Lumen Detection Research Articles

Support vector machine-based boundary recovery of a medical image segment in low resolution

A novel support vector machine (SVM)-based boundary recovery procedure for segmented medical objects in low-resolution images is proposed. The proposed procedure consists of two steps: segmentation and boundary interpolation steps. First, we initially estimate a coarse object region using an active contour-based segmentation method. Boundary recoveries from the first step exhibit considerably blocky artifacts and are easily misled by noise. Then, a reliable boundary recovery is achieved in the next step by the proposed support vector machines based interpolation scheme. In simulation, the proposed algorithm shows more reliable and better performance in the presence of noise and adequately preserves shapes and smooth boundaries that are essential characteristics of medical objects. We illustrate it using real-life data sets in regard to nonconvex tube detection in wall shear stress, lumen detection in carotid stenosis, micro-calcifications detection in digital mammography, and nonmedical fields as well.

In vivo comparison of arterial lumen dimensions assessed by co-registered three-dimensional (3D) quantitative coronary angiography, intravascular ultrasound and optical coherence tomography

This study sought to compare lumen dimensions as assessed by 3D quantitative coronary angiography (QCA) and by intravascular ultrasound (IVUS) or optical coherence tomography (OCT), and to assess the association of the discrepancy with vessel curvature. Coronary lumen dimensions often show discrepancies when assessed by X-ray angiography and by IVUS or OCT. One source of error concerns a possible mismatch in the selection of corresponding regions for the comparison. Therefore, we developed a novel, real-time co-registration approach to guarantee the point-to-point correspondence between the X-ray, IVUS and OCT images. A total of 74 patients with indication for cardiac catheterization were retrospectively included. Lumen morphometry was performed by 3D QCA and IVUS or OCT. For quantitative analysis, a novel, dedicated approach for co-registration and lumen detection was employed allowing for assessment of lumen size at multiple positions along the vessel. Vessel curvature was automatically calculated from the 3D arterial vessel centerline. Comparison of 3D QCA and IVUS was performed in 519 distinct positions in 40 vessels. Correlations were r = 0.761, r = 0.790, and r = 0.799 for short diameter (SD), long diameter (LD), and area, respectively. Lumen sizes were larger by IVUS (P < 0.001): SD, 2.51 ± 0.58 mm versus 2.34 ± 0.56 mm; LD, 3.02 ± 0.62 mm versus 2.63 ± 0.58 mm; Area, 6.29 ± 2.77 mm2 versus 5.08 ± 2.34 mm2. Comparison of 3D QCA and OCT was performed in 541 distinct positions in 40 vessels. Correlations were r = 0.880, r = 0.881, and r = 0.897 for SD, LD, and area, respectively. Lumen sizes were larger by OCT (P < 0.001): SD, 2.70 ± 0.65 mm versus 2.57 ± 0.61 mm; LD, 3.11 ± 0.72 mm versus 2.80 ± 0.62 mm; Area 7.01 ± 3.28 mm2 versus 5.93 ± 2.66 mm2. The vessel-based discrepancy between 3D QCA and IVUS or OCT long diameters increased with increasing vessel curvature. In conclusion, our comparison of co-registered 3D QCA and invasive imaging data suggests a bias towards larger lumen dimensions by IVUS and by OCT, which was more pronounced in larger and tortuous vessels.

Improved automated lumen contour detection by novel multifrequency processing algorithm with current intravascular ultrasound system

The aim of this study was to evaluate a new fully automated lumen border tracing system based on a novel multifrequency processing algorithm. We developed the multifrequency processing method to enhance arterial lumen detection by exploiting the differential scattering characteristics of blood and arterial tissue. The implementation of the method can be integrated into current intravascular ultrasound (IVUS) hardware. This study was performed in vivo with conventional 40-MHz IVUS catheters (Atlantis SR Pro™, Boston Scientific Corp, Natick, MA) in 43 clinical patients with coronary artery disease. A total of 522 frames were randomly selected, and lumen areas were measured after automatically tracing lumen borders with the new tracing system and a commercially available tracing system (TraceAssist™) referred to as the "conventional tracing system." The data assessed by the two automated systems were compared with the results of manual tracings by experienced IVUS analysts. New automated lumen measurements showed better agreement with manual lumen area tracings compared with those of the conventional tracing system (correlation coefficient: 0.819 vs. 0.509). When compared against manual tracings, the new algorithm also demonstrated improved systematic error (mean difference: 0.13 vs. -1.02 mm(2) ) and random variability (standard deviation of difference: 2.21 vs. 4.02 mm(2) ) compared with the conventional tracing system. This preliminary study showed that the novel fully automated tracing system based on the multifrequency processing algorithm can provide more accurate lumen border detection than current automated tracing systems and thus, offer a more reliable quantitative evaluation of lumen geometry.

Reproducible coronary plaque quantification by multislice computed tomography

The aim of this study was to investigate reproducibility and accuracy of computer-assisted coronary plaque measurements by multislice computed tomography coronary angiography (QMSCT-CA). Forty-eight patients undergoing MSCT-CA and coronary arteriography for symptomatic coronary artery disease and quantitative intravascular ultrasound (IVUS, QCU) were examined. Two investigators performed the QMSCT-CA twice and a third investigator performed the QCU, all blinded for each other's results. There was no difference found for the matched region of interest (ROI) lengths (QCU 29.4 +/- 13 mm vs. QMSCT-CA 29.6 +/- 13 mm, P = 0.6; total length = 1,400 mm). The comparison of volumetric measurements showed (lumen QCU 267 +/- 139 mm(3) vs. mean QMSCT-CA 177 +/- 91 mm(3), P < 0.001; vessel 454 +/- 194 mm(3) vs. 398 +/- 187 mm(3), P <<0.001; and plaque 189 +/- 93 mm(3) vs. 222 +/- 121 mm(3); investigator 1, P = 0.02; and investigator 2, P = 0.07) significant differences. Automated lumen detection was also applied for QMSCT-CA (218 +/- 112 mm(3), P < 0.001 vs. QCU). The interinvestigator variability measurements for QMSCT-CA showed no significant differences. QMSCT-CA systematically underestimates absolute coronary lumen- and vessel dimensions when compared with QCU. However, repeated measurements of coronary plaque by QMSCT-CA showed no statistically significant differences, although, the outcome showed a scattered result. Automated lumen detection for QMSCT-CA showed improved results when compared with those of human investigators.

Automatic border detection in IntraVascular UltraSound images for quantitative measurements of the vessel, lumen and stent parameters

This paper describes a knowledge- and model-guided system for the automatic contour detection of the vessel, lumen and stent borders in IntraVascular UltraSound (IVUS images). The different steps of a complete analysis of an IVUS pullback sequence are carried out such that the different components assist each other. The stent detection assists the vessel detection and both detections assist the lumen detection. Finally, the applications of the quantification software in the catheterization laboratory (on-line) as well as for research studies (off-line) are discussed.

Recent Developments in IVUS

In the development of intravascular ultrasound (IVUS), a serious emphasis has been given to the improvement of the image quality in terms of resolution. The image quality is indeed a very important issue, but there is a lot more information hidden in the ultrasound signals than is currently exploited by commercially available IVUS equipment. For several years, at the Thorax Centre, we have been exploring the possibilities of analysing sequences of radiofrequency (RF) traces to extend the functionality of the IVUS machines. It gives possibilities for local elasticity assessment, flow estimation and enhanced lumen detection. This paper is an up to date report of where RF-data analysis has taken us.

1000–33 On-line Automated Lumen Volume Measurement With 3-D Intracoronary Ultrasound During Coronary Interventions

The aim of the study was the clinical validation of an on-line fully automated lumen detection technique with intravascular ultrasound (IVUS). Assessment of lumen dimensions with IVUS was performed in 10 pts undergoing transcatheter therapy (5 PTCA, 2 DCA, 3 Stents). The images of 10 coronary segments 20 mm long in the treated area were acquired during motorized pull-back (1 mm/s) and processed using an automated contour detection algorithm based on acoustic quantification of blood backscatter and coronary wall. For each segment, 171 ultrasound images (8.5/mm) were processed in the laboratory, immediately after acquisition (processing time: 1.35 min.) and subsequently corrected off-line by an experienced analyst. Segment lumen volume was calculated by multiplying mean lumen area by segment length. Of the 1,710 on-line IVUS cross-sections obtained, 232 (13.6%) could not be analyzed due to failure of the algorithm used for lumen detection induced by presence of side branches, lack of sharpness of the lumen-wall interface or shadowing from calcium interpreted as lumen. On the remaining frames (1,478; 86.4%), on-line vs off-line comparison for mean lumen area and segment volume was performed. On-line measurement of lumen area and volume were highly correlated with the off-line measurements (r = 0.85 for area; see fig. for lumen), with a mean difference of 7.4 ± 14.1% and 5.9 ± 5.4% for lumen area and volume, respectively. In selected optimal sequences of images acquired during coronary interventions, on-line automated quantitative 3-D provides reliable measurements of lumen dimension, requiring limited manual interaction.Download : Download high-res image (57KB)Download : Download full-size image