Local Phase Features Research Articles

Multimodal brain image fusion based on error texture elimination and salient feature detection.

As an important clinically oriented information fusion technology, multimodal medical image fusion integrates useful information from different modal images into a comprehensive fused image. Nevertheless, existing methods routinely consider only energy information when fusing low-frequency or base layers, ignoring the fact that useful texture information may exist in pixels with lower energy values. Thus, erroneous textures may be introduced into the fusion results. To resolve this problem, we propose a novel multimodal brain image fusion algorithm based on error texture removal. A two-layer decomposition scheme is first implemented to generate the high- and low-frequency subbands. We propose a salient feature detection operator based on gradient difference and entropy. The proposed operator integrates the gradient difference and amount of information in the high-frequency subbands to effectively identify clearly detailed information. Subsequently, we detect the energy information of the low-frequency subband by utilizing the local phase feature of each pixel as the intensity measurement and using a random walk algorithm to detect the energy information. Finally, we propose a rolling guidance filtering iterative least-squares model to reconstruct the texture information in the low-frequency components. Through extensive experiments, we successfully demonstrate that the proposed algorithm outperforms some state-of-the-art methods. Our source code is publicly available at https://github.com/ixilai/ETEM.

Main Paths of Brain Fibers in Diffusion Images Mixed with a Noise to Improve Performance of Tractography Algorithm-Evaluation in Phantom.

Some voxels may alter the tractography results due to unintentional alteration of noises and other unwanted factors. This study aimed to investigate the effect of local phase features on tractography results providing data are mixed by a Gaussian or random distribution noise. In this simulation study, a mask was firstly designed based on the local phase features to decrease false-negative and -positive tractography results. The local phase features are calculated according to the local structures of images, which can be zero-dimensional, meaning just one point (equivalent to noise in tractography algorithm), a line (equivalent to a simple fiber), or an edge (equivalent to structures more complex than a simple fiber). A digital phantom evaluated the feasibility current model with the maximum complexities of configurations in fibers, including crossing fibers. In this paper, the diffusion images were mixed separately by a Gaussian or random distribution noise in 2 forms a zero-mean noise and a noise with a mean of data. The local mask eliminates the pixels of unfitted values with the main structures of images, due to noise or other interferer factors. The local phase features of diffusion images are an innovative solution to determine principal diffusion directions.

Phase-Based Palmprint Identification With Convolutional Sparse Coding

Phase-based image matching has shown high recognition accuracy in palmprint verification. The algorithm compares a pair of palmprint images by extracting local phase features from the images and computing local correlation functions between them. A major drawback of this algorithm is its high computational cost associated with the evaluation of local correlation functions. This needs to be addressed, especially in the case of one-to-many comparisons required for palmprint identification. The problem becomes increasingly severe as the number of enrolled images increases. In this paper, we propose a novel palmprint identification algorithm with low computational complexity, which employs a sparse representation of enrolled phase features (i.e., phase templates) to evaluate local correlation functions. For this purpose, we also develop an efficient Convolutional Sparse Coding (CSC) algorithm that can derive a compact representation of phase templates. The proposed method reduces the computational cost of phase-based palmprint identification without significant degradation of recognition performance. Our experiments using public databases clearly demonstrate the advantage of the proposed method over conventional methods.

Fetal biometric based abnormality detection during prenatal development using deep learning techniques

Early in pregnancy, ultrasounds are used to confirm the fetal heartbeat and a uterine pregnancy. Later, ultrasounds screen for fetal growth, placenta location and umbilical cord, as well as the baby's general health and anatomy. Identifying and interpreting fetal standard scan planes during 2-D ultrasound mid-pregnancy examinations are highly complex tasks, which require years of training. Apart from guiding the probe to the correct location, it is equally difficult for a non-expert to identify relevant structures within the image. The procedure requires a sonographer to find the standardized visualization planes with a probe and manually place measurement calipers on the structures of interest. The process is tedious, time consuming, and introduces user variability into the measurements. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. The proposed method is realized with deep convolutional neural network models to find the region of interest (ROI) of the fetal biometric and organs region in the US image. Based on the ROI, AlexNet, GoogleNet and CNN evaluate the image quality by assessing the goodness of depiction for the key structures of fetal biometrics. In this method both normal and abnormal US data are considered. In addition with that the input sources of the neural network are augmented with the local phase features along with the original US data. These augmented input sources helps to improve the performance of the various Neural Networks. The input sources are trained by AlexNet, GoogleNet and CNN. Then the process of validation is done by performance in proposed Networks for evaluating the accuracy. The performance of proposed work is evaluated with different network configuration. On the dataset of 400 images used in this classification task, proposed work of AlexNet, GoogleNet and CNN achieves accuracy of 90.43%, 88.70%, and 81.25% with reference to expert’s ground truth results respectively.

Phase-based thresholding schemes for segmentation of fetal thigh cross-sectional region in ultrasound images

Measurement of thigh circumference has been used as an indication of nutritional adequacy and fetal growth in the womb. Fractional thigh volume is one of the variables for estimating fetal weight that is also calculated using the fetal thigh circumference. This cross-section of the fetal thigh is an intricate part of detecting because it has a less clear boundary and sometimes attached to other tissue in the ultrasound image. This study aims to establish a detection scheme and segmentation of the fetal thigh cross-sectional area automatically on ultrasound images. We propose a segmentation framework that integrates two phase-based thresholding schemes and a gap connecting step. In the first scheme, we combine the phase congruency of the local phase features and the fuzzy entropy method. The second thresholding scheme is a combination of the phase symmetry method and the saliency visual attention model. The proposed framework has the advantage of being able to segment cross-sectional objects more precisely, even though there are other objects attached to them. This method also works well when applied to low-quality ultrasound images taken using a portable ultrasound scanner. This proposed segmentation method exhibits an average performance precision of 0.922 and a Dice similarity of 0.928.

Automatic segmentation of bone surfaces from ultrasound using a filter-layer-guided CNN.

Ultrasound (US) provides real-time, two-/three-dimensional safe imaging. Due to these capabilities, it is considered a safe alternative to intra-operative fluoroscopy in various computer-assisted orthopedic surgery (CAOS) procedures. However, interpretation of the collected bone US data is difficult due to high levels of noise, various imaging artifacts, and bone surfaces response appearing several millimeters (mm) in thickness. For US-guided CAOS procedures, it is an essential objective to have a segmentation mechanism, that is both robust and computationally inexpensive. In this paper, we present our development of a convolutional neural network-based technique for segmentation of bone surfaces from in vivo US scans. The novelty of our proposed design is that it utilizes fusion of feature maps and employs multi-modal images to abate sensitivity to variations caused by imaging artifacts and low intensity bone boundaries. B-mode US images, and their corresponding local phase filtered images are used as multi-modal inputs for the proposed fusion network. Different fusion architectures are investigated for fusing the B-mode US image and the local phase features. The proposed methods was quantitatively and qualitatively evaluated on 546 in vivo scans by scanning 14 healthy subjects. We achieved an average F-score above 95% with an average bone surface localization error of 0.2mm. The reported results are statistically significant compared to state-of-the-art. Reported accurate and robust segmentation results make the proposed method promising in CAOS applications. Further extensive validations are required in order to fully understand the clinical utility of the proposed method.

A Multi-Scale Learning Local Phase and Amplitude Blind Image Quality Assessment for Multiply Distorted Images

In the real application, digital images may undergo the process of acquisition, compression, and transmission, which causes the excess blurring, quantization, and noise. However, the metrics of image quality assessment for multiply distorted images are very limited. In this paper, we propose a new multi-scale learning quality-aware features blind image quality assessment algorithm for multiply distorted images by using both local phase and local amplitude. In the new model, a distorted image is decomposed into three scales by Gabor transform, and its phase congruency image (PCI), phase congruency covariance maximum image (PCCmax), and phase congruency covariance minimum image (PCCmin) are produced. Then, we calculate contrast sensitivity function and gray level-gradient co-occurrence matrix features from distorted image and its PCI, PCCmax, and PCCmin, and mean value of intensity of PCI, PCCmax, PCCmin, and overlapping blocked local amplitude of distorted image. At last, SVR is used to build the approximating function between these features and subjective mean opinion scores. Both local phase and local amplitude features are extracted at multi-scale images, which supply more flexibility than the previous single-scale methods in incorporating the variations of viewing scene. Comparative experiments between our proposed metric and the state-of-the-art full-reference and no-reference metrics are conducted on two newly released multiply distorted image databases (LIVEMD and MDID2013) that demonstrate the effectiveness of our proposed method.

Automatic 2-D/3-D Vessel Enhancement in Multiple Modality Images Using a Weighted Symmetry Filter.

Automated detection of vascular structures is of great importance in understanding the mechanism, diagnosis, and treatment of many vascular pathologies. However, automatic vascular detection continues to be an open issue because of difficulties posed by multiple factors, such as poor contrast, inhomogeneous backgrounds, anatomical variations, and the presence of noise during image acquisition. In this paper, we propose a novel 2-D/3-D symmetry filter to tackle these challenging issues for enhancing vessels from different imaging modalities. The proposed filter not only considers local phase features by using a quadrature filter to distinguish between lines and edges, but also uses the weighted geometric mean of the blurred and shifted responses of the quadrature filter, which allows more tolerance of vessels with irregular appearance. As a result, this filter shows a strong response to the vascular features under typical imaging conditions. Results based on eight publicly available datasets (six 2-D data sets, one 3-D data set, and one 3-D synthetic data set) demonstrate its superior performance to other state-of-the-art methods.

FUIQA: Fetal Ultrasound Image Quality Assessment With Deep Convolutional Networks.

The quality of ultrasound (US) images for the obstetric examination is crucial for accurate biometric measurement. However, manual quality control is a labor intensive process and often impractical in a clinical setting. To improve the efficiency of examination and alleviate the measurement error caused by improper US scanning operation and slice selection, a computerized fetal US image quality assessment (FUIQA) scheme is proposed to assist the implementation of US image quality control in the clinical obstetric examination. The proposed FUIQA is realized with two deep convolutional neural network models, which are denoted as L-CNN and C-CNN, respectively. The L-CNN aims to find the region of interest (ROI) of the fetal abdominal region in the US image. Based on the ROI found by the L-CNN, the C-CNN evaluates the image quality by assessing the goodness of depiction for the key structures of stomach bubble and umbilical vein. To further boost the performance of the L-CNN, we augment the input sources of the neural network with the local phase features along with the original US data. It will be shown that the heterogeneous input sources will help to improve the performance of the L-CNN. The performance of the proposed FUIQA is compared with the subjective image quality evaluation results from three medical doctors. With comprehensive experiments, it will be illustrated that the computerized assessment with our FUIQA scheme can be comparable to the subjective ratings from medical doctors.

Local Phase Features in Chromatic Domain for Human Detection

In this paper, a new descriptor based on phase congruency concept and LUV color space features is presented. Since the phase of the signal conveys more information regarding signal structure than the magnitude and the indispensable quality of the color in describing the world around us, the proposed descriptor can precisely identify and localize image features over the gradient based techniques, especially in the regions affected by illumination changes. The proposed features can be formed by extracting the phase congruency information for each pixel in the three-color image channels. The maximum phase congruency values are selected from the corresponding color channels. Histograms of the phase congruency values of the local regions in the image are computed with respect to its orientation. These histograms are concatenated to construct the proposed descriptor. Results of the experiments performed on the proposed descriptor show that it has better detection performance and lower error rates than a set of the state of the art feature extraction methodologies.

A computer-aided tracking and motion analysis with ultrasound (CAT & MAUS) system for the description of hip joint kinematics.

Investigation of joint kinematics contributes to developing a better understanding of musculoskeletal conditions. However, the most commonly used optoelectronic motion analysis systems cannot determine the movements of underlying bone landmarks with high accuracy because of soft tissue artefacts. The aim of this paper was to present a computer-aided measurement system to track the underlying bone anatomy in a 3D global coordinate frame and describe hip joint kinematics of ten healthy volunteers during gait. We have developed a measurement tool with an image-based computer-aided post-processing pipeline for automatic bone segmentation in ultrasound (US) images and a globally optimal 3D surface-to-surface registration method to quantify hip joint movements. The segmentation algorithm exploits US intensity profiles, including information about the integrated backscattering, acoustic shadows, and local phase features. A global optimization method is applied based on the traditional iterative closest point registration algorithm, which is robust to initialization. The International Society of Biomechanics recommended joint kinematics descriptor has been adapted to calculate the joint kinematics. The developed system prototype has been validated with a ball-joint femoral phantom and tested in vivo with 10 volunteers. The maximum Euclidean distance error of the automatic bone segmentation is less than 2 pixels (approximately 0.2mm). The maximum absolute rotation angle error is less than [Formula: see text]. This computer-aided tracking and motion analysis with ultrasound (CAT & MAUS) system shows the feasibility of describing hip joint kinematics for clinical investigation and diagnosis using an image-based solution.

Hybrid human detection and recognition in surveillance

In this paper, we present a hybrid human recognition system for surveillance. A Cascade Head–Shoulder Detector (CHSD) with human body model is proposed to find the face region in a surveillance video frame image. The CHSD is a chain of rejecters which combines the advantages of Haar-like feature and HoG feature to make the detector more efficient and effective. For human recognition, we introduce an Overlapping Local Phase Feature (OLPF) to describe the face region, which can improve the robustness to pose change and blurring. To well model the variations of faces, an Adaptive Gaussian Mixture Model (AGMM) is presented to describe the distributions of the face images. Since AGMM does not need the facial topology, the proposed method is resistant to face detection error caused by imperfect localization or misalignment. Experimental results demonstrate the effectiveness of the proposed method in public dataset as well as real surveillance video.

A flexible hierarchical approach for facial age estimation based on multiple features

Age estimation from facial images is increasingly receiving attention to solve age-based access control, age-adaptive targeted marketing, amongst other applications. Since even humans can be induced in error due to the complex biological processes involved, finding a robust method remains a research challenge today. In this paper, we propose a new framework for the integration of Active Appearance Models (AAM), Local Binary Patterns (LBP), Gabor wavelets (GW) and Local Phase Quantization (LPQ) in order to obtain a highly discriminative feature representation which is able to model shape, appearance, wrinkles and skin spots. In addition, this paper proposes a novel flexible hierarchical age estimation approach consisting of a multi-class Support Vector Machine (SVM) to classify a subject into an age group followed by a Support Vector Regression (SVR) to estimate a specific age. The errors that may happen in the classification step, caused by the hard boundaries between age classes, are compensated in the specific age estimation by a flexible overlapping of the age ranges. The performance of the proposed approach was evaluated on FG-NET Aging and MORPH Album 2 datasets and a mean absolute error (MAE) of 4.50 and 5.86 years was achieved respectively. The robustness of the proposed approach was also evaluated on a merge of both datasets and a MAE of 5.20 years was achieved. Furthermore, we have also compared the age estimation made by humans with the proposed approach and it has shown that the machine outperforms humans. The proposed approach is competitive with current state-of-the-art and it provides an additional robustness to blur, lighting and expression variance brought about by the local phase features.

Image Registration Based on Autocorrelation of Local Structure.

Registration of images in the presence of intra-image signal fluctuations is a challenging task. The definition of an appropriate objective function measuring the similarity between the images is crucial for accurate registration. This paper introduces an objective function that embeds local phase features derived from the monogenic signal in the modality independent neighborhood descriptor (MIND). The image similarity relies on the autocorrelation of local structure (ALOST) which has two important properties: 1) low sensitivity to space-variant intensity distortions (e.g., differences in contrast enhancement in MRI); 2) high distinctiveness for 'salient' image features such as edges. The ALOST method is quantitatively compared to the MIND approach based on three different datasets: thoracic CT images, synthetic and real abdominal MR images. The proposed method outperformed the NMI and MIND similarity measures on these three datasets. The registration of dynamic contrast enhanced and post-contrast MR images of patients with Crohn's disease led to relative contrast enhancement measures with the highest correlation (r=0.56) to the Crohn's disease endoscopic index of severity.

Semi-automatic segmentation of subcutaneous tumours from micro-computed tomography images

This paper outlines the first attempt to segment the boundary of preclinical subcutaneous tumours, which are frequently used in cancer research, from micro-computed tomography (microCT) image data. MicroCT images provide low tissue contrast, and the tumour-to-muscle interface is hard to determine, however faint features exist which enable the boundary to be located. These are used as the basis of our semi-automatic segmentation algorithm. Local phase feature detection is used to highlight the faint boundary features, and a level set-based active contour is used to generate smooth contours that fit the sparse boundary features. The algorithm is validated against manually drawn contours and micro-positron emission tomography (microPET) images. When compared against manual expert segmentations, it was consistently able to segment at least 70% of the tumour region (n = 39) in both easy and difficult cases, and over a broad range of tumour volumes. When compared against tumour microPET data, it was able to capture over 80% of the functional microPET volume. Based on these results, we demonstrate the feasibility of subcutaneous tumour segmentation from microCT image data without the assistance of exogenous contrast agents. Our approach is a proof-of-concept that can be used as the foundation for further research, and to facilitate this, the code is open-source and available from www.setuvo.com.

Palm-Dorsal Vein Recognition Method Based on Histogram of Local Gabor Phase XOR Pattern with Second Identification

This paper proposes a palm-dorsal vein recognition method with local Gabor phase features, which includes a second identification for more accuracy. First, we extract quadrant-bit codes from the 2D Gabor transformation of a vein image. Then using the Histogram of the Local Gabor Phase XOR Pattern (HLGPXP) obtains the vein texture features, which combines the global information and the local information. Finally, the chi-square distance is adopted for recognition. Using the texture features based on the local Gabor codes above, the Second Identification (SI) segments the vein images and regards the non-overlap degree between images as a matching criterion. The experimental results show the Error Equation Rate (EER) of our method (HLGPXP-SI) decreases by 11.7 %, 4.8 % respectively than Modified Local Binary Pattern (MLBP) [1], Local Gabor Binary Pattern (LGBP) [2] on Database A (204 high-quality palm-dorsal vein images from 68 hands), and on Database B (400 low-quality palm-dorsal vein images from 100 hands), it decreases by 18.94 %, 15.51 % respectively than Selected Gabor Phase and Amplitude Features (SGPAF) [3], Direct Gabor Phase Coding (DGPC) [4].

Automatic Bone Localization and Fracture Detection from Volumetric Ultrasound Images Using 3-D Local Phase Features

This article presents a novel method for bone segmentation from three-dimensional (3-D) ultrasound images that derives intensity-invariant 3-D local image phase measures that are then employed for extracting ridge-like features similar to those that occur at soft tissue/bone interfaces. The main contributions in this article include: (1) the extension of our previously proposed phase-symmetry-based bone surface extraction from two-dimensional (2-D) to 3-D images using 3-D Log-Gabor filters; (2) the design of a new framework for accuracy evaluation based on using computed tomography as a gold standard that allows the assessment of surface localization accuracy across the entire 3-D surface; (3) the quantitative validation of accuracy of our 3-D phase-processing approach on both intact and fractured bone surfaces using phantoms and ex vivo 3-D ultrasound scans; and (4) the qualitative validation obtained by scanning emergency room patients with distal radius and pelvis fractures. We show a 41% improvement in surface localization error over the previous 2-D phase symmetry method. The results demonstrate clearly visible segmentations of bone surfaces with a localization accuracy of <0.6 mm and mean errors in estimating fracture displacements below 0.6 mm. The results show that the proposed method is successful even for situations when the bone surface response is weak due to shadowing from muscle and fascia interfaces above the bone, which is a situation where the 2-D method fails.

Automatic Adaptive Parameterization in Local Phase Feature-Based Bone Segmentation in Ultrasound

Intensity-invariant local phase features based on Log-Gabor filters have been recently shown to produce highly accurate localizations of bone surfaces from three-dimensional (3-D) ultrasound. A key challenge, however, remains in the proper selection of filter parameters, whose values have so far been chosen empirically and kept fixed for a given image. Since Log-Gabor filter responses widely change when varying the filter parameters, actual parameter selection can significantly affect the quality of extracted features. This article presents a novel method for contextual parameter selection that autonomously adapts to image content. Our technique automatically selects the scale, bandwidth and orientation parameters of Log-Gabor filters for optimizing local phase symmetry. The proposed approach incorporates principle curvature computed from the Hessian matrix and directional filter banks in a phase scale-space framework. Evaluations performed on carefully designed in vitro experiments demonstrate 35% improvement in accuracy of bone surface localization compared with empirically-set parameterization results. Results from a pilot in vivo study on human subjects, scanned in the operating room, show similar improvements.

Improved Gabor Filter for Extracting Texture Edge Features in Ultrasound Kidney Images

The diagnosis of urinary tract calculi begins with a focused history of calculi, evolution of its symptoms and the duration. Computer assisted approaches for analyzing the images have increased since the manual interpretation of the image is a time consuming process and susceptible to human errors. The proposed system develops a multi-scale wavelet based Bayesian speckle suppression method for ultrasound kidney images. The logarithmic transform of the original image is analyzed into the multi-scale wavelet domain. The subband decompositions of ultrasound images have significantly non-Gaussian statistics that are best described by families of heavy-tailed distributions. Bayesian estimators are designed to exploits these statistics. Ultrasound (US) is increasingly considered as a viable alternative imaging modality in computer-assisted Kidney segmentation and disease diagnosis applications. Automatic Kidney segmentation from US images, however, remains a challenge due to speckle noise and various other artifacts inherent to US. This paper, design intensity invariant local image phase features, obtained using improved Gabor filter banks, for extracting edge texture features that occur at core and intermediate layer interfaces. The proposed model does the extension of phase symmetry features to modified gabor mode and their use in automatic extraction of kidney edge texture features from US normal and diseased patient images. The system functionality is proved qualitatively and quantitatively through experimentation for synthetic and real data sets. The localization feature value threshold is evaluated with the training samples of US images. The speckle noise error ratio with respect to the standard US image are compared and experimented.

Objective quantification of global and regional left ventricular systolic function by endocardial tracking of contrast echocardiographic sequences

Background Echocardiographic assessment of LV wall motion is still most frequently done visually. This study was designed to validate a new system for semi-automatic quantification of global and regional LV systolic function from contrast-enhanced cross-sectional echocardiograms. Methods Measurements of LV volumes were validated in 50 patients using magnetic resonance (MR) as reference. The regional identification of the endocardial boundary was validated frame-by-frame against the visually identified border in another 27 patients. Finally, the applicability of the system for quantifying stress-echocardiographic exams was assessed in 52 patients undergoing dobutamine interventions. Echocardiographic sequences were digitally processed using custom-built algorithms, based on local phase feature descriptors, deformable contour fitting, and prospective training. Results Compared to MR, the tracing system showed reasonable accuracy, with relative errors for end-diastolic volume, end-systolic volume, and EF of 21 ± 20%, 27 ± 33%, and − 4 ± 18%, respectively. Regional agreement of the instantaneous contours with visually traced borders was within the limits of visual reproducibility. The system was suitable for tracking stress-echo studies from all patients except two (96%). Quantification of regional radial shortening allowed to discriminate segments showing an abnormal regional wall motion with an overall area under the ROC curve of 0.87. Conclusions A reliable and accurate quantification of LV systolic function can be obtained by processing contrast echocardiograms. Values of LV volumes, ejection fraction, and regional endocardial shortening adequately correlate with currently available reference methods. Readily applicable to baseline and stress studies, endocardial tracking techniques increase the reliability of echocardiography for the assessment of global and regional systolic function.