Arunabha S. Roy

A framework for automated tumor detection in thoracic FDG pet images using texture-based features

This paper proposes a novel framework for tumor detection in Positron Emission Tomography (PET) images. A set of 8 second-order texture features obtained from the gray level co-occurrence matrix (GLCM) across 26 offsets, together with uptake value was used to construct a feature vector at each voxel in the data. Volume of Interest (VOI) samples from 42 images (7 patients with 6 gates each), marked by a radiologist, representing 5 distinct anatomy types and pathology were used to train a logit boost classifier. A ten-fold cross-validation showed a true positive rate of 96%and a false positive rate of 8% for tumor classification. The test dataset consisted of 50 × 50 × 40 representative VOIs from gated PET images of 3 patients. The classifier was run on the test data, followed by an SUV-based thresholding and elimination of noise using connected component analysis. The method detected 10/12 (83%) tumors while detecting an average of 20 false positive structures.

Deformable density matching for 3D non-rigid registration of shapes

There exists a large body of literature on shape matching and registration in medical image analysis. However, most of the previous work is focused on matching particular sets of features--point-sets, lines, curves and surfaces. In this work, we forsake specific geometric shape representations and instead seek probabilistic representations--specifically Gaussian mixture models--of shapes. We evaluate a closed-form distance between two probabilistic shape representations for the general case where the mixture models differ in variance and the number of components. We then cast non-rigid registration as a deformable density matching problem. In our approach, we take one mixture density onto another by deforming the component centroids via a thin-plate spline (TPS) and also minimizing the distance with respect to the variance parameters. We validate our approach on synthetic and 3D arterial tree data and evaluate it on 3D hippocampal shapes.

Robust motion correction for respiratory gated PET/CT using weighted averaging

Movement degrades image quality in PET/CT. A common strategy is to gate the PET data, reconstruct the images, register each image to a reference gate, and average the registered images (Reconstruct, Registered, Average or RRA).

Bi-directional labeled point matching

Robust point matching (RPM) simultaneously estimates correspondences and non-rigid warps between unstructured point-sets. While RPM is robust to outliers in the target (fixed) point-set, its performance degrades when the template (moving) point-set contains outliers. Additionally, RPM does not utilize information about the topological structure or group memberships of the data it is matching. Bi-directional Labeled Point Matching (BLPM) extends the RPM objective function by introducing (i) Bi-Directionality (BD) and (ii) a Label Entropy (LE) term. BD aids in outlier rejection in both point-sets and LE discourages mappings that transform points within a single group in one point-set onto points from multiple distinct groups in the other point-set. The resulting BLPM algorithm translates into simple modifications to the standard RPM update rules.

Improved Robust Point Matching with Label Consistency

Robust point matching (RPM) jointly estimates correspondences and non-rigid warps between unstructured point-clouds. RPM does not, however, utilize information of the topological structure or group memberships of the data it is matching. In numerous medical imaging applications, each extracted point can be assigned group membership attributes or labels based on segmentation, partitioning, or clustering operations. For example, points on the cortical surface of the brain can be grouped according to the four lobes. Estimated warps should enforce the topological structure of such point-sets, e.g. points belonging to the temporal lobe in the two point-sets should be mapped onto each other. We extend the RPM objective function to incorporate group membership labels by including a Label Entropy (LE) term. LE discourages mappings that transform points within a single group in one point-set onto points from multiple distinct groups in the other point-set. The resulting Labeled Point Matching (LPM) algorithm requires a very simple modification to the standard RPM update rules. We demonstrate the performance of LPM on coronary trees extracted from cardiac CT images. We partitioned the point sets into coronary sections without a priori anatomical context, yielding potentially disparate labelings (e.g. [1,2,3] → [a,b,c,d]). LPM simultaneously estimated label correspondences, point correspondences, and a non-linear warp. Non-matching branches were treated wholly through the standard RPM outlier process akin to non-matching points. Results show LPM produces warps that are more physically meaningful than RPM alone. In particular, LPM mitigates unrealistic branch crossings and results in more robust non-rigid warp estimates.

Nonrigid registration framework for bronchial tree labeling using robust point matching

Automated labeling of the bronchial tree is essential for localization of airway related diseases (e.g. chronic bronchitis) and is also a useful precursor to lung-lobe labeling. We describe an automated method for registration-based labeling of a bronchial tree. The bronchial tree is segmented from a CT image using a region-growing based algorithm. The medial line of the extracted tree is then computed using a potential field based approach. The expert-labeled target (atlas) and the source bronchial trees in the form of extracted centerline point sets are brought into alignment by calculating a non-rigid thin-plate spline (TPS) mapping from the source to the target. The registration takes into account global as well as local variations in anatomy between the two images through the use of separable linear and non-linear components of the transformation; as a result it is well suited to matching structures that deviate at finer levels: namely higher order branches. The method is validated by registering together pairs of datasets for which the ground truth labels are known in advance: the labels are transferred after matching target to source and then compared with the true values. The method was tested on datasets each containing 18 branch centerpoints and 12 bifurcation locations (30 landmarks in total) annotated manually by a radiologist, where the performance was measured as the number of landmarks having the correct transfer of labels. An overall accuracy of labeling of 91.5 % was obtained in matching 23 pairs of datasets obtained from different patients.