Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 133.621
Filtrar
Mais filtros








Intervalo de ano de publicação
1.
Talanta ; 237: 122908, 2022 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-34736645

RESUMO

Here we describe an automated and compact pollen detection system that integrates enrichment, in-situ detection and self-cleaning modules. The system can achieve continuous capture and enrichment of pollen grains in air samples by electrostatic adsorption. The captured pollen grains are imaged with a digital camera, and an automated image analysis based on machine vision is performed, which enables a quantification of the number of pollen particles as well as a preliminary classification into two types of pollen grains. In order to optimize and evaluate the system performance, we developed a testing approach that utilizes an airflow containing a precisely metered amount of pollen particles surrounded by a sheath flow to achieve the generation and lossless transmission of standard gas samples. We studied various factors affecting the pollen capture efficiency, including the applied voltage, air flow rate and humidity. Under optimized conditions, the system was successfully used in the measurement of airborne pollen particles within a wide range of concentrations, spanning 3 orders of magnitude.


Assuntos
Poluentes Atmosféricos , Pólen , Poluentes Atmosféricos/análise , Alérgenos/análise , Processamento de Imagem Assistida por Computador , Pólen/química , Eletricidade Estática
2.
PET Clin ; 17(1): 175-182, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34809865

RESUMO

Artificial intelligence (AI) has been widely used throughout medical imaging, including PET, for data correction, image reconstruction, and image processing tasks. However, there are number of opportunities for the application of AI in photon detector performance or the data collection process, such as to improve detector spatial resolution, time-of-flight information, or other PET system performance characteristics. This review outlines current topics, research highlights, and future directions of AI in PET instrumentation.


Assuntos
Inteligência Artificial , Tomografia por Emissão de Pósitrons , Humanos , Processamento de Imagem Assistida por Computador , Radiografia
3.
PET Clin ; 17(1): 85-94, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34809873

RESUMO

Artificial intelligence is an important technology, with rapidly expanding applications for cardiac PET. We review the common terminology, including methods for training and testing, which are fundamental to understanding artificial intelligence. Next, we highlight applications to improve image acquisition, reconstruction, and segmentation. Computed tomographic imaging is commonly acquired in conjunction with PET and various artificial intelligence methods have been applied, including methods to automatically extract anatomic information or generate synthetic attenuation images. Last, we describe methods to automate disease diagnosis or risk stratification. This summary highlights the current and future clinical applications of artificial intelligence to cardiovascular PET imaging.


Assuntos
Inteligência Artificial , Sistema Cardiovascular , Humanos , Processamento de Imagem Assistida por Computador , Tomografia por Emissão de Pósitrons , Tomografia Computadorizada por Raios X
4.
Meat Sci ; 183: 108654, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34419789

RESUMO

In the European Community, conformation and fat cover of bovine carcasses is assessed using the SEUROP grading system. In this study we pursued the development of an application software (App) based on Visual Image Analysis, useful for SEUROP and Fat Cover grading of bovine carcasses using a smartphone. The App was trained using 500 bovine carcasses. Carcass conformation and Fat Cover classes were assessed in parallel by expert evaluators and by App. Overall, a high correspondence was found between the measurements of carcasses parameters by operators and by the App, as high as 84.2% for SEUROP and 86.4% for the Fat Cover. In the 15.8% of samples with discordant SEUROP evaluation, and in the 13.6% of samples with discordant Fat Cover evaluation, the operators' and App measurements deviated by only one class. All values also aligned with the requirements expected by the current legislation for the use of automated and/or semi-automated systems able to determine the market value of carcasses.


Assuntos
Tecido Adiposo/anatomia & histologia , Processamento de Imagem Assistida por Computador/métodos , Carne Vermelha/análise , Animais , Composição Corporal , Bovinos , União Europeia , Carne Vermelha/normas
5.
Nihon Hoshasen Gijutsu Gakkai Zasshi ; 77(11): 1279-1287, 2021.
Artigo em Japonês | MEDLINE | ID: mdl-34803108

RESUMO

Dynamic chest radiography (DCR) is a flat-panel detector (FPD) -based functional X-ray imaging, which is performed as an additional examination in chest radiography. The large field of view of FPDs permits real-time observation of motion/kinetic findings on the entire lungs, right and left diaphragm, ribs, and chest wall; heart wall motions; respiratory changes in lung density; and diameter of the intrathoracic trachea. Since the dynamic FPDs had been developed in the early 2000s, we focused on the potential of dynamic FPDs for functional X-ray imaging and have launched a research project for the development of an imaging protocol and digital image-processing techniques for the DCR. The quantitative analysis of motion/kinetic findings is helpful for a better understanding of pulmonary function, because the interpretation of dynamic chest radiographs is challenging and time-consuming for radiologists, pulmonologists, and surgeons. Recent clinical studies have demonstrated the usefulness of DCR combined with the digital image processing techniques for the evaluation of pulmonary function and circulation. Especially, there is a major concern in color-mapping images based on dynamic changes in radiographic lung density, where pulmonary impairments can be detected as color defects, even without the use of contrast media or radioactive medicine. Dynamic chest radiography is now commercially available for the use in general X-ray room and therefore can be deployed as a simple and rapid means of functional imaging in both routine and emergency medicine. This review article describes the current status and future prospects of DCR, which might bring a paradigm shift in respiratory diagnosis.


Assuntos
Pneumopatias , Humanos , Processamento de Imagem Assistida por Computador , Pulmão/diagnóstico por imagem , Pneumopatias/diagnóstico por imagem , Intensificação de Imagem Radiográfica , Radiografia , Radiografia Torácica
6.
Comput Intell Neurosci ; 2021: 3812865, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34804140

RESUMO

Diabetic retinopathy (DR) is a diabetic complication affecting the eyes, which is the main cause of blindness in young and middle-aged people. In order to speed up the diagnosis of DR, a mass of deep learning methods have been used for the detection of this disease, but they failed to attain excellent results due to unbalanced training data, i.e., the lack of DR fundus images. To address the problem of data imbalance, this paper proposes a method dubbed retinal fundus images generative adversarial networks (RF-GANs), which is based on generative adversarial network, to synthesize retinal fundus images. RF-GANs is composed of two generation models, RF-GAN1 and RF-GAN2. Firstly, RF-GAN1 is employed to translate retinal fundus images from source domain (the domain of semantic segmentation datasets) to target domain (the domain of EyePACS dataset connected to Kaggle (EyePACS)). Then, we train the semantic segmentation models with the translated images, and employ the trained models to extract the structural and lesion masks (hereafter, we refer to it as Masks) of EyePACS. Finally, we employ RF-GAN2 to synthesize retinal fundus images using the Masks and DR grading labels. This paper verifies the effectiveness of the method: RF-GAN1 can narrow down the domain gap between different datasets to improve the performance of the segmentation models. RF-GAN2 can synthesize realistic retinal fundus images. Adopting the synthesized images for data augmentation, the accuracy and quadratic weighted kappa of the state-of-the-art DR grading model on the testing set of EyePACS increase by 1.53% and 1.70%, respectively.


Assuntos
Processamento de Imagem Assistida por Computador , Fundo de Olho , Humanos , Pessoa de Meia-Idade
7.
Comput Intell Neurosci ; 2021: 4395646, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34804141

RESUMO

Day by day, all the research communities have been focusing on digital image retrieval due to more internet and social media uses. In this paper, a U-Net-based neural network is proposed for the segmentation process and Haar DWT and lifting wavelet schemes are used for feature extraction in content-based image retrieval (CBIR). Haar wavelet is preferred as it is easy to understand, very simple to compute, and the fastest. The U-Net-based neural network (CNN) gives more accurate results than the existing methodology because deep learning techniques extract low-level and high-level features from the input image. For the evaluation process, two benchmark datasets are used, and the accuracy of the proposed method is 93.01% and 88.39% on Corel 1K and Corel 5K. U-Net is used for the segmentation purpose, and it reduces the dimension of the feature vector and feature extraction time by 5 seconds compared to the existing methods. According to the performance analysis, the proposed work has proven that U-Net improves image retrieval performance in terms of accuracy, precision, and recall on both the benchmark datasets.


Assuntos
Processamento de Imagem Assistida por Computador , Redes Neurais de Computação , Humanos
8.
Comput Intell Neurosci ; 2021: 4824613, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34804142

RESUMO

In this paper, we propose a novel method, an adaptive localizing region-based level set using convolutional neural network, for improving performance of maxillary sinus segmentation. The healthy sinus without lesion inside is easy for conventional algorithms. However, in practice, most of the cases are filled with lesions of great heterogeneity which lead to lower accuracy. Therefore, we provide a strategy to avoid active contour from being trapped into a nontarget area. First, features of lesion and maxillary sinus are studied using a convolutional neural network (CNN) with two convolutional and three fully connected layers in architecture. In addition, outputs of CNN are devised to evaluate possibilities of zero level set location close to lesion or not. Finally, the method estimates stable points on the contour by an interactive process. If it locates in the lesion, the point needs to be paid a certain speed compensation based on the value of possibility via CNN, assisting itself to escape from the local minima. If not, the point preserves current status till convergence. Capabilities of our method have been demonstrated on a dataset of 200 CT images with possible lesions. To illustrate the strength of our method, we evaluated it against state-of-the-art methods, FLS and CRF-FCN. For all cases, our method, as assessed by Dice similarity coefficients, performed significantly better compared with currently available methods and obtained a significant Dice improvement, 0.25 than FLS and 0.12 than CRF-FCN, respectively, on an average.


Assuntos
Seio Maxilar , Redes Neurais de Computação , Algoritmos , Processamento de Imagem Assistida por Computador , Seio Maxilar/diagnóstico por imagem
9.
Comput Intell Neurosci ; 2021: 9996232, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34804153

RESUMO

The most advanced method for crowd counting uses a fully convolutional network that extracts image features and then generates a crowd density map. However, this process often encounters multiscale and contextual loss problems. To address these problems, we propose a multiscale aggregation network (MANet) that includes a feature extraction encoder (FEE) and a density map decoder (DMD). The FEE uses a cascaded scale pyramid network to extract multiscale features and obtains contextual features through dense connections. The DMD uses deconvolution and fusion operations to generate features containing detailed information. These features can be further converted into high-quality density maps to accurately calculate the number of people in a crowd. An empirical comparison using four mainstream datasets (ShanghaiTech, WorldExpo'10, UCF_CC_50, and SmartCity) shows that the proposed method is more effective in terms of the mean absolute error and mean squared error. The source code is available at https://github.com/lpfworld/MANet.


Assuntos
Processamento de Imagem Assistida por Computador , Redes Neurais de Computação , Humanos , Software
10.
J Biomed Opt ; 26(11)2021 11.
Artigo em Inglês | MEDLINE | ID: mdl-34729970

RESUMO

SIGNIFICANCE: The non-destructive characterization of cardiac tissue composition provides essential information for both planning and evaluating the effectiveness of surgical interventions such as ablative procedures. Although several methods of tissue characterization, such as optical coherence tomography and fiber-optic confocal microscopy, show promise, many barriers exist that reduce effectiveness or prevent adoption, such as time delays in analysis, prohibitive costs, and limited scope of application. Developing a rapid, low-cost non-destructive means of characterizing cardiac tissue could improve planning, implementation, and evaluation of cardiac surgical procedures. AIM: To determine whether a new light-scattering spectroscopy (LSS) system that analyzes spectra via neural networks is capable of predicting the nuclear densities (NDs) of ventricular tissues. APPROACH: We developed an LSS system with a fiber-optics probe and applied it for measurements on cardiac tissues from an ovine model. We quantified the ND in the cardiac tissues using fluorescent labeling, confocal microscopy, and image processing. Spectra acquired from the same cardiac tissues were analyzed with spectral clustering and convolutional neural networks (CNNs) to assess the feasibility of characterizing the ND of tissue via LSS. RESULTS: Spectral clustering revealed distinct groups of spectra correlated to ranges of ND. CNNs classified three groups of spectra with low, medium, or high ND with an accuracy of 95.00 ± 11.77 % (mean and standard deviation). Our analyses revealed the sensitivity of the classification accuracy to wavelength range and subsampling of spectra. CONCLUSIONS: LSS and machine learning are capable of assessing ND in cardiac tissues. We suggest that the approach is useful for the diagnosis of cardiac diseases associated with changes of ND, such as hypertrophy and fibrosis.


Assuntos
Aprendizado de Máquina , Redes Neurais de Computação , Animais , Tecnologia de Fibra Óptica , Processamento de Imagem Assistida por Computador , Ovinos , Análise Espectral
11.
BMC Bioinformatics ; 22(1): 550, 2021 Nov 11.
Artigo em Inglês | MEDLINE | ID: mdl-34763653

RESUMO

BACKGROUND: Ancestry estimation of skulls is under a wide range of applications in forensic science, anthropology, and facial reconstruction. This study aims to avoid defects in traditional skull ancestry estimation methods, such as time-consuming and labor-intensive manual calibration of feature points, and subjective results. RESULTS: This paper uses the skull depth image as input, based on AlexNet, introduces the Wide module and SE-block to improve the network, designs and proposes ANINet, and realizes the ancestry classification. Such a unified model architecture of ANINet overcomes the subjectivity of manually calibrating feature points, of which the accuracy and efficiency are improved. We use depth projection to obtain the local depth image and the global depth image of the skull, take the skull depth image as the object, use global, local, and local + global methods respectively to experiment on the 95 cases of Han skull and 110 cases of Uyghur skull data sets, and perform cross-validation. The experimental results show that the accuracies of the three methods for skull ancestry estimation reached 98.21%, 98.04% and 99.03%, respectively. Compared with the classic networks AlexNet, Vgg-16, GoogLenet, ResNet-50, DenseNet-121, and SqueezeNet, the network proposed in this paper has the advantages of high accuracy and small parameters; compared with state-of-the-art methods, the method in this paper has a higher learning rate and better ability to estimate. CONCLUSIONS: In summary, skull depth images have an excellent performance in estimation, and ANINet is an effective approach for skull ancestry estimation.


Assuntos
Processamento de Imagem Assistida por Computador , Redes Neurais de Computação , Crânio/diagnóstico por imagem
12.
Sensors (Basel) ; 21(21)2021 Oct 20.
Artigo em Inglês | MEDLINE | ID: mdl-34770267

RESUMO

For high-resolution side scan sonar images, accurate and fast segmentation of sonar images is crucial for underwater target detection and recognition. However, due to the characteristics of low signal-to-noise ratio (SNR) and complex environmental noise of sonar, the existing methods with high accuracy and good robustness are mostly iterative methods with high complexity and poor real-time performance. For this purpose, a region growing based segmentation using the likelihood ratio testing method (RGLT) is proposed. This method obtains the seed points in the highlight and the shadow regions by likelihood ratio testing based on the statistical probability distribution and then grows them according to the similarity criterion. The growth avoids the processing of the seabed reverberation regions, which account for the largest proportion of sonar images, thus greatly reducing segmentation time and improving segmentation accuracy. In addition, a pre-processing filtering method called standard deviation filtering (STDF) is proposed to improve the SNR and remove the speckle noise. Experiments were conducted on three sonar databases, which showed that RGLT has significantly improved quantitative metrics such as accuracy, speed, and segmentation visual effects. The average accuracy and running times of the proposed segmentation method for 100 × 400 images are separately 95.90% and 0.44 s.


Assuntos
Algoritmos , Processamento de Imagem Assistida por Computador , Funções Verossimilhança , Razão Sinal-Ruído , Som
13.
Sensors (Basel) ; 21(21)2021 Oct 21.
Artigo em Inglês | MEDLINE | ID: mdl-34770302

RESUMO

Sky and ground are two essential semantic components in computer vision, robotics, and remote sensing. The sky and ground segmentation has become increasingly popular. This research proposes a sky and ground segmentation framework for the rover navigation visions by adopting weak supervision and transfer learning technologies. A new sky and ground segmentation neural network (network in U-shaped network (NI-U-Net)) and a conservative annotation method have been proposed. The pre-trained process achieves the best results on a popular open benchmark (the Skyfinder dataset) by evaluating seven metrics compared to the state-of-the-art. These seven metrics achieve 99.232%, 99.211%, 99.221%, 99.104%, 0.0077, 0.0427, and 98.223% on accuracy, precision, recall, dice score (F1), misclassification rate (MCR), root mean squared error (RMSE), and intersection over union (IoU), respectively. The conservative annotation method achieves superior performance with limited manual intervention. The NI-U-Net can operate with 40 frames per second (FPS) to maintain the real-time property. The proposed framework successfully fills the gap between the laboratory results (with rich idea data) and the practical application (in the wild). The achievement can provide essential semantic information (sky and ground) for the rover navigation vision.


Assuntos
Processamento de Imagem Assistida por Computador , Robótica , Benchmarking , Redes Neurais de Computação , Semântica
14.
Sensors (Basel) ; 21(21)2021 Oct 23.
Artigo em Inglês | MEDLINE | ID: mdl-34770337

RESUMO

It is necessary to locate microplastic particles mixed with beach sand to be able to separate them. This paper illustrates a kernel weight histogram-based analytical process to determine an appropriate neural network to perform tiny object segmentation on photos of sand with a few microplastic particles. U-net and MultiResUNet are explored as target networks. However, based on our observation of kernel weight histograms, visualized using TensorBoard, the initial encoder stages of U-net and MultiResUNet are useful for capturing small features, whereas the later encoder stages are not useful for capturing small features. Therefore, we derived reduced versions of U-net and MultiResUNet, such as Half U-net, Half MultiResUNet, and Quarter MultiResUNet. From the experiment, we observed that Half MultiResUNet displayed the best average recall-weighted F1 score (40%) and recall-weighted mIoU (26%) and Quarter MultiResUNet the second best average recall-weighted F1 score and recall-weighted mIoU for our microplastic dataset. They also require 1/5 or less floating point operations and 1/50 or a smaller number of parameters over U-net and MultiResUNet.


Assuntos
Processamento de Imagem Assistida por Computador , Microplásticos , Redes Neurais de Computação , Plásticos
15.
Sensors (Basel) ; 21(21)2021 Oct 24.
Artigo em Inglês | MEDLINE | ID: mdl-34770350

RESUMO

In the field of underwater vision, image matching between the main two sensors (sonar and optical camera) has always been a challenging problem. The independent imaging mechanism of the two determines the modalities of the image, and the local features of the images under various modalities are significantly different, which makes the general matching method based on the optical image invalid. In order to make full use of underwater acoustic and optical images, and promote the development of multisensor information fusion (MSIF) technology, this letter proposes to apply an image attribute transfer algorithm and advanced local feature descriptor to solve the problem of underwater acousto-optic image matching. We utilize real and simulated underwater images for testing; experimental results show that our proposed method could effectively preprocess these multimodal images to obtain an accurate matching result, thus providing a new solution for the underwater multisensor image matching task.


Assuntos
Algoritmos , Processamento de Imagem Assistida por Computador , Acústica , Som , Visão Ocular
16.
Sensors (Basel) ; 21(21)2021 Oct 25.
Artigo em Inglês | MEDLINE | ID: mdl-34770364

RESUMO

In this paper, we use Frame Theory to develop a generalized OCT image reconstruction method using redundant and non-uniformly spaced frequency domain samples that includes using non-redundant and uniformly spaced samples as special cases. We also correct an important theoretical error in the previously reported results related to OCT image reconstruction using the Non-uniform Discrete Fourier Transform (NDFT). Moreover, we describe an efficient method to compute our corrected reconstruction transform, i.e., a scaled NDFT, using the Fast Fourier Transform (FFT). Finally, we demonstrate different advantages of our generalized OCT image reconstruction method by achieving (1) theoretically corrected OCT image reconstruction directly from non-uniformly spaced frequency domain samples; (2) a novel OCT image reconstruction method with a higher signal-to-noise ratio (SNR) using redundant frequency domain samples. Our new image reconstruction method is an improvement of OCT technology, so it could benefit all OCT applications.


Assuntos
Algoritmos , Tomografia de Coerência Óptica , Análise de Fourier , Processamento de Imagem Assistida por Computador , Razão Sinal-Ruído
17.
Sensors (Basel) ; 21(21)2021 Oct 25.
Artigo em Inglês | MEDLINE | ID: mdl-34770380

RESUMO

This research is aimed to detect defects on the surface of the fabric and deep learning model optimization. Since defect detection cannot effectively solve the fabric with complex background by image processing, this research uses deep learning to identify defects. However, the current network architecture mainly focuses on natural images rather than the defect detection. As a result, the network architecture used for defect detection has more redundant neurons, which reduces the inference speed. In order to solve the above problems, we propose network pruning with the Bayesian optimization algorithm to automatically tune the network pruning parameters, and then retrain the network after pruning. The training and detection process uses the above-mentioned pruning network to predict the defect feature map, and then uses the image processing flow proposed in this research for the final judgment during fabric defect detection. The proposed method is verified in the two self-made datasets and the two public datasets. In the part of the proposed network optimization results, the Intersection over Union (IoU) of four datasets are dropped by 1.26%, 1.13%, 1.21%, and 2.15% compared to the original network model, but the inference time is reduced to 20.84%, 40.52%, 23.02%, and 23.33% of the original network model using Geforce 2080 Ti. Furthermore, the inference time is also reduced to 17.56%, 37.03%, 19.67%, and 22.26% using the embedded system AGX Xavier. After the image processing part, the accuracy of the four datasets can reach 92.75%, 94.87%, 95.6%, and 81.82%, respectively. In this research, Yolov4 is also trained with fabric defects, and the results showed this model are not conducive to detecting long and narrow fabric defects.


Assuntos
Processamento de Imagem Assistida por Computador , Redes Neurais de Computação , Algoritmos , Teorema de Bayes , Neurônios
18.
Sensors (Basel) ; 21(21)2021 Oct 25.
Artigo em Inglês | MEDLINE | ID: mdl-34770383

RESUMO

To improve the recognition rate of chip appearance defects, an algorithm based on a convolution neural network is proposed to identify chip appearance defects of various shapes and features. Furthermore, to address the problems of long training time and low accuracy caused by redundant input samples, an automatic data sample cleaning algorithm based on prior knowledge is proposed to reduce training and classification time, as well as improve the recognition rate. First, defect positions are determined by performing image processing and region-of-interest extraction. Subsequently, interference samples between chip defects are analyzed for data cleaning. Finally, a chip appearance defect classification model based on a convolutional neural network is constructed. The experimental results show that the recognition miss detection rate of this algorithm is zero, and the accuracy rate exceeds 99.5%, thereby fulfilling industry requirements.


Assuntos
Algoritmos , Redes Neurais de Computação , Processamento de Imagem Assistida por Computador , Reconhecimento Psicológico , Projetos de Pesquisa
19.
Sensors (Basel) ; 21(21)2021 Oct 26.
Artigo em Inglês | MEDLINE | ID: mdl-34770384

RESUMO

A major cause of bone mass loss worldwide is osteoporosis. X-ray is considered to be the gold-standard technique to diagnose this disease. However, there is currently a need for an alternative modality due to the ionizing radiations used in X-rays. In this vein, we conducted a numerical study herein to investigate the feasibility of using microwave tomography (MWT) to detect bone density variations that are correlated to variations in the complex relative permittivity within the reconstructed images. This study was performed using an in-house finite-element method contrast source inversion algorithm (FEM-CSI). Three anatomically-realistic human leg models based on magnetic resonance imaging reconstructions were created. Each model represents a leg with a distinct fat layer thickness; thus, the three models are for legs with thin, medium, and thick fat layers. In addition to using conventional matching media in the numerical study, the use of commercially available and cheap ultrasound gel was evaluated prior to bone image analysis. The inversion algorithm successfully localized bones in the thin and medium fat scenarios. In addition, bone volume variations were found to be inversely proportional to their relative permittivity in the reconstructed images with the root mean square error as low as 2.54. The observations found in this study suggest MWT as a promising bone imaging modality owing to its safe and non-ionizing radiations used in imaging objects with high quality.


Assuntos
Perna (Membro) , Imageamento de Micro-Ondas , Densidade Óssea , Estudos de Viabilidade , Humanos , Processamento de Imagem Assistida por Computador
20.
Sensors (Basel) ; 21(21)2021 Oct 26.
Artigo em Inglês | MEDLINE | ID: mdl-34770394

RESUMO

Image quality, resolution and scanning time are critical in digital pathology. In order to create a high-resolution digital image, the scanner systems execute stitching algorithms to the digitized images. Due to the heterogeneity of the tissue sample, complex optical path, non-acceptable sample quality or rapid stage movement, the intensities on pictures can be uneven. The evincible and visible intensity distortions can have negative effect on diagnosis and quantitative analysis. Utilizing the common areas of the neighboring field-of-views, we can estimate compensations to eliminate the inhomogeneities. We implemented and validated five different approaches for compensating output images created with an area scanner system. The proposed methods are based on traditional methods such as adaptive histogram matching, regression-based corrections and state-of-the art methods like the background and shading correction (BaSiC) method. The proposed compensation methods are suitable for both brightfield and fluorescent images, and robust enough against dust, bubbles, and optical aberrations. The proposed methods are able to correct not only the fixed-pattern artefacts but the stochastic uneven illumination along the neighboring or above field-of-views utilizing iterative approaches and multi-focal compensations.


Assuntos
Processamento de Imagem Assistida por Computador , Iluminação , Algoritmos , Artefatos , Cintilografia
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA