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Journal articles on the topic 'Biomedical images'

To see the other types of publications on this topic, follow the link: Biomedical images.

Author: Grafiati

Published: 10 March 2023

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1

Thanh, D. N. H., and S. D. Dvoenko. "A DENOISING OF BIOMEDICAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5/W6 (May 18, 2015): 73–78. http://dx.doi.org/10.5194/isprsarchives-xl-5-w6-73-2015.

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Today imaging science has an important development and has many applications in different fields of life. The researched object of imaging science is digital image that can be created by many digital devices. Biomedical image is one of types of digital images. One of the limits of using digital devices to create digital images is noise. Noise reduces the image quality. It appears in almost types of images, including biomedical images too. The type of noise in this case can be considered as combination of Gaussian and Poisson noises. In this paper we propose method to remove noise by using total variation. Our method is developed with the goal to combine two famous models: ROF for removing Gaussian noise and modified ROF for removing Poisson noise. As a result, our proposed method can be also applied to remove Gaussian or Poisson noise separately. The proposed method can be applied in two cases: with given parameters (generated noise for artificial images) or automatically evaluated parameters (unknown noise for real images).

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2

Santosh, K. C., Naved Alam, Partha Pratim Roy, Laurent Wendling, Sameer Antani, and GeorgeR Thoma. "Arrowhead detection in biomedical images." Electronic Imaging 2016, no. 17 (February 17, 2016): 1–7. http://dx.doi.org/10.2352/issn.2470-1173.2016.17.drr-054.

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3

Kundu, Amlan. "Local segmentation of biomedical images." Computerized Medical Imaging and Graphics 14, no. 3 (May 1990): 173–83. http://dx.doi.org/10.1016/0895-6111(90)90057-i.

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4

Zhang, Yinghui, Fengyuan Zhang, Yantong Cui, and Ruoci Ning. "CLASSIFICATION OF BIOMEDICAL IMAGES USING CONTENT BASED IMAGE RETRIEVAL SYSTEMS." International Journal of Engineering Technologies and Management Research 5, no. 2 (February 8, 2020): 181–89. http://dx.doi.org/10.29121/ijetmr.v5.i2.2018.161.

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Because of the numerous application of Content-based image retrieval (CBIR) system in various areas, it has always remained a topic of keen interest by the researchers. Fetching of the most similar image from the complete repository by comparing it to the input image in the minimum span of time is the main task of the CBIR. The purpose of the CBIR can vary from different types of requirements like a diagnosis of the illness by the physician, crime investigation, product recommendation by the e-commerce companies, etc. In the present work, CBIR is used for finding the similar patients having Breast cancer. Gray-Level Co-Occurrence Matrix along with histogram and correlation coefficient is used for creating CBIR system. Comparing the images of the area of interest of a present patient with the complete series of the image of a past patient can help in early diagnosis of the disease. CBIR is so much effective that even when the symptoms are not shown by the body the disease can be diagnosed from the sample images.

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5

Badaoui, S., V. Chameroy, and F. Aubry. "A database manager of biomedical images." Medical Informatics 18, no. 1 (January 1993): 23–33. http://dx.doi.org/10.3109/14639239309034465.

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6

Gil, Debora, Aura Hernàndez-Sabaté, Mireia Brunat, Steven Jansen, and Jordi Martínez-Vilalta. "Structure-preserving smoothing of biomedical images." Pattern Recognition 44, no. 9 (September 2011): 1842–51. http://dx.doi.org/10.1016/j.patcog.2010.08.003.

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7

Vitulano, S., C. Di Ruberto, and M. Nappi. "Different methods to segment biomedical images." Pattern Recognition Letters 18, no. 11-13 (November 1997): 1125–31. http://dx.doi.org/10.1016/s0167-8655(97)00097-4.

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8

Taratorin, A. M., E. E. Godik, and Yu V. Guljaev. "Functional mapping of dynamic biomedical images." Measurement 8, no. 3 (July 1990): 137–40. http://dx.doi.org/10.1016/0263-2241(90)90055-b.

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9

Sakr, Majd, Mohammad Hammoud, and Manoj Dareddy Reddy. "Image processing on the Cloud: Characterizing edge detection on biomedical images." Qatar Foundation Annual Research Forum Proceedings, no. 2012 (October 2012): CSPS11. http://dx.doi.org/10.5339/qfarf.2012.csps11.

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10

Korenblum, Daniel, Daniel Rubin, Sandy Napel, Cesar Rodriguez, and Chris Beaulieu. "Managing Biomedical Image Metadata for Search and Retrieval of Similar Images." Journal of Digital Imaging 24, no. 4 (September 16, 2010): 739–48. http://dx.doi.org/10.1007/s10278-010-9328-z.

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11

Kaur, Jaspreet, and Rajneet Kaur. "Speckle Noise Reduction in Biomedical Images Using Haar Wavelets with Wiener Filter." International Journal of Scientific Research 2, no. 5 (June 1, 2012): 120–22. http://dx.doi.org/10.15373/22778179/may2013/43.

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12

YOSHIZAWA, Shin, and Hideo YOKOTA. "9D-07 Noise Reduction for Biomedical Images." Proceedings of the Bioengineering Conference Annual Meeting of BED/JSME 2010.23 (2011): 365–66. http://dx.doi.org/10.1299/jsmebio.2010.23.365.

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13

Renukalatha, S., and K. V. Suresh. "A REVIEW ON BIOMEDICAL IMAGE ANALYSIS." Biomedical Engineering: Applications, Basis and Communications 30, no. 04 (August 2018): 1830001. http://dx.doi.org/10.4015/s1016237218300018.

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Bio-medical image analysis is an interdisciplinary field which includes: biology, physics, medicine and engineering. It deals with application of image processing techniques to biological or medical problems. Medical images to be analyzed contain a lot of information regarding the anatomical structure under investigation to reveal valid diagnosis and thereby helping doctors to choose adequate therapy. Doctors usually analyse these medical images manually through visual interpretation. But visual analysis of these images by human observers is limited due to variation in interpersonal interpretations, fatigue errors, surrounding disturbances and moreover this kind of analysis is purely subjective. On the other hand, automated analysis of these images using computers with suitable techniques favours the objective analysis by an expert and thereby improving the diagnostic confidence and accuracy of analysis. This survey is a consolidation of the exhaustive literature records related to biomedical image analysis.

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14

V, Bindhu. "BIOMEDICAL IMAGE ANALYSIS USING SEMANTIC SEGMENTATION." Journal of Innovative Image Processing 1, no. 02 (December 15, 2019): 91–101. http://dx.doi.org/10.36548/jiip.2019.2.004.

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Semantic Segmentation is a very active area of research in the examining the medical images. The failure in the conventional segmentation methods to preserve the full resolution throughout the network led to the research’s that developed methods to protect the resolution of the images. The proposed method involves the semantic segmentation model for the biomedical images by utilizing the encoder/decoder structure to down sample the spatial resolution of the input data and develop a lower resolution feature mapping that are very effective at distinguishing between the classes and then perform the up samples to have a full-resolution segmentation map of the biomedical images reducing the diagnostic time. The frame work put forth utilizes a pixel to pixel fully trained cascaded convolutional neural network for the task of image segmentation. The evaluation biomedical image analysis using the semantic segmentation shows the performance improvement achieved by the minimization of the time required in testing and the augmentation in the analysis performed by the radiologist.

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15

Ahmed, Zeeshan, Saman Zeeshan, and Thomas Dandekar. "Mining biomedical images towards valuable information retrieval in biomedical and life sciences." Database 2016 (2016): baw118. http://dx.doi.org/10.1093/database/baw118.

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Ahmed, Zeeshan, Saman Zeeshan, and Thomas Dandekar. "Mining biomedical images towards valuable information retrieval in biomedical and life sciences." Database 2016 (2016): baw134. http://dx.doi.org/10.1093/database/baw134.

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17

De, Soumya, R. Joe Stanley, Beibei Cheng, Sameer Antani, Rodney Long, and George Thoma. "Automated Text Detection and Recognition in Annotated Biomedical Publication Images." International Journal of Healthcare Information Systems and Informatics 9, no. 2 (April 2014): 34–63. http://dx.doi.org/10.4018/ijhisi.2014040103.

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Images in biomedical publications often convey important information related to an article's content. When referenced properly, these images aid in clinical decision support. Annotations such as text labels and symbols, as provided by medical experts, are used to highlight regions of interest within the images. These annotations, if extracted automatically, could be used in conjunction with either the image caption text or the image citations (mentions) in the articles to improve biomedical information retrieval. In the current study, automatic detection and recognition of text labels in biomedical publication images was investigated. This paper presents both image analysis and feature-based approaches to extract and recognize specific regions of interest (text labels) within images in biomedical publications. Experiments were performed on 6515 characters extracted from text labels present in 200 biomedical publication images. These images are part of the data set from ImageCLEF 2010. Automated character recognition experiments were conducted using geometry-, region-, exemplar-, and profile-based correlation features and Fourier descriptors extracted from the characters. Correct recognition as high as 92.67% was obtained with a support vector machine classifier, compared to a 75.90% correct recognition rate with a benchmark Optical Character Recognition technique.

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18

Chaabane, Salim Ben. "Color Image Segmentation Using Fuzzy Clustering and Fusion: Application to Biomedical Images." International Journal for Research in Applied Science and Engineering Technology V, no. XI (November 23, 2017): 2954–61. http://dx.doi.org/10.22214/ijraset.2017.11408.

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19

Xu, S., J. McCusker, and M. Krauthammer. "Yale Image Finder (YIF): a new search engine for retrieving biomedical images." Bioinformatics 24, no. 17 (July 9, 2008): 1968–70. http://dx.doi.org/10.1093/bioinformatics/btn340.

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20

Hamdi, Med. "A Comparative Study in Wavelets, Curvelets and Contourlets as Denoising biomedical Images." Image Processing & Communications 16, no. 3-4 (January 1, 2011): 13–20. http://dx.doi.org/10.2478/v10248-012-0007-1.

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A Comparative Study in Wavelets, Curvelets and Contourlets as Denoising biomedical ImagesA special member of the emerging family of multi scale geometric transforms is the contourlet transform which was developed in the last few years in an attempt to overcome inherent limitations of traditional multistage representations such as curvelets and wavelets. The biomedical images were denoised using firstly wavelet than curvelets and finally contourlets transform and results are presented in this paper. It has been found that the contourlets transform outperforms the curvelets and wavelet transform in terms of signal noise ratio

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21

Bergonzi, Luca, Giorgio Colombo, Davide Redaelli, and Marcello Lorusso. "An Augmented Reality Approach to Visualize Biomedical Images." Computer-Aided Design and Applications 16, no. 6 (March 13, 2019): 1195–208. http://dx.doi.org/10.14733/cadaps.2019.1195-1208.

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22

Rajyaguru, Vipul C., and Rohit M. Thanki. "Performance Analysis of Quality Measurement for Biomedical Images." IOP Conference Series: Materials Science and Engineering 561 (November 12, 2019): 012101. http://dx.doi.org/10.1088/1757-899x/561/1/012101.

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23

Kozlov, K. N., P. Baumann, J. Waldmann, and M. G. Samsonova. "TeraPro, a system for processing large biomedical images." Pattern Recognition and Image Analysis 23, no. 4 (December 2013): 488–97. http://dx.doi.org/10.1134/s105466181304007x.

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24

Walker, Nicholas, and John Fox. "Knowledge based interpretation of images: a biomedical perspective." Knowledge Engineering Review 2, no. 4 (December 1987): 249–64. http://dx.doi.org/10.1017/s0269888900004148.

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AbstractThe traditions of image processing and knowledge engineering have developed separately. Work on AI vision systems lies between the two traditions but only recently has attention been given to combining practical imaging systems with methods for exploiting knowledge in interpreting the contents of an image. Five general approaches to combining knowledge based expert systems with imaging technologies are discussed. Particular attention is paid to the requirement for techniques which transform a pixel array into a symbolic form suitable for interpretation, and current obstacles to a general solution. Interpretation of biomedical images is particularly problematic because of statistical, structural and temporal variation in morphology of objects and structures. Some ways in which knowledge of shape, structure, and object classifications may contribute to this interpretation are discussed. The survey focuses on biomedical images but many of the issues are of general relevance to work in image understanding.

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25

Sanromán-Junquera, M., I. Mora-Jiménez, A. J. Caamaño, J. Almendral, F. Atienza, L. Castilla, A. García-Alberola, and J. L. Rojo-Álvarez. "Digital recovery of biomedical signals from binary images." Signal Processing 92, no. 1 (January 2012): 43–53. http://dx.doi.org/10.1016/j.sigpro.2011.05.023.

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26

Iglesias, Juan Eugenio, and Mert R. Sabuncu. "Multi-atlas segmentation of biomedical images: A survey." Medical Image Analysis 24, no. 1 (August 2015): 205–19. http://dx.doi.org/10.1016/j.media.2015.06.012.

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27

Santosh, K. C., and Partha Pratim Roy. "Arrow detection in biomedical images using sequential classifier." International Journal of Machine Learning and Cybernetics 9, no. 6 (January 3, 2017): 993–1006. http://dx.doi.org/10.1007/s13042-016-0623-y.

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28

Baldock, Richard A. "Trainable models for the interpretation of biomedical images." Image and Vision Computing 10, no. 6 (July 1992): 444–49. http://dx.doi.org/10.1016/0262-8856(92)90029-3.

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29

Fernandez-Lozano, Carlos, Marcos Gestal, Nieves Pedreira, Julian Dorado, and Alejandro Pazos. "High Order Texture-Based Analysis in Biomedical Images." Current Medical Imaging Reviews 9, no. 4 (January 31, 2014): 309–17. http://dx.doi.org/10.2174/15734056113096660005.

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30

Obara, B., M. Fricker, D. Gavaghan, and V. Grau. "Contrast-Independent Curvilinear Structure Detection in Biomedical Images." IEEE Transactions on Image Processing 21, no. 5 (May 2012): 2572–81. http://dx.doi.org/10.1109/tip.2012.2185938.

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31

Babyuk, N., S. Pavlov, P. Kolisnyk, and Yang Longyin. "Peculiarities of Computer Analysis of Biomedical Images of Microcirculation of the Conjunctiva of the Eye." Optoelectronic Information-Power Technologies 42, no. 2 (October 26, 2022): 53–65. http://dx.doi.org/10.31649/1681-7893-2021-42-2-53-65.

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An analysis was carried out, which showed that the task of creating a system for evaluating biomedical images of pathologies of the vessels of the human eye is relevant and needs to be solved, since the existing methods and systems of evaluation, as well as the existing methods of processing biomedical images do not meet the modern requirements for such systems in terms of accuracy , the reliability of processing biomedical images, which leads to the occurrence of errors in the diagnosis in ophthalmological studies. The results of the comparison of the effectiveness of the Kirsch, Sobel, Roberts, Wallace, SUSAN algorithms for processing biomedical images based on the set of information diagnostic features formed are the most informative for image segmentation, the algorithms based on Kirsch filtering and the nonlinear Sobel filter. A system has been developed for evaluating dynamic changes in biomedical images, which allows to evaluate the state of blood vessels and determine the conjunctival index based on the following indicators: the ratio of the diameters of arterioles and corresponding venules; caliber irregularities; meander windings; microaneurysms; glomeruli; reticular structures of vessels; changes in the number of functioning capillaries; arteriolo-venular anastomoses; hemorrhages; perivascular edema; sludge phenomena; microthrombi

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32

ESENALIEV, RINAT O. "BIOMEDICAL OPTOACOUSTICS." Journal of Innovative Optical Health Sciences 04, no. 01 (January 2011): 39–44. http://dx.doi.org/10.1142/s1793545811001253.

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Optoacoustics is a promising modality for biomedical imaging, sensing, and monitoring with high resolution and contrast. In this paper, we present an overview of our studies for the last two decades on optoacoustic effects in tissues and imaging capabilities of the optoacoustic technique. In our earlier optoacoustic works we studied laser ablation of tissues and tissue-like media and proposed to use optoacoustics for imaging in tissues. In mid-90s we demonstrated detection of optoacoustic signals from tissues at depths of up to several centimeters, well deeper than the optical diffusion limit. We then obtained optoacoustic images of tissues both in vitro and in vivo. In late 90s we studied optoacoustic monitoring of thermotherapy: hyperthermia, coagulation, and freezing. Then we proposed and studied optoacoustic monitoring of blood oxygenation, hemoglobin concentration, and other physiologic parameters.

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33

NEUBAUER, A., A. M. TOMÉ, A. KODEWITZ, J. M. GÓRRIZ, C. G. PUNTONET, and E. W. LANG. "BIDIMENSIONAL ENSEMBLE EMPIRICAL MODE DECOMPOSITION OF FUNCTIONAL BIOMEDICAL IMAGES." Advances in Adaptive Data Analysis 06, no. 01 (January 2014): 1450004. http://dx.doi.org/10.1142/s1793536914500046.

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Positron emission tomography (PET) provides a functional imaging modality to detect signs of dementias in human brains. Two-dimensional empirical mode decomposition (2D EMD) provides means to analyze such images. It extracts characteristic textures from these images which may be fed into powerful classifiers trained to group these textures into several classes depending on the problem at hand. The study investigates the potential use of 2D EEMD in combination with proper classifiers to form a computer aided diagnosis (CAD) system to assist clinicians in identifying various diseases from functional images alone. PET images of subjects suffering from a dementia are taken to illustrate this ability.

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34

Götz, Andreas, Niels Grabow, Sabine Illner, and Volkmar Senz. "Fiber statistics of nonwoven materials by SEM images - influence of number of images." Current Directions in Biomedical Engineering 7, no. 2 (October 1, 2021): 652–55. http://dx.doi.org/10.1515/cdbme-2021-2166.

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Abstract Electrospun nonwovens are widely applied in biomedicine and various other fields. For control of the manufacturing process and quality assurance Scanning electron microscopy (SEM) imaging is one standard practice. In this study, statistical datasets of 60 SEM images of three nonwoven samples were evaluated using Gaussian fit to obtain numerical results of their fiber diameter distributions. The question of how much effort is required for acceptable imaging and processing is being discussed. As determined here, for reliable statistics, a minimum surface area of the nonwoven has to be evaluated. The fiber diameter should be in a range of approximately 2 - 3% of the edge length of the square equivalent of the evaluated image area, using sufficiently magnified SEM images, in which the fiber diameter is imaged over at least 30 pixels.

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35

Lotz, J., J. Olesch, B. Muller, T. Polzin, P. Galuschka, J. M. Lotz, S. Heldmann, et al. "Patch-Based Nonlinear Image Registration for Gigapixel Whole Slide Images." IEEE Transactions on Biomedical Engineering 63, no. 9 (September 2016): 1812–19. http://dx.doi.org/10.1109/tbme.2015.2503122.

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36

Gaidel, A. V. "A METHOD FOR ADJUSTING DIRECTED TEXTURE FEATURES IN BIOMEDICAL IMAGE ANALYSIS PROBLEM IMAGES." Computer Optics 39, no. 2 (January 1, 2015): 287–93. http://dx.doi.org/10.18287/0134-2452-2015-39-2-287-293.

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37

Saudagar, Abdul Khader Jilani, and Abdul Sattar Syed. "Image compression approach with ridgelet transformation using modified neuro modeling for biomedical images." Neural Computing and Applications 24, no. 7-8 (April 19, 2013): 1725–34. http://dx.doi.org/10.1007/s00521-013-1414-y.

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38

Saudagar, Abdul Khader Jilani. "Biomedical Image Compression Techniques for Clinical Image Processing." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 12 (October 19, 2020): 133. http://dx.doi.org/10.3991/ijoe.v16i12.17019.

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Image processing is widely used in the domain of biomedical engineering especially for compression of clinical images. Clinical diagnosis receives high importance which involves handling patient’s data more accurately and wisely when treating patients remotely. Many researchers proposed different methods for compression of medical images using Artificial Intelligence techniques. Developing efficient automated systems for compression of medical images in telemedicine is the focal point in this paper. Three major approaches were proposed here for medical image compression. They are image compression using neural network, fuzzy logic and neuro-fuzzy logic to preserve higher spectral representation to maintain finer edge information’s, and relational coding for inter band coefficients to achieve high compressions. The developed image coding model is evaluated over various quality factors. From the simulation results it is observed that the proposed image coding system can achieve efficient compression performance compared with existing block coding and JPEG coding approaches, even under resource constraint environments.

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39

Cheng, Beibei, R. Joe Stanley, Soumya De, Sameer Antani, and George R. Thoma. "Automatic Detection of Arrow Annotation Overlays in Biomedical Images." International Journal of Healthcare Information Systems and Informatics 6, no. 4 (October 2011): 23–41. http://dx.doi.org/10.4018/jhisi.2011100102.

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Images in biomedical articles are often referenced for clinical decision support, educational purposes, and medical research. Authors-marked annotations such as text labels and symbols overlaid on these images are used to highlight regions of interest which are then referenced in the caption text or figure citations in the articles. Detecting and recognizing such symbols is valuable for improving biomedical information retrieval. In this research, image processing and computational intelligence methods are integrated for object segmentation and discrimination and applied to the problem of detecting arrows on these images. Evolving Artificial Neural Networks (EANNs) and Evolving Artificial Neural Network Ensembles (EANNEs) computational intelligence-based algorithms are developed to recognize overlays, specifically arrows, in medical images. For these discrimination techniques, EANNs use particle swarm optimization and genetic algorithm for artificial neural network (ANN) training, and EANNEs utilize the number of ANNs generated in an ensemble and negative correlation learning for neural network training based on averaging and Linear Vector Quantization (LVQ) winner-take-all approaches. Experiments performed on medical images from the imageCLEFmed’08 data set, yielded area under the receiver operating characteristic curve and precision/recall results as high as 0.988 and 0.928/0.973, respectively, using the EANNEs method with the winner-take-all approach.

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40

Hassan, Gaber, Khalid M. Hosny, R. M. Farouk, and Ahmed M. Alzohairy. "EFFICIENT QUATERNION MOMENTS FOR REPRESENTATION AND RETRIEVAL OF BIOMEDICAL COLOR IMAGES." Biomedical Engineering: Applications, Basis and Communications 32, no. 05 (October 2020): 2050039. http://dx.doi.org/10.4015/s1016237220500398.

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Biomedical color (BMC) images are being used on a wide scale by physicians, where their diagnosis would be more accurate. Hence, it is recommended to develop new approaches that are able to represent and retrieve the BMC images efficiently. This work proposes two methods to represent BMC images: Quaternion Associated Laguerre. Moments (Q_ALMs), and Quaternion Chebyshev Moments (Q_CMs). Q_ALMs and Q_CMs are derived by extending the ALMs and CMs to the quaternion field. ALMs and CMs represent discrete orthogonal moments, and they are defined using the Associated Laguerre Polynomials (ALPs) and Chebychev Polynomials, respectively. Hospitals and medical institutes everywhere in the world create and store a large variety of datasets of BMC images during the routine clinical practices; hence, the mastery to retrieve the BMC images correctly is crucial for precise diagnoses and also for the researchers in medical sciences. So that in this study, we also introduced two image retrieval systems for BMC images based on the Q_CMs and Q_ALMs approaches. Our approaches extensively assessed with two standard benchmark datasets: LGG Segmentation dataset for brain magnetic resonance MR images and NEMA-CT for the computed tomography (CT) images. The performance of the proposed retrieval systems is assessed through three performance metrics: Average retrieval precision (ARP), average retrieval rate (ARR), and F_score. Results have shown the outperformance of Q_CMs over Q_ALMs in both the cases of representing and retrieval of BMC images.

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41

Pasquini, Luca, Antonio Napolitano, Matteo Pignatelli, Emanuela Tagliente, Chiara Parrillo, Francesco Nasta, Andrea Romano, Alessandro Bozzao, and Alberto Di Napoli. "Synthetic Post-Contrast Imaging through Artificial Intelligence: Clinical Applications of Virtual and Augmented Contrast Media." Pharmaceutics 14, no. 11 (November 4, 2022): 2378. http://dx.doi.org/10.3390/pharmaceutics14112378.

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Contrast media are widely diffused in biomedical imaging, due to their relevance in the diagnosis of numerous disorders. However, the risk of adverse reactions, the concern of potential damage to sensitive organs, and the recently described brain deposition of gadolinium salts, limit the use of contrast media in clinical practice. In recent years, the application of artificial intelligence (AI) techniques to biomedical imaging has led to the development of ‘virtual’ and ‘augmented’ contrasts. The idea behind these applications is to generate synthetic post-contrast images through AI computational modeling starting from the information available on other images acquired during the same scan. In these AI models, non-contrast images (virtual contrast) or low-dose post-contrast images (augmented contrast) are used as input data to generate synthetic post-contrast images, which are often undistinguishable from the native ones. In this review, we discuss the most recent advances of AI applications to biomedical imaging relative to synthetic contrast media.

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42

Lim, Laurence A. Gan, Raouf N. G. Naguib, Elmer P. Dadios, and Jose Maria C. Avila. "Image classification of microscopic colonic images using textural properties and KSOM." International Journal of Biomedical Engineering and Technology 3, no. 3/4 (2010): 308. http://dx.doi.org/10.1504/ijbet.2010.032698.

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43

Haimerl, M., J. Moldenhauer, U. Mende, and T. Beth. "COMPARATIVE ANALYSIS OF LOCALLY ADAPTIVE IMAGE ENHANCEMENT FOR 3D ULTRASOUND IMAGES." Biomedizinische Technik/Biomedical Engineering 47, s1b (2002): 629–32. http://dx.doi.org/10.1515/bmte.2002.47.s1b.629.

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44

Berezsky, O. M., Petro B. Liashchynskyi, Pavlo B. Liashchynskyi, A. R. Sukhovych, and T. M. Dolynyuk. "SYNTHESIS OF BIOMEDICAL IMAGES BASED ON GENERATIVE ADVERSARIAL NETWORKS." Ukrainian Journal of Information Technology 1, no. 1 (2019): 35–40. http://dx.doi.org/10.23939/ujit2019.01.035.

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Modern databases of biomedical images have been investigated. Biomedical imaging has been shown to be expensive and time consuming. A database of images of precancerous and cancerous breasts "BPCI2100" was developed. The database consists of 2,100 image files and a MySQL database of medical research information (patient information and image features). Generative adversarial networks (GAN) have been found to be an effective means of image generation. The architecture of the generative adversarial network consisting of a generator and a discriminator has been developed.The discriminator is a deep convolutional neural network with color images of 128×128 pixels. This network consists of six convolutional layers with a window size of 5×5 pixels. Leaky ReLU type activation function for convolutional layers is used. The last layer used a sigmoid activation function. The generator is a neural network consisting of a fully connected layer and seven deconvolution layers with a 5×5 pixel window size. Leaky ReLU activation function is used for all layers. The last layer uses the hyperbolic tangent activation function. Google Cloud Compute Instance tools have been used to train the the generative adversarial network. Generation of histological and cytological images on the basis of the generative adversarial network is conducted. As a result, the training sample for classifiers has been significantly increased.

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A., Sangita, and Vijay R. "Classification of Biomedical Images using Counter Propagation Neural Network." International Journal of Computer Applications 182, no. 10 (August 14, 2018): 23–27. http://dx.doi.org/10.5120/ijca2018917714.

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Berezsky, Oleg N. "The Algorithm of Analysis and Synthesis of Biomedical Images." Journal of Automation and Information Sciences 39, no. 4 (2007): 69–80. http://dx.doi.org/10.1615/jautomatinfscien.v39.i4.60.

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Ye, Qing, and Hanfeng Lin. "Misconduct of images: Guidance for biomedical authors and editors." Science Editor and Publisher 5, no. 1 (2020): 40–46. http://dx.doi.org/10.24069/2542-0267-2020-1-40-46.

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Laine, Andrew F. "Wavelets in Temporal and Spatial Processing of Biomedical Images." Annual Review of Biomedical Engineering 2, no. 1 (August 2000): 511–50. http://dx.doi.org/10.1146/annurev.bioeng.2.1.511.

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Mahadevan, V., H. Narasimha-Iyer, B. Roysam, and H. L. Tanenbaum. "Robust Model-Based Vasculature Detection in Noisy Biomedical Images." IEEE Transactions on Information Technology in Biomedicine 8, no. 3 (September 2004): 360–76. http://dx.doi.org/10.1109/titb.2004.834410.

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Mohammad Helal, Khalifa. "Unsupervised Random Forest Based Analysis of Biomedical Raman Images." Annals of Pure and Applied Mathematics 21, no. 1 (January 2020): 27–40. http://dx.doi.org/10.22457/apam.v21n1a4652.

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