Phase-Based Level Set Segmentation of Ultrasound Images

Segmentation of ultrasonic breast tumors based on homogeneous patch

PURPOSE

Accurately segmenting breast tumors in ultrasound (US) images is a difficult problem due to their specular nature and appearance of sonographic tumors. The current paper presents a variant of the normalized cut (NCut) algorithm based on homogeneous patches (HP-NCut) for the segmentation of ultrasonic breast tumors.

METHODS

A novel boundary-detection function is defined by combining texture and intensity information to find the fuzzy boundaries in US images. Subsequently, based on the precalculated boundary map, an adaptive neighborhood according to image location referred to as a homogeneous patch (HP) is proposed. HPs are guaranteed to spread within the same tissue region; thus, the statistics of primary features within the HPs is more reliable in distinguishing the different tissues and benefits subsequent segmentation. Finally, the fuzzy distribution of textons within HPs is used as final image features, and the segmentation is obtained using the NCut framework.

RESULTS

The HP-NCut algorithm was evaluated on a large dataset of 100 breast US images (50 benign and 50 malignant). The mean Hausdorff distance measure, the mean minimum Euclidean distance measure and similarity measure achieved 7.1 pixels, 1.58 pixels, and 86.67%, respectively, for benign tumors while those achieved 10.57 pixels, 1.98 pixels, and 84.41%, respectively, for malignant tumors.

CONCLUSIONS

The HP-NCut algorithm provided the improvement in accuracy and robustness compared with state-of-the-art methods. A conclusion that the HP-NCut algorithm is suitable for ultrasonic tumor segmentation problems can be drawn.

Segmentation of skin lesions in 2-D and 3-D ultrasound images using a spatially coherent generalized Rayleigh mixture model

This paper addresses the problem of jointly estimating the statistical distribution and segmenting lesions in multiple-tissue high-frequency skin ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled by enforcing local dependence between the mixture components. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then proposed to jointly estimate the mixture parameters and a label-vector associating each voxel to a tissue. More precisely, a hybrid Metropolis-within-Gibbs sampler is used to draw samples that are asymptotically distributed according to the posterior distribution of the Bayesian model. The Bayesian estimators of the model parameters are then computed from the generated samples. Simulation results are conducted on synthetic data to illustrate the performance of the proposed estimation strategy. The method is then successfully applied to the segmentation of in vivo skin tumors in high-frequency 2-D and 3-D ultrasound images.

Registration-based segmentation of three-dimensional ultrasound images for quantitative measurement of fetal craniofacial structure

Segmentation of a fetal head from three-dimensional (3-D) ultrasound images is a critical step in the quantitative measurement of fetal craniofacial structure. However, two main issues complicate segmentation, including fuzzy boundaries and large variations in pose and shape among different ultrasound images. In this article, we propose a new registration-based method for automatically segmenting the fetal head from 3-D ultrasound images. The proposed method first detects the eyes based on Gabor features to identify the pose of the fetus image. Then, a reference model, which is constructed from a fetal phantom and contains prior knowledge of head shape, is aligned to the image via feature-based registration. Finally, 3-D snake deformation is utilized to improve the boundary fitness between the model and image. Four clinically useful parameters including inter-orbital diameter (IOD), bilateral orbital diameter (BOD), occipital frontal diameter (OFD) and bilateral parietal diameter (BPD) are measured based on the results of the eye detection and head segmentation. Ultrasound volumes from 11 subjects were used for validation of the method accuracy. Experimental results showed that the proposed method was able to overcome the aforementioned difficulties and achieve good agreement between automatic and manual measurements.

Tracking pylorus in ultrasonic image sequences with edge-based optical flow

Tracking pylorus in ultrasonic image sequences is an important step in the analysis of duodenogastric reflux (DGR). We propose a joint prediction and segmentation method (JPS) which combines optical flow with active contour to track pylorus. The goal of the proposed method is to improve the pyloric tracking accuracy by taking account of not only the connection information among edge points but also the spatio-temporal information among consecutive frames. The proposed method is compared with other four tracking methods by using both synthetic and real ultrasonic image sequences. Several numerical indexes: Hausdorff distance (HD), average distance (AD), mean edge distance (MED), and edge curvature (EC) have been calculated to evaluate the performance of each method. JPS achieves the minimum distance metrics (HD, AD, and MED) and a smaller EC. The experimental results indicate that JPS gives a better tracking performance than others by the best agreement with the gold curves while keeping the smoothness of the result.

A computer-aided method for automatic localization and thickness measurement of peritoneum in ultrasound images

This paper presents a method of automatically measuring peritoneum thickness in ultrasound images. In our previous work, a method of manually selecting the region of interest (ROI) area has been developed. To achieve an automatic ROI area selection, two phases: Gaussian high-pass filtering and bilateral filtering, are used in the proposed method. In the bilateral filtering phase, the ultrasound image is enhanced for obtaining more details of the peritoneum so that probable areas can be extracted. In the other phase, the ultrasound image is processed with a Gaussian high pass filter, and the result is used to locate the precise area of peritoneum in the first phase result. The experimental results show that the proposed method has high accuracy and fast processing speed in determining the peritoneum area and its thickness distribution.