In this paper, we present a computer-aided detection (CAD) method to extract and use internal features to reduce false positive (FP) rate generated by surface-based measures on the inner colon wall in computed tomographic (CT) colonography. Firstly, a new shape description global curvature, which can provide an overall shape description of the colon wall, is introduced to improve the detection of suspicious patches on the colon wall whose geometrical features are similar to that of the colonic polyps. By a ray-driven edge finder, the volume of each detected patch is extracted as a fitted ellipsoid model. Within the ellipsoid model, CT image density distribution is analyzed. Three types of (geometrical, morphological and textural) internal features are extracted and applied to eliminate the FPs from the detected patches. The presented CAD method was tested by a total of 153 patient datasets in which 45 patients were found with 61 polyps of sizes 4–30 mm by optical colonoscopy. For a 100% detection sensitivity (on polyps), the presented CAD method had an average FPs of 2.68 per patient dataset and eliminated 93.1% of FPs generated by the surface-based measures. The presented CAD method was also evaluated by different polyp sizes. For polyp sizes of 10–30 mm, the method achieved mean number of FPs per dataset of 2.0 with 100% sensitivity. For polyp sizes of 4–10 mm, the method achieved 3.44 FP per dataset with 100% sensitivity.

Reduction of False Positives by Internal Features for Polyp Detection in CT-Based Virtual Colonoscopy

One of the drawbacks of standard volume rendering techniques is that is it often difficult to comprehend the three-dimensional structure of the volume from a single frame; this is especially true in cases where there is no solid surface. Generally, several frames must be generated and viewed sequentially, using motion parallax to relay depth. Another option is to generate a single spectroscopic pair, resulting in clear and unambiguous depth information in both static and moving images. Methods have been developed which take advantage of the coherence between the two halves of a stereo pair for polygon rendering and ray-tracing, generating the second half of the pair in significantly less time than that required to completely render a single image. This paper reports the results of implementing these techniques with parallel ray-traced volume rendering. In tests with different data types, the time savings is in the range of 70--80%.

Fast Stereoscopic Images with Ray-Traced Volume Rendering.

[Paper] Design and Construction of Data Acquisition Facilities for Diminished Reality Research

Bladder cancer screening by magnetic resonance imaging

We present a framework for computer aided detection and diagnosis based on segmented tissue mixtures in each voxel of the image. Detection is based on both the geometry and texture. Diagnosis is based on both the texture and feature extracted from the mixture segmentation.

Feature-based approach toward computer aided detection and diagnosis

Light field algorithm is one of the most famous image-based rendering techniques. In this paper, an improved light field algorithm - sphere light field algorithm - is proposed. This new algorithm replaces the parallel planes used in traditional light field algorithms by a triangularly parameterized sphere surface. Comparing to the traditional light field algorithms, this new algorithm achieves a more uniform distribution of light slabs in the three dimensional space. This improves the quality of the rendered images. Using the triangular parameterization and subdivision of the whole light field, less calculation and less memory are needed, resulting in improved real-time rendering.

Sphere light field rendering

In this paper, a new feature based rendering algorithm for partial volume is presented. This algorithm utilizes both surface and volume information for the rendering of the partial volume segmentation data with associated features. First principal directions are extracted from the partial volume segmentation dataset to construct a feature vector dataset. Using the improved LIC (line integral convolution) algorithm, our rendering algorithm can integrate the feature dataset into the traditional volume rendering process. Combining the features and the traditional volume rendering, the partial volume could be visualized clearly. This can result in an improved diagnosis in clinic, because the partial volume quantities carry very rich diagnostic information.© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Feature-based rendering for 2D/3D partial volume segmentation datasets

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Sphere light field rendering

Feature-based rendering for 2D/3D partial volume segmentation datasets