Medical Imaging Research and Development

Artificial Intelligence - AI, Machine Learning - ML, Deep Learning - DL, Artificial Neural Networks - ANN, Computer Vision - CV, Machine Vision - MV, Virtual Reality - VR & Augmented Reality - AR for Medical Imaging


Design and Development of Artificial Intelligence, Machine Learning, Deep Learning, Artificial Neural Networks, Computer Vision, Machine Vision, Virtual Reality and Augmented Reality for Medical Imaging

HubBucket Medical Imaging ("HubMedx")

HubMedx is a research and development - R&D department within HubBucket, Inc. ("HubBucket") that focuses on the design and development of Artificial Intelligence - A.I., i.e.., Machine Learning - ML, Deep Learning - DL, Artificial Neural Networks - ANN, coupled with Machine Vision and Computer Vision to enhance the capabilities of Medical Imaging, for Healthcare Providers and Medical Researchers.

Medical imaging is the technique and process of creating visual representations of the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues (physiology). Medical imaging seeks to reveal internal structures hidden by the skin and bones, as well as to diagnose and treat disease. Medical imaging also establishes a database of normal anatomy and physiology to make it possible to identify abnormalities. Although imaging of removed organs and tissues can be performed for medical reasons, such procedures are usually considered part of pathology instead of medical imaging.

As a discipline and in its widest sense, it is part of biological imaging and incorporates radiology which uses the imaging technologies of X-ray radiography, magnetic resonance imaging, medical ultrasonography or ultrasound, endoscopy, elastography, tactile imaging, thermography, medical photography and nuclear medicine functional imaging techniques as positron emission tomography (PET) and Single-photon emission computed tomography (SPECT).

Measurement and recording techniques which are not primarily designed to produce images, such as electroencephalography (EEG), magnetoencephalography (MEG), electrocardiography (ECG), and others represent other technologies which produce data susceptible to representation as a parameter graph vs. time or maps which contain data about the measurement locations. In a limited comparison these technologies can be considered as forms of medical imaging in another discipline.

Up until 2010, 5 billion medical imaging studies had been conducted worldwide. Radiation exposure from medical imaging in 2006 made up about 50% of total ionizing radiation exposure in the United States.

Medical imaging is often perceived to designate the set of techniques that noninvasively produce images of the internal aspect of the body. In this restricted sense, medical imaging can be seen as the solution of mathematical inverse problems. This means that cause (the properties of living tissue) is inferred from effect (the observed signal). In the case of medical ultrasonography, the probe consists of ultrasonic pressure waves and echoes that go inside the tissue to show the internal structure. In the case of projectional radiography, the probe uses X-ray radiation, which is absorbed at different rates by different tissue types such as bone, muscle and fat.

The term noninvasive is used to denote a procedure where no instrument is introduced into a patient's body which is the case for most imaging techniques used.




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