Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000最新文献

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Texture based classification of mass abnormalities in mammograms 乳房x光片中肿块异常的纹理分类
Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000 Pub Date : 2000-06-23 DOI: 10.1109/CBMS.2000.856894
Sooncheol Baeg, N. Kehtarnavaz
{"title":"Texture based classification of mass abnormalities in mammograms","authors":"Sooncheol Baeg, N. Kehtarnavaz","doi":"10.1109/CBMS.2000.856894","DOIUrl":"https://doi.org/10.1109/CBMS.2000.856894","url":null,"abstract":"This paper presents a scheme for the classification of mass abnormalities in digitized or digital mammograms based on two novel image texture features. The first texture feature provides a measure of smoothness/denseness and is obtained by applying a morphological operator to maxima/minima image points. The second texture feature reflects a measure of architectural distortion and is derived from image gradients. A three-layer backpropagation neural network is used as the classifier. The performance of the classification scheme is evaluated by carrying out a receiver operating characteristic (ROC) analysis. Classification of 150 biopsy proven masses into benign and malignant classes resulted in a ROC area of 0.91. The results obtained demonstrate the potential of using this scheme as an electronic second opinion to lower the number of unnecessary biopsies.","PeriodicalId":189930,"journal":{"name":"Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130406165","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}
引用次数: 25
Advanced local feature selection in medical diagnostics 医学诊断中的高级局部特征选择
Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000 Pub Date : 2000-06-23 DOI: 10.1109/CBMS.2000.856868
S. Puuronen, A. Tsymbal, Iryna Skrypnyk
{"title":"Advanced local feature selection in medical diagnostics","authors":"S. Puuronen, A. Tsymbal, Iryna Skrypnyk","doi":"10.1109/CBMS.2000.856868","DOIUrl":"https://doi.org/10.1109/CBMS.2000.856868","url":null,"abstract":"Current electronic data repositories contain enormous amounts of data, especially in medical domains, where data is often feature-space heterogeneous, so that different features have different importance in different sub-areas of the whole space. In this paper, we suggest a technique that searches for a strategic splitting of the feature space, identifying the best subsets of features for each instance. Our technique is based on the wrapper approach, where a classification algorithm is used as the evaluation function to differentiate between several feature subsets. We apply a recently developed technique for the dynamic integration of classifiers and use decision trees. For each test instance, we consider only those feature combinations that include features that are present in the path taken by the test instance in the decision tree. We evaluate our technique on medical data sets from the UCI machine learning repository. The experiments show that local feature selection is often advantageous in comparison with feature selection on the whole space.","PeriodicalId":189930,"journal":{"name":"Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129240968","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}
引用次数: 19
Parallel implementation of a MR-mammography matching algorithm 一种磁共振乳房x线摄影匹配算法的并行实现
Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000 Pub Date : 2000-06-23 DOI: 10.1109/CBMS.2000.856891
K. Dirk, M. Rainer, W. Aldo
{"title":"Parallel implementation of a MR-mammography matching algorithm","authors":"K. Dirk, M. Rainer, W. Aldo","doi":"10.1109/CBMS.2000.856891","DOIUrl":"https://doi.org/10.1109/CBMS.2000.856891","url":null,"abstract":"We present a parallel matching component of an integrated system for the automatic analysis of MRI-breast images towards the early detection of breast cancer. The system operates on images using the method of dynamic contrast-enhanced MRI. Suspicious breast lesions are automatically marked with colours, thus directing the physician's attention towards the critical regions. A proper and careful decision procedure is needed to differentiate between increases of signal intensity triggered by noise and tissue dislocations (motion artifacts) and increases that are triggered by an accumulation of contrast agent in the related breast region. We present our component for image matching using self organising maps (SOM), which enables the system to work properly even with image sequences that are strongly deformed by the patients breathing movements. To reach the time constraint of 15 minutes in medical practice we decide to implement a parallel architecture for the neural network matcher, which works on all computers in the heterogeneous network of our medical partners. The system is tested on real patient data and is now being refined in cooperation with our partner hospital for Radiology and Nuclear Medicine in Mainz.","PeriodicalId":189930,"journal":{"name":"Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130579669","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}
引用次数: 1
Analysis of 1/f fluctuation in walking using gyro sensor system 陀螺传感系统对行走1/f波动的分析
Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000 Pub Date : 2000-06-23 DOI: 10.1109/CBMS.2000.856882
M. Tsuruoka, R. Shibasaki, Y. Yasuoka, S. Murai, S. Minakuchi, Y. Tsuruoka
{"title":"Analysis of 1/f fluctuation in walking using gyro sensor system","authors":"M. Tsuruoka, R. Shibasaki, Y. Yasuoka, S. Murai, S. Minakuchi, Y. Tsuruoka","doi":"10.1109/CBMS.2000.856882","DOIUrl":"https://doi.org/10.1109/CBMS.2000.856882","url":null,"abstract":"Presents a useful walking analyser system (WAS). The set-up for acquiring data is a small wearable personal computer (WPC) assisted system. It employs a lithium battery-powered gyro-sensor. While walking, the WPC is worn like a wrist-watch, the small sensor is fixed to the person's back, near the body's centre of gravity, and the batteries are inserted into a waist pouch. When the WPC switch is turned on, three relative angles (i.e. roll, pitch and yaw) and accelerations in the X, Y and Z axes of the person's back are recorded at 30 Hz. People who wear dentures were selected as subjects at the department of geriatric dentistry in a dental hospital. It was observed that, in the case of people wearing dentures, the angular displacements and the accelerations of the back in walking had a better rhythm compared to those without dentures. As an effective analyser system for walking stability, power spectral analysis is used, utilizing autoregressive modelling. In the case of people not wearing dentures, their power spectrum densities of the three angles and accelerations of back fluctuation were wavy, showing unstable walking. In the case of people wearing dentures, their power spectrum densities were approximately close to a 1/f/sup 3/ fluctuation (f = frequency), i.e. they were stable when walking in all directions. Using this WAS, it is easy to discover people's walking stability in rehabilitation and physical fitness.","PeriodicalId":189930,"journal":{"name":"Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126763610","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}
引用次数: 9
Detecting and correcting failed segmentations of radiological images using a knowledge-based approach 使用基于知识的方法检测和纠正放射图像的失败分割
Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000 Pub Date : 2000-06-23 DOI: 10.1109/CBMS.2000.856896
A. V. Wangenheim, H. Wagner, D. Krechel, Peter Conrad
{"title":"Detecting and correcting failed segmentations of radiological images using a knowledge-based approach","authors":"A. V. Wangenheim, H. Wagner, D. Krechel, Peter Conrad","doi":"10.1109/CBMS.2000.856896","DOIUrl":"https://doi.org/10.1109/CBMS.2000.856896","url":null,"abstract":"The segmentation of images with poor contrast characteristics is an important issue in medical computer vision. Often image segmentation results are either oversegmented, with \"objects\" divided into parts, or incorrectly segmented, with two or more anatomies segmented as one single object. This problem occurs in all types of segmentation approaches, but is of particular importance in the field of region-growing algorithms, which are used in many medical applications, presenting the definition of stable and reliable segmentation parameters. We present a new knowledge-based method, based on an extension of the inexact consistent labelling method, that enables the automated consistency checking of the results of region-growing segmentations and is capable to automatically \"fitting\" erroneous segmentations, when they are oversegmented, when there exists a reliable domain model that can be used to guide a tree search procedure in the space. This allows the use of oversensitive parameters when an exact segmentation is not reliable.","PeriodicalId":189930,"journal":{"name":"Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126878059","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}
引用次数: 3
Use of shape models to search digitized spine X-rays 使用形状模型搜索数字化脊柱x射线
Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000 Pub Date : 2000-06-22 DOI: 10.1109/CBMS.2000.856908
L. Long, G. Thoma
{"title":"Use of shape models to search digitized spine X-rays","authors":"L. Long, G. Thoma","doi":"10.1109/CBMS.2000.856908","DOIUrl":"https://doi.org/10.1109/CBMS.2000.856908","url":null,"abstract":"We are building a biomedical information resource consisting of digitized X-ray images and associated textual data from national health surveys. This resource, the Web-based Medical Information Retrieval System, or WebMIRS, is currently in beta test. In a future WebMIRS system, we plan to have not only text and raw image data, but quantitative anatomical feature information derived from the images and capability to retrieve images based on image characteristics, either alone or in conjunction with text descriptions associated with the images. Our archive consists of data collected in the second and third National Health and Nutrition Examination Surveys (NHANES), conducted by the National Center for Health Statistics. For the NHANES II survey, the records contain information for approximately 20,000 participants. Each record contains about two thousand data points, including demographic information, answers to health questionnaires, anthropometric information, and the results of a physician's examination. In addition, approximately 10,000 cervical spine and 7,000 lumbar spine X-rays were collected. WebMIRS makes the text and images retrievable. Only raw images are returned; no quantitative or descriptive information about the images is stored in the database. We are conducting research into the problem of automatically or semi-automatically segmenting spine vertebrae in these images and determining vertebral boundaries with enough accuracy to be useful in classifying the vertebrae into categories of interest to researchers in osteoarthritis.","PeriodicalId":189930,"journal":{"name":"Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134565274","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}
引用次数: 47
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