Timor Kadir

Geometric and photometric invariant distinctive regions detection

In this paper, we present a number of enhancements to the Kadir/Brady salient region detector which result in a significant improvement in performance. The modifications we make include: stabilising the difference between consecutive scales when calculating the inter-scale saliency, a new sampling strategy using overlap of pixels, partial volume estimation and parzen windowing. Repeatability is used as the criterion for evaluating the performance of the algorithm. We observe the repeatability for distinctive regions selected from an image and from the same image after applying a particular transformation. The transformations we use include planar rotation, pixel translation, spatial scaling, and intensity shifts and scaling. Experimental results show that the average repeatability rate is improved from 46% to approximately 78% when all the enhancements are applied. We also compare our algorithm with other region detectors on a set of sequences of real images, and our detector outperforms most of the state of the art detectors.

Estimating the Joint Statistics of Images Using Nonparametric Windows with Application to Registration Using Mutual Information

Recently, the Non-Parametric (NP) Windows has been proposed to estimate the statistics of real 1D and 2D signals. NP Windows is accurate, because it is equivalent to sampling images at a high (infinite) resolution for an assumed interpolation model. This paper extends the proposed approach to consider joint distributions of image-pairs. Secondly, Greens Theorem is used to simplify the previous NP Windows algorithm. Finally, a resolution aware NP Windows algorithm is proposed, to improve robustness to relative scaling between an image-pair. Comparative testing of 2D image registration was performed using translation-only and affine transformations. Although more expensive than other methods, NP Windows frequently demonstrated superior performance for bias (distance between ground truth and global maximum) and frequency of convergence. Unlike other methods, the number of samples and histogram bin-size has little effect on NP Windows, and the prior selection of a kernel is not required.

Organ Segmentation with Level Sets Using Local Shape and Appearance Priors

Organ segmentation is a challenging problem on which recent progress has been made by incorporation of local image statistics that model the heterogeneity of structures outside of an organ of interest. However, most of these methods rely on landmark based segmentation, which has certain drawbacks. We propose to perform organ segmentation with a novel level set algorithm that incorporates local statistics via a highly efficient point tracking mechanism. Specifically, we compile statistics on these tracked points to allow for a local intensity profile outside of the contour and to allow for a local surface area penalty, which allows us to capture fine detail where it is expected. The local intensity and curvature models are learned through landmarks automatically embedded on the surface of the training shapes. We use Parzen windows to model the internal organ intensities as one distribution since this is sufficient for most organs. In addition, since the method is based on level sets, we are able to naturally take advantage of recent work on global shape regularization. We show state-of-the-art results on the challenging problems of liver and kidney segmentation.

Segmentation of the liver in ultrasound: a dynamic texture approach

ObjectiveThe segmentation of ultrasound (US) images is useful for several applications in computer aided interventions including the registration of pre-operative CT or MRI to intra-operative US. Shadowing, intensity inhomogeneity and speckle are the common effects on US images. They render the segmentation algorithms developed for other modalities inappropriate due to poor robustness. We present a novel method for classification of hepatic structures including vasculature and liver parenchyma on US images.MethodsThe method considers B-mode US images as a dynamic texture. The dynamics of each pixel are modelled as an auto regressive (AR) process perturbed with Gaussian noise. The linear coefficients and noise variance are estimated pixel-wise using Neumaier and Schneider’s algorithm. Together with mean intensity they comprise a parametric space in which classification (maximum a posteriori or K-nearest neighbour) of each pixel is performed. We emphasize the novelty of studying dynamics rather than static features such as intensity in the segmentation of various structures.ResultsWe assessed the automatic segmentations of ten US sequences using Dice Similarity Coefficients. The algorithm’s capability of vessel extraction was tested on three sequences where Doppler US failed to capture vasculature.ConclusionThe modelling of image dynamics with AR process combined with MAP classifier produced robust segmentation results indicating that the method has a good potential for intra-operative use.

Automatic registration of cardiac PET/CT for attenuation correction

Misalignments of images in cardiac Positron Emission Tomography (PET)-CT imaging may lead to erroneous Attenuation Correction (AC) and mis-diagnosis. Such misalignment may be corrected manually prior to reconstruction and clinical assessment; however this step is laborious and may be subject to operator variability. The aim of this study is to assess the performance of an algorithm to automatically align CT to PET prior to AC. We conclude that automatic registration is a viable option for the task of aligning cardiac CT and PET for AC, with a consistency comparable to that of using manual alignment.

Image template matching using Mutual Information and NP-Windows

A non-parametric (NP) sampling method is introduced for obtaining the joint distribution of a pair of images. This method based on NP windowing and is equivalent to sampling the images at infinite resolution. Unlike existing methods, arbitrary selection of kernels is not required and the spatial structure of images is used. NP windowing is applied to a registration application where the mutual information (MI) between a reference image and a warped template is maximised with respect to the warp parameters. In comparisons against the current state of the art MI registration methods NP windowing yielded excellent results with lower bias and improved convergence rates

A Novel Method for Registration of US/MR of the Liver Based on the Analysis of US Dynamics

Radiofrequency ablation of liver cancer is a minimally invasive alternative to open surgery. Typically, the preoperative planning is done on an MR (or CT) scan, while the intervention relies on ultrasound (US) guidance. Registration of intra-operative US and preoperative MR (or CT) would assist navigation and increase the confidence of RFA needle positioning. In this paper we present a novel method for registration of US and MR images of the liver. Hepatic vessels are extracted from 2D US by an algorithm that models US dynamics. It generates 2D probability maps representing hepatic vessels which are then combined into probability volumes. A multi-resolution registration framework performs registration of the pre-processed MR with two 3D vessel probability images. The accuracy, robustness and speed of the method were assessed by registering eight US/MR datasets. High robustness (86%) and reasonable accuracy (1.98 degrees, 4.10 mm), acceptable for the RFA clinical application, suggest that the method has a good potential for intra-operative use.