation within a Markov random field (MRF) framework. It is capable of automatically extracting white matter tracts that are included in the DTI atlas. The algorithm directly labels the voxels and can handle fiber crossing. Orientation field computed by minimizing and energy function with 2 terms: (1) first term guarantees the smoothness of the extracted fibers, and (2) second term makes sure that the fibers propagate along the volumetric segmentation (coming from DOTS).

6. Automatic Detection of Subcellular Particles in Fluorescence Microscopy via Feature Clustering and Bayesian AnalysisAuthor: Liang et al. (Yale) The paper proposes a method for sub-cellular particle detection in 2D fluorescence images, which can estimate x-y positions, relative sizes, and signal amplitudes of individual particles. For the initial stage, they use image filters to locate particle candidates and apply clustering algorithms to separate true particles from noises. Laplacian of Gaussian (LoG) and Top-Hat filters are employed during localization, and PCA based clustering for noise elimination. In the second stage MAP-Bayesian analysis is perform to refine the positions and determine other features.

7. Supervised localization of cell nuclei on TMA imagesAuthor: Ibba et al. (Delft) The paper proposes a method to automatically localize cell nuclei in tissue micro array (TMA) images. The method (1) a simple image processing based step finds the potential cell centers, (2) a classification step prunes the results, (3) post processing step clusters the final blobs. First step is performed using LoG filter followed by thresholding. For the second stage, features for the candidate cells are collected (using a small window around the center). These features are derived from local intensity histograms, shape properties. For the classification, Parzen windows are used with RBF kernel with 2 classes (correct or wrong). [What about the curse of dimensionality????] Finally, the cell centers are clustered using their cell center distances.

8. Automatic Atlas-based Three-label Cartilage Segmentation from MR Knee ImagesAuthor: Shan et al. (UNC) The paper proposes a method to automatically segment femoral and tibial cartilage from T1 weighted magnetic resonance (MR) images using a bone-cartilage atlas of the knee. Three-label segmentation formulation, with the 1-tibial 2-femoral 3-background classes, is employed (see formula). The kNN classification algorithm (kernel based classifier which also suffers from curse of dimensionality) used with 15 features (!): intensities on three scales, first-order derivatives in three directions on three scales and second-order derivatives in axial direction on three scales (different scales obtained by convolving the image with different Gaussian kernels). The probabilistic atlas restricts the segmentation ROI; improves the robustness. The training data is affinely aligned to generate atlas (the image show hard classes).

9. Vascular Bifurcation Detection in Scale-SpaceAuthor: Baboiu et al. (Simon Fraser) The paper proposes a modality independent method for the detection of bifurcations in 2D/3D images. Scale space Hessian analysis produces low vesselness values at bifurcations… The authors proposed 2 new bifurcation-ness metrics: determinant of Hessian (product of eigenvalues?) and |?1|(|?3|-|?2|). They are separated from blob like features using codimension (is the number of dimensions in which diffusion can occur). The method was tested using 5 retinal angiograms. For 3D, only a set of synthetic data was used (hence no valid validations around yet).

10. Segmentation of Left Ventricles from Echocardiographic Sequences via Sparse Appearance RepresentationAuthor: Huang et al. (Yale) A method employs sparse appearance representations for segmenting left ventricular endocardial and epicardial boundaries from 2D echocardiographic sequences.

11. Learning Features for Streak Detection in Dermoscopic Color Images using Localized Radial Flux of Principal Intensity CurvatureAuthor: Mirzaalian et al. (Simon Fraser)

12. Using a Flexibility Constrained 3D Statistical Shape Model for Robust MRF-Based SegmentationAuthor: Majeed et al. (U Basel)

13. Automatic Extraction of Coronary Artery Tree in 2D X-ray AngiographyAuthor: Tanveer et al. (IBM)

14. Toward Whole-Brain Maps of Neural Connections: Logical Framework and Fast ImplementationAuthor: Zhang et al. (Cleveland Clinic)

15. THANK YOU!