PURPOSE: In this study we propose a block-iterative method for reconstructing Compton scattered data. This study shows that the well-known expectation maximization (EM) approach along with its accelerated version based on the ordered subsets principle can be applied to the problem of image reconstruction for Compton camera. This study also compares several methods of constructing subsets for optimal performance of our algorithms. MATERIALS AND METHODS: Three reconstruction algorithms were implemented; simple backprojection (SBP), EM, and ordered subset EM (OSEM). For OSEM, the projection data were grouped into subsets in a predefined order. Three different schemes for choosing nonoverlapping subsets were considered; scatter angle-based subsets, detector position-based subsets, and both scatter angle- and detector position-based subsets. EM and OSEM with 16 subsets were performed with 64 and 4 iterations, respectively. The performance of each algorithm was evaluated in terms of computation time and normalized mean-squared error. RESULTS: Both EM and OSEM clearly outperformed SBP in all aspects of accuracy. The OSEM with 16 subsets and 4 iterations, which is equivalent to the standard EM with 64 iterations, was approximately 14 times faster in computation time than the standard EM. In OSEM, all of the three schemes for choosing subsets yielded similar results in computation time as well as normalized mean-squared error. CONCLUSION: Our results show that the OSEM algorithm, which have proven useful in emission tomography, can also be applied to the problem of image reconstruction for Compton camera. With properly chosen subset construction methods and moderate numbers of subsets, our OSEM algorithm significantly improves the computational efficiency while keeping the original quality of the standard EM reconstruction. The OSEM algorithm with scatter angle- and detector position-based subsets is most available.
Image Processing, Computer-Assisted

PURPOSE: To generate the axial, coronal and sagittal images from conventional simulation images, as a preliminary study of broad-beam simulator CT. METHODS AND MATERIALS: Volumetric filtered back-projection was performed using 90 sheets of films from conventional simulator for every 4. gantry angle. Two mAs exposure condition for 120kVp beam quality at SFD 140cm was given to each film. Outside the silhouette portion was removed and scatter component was deconvolved before back-projection. RESULTS: The axial, the sagittal and the coronal images with same spatial resolutions over all direction could be obtained. But image quality was very poor. CONCLUSION: CT images could be obtained using broad-beam. Scatter deconvolution technique was effective for this reconstruction. The fact that same spatial resolutions over all direction tells us the possiblility of application of this technique to DRR or Simulator-CT. But the quality of image should be improved for clinical application practicaly.
Image Processing, Computer-Assisted*

Medical image segmentation based on deep learning has become a powerful tool in the field of medical image processing. Due to the special nature of medical images, image segmentation algorithms based on deep learning face problems such as sample imbalance, edge blur, false positive, false negative, etc. In view of these problems, researchers mostly improve the network structure, but rarely improve from the unstructured aspect. The loss function is an important part of the segmentation method based on deep learning. The improvement of the loss function can improve the segmentation effect of the network from the root, and the loss function is independent of the network structure, which can be used in various network models and segmentation tasks in plug and play. Starting from the difficulties in medical image segmentation, this paper first introduces the loss function and improvement strategies to solve the problems of sample imbalance, edge blur, false positive and false negative. Then the difficulties encountered in the improvement of the current loss function are analyzed. Finally, the future research directions are prospected. This paper provides a reference for the reasonable selection, improvement or innovation of loss function, and guides the direction for the follow-up research of loss function.
Algorithms ; Image Processing, Computer-Assisted

Remarkable results have been realized by the U-Net network in the task of medical image segmentation. In recent years, many scholars have been researching the network and expanding its structure, such as improvement of encoder and decoder and improvement of skip connection. Based on the optimization of U-Net structure and its medical image segmentation techniques, this paper elucidates in the following: First, the paper elaborates on the application of U-Net in the field of medical image segmentation; Then, the paper summarizes the seven improvement mechanism of U-Net: dense connection mechanism, residual connection mechanism, multi-scale mechanism, ensemble mechanism, dilated mechanism, attention mechanism, and transformer mechanism; Finally, the paper states the ideas and methods on the U-Net structure improvement in a bid to provide a reference for later researches, which plays a significant part in advancing U-Net.
Image Processing, Computer-Assisted/methods*

OBJECTIVEDue to random sampling of non-adaptive, high-quality reconstruction of the original signal, one-dimensional non-stationary multi-channel EEG signal can be achieved automatic detection and analysis.

METHODSA new multicomponent redundant dictionaries with the atoms of the Gaussian function and its first and second derivatives was built in the paper, and reconstructed signal base on compressed sensing measurement model.

RESULTSThe selected dictionary atoms can more effectively match the EEG signals in a variety of transient characteristics of the waveform, allowing the formation of EEG signal is more sparse matching pursuit decomposition. With the theory based on compressed sensing signal sampling, only half of the original signal with different sample size can be used to reconstruct the original signal quality, the important instantaneous features of the waveform can well be maintained.

CONCLUSIONSignal sampling based on the theory of compressed sensing contains enough information of the original signal, using the prior conditions of EEG signals (or compressibility) sparsity, high-dimensional signal and original image can be reconstructed through a certain decoding of linear or nonlinear model.

In order to improve the storage and transmission efficiency of dynamic photoplethysmography (PPG) signals in the detection process and reduce the redundancy of signals, the modified adaptive matching pursuit (MAMP) algorithm was proposed according to the sparsity of the PPG signal. The proposed algorithm which is based on reconstruction method of sparse adaptive matching pursuit (SAMP), could improve the accuracy of the sparsity estimation of signals by using both variable step size and the double threshold conditions. After experiments on the simulated and the actual PPG signals, the results show that the modified algorithm could estimate the sparsity of signals accurately and quickly, and had good anti-noise performance. Contrasting with SAMP and orthogonal matching pursuit (OMP), the reconstruction speed of the algorithm was faster and the accuracy was high.
Algorithms ; Humans ; Image Processing, Computer-Assisted ; Photoplethysmography

The existing analog reordering and folding technology has the following problems: it cause the attenuation of the ultrasonic signal, and it is difficult to achieve beam steering in color Doppler ultrasonic diagnostic instrument. This article proposes a design method to achieve digital reordering of dynamic focusing data. The digital reordering is composed of two parts, bit reordering which is implemented with multiplier and byte reordering using switch selection. The results show that it can meet the design requirements using fewer resources.
Image Enhancement ; Signal Processing, Computer-Assisted ; Ultrasonics

Recently, medical image processing systems which take advantages of computer information technology have been used usefully in the hospitals. In particular, the 3D volume rendering technique with a sets of 2D medical images can visualize the organs of human body into 3D image virtually and such systems have started to be used widely to find diseases and to build up the plan of treatment. In this paper, we introduce a medical image processing system which uses a public graphic library VTK so that the system can be developed in the low price with the same power compared to other commercial systems. The system supports 2D and 3D image processing such as image editing, filtering, 3D image reconstruction and etc. by accepting multiple 2D original medical images.
Human Body ; Image Processing, Computer-Assisted

After a study on the characteristic of ECG data, we propose here in this paper a lossless compression method of ECG data, which is based on JPEG2000. It integrates both 1D and 2D compression. The method has been verified through all forty-eight records in MIT-BIH Arrhythmia database. And the result shows that the method has a better compression rate and a good computational efficiency.
Algorithms ; Electrocardiography ; methods ; Image Processing, Computer-Assisted

Image analysis plays an important role in morphological study of tissues and cells and their quantitative analysis. It also contributes to clinical pathological diagnosis. With the rapid development of computer technology great progress has been made in the image analysis system, its measurement, rapidity, accuracy and the extent of automation have been greatly enhanced. In the meantime advances in medicine give impetus to its improvement. In this paper, the process of development, the basic structure and mechanism of image analysis system are discussed and the application of image analysis system to the study of biomedicinal is presented.
Image Processing, Computer-Assisted ; instrumentation ; methods ; Medicine