Biomedical Image Volumes Denoising via the Wavelet Transform

The biomedical imaging and image processing course in Shanghai Jiao Tong University is a core course in the Department of Biomedical Engineering. We have taken many measures in the course system, student practice, classroom teaching, and active and creative learning. The effectiveness of the measures and the results are reported. The course is highly appraised by teachers and students and won many awards. The measures and experience of our course may enrich the teaching and learning method bank in Biomedical Engineering (BME) education.

Biomedical image analysis has become critically important to the public health and welfare. However, analyzing biomedical images is time-consuming and labor-intensive, and has long been performed manually by highly trained human experts. As a result, there has been an increasing interest in applying machine learning to automate biomedical image analysis. Recent progress in deep learning research has catalyzed the development of machine learning in learning discriminative features from data with minimum human intervention. Many deep learning models have been designed and achieved superior performance in various data analysis applications. This chapter starts with the basic of deep learning models and some practical strategies for handling biomedical image applications with limited data. After that, case studies of deep feature extraction for gene expression pattern image annotations, imaging data completion for brain disease diagnosis, and segmentation of infant brain tissue images are discussed to demonstrate the effectiveness of deep learning in biomedical image analysis.

Intense interest in applying convolutional neural networks (CNNs) in biomedical image analysis is wide spread, but its success is impeded by the lack of large annotated datasets in biomedical imaging. Annotating biomedical images is not only tedious and time consuming, but also demanding of costly, specialty-oriented knowledge and skills, which are not easily accessible. To dramatically reduce annotation cost, this paper presents a novel method called AIFT (active, incremental fine-tuning) to naturally integrate active learning and transfer learning into a single framework. AIFT starts directly with a pre-trained CNN to seek "worthy" samples from the unannotated for annotation, and the (fine-tuned) CNN is further fine-tuned continuously by incorporating newly annotated samples in each iteration to enhance the CNN's performance incrementally. We have evaluated our method in three different biomedical imaging applications, demonstrating that the cost of annotation can be cut by at least half. This performance is attributed to the several advantages derived from the advanced active and incremental capability of our AIFT method.

Automatic biomedical image processing has enjoyed increased popularity of late, primarily because it can be used to enhance images to measure and count accurately and quickly in various types of applications. Preliminary background and basic terminology commonly used in biomedical image processing will be reviewed. Among these are sources and forms of biomedical images, image enhancement, searching and analysis modes in biomedical image processing, and possible output formats.

Background: Carbon-based nanoparticle classes consisting of various subgroups based on morphology and crystallinity are called carbon dots (CQDs). The physical, chemical, and optical properties of CQDs can be modified using the simple pot synthesis technique. Additionally, its non-toxic nature, biocompatibility, physical and chemical responsiveness, resistance to chemical and photo bleaching, and low cost make it suitable for various purposes, such as biomedical imaging applications. Biomass waste, which has been widely discarded without economic utilization and potential, can surprisingly be used as a precursor for CQDs. Method: The literature was systematically collected from major databases. Studies from 2017–2025 were analyzed based on synthesis strategies, surface functionalization, and biological performance. Its potential in the medical field is highly advantageous. CQDs have fluorescence that is useful for biomedical imaging both in vivo and in vitro. The hydrothermal carbonization approach is also discussed in more detail, highlighting its green and sustainable synthesis, as well as the ease of the synthesis process. Finding: It was found that CQDs have compatibility and adjustable optical properties. Its fluorescence can clearly record tissues, body care, aging, and living cells. Utilizing renewable biomass precursors offers an environmentally friendly and cost-effective route for synthesizing fluorescent nanoprobes with excellent water solubility, tunable emission, and low cytotoxicity. Additionally, in vitro studies reinforce CQDs as multicolor fluorescent probes, and in vivo studies demonstrate that CQDs have low toxicity, rapid clearance, are safe, and biocompatible. Conclusion: This paper delves into the remarkable potential of CQDs to provide insights into how fluorescent inks are truly essential in biomedical imaging. Novelty/Originality of this article: This study provides a comprehensive and updated synthesis of CQD research spanning up to 2025, specifically focusing on the transition from "waste to nanoprobe."

In the last years, it has become very popular as well as significant to diagnose patients as less invasively as only it is possible. This becomes extremely important mainly when diagnosing adolescent patients or even babies. This paper describes the possibilities of medical diagnostics from a different point of view which is the evaluation of microscopic biomedical images of patient's body instead of examining him directly, i.e., invasively. The subject of research is human respiratory system and its diseases which are often caused or closely connected with proper function of small microscopic structures which should serve to clean one's airways. Such structures are called cilia of respiratory epithelium and can be found within nose, trachea or bronchi. The old way of diagnostic of a patient who is suspected to suffer from a disease called Primary ciliary dyskinesia (PCD), as he often undergoes serious inflammations and other breathing complications, comprises several repeated bronchoscopies which cut a small pieces of epithelium from selected parts of respiratory apparatus that are consequently observed using microscope. This is very uncomfortable and many times very hard to perform as the most affected group of patients are children under the age of three. Moreover, evaluation of microscopic samples is often imprecise because doctors assess it subjectively and the proper frequency of ciliary movement is only estimated and classified “slow” or “quick enough”, without accurate value. This paper proposes automated, much faster and much more exact ways of frequency measurements of ciliary movement based on processing and segmentation of biomedical microscopic images, which means more precise evaluation of patient's ciliary health status without such invasiveness as it is used during older methods of examination.

This paper discussed some highlights on biomedical image processing. Some topics discussed were; diffusion tensor imaging, MR spectroscopy, cardiac 4D ultrasound,electron crystallography of proteins, and learning based medical computer vision.

Image segmentation is an important problem in image processing that has a wide range of applications in medicine, biomedicine and other fields of science and engineering. During the non-learning-based approaches, the techniques based on the partial differential equations and calculus of variation have attracted a lot of attention and acquired many achievements. Among the variational models, the Chan-Vese variational segmentation is a well-known model to solve the image segmentation problem. The level set methods are highly accurate methods to solve this model, and they do not depend on the edges. However, the performance of these methods depends on the level set function and its initial boundary too much. In this paper, we propose automatic initial boundary generation methods based on the edge detectors: Sobel, Prewitt, Roberts and Canny. In the experiments, we prove that among the four proposed initial boundary generation methods, the method based on the Canny edge detector brings the highest performance for the segmentation method. By combining the proposed initial boundary generation method based on the Canny edge detector, we implement the Chan-Vese model to segment biomedical images. Experimental results indicate we obtain improved segmentation results and compare different edge detectors in terms of performance.

Biomedical image processing is widely utilized for disease detection and classification of biomedical images. Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere. For removing the qualitative aspect, tongue images are quantitatively inspected, proposing a novel disease classification model in an automated way is preferable. This article introduces a novel political optimizer with deep learning enabled tongue color image analysis (PODL-TCIA) technique. The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue. To attain this, the PODL-TCIA model initially performs image pre-processing to enhance medical image quality. Followed by, Inception with ResNet-v2 model is employed for feature extraction. Besides, political optimizer (PO) with twin support vector machine (TSVM) model is exploited for image classification process, shows the novelty of the work. The design of PO algorithm assists in the optimal parameter selection of the TSVM model. For ensuring the enhanced outcomes of the PODL-TCIA model, a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.

Academic radiology the first six years in the new millenium 1

Edge detection is an essential procedure in biomedical image processing. Note that several edge detection schemes based on ghost imaging (GI) have been proposed, while a practical application environment is not considered. In this paper, we first investigate the edge detection of target objects inside biological tissue in a GI system. The performances of four edge detection operators are compared in GI through biological tissue when refractive index turbulence is considered. The numerical results demonstrate that the Standard operator performs better in edge extraction regardless of the strength of refractive index fluctuations. Our results may provide potential applications of GI in the field of biomedical imaging.

For the past few years, deep learning-based methods have been widely used in the field of biomedical imaging. In biomedical image processing, the typical application of deep learning is semantic segmentation. However, the classical deep learning methods require higher hardware consumption and computational costs. In order to resolve this problem, we propose a new lightweight fully convolutional network (L-FCN). L-FCN consists of traditional watershed algorithm and fully convolutional network, which eliminates useless non-edge pixels to improves the efficiency of semantic segmentation. Experimental evaluations on ISBI 2012 dataset indicate that our L-FCN algorithm outperforms U-net in time efficiency and computational costs, meanwhile, maintain approximate image segmentation effect. Due to its portability, L-FCN can be applied to many biomedical areas easily.

Knowledge discovery and computer-based decision support in biomedicine

It is estimated that approximately fifty image processing languages are in common use worldwide. Those languages which are specific to biomedical image analysis are the subject of this paper. In general, image processing languages range from those which are self-contained, i.e., include their own monitor, parser, interpretor, memory management, and input/output routines, to those which are simply collections of FORTRAN subroutines called by a command language using the command substitution system present in most modern computer operating systems. Biomedical image analysis systems are divided into (1) those which are available commercially for clin ical image processing and (2) those which are designed for research purposes, concentrating especially, on the analysis of images of cells and cellular architecture in tissues and, fi nally, in radiographic images produced by x-ray equipment and equipment for use in generating computerized tomograms.

UPAMNet: A unified network with deep knowledge priors for photoacoustic microscopy