Field Of Image Acquisition Research Articles

Conventional and artificial intelligence-based imaging for biomarker discovery in chronic liver disease.

Chronic liver diseases, resulting from chronic injuries of various causes, lead to cirrhosis with life-threatening complications including liver failure, portal hypertension, hepatocellular carcinoma. A key unmet medical need is robust non-invasive biomarkers to predict patient outcome, stratify patients for risk of disease progression and monitor response to emerging therapies. Quantitative imaging biomarkers have already been developed, for instance, liver elastography for staging fibrosis or proton density fat fraction on magnetic resonance imaging for liver steatosis. Yet, major improvements, in the field of image acquisition and analysis, are still required to be able to accurately characterize the liver parenchyma, monitor its changes and predict any pejorative evolution across disease progression. Artificial intelligence has the potential to augment the exploitation of massive multi-parametric data to extract valuable information and achieve precision medicine. Machine learning algorithms have been developed to assess non-invasively certain histological characteristics of chronic liver diseases, including fibrosis and steatosis. Although still at an early stage of development, artificial intelligence-based imaging biomarkers provide novel opportunities to predict the risk of progression from early-stage chronic liver diseases toward cirrhosis-related complications, with the ultimate perspective of precision medicine. This review provides an overview of emerging quantitative imaging techniques and the application of artificial intelligence for biomarker discovery in chronic liver disease.

Dual Optical Path Based Adaptive Compressive Sensing Imaging System.

Compressive Sensing (CS) has proved to be an effective theory in the field of image acquisition. However, in order to distinguish the difference between the measurement matrices, the CS imaging system needs to have a higher signal sampling accuracy. At the same time, affected by the noise of the light path and the circuit, the measurements finally obtained are noisy, which directly affects the imaging quality. We propose a dual-optical imaging system that uses the bidirectional reflection characteristics of digital micromirror devices (DMD) to simultaneously acquire CS measurements and images under the same viewing angle. Since deep neural networks have powerful modeling capabilities, we trained the filter network and the reconstruction network separately. The filter network is used to filter the noise in the measurements, and the reconstruction network is used to reconstruct the CS image. Experiments have proved that the method we proposed can filter the noise in the sampling process of the CS system, and can significantly improve the quality of image reconstruction under a variety of algorithms.

Improve Image Decoding in Lightweight Environment Using a Coroutines Based Approach

The JPEG2000 still image compression standard, while providing a remedy for the many shortcomings of its predecessor JPEG, is still slow to establish itself on the Internet. This problem is mainly due to the complexity of the COder-DECoder (CODEC) which implies its non-adoption by large firms and platforms in the field of image acquisition, processing and transmission. Indeed, the encoding and decoding process consumes a lot of CPU, memory and energy resources and takes a lot of computing time. The objective of this paper is to propose a model for decoding jpeg2000 on lightweight devices running on the Android mobile operating system. This implementation uses coroutines, which are a lightweight process model with reduced resource consumption costs compared to conventional AsyncTask threadsets. The model minimizes decoding time while minimizing CPU and memory usage, resulting in a fast and energetically economical decoded image. The results of integrating the coroutines from the main thread into the decoding process instead of the AsyncTask from the main thread produced better performance in terms of computation time, CPU and memory utilization. Indeed, the use of our model has led to a gain of around 23.41% in execution time, 9.8% in CPU utilization rate and 18.56% in memory utilization rate, compared to the model proposed in the literature which uses the threads.

Automated Analysis and Detailed Quantification of Biomedical Images Using Definiens Cognition Network Technology®

Biomedicine has seen tremendous advances in the field of image acquisition. The generation of digital images of high information content has become so straightforward and efficient that the volume of images accumulating in biomedical disciplines is posing significant challenges. Until now, conventional image analysis solutions are generally pixel-based and limited in the amount of information that they extract. However, a software system enabling the complex analysis of biomedical images should not impose restrictions on detection, classification and quantification of structures, but rather allow unlimited freedom to answer exhaustively all conceivable questions about the interactions and relationships between structures. Crucial to this is the precise and robust segmentation of relevant structures in digital micrographs. This challenge involves bringing structure, morphology and context into play. Based on the Definiens Cognition Network Technology, solutions have been deployed for use in biomedicine. The technology is object-oriented, multi-scale, context-driven and knowledge-based. Images are interpreted on the properties of networked image objects, which results in numerous advantages. This approach enables users to bring in detailed expert knowledge and enables complex analyses to be performed with unprecedented accuracy, even on poor quality data or for structures exhibiting heterogeneous properties or variable phenotypes. Extracted structures are the basis for detailed morphometric, structural and relational measurements which can be exported for each individual structure. These data can be used for decision support or correlated against experimental or molecular data, thus bridging classical biomedicine with molecular biology. An overview of the technology is provided with examples from different biomedical applications.