Abstract Chromatic adaptation refers to the sensing and preprocessing of spectral composition incident light on retina, it is important for color‐image recognition. It challenging apply sensing, memory, processing functions color images via same physical process using complementary metal–oxide–semiconductor technology because redundant data detection, complicated signal conversion processes, requirement additional memory modules. Inspired by highly efficient chromatic human a 2D...

Abstract To improve image quality and CT number accuracy of fast-scan low-dose cone-beam computed tomography (CBCT) through a deep-learning convolutional neural network (CNN) methodology for head-and-neck (HN) radiotherapy. Fifty-five paired CBCT images from HN patients were retrospectively analysed. Among them, 15 underwent adaptive replanning during treatment, thus had same-day CT/CBCT pairs. The remaining 40 (post-operative) planning 1st fraction with minimal anatomic changes. A 2D U-Net...

Fast and accurate segmentation of knee bone cartilage on MRI images is becoming increasingly important in the orthopaedic area, as an essential prerequisite step to a patient-specific diagnosis, optimising implant design preoperative intraoperative planning. However, manual time-intensive subjected inter- intra-observer variations. Hence, this study, three-dimensional (3D) deep neural network using adversarial loss was proposed automatically segment resampled image volume order enlarge...

White matter hyperintensities (WMH) seen on T2WI are a hallmark of multiple sclerosis (MS) as it indicates inflammation associated with the disease. Automatic detection WMH can be valuable in diagnosing and monitoring treatment effectiveness. T2 fluid attenuated inversion recovery (FLAIR) MR images provided good contrast between lesions other tissue; however signal intensity gray tissue was close to FLAIR that may cause more false positives segment result. We developed evaluated tool for...

Deep learning (DL) has emerged as a powerful image processing technique that learns the features of data and produces state-of-the-art prediction results. The decade from 2010 to 2020 is real revival DL, which come turning point in history. In classification, many deep networks have been proposed by scholars, each them its own strengthness. It very important efficient for researchers developers know performance these networks, especially beginners, so give transplant instruction an objective...

Computer-aided diagnosis (CAD) system can promote the detection accuracy by providing a "second opinion" to radiologist, so high of mass in mammogram is critical for improving performance and efficiency. In this paper, we designed auto-diagnosis method based on texture features. First, was detected base bilateral comparison, center region interest (ROI) located. Second, fractal dimension two-dimensional entropy were calculated as Last, kinds ROI diagnosed Support Vector Machine (SVM), or...

<abstract> <p>Accurate determination of the onset time in acute ischemic stroke (AIS) patients helps to formulate more beneficial treatment plans and plays a vital role recovery patients. Considering that whole brain may contain some critical information, we combined Radiomics features infarct lesions improve prediction accuracy. First, radiomics were separately calculated using apparent diffusion coefficient (ADC), diffusion-weighted imaging (DWI) fluid-attenuated inversion...

Image retrieval methods in the fashion field mainly take advantage of query images that reflect user needs, without considering additional keywords users can provide to specify attributes their interests. To achieve fine-grained retrieval, we propose an iterative similarity learning network (ISLN) for attribute-guided image which takes a and specified attribute as input, outputs other with same or similar values. The core is module, leverages aggressive ability deep neural (DNN) focus on...

Objective The precise segmentation of kidneys from a 2D ultrasound (US) image is crucial for diagnosing and monitoring kidney diseases. However, achieving detailed difficult due to US images’ low signal-to-noise ratio low-contrast object boundaries. Methods This paper presents an approach called deep supervised attention with multi-loss functions (MLAU-Net) segmentation. MLAU-Net model combines the benefits mechanisms supervision improve accuracy. mechanism allows selectively focus on...

Total variation minimization (TVM) is a popular and useful method for accurate CT reconstruction from few-views limited-angle data. However, the optimization procedure of previous TVM-based algorithms very time-consuming. The purpose this paper was to accelerate high image quality data.A new algorithm based on transfer principle proposed. proposed uses TVM as regularization term cost function that ensures reconstructed Additionally, half square difference between original projection forward...

The segmentation of the ascending aorta from multislice computed tomography (MSCT) volume data is one critical steps for quantitative analysis coronary artery. In this paper, a fast and automatic iterative method presented. locates interest (VOI) detects seed point automatically. Then an procedure implemented adaptively to segment slice by slice. was evaluated in various clinical datasets using three criteria: sensitivity noise, successful rate running time. Experiments show that robust...

Patient-specific implant design and pre- intra-operative planning is becoming increasingly important in the orthopaedic field. For clinical feasibility of these techniques, fast accurate segmentation bone structures from MRI essential. However, manual time intensive subject to inter- intra-observer variation. The challenge developing automatic algorithms for data mainly exists inhomogeneity problem low contrast among cortical adjacent tissues. In this paper, we proposed a method knee with 3D...

Bone subtraction computed tomography angiography (BSCTA) is better able to facilitate the detection of intracranial aneurysms adjacent bone structures compared conventional non-subtracted CTA (CNSCTA). However, comparison diagnostic accuracy three-dimensional (3D) and two-dimensional (2D) BSCTA in evaluating remains unclear.To evaluate whether 3D has a superior those 2D CNSCTA single center with same instrument.Sixty-three patients received BSCTA, NSCTA for treatment planning suspected...

Fetal head circumference (HC) is an important biological parameter to monitor the healthy development of fetus. Since there are some HC measurement errors that affected by skill and experience sonographers, a rapid, accurate automatic for fetal in prenatal ultrasound great significance. We proposed new one-stage network rotating elliptic object detection based on anchor-free method, which also end-to-end auto-measurement no need any post-processing. The structure used simple transformer...

A new automatic skull stripping method for fluid attenuated inversion recovery (FLAIR) MR images is proposed the quantification of brain volume in multiple sclerosis (MS) patients. The based on use local moment inertia structure tensor and morphological operators. used to determine boundary brain, instead conventional edge detection algorithms. Data from 30 MS patients were processed by Brain Extraction Tool (BET); results which also compared with manual segmentation. It was shown that able...

The rigid registration is a key step of Image Guided Surgery (IGS), and the point-pair method main way used for registration. However configuration fiducial points has great influence on accuracy at target point.

The detection of hydrophobicity is an important way to evaluate the performance composite insulators, which helpful safe operation insulators.Image processing technology used judge makes results more accurate and overcomes subjective drawbacks traditional methods.As Canny operator requires manual intervention in selecting variance Gaussian filter threshold, paper presents a method edge based on improved operator.First, adaptive median replaces filter, can eliminate impact from remove noise...

The purpose of this paper is to improve the quality low dose Computed Tomography (CT) images. Low artifacts are Gaussian noises superimposed on CT images and often caused by insufficient calibrated detector photon starvation. A noise reduction method via three dimensional total variation using Compute Unified Device Architecture (CUDA) proposed image. This can also be employed for removal two decreasing a dimension processing technique. performance algorithm has been tested quantitative...