Quality evaluation of ultrasound imaging in the carotid artery based on normalization and speckle reduction filtering

Abstract

Image quality is important when evaluating ultrasound images of the carotid for the assessment of the degree of atherosclerotic disease, or when transferring images through a telemedicine channel, and/or in other image processing tasks. The objective of this study was to investigate the usefulness of image quality evaluation based on image quality metrics and visual perception, in ultrasound imaging of the carotid artery after normalization and speckle reduction filtering. Image quality was evaluated based on statistical and texture features, image quality evaluation metrics, and visual perception evaluation made by two experts. These were computed on 80 longitudinal ultrasound images of the carotid bifurcation recorded from two different ultrasound scanners, the HDI ATL-3000 and the HDI ATL-5000 scanner, before (NF) and after (DS) speckle reduction filtering, after normalization (N), and after normalization and speckle reduction filtering (NDS). The results of this study showed that: (1) the normalized speckle reduction, NDS, images were rated visually better on both scanners; (2) the NDS images showed better statistical and texture analysis results on both scanners; (3) better image quality evaluation results were obtained between the original (NF) and normalized (N) images, i.e. NF-N, for both scanners, followed by the NF-DS images for the ATL HDI-5000 scanner and the NF-DS on the HDI ATL-3000 scanner; (4) the ATL HDI-5000 scanner images have considerable higher entropy than the ATL HDI-3000 scanner and thus more information content. However, based on the visual evaluation by the two experts, both scanners were rated similarly. The above findings are also in agreement with the visual perception evaluation, carried out by the two vascular experts. The results of this study showed that ultrasound image normalization and speckle reduction filtering are important preprocessing steps favoring image quality, and should be further investigated.

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

“…Extensive validation of this technique showed that performance was limited by speckle noise. Even by adopting advanced techniques for speckle reduction [18] and wall clutter reduction [13], the automated local statistics based technique failed segmentation in about 8-10% of the images [21,26]. This is due to the fact that local statistics are very sensitive to noise; hence, a low signal-to-noise ratio precluded a correct initialization of the snake.…”

Medical Decision Support System of Common Carotid Artery Using Image Mining and Dual Snake Segmentation Procedure

“…Retrieving images from large and varied collections using image content as a key is a challenging and important problem 6,7 . A new image representation is improved which provides a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture space.…”

Compresión de imágenes médicas

“…El desempeño de un algoritmo de compresión se mide usando sistemas métricos encargados de determinar las diferencias entre dos imágenes (17). El resultado de estos sistemas métricos de calidad puede ser un número que representa la probabilidad de que el ojo humano pueda detectar una diferencia entre las dos imágenes o un número que cuantifica la similitud en su percepción.…”