Objective: To design a upper computer software which can achieve data acquisition, display, motion control for sickbed-control test system. Methods: In Visual C++6.0 environment, take advantage of Advantech's development kit, and integrate multi-thread and dual-buffer technology to achieve. PC translate user's intentions into control commands, then sent commands to PCI1240, PCI1716 through the PCI interface, PCI1240 drive stepper motor to control the movement of the bed, while PCI1716 collect movement state information. Results: Movement can be stopped immediately by clicking the stop button even during the reciprocating motion, and solve screen flicker when drawing the real-time curves. The software has been test in bed-control system many times and achieved good results. Conclusions: This paper's method realized the sickbed's motion control, data acquisition, data storage and display, compared with the method that using single chip machine and general electromotor, our method makes bed movement more precise and smooth, more function are achieved, and the software has been successfully used in the sickbed-control system.

Objective: To summarize the major progress in medical image registration in recent years. Furthermore, based on the recent advances in this field, this paper can provide a reference in following domains: three-dimensional medical image reconstruction, medical image visualization, quantitative analysis. Methods: Firstly, referring to a myriad of latest papers on medical image registration. Secondly, analyzing traits and exiting problems of techniques which presented in those papers. Finally, putting forward some efficient methods for solving these problems. Results: This paper compares the characteristics of some typical algorithms and its application and looks forward to the future research work. Conclusion: Using optimization strategy to improve the quality of image registration and studying on non-rigid image registration are the directions for future research in medical image registration field.

Multi?modality medical image processing has become a hot topic for research in the field of image processing and plays an important role in clinical diagnosis and treatment. Images with different modalities provide different information on patients. Anatomical images ( such as computed tomography and magnetic resonance imaging ) provide information on anatomical morphology and the structure of human body, and functional images ( such as single?photon emission computed tomography and positron emission tomography) provide the functional information on the distribution of radioactive concentration within human body. Such information needs to be fused to obtain comprehensive fusion images, and the images with different modalities need to be registered to obtain useful fusion images. This article reviews several image registration and fusion techniques used in the medical field, points out their advantages and shortcomings, and introduces the application of various processing techniques in clinical practice.

In this paper, an improved empirical mode decomposition (EMD) algorithm for phonocardiogram (PCG) signal de-noising is proposed. Based on PCG signal processing theory, the S1/S2 components can be extracted by combining the improved EMD-Wavelet algorithm and Shannon energy envelope algorithm. Firstly, by applying EMD-Wavelet algorithm for pre-processing, the PCG signal was well filtered. Then, the filtered PCG signal was saved and applied in the following processing steps. Secondly, time domain features, frequency domain features and energy envelope of the each intrinsic mode function's (IMF) were computed. Based on the time frequency domain features of PCG's IMF components which were extracted from the EMD algorithm and energy envelope of the PCG, the S1/S2 components were pinpointed accurately. Meanwhile, a detecting fixed method, which was based on the time domain processing, was proposed to amend the detection results. Finally, to test the performance of the algorithm proposed in this paper, a series of experiments was contrived. The experiments with thirty samples were tested for validating the effectiveness of the new method. Results of test experiments revealed that the accuracy for recognizing S1/S2 components was as high as 99.75%. Comparing the results of the method proposed in this paper with those of traditional algorithm, the detection accuracy was increased by 5.56%. The detection results showed that the algorithm described in this paper was effective and accurate. The work described in this paper will be utilized in the further studying on identity recognition.
Algorithms ; Humans ; Phonocardiography ; Signal Processing, Computer-Assisted

Computer-aided detection (CAD) of pulmonary nodule technology can effectively assist the radiologist to enhance lung nodule detection efficiency and accuracy rate, so it can lay the foundation for the early diagnosis of lung cancer. In order to provide reference for the scholars and to develop the CAD technology, we in this paper review the technology research and development of CAD of the pulmonary nodules which is based on CT image in recent years both home and abroad. At the same time, we also analyse the advantages and shortcomings of different methods. Then we present the improvement direction for reference. According to the literature in recent years, there still has been large development space in CAD technology for pulmonary nodules. The establishment and improvement of the CAD system in each step would be of great scientific value.
Computer Systems ; Diagnosis, Computer-Assisted ; Humans ; Lung ; pathology ; Lung Neoplasms ; diagnosis ; Software ; Tomography, X-Ray Computed

This paper presents a probability segmentation algorithm for lung nodules based on three-dimensional features. Firstly, we computed intensity and texture features in region of interest (ROI) pixel by pixel to get their feature vector, and then classified all the pixels based on their feature vector. At last, we carried region growing on the classified result, and got the final segmentation result. Using the public Lung Imaging Database Consortium (LIDC) lung nodule datasets, we verified the performance of proposed method by comparing the probability map within LIDC datasets, which was drawn by four radiology doctors separately. The experimental results showed that the segmentation algorithm using three-dimensional intensity and texture features would be effective.
Algorithms ; Databases, Factual ; Humans ; Imaging, Three-Dimensional ; Lung ; pathology ; Probability

In this paper, we present an experimental research on the frameless registration of DSA/CT images based on frameless localization algorithm. The result shows that, 3D fusion and registration of vessels in the DSA images and anatomical structures in CT images will help surgeons to make accurate diagnosis and on plann operative.
Algorithms ; Angiography, Digital Subtraction ; methods ; Cerebral Angiography ; Humans ; Image Processing, Computer-Assisted ; methods ; Imaging, Three-Dimensional ; Tomography, X-Ray Computed ; methods

Lung cancer is one of the most serious malignant tumors threatening human health. Early detection and accurate risk assessment are crucial for improving the survival rate and prognosis of lung cancer patients. In this review paper, the research progress in early-stage lung cancer risk assessment methods based on predictive factors and medical imaging was summarized. The results indicated that by utilizing more diverse machine learning algorithms and larger-scale datasets, independent risk prediction factors can be further mined to achieve an accurate assessment of individual lung cancer risk.

Objective:To design a lung function prediction method that combines transfer learning and multimodal feature fusion, aiming to improve the accuracy of lung function prediction in patients with idiopathic pulmonary fibrosis (IPF).Methods:CT images and clinical text data were reprocessed, and an adaptive module was designed to find the most suitable lung function attenuation function for IPF patients. The feature extraction module was utilized to comprehensively extract features. The feature extraction module comprises three sub-modules, including CT feature extraction, clinical text feature extraction, and lung function feature extraction. A multimodal feature prediction network was used to comprehensively evaluate the attenuation of lung function. The pre-trained model was fine-tuned to improve the predictive performance of the model.Results:Based on the OSIC pulmonary fibrosis progression competition dataset, it is found through the adaptive module that the linear attenuation hypothesis is more in line with the trend of pulmonary function decline in patients. Different modal data prediction experiments show that the model incorporating clinical text features has better predictive ability than the model using only CT images. The model combining CT images, clinical text features, and lung function features have optimal predictive results. The lung function prediction method combining transfer learning and multimodal feature fusion has modified version of the Laplace log likelihood (LLLm) of ?6.706 5, root mean squared error (RMSE) of 184.5, and mean absolute error (MAE) of 146.2, which outperforms other methods in terms of performance. The pre-trained model has higher prediction accuracy compared to the zero base training model.Conclusions:The lung function prediction method designed by combining transfer learning and multimodal feature fusion can effectively predict the lung function status of IPF patients at different weeks, providing important support for patient health management and disease diagnosis.

Rectal cancer is one of the most common gastrointestinal malignancies in China. Accurate and reasonable assessment of the preoperative staging of rectal cancer can significantly enhance treatment outcomes and improve patient prognosis. Magnetic resonance imaging is the technique of choice for local staging of rectal cancer and has significant advantages in the diagnosis of rectal primary tumors (T) and peri-intestinal lymph nodes (N). In this review paper, the research ideas and progress of traditional radiomics and deep learning methods for preoperative TN staging prediction of rectal cancer were reviewed around multimodal magnetic resonance images, with the aim of providing new ideas for realizing fully automated TN staging algorithms for rectal cancer.