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Contouring Blood Pool Myocardial Gated SPECT Images with a Sequence of Three Techniques Based on Wavelets, Neural Networks, and Fuzzy Logic
Published in Horia-Nicolai Teodorescu, Abraham Kandel, Lakhmi C. Jain, FUZZY and NEURO-FUZZY SYSTEMS in MEDICINE, 2017
Luis Patino, André Constantinesco, Ernest Hirsch
Medical imaging currently makes use of a wide range of non-invasive modalities such as Magnetic Resonance Imaging (MRI), Ultrasound, Computerized Tomography, Nuclear Imaging, and Radiography. Although each acquisition modality has its specific application domain, a common point relates them all. From the images acquired, the medical specialists have to recognize a given pathology after adequate presentation or processing of the data. However, due to various reasons (acquisition conditions, distortion, noise, attenuation of the signal, etc.), the images contain only variable amounts of approximated or sometimes incomplete information. In such situations, fuzzy logic [1], which permits us to deal with inaccurate or ill-defined data, is often applied in the field of medical imaging. Over the past years, an increasing number of applications using this approach have been reported in the literature. For example, Boegl et al. [2] have implemented a computer-assisted on-line diagnosis system, based on fuzzy reasoning, to detect rheumatic diseases in radiological images. The assessment of the age of bones based on features automatically extracted from hand radiographs is another example in the field [3]. Other applications of fuzzy logic and reasoning can be found for diagnosing chronic liver diseases in liver scintiscans; see, e.g., Shiomi S. et al. [4]. Further, in the domain of MRI, image segmentation to achieve tissue differentiation is described in several publications [5], [6]. Measuring the volumes of cerebrospinal fluid, and white matter and gray matter in brain images (see, as an example, [7]) is also frequently investigated. Bezdek published an interesting survey paper showing the fuzzy methods currently employed in medical imaging [8] and, in particular, emphasizing the fuzzy c-means clustering method.
Architecture, biometrics, and virtual environments triangulation: a research review
Published in Architectural Science Review, 2022
Non-invasive sensors are devices that do not require incision or injection (Bandodkar and Wang 2014). Neuroscience approaches and procedures deploy a wide range of tools and techniques to assess and map human responses and reactions to different somatosensory stimuli. Depending on the type of method, valence and arousal measurements can be recorded to acquire and analyse emotional, cognitive and behavioural information (Mauss and Robinson 2009). A body area sensor network allows the integration of intelligent, miniaturized, low-power sensor nodes in, on or around a human body to monitor body functions and the surrounding environment (Ullah et al. 2012). Small sensors can be placed on the human body to record various physiological parameters and send data to other devices so that further actions can be taken (Khan and Pathan 2018). Available methods and tools for brain measurement can make architects and designers aware of these effects on the human brain.
Graphene-based composites for biomedical applications
Published in Green Chemistry Letters and Reviews, 2022
Selsabil Rokia Laraba, Wei Luo, Amine Rezzoug, Qurat ul ain Zahra, Shihao Zhang, Bozhen Wu, Wen Chen, Lan Xiao, Yuhao Yang, Jie Wei, Yulin Li
In a review paper, Singh et al. (113) have indicated that GO-based materials could be used in biomedical as sensors for several applications such as DNA/RNA detection, Aptamer-based DNA detection, glucose detection, and cancer bio-sensing (113). In another review paper, Huang et al. (111) have thoroughly investigated graphene-based materials as sensors for human health monitoring. The authors give an overview of both non-invasive and invasive sensors by exploring their potential application (Figure 8). The non-invasive sensors are materials that do not infiltrate and break in the tissue or skin while in operation, which include detecting vital signals, such as biophysical signals (electrophysiological measurement, kinematic detection, thermometer), biochemical signals (metabolites, electrolyte, and volatile biomarker gases), and environmental signals (gases, light, heavy metals). On the other hand, the invasive sensors are more close to the target tissues or organs within human body significantly improve the sensing accuracy. Thereby, this kind of sensors exhibits huge interest in medical applications, such as implants for digestive system, nervous system (Figure 8(a, e)), cardiovascular system, and locomotor system (111).