Bone Surface Localization in Ultrasound Using Image Phase-Based Features

Hacihaliloglu, Ilker; Abugharbieh, Rafeef; Hodgson, Antony J.; Rohling, Robert

doi:10.1016/j.ultrasmedbio.2009.04.015

Cited by 125 publications

(72 citation statements)

References 29 publications

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“…Several feature extraction methods previously used by others for describing bones surfaces, including image gradient, Foroughi et al (2007)'s bone probability map and phase symmetry (Hacihaliloglu et al, 2009), were implemented. In addition, to characterize the acoustic shadow, the rupture points described by Hellier et al (2010) were also detected.…”

Section: Feature Extractionmentioning

confidence: 99%

“…2, T r is a noise threshold, and e rm (x, y) and o rm (x, y) correspond to the responses of quadrature 2D Log Gabor filters (Hacihaliloglu et al, 2009) with scale r and orientation m in the frequency domain:…”

Section: Phase Symmetrymentioning

confidence: 99%

“…The latter two methods exploited the continuity of bone regions for segmentation, which is very effective for large bone structures, but not for short surfaces like that of the spinous process. Another method proposed by Hacihaliloglu et al (2009) measures the presence of a bone surface in ultrasound images using local phase symmetry. This was applied to ultra-sound images of the radius.…”

Section: Introductionmentioning

confidence: 99%

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Segmentation of the spinous process and its acoustic shadow in vertebral ultrasound images

Berton

Cheriet

Marie-Claude

et al. 2016

Computers in Biology and Medicine

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Spinal ultrasound imaging is emerging as a low-cost, radiation-free alternative to conventional X-ray imaging for the clinical follow-up of patients with scoliosis. Currently, deformity measurement relies almost entirely on manual identification of key vertebral landmarks. However, the interpretation of vertebral ultrasound images is challenging, primarily because acoustic waves are entirely reflected by bone. To alleviate this problem, we propose an algorithm to segment these images into three regions: the spinous process, its acoustic shadow and other tissues. This method consists, first, in the extraction of several image features and the selection of the most relevant ones for the discrimination of the three regions. Then, using this set of features and a LDA classifier, each pixel of the image is classified as belonging to one of the three * Corresponding author Email addresses: florian.berton@polymtl.ca (Florian Berton), farida.cheriet@polymtl.ca (Farida Cheriet), marie-claude_miron@ssss.gouv.qc.ca regions. Finally, the image is segmented by regularizing the pixel-wise classification results to account for some geometrical properties of vertebrae. The feature set was first validated by analyzing the classification results across a learning database. The database contained 107 vertebral ultrasound images acquired with convex and linear probes. Classification rates of 84%, 92% and 91% were achieved for the spinous process, the acoustic shadow and other tissues, respectively. Dice similarity coefficients of 0.72 and 0.88 were obtained respectively for the spinous process and acoustic shadow, confirming that the proposed method accurately segments the spinous process and its acoustic shadow in vertebral ultrasound images. Furthermore, the centroid of the automatically segmented spinous process was located at an average distance of 0.38 mm from that of the manually labeled spinous process, which is on the order of image resolution. This suggests that the proposed method is a promising tool for the measurement of the Spinous Process Angle and, more generally, for assisting ultrasound-based assessment of scoliosis progression.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Phase Symmetrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Segmentation of the spinous process and its acoustic shadow in vertebral ultrasound images

Berton

Cheriet

Marie-Claude

et al. 2016

Computers in Biology and Medicine

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“…Noise parameter estimation of local phase features is critical for retaining a high signal-to-noise ratio in clinical applications such as accurate bone surface extraction [1]. The logGabor filters used in calculating the local phase features are very sensitive to noise, especially at small scales, since a highfrequency noise response is typically present at smaller wavelengths.…”

Section: Noise Parameter Estimationmentioning

confidence: 99%

“…For instance, image features extracted from local phase information have recently been shown to be robust in computer aided orthopedic applications. In previous works [1] we demonstrated the effectiveness of phase symmetry (PS) features for segmentation and localization of bone fractures in 3D ultrasound. In an extension to this work we implemented a step towards clinical ultrasound guided intervention [2], in which we used PS features to register intra-operative ultrasound to pre-operative computed tomography (CT) images.…”

Section: Introductionmentioning

confidence: 99%

Real-time extraction of local phase features from volumetric medical image data

Amir-Khalili¹,

Hodgson²,

Abugharbieh³

2013

2013 IEEE 10th International Symposium on Biomedical Imaging

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We present a novel real-time implementation of local phase feature extraction from volumetric image data based on 3D directional (log-Gabor) filters. We achieve drastic performance gains without compromising the signal-to-noise ratio by pre-computing the filters and adaptive noise estimation parameters, and streamlining the remainder of the computations to efficiently run on a multi-processor graphic processing unit (GPU). We validate our method on clinical ultrasound data and demonstrate a 15-fold speedup in computation time over state-of-the art methods, which could potentially facilitate a wide range of practical applications for real-time image-guided procedures.

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