He Deng

Small Infrared Target Detection Based on Weighted Local Difference Measure

Against an intricate infrared cloudy-sky background, jamming objects such as the edges of clouds in the scene have a similar thermal intensity measure with respect to the background as small targets. This may cause high false alarm rates and low probabilities of detection according to conventional small target detection methods. In this paper, we propose a weighted local difference measure (WLDM)-based scheme for the detection of small targets against various complex cloudy-sky backgrounds. Initially, a WLDM map is achieved to simultaneously enhance targets and suppress background clutters and noise. In this way, the true targets can be easily separated from jamming objects. After that, a simple adaptive threshold is used to segment the targets. More than 460 infrared small target images against diverse intricate cloudy-sky backgrounds were utilized to validate the detection capability of the WLDM-based method. Experimental results demonstrate that the proposed algorithm not only works more robustly for different cloudy-sky backgrounds, target movements, and signal-to-clutter ratio (SCR) values but also has a better performance with regard to the detection accuracy, in comparison to traditional baseline methods. In particular, the proposed method is able to significantly improve SCR values of the images.

Infrared small-target detection using multiscale gray difference weighted image entropy

We propose an effective small-target detection approach based on weighted image entropy. The approach weights the local entropy measure by the multiscale grayscale difference followed by an adaptive threshold operation, which aims to improve the signal-to-noise ratio for cases in which jamming objects in the scene have similar thermal intensity measure with respect to the background as small target. The detection capability of the proposed approach has been validated on six real sequences, and the results demonstrate its significance and improvement.

Image enhancement based on intuitionistic fuzzy sets theory

Enhancement of images with weak edges faces great challenges in imaging applications. In this study, the authors propose a novel image enhancement approach based on intuitionistic fuzzy sets. The proposed method first divides an image into sub-object and sub-background areas, and then successively implements new fuzzification, hyperbolisation, and defuzzification operations on each area. In this way, an enhanced image is obtained, where the visual quality of region of interest (ROI) is significantly improved. Several types of images are utilised to validate the proposed method with respect to the enhancement performance. Experimental results demonstrate that the proposed algorithm not only works more stably for different types of images, but also has better enhancement performance, in comparison to conventional methods. This is a great merit of such design for discerning specific ROIs.

Background suppression of small target image based on fast local reverse entropy operator

Background suppression is vitally important for the small target detection, which aims to enhance targets and improve the signal-to-noise ratio of small target images. Consequently, the study proposes a background suppression approach based on the fast local reverse entropy operator, which is designed according to the fact that the appearance of a small target could result in the great change of the value of local reverse entropy in the local region. The operator is adopted to suppress complex backgrounds of small target images in order to enhance small targets, and then bring about high probabilities of detection and low probabilities of false alarm in the small target detection. Both quantitative and qualitative analyses contribute to confirm the validity and efficiency of the proposed approach.

Entropy-based window selection for detecting dim and small infrared targets

Dim and small target detection in complex background is considered a difficult and challenging problem. Conventional algorithms using the local difference/mutation possibly produce high missed or mistaken detection rates. In this paper, we propose an effective algorithm for detecting dim and small infrared targets. In order to synchronously enhance targets and suppress complex background clutters, we adopt an adaptive entropy-based window selection technique to construct a novel local difference measure (LDM) map of an input image, which measures the dissimilarity between the current region and its neighboring ones. In this way, the window size can be adaptively regulated according to local statistical properties. Compared with the original image, the LDM map has less background clutters and noise residual. This guarantees the lower false alarm rates under the same probability of detection. Subsequently, a simple threshold is used to segment the target. More than 600 dim and small infrared target images against different complex and noisy backgrounds were utilized to validate the detection performance of the proposed approach. Extensive experimental results demonstrate that the proposed method not only works more stably for different target movements and signal-to-clutter ratio values, but also has a better performance compared with classical baseline methods. The evaluation results suggest that the proposed method is simple and effective with regard to detection accuracy. We present an adaptive entropy-based window selection scheme.The novel local difference measure map can keep low false alarm rates under the same probability of detection.The proposed method is simple and effective with regard to detection accuracy.

Constant-variable flip angles for hyperpolarized media MRI

The longitudinal magnetization of hyperpolarized media, such as hyperpolarized (129)Xe, (3)He, etc., is nonrenewable. When the MRI data acquisition begins at the k-domain center, a constant flip angle (CFA) results in an image of high signal-to-noise ratio (SNR) but sacrifices the accuracy of spatial information. On the other hand, a variable flip angle (VFA) strategy results in high accuracy but suffers from a low SNR. In this paper, we propose a novel scheme to optimize both the SNR and accuracy, called constant-variable flip angles (CVFA). The proposed scheme suggests that hyperpolarized magnetic resonance signals are firstly acquired through a train of n(∗) CFA excitation pulses, followed by a train of N-n(∗) VFA excitation pulses. We simulate and optimize the flip angle used in the CFA section, the number of CFA excitation pulses, the number of VFA excitation pulses, and the initial and final variable flip angles adopted in the VFA section. Phantom and in vivo experiments demonstrate the good performance of the CVFA designs and their ability to maintain both high SNR and spatial resolution.

Adaptive Intuitionistic Fuzzy Enhancement of Brain Tumor MR Images

Image enhancement techniques are able to improve the contrast and visual quality of magnetic resonance (MR) images. However, conventional methods cannot make up some deficiencies encountered by respective brain tumor MR imaging modes. In this paper, we propose an adaptive intuitionistic fuzzy sets-based scheme, called as AIFE, which takes information provided from different MR acquisitions and tries to enhance the normal and abnormal structural regions of the brain while displaying the enhanced results as a single image. The AIFE scheme firstly separates an input image into several sub images, then divides each sub image into object and background areas. After that, different novel fuzzification, hyperbolization and defuzzification operations are implemented on each object/background area, and finally an enhanced result is achieved via nonlinear fusion operators. The fuzzy implementations can be processed in parallel. Real data experiments demonstrate that the AIFE scheme is not only effectively useful to have information from images acquired with different MR sequences fused in a single image, but also has better enhancement performance when compared to conventional baseline algorithms. This indicates that the proposed AIFE scheme has potential for improving the detection and diagnosis of brain tumors.

Mammogram Enhancement Using Intuitionistic Fuzzy Sets

Objective: Conventional mammogram enhancement methods use transform-domain filtering, which possibly produce some artifacts or not well highlight all local details in images. This paper presents a new enhancement method based on intuitionistic fuzzy sets. Methods: The presented algorithm initially separates a mammogram via a global threshold and then fuzzifies the image utilizing the intuitionistic fuzzy membership function that adopts restricted equivalence functions. After that, the presented scheme hyperbolizes membership degrees of foreground and background areas, defuzzifies the fuzzy plane, and achieves a filtered image via normalization. Finally, an enhanced mammogram is obtained by fusing the original image with filtered one. These implementations can be processed in parallel. Results: This algorithm can improve the contrast and visual quality of regions of interest. Conclusion: Real data experiments demonstrate that our method has better performance regarding the improvement of contrast and visual quality of abnormalities in mammograms (such as masses and/or microcalcifications), compared with classical baseline methods. Significance: This algorithm has potential for understanding and determining abnormalities.

Simultaneous assessment of both lung morphometry and gas exchange function within a single breath‐hold by hyperpolarized 129Xe MRI

During the measurement of hyperpolarized 129Xe magnetic resonance imaging (MRI), the diffusion‐weighted imaging (DWI) technique provides valuable information for the assessment of lung morphometry at the alveolar level, whereas the chemical shift saturation recovery (CSSR) technique can evaluate the gas exchange function of the lungs. To date, the two techniques have only been performed during separate breaths. However, the request for multiple breaths increases the cost and scanning time, limiting clinical application. Moreover, acquisition during separate breath‐holds will increase the measurement error, because of the inconsistent physiological status of the lungs. Here, we present a new method, referred to as diffusion‐weighted chemical shift saturation recovery (DWCSSR), in order to perform both DWI and CSSR within a single breath‐hold. Compared with sequential single‐breath schemes (namely the ‘CSSR + DWI’ scheme and the ‘DWI + CSSR’ scheme), the DWCSSR scheme is able to significantly shorten the breath‐hold time, as well as to obtain high signal‐to‐noise ratio (SNR) signals in both DWI and CSSR data. This scheme enables comprehensive information on lung morphometry and function to be obtained within a single breath‐hold. In vivo experimental results demonstrate that DWCSSR has great potential for the evaluation and diagnosis of pulmonary diseases.

Considering low-rank, sparse and gas-inflow effects constraints for accelerated pulmonary dynamic hyperpolarized 129 Xe MRI

Dynamic hyperpolarized (HP) 129Xe MRI is able to visualize the process of lung ventilation, which potentially provides unique information about lung physiology and pathophysiology. However, the longitudinal magnetization of HP 129Xe is nonrenewable, making it difficult to achieve high image quality while maintaining high temporal-spatial resolution in the pulmonary dynamic MRI. In this paper, we propose a new accelerated dynamic HP 129Xe MRI scheme incorporating the low-rank, sparse and gas-inflow effects (L + S + G) constraints. According to the gas-inflow effects of HP gas during the lung inspiratory process, a variable-flip-angle (VFA) strategy is designed to compensate for the rapid attenuation of the magnetization. After undersampling k-space data, an effective reconstruction algorithm considering the low-rank, sparse and gas-inflow effects constraints is developed to reconstruct dynamic MR images. In this way, the temporal and spatial resolution of dynamic MR images is improved and the artifacts are lessened. Simulation and in vivo experiments implemented on the phantom and healthy volunteers demonstrate that the proposed method is not only feasible and effective to compensate for the decay of the magnetization, but also has a significant improvement compared with the conventional reconstruction algorithms (P-values are less than 0.05). This confirms the superior performance of the proposed designs and their ability to maintain high quality and temporal-spatial resolution.