Speckle reduction in ultrasound medical images using adaptive filter based on second order statistics

A fusion method of Gabor wavelet transform and unsupervised clustering algorithms for tissue edge detection

This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT) and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM) clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.

A novel approach to speckle noise filtering based on Artificial Bee Colony algorithm: an ultrasound image application

In this study a novel approach based on 2D FIR filters is presented for denoising digital images. In this approach the filter coefficients of 2D FIR filters were optimized using the Artificial Bee Colony (ABC) algorithm. To obtain the best filter design, the filter coefficients were tested with different numbers (3×3, 5×5, 7×7, 11×11) and connection types (cascade and parallel) during optimization. First, the speckle noise with variances of 1, 0.6, 0.8 and 0.2 respectively was added to the synthetic test image. Later, these noisy images were denoised with both the proposed approach and other well-known filter types such as Gaussian, mean and average filters. For image quality determination metrics such as mean square error (MSE), peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR) were used. Even in the case of noise having maximum variance (the most noisy), the proposed approach performed better than other filtering methods did on the noisy test images. In addition to test images, speckle noise with a variance of 1 was added to a fetal ultrasound image, and this noisy image was denoised with very high PSNR and SNR values. The performance of the proposed approach was also tested on several clinical ultrasound images such as those obtained from ovarian, abdomen and liver tissues. The results of this study showed that the 2D FIR filters designed based on ABC optimization can eliminate speckle noise quite well on noise added test images and intrinsically noisy ultrasound images.

Minimum requirement of artificial noise level for using noise-assisted correlation algorithm to suppress artifacts in ultrasonic Nakagami images

The Nakagami image is a complementary imaging mode for pulse-echo ultrasound B-scan to characterize tissues. White noise in anechoic areas induces artifacts in the Nakagami image. Recently, we proposed a noise-assisted correlation algorithm (NCA) for suppressing the Nakagami artifact. In the NCA, artificial white noise is intentionally added twice to backscattered signals to produce two noisy data, which are used to establish a correlation profile for rejecting noise. This study explored the effects of artificial noise level on the NCA to suppress the artifact of the Nakagami image. Simulations were conducted to produce B-mode images of anechoic regions under signal-to-noise ratios (SNRs) of 20, 10 and 5 dB. Various artificial noise levels ranging from 0.1- to 1-fold of the intrinsic noise amplitude were used in the NCA for constructing the Nakagami images. Phantom experiments were conducted to validate the performance of using the optimal artificial noise level suggested by the simulation results to suppress the Nakagami artifacts by the NCA. The simulation results indicated that the artifacts of the Nakagami image in the anechoic regions can be gradually suppressed by increasing the artificial noise level used in the NCA to improve the image contrast-to-noise ratio (CNR). The CNR of the Nakagami image reached 20 dB when the artificial noise level was 0.7-fold of the intrinsic noise amplitude. This criterion was demonstrated by the phantom results to provide the NCA with an excellent ability to obtain artifact-free Nakagami images.

SRBF: Speckle reducing bilateral filtering

Speckle noise negatively affects medical ultrasound image shape interpretation and boundary detection. Speckle removal filters are widely used to selectively remove speckle noise without destroying important image features to enhance object boundaries. In this article, a fully automatic bilateral filter tailored to ultrasound images is proposed. The edge preservation property is obtained by embedding noise statistics in the filter framework. Consequently, the filter is able to tackle the multiplicative behavior modulating the smoothing strength with respect to local statistics. The in silico experiments clearly showed that the speckle reducing bilateral filter (SRBF) has superior performances to most of the state of the art filtering methods. The filter is tested on 50 in vivo US images and its influence on a segmentation task is quantified. The results using SRBF filtered data sets show a superior performance to using oriented anisotropic diffusion filtered images. This improvement is due to the adaptive support of SRBF and the embedded noise statistics, yielding a more homogeneous smoothing. SRBF results in a fully automatic, fast and flexible algorithm potentially suitable in wide ranges of speckle noise sizes, for different medical applications (IVUS, B-mode, 3-D matrix array US).