Biomedical subjects

J M Reinhardt

Publications and source records attributed to J M Reinhardt.

3 recordsLinked to original sources

PubMed2001

Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images.

Segmentation of pulmonary X-ray computed tomography (CT) images is a precursor to most pulmonary image analysis applications. This paper presents a fully automatic method for identifying the lungs in three-dimensional (3-D) pulmonary X-ray CT images. The method has three main steps. First, the lung region is extracted from the CT images by gray-level thresholding. Then, the left and right lungs are separated by identifying the anterior and posterior junctions by dynamic programming. Finally, a sequence of morphological operations is used to smooth the irregular boundary along the mediastinum in order to obtain results consistent with those obtained by manual analysis, in which only the most central pulmonary arteries are excluded from the lung region. The method has been tested by processing 3-D CT data sets from eight normal subjects, each imaged three times at biweekly intervals with lungs at 90% vital capacity. We present results by comparing our automatic method to manually traced borders from two image analysts. Averaged over all volumes, the root mean square difference between the computer and human analysis is 0.8 pixels (0.54 mm). The mean intrasubject change in tissue content over the three scans was 2.75% +/- 2.29% (mean +/- standard deviation).

Algorithms↗

PubMed1998

Quantitative pulmonary imaging: spatial and temporal considerations in high-resolution CT.

RATIONALE AND OBJECTIVES: The authors performed this study to determine how scanner temporal resolution affects image quality in high-resolution computed tomography (CT)-based quantitative analysis of lung parenchyma. MATERIALS AND METHODS: A 37-kg, anesthetized white pig, in which 1.5-mm-diameter radiopaque markers were bronchoscopically implanted, was scanned supine with electron-beam CT and spiral CT scanners. Images were first acquired during apnea at functional residual capacity with electron-beam CT at a single level with multiple scan apertures. Without altering body posture, the animal was transported to the spiral CT scanner and scanned at the same lung volume and level. The animal was then euthanized and rescanned with spiral CT. All images were reconstructed with high-spatial-frequency algorithms resident on the respective scanners. RESULTS: High-resolution CT images of the live animal in the spiral scanner showed substantial cardiogenic motion artifacts for markers both near and distant from the heart. The matched postmortem images showed no motion artifacts. While line-pair phantom scans showed reduced spatial resolution of electron-beam CT compared with spiral CT scans, the electron-beam CT images of the markers in the live animal were free of artifacts, even with scan apertures of up to 700 msec. CONCLUSION: Motion artifacts may be accentuated by differences in high-resolution CT implementations. Applications such as lung parenchyma texture analysis, which regionally quantifies the subtle tissue density variations, will likely benefit from short scanning apertures.

Animals↗

PubMed1997

Accurate measurement of intrathoracic airways.

Airway geometry measurements can provide information regarding pulmonary physiology and pathophysiology. There has been considerable interest in measuring intrathoracic airways in two-dimensional (2-D) slices from volumetric X-ray computed tomography (CT). Such measurements can be used to evaluate and track the progression of diseases affecting the airways. A popular airway measurement method uses the "half-max" criteria, in which the gray level at the airway wall is estimated to be halfway between the minimum and maximum gray levels along a ray crossing the edge. However, because the scanning process introduces blurring, the half-max approach may not be applicable across all airway sizes. We propose a new measurement method based on a model of the scanning process. In our approach, we examine the gray-level profile of a ray crossing the airway wall and use a maximum-likelihood method to estimate the airway inner and outer radius. Using CT scans of a physical phantom, we present results showing that the new approach is more accurate than the half-max method at estimating wall location for thin-walled airways.

Bronchi↗