Texture preservation and speckle reduction in poor optical coherence tomography using the convolutional neural network

For a poor quality optical coherence tomography (OCT) image, quality enhancement is limited to speckle residue and edge blur as well as texture loss, especially at the background region near edges. To solve this problem, in this paper we propose a de-speckling method based on the convolutional neural network (CNN). In the proposed method, we use a deep nonlinear CNN mapping model in the serial architecture, here named as OCTNet. Our OCTNet in the proposed method can fully utilize the deep information on speckles and edges as well as fine textures of an original OCT image. And also we construct an available pertinent dataset by combining three existing methods to train the model. With the proposed method, we can accurately get the speckle noise from an original OCT image. We test our method on four experimental human retinal OCT images and also compare it with three state-of-the-art methods, including the adaptive complex diffusion (ACD) method and the curvelet shrinkage (Curvelet) method as well as the shearlet-based total variation (STV) method. The performance of these methods is quantitatively evaluated in terms of image distinguishability, contrast, smoothness and edge sharpness, and also qualitatively analyzed at aspects of speckle reduction, texture protection and edge preservation. The experimental results show that our OCTNet can reduce the speckle noise and protect the structural information as well as preserve the edge features effectively and simultaneously, even where the background region near edges. And also our OCTNet has full advantages on excellent generalization, adaptiveness, robust and batch performance. These advantages make our method be suitable to process a great mass of different images rapidly without any parameter fine-turning under a time-constrained real-time situation.     

基于小波变换的医学超声图像去噪及增强方法

目的探求一种基于小波变换的医学超声图像去噪及增强方法。方法提出了一种基于小波分析理论的医学超声图像噪声的综合抑制方法,首先对医学超声图像进行对数变换,将乘性噪声变成加性噪声;然后进行多尺度小波变换,将图像分解成一系列不同尺度上的小波系数,对变换后不同尺度的高频子图像进行非线性小波软阈值处理,阈值处理后的高频子图像进行增强;最后,经小波逆变换和指数变换恢复去噪后图像。结果原图像中斑纹噪声被有效去除,图像边缘细节得以保留。结论该方法可有效保留细节信号,极大限度地去除斑纹噪声。     

Visual enhancement of digital ultrasound images: wavelet versus Gauss–Laplace contrast pyramid

Purpose Noise is the principal factor which hampers the visual quality of ultrasound images, sometimes leading to misdiagnosis. Speckle noise in ultrasound images can be modeled as a random multiplicative process. Speckle reduction techniques were applied to digital ultrasound images to suppress noise and improve visual quality. Rationale Previous reports indicate that wavelet filtering performs best for speckle reduction in digital ultrasound images. Reportes on x-ray images compared wavelet filtering with Laplace-Gauss contrast enhancement (LGCE) showed that the LCGE performed better. As LGCE was never been applied to Ultrasound images, this study compared two filtering approaches for speckle reduction on digital ultrasound images. Methods Two methods were implemented and compared. The first method uses the wavelet soft threshold (WST) approach for enhancement. The second method is based on multiscale Laplacian-Gaussian contrast enhancement (LGCE). LGCE is derived from the combination of a Gaussian pyramid and a Laplacian one. Contrast enhancement is applied on local scale by using varying sizes of median filter. Results The two methods were applied to synthetic and real ultrasound images. A comparison between WST and LGCE methods was performed based on noise level, artifacts and subjective image quality. Conclusion WST visual enhancement provided better results than LGCE for selected ultrasound images.     

Speckles Suppression Techniques for Ultrasound Images

Most medical images have a poorer signal to noise ratio than scenes taken with a digital camera, which often leads to incorrect diagnosis. Speckles suppression from ultrasound images is one of the most important concerns in computer-aided diagnosis. This article proposes two novel, robust and efficient ultrasound images denoising techniques. The first technique is the enhanced ultrasound images denoising (EUID) technique, which estimates automatically the speckle noise amount in the ultrasound images by estimating important input parameters of the filter and then denoising the image using the sigma filter. The second technique is the ultrasound image denoising using neural network (UIDNN) that is based on the second-order difference of pixels with adaptive threshold value in order to identify random valued speckles from images to achieve high efficient image restoration. The performances of the proposed techniques are analyzed and compared with those of other image denoising techniques. The experimental results show that the proposed techniques are valuable tools for speckles suppression, being accurate, less tedious, and preventing typical human errors associated with manual tasks in addition to preserving the edges from the image. The EUID algorithm has nearly the same peak signal to noise ratio (PSNR) as Frost and speckle-reducing anisotropic diffusion 1, whereas it achieves higher gains, on average—0.4 dB higher PSNR—than the Lee, Kuan, and anisotropic diffusion filters. The UIDNN technique outperforms all the other techniques since it can determine the noisy pixels and perform filtering for these pixels only. Generally, when relatively high levels of noise are added, the proposed algorithms show better performances than the other conventional filters.     

A textural approach based on Gabor functions for texture edge detection in ultrasound images

Edge detection is an important, but difficult, step in quantitative ultrasound (US) image analysis. In this paper, we present a new textural approach for detecting a class of edges in US images; namely, the texture edges with a weak regional mean gray-level difference (RMGD) between adjacent regions. The proposed approach comprises a vision model-based texture edge detector using Gabor functions and a new texture-enhancement scheme. The experimental results on the synthetic edge images have shown that the performances of the four tested textural and nontextural edge detectors are about 20%-95% worse than that of the proposed approach. Moreover, the texture enhancement may improve the performance of the proposed texture edge detector by as much as 40%. The experiments on 20 clinical US images have shown that the proposed approach can find reasonable edges for real objects of interest with the performance of 0.4 +/- 0.08 in terms of the Pratt's figure.     

应用去噪后增强算法辅助乳腺肿块的超声诊断研究

目的探讨超声图像去噪后增强算法对乳腺肿块良恶性检测与分类的价值。方法选用211例603幅乳腺肿块超声图片(良性109例,恶性102例)进行去噪后增强处理,以手术病理结果作为金标准,对乳腺肿块原始图片和处理后图片进行分析,来区分乳腺肿块的良、恶性。利用ROC曲线下面积表现去噪后增强前后的诊断性能,计算超声诊断的准确率。结果通过去噪后增强算法处理后,使腺体和周围组织能分离,突出了腺体和病灶的部位,细节显示更加清晰,超声与病理诊断的各项指标符合率明显提高,准确率提高至92.73%,原片与处理后图片ROC曲线下面积二者之间差异有显著性统计学意义(P0.001)。结论新的超声图像去噪后增强算法可明显地改善了图像质量,提高了乳腺肿块的正确诊断率。     

Multi-penalty conditional random field approach to super-resolved reconstruction of optical coherence tomography images

Improving the spatial resolution of Optical Coherence Tomography (OCT) images is important for the visualization and analysis of small morphological features in biological tissue such as blood vessels, membranes, cellular layers, etc. In this paper, we propose a novel reconstruction approach to obtaining super-resolved OCT tomograms from multiple lower resolution images. The proposed Multi-Penalty Conditional Random Field (MPCRF) method combines four different penalty factors (spatial proximity, first and second order intensity variations, as well as a spline-based smoothness of fit) into the prior model within a Maximum A Posteriori (MAP) estimation framework. Test carried out in retinal OCT images illustrate the effectiveness of the proposed MPCRF reconstruction approach in terms of spatial resolution enhancement, as compared to previously published super resolved image reconstruction methods. Visual assessment of the MPCRF results demonstrate the potential of this method in better preservation of fine details and structures of the imaged sample, as well as retaining the sharpness of biological tissue boundaries while reducing the effects of speckle noise inherent to OCT. Quantitative evaluation using imaging metrics such as Signal-to-Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Equivalent Number of Looks (ENL), and Edge Preservation Parameter show significant visual quality improvement with the MPCRF approach. Therefore, the proposed MPCRF reconstruction approach is an effective tool for enhancing the spatial resolution of OCT images without the necessity for significant imaging hardware modifications.OCIS codes: (100.0100) Image processing, (110.4500) Optical coherence tomography, (330.6130) Spatial resolution     

A Novel Approach to Speckle Reduction in Ultrasound Imaging

Speckle noise is inherent in ultrasound images, and it generally tends to reduce the resolution and contrast, thereby degrading the diagnostic accuracy of this modality. Speckle reduction is very important and critical for ultrasound imaging. In this paper, we propose a novel approach for speckle reduction using 2-D homogeneity and directional average filters. We have conducted experiments on numerous artificial images, clinic breast ultrasound images and vascular images. The experimental results are compared with that of other methods and the performance is evaluated using several merits, and they demonstrate that the proposed approach can reduce the speckle noise effectively without blurring the edges and damaging the textual information. It will be very useful for computer-aided diagnosis systems using ultrasound images. (E-mail: hengda.cheng@usu.edu)     

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). (E-mail: balocco.simone@gmail.com)     

Dynamic Enhancement of B-Mode Cardiac Ultrasound Image Sequences

Limited contrast, along with speckle and acoustic noise, can reduce the diagnostic value of echocardiographic images. This study introduces dynamic histogram-based intensity mapping (DHBIM), a novel approach employing temporal variations in the cumulative histograms of cardiac ultrasound images to contrast enhance the imaged structures. DHBIM is then combined with spatial compounding to compensate for noise and speckle. The proposed techniques are quantitatively assessed (32 clinical data sets) employing (i) standard image quality measures and (ii) the repeatability of routine clinical measurements, such as chamber diameter and wall thickness. DHBIM introduces a mean increase of 120.9% in tissue/chamber detectability, improving the overall repeatability of clinical measurements by 17%. The integrated approach of DHBIM followed by spatial compounding provides the best overall enhancement of image quality and diagnostic value, consistently outperforming the individual approaches and achieving a 401.4% average increase in tissue/chamber detectability with an associated 24.3% improvement in the overall repeatability of clinical measurements.     

Fetal abdominal contour extraction and measurement in ultrasound images

A novel method is developed for the fetal abdominal contour extraction and measurement in ultrasound images. Fetal abdominal circumference (AC) is one of the standardized measurements in the antepartum ultrasound monitoring. Among several standardized measurements, AC is best correlated with fetal growth but is also the most difficult to be accurately measured. To overcome the difficulties in the abdominal contour extraction, the proposed method is a four-step procedure that integrates several image segmentation techniques. The proposed method is able to make the best use of the strength of different segmentation algorithms, while avoiding their deficiencies. An enhanced instantaneous coefficient of variation (ICOV) edge detector is first developed to detect edges of the abdominal contour and alleviate the effects of most speckle noise. Then, the Fuzzy C-Means clustering is employed to distinguish salient edges attributable to the abdominal contour from weak edges due to the other texture. Subsequently, the iterative Hough transform is applied to determine an elliptical contour and obtain an initial estimation of the AC. Finally, the gradient vector field (GVF) snake adapts the initial ellipse to the real edges of the abdominal contour. Quantitative validation of the proposed method on synthetic images under different imaging conditions achieves satisfactory segmentation accuracy (98.78%+/-0.16%). Experiments on 150 clinical images are carried out in three aspects: comparisons between inter-observer and inter-run variation, the fitness analysis between the automatically detected ellipse and the manual delineation, and the accuracy comparisons between automatic measurements and manual measurements in estimation of fetal weight (EFW). Experimental results show that the proposed method can provide consistent and accurate measurements. The reductions of the mean absolute difference and the standard deviation of EFW based on automatic measurements are about 1.2% and 2.1%, respectively, which indicate its potential in clinical antepartum monitoring application.     

Improved medical image fusion based on cascaded PCA and shift invariant wavelet transforms

Purpose

In the medical field, radiologists need more informative and high-quality medical images to diagnose diseases. Image fusion plays a vital role in the field of biomedical image analysis. It aims to integrate the complementary information from multimodal images, producing a new composite image which is expected to be more informative for visual perception than any of the individual input images. The main objective of this paper is to improve the information, to preserve the edges and to enhance the quality of the fused image using cascaded principal component analysis (PCA) and shift invariant wavelet transforms.

Methods

A novel image fusion technique based on cascaded PCA and shift invariant wavelet transforms is proposed in this paper. PCA in spatial domain extracts relevant information from the large dataset based on eigenvalue decomposition, and the wavelet transform operating in the complex domain with shift invariant properties brings out more directional and phase details of the image. The significance of maximum fusion rule applied in dual-tree complex wavelet transform domain enhances the average information and morphological details.

Results

The input images of the human brain of two different modalities (MRI and CT) are collected from whole brain atlas data distributed by Harvard University. Both MRI and CT images are fused using cascaded PCA and shift invariant wavelet transform method. The proposed method is evaluated based on three main key factors, namely structure preservation, edge preservation, contrast preservation. The experimental results and comparison with other existing fusion methods show the superior performance of the proposed image fusion framework in terms of visual and quantitative evaluations.

Conclusion

In this paper, a complex wavelet-based image fusion has been discussed. The experimental results demonstrate that the proposed method enhances the directional features as well as fine edge details. Also, it reduces the redundant details, artifacts, distortions.

     

Directional feature: a novel feature for group target detection in high resolution SAR images

In this letter, we propose a new feature for group target detection in high resolution Synthetic Aperture Radar (SAR) images. This study aims to reduce the false alarm rate by adding a novel directional feature to typical SAR target detection algorithms. Unlike other shape- or contrast-based features, the directional feature contains the orientation and angle information of targets. Based on this feature, we can distinguish group targets from false alarms by analysing the correlation of their directional features. First, the proposed feature extraction approach generates an enhanced image to overcome speckle noises and a low signal to noise ratio (SNR). Next, a contour extraction algorithm is used to generate a line-drawing edge map of the target. Finally, the directional feature is obtained based on the major principal axes of the edge map. The experimental results using real high resolution SAR images verify the validity and effectiveness of the SAR detection algorithm.     

Development of an efficient algorithm for the detection of macular edema from optical coherence tomography images

Purpose

Detection of eye diseases and their treatment is a key to reduce blindness, which impacts human daily needs like driving, reading, writing, etc. Several methods based on image processing have been used to monitor the presence of macular diseases. Optical coherence tomography (OCT) imaging is the most efficient technique used to observe eye diseases. This paper proposes an efficient algorithm to automatically classify normal as well as disease-affected (macular edema) retinal OCT images by using segmentation of Inner Limiting Membrane and the Choroid Layer.

Methods

In the proposed method, preprocessing of the input image is done to improve the quality and reduce the speckle noise. The layer segmentation is done on the gradient image, and graph theory and dynamic programming algorithm is performed. The feature vectors from segmented image are in terms of thickness profile and cyst fluid parameter, and these features are applied to various classifiers.

Results

The proposed method was tested with the standard dataset collected from the Department of Ophthalmology, Duke University, and achieved a high accuracy rate of 99.4975%, sensitivity of 100%, and specificity of 99% for the SVM classifier.

Conclusions

An efficient algorithm is proposed for macular edema detection from OCT images using segmentation based on graph theory and dynamic programming algorithm. The comparison with alternative methods yielded results that demonstrate the superiority of the proposed algorithm for macular edema detection.

     

Resolution enhancement and realistic speckle recovery with generative adversarial modeling of micro-optical coherence tomography

A resolution enhancement technique for optical coherence tomography (OCT), based on Generative Adversarial Networks (GANs), was developed and investigated. GANs have been previously used for resolution enhancement of photography and optical microscopy images. We have adapted and improved this technique for OCT image generation. Conditional GANs (cGANs) were trained on a novel set of ultrahigh resolution spectral domain OCT volumes, termed micro-OCT, as the high-resolution ground truth (∼1 μm isotropic resolution). The ground truth was paired with a low-resolution image obtained by synthetically degrading resolution 4x in one of (1-D) or both axial and lateral axes (2-D). Cross-sectional image (B-scan) volumes obtained from in vivo imaging of human labial (lip) tissue and mouse skin were used in separate feasibility experiments. Accuracy of resolution enhancement compared to ground truth was quantified with human perceptual accuracy tests performed by an OCT expert. The GAN loss in the optimization objective, noise injection in both the generator and discriminator models, and multi-scale discrimination were found to be important for achieving realistic speckle appearance in the generated OCT images. The utility of high-resolution speckle recovery was illustrated by an example of micro-OCT imaging of blood vessels in lip tissue. Qualitative examples applying the models to image data from outside of the training data distribution, namely human retina and mouse bladder, were also demonstrated, suggesting potential for cross-domain transferability. This preliminary study suggests that deep learning generative models trained on OCT images from high-performance prototype systems may have potential in enhancing lower resolution data from mainstream/commercial systems, thereby bringing cutting-edge technology to the masses at low cost.     

Automatic segmentation of choroidal thickness in optical coherence tomography

The assessment of choroidal thickness from optical coherence tomography (OCT) images of the human choroid is an important clinical and research task, since it provides valuable information regarding the eye’s normal anatomy and physiology, and changes associated with various eye diseases and the development of refractive error. Due to the time consuming and subjective nature of manual image analysis, there is a need for the development of reliable objective automated methods of image segmentation to derive choroidal thickness measures. However, the detection of the two boundaries which delineate the choroid is a complicated and challenging task, in particular the detection of the outer choroidal boundary, due to a number of issues including: (i) the vascular ocular tissue is non-uniform and rich in non-homogeneous features, and (ii) the boundary can have a low contrast. In this paper, an automatic segmentation technique based on graph-search theory is presented to segment the inner choroidal boundary (ICB) and the outer choroidal boundary (OCB) to obtain the choroid thickness profile from OCT images. Before the segmentation, the B-scan is pre-processed to enhance the two boundaries of interest and to minimize the artifacts produced by surrounding features. The algorithm to detect the ICB is based on a simple edge filter and a directional weighted map penalty, while the algorithm to detect the OCB is based on OCT image enhancement and a dual brightness probability gradient. The method was tested on a large data set of images from a pediatric (1083 B-scans) and an adult (90 B-scans) population, which were previously manually segmented by an experienced observer. The results demonstrate the proposed method provides robust detection of the boundaries of interest and is a useful tool to extract clinical data.OCIS codes: (100.0100) Image processing, (100.2960) Image analysis, (110.4500) Optical coherence tomography, (170.4470) Ophthalmology     

Wavelet denoising of multiframe optical coherence tomography data

We introduce a novel speckle noise reduction algorithm for OCT images. Contrary to present approaches, the algorithm does not rely on simple averaging of multiple image frames or denoising on the final averaged image. Instead it uses wavelet decompositions of the single frames for a local noise and structure estimation. Based on this analysis, the wavelet detail coefficients are weighted, averaged and reconstructed. At a signal-to-noise gain at about 100% we observe only a minor sharpness decrease, as measured by a full-width-half-maximum reduction of 10.5%. While a similar signal-to-noise gain would require averaging of 29 frames, we achieve this result using only 8 frames as input to the algorithm. A possible application of the proposed algorithm is preprocessing in retinal structure segmentation algorithms, to allow a better differentiation between real tissue information and unwanted speckle noise.     

Speckle reduction in ultrasound images using nonisotropic adaptive filtering

In this article, a speckle reduction approach for ultrasound imaging that preserves important features such as edges, corners and point targets is presented. Speckle reduction is an important problem in coherent imaging, such as ultrasound imaging or synthetic aperture radar, and many speckle reduction algorithms have been developed. Speckle is a non-additive and non-white process and the reduction of speckle without blurring sharp features is known to be difficult. The new speckle reduction algorithm presented in this article utilizes a nonhomogeneous filter that adapts to the proximity and direction of the nearest important features. To remove speckle without blurring important features, the location and direction of edges in the image are estimated. Then for each pixel in the image, the distance and angle to the nearest edge are efficiently computed by a two-pass algorithm and stored in distance and angle maps. Finally for each pixel, an adaptive directional filter aligned to the nearest edge is applied. The shape and orientation of the adaptive filter are determined from the distance and angle maps. The new speckle reduction algorithm is tested with both synthesized and real ultrasound images. The performance of the new algorithm is also compared with those of other speckle reduction approaches and it is shown that the new algorithm performs favorably in reducing speckle without blurring important features.     

Speckle reduction with edge preservation in medical ultrasonic images using a homogeneous region growing mean filter (HRGMF)

In this paper, we propose a new post formation adaptive image filtering technique, to be called the homogeneous region growing mean filter, in order to reduce speckle noise with edge preservation in medical ultrasonic images. First, to find a proper seed region, an initially assumed seed region, which is larger than the average speckle size around a given filtering point, is successively contracted according to a certain local homogeneity criterion. Once the seed region is determined, the next step is to grow the homogeneous region successively based on some measures of local homogeneity and similarity of the neighboring region. The output of the proposed filter for each filtering point is obtained from the arithmetic mean of the grown locally homogeneous region. Several simulation results are presented to illustrate the performance of the proposed filter. They show that the proposed technique effectively smoothes ultrasonic speckle and completely suppresses isolated impulsive noise over the entire texture in addition to preserving the edge information.     

超声乳腺肿瘤图像的边缘提取

目的探求乳腺肿瘤超声图像的边缘提取。方法广义梯度矢量流Snake模型已经成功地用于噪声相对比较小的CT、MRI等医学图像,然而乳腺肿瘤超声图像对比度低,斑点噪声大,很难将该模型直接应用于乳腺肿瘤超声图像。本文针对乳腺肿瘤超声图像的特点如图像对比度低,斑点噪声大,部分边缘缺失,肿瘤内部微细结构分布复杂(如血管,钙化灶等),特别恶性肿瘤还具有复杂形状等,采用相应的图像处理技术如非线性各向异性扩散滤除斑点噪声,形态学滤波器平滑图像,直方图均衡化提高图像的对比度,最后将该模型引入到乳腺肿瘤超声图像边缘提取。结果实验对158例乳腺肿瘤超声图像进行边缘提取,定量和定性分析均获得满意的结果。结论本文方法可以有效地用于超声乳腺肿瘤图像的边缘提取。