Biomedical subjects

Adam Huang

Publications and source records attributed to Adam Huang.

2 recordsLinked to original sources

PubMed2006

Assessment of polyp and mass histopathology by intravenous contrast-enhanced CT colonography.

RATIONALE AND OBJECTIVES: We sought to demonstrate that intravenous contrast-enhanced CT colonography (CTC) can distinguish colonic adenomas from carcinomas. METHODS: Supine intravenous contrast-enhanced CTC with colonoscopic and/or surgical correlation was performed on 25 patients with colonic adenomas or carcinomas. Standard deviation of mean polyp CT attenuation was computed and assessed using ANOVA and receiver-operating characteristic analyses. RESULTS: Colonoscopy confirmed 32 polyps or masses 1 to 8 cm in size. The standard deviations of CT attenuation were carcinomas (n = 13; 36 +/- 6 HU; range 28-48 HU) and adenomas (n = 19; 49 +/- 14 HU; range 31-100 HU) (P = 0.005). At a standard deviation threshold of 42 HU, the sensitivity and specificity for classifying a polyp or mass as a carcinoma were 92% and 79%, respectively. The area under the receiver-operating characteristic curve was 0.89 +/- 0.06 (95% confidence interval 0.73-0.96). CONCLUSIONS: Measurement of the standard deviation of CT attenuation on intravenous contrast-enhanced CTC permits histopathologic classification of polyps 1 cm or larger as carcinomas versus adenomas. The presence of ulceration or absence of muscular invasion in carcinomas creates overlap with adenomas, reducing the specificity of carcinoma classification.

Adenoma↗

PubMedDate not supplied

Thin structure segmentation and visualization in three-dimensional biomedical images: a shape-based approach.

This paper presents a shape-based approach in extracting thin structures, such as lines and sheets, from three-dimensional (3D) biomedical images. Of particular interest is the capability to recover cellular structures, such as microtubule spindle fibers and plasma membranes, from laser scanning confocal microscopic (LSCM) data. Hessian-based shape methods are reviewed. A synthesized linear structure is used to evaluate the sensitivity of the multiscale filtering approach in extracting closely positioned fibers. We find that the multiscale approach tends to fuse lines together, which makes it unsuitable for visualizing mouse egg spindle fibers. Single-scale Gaussian filters, balanced between sensitivity and noise resistance, are adopted instead. In addition, through an ellipsoidal Gaussian model, the eigenvalues of the Hessian matrix are quantitatively associated with the standard deviations of the Gaussian model. Existing shape filters are simplified and applied to LSCM data. A significant improvement in extracting closely positioned thin lines is demonstrated by the resultant images. Further, the direct association of shape models and eigenvalues makes the processed images more understandable qualitatively and quantitatively.

Algorithms↗