Using a PIM interface for improving computer medical image processing abilities

This book places an emphasis on the basic principles and recent advances in medical imaging technologies. After an introduction, the rest of the text can be categorized into three main parts. Part 1 provides a comprehensive tutorial on the fundamentals of medical imaging modalities and image processing methodologies. Part 2 highlights the cutting-edge imaging modalities for radiography and the recent developments of information technology in biomedicine. Part 3 provides some case studies and applications in radiation therapy and computer-aided diagnosis along with a perspective on future trends in medical and molecular imaging. The book is well organized and well presented. A concise summary before each chapter highlights the most important points of the text. This book can be used as a textbook for graduate courses and as an excellent reference for radiologists, neurologists, biomedical engineers, and researchers who are interested in imaging processing and modeling.

The fields of medical imaging and medical imaging instrumentation are increasingly important. The state-of-the-art continues to advance at a very rapid pace. In fact, various medical imaging modalities are under development at the National Synchrotron Light Source (such as MECT and Transvenous Angiography.) It is important to understand how these techniques compare with today`s more conventional imaging modalities. The purpose of this report is to provide some basic information about the various medical imaging technologies currently in use and their potential developments as a basis for this comparison. This report is by no means an in-depth study of the physics and instrumentation of the various imaging modalities; instead, it is an attempt to provide an explanation of the physical bases of these techniques and their principal clinical and research capabilities.

Artificial intelligence for medical imaging: U-Net technology for anatomical feature analysis

Many modern workloads, such as neural networks, databases, and graph processing, are fundamentally memory-bound. For such workloads, the data movement between main memory and CPU cores imposes a significant overhead in terms of both latency and energy. A major reason is that this communication happens through a narrow bus with high latency and limited bandwidth, and the low data reuse in memory-bound workloads is insufficient to amortize the cost of main memory access. Fundamentally addressing this data movement bottleneck requires a paradigm where the memory system assumes an active role in computing by integrating processing capabilities. This paradigm is known as processing-in-memory (PIM). Recent research explores different forms of PIM architectures, motivated by the emergence of new 3D-stacked memory technologies that integrate memory with a logic layer where processing elements can be easily placed. Past works evaluate these architectures in simulation or, at best, with simplified hardware prototypes. In contrast, the UPMEM company has designed and manufactured the first publicly-available real-world PIM architecture. The UPMEM PIM architecture combines traditional DRAM memory arrays with general-purpose in- order cores, called DRAM Processing Units (DPUs), integrated in the same chip. This paper provides the first comprehensive analysis of the first publicly-available real-world PIM architecture. We make two key contributions. First, we conduct an experimental characterization of the UPMEM-based PIM system using microbenchmarks to assess various architecture limits such as compute throughput and memory bandwidth, yielding new insights. Second, we present PrIM (Processing-In-Memory benchmarks), a benchmark suite of 16 workloads from different application domains (e.g., dense/sparse linear algebra, databases, data analytics, graph processing, neural networks, bioinformatics, image processing), which we identify as memory-bound. We evaluate the performance and ...

The discovery of X-ray in 1895 initiated the era of medical imaging diagnostics. Since then, medical imaging systems have realized unprecedented advancements. These systems have also turned out to be invaluable tools in the practice of diagnostic medicine. However, despite the significant development in medical imaging technologies, processing medical images still pose a substantial challenge especially when it comes to image segmentation. That problem is gradually being alleviated by the implementation of digital medical image processing, especially in the diagnosis and treatment of brain tumors. But the capability of most of the contemporary image delineating algorithms remains limited. Therefore, there is a need to come up with the new medical image segmentation programs to fully utilize the power of digital image processing. In light that, this article reviews some of the contemporary algorithmic protocols for brain tumor delineation systems and how effective they are.

This article introduces a medical imaging and image processing knowledge center which serves as an e-learning platform based on network technology. This platform includes contents of medical imaging and medical image processing for the teaching activities, virtual reality tutoring system of human skeleton, medical image database, etc. The purpose of establishing this platform is to recruit and excavate information records which usually associated with the daily data exchanged in teaching and research activities. Then it will gradually form an open professional knowledge database. At present, this platform plays the role of knowledge community in the educational activities of BME program in Tsinghua University.

Biomedical Engineering: Applications, Basis and CommunicationsVol. 19, No. 06, pp. 359-374 (2007) No AccessHYBRID REGISTRATION OF CORRESPONDING MAMMOGRAM IMAGES FOR AUTOMATIC DETECTION OF BREAST CANCERYih-Chih Chiou, Chern-Sheng Lin, and Cheng-Yu LinYih-Chih ChiouDepartment of Mechanical Engineering, Chung Hua University, Hsinchu, Taiwan, R. O. C. Search for more papers by this author , Chern-Sheng LinDepartment of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan, R. O. C.Corresponding author: Chern Sheng Lin, Professor, Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan, R. O. C. Search for more papers by this author , and Cheng-Yu LinDepartment of Mechanical Engineering, Chung Hua University, Hsinchu, Taiwan, R. O. C. Search for more papers by this author https://doi.org/10.4015/S101623720700046XCited by:2 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail AbstractMammogram registration is a critical step in automatic detection of breast cancer. Much research has been devoted to registering mammograms using either feature-matching or similarity measure. However, a few studies have been done on combining these two methods. In this research, a hybrid mammogram registration method for the early detection of breast cancer is developed by combining feature-based and intensity-based image registration techniques. Besides, internal and external features were used simultaneously during the registration to obtain a global spatial transformation. The experimental results indicates that the similarity between the two mammograms increases significantly after a proper registration using the proposed TPS-registration procedures.Keywords:Mutual informationMammogram registrationThin-plate splinesFeature matching References S. V. Engelandet al., IEEE Trans. Med. Imag. 22(11), 1436 (2003), DOI: 10.1109/TMI.2003.819273. Crossref, ISI, Google ScholarG. Ertas, H. O. Gulcur and K. Gursoy, Alignment of mammogram pairs, Proceedings of the Second Joint EMBS/BMES Conference2 (2002) pp. 1013–1014. Google ScholarF. L. Bookstein, IEEE Trans. pattern Anal. Machine Intell. 11(6), 567 (1989), DOI: 10.1109/34.24792. Crossref, ISI, Google ScholarL. G. Brown, ACM Computing Surveys 24(4), 325 (1992), DOI: 10.1145/146370.146374. Crossref, ISI, Google ScholarH. M. Chen, P. K. Varshney and M. K. Arora, IEEE Trans. Geosci. Remote Sensing 41(11), 2445 (2003), DOI: 10.1109/TGRS.2003.817664. Crossref, ISI, Google ScholarM. L. 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Rubin, Tracking mammographic structures over time, Proceedings of the 12th British Machine Vision Conference (2001) pp. 143–152. Google ScholarJ. P. W. Pluim, J. B. A. Maintz and M. A. Viergerver, IEEE Trans. Med. Imag. 22(8), 986 (2003), DOI: 10.1109/TMI.2003.815867. Crossref, ISI, Google ScholarD. Rueckertet al., Non-rigid Registration of Breast MR Images using Mutual Information, Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention (1998) pp. 1144–1152. Google ScholarM. Y. Sallam and K. W. Bowyer, Medical Image Analysis 3(2), 103 (1999), DOI: 10.1016/S1361-8415(99)80001-2. Crossref, Google ScholarR. Sivaramakrishna, Computer Systems Engineering, Medical Physics 25(11), 2249 (1998). ISI, Google ScholarE. A. Stamatakiset al., Detecting abnormalities on mammograms by bilateral comparison, IEEE Colloquium on Digital Mammography12 (1996) pp. 1–4. Google ScholarN. Vujovic and D. Brzakovic, IEEE Trans. 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FiguresReferencesRelatedDetailsCited By 2LYMPHATIC INFILTRATION DETECTION IN BREAST CANCER H&E IMAGE PRIOR TO LYMPHADENECTOMYYung-Lung Kuo, Chien-Chuan Ko, and Ming-Ji Lee6 August 2014 | Biomedical Engineering: Applications, Basis and Communications, Vol. 26, No. 04Visual Perception Driven Registration of MammogramsA. Boucher, F. Cloppet, N. Vincent and P. Jouve1 Aug 2010 Recommended Vol. 19, No. 06 Metrics History Accepted 13 November 2007 KeywordsMutual informationMammogram registrationThin-plate splinesFeature matchingPDF download

Today’s systems are capable of storing large amounts of data in main memory. Particularly, in-memory DBMSs benefit from this development. However, the processing of data from the main memory necessarily has to run via the CPU. This creates a bottleneck, which affects the possible performance of the DBMS. Processing-In-Memory (PIM) is a paradigm to overcome this problem, which was not available in commercial systems for a long time. With the availability of UPMEM, a commercial product is finally available that provides PIM technology in hardware. In this work, we focus on the acceleration of the table scan, a fundamental database query operation. We show and investigate an approach that can be used to optimize this operation by using PIM. We evaluate the PIM scan in terms of parallelism and execution time in benchmarks with different table sizes and compare it to a traditional CPU-based table scan. The result is a PIM table scan that outperforms the CPU-based scan significantly.

We study the problem of retrieving images from a large digital library, by searching the image contents with a specified pattern, using a low cost processing-in-memory (PIM) parallel processor architecture called Shamrock. We consider large databases with images that are rich in details, such as satellite and medical images. For the algorithmic part we focus on Fourier transforms and analyze the time complexities for performing the required operations on the Shamrock architecture. From the architectural point of view, we use a parallel processor architecture called Shamrock, which contains large amounts of memory and logic on the same chip, organized in a "mesh-like" structure. We conclude that content-based image processing, under some relaxed conditions, such as the existence of descriptors and special I/O organizations, can be done at high rate on a low cost PIM architecture, down to a single chip, by the year 2010.

Medical imaging technology is an important course in biomedical engineering. It is a multidisciplinary field integrating advanced technologies from physics, electronic engineering, computer science, engineering mathematics, material science, and fine processing. This course lays the foundation for the implementation of imaging diagnostics essential for medical automation. It enables participants to systematically grasp the fundamental knowledge in medical imaging principles, equipment, and system analysis, as well as to understand the direction of the latest developments in this field. This paper discusses the basic principles and performance of various basic imaging devices, such as X-ray imaging, magnetic resonance imaging, nuclear medicine imaging, and ultrasound imaging. In addition, it explores the systems involved and the future prospects of medical imaging technology.

In the last few years, with the rapid development of artificial intelligence (AI) technology, its application in the medical field has become more and more in-depth and extensive, especially the research of computer vision technology in medical image analysis and recognition has achieved many results. It is recognized by experts engaged in imaging diagnosis in the medical field, which has provided a great boost to the development of precision medicine. However, the application and development of artificial intelligence in the field of medical imaging is still in its infancy, and faces many technical and ethical challenges, such as data quality issues, data security issues and clinical application issues. The application of AI technology in medical imaging needs to be further adjusted and improved in combination with social development and actual needs. In this paper, we discussed the usages of artificial intelligence in medical imaging and provided new sights for the sustainable development and improvement in this area, as well as the challenges faced by others.

The Joint Commission in the United States disseminated a Sentinel Event Alert because of the number of adverse outcomes from problems with health information technology (HIT). The HITs were trading off safety and quality against throughput or efficiency. The Alert urged healthcare providers to improve process measurement and provide leadership in mitigating the risks. In order to understand what problems compromise safety and efficiency, this study has accessed, deconstructed, categorized and analyzed Australian patient safety incident reports of the things that go wrong in medical imaging, and their impact on both patients and the medical imaging acquisition and processing systems. Data Sources comprised two sets of voluntary incident reports and convenience samples of interviews with radiology staff. A special targeted search was undertaken for identifying HIT related incidents so that they could be deconstructed with the health information technology classification system. This resulted in 436 HIT related incidents. Within these incidents, 623 HIT related issues were found. These included use or human factor related issues (40%), software and hardware related issues (30%) and machine related issues (30%). Although many technical problems and deficiencies were detected in the reports identified, we did not anticipate that more than half of the incidents would have involved failures of human performance. Identifying and characterizing the things that are going wrong, related to HIT through the lens of medical imaging incident reports can provide a basis for preventing issues and improving clinical practice.

Medical Imaging has been playing one of the most important roles in medical diagnosis. As the technologies advance, so does medical imaging, and it gets an increasing role, not only in diagnosis, but also in treatment, such as image-guided therapies. Medical imaging also has a great impact on many research areas including neuroscience, drug development, phycology, psychiatry, cardiology, and so on. Advancement of the medical imaging technologies involves multidisciplinary research fields that encompass physics, medicine, electrical and mechanical engineering, biology and chemical engineering, etc. Considering the impact of medical imaging on clinics, research, and engineering, we select “Medical Imaging” as the theme of this Special Issue of Biomedical Engineering Letters. This special issue includes five review papers on recent advances of several medical imaging technologies and their applications. The first review paper entitled, “Model based filtered backprojection algorithm: a tutorial”, by Gengsheng L. Zeng, IEEE Fellow, USA, provides in-depth knowledge of very interesting new type of linear reconstruction algorithm with incorporation of noise models in it [1]. For many decades, the filtered backprojection (FBP) algorithm has been a popular choice in image reconstruction. However, recent developments of new medical imaging systems such as lowdose x-ray CT, nuclear medicine, and real-time MRI applications require more advanced reconstruction algorithms than the conventional FBP in order to incorporate measurement noise and system models. In recent years, many researchers have been developing various iterative algorithms to meet this demand and improve the final reconstructed images. However, the drawback of most iterative algorithms is the heavy computational cost. Therefore, it would be wonderful if we could have an FBP algorithm that performs comparable tasks to the iterative algorithms. In this first review paper, Dr. Zeng provides a way to develop such an FBP algorithm with incorporation of noise models, which may replace the state-of-the-art iterative algorithms. The second review paper, “Challenges and advances in MR imaging of the intervertebral disc could the cartilaginous endplate be a biomarker for the disc health?,” by Sung M. Moon, Ph.D., GE Healthcare, USA, provides an overview of new magnetic resonance imaging (MRI) techniques and findings on disc disease and cartilaginous endplate (CEP) [2]. A brief description on the anatomy of the CEP and intervertebral disc is followed by their roles in the disc Dosik Hwang ( ) School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea Tel : +82-2-2123-5771 E-mail : dosik.hwang@yonsei.ac.kr

The rapid development of medical practices and imaging technology tools creates substantial growth in the amount of medical image data each year in our present era. This research aims to develop a hybrid approach that integrates Machine Learning (ML) and Deep Learning (DL) techniques to enhance the accuracy and reliability of medical image classification for diagnostic purposes. Medical imaging data complexity and growing volume serve as the research motivation, which leads to an investigation of standalone ML or DL limitations and their combination into a single framework. The medical image processing starts with normalization, then noise reduction, and continues to grayscale conversion before performing histogram equalization. This research uses VGG16 and ResNet50 alongside MobileNet and InceptionV3 for feature extraction, then applies ten different ML algorithms, including SVM and MLP, and Ran-dom Forest, for classification. Five public medical image datasets from Kaggle are used: COVID-19 chest X-rays, melanoma skin lesions, pneumonia chest X-rays, acute stroke facial images, and various eye diseases. Hybrid models display superior performance compared to stand-alone ML or DL models based on accuracy, precision, recall, and F1-score evaluation measures. Multiple datasets demonstrate that the MobileNet+MLP combination de-livers the most accurate results, which demonstrates its reliable and efficient performance. The developed AI diagnostic tool presents a scalable system alongside accuracy and interpretability to enhance clinical decision outcomes.

Medical images are an important basis for medical diagnosis. Nowadays, Artificial intelligence algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. In the context of COVID-19, image recognition functions based on artificial intelligence have been gradually applied to the recognition of various medical images. In medical imaging, AI has shown great potential by revolutionizing diagnostic imaging, improving accuracy, efficiency, and improved patient outcomes. At present, the three types of AI application in the field of medical imaging mainly include image recognition and analysis, auxiliary diagnosis and treatment scheme optimization. Firstly, this paper introduces the current research progress of artificial intelligence technology. At the same time, the current deep learning algorithm based on one of the artificial intelligence algorithms has achieved good results in medical image processing, so as to carry out related application research. Secondly, according to the current situation of clinical diagnosis, aiming at the problems in the current society, this paper lists the existing research results. Finally, the future development trend of deep learning based on artificial intelligence technology is prospected, and the medical community is expected to seize the application opportunities of artificial intelligence in the medical field and meet the corresponding challenges.