Pub Date : 2013-07-30DOI: 10.7490/F1000RESEARCH.1113537.1
Zayapragassarazan Z, Santosh Kumar
In this pilot study an attempt was made to assess the training needs of telemedicine practioners and telemedicine project staff working in the telemedicine units. A pre-validated self administered ‘Telemedicine Training Needs Assessment Tool’ containing 12 items was administered among 92 telemedicine practioners and project staff working in various telemedicine units in India. The responses revealed the mixed expectations with regard to their training in telemedicine. About 28% of the respondents were found to possess high level of competencies, 43% possess moderate level of competencies and 29% possessed low level of competencies with respect to telemedicine and its related activities. The findings of the study suggest that although the respondents of the present study are involved in telemedicine related works three-fourth of them requires formal training in the telemedicine.
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Pub Date : 2012-09-05DOI: 10.3850/978-981-07-1403-1_871
M. Jain
Introduction Medical images should be subjected to loss-less compression, a technique that stems from mathematical theory of communication (Shannon, 1948). Loss-less compression techniques use variable length codes, (David Huffman, 1952). Compression ratio achieved is not very satisfactory in Huffman. Hence Run length encoding, yields a more effective compression algorithm that increases the compression ratio on medical images. Objectives - To provide lossless compression of Medical images by applying Wavelet transform and run length encoding - To provide security mechanism by eliminating the textual content in Medical images Proposed System With the DICOM standard, it is easy to eliminate textual information such as patient name and ID.We use Daubechies' wavelets and analysis techniques to detect the high frequency variation in the diagonal direction that is indicative of text. Only sensitive patient identification information is eliminated while retaining the medical information in the image. Encoding and decoding can be done by applying Run length technique. Excellent results have been obtained in experiments using a large set of real world medical images many with superimposed text. Methodology Matlab Software Version 7.0.1 consists of various modules: 1.The Input Module to retrieve the Medical Image as input. 2.Provide security feature by changing the DICOM unique identifier (UID). 3.Wavelet decomposition module to provide wavelet compression using Daubechies wavelet of order 2. 4.Compression Module to compress the input image by applying Run Length encoding. 5. Reconstruct original image from compressed image data applying Run Length decoding . 6.Wavelet reconstruction to decompress the image and extract the Original Image. Simulation Result and discussion Simulation: The Images used in this project are shown in the Figure below.The Images for transformation are scanned directly from IPRO GE SYTEC 1800-i CT SCANNER.These Images are in DICOM format and are then converted to .dcm. Results In this thesis we have developed a technique for wavelet transforms.Wavelet transform making it attractive both in terms of speed and memory needs and enhancing security features also. It is found that the proposed method gives more than 34% average improvement in the PSNR value in the bpp range of 0.0625 to 1.00 and highly reduction in Mean square error with a better quality of the reconstructed medical image judged on the basis of the human visual system (HVS). So, finally we can conclude that the proposed Wavelet based method is very suitable for low bit rate compression, high compression ratios, can perform lossless coding, high PSNR, low MSEs as well as good visual quality of the reconstructed medical image at low bit rates. It can also maintain the high diagnostic quality of the compressed image.
医学图像应该进行无损压缩，这是一种源于通信数学理论的技术(Shannon, 1948)。无损压缩技术使用可变长度代码，(David Huffman, 1952)。在霍夫曼下得到的压缩比并不令人满意。因此，运行长度编码产生了一种更有效的压缩算法，可以提高医学图像的压缩比。目的-应用小波变换和运行长度编码对医学图像进行无损压缩-通过消除医学图像中的文本内容提供安全机制建议系统使用DICOM标准，可以很容易地消除患者姓名和ID等文本信息。我们使用Daubechies的小波和分析技术来检测对角线方向的高频变化，这是文本的指示。在保留图像中的医疗信息的同时，仅消除敏感的患者识别信息。编码和解码可以通过应用运行长度技术来完成。在使用大量真实医学图像的实验中获得了出色的结果，其中许多图像带有叠加文本。Matlab Software Version 7.0.1由多个模块组成:检索医学图像作为输入的输入模块。2.通过更改DICOM唯一标识符(UID)提供安全特性。3.小波分解模块提供小波压缩使用的Daubechies小波的顺序为2。4.压缩模块通过应用运行长度编码来压缩输入图像。5. 利用行长度解码技术从压缩后的图像数据中重构原始图像。6.小波重构对图像进行解压缩，提取原始图像。仿真结果和讨论仿真:本项目中使用的图像如下图所示。用于转换的图像直接从IPRO GE SYTEC 1800-i CT扫描仪扫描。这些图像是DICOM格式，然后转换为。dcm。结果本文发展了一种小波变换技术。小波变换使其在速度和内存需求以及增强安全特性方面都具有吸引力。结果表明，该方法在bpp值为0.0625 ~ 1.00的范围内，PSNR值平均提高34%以上，均方误差大幅度降低，重建的医学图像具有较好的人眼视觉系统(HVS)质量。最后，我们可以得出结论，基于小波的方法非常适合于低比特率压缩，压缩比高，可以在低比特率下实现无损编码，高PSNR，低mse和良好的视觉质量。它还可以保持压缩图像的高诊断质量。
{"title":"Error Resilient Transmission and Security filtering of Medical Images","authors":"M. Jain","doi":"10.3850/978-981-07-1403-1_871","DOIUrl":"https://doi.org/10.3850/978-981-07-1403-1_871","url":null,"abstract":"Introduction \u0000Medical images should be subjected to loss-less compression, a technique that stems from mathematical theory of communication (Shannon, 1948). Loss-less compression techniques use variable length codes, (David Huffman, 1952). Compression ratio achieved is not very satisfactory in Huffman. Hence Run length encoding, yields a more effective compression algorithm that increases the compression ratio on medical images. \u0000 \u0000Objectives \u0000- To provide lossless compression of Medical images by applying Wavelet transform and run length encoding \u0000- To provide security mechanism by eliminating the textual content in Medical images \u0000 \u0000Proposed System \u0000With the DICOM standard, it is easy to eliminate textual information such as patient name and ID.We use Daubechies' wavelets and analysis techniques to detect the high frequency variation in the diagonal direction that is indicative of text. Only sensitive patient identification information is eliminated while retaining the medical information in the image. Encoding and decoding can be done by applying Run length technique. Excellent results have been obtained in experiments using a large set of real world medical images many with superimposed text. \u0000Methodology \u0000 \u0000Matlab Software Version 7.0.1 consists of various modules: \u00001.The Input Module to retrieve the Medical Image as input. \u00002.Provide security feature by changing the DICOM unique identifier (UID). \u00003.Wavelet decomposition module to provide wavelet compression using Daubechies wavelet of order 2. \u00004.Compression Module to compress the input image by applying Run Length encoding. \u00005. Reconstruct original image from compressed image data applying Run Length decoding . \u00006.Wavelet reconstruction to decompress the image and extract the Original Image. \u0000 \u0000Simulation Result and discussion \u0000Simulation: The Images used in this project are shown in the Figure below.The Images for transformation are scanned directly from IPRO GE SYTEC 1800-i CT SCANNER.These Images are in DICOM format and are then converted to .dcm. \u0000Results \u0000In this thesis we have developed a technique for wavelet transforms.Wavelet transform making it attractive both in terms of speed and memory needs and enhancing security features also. It is found that the proposed method gives more than 34% average improvement in the PSNR value in the bpp range of 0.0625 to 1.00 and highly reduction in Mean square error with a better quality of the reconstructed medical image judged on the basis of the human visual system (HVS). \u0000 \u0000So, finally we can conclude that the proposed Wavelet based method is very suitable for low bit rate compression, high compression ratios, can perform lossless coding, high PSNR, low MSEs as well as good visual quality of the reconstructed medical image at low bit rates. It can also maintain the high diagnostic quality of the compressed image.","PeriodicalId":91274,"journal":{"name":"Indian journal of medical informatics","volume":"6 1","pages":"52-55"},"PeriodicalIF":0.0,"publicationDate":"2012-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70004586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vijayaraghavan Bashyam, Craig Morioka, Suzie El-Saden, Alex At Bui, Ricky K Taira
A patient's electronic medical record contains a large number of medical reports and imaging studies. Identifying the relevant information in order to make a diagnosis can be a time consuming process that can easily overwhelm the physician. Summarizing key clinical information for physicians evaluating brain tumor patients is an ongoing research project at our institution. Notably, identifying documents associated with brain tumor is an important step in collecting the data relevant for summarization. Current electronic medical record systems lack meta-information which is useful in structuring heterogeneous medical information. Thus, identifying reports relevant to a particular task cannot be easily retrieved from a structured database. This necessitates content analysis methods for identifying relevant reports. This paper reports a system designed to identify brain-tumor related reports from an assorted collection of clinical reports. A large collection of clinical reports was obtained from our university hospital database. A domain expert manually annotated the documents classifying them into `related' and ùnrelated' categories. A multinomial naïve Bayes classifier was trained to use word level and UMLS concept level features from the reports to identify brain tumor related reports from the assorted collection. The system was trained on 90% and tested on 10% of the manually annotated corpus. A ten-fold cross validation is reported. Performance of the system was best (f-score 94.7) when the system was trained using both word level and UMLS concept level features. Using UMLS concepts improved classifier accuracy.
{"title":"Identifying relevant medical reports from an assorted report collection using the multinomial naïve Bayes classifier and the UMLS.","authors":"Vijayaraghavan Bashyam, Craig Morioka, Suzie El-Saden, Alex At Bui, Ricky K Taira","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>A patient's electronic medical record contains a large number of medical reports and imaging studies. Identifying the relevant information in order to make a diagnosis can be a time consuming process that can easily overwhelm the physician. Summarizing key clinical information for physicians evaluating brain tumor patients is an ongoing research project at our institution. Notably, identifying documents associated with brain tumor is an important step in collecting the data relevant for summarization. Current electronic medical record systems lack meta-information which is useful in structuring heterogeneous medical information. Thus, identifying reports relevant to a particular task cannot be easily retrieved from a structured database. This necessitates content analysis methods for identifying relevant reports. This paper reports a system designed to identify brain-tumor related reports from an assorted collection of clinical reports. A large collection of clinical reports was obtained from our university hospital database. A domain expert manually annotated the documents classifying them into `related' and ùnrelated' categories. A multinomial naïve Bayes classifier was trained to use word level and UMLS concept level features from the reports to identify brain tumor related reports from the assorted collection. The system was trained on 90% and tested on 10% of the manually annotated corpus. A ten-fold cross validation is reported. Performance of the system was best (f-score 94.7) when the system was trained using both word level and UMLS concept level features. Using UMLS concepts improved classifier accuracy.</p>","PeriodicalId":91274,"journal":{"name":"Indian journal of medical informatics","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2007-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9592058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142141994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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