Speckle noise is a phenomenon inherent in any coherent imaging process and decreases the signal-to-noise ratio (SNR), which brings down the imaging quality. Speckle noise reduction is particularly important in the tissue harmonic imaging (THI) since it has the lower energy and the poorer SNR than the fundamental imaging (FI). Recently plane wave imaging (PWI) has been widely explored. Since the entire imaging region can be covered in one emission, the frame rate increases greatly. In PWI, speckle can be reduced by incoherently averaging images with different speckle patterns. Such images can be acquired by varying the angle from which a target is imaged (spatial compounding, SC) or by changing the spectrum of the pulse (frequency compounding, FC). In this paper we demonstrate here that each approach is only a partial solution and that combining them provides a better result than applying either approach separately. We propose a spatial-frequency compounding (SFC) method for THI. The new method brings a good speckle suppression result. To illustrate the performance of our method, experiments have been conducted on the simulated data. A nonlinear simulation platform based on the full-wave model is used in the harmonic imaging simulation. Results show that our method brings the SNR an improvement of up to 50% in comparison with the single frame HI while maintaining a far better performance in both terms of resolution and contrast than the FI. Similar results can be obtained from our further experiments.
The detection of brain tumor from MR images is very significant for medical diagnosis and treatment. However, the existing methods are mostly based on manual or semiautomatic segmentation which are awkward when dealing with a large amount of MR slices. In this paper, a new fully automatic method for the segmentation of brain tumors in MR slices is presented. Based on the hypothesis of the symmetric brain structure, the method improves the interactive GrowCut algorithm by further using the bounding box algorithm in the pre-processing step. More importantly, local reflectional symmetry is used to make up the deficiency of the bounding box method. After segmentation, 3D tumor image is reconstructed. We evaluate the accuracy of the proposed method on MR slices with synthetic tumors and actual clinical MR images. Result of the proposed method is compared with the actual position of simulated 3D tumor qualitatively and quantitatively. In addition, our automatic method produces equivalent performance as manual segmentation and the interactive GrowCut with manual interference while providing fully automatic segmentation.
Analysis of ultrasound fetal head images is a daily routine for medical professionals in obstetrics. The contours of
fetal skulls often appear discontinuous and irregular in clinical ultrasound images, making it difficult to measure
the fetal head size automatically. In addition, the presence of heavy noise in ultrasound images is another
challenge for computer aided automatic fetal head detection. In this paper, we first utilize the stick method to
suppress the noise and compute an adaptive threshold for fetal skull segmentation. Morphological thinning is then
performed to obtain a skeleton image, which is used as an input to the Hough transform. Finally, automatic fetal
skull detection is realized by Iterative Randomized Hough Transform (IRHT). The elliptic eccentricity is used
in the IRHT to reduce the number of invalid accumulations in the parameter space, improving the detection
accuracy. Furthermore, the target region is adaptively adjusted in the IRHT. To evaluate the performance
of IRHT, we also developed a simulation user interface for comparing results produced by the conventional
randomized Hough transform (RHT) and the IRHT. Experimental results showed that the proposed method is
effective for automatic fetal head detection in ultrasound images.