Hypothesis Validation of Far-Wall Brightness in Carotid-Artery Ultrasound for Feature-Based IMT Measurement Using a Combination of Level-Set Segmentation and Registration

Intima-media thickness (IMT) is now being considered as an indicator of atherosclerosis. Our group has developed several feature-based IMT measurement algorithms such as the Completely Automated Layer EXtraction (CALEX) (which is a class of patented AtheroEdge Systems from Global Biomedical Technologies, Inc., CA, USA). These methods are based on the hypothesis that the highest pixel intensities are in the far wall of the common carotid artery (CCA) or the internal carotid artery (ICA). In this paper, we verify that this hypothesis holds true for B-mode longitudinal ultrasound (US) images of the carotid wall. This patented methodology consists of generating the composite image (the arithmetic sum of images) from the database by first registering the carotid image frames with respect to a nearly straight carotid-artery frame from the same database using: 1) B-spline-based nonrigid registration and 2) affine registration. Prior to registration, we segment the carotid-artery lumen using a level-set-based algorithm followed by morphological image processing. The binary lumen images are registered, and the transformations are applied to the original grayscale CCA images. We evaluated our technique using a database of 200 common carotid images of normal and pathologic carotids. The composite image presented the highest intensity distribution in the far wall of the CCA/ICA, validating our hypothesis. We have also demonstrated the accuracy and improvement in the IMT segmentation result with our CALEX 3.0 system. The CALEX system, when run on newly acquired US images, shows the IMT error of about 30 mu m. Thus, we have shown that the CALEX algorithm is able to exploit the far-wall brightness for accurate IMT measurements

Cardiac state diagnosis using adaptive neuro-fuzzy technique

Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings7 VOLS3864-3867CEMB

Completely automated robust edge snapper for carotid ultrasound IMT measurement on a multi-institutional database of 300 images

The carotid intima-media thickness (IMT) is the most used marker for the progression of atherosclerosis and onset of cardiovascular diseases. Computer-aided measurements improve accuracy and precision, but usually require user interaction. In this paper we characterized a new and completely automated technique for carotid segmentation and IMT measurement based on the merits of two previously developed techniques. We used an integrated approach of intelligent image feature extraction and line fitting for automatically locating the carotid artery in the image frame, followed by wall interfaces extraction based on a Gaussian edge operator. We called our system—CARES. We validated CARES on a multi-institutional database of 300 carotid ultrasound images. The IMT measurement bias was 0.032 ± 0.141 mm. Our novel approach of CARES processed 96% of the images in the database taken from two different institutions. In order to evaluate its performance, the figure-of-merit (FoM) was defined as the percent ratio between the average IMT computed by CARES and the one obtained from manual tracings by expert sonographers. The estimated FoM by CARES was 95.7%. Comparing the IMT bias of CARES with our previously published method CALEX that showed an IMT bias equal to 0.099 ± 0.137 mm, CARES improved the IMT accuracy by 67%, while increasing the standard deviation by 3%. CARES could be a useful research tool for processing large datasets in multi-center studies involving atherosclerosi

CARES 2.0: Completely Automated Robust Edge Snapper for CIMT measurement in 300 ultrasound images—A two stage paradigm

The carotid intima-media thickness (IMT) is a widely used marker associated to the risk of cardiovascular diseases and to atherosclerosis progression. IMT measurement requires high accuracy and reproducibility. Computer-aided measurements improve accuracy and precision, but usually require user interaction. In this paper we proposed an improved method (called CARES 2.0) over the previously developed technique (called CARES 1.0). CARES 2.0 is a two stage process: Stage-I adapts an integrated approach of intelligent image feature extraction and line fitting for far adventitia border detection. Stage-II is a first order absolute moment (FOAM 1.0) coupled to a novel and improved heuristic search for the lumen-intima (LI) and media-adventitia (MA) peaks. CARES 2.0 brings in two novel scientific contributions: (a) ability to improve Stage-I to compare jugular vein versus carotid artery and (b) introduction bi-directional and robust FOAM. The improved method is a fully automated IMT measurement technique, and was validated on a multi-institutional database of 300 images exhibiting normal and pathologic carotids. We benchmarked CARES 2.0 against previously developed CALEX 1.0 and user-driven FOAM 1.0. CARES 2.0 showed an IMT measurement bias equal to -0.032 +/- 0.178 mm, which was better than CALEX 1.0 (0.070 +/- 0.331 mm), FOAM 1.0 (-0.091 +/- 0.161 mm) and CARES 1.0 (0.035 +/- 0.198 mm), respectively. Thus CARES 2.0 showed an improvement of 54% over CALEX 1.0, 65% over stand alone FOAM 1.0 and 9% over CARES 1.0. Compared to CARES 1.0, CARES 2.0 improved the reproducibility by 10%. CARES 2.0 ensured complete automation and increased the reproducibility of the IMT measurement, a step closer for clinical usage

Symptomatic vs. Asymptomatic Plaque Classification in Carotid Ultrasound

Quantitative characterization of carotid atherosclerosis and classification into symptomatic or asymptomatic type is crucial in both diagnosis and treatment planning. This paper describes a computer-aided diagnosis (CAD) system which analyzes ultrasound images and classifies them into symptomatic and asymptomatic based on the textural features. The proposed CAD system consists of three modules. The first module is preprocessing, which conditions the images for the subsequent feature extraction. The feature extraction stage uses image texture analysis to calculate Standard deviation, Entropy, Symmetry, and Run Percentage. Finally, classification is performed using AdaBoost and Support Vector Machine for automated decision making. For Adaboost, we compared the performance of five distinct configurations (Least Squares, Maximum- Likelihood, Normal Density Discriminant Function, Pocket, and Stumps) of this algorithm. For Support Vector Machine, we compared the performance using five different configurations (linear kernel, polynomial kernel configurations of different orders and radial basis function kernels). SVM with radial basis function kernel for support vector machine presented the best classification result: classification accuracy of 82.4%, sensitivity of 82.9%, and specificity of 82.1%. We feel that texture features coupled with the Support Vector Machine classifier can be used to identify the plaque tissue type. An Integrated Index, called symptomatic asymptomatic carotid index (SACI), is proposed using texture features to discriminate symptomatic and asymptomatic carotid ultrasound images using just one index or number. We hope this SACI can be used as an adjunct tool by the vascular surgeons for daily screenin

Acupuncture and herbal formulation compared with artificial tears alone: Evaluation of dry eye symptoms and associated tests in randomised clinical trial

10.1136/bmjophth-2018-000150BMJ Open Ophthalmology31e00015