Simulation of Quantum Noise in the Low-Light-Level Imaging System

A mathematical model of quantum noise having much effect on the low light imaging system is set up. To simulate the quantum noise, the random numbers obeying noise distribution must be formed and are weighted on the basis of the model created. Three uniform random sequences are built by the linear congruential method, of which two are used to form integer number and decimal fraction parts of the new random sequence respectively and the third to shuffle the new sequence. And then a Gauss sequence is formed out of uniform distribution by a function transforming method. It actualizes the simulation in real time of quantum noise in the low light imaging system, where video flow is extracted in real time, the noise summed up and played back side by side with the original video signs by a simulation software.

Laser triangulation measurement of the level in a coal silo

Laser triangulation theory was used to develop a novel contact-free method for measuring the coal level in a silo under harsh environmental conditions found in coal mines, such as the presence of dense dust, high humidity, and low illumination. A laser source and a camera were mounted at the top of the silo. The laser spot projected into the silo was imaged by the camera. The pinhole imaging principle allows the level to be found from the lateral shift of the spot image on the sensor. A pre-calibrated look-up table of the coal depth versus spot position was used to obtain the depth. The measurement accuracy depends on the step size used during pre-calibration. The actual application of a device designed according to these principles shows that it is easy to implement. The detection of the coal level in a silo at the low illumination level found in coal mines is demonstrated.

Novel Frame Shift and Integral Technique for Enhancing Low-Light-Level Moving Images

A novel frame shift and integral technique for the enhancement of low light level moving image sequence is introduced. According to the technique, motion parameters of target are measured by algorithm based on difference processing. To obtain spatial relativity, images are shifted according to the motion parameters. As a result, the processing of integral and average can be applied to images that have been shifted. The technique of frame shift and integral that includes the algorithm of motion parameter determination is discussed, experiments with low light level moving image sequences are also described. The experiment results show the effectiveness and the robustness of the parameter determination algorithm, and the improvement in the signal-to-noise ratio （SNR） of low light level moving images.

BOLD-fMRI study of auditory cortex in patients with tinnitus

Objective Blood oxygenation level-dependent functional magnetic resonance imaging (BOLD-fMRI) was used to study activation signals in the brain cortex evoked by tone stimulation in patients with tinnitus for its potential utility as an objective indicator of tinnitus. Methods BOLD-fMRI examination was conducted in 7 patients with chronic tinnitus and 15 control subjects. The activation signal in the brain cortex was recorded. Results Significant activation was found in temporal lobe in control subjects, with greater signal volume and intensity on the contralateral than ipsilateral auditory cortex (P < 0.01). However, there was no discernable patterns in the anatomical location, volume and intensity of cortical activation signals in patients with chronic tinnitus. Conclusions Patients with chronic tinnitus may have abnormal neural activities in the auditory cortex.

MTF Measurement of EBCCD Imaging System by Using Super Resolution Technique

Existing methods of measurement MTF for discrete imaging system are analysed. A slit target is frequently used to measure the MTF for an imaging system. Usually there are four methods to measure the MTF for a discrete imaging system by using a slit. These methods have something imperfect respectively. But for the discrete imaging systems of under sampling it is difficult to reproduce this type of target properly since frequencies above Nyquist are folded into those below Nyquist, resulting in aliasing effect. To tackle the aliasing problem, a super resolution technique is introduced into our measurement, which gives MTF values both above and below Nyquist more accurately.

Detection of Angioectasias and Haemorrhages Incorporated into a Multi-Class Classification Tool for the GI Tract Anomalies by Using Binary CNNs

The proposed deep learning algorithm will be integrated as a binary classifier under the umbrella of a multi-class classification tool to facilitate the automated detection of non-healthy deformities, anatomical landmarks, pathological findings, other anomalies and normal cases, by examining medical endoscopic images of GI tract. Each binary classifier is trained to detect one specific non-healthy condition. The algorithm analyzed in the present work expands the ability of detection of this tool by classifying GI tract image snapshots into two classes, depicting haemorrhage and non-haemorrhage state. The proposed algorithm is the result of the collaboration between interdisciplinary specialists on AI and Data Analysis, Computer Vision, Gastroenterologists of four University Gastroenterology Departments of Greek Medical Schools. The data used are 195 videos (177 from non-healthy cases and 18 from healthy cases) videos captured from the PillCam(R) Medronics device, originated from 195 patients, all diagnosed with different forms of angioectasia, haemorrhages and other diseases from different sites of the gastrointestinal (GI), mainly including difficult cases of diagnosis. Our AI algorithm is based on convolutional neural network (CNN) trained on annotated images at image level, using a semantic tag indicating whether the image contains angioectasia and haemorrhage traces or not. At least 22 CNN architectures were created and evaluated some of which pre-trained applying transfer learning on ImageNet data. All the CNN variations were introduced, trained to a prevalence dataset of 50%, and evaluated of unseen data. On test data, the best results were obtained from our CNN architectures which do not utilize backbone of transfer learning. Across a balanced dataset from no-healthy images and healthy images from 39 videos from different patients, identified correct diagnosis with sensitivity 90%, specificity 92%, precision 91.8%, FPR 8%, FNR 10%. Besides, we compared the performance of our best CNN algorithm versus our same goal algorithm based on HSV colorimetric lesions features extracted of pixel-level annotations, both algorithms trained and tested on the same data. It is evaluated that the CNN trained on image level annotated images, is 9% less sensitive, achieves 2.6% less precision, 1.2% less FPR, and 7% less FNR, than that based on HSV filters, extracted from on pixel-level annotated training data.

Functional magnetic resonance imaging and the brain: A brief review

Functional magnetic resonance imaging(fMRI) is em-ployed in many behavior analysis studies, with blood oxygen level dependent-(BOLD-) contrast imaging being the main method used to generate images. The use of BOLD-contrast imaging in f MRI has been refined over the years, for example, the inclusion of a spin echo pulse and increased magnetic strength were shown to produce better recorded images. Taking careful precautions to control variables during measurement, comparisons between different specimen groups can be illustrated by f MRI imaging using both quantitative and qualitative methods. Differences have been observed in comparisons of active and resting, developing and aging, and defective and damaged brains in various studies. However, cognitive studies using f MRI still face a number of challenges in interpretation that can only be overcome by imaging large numbers of samples. Furthermore, f MRI studies of brain cancer, lesions and other brain pathologies of both humans and animals are still to be explored.

Multimodality magnetic resonance imaging in hepatic encephalopathy: An update

Hepatic encephalopathy(HE) is a neuropsychiatric complication of cirrhosis or acute liver failure. Currently, HE is regarded as a continuous cognitive impairment ranging from the mildest stage, minimal HE to overt HE. Hyperammonaemia and neuroinflammation are two main underlying factors which contribute to the neurological alterations in HE. Both structural and functional impairments are found in the white mater and grey mater involved in HE. Although the investigations into HE pathophysiological mechanism are enormous, the exact pathophysiological causes underlying HE remain controversial. Multimodality magnetic resonance imaging(MRI) plays an important role in helping to understand the pathological process of HE. This paper reviews the up-to-date multimodality MRI methods and predominant findings in HE patients with a highlight ofthe increasingly important role of blood oxygen level dependent functional MRI.

Double regularization control based on level set evolution for tablet packaging image segmentation

This paper proposes a novel double regular- ization control （DRC） method which is used for tablet packaging image segmentation. Since the intensities of tablet packaging images are inhomogenous, it is difficult to make image segmentation. Compared to methods based on level set, the proposed DRC method has some advantages for tablet packaging image segmentation. The local re- gional control term and the rectangle initialization contour are first employed in this method to quickly segment un- even grayscale images and accelerate the curve evolution rate. Gaussian filter operator and the convolution calcula- tion are then adopted to remove the effects of texture noises in image segmentation. The developed penalty energy function, as regularization term, increases the constrained conditions based on the gradient flow conditions. Since the potential function is embedded into the level set of evo- lution equations and the image contour evolutions are bi- laterally extended, the proposed method further improves the accuracy of image contours. Experimental studies show that the DRC method greatly improves the computational efficiency and numerical accuracy, and achieves better results for image contour segmentation compared to other level set methods.