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1.
Diagn Pathol ; 17(1): 45, 2022 May 06.
Artículo en Inglés | MEDLINE | ID: mdl-35524221

RESUMEN

BACKGROUND: In breast cancer (BC) Ki-67 cut-off levels, counting methods and inter- and intraobserver variation are still unresolved. To reduce inter-laboratory differences, it has been proposed that cut-off levels for Ki-67 should be determined based on the in-house median of 500 counted tumour cell nuclei. Digital image analysis (DIA) has been proposed as a means to standardize assessment of Ki-67 staining in tumour tissue. In this study we compared digital and visual assessment (VA) of Ki-67 protein expression levels in full-face sections from a consecutive series of BCs. The aim was to identify the number of tumour cells necessary to count in order to reflect the growth potential of a given tumour in both methods, as measured by tumour grade, mitotic count and patient outcome. METHODS: A series of whole sections from 248 invasive carcinomas of no special type were immunohistochemically stained for Ki-67 and then assessed by VA and DIA. Five 100-cell increments were counted in hot spot areas using both VA and DIA. The median numbers of Ki-67 positive tumour cells were used to calculate cut-off levels for Low, Intermediate and High Ki-67 protein expression in both methods. RESULTS: We found that the percentage of Ki-67 positive tumour cells was higher in DIA compared to VA (medians after 500 tumour cells counted were 22.3% for VA and 30% for DIA). While the median Ki-67% values remained largely unchanged across the 100-cell increments for VA, median values were highest in the first 1-200 cells counted using DIA. We also found that the DIA100 High group identified the largest proportion of histopathological grade 3 tumours 70/101 (69.3%). CONCLUSIONS: We show that assessment of Ki-67 in breast tumours using DIA identifies a greater proportion of cases with high Ki-67 levels compared to VA of the same tumours. Furthermore, we show that diagnostic cut-off levels should be calibrated appropriately on the introduction of new methodology.


Asunto(s)
Neoplasias de la Mama , Neoplasias de la Mama/patología , Proliferación Celular , Femenino , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Inmunohistoquímica , Antígeno Ki-67/análisis , Pronóstico
2.
PLoS One ; 17(5): e0267056, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35511914

RESUMEN

Image processing algorithms are used to improve digital image representations in either their appearance or storage efficiency. The merit of these algorithms depends, in part, on visual perception by human observers. However, in practice, most are assessed numerically, and the perceptual metrics that do exist are criterion sensitive with several shortcomings. Here we propose an objective performance-based perceptual measure of image quality and demonstrate this by comparing the efficacy of a denoising algorithm for a variety of filters. For baseline, we measured detection thresholds for a white noise signal added to one of a pair of natural images in a two-alternative forced-choice (2AFC) paradigm where each image was selected randomly from a set of n = 308 on each trial. In a series of experimental conditions, the stimulus image pairs were passed through various configurations of a denoising algorithm. The differences in noise detection thresholds with and without denoising are objective perceptual measures of the ability of the algorithm to render noise invisible. This was a factor of two (6dB) in our experiment and consistent across a range of filter bandwidths and types. We also found that thresholds in all conditions converged on a common value of PSNR, offering support for this metric. We discuss how the 2AFC approach might be used for other algorithms including compression, deblurring and edge-detection. Finally, we provide a derivation for our Cartesian-separable log-Gabor filters, with polar parameters. For the biological vision community this has some advantages over the more typical (i) polar-separable variety and (ii) Cartesian-separable variety with Cartesian parameters.


Asunto(s)
Compresión de Datos , Procesamiento de Imagen Asistido por Computador , Algoritmos , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Ruido , Relación Señal-Ruido
3.
PLoS One ; 17(5): e0267452, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35536793

RESUMEN

Development of automated analysis tools for "single ion channel" recording is hampered by the lack of available training data. For machine learning based tools, very large training sets are necessary with sample-by-sample point labelled data (e.g., 1 sample point every 100microsecond). In an experimental context, such data are labelled with human supervision, and whilst this is feasible for simple experimental analysis, it is infeasible to generate the enormous datasets that would be necessary for a big data approach using hand crafting. In this work we aimed to develop methods to generate simulated ion channel data that is free from assumptions and prior knowledge of noise and underlying hidden Markov models. We successfully leverage generative adversarial networks (GANs) to build an end-to-end pipeline for generating an unlimited amount of labelled training data from a small, annotated ion channel "seed" record, and this needs no prior knowledge of theoretical dynamical ion channel properties. Our method utilises 2D CNNs to maintain the synchronised temporal relationship between the raw and idealised record. We demonstrate the applicability of the method with 5 different data sources and show authenticity with t-SNE and UMAP projection comparisons between real and synthetic data. The model would be easily extendable to other time series data requiring parallel labelling, such as labelled ECG signals or raw nanopore sequencing data.


Asunto(s)
Procesamiento de Imagen Asistido por Computador , Redes Neurales de la Computación , Algoritmos , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Almacenamiento y Recuperación de la Información , Aprendizaje Automático
4.
Sci Data ; 9(1): 216, 2022 05 17.
Artículo en Inglés | MEDLINE | ID: mdl-35581201

RESUMEN

Baker's yeast (Saccharomyces cerevisiae) is a model organism for studying the morphology that emerges at the scale of multi-cell colonies. To look at how morphology develops, we collect a dataset of time-lapse photographs of the growth of different strains of S. cerevisiae. We discuss the general statistical challenges that arise when using time-lapse photographs to extract time-dependent features. In particular, we show how texture-based feature engineering and representative clustering can be successfully applied to categorize the development of yeast colony morphology using our dataset. The Local binary pattern (LBP) from image processing is used to score the surface texture of colonies. This texture score develops along a smooth trajectory during growth. The path taken depends on how the morphology emerges. A hierarchical clustering of the colonies is performed according to their texture development trajectories. The clustering method is designed for practical interpretability; it obtains the best representative colony image for any hierarchical cluster.


Asunto(s)
Saccharomyces cerevisiae , Procesamiento de Imagen Asistido por Computador , Imagen de Lapso de Tiempo
5.
Biomed Res Int ; 2022: 5426643, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35586813

RESUMEN

Medical imaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) imaging are indispensable for contemporary neurorehabilitation diagnostics, intervention, and monitoring. It would be desirable to reconstruct images from sparse measurements to reduce the ionizing radiation and motion artifacts. Although recent coordinate-based representation methods have shown promise advances for sparse-view reconstruction, they overfit a single MLP on a single patient. In this work, we generalize it across many patients by incorporating an interpatient prior into the ill-posed inverse/reconstruction problem, which is the missing ingredient in the previous works. The experiment demonstrates that our method significantly improves image quality over the state-of-the-art both qualitatively and quantitatively. Thus, our method provides a powerful and principled means to deal with the measurement-scarce problem.


Asunto(s)
Procesamiento de Imagen Asistido por Computador , Rehabilitación Neurológica , Artefactos , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Fantasmas de Imagen
6.
Sci Rep ; 12(1): 8508, 2022 May 20.
Artículo en Inglés | MEDLINE | ID: mdl-35595784

RESUMEN

Detection, diagnosis, and treatment of ophthalmic diseases depend on extraction of information (features and/or their dimensions) from the images. Deep learning (DL) model are crucial for the automation of it. Here, we report on the development of a lightweight DL model, which can precisely segment/detect the required features automatically. The model utilizes dimensionality reduction of image to extract important features, and channel contraction to allow only the required high-level features necessary for reconstruction of segmented feature image. Performance of present model in detection of glaucoma from optical coherence tomography angiography (OCTA) images of retina is high (area under the receiver-operator characteristic curve AUC ~ 0.81). Bland-Altman analysis gave exceptionally low bias (~ 0.00185), and high Pearson's correlation coefficient (p = 0.9969) between the parameters determined from manual and DL based segmentation. On the same dataset, bias is an order of magnitude higher (~ 0.0694, p = 0.8534) for commercial software. Present model is 10 times lighter than Unet (popular for biomedical image segmentation) and have a better segmentation accuracy and model training reproducibility (based on the analysis of 3670 OCTA images). High dice similarity coefficient (D) for variety of ophthalmic images suggested it's wider scope in precise segmentation of images even from other fields. Our concept of channel narrowing is not only important for the segmentation problems, but it can also reduce number of parameters significantly in object classification models. Enhanced disease diagnostic accuracy can be achieved for the resource limited devices (such as mobile phone, Nvidia's Jetson, Raspberry pi) used in self-monitoring, and tele-screening (memory size of trained model ~ 35 MB).


Asunto(s)
Aprendizaje Profundo , Angiografía , Procesamiento de Imagen Asistido por Computador/métodos , Reproducibilidad de los Resultados , Retina , Tomografía de Coherencia Óptica
7.
Sci Rep ; 12(1): 8578, 2022 May 20.
Artículo en Inglés | MEDLINE | ID: mdl-35595829

RESUMEN

Magnetic Resonance Imaging (MRI) has been widely used to acquire structural and functional information about the brain. In a group- or voxel-wise analysis, it is essential to correct the bias field of the radiofrequency coil and to extract the brain for accurate registration to the brain template. Although automatic methods have been developed, manual editing is still required, particularly for echo-planar imaging (EPI) due to its lower spatial resolution and larger geometric distortion. The needs of user interventions slow down data processing and lead to variable results between operators. Deep learning networks have been successfully used for automatic postprocessing. However, most networks are only designed for a specific processing and/or single image contrast (e.g., spin-echo or gradient-echo). This limitation markedly restricts the application and generalization of deep learning tools. To address these limitations, we developed a deep learning network based on the generative adversarial net (GAN) to automatically correct coil inhomogeneity and extract the brain from both spin- and gradient-echo EPI without user intervention. Using various quantitative indices, we show that this method achieved high similarity to the reference target and performed consistently across datasets acquired from rodents. These results highlight the potential of deep networks to integrate different postprocessing methods and adapt to different image contrasts. The use of the same network to process multimodality data would be a critical step toward a fully automatic postprocessing pipeline that could facilitate the analysis of large datasets with high consistency.


Asunto(s)
Aprendizaje Profundo , Encéfalo/diagnóstico por imagen , Imagen Eco-Planar/métodos , Procesamiento de Imagen Asistido por Computador/métodos , Imagen por Resonancia Magnética
8.
BMC Med Imaging ; 22(1): 94, 2022 May 20.
Artículo en Inglés | MEDLINE | ID: mdl-35596153

RESUMEN

BACKGROUND: Computer-aided methods for analyzing white blood cells (WBC) are popular due to the complexity of the manual alternatives. Recent works have shown highly accurate segmentation and detection of white blood cells from microscopic blood images. However, the classification of the observed cells is still a challenge, in part due to the distribution of the five types that affect the condition of the immune system. METHODS: (i) This work proposes W-Net, a CNN-based method for WBC classification. We evaluate W-Net on a real-world large-scale dataset that includes 6562 real images of the five WBC types. (ii) For further benefits, we generate synthetic WBC images using Generative Adversarial Network to be used for education and research purposes through sharing. RESULTS: (i) W-Net achieves an average accuracy of 97%. In comparison to state-of-the-art methods in the field of WBC classification, we show that W-Net outperforms other CNN- and RNN-based model architectures. Moreover, we show the benefits of using pre-trained W-Net in a transfer learning context when fine-tuned to specific task or accommodating another dataset. (ii) The synthetic WBC images are confirmed by experiments and a domain expert to have a high degree of similarity to the original images. The pre-trained W-Net and the generated WBC dataset are available for the community to facilitate reproducibility and follow up research work. CONCLUSION: This work proposed W-Net, a CNN-based architecture with a small number of layers, to accurately classify the five WBC types. We evaluated W-Net on a real-world large-scale dataset and addressed several challenges such as the transfer learning property and the class imbalance. W-Net achieved an average classification accuracy of 97%. We synthesized a dataset of new WBC image samples using DCGAN, which we released to the public for education and research purposes.


Asunto(s)
Procesamiento de Imagen Asistido por Computador , Redes Neurales de la Computación , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Recuento de Leucocitos , Leucocitos , Reproducibilidad de los Resultados
9.
Adv Exp Med Biol ; 1364: 197-225, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35508877

RESUMEN

In this chapter, we first review the reasons why conventional ultrasonography fails to image the interior of bones. Next we show our recent work on imaging a cortical bone layer with ultrasound. Revealing the shape of the cortex of a bone, in particular its thickness, is of interest for evaluating bone strength. In addition we describe how the process of reconstructing a truthful image of the bone cortex includes the estimation of ultrasound wave-speed in cortical bone tissue. Cortical bone exhibits elastic anisotropy, which causes anisotropy of ultrasound wave-speed as well. Therefore a faithful and high-quality picture of the bone cortex is obtained if wave-speed anisotropy is taken into account during image reconstruction. Capitalizing on prior knowledge on the elastic anisotropy of cortical bone, a procedure for estimating wave-speed and its anisotropy is described. It is based on the measurement of a head-wave velocity and an autofocus approach. The latter relies on the fact that the reconstructed ultrasound image shows optimal quality if the wave-speed model is correct. In order to achieve real-time imaging of a bone cortex, image reconstruction is performed with a delay-and-sum algorithm. Finally, we report recent advances in the measurement of blood flow in cortical bone.


Asunto(s)
Huesos , Hueso Cortical , Anisotropía , Huesos/diagnóstico por imagen , Hueso Cortical/diagnóstico por imagen , Procesamiento de Imagen Asistido por Computador/métodos , Ultrasonografía
10.
PLoS One ; 17(5): e0267976, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35500005

RESUMEN

Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Therefore, artificial intelligence has become a popular tool for the automatic processing of medical data, acting as a supportive tool for doctors. However, the machine learning models used to build these tools are highly dependent on the data used to train them. Large amounts of data can be difficult to obtain in medicine due to privacy reasons, expensive and time-consuming annotations, and a general lack of data samples for infrequent lesions. In this study, we present a novel synthetic data generation pipeline, called SinGAN-Seg, to produce synthetic medical images with corresponding masks using a single training image. Our method is different from the traditional generative adversarial networks (GANs) because our model needs only a single image and the corresponding ground truth to train. We also show that the synthetic data generation pipeline can be used to produce alternative artificial segmentation datasets with corresponding ground truth masks when real datasets are not allowed to share. The pipeline is evaluated using qualitative and quantitative comparisons between real data and synthetic data to show that the style transfer technique used in our pipeline significantly improves the quality of the generated data and our method is better than other state-of-the-art GANs to prepare synthetic images when the size of training datasets are limited. By training UNet++ using both real data and the synthetic data generated from the SinGAN-Seg pipeline, we show that the models trained on synthetic data have very close performances to those trained on real data when both datasets have a considerable amount of training data. In contrast, we show that synthetic data generated from the SinGAN-Seg pipeline improves the performance of segmentation models when training datasets do not have a considerable amount of data. All experiments were performed using an open dataset and the code is publicly available on GitHub.


Asunto(s)
Aprendizaje Profundo , Procesamiento de Imagen Asistido por Computador , Algoritmos , Inteligencia Artificial , Procesamiento de Imagen Asistido por Computador/métodos , Redes Neurales de la Computación
11.
J Forensic Odontostomatol ; 1(40): 65-73, 2022 Apr 30.
Artículo en Inglés | MEDLINE | ID: mdl-35499538

RESUMEN

Human identification using Forensic Dentistry occurs through comparative analysis of ante-mortem (AM) and post-mortem (PM) data. With the constant improvement of technology, photographs became a common source of AM data. When clinical dental records are not available, images showing the smile can be useful in human identification. The aim of this study was to investigate human identification techniques through the analysis of smile images in the available literature. Studies on human identification through the analysis of smile images were searched in the scientific literature. The search resulted in 4,043 studies. After screening, 14 studies were considered eligible. Eleven were case reports, two were pilot studies and one a technical note. From the eligible studies, in addition to the methodological data, information about the sample, used techniques and results regarding human identification were extracted. Three techniques were detected: direct comparison of morphological characteristics, AM/PM image overlap, and the analysis of smile lines. One or more associated techniques were used for human identification. Authors highlighted as a common limitation of the techniques the quality of the available images, the difficulty in reproducing PM the same images AM, and the eventual image modifications performed by the victim before posting in social media. Advantages included the low-cost aspect of the technique, as well as a potential fast and accurate procedure (depending on the quantity and quality of evidence). In general, studies considered the technique useful and adjuvant for human identification.


Asunto(s)
Odontología Forense , Sonrisa , Antropología Forense , Odontología Forense/métodos , Humanos , Procesamiento de Imagen Asistido por Computador
12.
Acta Crystallogr A Found Adv ; 78(Pt 3): 172-199, 2022 May 01.
Artículo en Inglés | MEDLINE | ID: mdl-35502711

RESUMEN

Statistically sound crystallographic symmetry classifications are obtained with information-theory-based methods in the presence of approximately Gaussian distributed noise. A set of three synthetic patterns with strong Fedorov-type pseudosymmetries and varying amounts of noise serve as examples. Contrary to traditional crystallographic symmetry classifications with an image processing program such as CRISP, the classification process does not need to be supervised by a human being and is free of any subjectively set thresholds in the geometric model selection process. This enables crystallographic symmetry classification of digital images that are more or less periodic in two dimensions (2D), also known as crystal patterns, as recorded with sufficient structural resolution from a wide range of crystalline samples with different types of scanning probe and transmission electron microscopes. Correct symmetry classifications enable the optimal crystallographic processing of such images. That processing consists of the averaging over all asymmetric units in all unit cells in the selected image area and significantly enhances both the signal-to-noise ratio and the structural resolution of a microscopic study of a crystal. For sufficiently complex crystal patterns, the information-theoretic symmetry classification methods are more accurate than both visual classifications by human experts and the recommendations of one of the popular crystallographic image processing programs of electron crystallography.


Asunto(s)
Aumento de la Imagen , Procesamiento de Imagen Asistido por Computador , Cristalografía por Rayos X , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Relación Señal-Ruido
13.
J Healthc Eng ; 2022: 3545831, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35529540

RESUMEN

At present, the incidence of emergencies in obstetric care environment is gradually increasing, and different obstetric wards often have a variety of situations. Therefore, it can provide great help in clinical medicine to give early warning and plan coping plans according to different situations. This paper studied an obstetrics central surveillance system based on a medical image segmentation algorithm. Images obtained by central obstetrics monitoring are segmented, magnified in detail, and image features are extracted, collated, and trained. The normal distribution rule is used to classify the features, which are included in the feature library of the obstetric central monitoring system. In the gray space of the medical image, the statistical distribution of gray features of the medical image is described by the mixture model of Rayleigh distribution and Gaussian distribution. In the gray space of the medical image, Taylor series expansion is used to describe the linear geometric structure of medicine. The eigenvalues of Hessian matrix are introduced to obtain high-order multiscale features of medicine. The multiscale feature energy function is introduced into Markov random energy objective function to realize medical image segmentation. Compared with other segmentation algorithms, the accuracy and sensitivity of the proposed algorithm are 87.98% and 86.58%, respectively, which can clearly segment small medical features.


Asunto(s)
Algoritmos , Procesamiento de Imagen Asistido por Computador , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Distribución Normal
14.
Sci Rep ; 12(1): 7523, 2022 May 07.
Artículo en Inglés | MEDLINE | ID: mdl-35525857

RESUMEN

An accurate three-dimensional (3D) segmentation of the maxillary sinus is crucial for multiple diagnostic and treatment applications. Yet, it is challenging and time-consuming when manually performed on a cone-beam computed tomography (CBCT) dataset. Recently, convolutional neural networks (CNNs) have proven to provide excellent performance in the field of 3D image analysis. Hence, this study developed and validated a novel automated CNN-based methodology for the segmentation of maxillary sinus using CBCT images. A dataset of 264 sinuses were acquired from 2 CBCT devices and randomly divided into 3 subsets: training, validation, and testing. A 3D U-Net architecture CNN model was developed and compared to semi-automatic segmentation in terms of time, accuracy, and consistency. The average time was significantly reduced (p-value < 2.2e-16) by automatic segmentation (0.4 min) compared to semi-automatic segmentation (60.8 min). The model accurately identified the segmented region with a dice similarity co-efficient (DSC) of 98.4%. The inter-observer reliability for minor refinement of automatic segmentation showed an excellent DSC of 99.6%. The proposed CNN model provided a time-efficient, precise, and consistent automatic segmentation which could allow an accurate generation of 3D models for diagnosis and virtual treatment planning.


Asunto(s)
Seno Maxilar , Redes Neurales de la Computación , Tomografía Computarizada de Haz Cónico/métodos , Procesamiento de Imagen Asistido por Computador , Seno Maxilar/diagnóstico por imagen , Reproducibilidad de los Resultados
15.
Comput Intell Neurosci ; 2022: 2819269, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35528331

RESUMEN

In order to improve the artistic expression effect of photographic images, this article combines the deep learning model to conduct multicamera photographic image art research in BERT motion. Moreover, this article analyzes the external parameter errors caused in the calibration process and uses the checkerboard in the common field of view to calibrate the spatial coordinates of the corners of the board in multiple camera coordinate systems. In addition, this article aims to match the spatial coordinates of the corresponding points to each other and solve the rotation and translation matrix in the transformation process. Finally, this article uses the LM algorithm to optimize the calibration parameters of the camera and combines the deep learning algorithm to perform image processing. The experimental research results show that the research method of multicamera photography image art in BERT motion based on the deep learning mode proposed in this article can effectively improve the expression effect of image art.


Asunto(s)
Aprendizaje Profundo , Algoritmos , Procesamiento de Imagen Asistido por Computador/métodos , Movimiento (Física) , Fotograbar
16.
Comput Intell Neurosci ; 2022: 5140148, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35528341

RESUMEN

White blood cells (WBCs) are blood cells that fight infections and diseases as a part of the immune system. They are also known as "defender cells." But the imbalance in the number of WBCs in the blood can be hazardous. Leukemia is the most common blood cancer caused by an overabundance of WBCs in the immune system. Acute lymphocytic leukemia (ALL) usually occurs when the bone marrow creates many immature WBCs that destroy healthy cells. People of all ages, including children and adolescents, can be affected by ALL. The rapid proliferation of atypical lymphocyte cells can cause a reduction in new blood cells and increase the chances of death in patients. Therefore, early and precise cancer detection can help with better therapy and a higher survival probability in the case of leukemia. However, diagnosing ALL is time-consuming and complicated, and manual analysis is expensive, with subjective and error-prone outcomes. Thus, detecting normal and malignant cells reliably and accurately is crucial. For this reason, automatic detection using computer-aided diagnostic models can help doctors effectively detect early leukemia. The entire approach may be automated using image processing techniques, reducing physicians' workload and increasing diagnosis accuracy. The impact of deep learning (DL) on medical research has recently proven quite beneficial, offering new avenues and possibilities in the healthcare domain for diagnostic techniques. However, to make that happen soon in DL, the entire community must overcome the explainability limit. Because of the black box operation's shortcomings in artificial intelligence (AI) models' decisions, there is a lack of liability and trust in the outcomes. But explainable artificial intelligence (XAI) can solve this problem by interpreting the predictions of AI systems. This study emphasizes leukemia, specifically ALL. The proposed strategy recognizes acute lymphoblastic leukemia as an automated procedure that applies different transfer learning models to classify ALL. Hence, using local interpretable model-agnostic explanations (LIME) to assure validity and reliability, this method also explains the cause of a specific classification. The proposed method achieved 98.38% accuracy with the InceptionV3 model. Experimental results were found between different transfer learning methods, including ResNet101V2, VGG19, and InceptionResNetV2, later verified with the LIME algorithm for XAI, where the proposed method performed the best. The obtained results and their reliability demonstrate that it can be preferred in identifying ALL, which will assist medical examiners.


Asunto(s)
Inteligencia Artificial , Leucemia , Adolescente , Niño , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Leucemia/diagnóstico , Aprendizaje Automático , Reproducibilidad de los Resultados
17.
Comput Intell Neurosci ; 2022: 3814252, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35528353

RESUMEN

Fuzzy clustering algorithms have received widespread attention in various fields. Point tracking technology has significant application importance in sports image data analysis. In order to solve the problem of limited tracking performance caused by the fuzzy and rough division of moving image edges, this paper proposes a point tracking technology based on a fuzzy clustering algorithm, which is used for the point tracking of moving image sequence signs. This article analyzes the development status of sports image sequence analysis and processing technology and introduces some basic theories about fuzzy clustering algorithms. On the basis of the fuzzy clustering algorithm, the positioning and tracking of the marker points of the moving image sequence are studied. A series of experiments have proved that the fuzzy clustering algorithm can improve the recognition rate of the landmark points of the moving image. For the detection and tracking of moving targets, the fuzzy clustering algorithm can reach the limit faster under the same number of iterations, and the image noise can be reduced to 60% of the original by 5 iterations. This has excellent development value in application.


Asunto(s)
Algoritmos , Lógica Difusa , Análisis por Conglomerados , Procesamiento de Imagen Asistido por Computador , Tecnología
18.
Comput Intell Neurosci ; 2022: 1274260, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35528354

RESUMEN

The effectiveness of CycleGAN is demonstrated to outperform recent approaches for semisupervised semantic segmentation on public segmentation benchmarks. In contrast to analog images, however, the acoustic images are unbalanced and often exhibit speckle noise. As a consequence, CycleGAN is prone to mode-collapse and cannot retain target details when applied directly to the sonar image dataset. To address this problem, a spectral normalized CycleGAN network is presented, which applies spectral normalization to both generators and discriminators to stabilize the training of GANs. Without using a pretrained model, the experimental results demonstrate that our simple yet effective method helps to achieve reasonably accurate sonar targets segmentation results.


Asunto(s)
Procesamiento de Imagen Asistido por Computador , Semántica , Procesamiento de Imagen Asistido por Computador/métodos
19.
Comput Intell Neurosci ; 2022: 1181189, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35528371

RESUMEN

With the continuous development of imaging sensors, images contain more and more information, the images presented by different types of sensors are different, and the images obtained by the same type of sensors under different parameters or conditions are also different. Multisource image fusion technology combines images acquired by different types of sensors or the same type of sensors with different parameter settings, which makes the image information more complete, compensates for the limitations of images of the same type, and also allows you to save information about the characteristics of the original image. Multimodal image mosaic and multifocal image mosaic have been studied in detail in two directions. On the one hand, a method based on frequency domain transformation is used for multiscale image decomposition. On the other hand, image extraction with neural network-based methods is proposed. The technology of convolutional neural networks (CNNs) allows to extract richer texture features. However, when using this method for fusion, it is difficult to obtain an accurate decision map, and there are artifacts in the fusion boundary. Based on this, a multifocal fusion method based on a two-stage CNN is proposed. Train the advanced intensive network to classify input image blocks as focus, and then use the appropriate merge rules to get the ideal decision tree. In addition, several versions of the fuzzy learning set have been developed to improve network performance. Experimental results show that the frames of the first stage proposed by the algorithm make it possible to obtain an accurate decision scheme and that the frames of the second stage make it possible to eliminate the pseudo-shadow of the integration boundary.


Asunto(s)
Procesamiento de Imagen Asistido por Computador , Redes Neurales de la Computación , Algoritmos , Artefactos , Procesamiento de Imagen Asistido por Computador/métodos , Aprendizaje
20.
Comput Math Methods Med ; 2022: 5435207, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35529256

RESUMEN

It is important to study the evaluation algorithm for the stroke rehabilitation treatment effect to make accurate evaluation and optimize the stroke disease treatment plan according to the evaluation results. To address the problems of poor restoration effect of positron emission tomography (PET) image and recognition restoration effect of evaluation data and so on. In the paper, we propose a stroke rehabilitation treatment effect evaluation algorithm based on cross-modal deep learning. Magnetic resonance images (MRI) and PET of stroke patients were collected as evaluation data to construct a multimodal evaluation dataset, and the data were divided into positive samples and negative samples. According to the mapping relationship between MRI and PET, three-dimensional cyclic adversarial is used to generate the neural network model to recover the missing PET data. Using the cross-modal depth learning network model, the RGB image, depth image, gray image, and normal images of MRI and PET are taken as the feature images and the multifeature fusion method is used to fuse the feature images, output the recognition results of MRI and PET, and evaluate the effect of stroke rehabilitation treatment according to the recognition results. The results show that the proposed algorithm can accurately restore PET images, the evaluation data recognition effect is good, and the evaluation data recognition accuracy is higher than 95%. The evaluation accuracy of stroke rehabilitation treatment effect is high, the evaluation time varies between 0.56 s and 0.91 s, and the practical application effect is good.


Asunto(s)
Aprendizaje Profundo , Rehabilitación de Accidente Cerebrovascular , Accidente Cerebrovascular , Algoritmos , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Imagen por Resonancia Magnética/métodos , Tomografía de Emisión de Positrones/métodos , Accidente Cerebrovascular/diagnóstico por imagen
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