J. Ken Leader

A simple method for automated lung segmentation in x-ray CT images

We developed and tested an automated scheme to segment lung areas depicted in CT images. The scheme includes a series of six steps. 1) Filtering and removing pixels outside the scanned anatomic structures. 2) Segmenting the potential lung areas using an adaptive threshold based on pixel value distribution in each CT slice. 3) Labeling all selected pixels ingo segmented regions and deleting isolated regions in non-lung area. 4) Labeling and filling interior cavities (e.g., pleural nodules, airway wall, and major blood vessels) inside lung areas. 5) Detecting and deleting the main airways (e.g., trachea and central bronchi) connected to the segmented lung areas. 6) Detecting and separating possible anterior or posterior junctions between the lungs. Five lung CT cases (7-10 mm in slice thickness) with variety of disease patterns were used to train or set up the classification rules in the scheme. Fifty examinations of emphysema patients were then used to test the scheme. The results were compared with the results generated from a semi-automated method with manual interaction by an expert observer. The experimental results showed that the average difference in estimated lung volumes between the automated scheme and manually corrected approach was 2.91%±0.88%. Visual examination of segmentation results indicated that the difference of the two methods was larger in the areas near the apices and the diaphragm. This preliminary study demonstrated that a simple multi-stage scheme had potential of eliminating the need for manual interaction during lunch segmentation. Hence, it can ultimately be integrated into computer schemes for quantitative analysis and diagnosis of lung diseases.

Multi-site telemammography system: preliminary assessment of technical and operational issues

Our goal was to develop an inexpensive, high-quality, multi-site telemammography system, implemented with low-level data connections that provided a communication link for an “almost real-time” response from a radiologist (central site) to remote “underserved” sites. The remote sites digitize mammographic films using high-resolution, laser digitizers. Images are automatically cropped, compressed (wavelet-based), and encrypted prior to transmission. At the central site images are decrypted, decompressed, unsharp masked, and displayed using automatically determined LUTs. The sites communicate instantly via a “chat box.” Remote sites 1, 2, and 3 are 15, 20, and 90 miles from the central site, respectively, and connected by POTS (sites 1 and 2) and LAN (site 3). Only minimal noticeable difference at compression levels of 50:1 and 75:1 could be identified unless magnified to extreme levels. Two experienced observers rated the LUTs for 200 images as “acceptable” to “excellent.” Average cycle times to digitize, transmit and receive cases (four films each) at 75:1 compression were 5.97, 6.85, and 5.77 min/case from sites 1, 2, and 3, respectively. Unique data-handling schemes significantly decrease the image file size and allow successful transmission in a reliable, timely manner. Over 1000 cases have been transmitted to date. Messaging was found to be easy to use.

Impedance measurements for early detection of breast cancer in younger women: a preliminary assessment

The purpose of this preliminary investigation is to explore the possibility that electrical impedance measurements of the breast can ultimately be used to screen younger women for early detection of breast cancer. As a part of a comprehensive protocol to compare different modalities, participating women undergo a series of diagnostic examinations, including impedance measurements under IRB-approved protocols. The results of the frequency-dependent algorithm are compared with the results of other imaging modalities as well as diagnostic outcome when available. In a preliminary series of 83 patients (divided into two groups) with varying risk levels, a significant correlation between impedance measurements and results from other diagnostic modalities was observed. The specific algorithm developed for high specificity resulted in an overall performance level of 90 percent specificity. The procedure was found to be “simple,” “fast,” and “easy to use” by the technologists. The interpretation of the results is straightforward. Our preliminary assessment is encouraging and indicates that the system may prove extremely useful for the purpose it was designed. Further technical improvements and clinical assessments are underway.

The relation of airway size to lung function

Chronic obstructive pulmonary disease may cause airway remodeling, and small airways are the mostly likely site of associated airway flow obstruction. Detecting and quantifying airways depicted on a typical computed tomography (CT) images is limited by spatial resolution. In this study, we examined the association between lung function and airway size. CT examinations and spirometry measurement of forced expiratory volume in one second as a percent predicted (FEV1%) from 240 subjects were used in this study. Airway sections depicted in axial CT section were automatically detected and quantified. Pearson correlation coefficients (PCC) were computed to compare lung function across three size categories: (1) all detected airways, (2) the smallest 50% of detected airways, and (3) the largest 50% of detected airways using the CORANOVA test. The mean number of all airways detected per subject was 117.4 (± 40.1) with mean size ranging from 20.2 to 50.0 mm2. The correlation between lung function (i.e., FEV1) and airway morphometry associated with airway remodeling and airflow obstruction (i.e., lumen perimeter and wall area as a percent of total airway area) was significantly stronger for smaller compared to larger airways (p < 0.05). The PCCs between FEV1 and all airways, the smallest 50%, and the largest 50% were 0.583, 0.617, 0.523, respectively, for lumen perimeter and -0.560, -0.584, and -0.514, respectively, for wall area percent. In conclusion, analyzing a set of smaller airways compared to larger airways may improve detection of an association between lung function and airway morphology change.

Texture-Based Segmentation and Analysis of Emphysema Depicted on CT Images

In this study we present a texture-based method of emphysema segmentation depicted on CT examination consisting of two steps. Step 1, a fractal dimension based texture feature extraction is used to initially detect base regions of emphysema. A threshold is applied to the texture result image to obtain initial base regions. Step 2, the base regions are evaluated pixel-by-pixel using a method that considers the variance change incurred by adding a pixel to the base in an effort to refine the boundary of the base regions. Visual inspection revealed a reasonable segmentation of the emphysema regions. There was a strong correlation between lung function (FEV1%, FEV1/FVC, and DLCO%) and fraction of emphysema computed using the texture based method, which were -0.433, -.629, and -0.527, respectively. The texture-based method produced more homogeneous emphysematous regions compared to simple thresholding, especially for large bulla, which can appear as speckled regions in the threshold approach. In the texture-based method, single isolated pixels may be considered as emphysema only if neighboring pixels meet certain criteria, which support the idea that single isolated pixels may not be sufficient evidence that emphysema is present. One of the strength of our complex texture-based approach to emphysema segmentation is that it goes beyond existing approaches that typically extract a single or groups texture features and individually analyze the features. We focus on first identifying potential regions of emphysema and then refining the boundary of the detected regions based on texture patterns.

Airway morphometry in the lungs as depicted in chest CT examinations variability of measurements

The purpose of the study was to decrease the variability of computed tomographic airway measurements. We to developed and evaluated a novel computer scheme to automatically segment airways depicted on chest CT examinations at the level of the lobar and segmental bronchi and to decrease. The computer scheme begins with manual selection of a seed point within the airway from which the airway wall and lumen are automatically segmented and airway pixels were assigned full or partial membership to the lumen or wall. Airway pixels not assigned full membership to the lumen (< -900 HU) or wall (> 0 HU) were assigned partial membership to the lumen and wall. In fifteen subjects with no visible signs of emphysema and a range of pulmonary obstruction from none to severe, airway measures were compared to pulmonary function parameters in a rank order analysis to evaluate measuring a single airway versus multiple airways. The quality of the automated airway segmentation was visually acceptable. The Pearson Correlation coefficients for the ranking of FEV1 versus wall area percent (percent of total airway size) and FVC versus wall area percent were 0.164 and 0.175 for a single measurement, respectively, and were 0.243 and 0.239 for multiple measurements, respectively. Our preliminary results suggest that averaging the measurements from multiple airways may improve the relation between airway measures and lung function compared to measurement from a single airway, which improve quantification of airway remodeling in COPD patients.

Subjective assessment of high-level image compression of digitized mammograms

This study was designed to evaluate radiologists’ ability to identify highly-compressed, digitized mammographic images displayed on high-resolution, monitors. Mammography films were digitized at 50 micron pixel dimensions using a high-resolution laser film digitizer. Image data were compressed using the irreversible (lossy), wavelet-based JPEG 2000 method. Twenty images were randomly presented in pairs (one image per monitor) in three modes: mode 1, non-compressed versus 50:1 compression; mode 2, non-compressed versus 75:1 compression; and mode 3, 50:1 versus 75:1 compression with 20 random pairs presented twice (80 pairs total). Six radiologists were forced to choose which image had the lower level of data compression in a two-alternative forced choice paradigm. The average percent correct across the six radiologists for modes 1, 2 and 3 were 52.5% (+/-11.3), 58.3% (+/-14.7), and 58.3% (+/-7.5), respectively. Intra-reader agreement ranged from 10 to 50% and Kappa from -0.78 to -0.19. Kappa for inter-reader agreement ranged from -0.47 to 0.37. The “monitor effect” (left/right) was of the same order of magnitude as the radiologists’ ability to identify the lower level of image compression. In this controlled evaluation, radiologists did not accurately discriminate non-compressed and highly-compressed images. Therefore, 75:1 image compression should be acceptable for review of digitized mammograms in a telemammography system.

Texture-based CT Image analysis of asthma

This study was motivated by anecdotal reports from our clinicians that the lung parenchyma appears “different” (more heterogeneous) in asthmatics compared to non-asthmatics. We investigated whether traditional texture features were different between severe asthmatics and non-asthmatics. CT examinations from 76 subjects classified as “severe asthma” (n = 51) and “normal control” (n = 25) based on Severe Asthma Research Program (SARP) criteria were used in this study. The CT exams were performed on a 64-detector or 16-detector GE scanner at a radiation exposure of 96.6 (±30.7) mAs with the subjects holding their breath at end-normal-expiration (functional residual capacity). The CT images were reconstructed at 0.625 or 1.25 mm thickness using either GE’s “standard” or “detail” kernels. Air trapping was computed as the percentage of voxels with a value less than -856 HU. Gray level co-occurrence matrices (GLCM) were computed from the CT images, and 15 Haralick texture descriptors were computed from the GLCM. Air trapping was significantly greater in the severe asthma subjects compared to the normal control subjects. Seven of the 15 texture features were significantly different between the severe asthma and normal control subjects. Our findings provide some validity to anecdotal reports of differences between the parenchyma of asthmatic and non-asthmatics. The significant texture features may ultimately be used to classify individuals as asthmatic or non-asthmatic, which should improve the limited performance of air trapping alone.

Preliminary clinical evaluation of a multi-site telemammography system in a screening mammography environment

We evaluated a telemammography system for reviewing and rating screening mammography in a clinical setting. Three remote sites transmitted 306 exams to a central site. Films were digitized at 50 micron pixel dimensions and compressed at a 50:1 ratio. At the central site images were displayed on a workstation with two high-resolution monitors. Five radiologists reviewed and rated the screens without the availability of prior images or additional information indicating: 1) if additional procedures were needed, 2) which breast was involved, and 3) when appropriate, the recommended additional procedures. During the actual clinical interpretation 13.7% (42 cases) of the patients were recalled for additional procedures. During the retrospective review radiologists 1, 2, 3, 4, and 5 recommended additional procedures for 26.1%, 29.1%, 36.3%, 45.1%, and 54.2% of the cases, respectively. The agreements between the clinical interpretation and radiologists 1, 2, 3, 4, and 5 were 77.8%, 76.1%, 69.0%, 62.7%, and 53.6%, respectively. The exceedingly high percentage of recommended additional procedures using the workstation was attributed to lack of prior images or additional information, the knowledge that case management was not affected, and the observers’ expectation for an enriched case mix.

Visualization of 3D geometric models of the breast created from contrast-enhanced MRI

Contrast enhanced breast MRI is currently used as an adjuvant modality to x-ray mammography because of its ability to resolve ambiguities and determine the extent of malignancy. This study described techniques to create and visualize 3D geometric models of abnormal breast tissue. MRIs were performed on a General Electric 1.5 Tesla scanner using dual phased array breast coils. Image processing tasks included: 1) correction of image inhomogeneity caused by the coils, 2) segmentation of normal and abnormal tissue, and 3) modeling and visualization of the segmented tissue. The models were visualized using object-based surface rendering which revealed characteristics critical to differentiating benign from malignant tissue. Surface rendering illustrated the enhancement distribution and enhancement patterns. The modeling process condensed the multi-slice MRI data information and standardized its interpretation. Visualizing the 3D models should improve the radiologists and/or surgeons impression of the 3D shape, extent, and accessibility of the malignancy compared to viewing breast MRI data slice by slice.