Biological Image Processing Research Articles

Malignant thyroid lump multi classification by TIRADS using DBA with transfer learning

Thyroid diseases have developed into significant illnesses in recent decades. These diseases affect the thyroid glands and are caused by elevated thyroid hormone levels or infections in the thyroid organs. It is challenging to resolve thyroid diagnosis using conventional parametric and nonparametric statistical techniques since it can be viewed as a classification problem. However, there are certain barriers in the manner of obtaining both efficacy and accuracy in thyroid nodule diagnosis. Deep learning (DL) and machine learning (ML) models have emerged as useful instruments for the diagnosis of sickness in the modern era. For the purpose of diagnosing and classifying thyroid diseases, this research introduces a novel deep belief network (DBF) with transfer learning, known as DBNTL. In this study, the pre-processed image was first pre-processed using a conventional multiresolution bilateral technique, and then it was subjected to a novel segmentation technique called fusion pooling integrated U-net segmentation. The DBN with transfer learning model is used to classify and grade malignant thyroid nodules in compliance with thyroid imaging-reporting-and-data-system (TIRADS) guidelines. In this model, the model's weights are obtained by transfer learning. A major metric for evaluating the efficacy of biological image processing applications, good sensitivity and specificity (97.28 and 97.22, respectively) were obtained for the recommended modes.

Designing efficient brain tumor classifier using hybrid EfficientNet-faster R-CNN deep learning model

Purpose. Brain tumors have historically posed a significant risk to individuals, potentially leading to fatality. However, in contemporary times, they have emerged as one of the most dangerous health issues affecting both children and adults. The effects of this phenomenon result in the unregulated proliferation of brain cells. Therefore, early detection and precise classification of brain tumor is very necessary and important for saving lives and avoid future complications. Within the field of biological image processing, the utilisation of deep learning algorithms offers a distinct and unparalleled experience. The Convolutional Neural Network (CNN) is a pivotal component in brain tumor categorization applications. However, a notable limitation of CNNs is their extensive execution time, primarily attributed to the substantial number of trainable parameters involved. Also, the automatic detection and classification of brain tumor is still a challenging task due to its variability in shape, size and location. Methods. This study proposes a hybrid model for brain tumor classification that combines faster R-CNN and EfficientNet. The objective is to minimise the time required for classification without compromising accuracy. The data collection from Figshare was employed during the model’s development. Results and Conclusion. The hybrid model proposed in this study demonstrates a notable accuracy of 98.96% during the training phase and 99.2% during the testing phase, surpassing the performance of both the EfficientNet model and the Faster R-CNN model when employed individually. The aforementioned findings were derived through a comparative analysis of the Hybrid model, the EfficientNet model, and the Faster R-CNN model.

3D Poissonian image deblurring via patch-based tensor logarithmic Schatten-p minimization

In medical and biological image processing, multi-dimensional images are often corrupted by blur and Poisson noise. In this paper, we first propose a new tensor logarithmic Schatten-p (t-log-Sp ) low-rank measure and a tensor iteratively reweighted Schatten-p minimization algorithm for minimizing such measure. Furthermore, we adopt this low-rank measure to regularize the non-local tensors formed by similar 3D image patches and develop a patch-based non-local low-rank model. The data fidelity term of the model characterizes the Poisson noise distribution and blur operator. The optimization model is further solved by an alternating minimization technique combined with variable splitting. Experimental results tested on 3D fluorescence microscope images show that the proposed patch-based tensor logarithmic Schatten-p minimization method outperforms state-of-the-art methods in terms of image evaluation metrics and visual quality.

Optical Inspection of Concrete Surface

In order to obtain accurate and reliable data on the properties of concrete surfaces, professional testing methods and specialised equipment may be required. Optical inspection can be carried out as part of, or as preparation for, diagnostic testing. This paper describes a procedure that can be applied to microscopic inspection too, as it is based on data obtained from the use of a software package developed for biological digital image processing. As an example, the quantification of cavities on the side of a concrete cube specimen is described, step-by-step, but such a convenient procedure could also be used to evaluate other surface features.

Views from 'crabworld': the spatial distribution of light in a tropical mudflat.

Natural scene analysis has been extensively used to understand how the invariant structure of the visual environment may have shaped biological image processing strategies. This paper deals with four crucial, but hitherto largely neglected aspects of natural scenes: (1) the viewpoint of specific animals; (2) the fact that image statistics are not independent of the position within the visual field; (3) the influence of the direction of illumination on luminance, spectral and polarization contrast in a scene; and (4) the biologically relevant information content of natural scenes. To address these issues, I recorded the spatial distribution of light in a tropical mudflat with a spectrographic imager equipped with a polarizing filter in an attempt to describe quantitatively the visual environment of fiddler crabs. The environment viewed by the crabs has a distinct structure. Depending on the position of the sun, the luminance, the spectral composition, and the polarization characteristics of horizontal light distribution are not uniform. This is true for both skylight and for reflections from the mudflat surface. The high-contrast feature of the line of horizon dominates the vertical distribution of light and is a discontinuity in terms of luminance, spectral distribution and of image statistics. On a clear day, skylight intensity increases towards the horizon due to multiple scattering, and its spectral composition increasingly resembles that of sunlight. Sky-substratum contrast is highest at short wavelengths. I discuss the consequences of this extreme example of the topography of vision for extracting biologically relevant information from natural scenes.

Biological Image Processing Algorithm Based on Attention Mechanism and Convolutional Neural Network

This paper proposes a model algorithm based on convolutional neural network combined with attention mechanism to realize fast and accurate identification of biological image. Firstly, deformable convolution is used to extract features in the horizontal and vertical directions, respectively. Secondly, attention modules are used to capture remote dependencies in one spatial direction, while accurate position information is retained in another spatial direction, so that information in both vertical and horizontal directions can be retained; after a series of transformations, the attention vector is obtained and multiplied back to the original feature vector as a weight factor. The experimental results show that the proposed algorithm can effectively improve the image quality, improve the image clarity, avoid color distortion, and achieve good results in both synthetic and real low-illumination images, and the subjective and objective evaluation indicators are better than the contrast algorithm.

Thin film notch filters as platforms for biological image processing

Many image processing operations involve the modification of the spatial frequency content of images. Here we demonstrate object-plane spatial frequency filtering utilizing the angular sensitivity of a commercial spectral bandstop filter. This approach to all-optical image processing is shown to generate real-time pseudo-3D images of transparent biological and other samples, such as human cervical cancer cells. This work demonstrates the potential of non-local, non-interferometric approaches to image processing for uses in label-free biological cell imaging and dynamical monitoring.

A VLSI Image Processing Algorithm for Biomedical Applications

The domain of signal processing that is dedicated to applications in the medical sector has shown phenomenal expansion over the course of the last decade. It’s possible that developments in fields such as computer-aided design (CAD), healthcare, and automated signal processing have played a key part in this expansion. As a result of the current COVID-19 epidemic, there is a greater need for biomedical signal analysis, and from an imaging point of view, biomedical research projects far exceed those that use more basic signal processing methodologies. The fundamentals of image processing and the processing of biological pictures have been the subject of a lot of studies, and the findings of these studies have shown a variety of possible applications in a variety of settings. A examination of the existing literature reveals that there are a limited number of solutions that are based on very large scale integration (VLSI). As a direct consequence of this, this chapter devotes a significant amount of its focus to the investigation of biological image processing, particularly in terms of its applications and VLSI implementations. It is hoped that researchers and investigators would find this useful in better preparing a solution to this issue so that it may be put into action. Following an analysis of the most recent developments in research, a number of possible directions for future research have been contemplated.

Molecular phylogeny of swallowtail butterflies (Lepidoptera: Papilionidae) based on mitochondrial cytochrome c oxidase I (COI) gene from Bangladesh

Swallowtail butterflies (Papilionidae) are among the best-known insects in the world. Mitochondrial cytochrome c oxidase I (COI, 642 bp) gene from nine species under four genera of swallowtail butterflies was sequenced and submitted to the GenBank. Basic local alignment search tool (BLAST) analysis showed that the COI gene sequences of these nine butterflies matched those of respective species from different geographical areas (99-96% similarity). The interspecific genetic divergence among swallowtail species was between 0.01–0.15%. Phylogenetic tree constructed using the maximum likelihood algorithm showed that the species of swallowtail butterflies were originated from a common ancestor where Papilio polytes, Papilio nephelus, Papilio demoleus, Papilio memnon and Papilio polymnestor belonged to a cluster. On the other hand, Graphium agamemnon, Atrophaneura varuna and Pachliopta aristolochiae belonged to another cluster. In the TCS haplotype network, considerable genetic divergence was observed among the nine species of swallowtail butterflies. Both phylogenetic and haplotype analyses showed that Graphium doson was genetically most divergent among the swallowtail butterflies studied in the present study. These results indicate that Graphium spp. might be the good model system to uncover many biological processes like color vision and image processing in organisms.

Deep Learning in Biological Image and Signal Processing [From the Guest Editors

Biological research on the fundamental structural and functional properties of life—from molecules to cells, tissues, organs, and complete organisms, including human life—relies heavily on advanced imaging systems and measurement devices generating data of ever-increasing quantity and complexity. Automated processing and analysis of these data through increasingly sophisticated computational methods have become indispensable in exploiting relevant information and enabling researchers to detect patterns that may be unnoticeable to human senses.

Urdnet: A Cryo-EM Particle Automatic Picking Method

Cryo-Electron Microscopy (Cryo-EM) images are characterized by the low signal-to-noise ratio, low contrast, serious background noise, more impurities, less data, difficult data labeling, simpler image semantics, and relatively fixed structure, while U-Net obtains low resolution when downsampling rate information to complete object category recognition, obtains high-resolution information during upsampling to complete precise segmentation and positioning, fills in the underlying information through skip connection to improve the accuracy of image segmentation, and has advantages in biological image processing like Cryo-EM image. This article proposes A U-Net based residual intensive neural network (Urdnet), which combines point-level and pixel-level tags, used to accurately and automatically locate particles from cryo-electron microscopy images, and solve the bottleneck that cryo-EM Single-particle biological macromolecule reconstruction requires tens of thousands of automatically picked particles. The 80S ribosome, HCN1 channel and TcdA1 toxin subunits, and other public protein datasets have been trained and tested on Urdnet. The experimental results show that Urdnet could reach the same excellent particle picking performances as the mainstream methods of RELION, DeepPicker, and acquire the 3D structure of picked particles with higher resolution.

New Extensibility and Scripting Tools in the ImageJ Ecosystem.

ImageJ provides a framework for image processing across scientific domains while being fully open source. Over the years ImageJ has been substantially extended to support novel applications in scientific imaging as they emerge, particularly in the area of biological microscopy, with functionality made more accessible via the Fiji distribution of ImageJ. Within this software ecosystem, work has been done to extend the accessibility of ImageJ to utilize scripting, macros, and plugins in a variety of programming scenarios, e.g., from Groovy and Python and in Jupyter notebooks and cloud computing. We provide five protocols that demonstrate the extensibility of ImageJ for various workflows in image processing. We focus first on Fluorescence Lifetime Imaging Microscopy (FLIM) data, since this requires significant processing to provide quantitative insights into the microenvironments of cells. Second, we show how ImageJ can now be utilized for common image processing techniques, specifically image deconvolution and inversion, while highlighting the new, built-in features of ImageJ-particularly its capacity to run completely headless and the Ops matching feature that selects the optimal algorithm for a given function and data input, thereby enabling processing speedup. Collectively, these protocols can be used as a basis for automating biological image processing workflows. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Using PyImageJ for FLIM data processing Alternate Protocol: Groovy FLIMJ in Jupyter Notebooks Basic Protocol 2: Using ImageJ Ops for image deconvolution Support Protocol 1: Using ImageJ Ops matching feature for image inversion Support Protocol 2: Headless ImageJ deconvolution.

ANALYSIS OF BIOLO ARTIFICIAL NEURAL NETWORK IN PREDICTION OF AEROBIC EXERCISE INDEX BASED ON ALGORITHM

ABSTRACT Objective: To study the relationship between aerobic activity and cardiac autonomic nerve activity by artificial neural network algorithm and biological image fusion; because of the artificial neural network model (ANN) problems, biological image processing technology is introduced based on ANN. Methods: An Ann under biological image intelligence algorithm is proposed, a classifier suitable for electrocardiograph (ECG) screening is designed, and an ECG signal screening system is successfully established. Moreover, the data set of normal recovered ECG signals of the subjects during the experimental period is constructed, and a classifier is used to extract the characteristic data of a normal ECG signal during the experimental period. Results: The changes in resting heart rate and other physical health indicators are analyzed by combining resting physiological indicators, namely heart rate, body weight, body mass index and body fat rate. The results show that the self-designed classifier can efficiently process the ECG images, and long-term regular activities can improve the physical conditions of most people. Most subjects’ body weight and body fat rate decrease with the extension of experiment time, and the resting heart rate decreases relatively. Conclusions: Certain indicators can be used to predict a person's dynamic physical health, which indicates that the experimental research of index prediction in this research has a good effect, which not only extends the application of artificial neural network but also lays a foundation for the research and implementation of ECG intelligent testing wearable devices. Level of evidence II; Therapeutic studies - investigation of treatment results.

Special Issue on Deep Learning in Biological Image and Signal Processing

Special Issue on Deep Learning in Biological Image and Signal Processing

Special Issue on Deep Learning in Biological Image and Signal Processing

Special Issue on Deep Learning in Biological Image and Signal Processing

Nonlinear retinal response modeling for future neuromorphic instrumentation

The development of a bio-inspired image sensor that can match the functionality of the vertebrate retina in terms of image resolution, power dissipation and dynamic resolution demands an extremely challenging standard in terms of efficiency and performance [1]–[4]. At present, functional biological image processing possesses extremely high temporal resolution by asynchronously optimizing the sampling rate of a scene but suffers from low spatial resolution, noise, and an inability to process low frequency content [5]. Regardless, the use of dynamic vision sensors that emulate biological retina behaviors are important in performing classification tasks using high-speed motion detection at low-power requirements [6]–[9]. The difficulty in pushing image sensor performance to meet the specifications of the retina lies in the biological complexities of the underlying neural networks. Therefore, a prerequisite for hardware mapping of biological vision systems is understanding the bioprocessing between various retina cells. Here, we present a biologically plausible cellular network retina simulator, where light stimuli are accepted at the front-end array of photoreceptor cells, and pass through a system of nonlinear integral equations derived from experimental voltage- and current-clamp data used to calculate the response of each sequential retina cell. It was shown in [10], [11] that integrating the system improves performance when compared with conventional numerical solvers. We expect that by placing our simulator in the hands of interdisciplinary researchers, especially computational neuroscientists, mathematical scientists and machine learning practitioners, it will foster the development of more efficient representations of visual inputs in deep learning applications.

Image Feature Extraction and Retrieval of the Euler Number to Chinese Herbal Medicine based on PCNN

In order to extract microscopic image feature information of traditional Chinese medicine effectively and simply and improve the precision of image retrieval, an image Euler number feature extraction and retrieval algorithm based on pulse coupled neural network (PCNN) was proposed. First of all, the PCNN model is introduced from the perspective of biological tissue image processing. Secondly, the CHM image is segmented into a series of binary correlation images using simplified PCNN. The Euler number of the edge information of each binary image in the generated binary image sequence is calculated, and a one-dimensional feature vector is constructed. Finally, the Euclidean distance is used to measure the similarity of the feature vector in the retrieval of CHM microscopic images. Euler number feature extraction and retrieval method is characterized by simple calculation and small amount of data. Compared with other methods, this algorithm has stronger robustness and retrieval accuracy, and certain feasibility.

Biological image analysis using deep learning-based methods: Literature review

Automatic processing large amount of microscopic images is important for medical and biological studies. Deep learning has demonstrated better performance than traditional machine learning methods for processing massive quantities of images; therefore, it has attracted increasing attention from the research and industry fields. This paper summarizes the latest progress of deep learning methods in biological microscopic image processing, including image classification, object detection, and image segmentation. Compared to the traditional machine learning methods, deep neural networks achieved better accuracy without tedious feature selection procedure. Obstacles of the biological image analysis with deep learning methods include limited training set and imperfect image quality. Viable solutions to these obstacles are discussed at the end of the paper. With this survey, we hope to provide a reference for the researchers conducting biological microscopic image processing.

Convolution Comparison Pattern: An Efficient Local Image Descriptor for Fingerprint Liveness Detection.

We present a new type of local image descriptor which yields binary patterns from small image patches. For the application to fingerprint liveness detection, we achieve rotation invariant image patches by taking the fingerprint segmentation and orientation field into account. We compute the discrete cosine transform (DCT) for these rotation invariant patches and attain binary patterns by comparing pairs of two DCT coefficients. These patterns are summarized into one or more histograms per image. Each histogram comprises the relative frequencies of pattern occurrences. Multiple histograms are concatenated and the resulting feature vector is used for image classification. We name this novel type of descriptor convolution comparison pattern (CCP). Experimental results show the usefulness of the proposed CCP descriptor for fingerprint liveness detection. CCP outperforms other local image descriptors such as LBP, LPQ and WLD on the LivDet 2013 benchmark. The CCP descriptor is a general type of local image descriptor which we expect to prove useful in areas beyond fingerprint liveness detection such as biological and medical image processing, texture recognition, face recognition and iris recognition, liveness detection for face and iris images, and machine vision for surface inspection and material classification.

Subcellular object quantification with Squassh3C and SquasshAnalyst.

Quantitative image analysis plays an important role in contemporary biomedical research. Squassh is a method for automatic detection, segmentation, and quantification of subcellular structures and analysis of their colocalization. Here we present the applications Squassh3C and SquasshAnalyst. Squassh3C extends the functionality of Squassh to three fluorescence channels and live-cell movie analysis. SquasshAnalyst is an interactive web interface for the analysis of Squassh3C object data. It provides segmentation image overview and data exploration, figure generation, object and image filtering, and a statistical significance test in an easy-to-use interface. The overall procedure combines the Squassh3C plug-in for the free biological image processing program ImageJ and a web application working in conjunction with the free statistical environment R, and it is compatible with Linux, MacOS X, or Microsoft Windows. Squassh3C and SquasshAnalyst are available for download at www.psi.ch/lbr/SquasshAnalystEN/SquasshAnalyst.zip.