Biomedical engineers have traditionally developed technologies in response to the needs of the developed world's medical community. As a result, the diagnostic systems on which they have worked have met the requirements of well-funded laboratories in highly regulated and quality-assessed environments. However, such approaches do not address the needs of the majority of the world's people afflicted with infectious diseases, who have, at best, access to poorly resourced health care facilities with almost no supporting clinical laboratory infrastructure. A major challenge for the biomedical engineering community is to develop diagnostic tests to meet the needs of these people, the majority of whom are in the developing world. We here review the context in which the diagnostics must operate, some of the appropriate diagnostic technologies already in distribution, and some emerging technologies that promise to address this challenge. However, there is much room for innovation, adaptation, and cost reduction be...

https://www.annualreviews.org/doi/pdf/10.1146/annurev.bioeng.10.061807.160524

Point-of-care diagnostics for global health.

Infrared and visible image fusion methods and applications: A survey

State of the art survey on MRI brain tumor segmentation.

Deep learning on image denoising: An overview.

The Self-Organizing Map (SOM) algorithm has attracted an ever increasing amount of interest among researches and practitioners in a wide variety of elds The SOM and a variant of it, the LVQ, have been analyzed extensively, a number of variants of them have been developed and, perhaps most notably, they have been applied extensively within elds ranging from engineering sciences to medicine, biology, and economics We have collected a comprehensive list of 3343 scienti c papers that use the algorithms, have bene ted from them, or contain analyses of them The list is intended to serve as a source for literature surveys We have provided both a thematic and a keyword index to help nding articles of interest

Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997

The preprocessing step of most computer-aided diagnosis (CAD) systems for identifying the lung diseases is lung segmentation. We present a novel lung segmentation technique based on watershed transform, which is fast and accurate. Lung region is precisely marked with internal and external markers. The markers are combined with the gradient image of the original data and watershed transform is applied on the combined data to find the lung borders. Rolling ball filter is used to smooth the contour and fill the cavities while preserving the original borders. The proposed method eliminates the tasks of finding an optimal threshold and separating the attached left and right lungs, which are two common practices in most lung segmentation methods and require a significant amount of time. We have applied our new approach on several pulmonary CT images and the results reveal the speed, robustness and accuracy of this method.

Automatic lung segmentation in CT images using watershed transform

Low-dose CT denoising is a challenging task that has been studied by many researchers. Some studies have used deep neural networks to improve the quality of low-dose CT images and achieved fruitful results. In this paper, we propose a deep neural network that uses dilated convolutions with different dilation rates instead of standard convolution helping to capture more contextual information in fewer layers. Also, we have employed residual learning by creating shortcut connections to transmit image information from the early layers to later ones. To further improve the performance of the network, we have introduced a non-trainable edge detection layer that extracts edges in horizontal, vertical, and diagonal directions. Finally, we demonstrate that optimizing the network by a combination of mean-square error loss and perceptual loss preserves many structural details in the CT image. This objective function does not suffer from over smoothing and blurring effects causing by per-pixel loss and grid-like artifacts resulting from perceptual loss. The experiments show that each modification to the network improves the outcome while changing the complexity of the network, minimally.

Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer

The authors present an unsupervised clustering technique for multispectral segmentation of magnetic resonance (MR) images of the human brain. Their scheme utilizes the Self Organizing Feature Map (SOFM) artificial neural network for feature mapping and generates a set of codebook vectors. By extending the network with an additional layer the map will be classified and each tissue class will be labeled. An algorithm has been developed for extracting the cerebrum from the head scan prior to the segmentation. Extracting the cerebrum is performed by stripping away the skull pixels from the T2 image. Three tissue types of the brain: white matter, gray matter and cerebral spinal fluid (CSF) are segmented accurately. To compare the results with other conventional approaches the authors applied the c-means algorithm to the problem.

Automatic segmentation of cerebral MR images using artificial neural networks

Presents a study investigating the potential of artificial neural networks (ANN's) for the classification and segmentation of magnetic resonance (MR) images of the human brain. In this study, the authors present the application of a learning vector quantization (LVQ) ANN for the multispectral supervised classification of MR images. The authors have modified the LVQ for better and more accurate classification. They have compared the results using LVQ ANN versus back-propagation ANN. This comparison shows that, unlike back-propagation ANN, the authors' method is insensitive to the gray-level variation of MR images between different slices. It shows that tissue segmentation using LVQ ANN also performs better and faster than that using back-propagation ANN.

Neural network based segmentation of magnetic resonance images of the brain

The light-scattering properties of submicroscopic metal particles ranging from 40 to 120 nm in diameter have recently been investigated. These particles scatter incident white light to generate monochromatic light, which can be seen either by the naked eye or by dark-field microscopy. The nanoparticles are well suited for detection in microchannel-based immunoassays. The goal of the present study was to detect Helicobacter pylori- and Escherichia coli O157:H7-specific antigens with biotinylated polyclonal antibodies. Gold particles (diameter, 80 nm) functionalized with a secondary antibiotin antibody were then used as the readout. A dark-field stereomicroscope was used for particle visualization in poly(dimethylsiloxane) microchannels. A colorimetric quantification scheme was developed for the detection of the visual color changes resulting from immune reactions in the microchannels. The microchannel immunoassays reliably detected H. pylori and E. coli O157:H7 antigens in quantities on the order of 10 ng, which provides a sensitivity of detection comparable to those of conventional dot blot assays. In addition, the nanoparticles within the microchannels can be stored for at least 8 months without a loss of signal intensity. This strategy provides a means for the detection of nanoparticles in microchannels without the use of sophisticated equipment. In addition, the approach has the potential for use for further miniaturization of immunoassays and can be used for long-term archiving of immunoassays.

Javad Alirezaie

Papers

Automatic lung segmentation in CT images using watershed transform

Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer

Automatic segmentation of cerebral MR images using artificial neural networks

Neural network based segmentation of magnetic resonance images of the brain

Development of a Nanoparticle-Labeled Microfluidic Immunoassay for Detection of Pathogenic Microorganisms