RF Ultrasound Distribution-Based Confidence Maps
Ultrasound is becoming an ever increasingly important modality in medical care. However, underlying physical acquisition principles are prone to image artifacts and result in overall quality variation. Therefore processing medical ultrasound data remains
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Abstract. Ultrasound is becoming an ever increasingly important modality in medical care. However, underlying physical acquisition principles are prone to image artifacts and result in overall quality variation. Therefore processing medical ultrasound data remains a challenging task. We propose a novel distribution-based measure of assessing the confidence in the signal, which emphasizes uncertainty in attenuated as well as shadow regions. In contrast to the similar recently proposed method that relies on image intensities, the new approach makes use of the enveloped-detected radio-frequency data, facilitating the use of Nakagami speckle statistics. Employing J-divergence as distance measure for the random-walk based algorithm, provides a natural measure of similarity, yielding a more reliable estimate of confidence. For evaluation of the model’s performance, tests are conducted on the application of shadow detection. Additionally, computed maps are presented for different organs such as neck, liver and prostate, showcasing the properties of the model. The probabilistic approach is shown to have beneficial features for image processing tasks. Keywords: Envelope-Detected RF, Ultrasound, Confidence, Nakagami.
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Introduction
Ultrasound (US) image processing is still commonly performed in the B-mode domain. However, proprietary filtering and non-linear compression to obtain Bmode from raw data distort the signal and effectively reduce the information content in the data in a non-linear fashion, which reduces its suitability for statistical modeling. A more natural way is to deal with the US characteristics in a statistical framework using unprocessed, radio frequency (RF) data. As US imaging is largely driven by patterns referred to as speckle noise, literature suggests a multitude of different models that accommodate the different noise scenarios, e.g. the Rician, generalized K, homodyned K, Rayleigh and Nakagami distribution. Shankar [13] showed that the envelope detected RF signal follows the Nakagami distribution [10]. This distribution has been shown to be a very general model for a multitude of speckle scenarios, often making it the model of choice as it is in this paper. We propose a novel method for estimating confidence maps [7] in envelope detected RF US images, which emphasizes uncertainty in attenuated as well as c Springer International Publishing Switzerland 2015 N. Navab et al. (Eds.): MICCAI 2015, Part II, LNCS 9350, pp. 595–602, 2015. DOI: 10.1007/978-3-319-24571-3_71
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T. Klein and W.M. Wells III
shadow regions. Confidence maps have shown to be useful for a number of applications such as registration, segmentation and shadow detection [7,14]. However, in contrast to the previously proposed method [7] that relies on B-mode image intensities, the novel approach makes use of the envelope detected RF data, facilitating the use of Nakagami speckle statistics. The main novelty lies in the use of a probabilistic distance measure that is used within the random walks framework originally introduced for segmentat
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