Study on the Data Mining of Image Storage on the Hadoop Cloud Platform

2014 ◽  
Vol 543-547 ◽  
pp. 3667-3670 ◽  
Author(s):  
Jian Xin Zhu
Keyword(s):  
Data Mining ◽  
Satellite Remote Sensing ◽  
Medical Image ◽  
Rapid Development ◽  
Image Data ◽  
Challenging Problem ◽  
Cloud Platform ◽  
Huge Number ◽  
Image Storage ◽  
Usable Information

Along with the rapid development of image acquisition and image storage, a huge number of usable image data are obtained by people, such as satellite remote sensing image data, medical image data, etc. Data mining of images is to analyze these useful images and extract the usable information from them. How to effectively store rapidly make data mining for the increasing images has become the most challenging problem faced by people. This paper focuses on data mining of the massive images with the help of the Hadoop cloud platform.

scholarly journals Multi-Trend Structure Descriptor at Micro-Level for Histological Image Retrieval

2019 ◽  
Vol 8 (3) ◽  
pp. 7539-7543 ◽  
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Representation ◽  
Image Data ◽  
Similarity Measurement ◽  
Challenging Problem ◽  
Histological Image ◽  
Illumination Changes ◽  
Micro Level ◽  
Structure Descriptor

Since hospitals are generating and using image data extensively, medical image databases and its size are rising rapidly. This led to difficulties in browsing and managing the huge databases. Therefore, the necessity for the development of efficient content-based medical image retrieval (CBMIR) system arises and is more challenging problem for researchers. In this paper, to alleviate the unbalanced distribution of image representation using multi-trend structure descriptor (MTSD), MTSD is computed at micro level i.e., image is divided into number of sub-images and for each sub-image MTSD is exploited. In similarity measurement, we compared the MTSDs of corresponding sub-images in query and target images than the liner ordered collection of smallest similarity values between the sub-images are considered for retrieval. Experiments revels that computation of proposed feature at micro level retains the localized representation and considering the liner ordered collection of smallest similarity values between the sub-images provides consistency under illumination changes and noise and thus proposed CBMIR achieves better results.

Research on Application of Ultrasound Medical Imaging Technology in Big Data Mining of Regional Medical Imaging

2021 ◽  
Vol 11 (3) ◽  
pp. 930-937
Author(s):  
Yubo Xie
Keyword(s):  
Data Mining ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Images ◽  
Image Data ◽  
Image Feature ◽  
Big Data Mining ◽  
Regional Medical ◽  
Mining Methods ◽  
Medical Image Data

Ultrasound medical imaging technology is one of the main methods of medical non-invasive diagnosis, and it is the focus of research in the medical field at home and abroad. Medical images have a large amount of data and contain a wealth of image feature information and rules, which need to be studied and understood. Therefore, the research of data mining technique for reading medical images has become a very important field in the interdisciplinary research of medical and computer science. The high resolution of medical images, the mass of data, and the complexity of image feature expressions make the research of data mining technology in medical images of great academic value and broad application prospects. At present, research on data mining for medical images has just started, and there are still many problems in the direct application of existing data mining methods. Researching and exploring the theoretical and practical problems of medical image data mining, such as data mining methods and algorithms suitable for medical image, which has significant and crucial value, and it is of great importance to help physicians in clinical diagnosis of medical images. This article introduces the background, definition and basic process of data mining technology, the characteristics of medical imaging data and the key techniques of medical image data mining. In view of the data mining research of human abdominal medical images is a completely new field, human abdominal imaging is the most complicated part of medical images. Solving the problem of abdominal imaging is of great value to the entire medical image. For regional medical image big data mining, we can use ultrasound images of the human abdomen. The clustering feature extraction algorithm and its implementation based on the approximate density structure of medical images proposed in this article, and innovative research results such as classification rule mining methods, are used to mine medical image data research, automatic diagnosis of clinical medical images, and early diagnosis of clinical medicine are of great significance.

Image Conversion from DICOM into Bitmap Based on VC Platform

2012 ◽  
Vol 591-593 ◽  
pp. 2487-2490
Author(s):  
Wen Juan Gu ◽  
Li Nan Fan ◽  
Shen Shen Sun ◽  
Xiang Li Hu ◽  
Xin Wang
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Rapid Development ◽  
Processing System ◽  
Medical Image Processing ◽  
Image Data ◽  
Research Direction ◽  
Image Conversion ◽  
Medical Image Data ◽  
Processing Platform

With the rapid development of medical equipment (such as CT, MRT, PACS), medical image data that need to process has become increasingly rich, which makes the design of medical image processing platform become a popular research direction. Through analyzing one medical image with communication standard (the DICOM protocol) in this paper. Research in-depth how medical images are stored in computer, and how to transform a medical image into the format of bitmap so that to see a medical image on screen , and it is good to software workers and doctors ,for it can help them have a clearer understanding of the medical image, and help them to see diseases clearly, it is also the basis for the subsequent design of medical image processing system .

scholarly journals Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

scholarly journals Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1263
Author(s):  
Zhaojun Wang ◽  
Jiangning Wang ◽  
Congtian Lin ◽  
Yan Han ◽  
Zhaosheng Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Digital Technology ◽  
Rapid Development ◽  
Image Data ◽  
Image Database ◽  
Test Accuracy ◽  
Practical Application ◽  
Accuracy Rate ◽  
Deep Convolutional Neural Networks

With the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird images. Here, we demonstrate the recognition of habitat elements using four DCNN models trained end-to-end directly based on images. To carry out this research, an image database called Habitat Elements of Bird Images (HEOBs-10) and composed of 10 categories of habitat elements was built, making future benchmarks and evaluations possible. Experiments showed that good results can be obtained by all the tested models. ResNet-152-based models yielded the best test accuracy rate (95.52%); the AlexNet-based model yielded the lowest test accuracy rate (89.48%). We conclude that DCNNs could be efficient and useful for automatically identifying habitat elements from bird images, and we believe that the practical application of this technology will be helpful for studying the relationships between birds and habitat elements.

Fault Diagnosis of Automobile ECUs with Data Mining Technologies

2010 ◽  
Vol 40-41 ◽  
pp. 156-161 ◽  
Author(s):  
Yang Li ◽  
Yan Qiang Li ◽  
Zhi Xue Wang
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Data Stream ◽  
Electronic Control Unit ◽  
Rapid Development ◽  
Reliability And Validity ◽  
Control Unit ◽  
Use Of Data ◽  
The Cost

With the rapid development of automotive ECUs(Electronic Control Unit), the fault diagnosis becomes increasingly complicated. And the link between fault and symptom becomes less obvious. In order to improve the maintenance quality and efficiency, the paper proposes a fault diagnosis approach based on data mining technologies. By making full use of data stream, we firstly extract fault symptom vectors by processing data stream, and then establish a diagnosis decision tree through the ID3 decision tree algorithm, and finally store the link rules between faults and the related symptoms into historical fault database as a foundation for the fault diagnosis. The database provides the basis of trend judgments for a future fault. To verify this approach, an example of diagnosing faults of entertainment ECU is showed. The test result testifies the reliability and validity of this diagnostic method and reduces the cost of ECU diagnosis.

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