Esmeraldo dos Santos Filho

Ultrasonic Tissue Characterization of Atherosclerosis by a Speed-of-Sound Microscanning System

We have been developing a scanning acoustic microscope (SAM) system for medicine and biology featuring quantitative measurement of ultrasonic parameters of soft tissues. In the present study, we propose a new concept sound speed microscopy that can measure the thickness and speed of sound in the tissue using fast Fourier transform of a single pulsed wave instead of burst waves used in conventional SAM systems. Two coronary arteries were frozen and sectioned approximately 10 mum in thickness. They were mounted on glass slides without cover slips. The scanning time of a frame with 300 X 300 pixels was 90 s and two- dimensional distribution of speed of sound was obtained. The speed of sound was 1680 plusmn 30 m/s in the thickened intima with collagen fiber, 1520 plusmn 8 m/s in the lipid deposition underlying the fibrous cap, and 1810 plusmn 25 m/s in a calcified lesion in the intima. These basic measurements will help in the understanding of echo intensity and pattern in intravascular ultrasound images.

Moment-based texture segmentation of luminal contour in intravascular ultrasound images

PurposeA system for luminal contour segmentation in intravascular ultrasound images is proposed.MethodsMoment-based texture features are used for clustering of the pixels in the input image. After the clustering, morphological smoothing and a boundary detection process are applied and the final image is obtained.ResultsThe proposed method was applied to 15 images from different patients, and a correlation coefficient of 0.86 was obtained between the areas of lumen automatically and manually defined.ConclusionMoment-based texture features together with the radial feature are powerful tools for identification of the lumen region in intravascular ultrasound images. Morphological filtering was useful for improving the segmentation results.

Automated Calcification Detection and Quantification in Intravascular Ultrasound Images by Adaptive Thresholding

An innovative application of adaptive thresholding is used for calcification regions detection in intravascular ultrasound images. A priori knowledge about the acoustic shadow that usually follows the calcification regions is used as discriminant of other bright regions of the image. Tests were carried out with 20 in vivo coronary artery images obtained from different patients. This proposed algorithm presented specificity of 88% and sensitivity of 84%. A receiver operating characteristic (ROC) curve, whose area under the curve (AUC) was equal to 0.87, was plotted for evaluation of the algorithm performance. A technique for measure of the arc of calcium from the segmented images is also presented. The measures of the arc of calcium calculated by the proposed algorithm presented a correlation coefficient of 0.92 when compared with measures performed manually by a human expert.

P1D-3 A System for Tissue Characterization and Quantification of Calcium Regions in Intravascular Ultrasound

This paper presents a combination of signal processing and image processing techniques for automatic segmentation and characterization of intravascular ultrasound images. This system is comprised of two modules, the tissue characterization module and the calcium quantification module. The tissue characterization module is based on classification of RF signal performed by a self-organizing map (SOM) previously trained. The calcium quantification module, using the image generated by the envelop of the RF signal, performs an adaptive thresholding based on the Otsus method. The thresholding process is followed by the analysis of the acoustic shadow regime of the input image which permits to distinguish calcification regions from other small bright regions that, usually, still remain in the image after the thresholding processing

Ultrasonic Nano-Imaging System for Medicine and Biology

The ultrasonic nano-imaging system for medicine and biology that has two measurement modes was developed. The transmission mode realized non-contact high resolution imaging of cultured cells. This mode can be applied for assessment of biomechanics of the cells and thinly sliced tissues. The reflection mode visualized fine structures of the brain of rat. This mode can be applied for intra-operative pathological examination because it does not require slicing or staining.

Radio Frequency Signal Analysis for Tissue Characterization of Coronary Artery: In Vivo Intravascular Ultrasound Study

Intravascular ultrasound (IVUS) is an important clinical tool that provides high resolution cross-sectional image of coronary artery. However, it is difficult to accurately classify plaque composition by conventional IVUS images. In the present study, we apply self-organizing map (SOM) of radiofrequency (RF) signal spectra for automatic plaque classification in IVUS diagnosis. IVUS data were acquired with a commercially available IVUS system with the central frequency of 40 MHz. We used double SOM classifier. The 1st classifier is supervised-SOM, learned four structures (blood, catheter, shadow, and outer lumen) based on spectral parameters. The 2nd classifier is unsupervised-SOM, used for classifying remained data, which were not classified the 1st classifier. We defined categories on the 2nd SOM by using K-means clustering method. Finally, color codes were assigned to the plaque component values, and the tissue color coded maps were reconstructed. Results suggest that the proposed technique is useful for automatic characterization of plaque components in IVUS image.