Atherosclerotic carotid plaque segmentation in ultrasound imaging of the carotid artery

In this chapter, we propose and evaluate an integrated system for the segmentation of atherosclerotic plaque in ultrasound imaging of the carotid artery based on normalization, speckle reduction filtering, and four different snakes segmentation methods. These methods are the Williams and Shah, Balloon, Lai and Chin, and the gradient vector flow (GVF) snake. The performance of the four different plaque snakes segmentation methods was tested on 80 longitudinal ultrasound images of the carotid artery using receiver operating characteristic (ROC) analysis and the manual delineations of an expert. All four methods performed very satisfactorily and similarly in all measures evaluated with no significant differences between them; however, the Lai and Chin snakes segmentation method gave slightly better results. Concluding, it is proposed that the integrated system investigated in this study could be used successfully for the automated segmentation of the carotid plaque

Chapter 7. Despeckle Filtering of Ultrasound Images

It is well known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. This necessitates the need for robust despeckling techniques for both routine clinical practice and tele-consultation. The recent advancements in ultrasound instrumentation and portable ultrasound devices necessitate the need of more robust despeckling techniques for enhanced ultrasound medical imaging for both routine clinical practice and tele-consultation. The objective of this chapter is to introduce the theoretical background of a number of despeckle filtering techniques and to carry out a comparative evaluation of despeckle filtering based on texture analysis, image quality evaluation metrics, and visual evaluation by medical experts, on ultrasound images of the carotid artery bifurcation. In this chapter, a total of ten despeckle filters are presented based on local statistics, median filtering, pixel homogeneity, geometric filtering, homomorphic filtering, anisotropic diffusion, nonlinear coherence diffusion, and wavelet filtering. Our results suggest that the first-order statistics filter DsFlsmv gave the best performance, followed by the geometric filter DsFgf4d and the homogeneous mask area filter DsFlsminsc. These filters improved the class separation between the asymptomatic and the symptomatic classes based on the statistics of the extracted texture features, gave only a marginal improvement in the classification success rate, and improved the visual assessment carried out by the two experts. Most importantly, a despeckle filtering and evaluation protocol is proposed based on texture analysis, image quality evaluation metrics, and visual evaluation by experts. In conclusion, the proper selection of a despeckle filter is very important in the enhancement of ultrasonic imaging of the carotid artery. Further work is needed to evaluate at a larger scale and in clinical practice the performance of the proposed despeckle filters in the automated segmentation, texture analysis, and classification of carotid ultrasound imaging. KeywordsSpeckle-Despeckle filtering-Ultrasound imaging-Texture analysis-Carotid arter

Measurement of ultrasonic diaphragmatic motion

The motion characteristics of the diaphragmatic muscle may provide useful information about normal and abnormal diaphragmatic function and indicate diaphragmatic weakness. The objective of this paper was to introduce a simple system for the quantitative analysis of ultrasonic diaphragmatic motion. The measurements routinely carried out by the experts were computed and these include: (i) excursion, (ii) inspiration time (Tinsp) and (iii) cycle duration (Ttot). The system was evaluated on four simulated videos and one real video. Manual and automated measurements were very close. Further work in a larger number of videos is needed for validating the proposed method © 2015 IEEE

Atherosclerotic carotid plaque segmentation

Atherosclerosis is the major cause of heart attack and stroke in the western world. In this paper we present a computerized method for segmenting the athrerosclerotic carotid plaque from ultrasound images. The method uses the blood flow image first to detect the initial contour of the plaque, and then despeckle filtering and snakes to deform the initial contour for best fit of plaque boundaries. The accuracy and reproducibility of this method was tested using 35 longitudinal ultrasound images of carotid arteries and the results were compared with the manual delineations of an expert. The comparison showed that the computerized method gives satisfactory results with no manual correction needed in most of the cases. The true positive fraction, TPF, true negative fraction, TNF, false negative fraction, FNF and false positive fraction, FPF, were 86.44%, 84.03%, 8.5%, and 7% respectively

Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery

It is well-known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. The recent advancements in ultrasound instrumentation and portable ultrasound devices necessitate the need of more robust despeckling techniques for enhanced ultrasound medical imaging for both routine clinical practice and teleconsultation. The objective of this work was to carry out a comparative evaluation of despeckle filtering based on texture analysis, image quality evaluation metrics, and visual evaluation by medical experts in the assessment of 440 (220 asymptomatic and 220 symptomatic) ultrasound images of the carotid artery bifurcation. In this paper a total of 10 despeckle filters were evaluated based on local statistics, median filtering, pixel homogeneity, geometric filtering, homomorphic filtering, anisotropic diffusion, nonlinear coherence diffusion, and wavelet filtering. The results of this study suggest that the first order statistics filter lsmv, gave the best performance, followed by the geometric filter gf4d, and the homogeneous mask area filter lsminsc. These filters improved the class separation between the asymptomatic and the symptomatic classes based on the statistics of the extracted texture features, gave only a marginal improvement in the classification success rate, and improved the visual assessment carried out by the two experts. More specifically, filters lsmv or gf4d can be used for despeckling asymptomatic images in which the expert is interested mainly in the plaque composition and texture analysis; and filters lsmv, gf4d, or lsminsc can be used for the despeckling of symptomatic images in which the expert is interested in identifying the degree of stenosis and the plaque borders. The proper selection of a despeckle filter is very important in the enhancement of ultrasonic imaging of the carotid artery. Further work is needed to evaluate at a larger scale and in clinical practice the performance of the proposed despeckle filters in the automated segmentation, texture analysis, and classification of carotid ultrasound imaging