Ultrasound (US) is a widely used as a low-cost alternative to computed tomography (CT) or magnetic resonance (MRI) and primarily for preliminary imaging. Since speckle intensity in US images is inherently stochastic, readers are often challenged in their ability to identify the pathological regions in a volume of a large number of images. This paper introduces a generalized approach for volumetric segmentation of structures in US images and volumes. We employ an iterative random walks (IRW) solver, random forest (RF) learning model, and a gradient vector flow (GVF) based inter-frame belief propagation technique for achieving cross-frame volumetric segmentation. At the start, a weak estimate the tissue structure is obtained using estimates of parameters of a statistical mechanics model of ultrasound tissue interaction. Ensemble learning of these parameters further using a random forest is used to initialize the segmentation pipeline. IRW is used for correcting the contour in various steps of the algorithm. Subsequently, a GVF based inter-frame belief propagation is applied to adjacent frames based on initialization of contour using information in the current frame to segment the complete volume by frame-wise processing. We have experimentally evaluated our approach using two different datasets. Intravascular Ultrasound (IVUS) segmentation was evaluated using 10 pullbacks acquired at 20 MHz and thyroid ultrasound segmentation is evaluated on 16 volumes acquired at 11 - 16 MHz. Our approach obtains a Jaccard score of 0.937 ± 0.022 for IVUS segmentation and 0.908 ± 0.028 for thyroid segmentation while processing each frame in 1.15 ± 0.05 s for IVUS and in 1.23 ± 0.27 s for thyroid segmentation without the need of any computing accelerators like GPUs.

In this paper, a novel methodology has been proposed for speckle noise reduction of intravascular ultrasound (IVUS). IVUS, a standard coronary artery diagnosis imaging protocol, is degraded with speckle noise due to the coherent interferences of the ultrasound reflected from the scatters. Presence of such noise creates problem in segmentation and classification of these images. Non local means filter has been applied in wavelet domain to smooth out noise interferences to improve the visual characteristics of IVUS insight. Eventually, a comparison study has also been performed with various filters viz. anisotropic diffusion filter, nonlinear median filter and geometric nonlinear diffusion filter. Mean squared error (MSE) and peak signal to noise ratio (PSNR) has been used to evaluate the efficiency of the proposed filtering methodology.

Ultrasound imaging is generally employed for real-time investigation of internal anatomy of the human body for disease identification. Delineation of the anatomical boundary of organs and pathological lesions is quite challenging due to the stochastic nature of speckle intensity in the images, which also introduces visual fatigue for the observer. This paper introduces a fully convolutional neural network based method to segment organ and pathologies in ultrasound volume by learning the spatial-relationship between closely related classes in the presence of stochastically varying speckle intensity. We propose a convolutional encoder-decoder like framework with (i) feature concatenation across matched layers in encoder and decoder and (ii) index passing based unpooling at the decoder for semantic segmentation of ultrasound volumes. We have experimentally evaluated the performance on publicly available datasets consisting of 10 intravascular ultrasound pullback acquired at 20 MHz and 16 freehand thyroid ultrasound volumes acquired 11 − 16 MHz. We have obtained a dice score of 0.93 ± 0.08 and 0.92 ± 0.06 respectively, following a 10-fold cross-validation experiment while processing frame of 256 × 384 pixel in 0.035s and a volume of 256 × 384 × 384 voxel in 13.44s.

Coronary artery disease accounts for a large number of deaths across the world and clinicians generally prefer using x-ray computed tomography or magnetic resonance imaging for localizing vascular pathologies. Interventional imaging modalities like intravascular ultrasound (IVUS) are used to adjunct diagnosis of atherosclerotic plaques in vessels, and help assess morphological state of the vessel and plaque, which play a significant role for treatment planning. Since speckle intensity in IVUS images are inherently stochastic in nature and challenge clinicians with accurate visibility of the vessel wall boundaries, it requires automation. In this paper we present a method for segmenting the lumen and external elastic laminae of the artery wall in IVUS images using random walks over a multiscale pyramid of Gaussian decomposed frames. The seeds for the random walker are initialized by supervised learning of ultrasonic backscattering and attenuation statistical mechanics from labelled training samples. We have experimentally evaluated the performance using 77 IVUS images acquired at 40 MHz that are available in the IVUS segmentation challenge dataset 4 to obtain a Jaccard score of 0.89 ± 0.14 for lumen and 0.85 ± 0.12 for external elastic laminae segmentation over a 10-fold cross-validation study.

The performance of the proposed methodology has been compared with the manually delineated images (ground truth) obtained from different experts, individually. Quantitative evaluation with respect to various performance measures (such as dice coefficient, Jaccard score, and correlation coefficient) shows the efficient performance of the proposed technique.