Osteoarthritis is the most commonly seen arthritis, where there are 30.8 million adults affected in 2015. Magnetic resonance imaging (MRI) plays a key role to provide direct visualization and quantitative measurement on knee cartilage to monitor the osteoarthritis progression. However, the visual quality of MRI data can be influenced by poor background luminance, complex human knee anatomy, and indistinctive tissue contrast. Typical histogram equalisation methods are proven to be irrelevant in processing the biomedical images due to their steep cumulative density function (CDF) mapping curve which could result in severe washout and distortion on subject details. In this paper, the prominent region of interest contrast enhancement method (PROICE) is proposed to separate the original histogram of a 16-bit biomedical image into two Gaussians that cover dark pixels region and bright pixels region respectively. After obtaining the mean of the brighter region, where our ROI – knee cartilage falls, the mean becomes a break point to process two Bezier transform curves separately. The Bezier curves are then combined to replace the typical CDF curve to equalize the original histogram. The enhanced image preserves knee feature as well as region of interest (ROI) mean brightness. The image enhancement performance tests show that PROICE has achieved the highest peak signal-to-noise ratio (PSNR=24.747±1.315dB), lowest absolute mean brightness error (AMBE=0.020±0.007) and notably structural similarity index (SSIM=0.935±0.019). In other words, PROICE has considerably outperformed the other approaches in terms of its noise reduction, perceived image quality, its precision and has shown great potential to visually assist physicians in their diagnosis and decision-making process.
Brain computer interface (BCI) system empowers command over external device by retrieving brain waves and interpreting them into machine instructions. The system utilizes electroencephalogram (EEG) for receiving, processing and classifying signals to control by means of brain generated signals. The paper focused on mental task designs for BCI by acquiring the signals generated by mental activity using EEG comb electrodes, placed over three-dimensional (3D) printed headset. The experiment involved the blinking of left and right eyes for the forward and backward movements of the prototype wheelchair. The experimental measurement was performed using a Cyton board where the information was transmitted through Bluetooth which were later processed and translated to the wheelchair to perform activities. The system has successfully achieved the real time control of an assistive device by using signals from the brain.
Ultraviolet-C (UVC) sourced has been widely used for the purpose of disinfection due to its germicidal spectrum. In this study, the effectiveness of Everlight’s 275 nm Ultraviolet-C surface mounted device (UVC-SMD) to disinfect Staphylococcus aureus (S. aureus) was investigated at three exposure durations (10, 30 and 60 s) for a standard distance of 5 cm. The inhibition zones were greater with the increment of exposure duration. The highest records of 4.53 ± 0.03 cm were achieved when 102 mJ/cm² of dose was applied at a distance of 5 cm for 60 s. Whereas, on the other side, the lowest inhibition was seen when the exposure was set for 10 s. Thus, the Everlight 6565 UVC-SMD with 275 nm of wavelength is capable in providing bacterial disinfection which could possibly be used for the development of disinfection system based on SMDs at longer exposure duration.
Brain computer interface (BCI) system empowers command over external device by retrieving brain waves and interpreting them into machine instructions. The system utilizes electroencephalogram (EEG) for receiving, processing and classifying signals to control by means of brain generated signals. The paper focuses on mental task design for BCI by acquiring the signals generated by mental activity through EEG comb electrodes, placed over three-dimensional (3D) printed headset. The experiment involved the blinking of left and right eye for the forward and backward movements of the prototype wheelchair. The experimental measurement was performed using a Cyton board where the information was transmitted through Bluetooth which were later processed and translated to the wheelchair to perform activities. The system has successfully achieved the real time control of an assistive device by using signals from the brain.
<div>The automatic retinal disease diagnosis by artificial intelligent is an interesting and challenging topic in the medical field. It requires an appropriate image enhancement technique and a sufficient training dataset for the specific retina conditions. The aim of this study was to design an automatic diagnosis convolutional neural network (CNN) model which does not require a large training dataset to specifically identify diabetic retinopathy symptoms, which are cotton wool, exudates spots and red lesionin colour fundus pictures. A novel framework comprised image enhancement method by using upgraded contrast limited adaptive histogram equalization (UCLAHE) filter and transferred pre-trained networks was developed to classify the retinal diseases regarding to the symptoms. The performance of the proposed framework was evaluated based on accuracy, sensitivity and specificity metrics. The collected results have proven the robustness of the proposed framework in offering good accuracy in retina diseases diagnosis. </div>
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