Iwo Willem Oscar Serlie

Visualization of boundaries in volumetric data sets using LH histograms

A crucial step in volume rendering is the design of transfer functions that highlights those aspects of the volume data that are of interest to the user. For many applications, boundaries carry most of the relevant information. Reliable detection of boundaries is often hampered by limitations of the imaging process, such as blurring and noise. We present a method to identify the materials that form the boundaries. These materials are then used in a new domain that facilitates interactive and semiautomatic design of appropriate transfer functions. We also show how the obtained boundary information can be used in region-growing-based segmentation.

Computed Cleansing for Virtual Colonoscopy Using a Three-Material Transition Model

Virtual colonoscopy is a non-invasive technique for the detection of polyps. Currently, a clean colon is required; as without cleansing the colonic wall cannot be segmented. Enhanced bowel preparation schemes opacify intraluminal remains to enable colon segmentation. Computed cleansing (as opposed to physical cleansing of the bowels) allows removal of tagged intraluminal remains. This paper describes a model that allows proper classification of transitions between three materials: gas, tissue and tagged intraluminal remains. The computed cleansing effectively detects and removes the remains from the data. Inspection of the ‘clean’ wall is possible using common surface visualization techniques.

Classifying CT Image Data Into Material Fractions by a Scale and Rotation Invariant Edge Model

A fully automated method is presented to classify 3-D CT data into material fractions. An analytical scale-invariant description relating the data value to derivatives around Gaussian blurred step edges - arch model - is applied to uniquely combine robustness to noise, global signal fluctuations, anisotropic scale, noncubic voxels, and ease of use via a straightforward segmentation of 3-D CT images through material fractions. Projection of noisy data value and derivatives onto the arch yields a robust alternative to the standard computed Gaussian derivatives. This results in a superior precision of the method. The arch-model parameters are derived from a small, but over-determined, set of measurements (data values and derivatives) along a path following the gradient uphill and downhill starting at an edge voxel. The model is first used to identify the expected values of the two pure materials (named and ) and thereby classify the boundary. Second, the model is used to approximate the underlying noise-free material fractions for each noisy measurement. An iso-surface of constant material fraction accurately delineates the material boundary in the presence of noise and global signal fluctuations. This approach enables straightforward segmentation of 3-D CT images into objects of interest for computer-aided diagnosis and offers an easy tool for the design of otherwise complicated transfer functions in high-quality visualizations. The method is applied to segment a tooth volume for visualization and digital cleansing for virtual colonoscopy.

Improved visualization in virtual colonoscopy using image-based rendering

Virtual colonoscopy (VC) is a patient-friendly alternative for colorectal endoscopic examination. We explore visualization aspects of VC such as surface in view, navigation and communication of a diagnosis. A series of unfolded cubes presents an animated full 360-degree omnidirectional field-of-view to the physician, to facilitate thorough and rapid inspection. For communication between physicians a tool has been designed that uses image-based rendering. Clinical evaluation has shown a reduction in inspection time from 19 minutes to 7 minutes without loss of sensitivity. With current virtual colonoscopy using a 2-sided view only 94% of the surface is available for exploration. In our approach the surface in view is increased to potentially 100%. Thus, the entire colon can be explored with better confidence that no regions are missed.

Electronic Cleansing for Computed Tomography (CT) Colonography Using a Scale-Invariant Three-Material Model

A well-known reading pitfall in computed tomography (CT) colonography is posed by artifacts at T-junctions, i.e., locations where air-fluid levels interface with the colon wall. This paper presents a scale-invariant method to determine material fractions in voxels near such T-junctions. The proposed electronic cleansing method particularly improves the segmentation at those locations. The algorithm takes a vector of Gaussian derivatives as input features. The measured features are made invariant to the orientation-dependent apparent scale of the data and normalized in a way to obtain equal noise variance. A so-called parachute model is introduced that maps Gaussian derivatives onto material fractions near T-junctions. Projection of the noisy derivatives onto the model yields improved estimates of the true, underlying feature values. The method is shown to render an accurate representation of the object boundary without artifacts near junctions. Therefore, it enhances the reading of CT colonography in a 3-D display mode.

Lesion Conspicuity and Efficiency of CT Colonography with Electronic Cleansing Based on a Three-Material Transition Model

OBJECTIVE The purpose of this article is to report the effect on lesion conspicuity and the practical efficiency of electronic cleansing for CT colonography (CTC). MATERIALS AND METHODS Patients were included from the Walter Reed Army Medical Center public database. All patients had undergone extensive bowel preparation with fecal tagging. A primary 3D display method was used. For study I, the data consisted of all patients with polyps > or = 6 mm. Two experienced CTC observers (observer 1 and observer 2) scored the lesion conspicuity considering supine and prone positions separately. For study II, data consisted of 19 randomly chosen patients from the database. The same observers evaluated the data before and after electronic cleansing. Evaluation time, assessment effort, and observer confidence were recorded. RESULTS In study I, there were 59 lesions partly or completely covered by tagged material (to be uncovered by electronic cleansing) and 70 lesions surrounded by air (no electronic cleansing required). The conspicuity did not differ significantly between lesions that were uncovered by electronic cleansing and lesions surrounded by air (observer 1, p < 0.5; observer 2, p < 0.6). In study II, the median evaluation time per patient after electronic cleansing was significantly shorter than for original data (observer 1, 20 reduced to 12 minutes; observer 2, 17 reduced to 12 minutes). Assessment effort was significantly smaller for both observers (p < 0.0000001), and observer confidence was significantly larger (observer 1, p < 0.007; observer 2, p < 0.0002) after electronic cleansing. CONCLUSION Lesions uncovered by electronic cleansing have comparable conspicuity with lesions surrounded by air. CTC with electronic cleansing sustains a shorter evaluation time, lower assessment effort, and larger observer confidence than without electronic cleansing.

Visualization of noisy and biased volume data using first and second order derivative techniques

The quality of volume visualization depends strongly on the quality of the underlying data. In virtual colonoscopy, CT data should be acquired at a low radiation dose that results in a low signal-to-noise ratio. Alternatively, MRI data is acquired without ionizing radiation, but suffers from noise and bias (global signal fluctuations). Current volume visualization techniques often do not produce good results with noisy or biased data. This paper describes methods for volume visualization that deal with these imperfections. The techniques are based on specially adapted edge detectors using first and second order derivative filters. The filtering is integrated into the visualization process. The first order derivative method results in good quality images but suffers from localization bias. The second order method has better surface localization, especially in highly curved areas. It guarantees minimal detail smoothing resulting in a better visualization of polyps.

Probabilistic method for virtual colonoscopy cleansing

Currently, virtual colonoscopy examinations require extensive bowel preparation because residual materials can occlude lesions or can be misinterpreted as polyps. Our goal is to investigate a probabilistic method to segment contrast enhanced residual materials and remove them from the rendering. The region around a sample position is modeled to contain mixtures of air, tissue and tagged intraluminal remains. For each image sample a probability vector is calculated expressing the probability that the materials of interest are present. A probability space is defined using the probabilities for pure materials as base vectors. Mixture vectors are constructed at 45-degree angles between the pure material vectors. The probability vectors are compared to the base vectors and the mixture vectors to classify them into material mixtures. Consider the layer between air and tagged fluid. Image intensities are similar to tissue. The scale at which the Gaussian averaged probability is calculated is increased until convergence: two successive scales result in the same classification. The Bayesian classification method shows good results with relatively large objects. However, edges of small or thin objects are likely to be misclassified: a too large environment is needed for convergence.

Thin layer tissue classification for electronic cleansing of CT colonography data

CT colonography (CTC) is a rapidly evolving technique to screen for colorectal polyps. Fecal residue may occlude or, reversely, mimic polyps. Electronic cleansing aims at removing contrast-enhanced fecal residue from the image. However, thin layers of soft tissue (the colon wall or a fold) or residue are easily misclassified by current electronic cleansing methods, thereby causing holes in the colon wall or other artefacts that hamper visualization and automated detection. We present a thin layer model to detect and characterize such layers to support electronic cleansing. It is demonstrated that the model sustains robust estimation of the location and thickness of such a layer. Such thicknesses of thin layers were measured in real data sets. A lower bound on the thickness of such layers exists and was found to be 1.0 mm for our data.

Unfolded Cube Projection of the Colon

In the evaluation of computed tomography (CT) colonography examinations generally two different approaches exist: