Optic cup segmentation from fundus images for glaucoma diagnosis

PubMed Central

Hu, Man; Zhu, Chenghao; Li, Xiaoxing; Xu, Yongli

2017-01-01

ABSTRACT Glaucoma is a serious disease that can cause complete, permanent blindness, and its early diagnosis is very difficult. In recent years, computer-aided screening and diagnosis of glaucoma has made considerable progress. The optic cup segmentation from fundus images is an extremely important part for the computer-aided screening and diagnosis of glaucoma. This paper presented an automatic optic cup segmentation method that used both color difference information and vessel bends information from fundus images to determine the optic cup boundary. During the implementation of this algorithm, not only were the locations of the 2 types of information points used, but also the confidences of the information points were evaluated. In this way, the information points with higher confidence levels contributed more to the determination of the final cup boundary. The proposed method was evaluated using a public database for fundus images. The experimental results demonstrated that the cup boundaries obtained by the proposed method were more consistent than existing methods with the results obtained by ophthalmologists. PMID:27764542

Analytical Procedures for Testability.

DTIC Science & Technology

1983-01-01

Beat Internal Classifications", AD: A018516. "A System of Computer Aided Diagnosis with Blood Serum Chemistry Tests and Bayesian Statistics", AD: 786284...6 LIST OF TALS .. 1. Truth Table ......................................... 49 2. Covering Problem .............................. 93 3. Primary and...quential classification procedure in a coronary care ward is evaluated. In the toxicology field "A System of Computer Aided Diagnosis with Blood Serum

[A computer-aided image diagnosis and study system].

PubMed

Li, Zhangyong; Xie, Zhengxiang

2004-08-01

The revolution in information processing, particularly the digitizing of medicine, has changed the medical study, work and management. This paper reports a method to design a system for computer-aided image diagnosis and study. Combined with some good idea of graph-text system and picture archives communicate system (PACS), the system was realized and used for "prescription through computer", "managing images" and "reading images under computer and helping the diagnosis". Also typical examples were constructed in a database and used to teach the beginners. The system was developed by the visual developing tools based on object oriented programming (OOP) and was carried into operation on the Windows 9X platform. The system possesses friendly man-machine interface.

Comparison of image features calculated in different dimensions for computer-aided diagnosis of lung nodules

NASA Astrophysics Data System (ADS)

Xu, Ye; Lee, Michael C.; Boroczky, Lilla; Cann, Aaron D.; Borczuk, Alain C.; Kawut, Steven M.; Powell, Charles A.

2009-02-01

Features calculated from different dimensions of images capture quantitative information of the lung nodules through one or multiple image slices. Previously published computer-aided diagnosis (CADx) systems have used either twodimensional (2D) or three-dimensional (3D) features, though there has been little systematic analysis of the relevance of the different dimensions and of the impact of combining different dimensions. The aim of this study is to determine the importance of combining features calculated in different dimensions. We have performed CADx experiments on 125 pulmonary nodules imaged using multi-detector row CT (MDCT). The CADx system computed 192 2D, 2.5D, and 3D image features of the lesions. Leave-one-out experiments were performed using five different combinations of features from different dimensions: 2D, 3D, 2.5D, 2D+3D, and 2D+3D+2.5D. The experiments were performed ten times for each group. Accuracy, sensitivity and specificity were used to evaluate the performance. Wilcoxon signed-rank tests were applied to compare the classification results from these five different combinations of features. Our results showed that 3D image features generate the best result compared with other combinations of features. This suggests one approach to potentially reducing the dimensionality of the CADx data space and the computational complexity of the system while maintaining diagnostic accuracy.

Computer-aided diagnosis of early knee osteoarthritis based on MRI T2 mapping.

PubMed

Wu, Yixiao; Yang, Ran; Jia, Sen; Li, Zhanjun; Zhou, Zhiyang; Lou, Ting

2014-01-01

This work was aimed at studying the method of computer-aided diagnosis of early knee OA (OA: osteoarthritis). Based on the technique of MRI (MRI: Magnetic Resonance Imaging) T2 Mapping, through computer image processing, feature extraction, calculation and analysis via constructing a classifier, an effective computer-aided diagnosis method for knee OA was created to assist doctors in their accurate, timely and convenient detection of potential risk of OA. In order to evaluate this method, a total of 1380 data from the MRI images of 46 samples of knee joints were collected. These data were then modeled through linear regression on an offline general platform by the use of the ImageJ software, and a map of the physical parameter T2 was reconstructed. After the image processing, the T2 values of ten regions in the WORMS (WORMS: Whole-organ Magnetic Resonance Imaging Score) areas of the articular cartilage were extracted to be used as the eigenvalues in data mining. Then,a RBF (RBF: Radical Basis Function) network classifier was built to classify and identify the collected data. The classifier exhibited a final identification accuracy of 75%, indicating a good result of assisting diagnosis. Since the knee OA classifier constituted by a weights-directly-determined RBF neural network didn't require any iteration, our results demonstrated that the optimal weights, appropriate center and variance could be yielded through simple procedures. Furthermore, the accuracy for both the training samples and the testing samples from the normal group could reach 100%. Finally, the classifier was superior both in time efficiency and classification performance to the frequently used classifiers based on iterative learning. Thus it was suitable to be used as an aid to computer-aided diagnosis of early knee OA.

Computer-Aided Diagnosis of Breast Cancer: A Multi-Center Demonstrator

DTIC Science & Technology

1998-10-01

Artificial Neural Network (ANN) approach to computer aided diagnosis of breast cancer from mammographic findings. An ANN has been developed to provide support for the clinical decision to perform breast biopsy. The system is designed to aid in the decision to biopsy those patients who have suspicious mammographic findings. The decision to biopsy can be viewed as a two stage process: 1)the mammographer views the mammogram and determines the presence or absence of image features such as calcifications and masses, 2) the presence and description of these features

Enhancements in medicine by integrating content based image retrieval in computer-aided diagnosis

NASA Astrophysics Data System (ADS)

Aggarwal, Preeti; Sardana, H. K.

2010-02-01

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. With cad, radiologists use the computer output as a "second opinion" and make the final decisions. Retrieving images is a useful tool to help radiologist to check medical image and diagnosis. The impact of contentbased access to medical images is frequently reported but existing systems are designed for only a particular context of diagnosis. The challenge in medical informatics is to develop tools for analyzing the content of medical images and to represent them in a way that can be efficiently searched and compared by the physicians. CAD is a concept established by taking into account equally the roles of physicians and computers. To build a successful computer aided diagnostic system, all the relevant technologies, especially retrieval need to be integrated in such a manner that should provide effective and efficient pre-diagnosed cases with proven pathology for the current case at the right time. In this paper, it is suggested that integration of content-based image retrieval (CBIR) in cad can bring enormous results in medicine especially in diagnosis. This approach is also compared with other approaches by highlighting its advantages over those approaches.

Computer-assisted education and interdisciplinary breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Whatmough, Pamela; Gale, Alastair G.; Wilson, A. R. M.

1996-04-01

The diagnosis of breast disease for screening or symptomatic women is largely arrived at by a multi-disciplinary team. We report work on the development and assessment of an inter- disciplinary computer based learning system to support the diagnosis of this disease. The diagnostic process is first modelled from different viewpoints and then appropriate knowledge structures pertinent to the domains of radiologist, pathologist and surgeon are depicted. Initially the underlying inter-relationships of the mammographic diagnostic approach were detailed which is largely considered here. Ultimately a system is envisaged which will link these specialties and act as a diagnostic aid as well as a multi-media educational system.

Computer-aided diagnosis of leukoencephalopathy in children treated for acute lymphoblastic leukemia

NASA Astrophysics Data System (ADS)

Glass, John O.; Li, Chin-Shang; Helton, Kathleen J.; Reddick, Wilburn E.

2005-04-01

The purpose of this study was to use objective quantitative MR imaging methods to develop a computer-aided diagnosis tool to differentiate white matter (WM) hyperintensities as either leukoencephalopathy (LE) or normal maturational processes in children treated for acute lymphoblastic leukemia with intravenous high dose methotrexate. A combined imaging set consisting of T1, T2, PD, and FLAIR MR images and WM, gray matter, and cerebrospinal fluid a priori maps from a spatially normalized atlas were analyzed with a neural network segmentation based on a Kohonen Self-Organizing Map. Segmented regions were manually classified to identify the most hyperintense WM region and the normal appearing genu region. Signal intensity differences normalized to the genu within each examination were generated for two time points in 203 children. An unsupervised hierarchical clustering algorithm with the agglomeration method of McQuitty was used to divide data from the first examination into normal appearing or LE groups. A C-support vector machine (C-SVM) was then trained on the first examination data and used to classify the data from the second examination. The overall accuracy of the computer-aided detection tool was 83.5% (299/358) with sensitivity to normal WM of 86.9% (199/229) and specificity to LE of 77.5% (100/129) when compared to the readings of two expert observers. These results suggest that subtle therapy-induced leukoencephalopathy can be objectively and reproducibly detected in children treated for cancer using this computer-aided detection approach based on relative differences in quantitative signal intensity measures normalized within each examination.

Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods.

PubMed

Shan, Juan; Alam, S Kaisar; Garra, Brian; Zhang, Yingtao; Ahmed, Tahira

2016-04-01

This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs categories were designed and tested using a database of 283 pathology-proven benign and malignant lesions. Features were selected based on classification performance using a "bottom-up" approach for different machine learning methods, including decision tree, artificial neural network, random forest and support vector machine. Using 10-fold cross-validation on the database of 283 cases, the highest area under the receiver operating characteristic (ROC) curve (AUC) was 0.84 from a support vector machine with 77.7% overall accuracy; the highest overall accuracy, 78.5%, was from a random forest with the AUC 0.83. Lesion margin and orientation were optimum features common to all of the different machine learning methods. These features can be used in CAD systems to help distinguish benign from worrisome lesions. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. All rights reserved.

[Computer aided diagnosis model for lung tumor based on ensemble convolutional neural network].

PubMed

Wang, Yuanyuan; Zhou, Tao; Lu, Huiling; Wu, Cuiying; Yang, Pengfei

2017-08-01

The convolutional neural network (CNN) could be used on computer-aided diagnosis of lung tumor with positron emission tomography (PET)/computed tomography (CT), which can provide accurate quantitative analysis to compensate for visual inertia and defects in gray-scale sensitivity, and help doctors diagnose accurately. Firstly, parameter migration method is used to build three CNNs (CT-CNN, PET-CNN, and PET/CT-CNN) for lung tumor recognition in CT, PET, and PET/CT image, respectively. Then, we aimed at CT-CNN to obtain the appropriate model parameters for CNN training through analysis the influence of model parameters such as epochs, batchsize and image scale on recognition rate and training time. Finally, three single CNNs are used to construct ensemble CNN, and then lung tumor PET/CT recognition was completed through relative majority vote method and the performance between ensemble CNN and single CNN was compared. The experiment results show that the ensemble CNN is better than single CNN on computer-aided diagnosis of lung tumor.

Combining destination diversion decisions and critical in-flight event diagnosis in computer aided testing of pilots

NASA Technical Reports Server (NTRS)

Rockwell, T. H.; Giffin, W. C.; Romer, D. J.

1984-01-01

Rockwell and Giffin (1982) and Giffin and Rockwell (1983) have discussed the use of computer aided testing (CAT) in the study of pilot response to critical in-flight events. The present investigation represents an extension of these earlier studies. In testing pilot responses to critical in-flight events, use is made of a Plato-touch CRT system operating on a menu based format. In connection with the typical diagnostic problem, the pilot was presented with symptoms within a flight scenario. In one problem, the pilot has four minutes for obtaining the information which is needed to make a diagnosis of the problem. In the reported research, the attempt has been made to combine both diagnosis and diversion scenario into a single computer aided test. Tests with nine subjects were conducted. The obtained results and their significance are discussed.

Sinus barotrauma--late diagnosis and treatment with computer-aided endoscopic surgery.

PubMed

Larsen, Anders Schermacher; Buchwald, Christian; Vesterhauge, Søren

2003-02-01

Sinus barotrauma is usually easy to diagnose, and treatment achieves good results. We present two severe cases where delayed diagnosis caused significant morbidity. The signs and symptoms were atypical and neither the patients themselves, nor the initial examiners recognized that the onset of symptoms coincided with descent in a commercial airliner. CT and MRI scans of the brain were normal, but in both cases showed opafication of the sphenoid sinuses, which lead to the correct diagnosis. Subsequent surgical intervention consisting of endoscopic computer-aided surgery showed blood and petechia in the affected sinuses. This procedure provided immediate relief.

Development of a Computer-Aided Diagnosis System for Early Detection of Masses Using Retrospectively Detected Cancers on Prior Mammograms

DTIC Science & Technology

2006-06-01

Hadjiiski, and N. Petrick, "Computerized nipple identification for multiple image analysis in computer-aided diagnosis," Medical Physics 31, 2871...candidates, 3 identification of suspicious objects, 4 feature extraction and analysis, and 5 FP reduc- tion by classification of normal tissue...detection of microcalcifi- cations on digitized mammograms.41 An illustration of a La- placian decomposition tree is shown on the left-hand side of Fig. 4

[Clinical skills and outcomes of chair-side computer aided design and computer aided manufacture system].

PubMed

Yu, Q

2018-04-09

Computer aided design and computer aided manufacture (CAD/CAM) technology is a kind of oral digital system which is applied to clinical diagnosis and treatment. It overturns the traditional pattern, and provides a solution to restore defect tooth quickly and efficiently. In this paper we mainly discuss the clinical skills of chair-side CAD/CAM system, including tooth preparation, digital impression, the three-dimensional design of prosthesis, numerical control machining, clinical bonding and so on, and review the outcomes of several common kinds of materials at the same time.

Computer-aided diagnosis in radiological imaging: current status and future challenges

NASA Astrophysics Data System (ADS)

Doi, Kunio

2009-10-01

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. Many different types of CAD schemes are being developed for detection and/or characterization of various lesions in medical imaging, including conventional projection radiography, CT, MRI, and ultrasound imaging. Commercial systems for detection of breast lesions on mammograms have been developed and have received FDA approval for clinical use. CAD may be defined as a diagnosis made by a physician who takes into account the computer output as a "second opinion". The purpose of CAD is to improve the quality and productivity of physicians in their interpretation of radiologic images. The quality of their work can be improved in terms of the accuracy and consistency of their radiologic diagnoses. In addition, the productivity of radiologists is expected to be improved by a reduction in the time required for their image readings. The computer output is derived from quantitative analysis of radiologic images by use of various methods and techniques in computer vision, artificial intelligence, and artificial neural networks (ANNs). The computer output may indicate a number of important parameters, for example, the locations of potential lesions such as lung cancer and breast cancer, the likelihood of malignancy of detected lesions, and the likelihood of various diseases based on differential diagnosis in a given image and clinical parameters. In this review article, the basic concept of CAD is first defined, and the current status of CAD research is then described. In addition, the potential of CAD in the future is discussed and predicted.

Computer-aided testing of pilot response to critical in-flight events

NASA Technical Reports Server (NTRS)

Giffin, W. C.; Rockwell, T. H.

1984-01-01

This research on pilot response to critical in-flight events employs a unique methodology including an interactive computer-aided scenario-testing system. Navigation displays, instrument-panel displays, and assorted textual material are presented on a touch-sensitive CRT screen. Problem diagnosis scenarios, destination-diversion scenarios and combined destination/diagnostic tests are available. A complete time history of all data inquiries and responses is maintained. Sample results of diagnosis scenarios obtained from testing 38 licensed pilots are presented and discussed.

Statistical-techniques-based computer-aided diagnosis (CAD) using texture feature analysis: application in computed tomography (CT) imaging to fatty liver disease

NASA Astrophysics Data System (ADS)

Chung, Woon-Kwan; Park, Hyong-Hu; Im, In-Chul; Lee, Jae-Seung; Goo, Eun-Hoe; Dong, Kyung-Rae

2012-09-01

This paper proposes a computer-aided diagnosis (CAD) system based on texture feature analysis and statistical wavelet transformation technology to diagnose fatty liver disease with computed tomography (CT) imaging. In the target image, a wavelet transformation was performed for each lesion area to set the region of analysis (ROA, window size: 50 × 50 pixels) and define the texture feature of a pixel. Based on the extracted texture feature values, six parameters (average gray level, average contrast, relative smoothness, skewness, uniformity, and entropy) were determined to calculate the recognition rate for a fatty liver. In addition, a multivariate analysis of the variance (MANOVA) method was used to perform a discriminant analysis to verify the significance of the extracted texture feature values and the recognition rate for a fatty liver. According to the results, each texture feature value was significant for a comparison of the recognition rate for a fatty liver ( p < 0.05). Furthermore, the F-value, which was used as a scale for the difference in recognition rates, was highest in the average gray level, relatively high in the skewness and the entropy, and relatively low in the uniformity, the relative smoothness and the average contrast. The recognition rate for a fatty liver had the same scale as that for the F-value, showing 100% (average gray level) at the maximum and 80% (average contrast) at the minimum. Therefore, the recognition rate is believed to be a useful clinical value for the automatic detection and computer-aided diagnosis (CAD) using the texture feature value. Nevertheless, further study on various diseases and singular diseases will be needed in the future.

Evaluation of computer-aided detection and diagnosis systems.

PubMed

Petrick, Nicholas; Sahiner, Berkman; Armato, Samuel G; Bert, Alberto; Correale, Loredana; Delsanto, Silvia; Freedman, Matthew T; Fryd, David; Gur, David; Hadjiiski, Lubomir; Huo, Zhimin; Jiang, Yulei; Morra, Lia; Paquerault, Sophie; Raykar, Vikas; Samuelson, Frank; Summers, Ronald M; Tourassi, Georgia; Yoshida, Hiroyuki; Zheng, Bin; Zhou, Chuan; Chan, Heang-Ping

2013-08-01

Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. Computer-aided detection systems mark regions of an image that may reveal specific abnormalities and are used to alert clinicians to these regions during image interpretation. Computer-aided diagnosis systems provide an assessment of a disease using image-based information alone or in combination with other relevant diagnostic data and are used by clinicians as a decision support in developing their diagnoses. While CAD systems are commercially available, standardized approaches for evaluating and reporting their performance have not yet been fully formalized in the literature or in a standardization effort. This deficiency has led to difficulty in the comparison of CAD devices and in understanding how the reported performance might translate into clinical practice. To address these important issues, the American Association of Physicists in Medicine (AAPM) formed the Computer Aided Detection in Diagnostic Imaging Subcommittee (CADSC), in part, to develop recommendations on approaches for assessing CAD system performance. The purpose of this paper is to convey the opinions of the AAPM CADSC members and to stimulate the development of consensus approaches and "best practices" for evaluating CAD systems. Both the assessment of a standalone CAD system and the evaluation of the impact of CAD on end-users are discussed. It is hoped that awareness of these important evaluation elements and the CADSC recommendations will lead to further development of structured guidelines for CAD performance assessment. Proper assessment of CAD system performance is expected to increase the understanding of a CAD system's effectiveness and limitations, which is expected to stimulate further research and development efforts on CAD technologies, reduce problems due to improper use, and eventually improve the utility and efficacy of CAD in clinical practice.

Evaluation of computer-aided detection and diagnosis systemsa)

PubMed Central

Petrick, Nicholas; Sahiner, Berkman; Armato, Samuel G.; Bert, Alberto; Correale, Loredana; Delsanto, Silvia; Freedman, Matthew T.; Fryd, David; Gur, David; Hadjiiski, Lubomir; Huo, Zhimin; Jiang, Yulei; Morra, Lia; Paquerault, Sophie; Raykar, Vikas; Samuelson, Frank; Summers, Ronald M.; Tourassi, Georgia; Yoshida, Hiroyuki; Zheng, Bin; Zhou, Chuan; Chan, Heang-Ping

2013-01-01

Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. Computer-aided detection systems mark regions of an image that may reveal specific abnormalities and are used to alert clinicians to these regions during image interpretation. Computer-aided diagnosis systems provide an assessment of a disease using image-based information alone or in combination with other relevant diagnostic data and are used by clinicians as a decision support in developing their diagnoses. While CAD systems are commercially available, standardized approaches for evaluating and reporting their performance have not yet been fully formalized in the literature or in a standardization effort. This deficiency has led to difficulty in the comparison of CAD devices and in understanding how the reported performance might translate into clinical practice. To address these important issues, the American Association of Physicists in Medicine (AAPM) formed the Computer Aided Detection in Diagnostic Imaging Subcommittee (CADSC), in part, to develop recommendations on approaches for assessing CAD system performance. The purpose of this paper is to convey the opinions of the AAPM CADSC members and to stimulate the development of consensus approaches and “best practices” for evaluating CAD systems. Both the assessment of a standalone CAD system and the evaluation of the impact of CAD on end-users are discussed. It is hoped that awareness of these important evaluation elements and the CADSC recommendations will lead to further development of structured guidelines for CAD performance assessment. Proper assessment of CAD system performance is expected to increase the understanding of a CAD system's effectiveness and limitations, which is expected to stimulate further research and development efforts on CAD technologies, reduce problems due to improper use, and eventually improve the utility and efficacy of CAD in clinical practice. PMID:23927365

Computer-aided diagnosis workstation and network system for chest diagnosis based on multislice CT images

NASA Astrophysics Data System (ADS)

Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru

2008-03-01

Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis likelihood by using helical CT scanner for the lung cancer mass screening. The function to observe suspicious shadow in detail are provided in computer-aided diagnosis workstation with these screening algorithms. We also have developed the telemedicine network by using Web medical image conference system with the security improvement of images transmission, Biometric fingerprint authentication system and Biometric face authentication system. Biometric face authentication used on site of telemedicine makes "Encryption of file" and Success in login" effective. As a result, patients' private information is protected. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new telemedicine network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our telemedicine network system can increase diagnostic speed, diagnostic accuracy and security improvement of medical information.

Deep-reasoning fault diagnosis - An aid and a model

NASA Technical Reports Server (NTRS)

Yoon, Wan Chul; Hammer, John M.

1988-01-01

The design and evaluation are presented for the knowledge-based assistance of a human operator who must diagnose a novel fault in a dynamic, physical system. A computer aid based on a qualitative model of the system was built to help the operators overcome some of their cognitive limitations. This aid differs from most expert systems in that it operates at several levels of interaction that are believed to be more suitable for deep reasoning. Four aiding approaches, each of which provided unique information to the operator, were evaluated. The aiding features were designed to help the human's casual reasoning about the system in predicting normal system behavior (N aiding), integrating observations into actual system behavior (O aiding), finding discrepancies between the two (O-N aiding), or finding discrepancies between observed behavior and hypothetical behavior (O-HN aiding). Human diagnostic performance was found to improve by almost a factor of two with O aiding and O-N aiding.

An evaluation of consensus techniques for diagnostic interpretation

NASA Astrophysics Data System (ADS)

Sauter, Jake N.; LaBarre, Victoria M.; Furst, Jacob D.; Raicu, Daniela S.

2018-02-01

Learning diagnostic labels from image content has been the standard in computer-aided diagnosis. Most computer-aided diagnosis systems use low-level image features extracted directly from image content to train and test machine learning classifiers for diagnostic label prediction. When the ground truth for the diagnostic labels is not available, reference truth is generated from the experts diagnostic interpretations of the image/region of interest. More specifically, when the label is uncertain, e.g. when multiple experts label an image and their interpretations are different, techniques to handle the label variability are necessary. In this paper, we compare three consensus techniques that are typically used to encode the variability in the experts labeling of the medical data: mean, median and mode, and their effects on simple classifiers that can handle deterministic labels (decision trees) and probabilistic vectors of labels (belief decision trees). Given that the NIH/NCI Lung Image Database Consortium (LIDC) data provides interpretations for lung nodules by up to four radiologists, we leverage the LIDC data to evaluate and compare these consensus approaches when creating computer-aided diagnosis systems for lung nodules. First, low-level image features of nodules are extracted and paired with their radiologists semantic ratings (1= most likely benign, , 5 = most likely malignant); second, machine learning multi-class classifiers that handle deterministic labels (decision trees) and probabilistic vectors of labels (belief decision trees) are built to predict the lung nodules semantic ratings. We show that the mean-based consensus generates the most robust classi- fier overall when compared to the median- and mode-based consensus. Lastly, the results of this study show that, when building CAD systems with uncertain diagnostic interpretation, it is important to evaluate different strategies for encoding and predicting the diagnostic label.

Computer-Assisted Digital Image Analysis of Plus Disease in Retinopathy of Prematurity.

PubMed

Kemp, Pavlina S; VanderVeen, Deborah K

2016-01-01

The objective of this study is to review the current state and role of computer-assisted analysis in diagnosis of plus disease in retinopathy of prematurity. Diagnosis and documentation of retinopathy of prematurity are increasingly being supplemented by digital imaging. The incorporation of computer-aided techniques has the potential to add valuable information and standardization regarding the presence of plus disease, an important criterion in deciding the necessity of treatment of vision-threatening retinopathy of prematurity. A review of literature found that several techniques have been published examining the process and role of computer aided analysis of plus disease in retinopathy of prematurity. These techniques use semiautomated image analysis techniques to evaluate retinal vascular dilation and tortuosity, using calculated parameters to evaluate presence or absence of plus disease. These values are then compared with expert consensus. The study concludes that computer-aided image analysis has the potential to use quantitative and objective criteria to act as a supplemental tool in evaluating for plus disease in the setting of retinopathy of prematurity.

Computer-aided classification of optical images for diagnosis of osteoarthritis in the finger joints.

PubMed

Zhang, Jiang; Wang, James Z; Yuan, Zhen; Sobel, Eric S; Jiang, Huabei

2011-01-01

This study presents a computer-aided classification method to distinguish osteoarthritis finger joints from healthy ones based on the functional images captured by x-ray guided diffuse optical tomography. Three imaging features, joint space width, optical absorption, and scattering coefficients, are employed to train a Least Squares Support Vector Machine (LS-SVM) classifier for osteoarthritis classification. The 10-fold validation results show that all osteoarthritis joints are clearly identified and all healthy joints are ruled out by the LS-SVM classifier. The best sensitivity, specificity, and overall accuracy of the classification by experienced technicians based on manual calculation of optical properties and visual examination of optical images are only 85%, 93%, and 90%, respectively. Therefore, our LS-SVM based computer-aided classification is a considerably improved method for osteoarthritis diagnosis.

Computer-Aided Characterization of Breast Masses on Volumetric Ultrasound Images: An Adjunct to Mammography

DTIC Science & Technology

2005-10-01

nearly setting-independent features and artificial neural networks. Radiology 2003; 226:504-514. 14. Horsch K, Giger ML, Venta LA, Vyborny CJ...Giger ML, Vyborny CJ, Venta LA. Performance of computer-aided diagnosis in the interpretation of lesions on breast sonography. Acad. Radiol. 2004; 11:272

PACS-Based Computer-Aided Detection and Diagnosis

NASA Astrophysics Data System (ADS)

Huang, H. K. (Bernie); Liu, Brent J.; Le, Anh HongTu; Documet, Jorge

The ultimate goal of Picture Archiving and Communication System (PACS)-based Computer-Aided Detection and Diagnosis (CAD) is to integrate CAD results into daily clinical practice so that it becomes a second reader to aid the radiologist's diagnosis. Integration of CAD and Hospital Information System (HIS), Radiology Information System (RIS) or PACS requires certain basic ingredients from Health Level 7 (HL7) standard for textual data, Digital Imaging and Communications in Medicine (DICOM) standard for images, and Integrating the Healthcare Enterprise (IHE) workflow profiles in order to comply with the Health Insurance Portability and Accountability Act (HIPAA) requirements to be a healthcare information system. Among the DICOM standards and IHE workflow profiles, DICOM Structured Reporting (DICOM-SR); and IHE Key Image Note (KIN), Simple Image and Numeric Report (SINR) and Post-processing Work Flow (PWF) are utilized in CAD-HIS/RIS/PACS integration. These topics with examples are presented in this chapter.

Computer-aided US diagnosis of breast lesions by using cell-based contour grouping.

PubMed

Cheng, Jie-Zhi; Chou, Yi-Hong; Huang, Chiun-Sheng; Chang, Yeun-Chung; Tiu, Chui-Mei; Chen, Kuei-Wu; Chen, Chung-Ming

2010-06-01

To develop a computer-aided diagnostic algorithm with automatic boundary delineation for differential diagnosis of benign and malignant breast lesions at ultrasonography (US) and investigate the effect of boundary quality on the performance of a computer-aided diagnostic algorithm. This was an institutional review board-approved retrospective study with waiver of informed consent. A cell-based contour grouping (CBCG) segmentation algorithm was used to delineate the lesion boundaries automatically. Seven morphologic features were extracted. The classifier was a logistic regression function. Five hundred twenty breast US scans were obtained from 520 subjects (age range, 15-89 years), including 275 benign (mean size, 15 mm; range, 5-35 mm) and 245 malignant (mean size, 18 mm; range, 8-29 mm) lesions. The newly developed computer-aided diagnostic algorithm was evaluated on the basis of boundary quality and differentiation performance. The segmentation algorithms and features in two conventional computer-aided diagnostic algorithms were used for comparative study. The CBCG-generated boundaries were shown to be comparable with the manually delineated boundaries. The area under the receiver operating characteristic curve (AUC) and differentiation accuracy were 0.968 +/- 0.010 and 93.1% +/- 0.7, respectively, for all 520 breast lesions. At the 5% significance level, the newly developed algorithm was shown to be superior to the use of the boundaries and features of the two conventional computer-aided diagnostic algorithms in terms of AUC (0.974 +/- 0.007 versus 0.890 +/- 0.008 and 0.788 +/- 0.024, respectively). The newly developed computer-aided diagnostic algorithm that used a CBCG segmentation method to measure boundaries achieved a high differentiation performance. Copyright RSNA, 2010

A Set of Image Processing Algorithms for Computer-Aided Diagnosis in Nuclear Medicine Whole Body Bone Scan Images

NASA Astrophysics Data System (ADS)

Huang, Jia-Yann; Kao, Pan-Fu; Chen, Yung-Sheng

2007-06-01

Adjustment of brightness and contrast in nuclear medicine whole body bone scan images may confuse nuclear medicine physicians when identifying small bone lesions as well as making the identification of subtle bone lesion changes in sequential studies difficult. In this study, we developed a computer-aided diagnosis system, based on the fuzzy sets histogram thresholding method and anatomical knowledge-based image segmentation method that was able to analyze and quantify raw image data and identify the possible location of a lesion. To locate anatomical reference points, the fuzzy sets histogram thresholding method was adopted as a first processing stage to suppress the soft tissue in the bone images. Anatomical knowledge-based image segmentation method was then applied to segment the skeletal frame into different regions of homogeneous bones. For the different segmented bone regions, the lesion thresholds were set at different cut-offs. To obtain lesion thresholds in different segmented regions, the ranges and standard deviations of the image's gray-level distribution were obtained from 100 normal patients' whole body bone images and then, another 62 patients' images were used for testing. The two groups of images were independent. The sensitivity and the mean number of false lesions detected were used as performance indices to evaluate the proposed system. The overall sensitivity of the system is 92.1% (222 of 241) and 7.58 false detections per patient scan image. With a high sensitivity and an acceptable false lesions detection rate, this computer-aided automatic lesion detection system is demonstrated as useful and will probably in the future be able to help nuclear medicine physicians to identify possible bone lesions.

Artefacts found in computed radiography.

PubMed

Cesar, L J; Schueler, B A; Zink, F E; Daly, T R; Taubel, J P; Jorgenson, L L

2001-02-01

Artefacts on radiographic images are distracting and may compromise accurate diagnosis. Although most artefacts that occur in conventional radiography have become familiar, computed radiography (CR) systems produce artefacts that differ from those found in conventional radiography. We have encountered a variety of artefacts in CR images that were produced from four different models plate reader. These artefacts have been identified and traced to the imaging plate, plate reader, image processing software or laser printer or to operator error. Understanding the potential sources of CR artefacts will aid in identifying and resolving problems quickly and help prevent future occurrences.

Persons with Alzheimer's Disease Make Phone Calls Independently Using a Computer-Aided Telephone System

ERIC Educational Resources Information Center

Perilli, Viviana; Lancioni, Giulio E.; Singh, Nirbhay N.; O'Reilly, Mark F.; Sigafoos, Jeff; Cassano, Germana; Cordiano, Noemi; Pinto, Katia; Minervini, Mauro G.; Oliva, Doretta

2012-01-01

This study assessed whether four patients with a diagnosis of Alzheimer's disease could make independent phone calls via a computer-aided telephone system. The study was carried out according to a non-concurrent multiple baseline design across participants. All participants started with baseline during which the telephone system was not available,…

Evaluating Imaging and Computer-aided Detection and Diagnosis Devices at the FDA

PubMed Central

Gallas, Brandon D.; Chan, Heang-Ping; D’Orsi, Carl J.; Dodd, Lori E.; Giger, Maryellen L.; Gur, David; Krupinski, Elizabeth A.; Metz, Charles E.; Myers, Kyle J.; Obuchowski, Nancy A.; Sahiner, Berkman; Toledano, Alicia Y.; Zuley, Margarita L.

2017-01-01

This report summarizes the Joint FDA-MIPS Workshop on Methods for the Evaluation of Imaging and Computer-Assist Devices. The purpose of the workshop was to gather information on the current state of the science and facilitate consensus development on statistical methods and study designs for the evaluation of imaging devices to support US Food and Drug Administration submissions. Additionally, participants expected to identify gaps in knowledge and unmet needs that should be addressed in future research. This summary is intended to document the topics that were discussed at the meeting and disseminate the lessons that have been learned through past studies of imaging and computer-aided detection and diagnosis device performance. PMID:22306064

Analysis of components of variance in multiple-reader studies of computer-aided diagnosis with different tasks

NASA Astrophysics Data System (ADS)

Beiden, Sergey V.; Wagner, Robert F.; Campbell, Gregory; Metz, Charles E.; Chan, Heang-Ping; Nishikawa, Robert M.; Schnall, Mitchell D.; Jiang, Yulei

2001-06-01

In recent years, the multiple-reader, multiple-case (MRMC) study paradigm has become widespread for receiver operating characteristic (ROC) assessment of systems for diagnostic imaging and computer-aided diagnosis. We review how MRMC data can be analyzed in terms of the multiple components of the variance (case, reader, interactions) observed in those studies. Such information is useful for the design of pivotal studies from results of a pilot study and also for studying the effects of reader training. Recently, several of the present authors have demonstrated methods to generalize the analysis of multiple variance components to the case where unaided readers of diagnostic images are compared with readers who receive the benefit of a computer assist (CAD). For this case it is necessary to model the possibility that several of the components of variance might be reduced when readers incorporate the computer assist, compared to the unaided reading condition. We review results of this kind of analysis on three previously published MRMC studies, two of which were applications of CAD to diagnostic mammography and one was an application of CAD to screening mammography. The results for the three cases are seen to differ, depending on the reader population sampled and the task of interest. Thus, it is not possible to generalize a particular analysis of variance components beyond the tasks and populations actually investigated.

Potential clinical impact of advanced imaging and computer-aided diagnosis in chest radiology: importance of radiologist's role and successful observer study.

PubMed

Li, Feng

2015-07-01

This review paper is based on our research experience in the past 30 years. The importance of radiologists' role is discussed in the development or evaluation of new medical images and of computer-aided detection (CAD) schemes in chest radiology. The four main topics include (1) introducing what diseases can be included in a research database for different imaging techniques or CAD systems and what imaging database can be built by radiologists, (2) understanding how radiologists' subjective judgment can be combined with technical objective features to improve CAD performance, (3) sharing our experience in the design of successful observer performance studies, and (4) finally, discussing whether the new images and CAD systems can improve radiologists' diagnostic ability in chest radiology. In conclusion, advanced imaging techniques and detection/classification of CAD systems have a potential clinical impact on improvement of radiologists' diagnostic ability, for both the detection and the differential diagnosis of various lung diseases, in chest radiology.

A novel approach to malignant-benign classification of pulmonary nodules by using ensemble learning classifiers.

PubMed

Tartar, A; Akan, A; Kilic, N

2014-01-01

Computer-aided detection systems can help radiologists to detect pulmonary nodules at an early stage. In this paper, a novel Computer-Aided Diagnosis system (CAD) is proposed for the classification of pulmonary nodules as malignant and benign. The proposed CAD system using ensemble learning classifiers, provides an important support to radiologists at the diagnosis process of the disease, achieves high classification performance. The proposed approach with bagging classifier results in 94.7 %, 90.0 % and 77.8 % classification sensitivities for benign, malignant and undetermined classes (89.5 % accuracy), respectively.

A handheld computer-aided diagnosis system and simulated analysis

NASA Astrophysics Data System (ADS)

Su, Mingjian; Zhang, Xuejun; Liu, Brent; Su, Kening; Louie, Ryan

2016-03-01

This paper describes a Computer Aided Diagnosis (CAD) system based on cellphone and distributed cluster. One of the bottlenecks in building a CAD system for clinical practice is the storage and process of mass pathology samples freely among different devices, and normal pattern matching algorithm on large scale image set is very time consuming. Distributed computation on cluster has demonstrated the ability to relieve this bottleneck. We develop a system enabling the user to compare the mass image to a dataset with feature table by sending datasets to Generic Data Handler Module in Hadoop, where the pattern recognition is undertaken for the detection of skin diseases. A single and combination retrieval algorithm to data pipeline base on Map Reduce framework is used in our system in order to make optimal choice between recognition accuracy and system cost. The profile of lesion area is drawn by doctors manually on the screen, and then uploads this pattern to the server. In our evaluation experiment, an accuracy of 75% diagnosis hit rate is obtained by testing 100 patients with skin illness. Our system has the potential help in building a novel medical image dataset by collecting large amounts of gold standard during medical diagnosis. Once the project is online, the participants are free to join and eventually an abundant sample dataset will soon be gathered enough for learning. These results demonstrate our technology is very promising and expected to be used in clinical practice.

[Computer-aided Diagnosis and New Electronic Stethoscope].

PubMed

Huang, Mei; Liu, Hongying; Pi, Xitian; Ao, Yilu; Wang, Zi

2017-05-30

Auscultation is an important method in early-diagnosis of cardiovascular disease and respiratory system disease. This paper presents a computer-aided diagnosis of new electronic auscultation system. It has developed an electronic stethoscope based on condenser microphone and the relevant intelligent analysis software. It has implemented many functions that combined with Bluetooth, OLED, SD card storage technologies, such as real-time heart and lung sounds auscultation in three modes, recording and playback, auscultation volume control, wireless transmission. The intelligent analysis software based on PC computer utilizes C# programming language and adopts SQL Server as the background database. It has realized play and waveform display of the auscultation sound. By calculating the heart rate, extracting the characteristic parameters of T1, T2, T12, T11, it can analyze whether the heart sound is normal, and then generate diagnosis report. Finally the auscultation sound and diagnosis report can be sent to mailbox of other doctors, which can carry out remote diagnosis. The whole system has features of fully function, high portability, good user experience, and it is beneficial to promote the use of electronic stethoscope in the hospital, at the same time, the system can also be applied to auscultate teaching and other occasions.

Monitoring and decision making by people in man machine systems

NASA Technical Reports Server (NTRS)

Johannsen, G.

1979-01-01

The analysis of human monitoring and decision making behavior as well as its modeling are described. Classic and optimal control theoretical, monitoring models are surveyed. The relationship between attention allocation and eye movements is discussed. As an example of applications, the evaluation of predictor displays by means of the optimal control model is explained. Fault detection involving continuous signals and decision making behavior of a human operator engaged in fault diagnosis during different operation and maintenance situations are illustrated. Computer aided decision making is considered as a queueing problem. It is shown to what extent computer aids can be based on the state of human activity as measured by psychophysiological quantities. Finally, management information systems for different application areas are mentioned. The possibilities of mathematical modeling of human behavior in complex man machine systems are also critically assessed.

A Computer-Aided Telephone System to Enable Five Persons with Alzheimer's Disease to Make Phone Calls Independently

ERIC Educational Resources Information Center

Perilli, Viviana; Lancioni, Giulio E.; Laporta, Dominga; Paparella, Adele; Caffo, Alessandro O.; Singh, Nirbhay N.; O'Reilly, Mark F.; Sigafoos, Jeff; Oliva, Doretta

2013-01-01

This study extended the assessment of a computer-aided telephone system to enable five patients with a diagnosis of Alzheimer's disease to make phone calls independently. The patients were divided into two groups and exposed to intervention according to a non-concurrent multiple baseline design across groups. All patients started with baseline in…

Bayes' theorem application in the measure information diagnostic value assessment

NASA Astrophysics Data System (ADS)

Orzechowski, Piotr D.; Makal, Jaroslaw; Nazarkiewicz, Andrzej

2006-03-01

The paper presents Bayesian method application in the measure information diagnostic value assessment that is used in the computer-aided diagnosis system. The computer system described here has been created basing on the Bayesian Network and is used in Benign Prostatic Hyperplasia (BPH) diagnosis. The graphic diagnostic model enables to juxtapose experts' knowledge with data.

Computer-aided diagnosis workstation and telemedicine network system for chest diagnosis based on multislice CT images

NASA Astrophysics Data System (ADS)

Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Ohmatsu, Hironobu; Kakinuma, Ryutaru; Moriyama, Noriyuki

2009-02-01

Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. Moreover, the doctor who diagnoses a medical image is insufficient in Japan. To overcome these problems, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis likelihood by using helical CT scanner for the lung cancer mass screening. The functions to observe suspicious shadow in detail are provided in computer-aided diagnosis workstation with these screening algorithms. We also have developed the telemedicine network by using Web medical image conference system with the security improvement of images transmission, Biometric fingerprint authentication system and Biometric face authentication system. Biometric face authentication used on site of telemedicine makes "Encryption of file" and "Success in login" effective. As a result, patients' private information is protected. We can share the screen of Web medical image conference system from two or more web conference terminals at the same time. An opinion can be exchanged mutually by using a camera and a microphone that are connected with workstation. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new telemedicine network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our telemedicine network system can increase diagnostic speed, diagnostic accuracy and security improvement of medical information.

Computer-aided diagnosis and artificial intelligence in clinical imaging.

PubMed

Shiraishi, Junji; Li, Qiang; Appelbaum, Daniel; Doi, Kunio

2011-11-01

Computer-aided diagnosis (CAD) is rapidly entering the radiology mainstream. It has already become a part of the routine clinical work for the detection of breast cancer with mammograms. The computer output is used as a "second opinion" in assisting radiologists' image interpretations. The computer algorithm generally consists of several steps that may include image processing, image feature analysis, and data classification via the use of tools such as artificial neural networks (ANN). In this article, we will explore these and other current processes that have come to be referred to as "artificial intelligence." One element of CAD, temporal subtraction, has been applied for enhancing interval changes and for suppressing unchanged structures (eg, normal structures) between 2 successive radiologic images. To reduce misregistration artifacts on the temporal subtraction images, a nonlinear image warping technique for matching the previous image to the current one has been developed. Development of the temporal subtraction method originated with chest radiographs, with the method subsequently being applied to chest computed tomography (CT) and nuclear medicine bone scans. The usefulness of the temporal subtraction method for bone scans was demonstrated by an observer study in which reading times and diagnostic accuracy improved significantly. An additional prospective clinical study verified that the temporal subtraction image could be used as a "second opinion" by radiologists with negligible detrimental effects. ANN was first used in 1990 for computerized differential diagnosis of interstitial lung diseases in CAD. Since then, ANN has been widely used in CAD schemes for the detection and diagnosis of various diseases in different imaging modalities, including the differential diagnosis of lung nodules and interstitial lung diseases in chest radiography, CT, and position emission tomography/CT. It is likely that CAD will be integrated into picture archiving and communication systems and will become a standard of care for diagnostic examinations in daily clinical work. Copyright © 2011 Elsevier Inc. All rights reserved.

Computer aided exercise electrocardiographic testing and coronary arteriography in patients with angina pectoris and with myocardial infarction.

PubMed Central

Angelhed, J E; Bjurö, T I; Ejdebäck, J; Selin, K; Schlossman, D; Griffith, L S; Bergstrand, R; Vedin, A; Wilhelmsson, C

1984-01-01

A set of electrocardiographic criteria for the diagnosis of coronary artery disease was evaluated in two different groups of patients examined by computer aided 12 lead exercise electrocardiographic stress testing and coronary arteriography. One group consisted of patients with severe angina pectoris and the other of patients who had suffered a myocardial infarction three years before the study. Angiographically determined categories of patients could be identified with satisfactory precision by the electrocardiographic criteria under test in the patients with angina pectoris but not in those with infarction. A new method of classifying patients on the basis of data from coronary arteriography improved the correlation with ST segment analysis compared with conventional classification. PMID:6743432

Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions.

PubMed

Loh, Brian C S; Then, Patrick H H

2017-01-01

Cardiovascular diseases are one of the top causes of deaths worldwide. In developing nations and rural areas, difficulties with diagnosis and treatment are made worse due to the deficiency of healthcare facilities. A viable solution to this issue is telemedicine, which involves delivering health care and sharing medical knowledge at a distance. Additionally, mHealth, the utilization of mobile devices for medical care, has also proven to be a feasible choice. The integration of telemedicine, mHealth and computer-aided diagnosis systems with the fields of machine and deep learning has enabled the creation of effective services that are adaptable to a multitude of scenarios. The objective of this review is to provide an overview of heart disease diagnosis and management, especially within the context of rural healthcare, as well as discuss the benefits, issues and solutions of implementing deep learning algorithms to improve the efficacy of relevant medical applications.

Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions

PubMed Central

Then, Patrick H. H.

2017-01-01

Cardiovascular diseases are one of the top causes of deaths worldwide. In developing nations and rural areas, difficulties with diagnosis and treatment are made worse due to the deficiency of healthcare facilities. A viable solution to this issue is telemedicine, which involves delivering health care and sharing medical knowledge at a distance. Additionally, mHealth, the utilization of mobile devices for medical care, has also proven to be a feasible choice. The integration of telemedicine, mHealth and computer-aided diagnosis systems with the fields of machine and deep learning has enabled the creation of effective services that are adaptable to a multitude of scenarios. The objective of this review is to provide an overview of heart disease diagnosis and management, especially within the context of rural healthcare, as well as discuss the benefits, issues and solutions of implementing deep learning algorithms to improve the efficacy of relevant medical applications. PMID:29184897

Multiple neural network approaches to clinical expert systems

NASA Astrophysics Data System (ADS)

Stubbs, Derek F.

1990-08-01

We briefly review the concept of computer aided medical diagnosis and more extensively review the the existing literature on neural network applications in the field. Neural networks can function as simple expert systems for diagnosis or prognosis. Using a public database we develop a neural network for the diagnosis of a major presenting symptom while discussing the development process and possible approaches. MEDICAL EXPERTS SYSTEMS COMPUTER AIDED DIAGNOSIS Biomedicine is an incredibly diverse and multidisciplinary field and it is not surprising that neural networks with their many applications are finding more and more applications in the highly non-linear field of biomedicine. I want to concentrate on neural networks as medical expert systems for clinical diagnosis or prognosis. Expert Systems started out as a set of computerized " ifthen" rules. Everything was reduced to boolean logic and the promised land of computer experts was said to be in sight. It never came. Why? First the computer code explodes as the number of " ifs" increases. All the " ifs" have to interact. Second experts are not very good at reducing expertise to language. It turns out that experts recognize patterns and have non-verbal left-brain intuition decision processes. Third learning by example rather than learning by rule is the way natural brains works and making computers work by rule-learning is hideously labor intensive. Neural networks can learn from example. They learn the results

Computer-aided head film analysis: the University of California San Francisco method.

PubMed

Baumrind, S; Miller, D M

1980-07-01

Computer technology is already assuming an important role in the management of orthodontic practices. The next 10 years are likely to see expansion in computer usage into the areas of diagnosis, treatment planning, and treatment-record keeping. In the areas of diagnosis and treatment planning, one of the first problems to be attacked will be the automation of head film analysis. The problems of constructing computer-aided systems for this purpose are considered herein in the light of the authors' 10 years of experience in developing a similar system for research purposes. The need for building in methods for automatic detection and correction of gross errors is discussed and the authors' method for doing so is presented. The construction of a rudimentary machine-readable data base for research and clinical purposes is described.

Computer-Aided Diagnosis of Different Rotator Cuff Lesions Using Shoulder Musculoskeletal Ultrasound.

PubMed

Chang, Ruey-Feng; Lee, Chung-Chien; Lo, Chung-Ming

2016-09-01

The lifetime prevalence of shoulder pain approaches 70%, which is mostly attributable to rotator cuff lesions such as inflammation, calcific tendinitis and tears. On clinical examination, shoulder ultrasound is recommended for the detection of lesions. However, there exists inter-operator variability in diagnostic accuracy because of differences in the experience and expertise of operators. In this study, a computer-aided diagnosis (CAD) system was developed to assist ultrasound operators in diagnosing rotator cuff lesions and to improve the practicality of ultrasound examination. The collected cases included 43 cases of inflammation, 30 cases of calcific tendinitis and 26 tears. For each case, the lesion area and texture features were extracted from the entire lesions and combined in a multinomial logistic regression classifier for lesion classification. The proposed CAD achieved an accuracy of 87.9%. The individual accuracy of this CAD system was 88.4% for inflammation, 83.3% for calcific tendinitis and 92.3% for tears. Cohen's k was 0.798. On the basis of its diagnostic performance, clinical use of this CAD technique has promise. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Computer-aided diagnosis of HIE based on segmentation of MRI

NASA Astrophysics Data System (ADS)

Sun, Ziguang; Li, Chungui; Wang, Qin

2009-10-01

Computer-aided diagnosis has become one of the major research subjects in medical imaging and diagnostic radiology. Hypoxic-ischemic encephalopathy (HIE), remains a serious condition that causes significant mortality and long-term morbidity to neonates. We adopt self-organizing feature maps to segment the tissues, such as white matter and grey matter in the magnetic resonance images. The borderline between white matter and grey matter can be found and the doubtful regions along with the borderline can be localized, then the feature in doubtful regions can be quantified. The method can assist doctors to easily diagnose whether a neonate is ill with mild HIE.

Black box integration of computer-aided diagnosis into PACS deserves a second chance: results of a usability study concerning bone age assessment.

PubMed

Geldermann, Ina; Grouls, Christoph; Kuhl, Christiane; Deserno, Thomas M; Spreckelsen, Cord

2013-08-01

Usability aspects of different integration concepts for picture archiving and communication systems (PACS) and computer-aided diagnosis (CAD) were inquired on the example of BoneXpert, a program determining the skeletal age from a left hand's radiograph. CAD-PACS integration was assessed according to its levels: data, function, presentation, and context integration focusing on usability aspects. A user-based study design was selected. Statements of seven experienced radiologists using two alternative types of integration provided by BoneXpert were acquired and analyzed using a mixed-methods approach based on think-aloud records and a questionnaire. In both variants, the CAD module (BoneXpert) was easily integrated in the workflow, found comprehensible and fitting in the conceptual framework of the radiologists. Weak points of the software integration referred to data and context integration. Surprisingly, visualization of intermediate image processing states (presentation integration) was found less important as compared to efficient handling and fast computation. Seamlessly integrating CAD into the PACS without additional work steps or unnecessary interrupts and without visualizing intermediate images may considerably improve software performance and user acceptance with efforts in time.

Computer-aided dermoscopy for diagnosis of melanoma

PubMed Central

Barzegari, Masoomeh; Ghaninezhad, Haiedeh; Mansoori, Parisa; Taheri, Arash; Naraghi, Zahra S; Asgari, Masood

2005-01-01

Background Computer-aided dermoscopy using artificial neural networks has been reported to be an accurate tool for the evaluation of pigmented skin lesions. We set out to determine the sensitivity and specificity of a computer-aided dermoscopy system for diagnosis of melanoma in Iranian patients. Methods We studied 122 pigmented skin lesions which were referred for diagnostic evaluation or cosmetic reasons. Each lesion was examined by two clinicians with naked eyes and all of their clinical diagnostic considerations were recorded. The lesions were analyzed using a microDERM® dermoscopy unit. The output value of the software for each lesion was a score between 0 and 10. All of the lesions were excised and examined histologically. Results Histopathological examination revealed melanoma in six lesions. Considering only the most likely clinical diagnosis, sensitivity and specificity of clinical examination for diagnosis of melanoma were 83% and 96%, respectively. Considering all clinical diagnostic considerations, the sensitivity and specificity were 100% and 89%. Choosing a cut-off point of 7.88 for dermoscopy score, the sensitivity and specificity of the score for diagnosis of melanoma were 83% and 96%, respectively. Setting the cut-off point at 7.34, the sensitivity and specificity were 100% and 90%. Conclusion The diagnostic accuracy of the dermoscopy system was at the level of clinical examination by dermatologists with naked eyes. This system may represent a useful tool for screening of melanoma, particularly at centers not experienced in the field of pigmented skin lesions. PMID:16000171

Decision support in psychiatry – a comparison between the diagnostic outcomes using a computerized decision support system versus manual diagnosis

PubMed Central

Bergman, Lars G; Fors, Uno GH

2008-01-01

Background Correct diagnosis in psychiatry may be improved by novel diagnostic procedures. Computerized Decision Support Systems (CDSS) are suggested to be able to improve diagnostic procedures, but some studies indicate possible problems. Therefore, it could be important to investigate CDSS systems with regard to their feasibility to improve diagnostic procedures as well as to save time. Methods This study was undertaken to compare the traditional 'paper and pencil' diagnostic method SCID1 with the computer-aided diagnostic system CB-SCID1 to ascertain processing time and accuracy of diagnoses suggested. 63 clinicians volunteered to participate in the study and to solve two paper-based cases using either a CDSS or manually. Results No major difference between paper and pencil and computer-supported diagnosis was found. Where a difference was found it was in favour of paper and pencil. For example, a significantly shorter time was found for paper and pencil for the difficult case, as compared to computer support. A significantly higher number of correct diagnoses were found in the diffilt case for the diagnosis 'Depression' using the paper and pencil method. Although a majority of the clinicians found the computer method supportive and easy to use, it took a longer time and yielded fewer correct diagnoses than with paper and pencil. Conclusion This study could not detect any major difference in diagnostic outcome between traditional paper and pencil methods and computer support for psychiatric diagnosis. Where there were significant differences, traditional paper and pencil methods were better than the tested CDSS and thus we conclude that CDSS for diagnostic procedures may interfere with diagnosis accuracy. A limitation was that most clinicians had not previously used the CDSS system under study. The results of this study, however, confirm that CDSS development for diagnostic purposes in psychiatry has much to deal with before it can be used for routine clinical purposes. PMID:18261222

Computer-aided diagnosis with radiogenomics: analysis of the relationship between genotype and morphological changes of the brain magnetic resonance images.

PubMed

Kai, Chiharu; Uchiyama, Yoshikazu; Shiraishi, Junji; Fujita, Hiroshi; Doi, Kunio

2018-05-10

In the post-genome era, a novel research field, 'radiomics' has been developed to offer a new viewpoint for the use of genotypes in radiology and medicine research which have traditionally focused on the analysis of imaging phenotypes. The present study analyzed brain morphological changes related to the individual's genotype. Our data consisted of magnetic resonance (MR) images of patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD), as well as their apolipoprotein E (APOE) genotypes. First, statistical parametric mapping (SPM) 12 was used for three-dimensional anatomical standardization of the brain MR images. A total of 30 normal images were used to create a standard normal brain image. Z-score maps were generated to identify the differences between an abnormal image and the standard normal brain. Our experimental results revealed that cerebral atrophies, depending on genotypes, can occur in different locations and that morphological changes may differ between MCI and AD. Using a classifier to characterize cerebral atrophies related to an individual's genotype, we developed a computer-aided diagnosis (CAD) scheme to identify the disease. For the early detection of cerebral diseases, a screening system using MR images, called Brain Check-up, is widely performed in Japan. Therefore, our proposed CAD scheme would be used in Brain Check-up.

A computer-aided diagnostic system for kidney disease

PubMed Central

Jahantigh, Farzad Firouzi; Malmir, Behnam; Avilaq, Behzad Aslani

2017-01-01

Background Disease diagnosis is complicated since patients may demonstrate similar symptoms but physician may diagnose different diseases. There are a few number of investigations aimed to create a fuzzy expert system, as a computer aided system for disease diagnosis. Methods In this research, a cross-sectional descriptive study conducted in a kidney clinic in Tehran, Iran in 2012. Medical diagnosis fuzzy rules applied, and a set of symptoms related to the set of considered diseases defined. The input case to be diagnosed defined by assigning a fuzzy value to each symptom and then three physicians asked about each suspected diseases. Then comments of those three physicians summarized for each disease. The fuzzy inference applied to obtain a decision fuzzy set for each disease, and crisp decision values attained to determine the certainty of existence for each disease. Results Results indicated that, in the diagnosis of seven cases of kidney disease by examining 21 indicators using fuzzy expert system, kidney stone disease with 63% certainty was the most probable, renal tubular was at the lowest level with 15%, and other kidney diseases were at the other levels. The most remarkable finding of this study was that results of kidney disease diagnosis (e.g., kidney stone) via fuzzy expert system were fully compatible with those of kidney physicians. Conclusion The proposed fuzzy expert system is a valid, reliable, and flexible instrument to diagnose several typical input cases. The developed system decreases the effort of initial physical checking and manual feeding of input symptoms. PMID:28392995

A computer-aided diagnostic system for kidney disease.

PubMed

Jahantigh, Farzad Firouzi; Malmir, Behnam; Avilaq, Behzad Aslani

2017-03-01

Disease diagnosis is complicated since patients may demonstrate similar symptoms but physician may diagnose different diseases. There are a few number of investigations aimed to create a fuzzy expert system, as a computer aided system for disease diagnosis. In this research, a cross-sectional descriptive study conducted in a kidney clinic in Tehran, Iran in 2012. Medical diagnosis fuzzy rules applied, and a set of symptoms related to the set of considered diseases defined. The input case to be diagnosed defined by assigning a fuzzy value to each symptom and then three physicians asked about each suspected diseases. Then comments of those three physicians summarized for each disease. The fuzzy inference applied to obtain a decision fuzzy set for each disease, and crisp decision values attained to determine the certainty of existence for each disease. Results indicated that, in the diagnosis of seven cases of kidney disease by examining 21 indicators using fuzzy expert system, kidney stone disease with 63% certainty was the most probable, renal tubular was at the lowest level with 15%, and other kidney diseases were at the other levels. The most remarkable finding of this study was that results of kidney disease diagnosis (e.g., kidney stone) via fuzzy expert system were fully compatible with those of kidney physicians. The proposed fuzzy expert system is a valid, reliable, and flexible instrument to diagnose several typical input cases. The developed system decreases the effort of initial physical checking and manual feeding of input symptoms.

Evaluation of a New Ensemble Learning Framework for Mass Classification in Mammograms.

PubMed

Rahmani Seryasat, Omid; Haddadnia, Javad

2018-06-01

Mammography is the most common screening method for diagnosis of breast cancer. In this study, a computer-aided system for diagnosis of benignity and malignity of the masses was implemented in mammogram images. In the computer aided diagnosis system, we first reduce the noise in the mammograms using an effective noise removal technique. After the noise removal, the mass in the region of interest must be segmented and this segmentation is done using a deformable model. After the mass segmentation, a number of features are extracted from it. These features include: features of the mass shape and border, tissue properties, and the fractal dimension. After extracting a large number of features, a proper subset must be chosen from among them. In this study, we make use of a new method on the basis of a genetic algorithm for selection of a proper set of features. After determining the proper features, a classifier is trained. To classify the samples, a new architecture for combination of the classifiers is proposed. In this architecture, easy and difficult samples are identified and trained using different classifiers. Finally, the proposed mass diagnosis system was also tested on mini-Mammographic Image Analysis Society and digital database for screening mammography databases. The obtained results indicate that the proposed system can compete with the state-of-the-art methods in terms of accuracy. Copyright © 2017 Elsevier Inc. All rights reserved.

Enormous knowledge base of disease diagnosis criteria.

PubMed

Xiao, Z H; Xiao, Y H; Pei, J H

1995-01-01

One of the problems in the development of the medical knowledge systems is the limitations of the system's knowledge. It is a common expectation to increase the number of diseases contained in a system. Using a high density knowledge representation method designed by us, we have developed the Enormous Knowledge Base of Disease Diagnosis Criteria (EKBDDC). It contains diagnostic criteria of 1,001 diagnostic entities and describes nearly 4,000 items of diagnostic indicators. It is the core of a huge medical project--the Electronic-Brain Medical Erudite (EBME). This enormous knowledge base was implemented initially on a low-cost popular microcomputer, which can aid in the prompting of typical disease and in teaching of diagnosis. The knowledge base is easy to expand. One of the main goals of EKBDDC is to increase the number of diseases included in it as far as possible using a low-cost computer with a comparatively small storage capacity. For this, we have designed a high density knowledge representation method. Criteria of various diagnostic entities are respectively stored in different records of the knowledge base. Each diagnostic entity corresponds to a diagnostic criterion data set; each data set consists of some diagnostic criterion data values (Table 1); each data is composed of two parts: integer and decimal; the integral part is the coding number of the given diagnostic information, and the decimal part is the diagnostic value of this information to the disease indicated by corresponding record number. For example, 75.02: the integer 75 is the coding number of "hemorrhagic skin rash"; the decimal 0.02 is the diagnostic value of this manifestation for diagnosing allergic purpura. TABULAR DATA, SEE PUBLISHED ABSTRACT. The algebraic sum method, a special form of the weighted summation, is adopted as mathematical model. In EKBDDC, the diagnostic values, which represent the significance of the disease manifestations for diagnosing corresponding diseases, were determined empirically. It is of a great economical, practical, and technical significance to realize enormous knowledge bases of disease diagnosis criteria on a low-cost popular microcomputer. This is beneficial for the developing countries to popularize medical informatics. To create the enormous international computer-aided diagnosis system, one may jointly develop the unified modules of disease diagnosis criteria used to "inlay" relevant computer-aided diagnosis systems. It is just like assembling a house using prefabricated panels.

EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN

PubMed Central

AlSharabi, Khalil; Ibrahim, Sutrisno; Alsuwailem, Abdullah

2017-01-01

Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD) of autism ‎based on electroencephalography (EEG) signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT), entropy (En), and artificial neural network (ANN). DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands. The feature vector is constructed by computing Shannon entropy values from each EEG subband. ANN classifies the corresponding EEG signal into normal or autistic based on the extracted features. The experimental results show the effectiveness of the proposed method for assisting autism diagnosis. A receiver operating characteristic (ROC) curve metric is used to quantify the performance of the proposed method. The proposed method obtained promising results tested using real dataset provided by King Abdulaziz Hospital, Jeddah, Saudi Arabia. PMID:28484720

A survey on computer aided diagnosis for ocular diseases

PubMed Central

2014-01-01

Background Computer Aided Diagnosis (CAD), which can automate the detection process for ocular diseases, has attracted extensive attention from clinicians and researchers alike. It not only alleviates the burden on the clinicians by providing objective opinion with valuable insights, but also offers early detection and easy access for patients. Method We review ocular CAD methodologies for various data types. For each data type, we investigate the databases and the algorithms to detect different ocular diseases. Their advantages and shortcomings are analyzed and discussed. Result We have studied three types of data (i.e., clinical, genetic and imaging) that have been commonly used in existing methods for CAD. The recent developments in methods used in CAD of ocular diseases (such as Diabetic Retinopathy, Glaucoma, Age-related Macular Degeneration and Pathological Myopia) are investigated and summarized comprehensively. Conclusion While CAD for ocular diseases has shown considerable progress over the past years, the clinical importance of fully automatic CAD systems which are able to embed clinical knowledge and integrate heterogeneous data sources still show great potential for future breakthrough. PMID:25175552

Deep Learning Role in Early Diagnosis of Prostate Cancer

PubMed Central

Reda, Islam; Khalil, Ashraf; Elmogy, Mohammed; Abou El-Fetouh, Ahmed; Shalaby, Ahmed; Abou El-Ghar, Mohamed; Elmaghraby, Adel; Ghazal, Mohammed; El-Baz, Ayman

2018-01-01

The objective of this work is to develop a computer-aided diagnostic system for early diagnosis of prostate cancer. The presented system integrates both clinical biomarkers (prostate-specific antigen) and extracted features from diffusion-weighted magnetic resonance imaging collected at multiple b values. The presented system performs 3 major processing steps. First, prostate delineation using a hybrid approach that combines a level-set model with nonnegative matrix factorization. Second, estimation and normalization of diffusion parameters, which are the apparent diffusion coefficients of the delineated prostate volumes at different b values followed by refinement of those apparent diffusion coefficients using a generalized Gaussian Markov random field model. Then, construction of the cumulative distribution functions of the processed apparent diffusion coefficients at multiple b values. In parallel, a K-nearest neighbor classifier is employed to transform the prostate-specific antigen results into diagnostic probabilities. Finally, those prostate-specific antigen–based probabilities are integrated with the initial diagnostic probabilities obtained using stacked nonnegativity constraint sparse autoencoders that employ apparent diffusion coefficient–cumulative distribution functions for better diagnostic accuracy. Experiments conducted on 18 diffusion-weighted magnetic resonance imaging data sets achieved 94.4% diagnosis accuracy (sensitivity = 88.9% and specificity = 100%), which indicate the promising results of the presented computer-aided diagnostic system. PMID:29804518

The Feasibility of Classifying Breast Masses Using a Computer-Assisted Diagnosis (CAD) System Based on Ultrasound Elastography and BI-RADS Lexicon.

PubMed

Fleury, Eduardo F C; Gianini, Ana Claudia; Marcomini, Karem; Oliveira, Vilmar

2018-01-01

To determine the applicability of a computer-aided diagnostic system strain elastography system for the classification of breast masses diagnosed by ultrasound and scored using the criteria proposed by the breast imaging and reporting data system ultrasound lexicon and to determine the diagnostic accuracy and interobserver variability. This prospective study was conducted between March 1, 2016, and May 30, 2016. A total of 83 breast masses subjected to percutaneous biopsy were included. Ultrasound elastography images before biopsy were interpreted by 3 radiologists with and without the aid of computer-aided diagnostic system for strain elastography. The parameters evaluated by each radiologist results were sensitivity, specificity, and diagnostic accuracy, with and without computer-aided diagnostic system for strain elastography. Interobserver variability was assessed using a weighted κ test and an intraclass correlation coefficient. The areas under the receiver operating characteristic curves were also calculated. The areas under the receiver operating characteristic curve were 0.835, 0.801, and 0.765 for readers 1, 2, and 3, respectively, without computer-aided diagnostic system for strain elastography, and 0.900, 0.926, and 0.868, respectively, with computer-aided diagnostic system for strain elastography. The intraclass correlation coefficient between the 3 readers was 0.6713 without computer-aided diagnostic system for strain elastography and 0.811 with computer-aided diagnostic system for strain elastography. The proposed computer-aided diagnostic system for strain elastography system has the potential to improve the diagnostic performance of radiologists in breast examination using ultrasound associated with elastography.

Texture feature extraction based on a uniformity estimation method for local brightness and structure in chest CT images.

PubMed

Peng, Shao-Hu; Kim, Deok-Hwan; Lee, Seok-Lyong; Lim, Myung-Kwan

2010-01-01

Texture feature is one of most important feature analysis methods in the computer-aided diagnosis (CAD) systems for disease diagnosis. In this paper, we propose a Uniformity Estimation Method (UEM) for local brightness and structure to detect the pathological change in the chest CT images. Based on the characteristics of the chest CT images, we extract texture features by proposing an extension of rotation invariant LBP (ELBP(riu4)) and the gradient orientation difference so as to represent a uniform pattern of the brightness and structure in the image. The utilization of the ELBP(riu4) and the gradient orientation difference allows us to extract rotation invariant texture features in multiple directions. Beyond this, we propose to employ the integral image technique to speed up the texture feature computation of the spatial gray level dependent method (SGLDM). Copyright © 2010 Elsevier Ltd. All rights reserved.

Computer-Aided Detection of Mammographic Masses in Dense Breast Images

DTIC Science & Technology

2005-06-01

Kinnard, Ph.D. CONTRACTING ORGANIZATION: Howard University Washington, DC 20059 REPORT DATE: June 2005 TYPE OF REPORT: Annual Summary PREPARED FOR: U.S...AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBER Howard University Washington, DC 20059 9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES...34, Preparing for the Postdoctoral Institute, August, 2004, Howard University and The University of Texas at El Paso. 2. "Computer-Aided Diagnosis and Image

Computer-aided diagnosis workstation and database system for chest diagnosis based on multi-helical CT images

NASA Astrophysics Data System (ADS)

Satoh, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Kakinuma, Ryutarou; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru; Sasagawa, Michizou

2006-03-01

Multi-helical CT scanner advanced remarkably at the speed at which the chest CT images were acquired for mass screening. Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router and Biometric fingerprint authentication system and Biometric face authentication system for safety of medical information. Based on these diagnostic assistance methods, we have now developed a new computer-aided workstation and database that can display suspected lesions three-dimensionally in a short time. This paper describes basic studies that have been conducted to evaluate this new system. The results of this study indicate that our computer-aided diagnosis workstation and network system can increase diagnostic speed, diagnostic accuracy and safety of medical information.

Model–Free Visualization of Suspicious Lesions in Breast MRI Based on Supervised and Unsupervised Learning

PubMed Central

Twellmann, Thorsten; Meyer-Baese, Anke; Lange, Oliver; Foo, Simon; Nattkemper, Tim W.

2008-01-01

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important tool in breast cancer diagnosis, but evaluation of multitemporal 3D image data holds new challenges for human observers. To aid the image analysis process, we apply supervised and unsupervised pattern recognition techniques for computing enhanced visualizations of suspicious lesions in breast MRI data. These techniques represent an important component of future sophisticated computer-aided diagnosis (CAD) systems and support the visual exploration of spatial and temporal features of DCE-MRI data stemming from patients with confirmed lesion diagnosis. By taking into account the heterogeneity of cancerous tissue, these techniques reveal signals with malignant, benign and normal kinetics. They also provide a regional subclassification of pathological breast tissue, which is the basis for pseudo-color presentations of the image data. Intelligent medical systems are expected to have substantial implications in healthcare politics by contributing to the diagnosis of indeterminate breast lesions by non-invasive imaging. PMID:19255616

Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

PubMed Central

El-Baz, Ayman; Beache, Garth M.; Gimel'farb, Georgy; Suzuki, Kenji; Okada, Kazunori; Elnakib, Ahmed; Soliman, Ahmed; Abdollahi, Behnoush

2013-01-01

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems. PMID:23431282

The LISS--a public database of common imaging signs of lung diseases for computer-aided detection and diagnosis research and medical education.

PubMed

Han, Guanghui; Liu, Xiabi; Han, Feifei; Santika, I Nyoman Tenaya; Zhao, Yanfeng; Zhao, Xinming; Zhou, Chunwu

2015-02-01

Lung computed tomography (CT) imaging signs play important roles in the diagnosis of lung diseases. In this paper, we review the significance of CT imaging signs in disease diagnosis and determine the inclusion criterion of CT scans and CT imaging signs of our database. We develop the software of abnormal regions annotation and design the storage scheme of CT images and annotation data. Then, we present a publicly available database of lung CT imaging signs, called LISS for short, which contains 271 CT scans and 677 abnormal regions in them. The 677 abnormal regions are divided into nine categories of common CT imaging signs of lung disease (CISLs). The ground truth of these CISLs regions and the corresponding categories are provided. Furthermore, to make the database publicly available, all private data in CT scans are eliminated or replaced with provisioned values. The main characteristic of our LISS database is that it is developed from a new perspective of CT imaging signs of lung diseases instead of commonly considered lung nodules. Thus, it is promising to apply to computer-aided detection and diagnosis research and medical education.

Research on computer aided testing of pilot response to critical in-flight events

NASA Technical Reports Server (NTRS)

Giffin, W. C.; Rockwell, T. H.; Smith, P. J.

1984-01-01

Experiments on pilot decision making are described. The development of models of pilot decision making in critical in flight events (CIFE) are emphasized. The following tests are reported on the development of: (1) a frame system representation describing how pilots use their knowledge in a fault diagnosis task; (2) assessment of script norms, distance measures, and Markov models developed from computer aided testing (CAT) data; and (3) performance ranking of subject data. It is demonstrated that interactive computer aided testing either by touch CRT's or personal computers is a useful research and training device for measuring pilot information management in diagnosing system failures in simulated flight situations. Performance is dictated by knowledge of aircraft sybsystems, initial pilot structuring of the failure symptoms and efficient testing of plausible causal hypotheses.

Artificial Intelligence: An Analysis of Potential Applications to Training, Performance Measurement, and Job Performance Aiding.

DTIC Science & Technology

1983-09-01

AD-Ali33 592 ARTIFICIAL INTELLIGENCE: AN ANALYSIS OF POTENTIAL 1/1 APPLICATIONS TO TRAININ..(U) DENVER RESEARCH INST CO JRICHARDSON SEP 83 AFHRL-TP...83-28 b ’ 3 - 4. TITLE (aied Suhkie) 5. TYPE OF REPORT & PERIOD COVERED ARTIFICIAL INTEL11GENCE: AN ANALYSIS OF Interim POTENTIAL APPLICATIONS TO...8217 sde if neceseamy end ides*f by black naumber) artificial intelligence military research * computer-aided diagnosis performance tests computer

Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning

PubMed Central

Wang, Shuihua; Yang, Ming; Du, Sidan; Yang, Jiquan; Liu, Bin; Gorriz, Juan M.; Ramírez, Javier; Yuan, Ti-Fei; Zhang, Yudong

2016-01-01

Highlights We develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging.Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems.The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls. Aim: Sensorineural hearing loss (SNHL) is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way. Materials: We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany). The subjects contain 14 patients with right-sided hearing loss (RHL), 15 patients with left-sided hearing loss (LHL), and 20 healthy controls (HC). Method: We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE) was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM). Results: The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier. Conclusions: This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss. PMID:27807415

Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning.

PubMed

Wang, Shuihua; Yang, Ming; Du, Sidan; Yang, Jiquan; Liu, Bin; Gorriz, Juan M; Ramírez, Javier; Yuan, Ti-Fei; Zhang, Yudong

2016-01-01

Highlights We develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging.Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems.The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls. Aim: Sensorineural hearing loss (SNHL) is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way. Materials: We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany). The subjects contain 14 patients with right-sided hearing loss (RHL), 15 patients with left-sided hearing loss (LHL), and 20 healthy controls (HC). Method: We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE) was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM). Results: The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier. Conclusions: This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss.

Computer Aided Reading Diagnosis.

ERIC Educational Resources Information Center

McEneaney, John E.

Computer technologies are having an ever-increasing influence on educational research and practice in Russia and the United States. In Russia, a number of recent papers have focused on the application of the computer as a teaching tool and on its influence in instructional organization and planning. In the United States, there is a great deal of…

GUIDON. Technical Report #9.

ERIC Educational Resources Information Center

Clancey, William J.

GUIDON is an intelligent computer-aided instruction (ICAI) program for teaching diagnosis, which has been tested using the infectious disease diagnosis rules of the MYCIN consultation system developed at the Stanford University School of Medicine. GUIDON engages a student in a dialogue about a patient suspected of having an infection and thus…

Student Achievement in Computer Programming: Lecture vs Computer-Aided Instruction

ERIC Educational Resources Information Center

Tsai, San-Yun W.; Pohl, Norval F.

1978-01-01

This paper discusses a study of the differences in student learning achievement, as measured by four different types of common performance evaluation techniques, in a college-level computer programming course under three teaching/learning environments: lecture, computer-aided instruction, and lecture supplemented with computer-aided instruction.…

Size assessment of breast lesions by means of a computer-aided detection (CAD) system for magnetic resonance mammography.

PubMed

Levrini, G; Sghedoni, R; Mori, C; Botti, A; Vacondio, R; Nitrosi, A; Iori, M; Nicoli, F

2011-10-01

The aim of this study was to investigate the efficacy of a dedicated software tool for automated volume measurement of breast lesions in contrast-enhanced (CE) magnetic resonance mammography (MRM). The size of 52 breast lesions with a known histopathological diagnosis (three benign, 49 malignant) was automatically evaluated using different techniques. The volume of all lesions was measured automatically (AVM) from CE 3D MRM examinations by means of a computer-aided detection (CAD) system and compared with the size estimates based on maximum diameter measurement (MDM) on MRM, ultrasonography (US), mammography and histopathology. Compared with histopathology as the reference method, AVM understimated lesion size by 4% on average. This result was similar to MDM (3% understimation, not significantly different) but significantly better than US and mammographic lesion measurements (24% and 33% size underestimation, respectively). AVM is as accurate as MDM but faster. Both methods are more accurate for size assessment of breast lesions compared with US and mammography.

Toward the detection of abnormal chest radiographs the way radiologists do it

NASA Astrophysics Data System (ADS)

Alzubaidi, Mohammad; Patel, Ameet; Panchanathan, Sethuraman; Black, John A., Jr.

2011-03-01

Computer Aided Detection (CADe) and Computer Aided Diagnosis (CADx) are relatively recent areas of research that attempt to employ feature extraction, pattern recognition, and machine learning algorithms to aid radiologists in detecting and diagnosing abnormalities in medical images. However, these computational methods are based on the assumption that there are distinct classes of abnormalities, and that each class has some distinguishing features that set it apart from other classes. However, abnormalities in chest radiographs tend to be very heterogeneous. The literature suggests that thoracic (chest) radiologists develop their ability to detect abnormalities by developing a sense of what is normal, so that anything that is abnormal attracts their attention. This paper discusses an approach to CADe that is based on a technique called anomaly detection (which aims to detect outliers in data sets) for the purpose of detecting atypical regions in chest radiographs. However, in order to apply anomaly detection to chest radiographs, it is necessary to develop a basis for extracting features from corresponding anatomical locations in different chest radiographs. This paper proposes a method for doing this, and describes how it can be used to support CADe.

AI in manufacturing

NASA Astrophysics Data System (ADS)

Gross, John E.; Minato, Rick; Smith, David M.; Loftin, R. B.; Savely, Robert T.

1991-10-01

AI techniques are shown to have been useful in such aerospace industry tasks as vehicle configuration layouts, process planning, tool design, numerically-controlled programming of tools, production scheduling, and equipment testing and diagnosis. Accounts are given of illustrative experiences at the production facilities of three major aerospace defense contractors. Also discussed is NASA's autonomous Intelligent Computer-Aided Training System, for such ambitious manned programs as Space Station Freedom, which employs five different modules to constitute its job-independent training architecture.

Medical imaging and computers in the diagnosis of breast cancer

NASA Astrophysics Data System (ADS)

Giger, Maryellen L.

2014-09-01

Computer-aided diagnosis (CAD) and quantitative image analysis (QIA) methods (i.e., computerized methods of analyzing digital breast images: mammograms, ultrasound, and magnetic resonance images) can yield novel image-based tumor and parenchyma characteristics (i.e., signatures that may ultimately contribute to the design of patient-specific breast cancer management plans). The role of QIA/CAD has been expanding beyond screening programs towards applications in risk assessment, diagnosis, prognosis, and response to therapy as well as in data mining to discover relationships of image-based lesion characteristics with genomics and other phenotypes; thus, as they apply to disease states. These various computer-based applications are demonstrated through research examples from the Giger Lab.

Digital model as an alternative to plaster model in assessment of space analysis

PubMed Central

Kumar, A. Anand; Phillip, Abraham; Kumar, Sathesh; Rawat, Anuradha; Priya, Sakthi; Kumaran, V.

2015-01-01

Introduction: Digital three-dimensional models are widely used for orthodontic diagnosis. The purpose of this study was to appraise the accuracy of digital models obtained from computer-aided design/computer-aided manufacturing (CAD/CAM) and cone-beam computed tomography (CBCT) for tooth-width measurements and the Bolton analysis. Materials and Methods: Digital models (CAD/CAM, CBCT) and plaster model were made for each of 50 subjects. Tooth-width measurements on the digital models (CAD/CAM, CBCT) were compared with those on the corresponding plaster models. The anterior and overall Bolton ratios were calculated for each participant and for each method. The paired t-test was applied to determine the validity. Results: Tooth-width measurements, anterior, and overall Bolton ratio of digital models of CAD/CAM and CBCT did not differ significantly from those on the plaster models. Conclusion: Hence, both CBCT and CAD/CAM are trustable and promising technique that can replace plaster models due to its overwhelming advantages. PMID:26538899

Computer aided diagnosis of diabetic foot using infrared thermography: A review.

PubMed

Adam, Muhammad; Ng, Eddie Y K; Tan, Jen Hong; Heng, Marabelle L; Tong, Jasper W K; Acharya, U Rajendra

2017-12-01

Diabetes mellitus (DM) is a chronic metabolic disorder that requires regular medical care to prevent severe complications. The elevated blood glucose level affects the eyes, blood vessels, nerves, heart, and kidneys after the onset. The affected blood vessels (usually due to atherosclerosis) may lead to insufficient blood circulation particularly in the lower extremities and nerve damage (neuropathy), which can result in serious foot complications. Hence, an early detection and treatment can prevent foot complications such as ulcerations and amputations. Clinicians often assess the diabetic foot for sensory deficits with clinical tools, and the resulting foot severity is often manually evaluated. The infrared thermography is a fast, nonintrusive and non-contact method which allows the visualization of foot plantar temperature distribution. Several studies have proposed infrared thermography-based computer aided diagnosis (CAD) methods for diabetic foot. Among them, the asymmetric temperature analysis method is more superior, as it is easy to implement, and yielded satisfactory results in most of the studies. In this paper, the diabetic foot, its pathophysiology, conventional assessments methods, infrared thermography and the different infrared thermography-based CAD analysis methods are reviewed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review.

PubMed

Yassin, Nisreen I R; Omran, Shaimaa; El Houby, Enas M F; Allam, Hemat

2018-03-01

The high incidence of breast cancer in women has increased significantly in the recent years. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerized features extraction and classification algorithms. This paper presents the conduction and results of a systematic review (SR) that aims to investigate the state of the art regarding the computer aided diagnosis/detection (CAD) systems for breast cancer. The SR was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The scientific databases used are Springer Link (SL), Science Direct (SD), IEEE Xplore Digital Library, and PubMed. Inclusion and exclusion criteria were defined and applied to each retrieved work to select those of interest. From 320 studies retrieved, 154 studies were included. However, the scope of this research is limited to scientific and academic works and excludes commercial interests. This survey provides a general analysis of the current status of CAD systems according to the used image modalities and the machine learning based classifiers. Potential research studies have been discussed to create a more objective and efficient CAD systems. Copyright © 2017 Elsevier B.V. All rights reserved.

A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications.

PubMed

Amirkhani, Abdollah; Papageorgiou, Elpiniki I; Mohseni, Akram; Mosavi, Mohammad R

2017-04-01

A high percentage of medical errors, committed because of physician's lack of experience, huge volume of data to be analyzed, and inaccessibility to medical records of previous patients, can be reduced using computer-aided techniques. Therefore, designing more efficient medical decision-support systems (MDSSs) to assist physicians in decision-making is crucially important. Through combining the properties of fuzzy logic and neural networks, fuzzy cognitive maps (FCMs) are among the latest, most efficient, and strongest artificial intelligence techniques for modeling complex systems. This review study is conducted to identify different FCM structures used in MDSS designs. The best structure for each medical application can be introduced by studying the properties of FCM structures. This paper surveys the most important decision- making methods and applications of FCMs in the medical field in recent years. To investigate the efficiency and capability of different FCM models in designing MDSSs, medical applications are categorized into four key areas: decision-making, diagnosis, prediction, and classification. Also, various diagnosis and decision support problems addressed by FCMs in recent years are reviewed with the goal of introducing different types of FCMs and determining their contribution to the improvements made in the fields of medical diagnosis and treatment. In this survey, a general trend for future studies in this field is provided by analyzing various FCM structures used for medical purposes, and the results from each category. Due to the unique specifications of FCMs in integrating human knowledge and experience with computer-aided techniques, they are among practical instruments for MDSS design. In the not too distant future, they will have a significant role in medical sciences. Copyright © 2017 Elsevier B.V. All rights reserved.

Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm.

PubMed

Pereira, Danilo Cesar; Ramos, Rodrigo Pereira; do Nascimento, Marcelo Zanchetta

2014-04-01

In Brazil, the National Cancer Institute (INCA) reports more than 50,000 new cases of the disease, with risk of 51 cases per 100,000 women. Radiographic images obtained from mammography equipments are one of the most frequently used techniques for helping in early diagnosis. Due to factors related to cost and professional experience, in the last two decades computer systems to support detection (Computer-Aided Detection - CADe) and diagnosis (Computer-Aided Diagnosis - CADx) have been developed in order to assist experts in detection of abnormalities in their initial stages. Despite the large number of researches on CADe and CADx systems, there is still a need for improved computerized methods. Nowadays, there is a growing concern with the sensitivity and reliability of abnormalities diagnosis in both views of breast mammographic images, namely cranio-caudal (CC) and medio-lateral oblique (MLO). This paper presents a set of computational tools to aid segmentation and detection of mammograms that contained mass or masses in CC and MLO views. An artifact removal algorithm is first implemented followed by an image denoising and gray-level enhancement method based on wavelet transform and Wiener filter. Finally, a method for detection and segmentation of masses using multiple thresholding, wavelet transform and genetic algorithm is employed in mammograms which were randomly selected from the Digital Database for Screening Mammography (DDSM). The developed computer method was quantitatively evaluated using the area overlap metric (AOM). The mean ± standard deviation value of AOM for the proposed method was 79.2 ± 8%. The experiments demonstrate that the proposed method has a strong potential to be used as the basis for mammogram mass segmentation in CC and MLO views. Another important aspect is that the method overcomes the limitation of analyzing only CC and MLO views. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Unusual Radiographic Presentation of Pneumocystis Pneumonia in a Patient with AIDS.

PubMed

Block, Brian L; Mehta, Tejas; Ortiz, Gabriel M; Ferris, Sean P; Vu, Thienkhai H; Huang, Laurence; Cattamanchi, Adithya

2017-01-01

Pneumocystis jirovecii pneumonia (PCP) typically presents as an interstitial and alveolar process with ground glass opacities on chest computed tomography (CT). The absence of ground glass opacities on chest CT is thought to have a high negative predictive value for PCP in individuals with AIDS. Here, we report a case of PCP in a man with AIDS who presented to our hospital with subacute shortness of breath and a nonproductive cough. While his chest CT revealed diffuse nodular rather than ground glass opacities, bronchoscopy with bronchoalveolar lavage and transbronchial biopsies confirmed the diagnosis of PCP and did not identify additional pathogens. PCP was not the expected diagnosis based on chest CT, but it otherwise fit well with the patient's clinical and laboratory presentation. In the era of combination antiretroviral therapy, routine prophylaxis for PCP, and increased use of computed tomography, it may be that PCP will increasingly present with nonclassical chest radiographic patterns. Clinicians should be aware of this presentation when selecting diagnostic and management strategies.

Unusual Radiographic Presentation of Pneumocystis Pneumonia in a Patient with AIDS

PubMed Central

Mehta, Tejas; Ortiz, Gabriel M.; Ferris, Sean P.; Vu, Thienkhai H.; Huang, Laurence; Cattamanchi, Adithya

2017-01-01

Pneumocystis jirovecii pneumonia (PCP) typically presents as an interstitial and alveolar process with ground glass opacities on chest computed tomography (CT). The absence of ground glass opacities on chest CT is thought to have a high negative predictive value for PCP in individuals with AIDS. Here, we report a case of PCP in a man with AIDS who presented to our hospital with subacute shortness of breath and a nonproductive cough. While his chest CT revealed diffuse nodular rather than ground glass opacities, bronchoscopy with bronchoalveolar lavage and transbronchial biopsies confirmed the diagnosis of PCP and did not identify additional pathogens. PCP was not the expected diagnosis based on chest CT, but it otherwise fit well with the patient's clinical and laboratory presentation. In the era of combination antiretroviral therapy, routine prophylaxis for PCP, and increased use of computed tomography, it may be that PCP will increasingly present with nonclassical chest radiographic patterns. Clinicians should be aware of this presentation when selecting diagnostic and management strategies. PMID:29362681

Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning.

PubMed

van Ginneken, Bram

2017-03-01

Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant technology for tackling CAD in the lungs, generally producing better results than do classical rule-based approaches, and how the field is now rapidly changing: in the last few years, we have seen how even better results can be obtained with deep learning. The key differences among rule-based processing, machine learning, and deep learning are summarized and illustrated for various applications of CAD in the chest.

Computer-aided diagnosis software for vulvovaginal candidiasis detection from Pap smear images.

PubMed

Momenzadeh, Mohammadreza; Vard, Alireza; Talebi, Ardeshir; Mehri Dehnavi, Alireza; Rabbani, Hossein

2018-01-01

Vulvovaginal candidiasis (VVC) is a common gynecologic infection and it occurs when there is overgrowth of the yeast called Candida. VVC diagnosis is usually done by observing a Pap smear sample under a microscope and searching for the conidium and mycelium components of Candida. This manual method is time consuming, subjective and tedious. Any diagnosis tools that detect VVC, semi- or full-automatically, can be very helpful to pathologists. This article presents a computer aided diagnosis (CAD) software to improve human diagnosis of VVC from Pap smear samples. The proposed software is designed based on phenotypic and morphology features of the Candida in Pap smear sample images. This software provide a user-friendly interface which consists of a set of image processing tools and analytical results that helps to detect Candida and determine severity of illness. The software was evaluated on 200 Pap smear sample images and obtained specificity of 91.04% and sensitivity of 92.48% to detect VVC. As a result, the use of the proposed software reduces diagnostic time and can be employed as a second objective opinion for pathologists. © 2017 Wiley Periodicals, Inc.

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review.

PubMed

Xing, Fuyong; Yang, Lin

2016-01-01

Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.

Computer aided lung cancer diagnosis with deep learning algorithms

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Zheng, Bin; Qian, Wei

2016-03-01

Deep learning is considered as a popular and powerful method in pattern recognition and classification. However, there are not many deep structured applications used in medical imaging diagnosis area, because large dataset is not always available for medical images. In this study we tested the feasibility of using deep learning algorithms for lung cancer diagnosis with the cases from Lung Image Database Consortium (LIDC) database. The nodules on each computed tomography (CT) slice were segmented according to marks provided by the radiologists. After down sampling and rotating we acquired 174412 samples with 52 by 52 pixel each and the corresponding truth files. Three deep learning algorithms were designed and implemented, including Convolutional Neural Network (CNN), Deep Belief Networks (DBNs), Stacked Denoising Autoencoder (SDAE). To compare the performance of deep learning algorithms with traditional computer aided diagnosis (CADx) system, we designed a scheme with 28 image features and support vector machine. The accuracies of CNN, DBNs, and SDAE are 0.7976, 0.8119, and 0.7929, respectively; the accuracy of our designed traditional CADx is 0.7940, which is slightly lower than CNN and DBNs. We also noticed that the mislabeled nodules using DBNs are 4% larger than using traditional CADx, this might be resulting from down sampling process lost some size information of the nodules.

SU-E-I-30: Image Analysis in Ultrasonography for Diagnosis of Sjoegren's Syndrome Using Dual-Tree Complex Wavelet Transform

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matsui, T; Ohki, M; Nakamura, T

Purpose: Sjoegren's syndrome (SS) is an autoimmune disease invading mainly salivary and lacrimal glands. Ultrasonography is used for an initial and non-invasive examination of this disease. However, the ultrasonography diagnosis tends to lack in objectivity and depends on the operator's skills. The purpose of this study is to propose a computer-aided diagnosis (CAD) system for SS based on a dual-tree complex wavelet transform (DT-CWT) and machine learning. Methods: The subjects of this study were 174 patients suspected of having SS at Nagasaki University Hospital and examined with ultrasonography of the parotid glands. Out of these patients, 77 patients were diagnosedmore » with SS by sialography. A region of interest (ROI) of 128 × 128 pixels was set within the parotid gland that was indicated by a dental radiologist. The DT-CWT was applied to the images in the ROI and every image was decomposed into 72 sub-images of the real and imaginary components in six different resolution levels and six orientations. The statistical features of the sub-image were calculated and used as data input for the support vector machine (SVM) classifier for the detection of SS. A ten-fold cross-validation was employed to verify the Resultof SVM. The accuracy of diagnosis was compared by a CAD system with a human observer performance. Results: The sensitivity, specificity, and accuracy in the detection of SS were 95%, 86%, and 91% through our CAD system respectively, while those by a human observer were 84%, 81%, and 83% respectively. Conclusion: The proposed computer-aided diagnosis system for Sjoegren's syndrome in ultrasonography based on dual-tree complex wavelet transform had a better performance than a human observer.« less

Three-dimensional surgical simulation.

PubMed

Cevidanes, Lucia H C; Tucker, Scott; Styner, Martin; Kim, Hyungmin; Chapuis, Jonas; Reyes, Mauricio; Proffit, William; Turvey, Timothy; Jaskolka, Michael

2010-09-01

In this article, we discuss the development of methods for computer-aided jaw surgery, which allows us to incorporate the high level of precision necessary for transferring virtual plans into the operating room. We also present a complete computer-aided surgery system developed in close collaboration with surgeons. Surgery planning and simulation include construction of 3-dimensional surface models from cone-beam computed tomography, dynamic cephalometry, semiautomatic mirroring, interactive cutting of bone, and bony segment repositioning. A virtual setup can be used to manufacture positioning splints for intraoperative guidance. The system provides further intraoperative assistance with a computer display showing jaw positions and 3-dimensional positioning guides updated in real time during the surgical procedure. The computer-aided surgery system aids in dealing with complex cases with benefits for the patient, with surgical practice, and for orthodontic finishing. Advanced software tools for diagnosis and treatment planning allow preparation of detailed operative plans, osteotomy repositioning, bone reconstructions, surgical resident training, and assessing the difficulties of the surgical procedures before the surgery. Computer-aided surgery can make the elaboration of the surgical plan a more flexible process, increase the level of detail and accuracy of the plan, yield higher operative precision and control, and enhance documentation of cases. 2010 American Association of Orthodontists. Published by Mosby, Inc. All rights reserved.

The electronic stethoscope.

PubMed

Leng, Shuang; Tan, Ru San; Chai, Kevin Tshun Chuan; Wang, Chao; Ghista, Dhanjoo; Zhong, Liang

2015-07-10

Most heart diseases are associated with and reflected by the sounds that the heart produces. Heart auscultation, defined as listening to the heart sound, has been a very important method for the early diagnosis of cardiac dysfunction. Traditional auscultation requires substantial clinical experience and good listening skills. The emergence of the electronic stethoscope has paved the way for a new field of computer-aided auscultation. This article provides an in-depth study of (1) the electronic stethoscope technology, and (2) the methodology for diagnosis of cardiac disorders based on computer-aided auscultation. The paper is based on a comprehensive review of (1) literature articles, (2) market (state-of-the-art) products, and (3) smartphone stethoscope apps. It covers in depth every key component of the computer-aided system with electronic stethoscope, from sensor design, front-end circuitry, denoising algorithm, heart sound segmentation, to the final machine learning techniques. Our intent is to provide an informative and illustrative presentation of the electronic stethoscope, which is valuable and beneficial to academics, researchers and engineers in the technical field, as well as to medical professionals to facilitate its use clinically. The paper provides the technological and medical basis for the development and commercialization of a real-time integrated heart sound detection, acquisition and quantification system.

Quantitative diagnosis of tongue cancer from histological images in an animal model

NASA Astrophysics Data System (ADS)

Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Chen, Zhuo G.; Fei, Baowei

2016-03-01

We developed a chemically-induced oral cancer animal model and a computer aided method for tongue cancer diagnosis. The animal model allows us to monitor the progress of the lesions over time. Tongue tissue dissected from mice was sent for histological processing. Representative areas of hematoxylin and eosin stained tissue from tongue sections were captured for classifying tumor and non-tumor tissue. The image set used in this paper consisted of 214 color images (114 tumor and 100 normal tissue samples). A total of 738 color, texture, morphometry and topology features were extracted from the histological images. The combination of image features from epithelium tissue and its constituent nuclei and cytoplasm has been demonstrated to improve the classification results. With ten iteration nested cross validation, the method achieved an average sensitivity of 96.5% and a specificity of 99% for tongue cancer detection. The next step of this research is to apply this approach to human tissue for computer aided diagnosis of tongue cancer.

Building a medical image processing algorithm verification database

NASA Astrophysics Data System (ADS)

Brown, C. Wayne

2000-06-01

The design of a database containing head Computed Tomography (CT) studies is presented, along with a justification for the database's composition. The database will be used to validate software algorithms that screen normal head CT studies from studies that contain pathology. The database is designed to have the following major properties: (1) a size sufficient for statistical viability, (2) inclusion of both normal (no pathology) and abnormal scans, (3) inclusion of scans due to equipment malfunction, technologist error, and uncooperative patients, (4) inclusion of data sets from multiple scanner manufacturers, (5) inclusion of data sets from different gender and age groups, and (6) three independent diagnosis of each data set. Designed correctly, the database will provide a partial basis for FDA (United States Food and Drug Administration) approval of image processing algorithms for clinical use. Our goal for the database is the proof of viability of screening head CT's for normal anatomy using computer algorithms. To put this work into context, a classification scheme for 'computer aided diagnosis' systems is proposed.

Computer aided diagnosis based on medical image processing and artificial intelligence methods

NASA Astrophysics Data System (ADS)

Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

2006-12-01

Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

Terrestrial implications of mathematical modeling developed for space biomedical research

NASA Technical Reports Server (NTRS)

Lujan, Barbara F.; White, Ronald J.; Leonard, Joel I.; Srinivasan, R. Srini

1988-01-01

This paper summarizes several related research projects supported by NASA which seek to apply computer models to space medicine and physiology. These efforts span a wide range of activities, including mathematical models used for computer simulations of physiological control systems; power spectral analysis of physiological signals; pattern recognition models for detection of disease processes; and computer-aided diagnosis programs.

Determinants of Progression to AIDS and Death Following HIV Diagnosis: A Retrospective Cohort Study in Wuhan, China

PubMed Central

Jiang, Hongbo; Xie, Nianhua; Cao, Beibei; Tan, Li; Fan, Yunzhou; Zhang, Fan; Yao, Zhongzhao; Liu, Li; Nie, Shaofa

2013-01-01

Objective To identify determinants associated with disease progression and death following human immunodeficiency virus (HIV) diagnosis. Methods Disease progression data from the diagnosis of HIV infection or acquiring immunodeficiency syndrome (AIDS) to February 29, 2012 were retrospectively collected from the national surveillance system databases and the national treatment database in Wuhan, China. Kaplan-Meier method, Logistic regression and Cox proportional hazards model were applied to identify the related factors of progression to AIDS or death following HIV diagnosis. Results By the end of February 2012, 181 of 691 HIV infectors developed to AIDS, and 129 of 470 AIDS patients died among whom 289 cases received concurrent HIV/AIDS diagnosis. Compared with men infected through homosexual behavior, injection drug users possessed sharply decreased hazard ratio (HR) for progression to AIDS following HIV diagnosis [HR = 0.31, 95% confidence interval (CI), 0.18–0.54, P = 4.01×10−5]. HIV infectors at least 60 years presented 1.15-fold (HR = 2.15, 95% CI, 1.15–4.03, P = 0.017) increased risk to develop AIDS when compared with those aged 17–29 years. Similarly, AIDS patients with diagnosis ages between 50 and 59 years were at a 1.60-fold higher risk of death (HR = 2.60, 95% CI, 1.18–5.72, P = 0.017) compared to those aged 19–29 years. AIDS patients with more CD4+ T-cells within 6 months at diagnosis (cell/µL) presented lower risk of death (HR = 0.29 for 50- vs <50, 95% CI, 0.15–0.59, P = 0.001). The highly active antiretroviral therapy (HAART) delayed progression to AIDS from HIV diagnosis (HR = 0.15, 95% CI, 0.07–0.34, P = 6.46×10−6) and reduced the risk of death after AIDS diagnosis (HR = 0.02, 95% CI, 0.01–0.04, P = 7.25×10−25). Conclusions Progression to AIDS and death following HIV diagnosis differed in age at diagnosis, transmission categories, CD4+ T-cell counts and HAART. Effective interventions should target those at higher risk for morbidity or mortality, ensuring early diagnosis and timely treatment to slow down the disease progression. PMID:24376638

Determinants of progression to AIDS and death following HIV diagnosis: a retrospective cohort study in Wuhan, China.

PubMed

Jiang, Hongbo; Xie, Nianhua; Cao, Beibei; Tan, Li; Fan, Yunzhou; Zhang, Fan; Yao, Zhongzhao; Liu, Li; Nie, Shaofa

2013-01-01

To identify determinants associated with disease progression and death following human immunodeficiency virus (HIV) diagnosis. Disease progression data from the diagnosis of HIV infection or acquiring immunodeficiency syndrome (AIDS) to February 29, 2012 were retrospectively collected from the national surveillance system databases and the national treatment database in Wuhan, China. Kaplan-Meier method, Logistic regression and Cox proportional hazards model were applied to identify the related factors of progression to AIDS or death following HIV diagnosis. By the end of February 2012, 181 of 691 HIV infectors developed to AIDS, and 129 of 470 AIDS patients died among whom 289 cases received concurrent HIV/AIDS diagnosis. Compared with men infected through homosexual behavior, injection drug users possessed sharply decreased hazard ratio (HR) for progression to AIDS following HIV diagnosis [HR = 0.31, 95% confidence interval (CI), 0.18-0.54, P = 4.01×10(-5)]. HIV infectors at least 60 years presented 1.15-fold (HR = 2.15, 95% CI, 1.15-4.03, P = 0.017) increased risk to develop AIDS when compared with those aged 17-29 years. Similarly, AIDS patients with diagnosis ages between 50 and 59 years were at a 1.60-fold higher risk of death (HR = 2.60, 95% CI, 1.18-5.72, P = 0.017) compared to those aged 19-29 years. AIDS patients with more CD4(+) T-cells within 6 months at diagnosis (cell/µL) presented lower risk of death (HR = 0.29 for 50- vs <50, 95% CI, 0.15-0.59, P = 0.001). The highly active antiretroviral therapy (HAART) delayed progression to AIDS from HIV diagnosis (HR = 0.15, 95% CI, 0.07-0.34, P = 6.46×10(-6)) and reduced the risk of death after AIDS diagnosis (HR = 0.02, 95% CI, 0.01-0.04, P = 7.25×10(-25)). Progression to AIDS and death following HIV diagnosis differed in age at diagnosis, transmission categories, CD4(+) T-cell counts and HAART. Effective interventions should target those at higher risk for morbidity or mortality, ensuring early diagnosis and timely treatment to slow down the disease progression.

Computer aided diagnosis and treatment planning for developmental dysplasia of the hip

NASA Astrophysics Data System (ADS)

Li, Bin; Lu, Hongbing; Cai, Wenli; Li, Xiang; Meng, Jie; Liang, Zhengrong

2005-04-01

The developmental dysplasia of the hip (DDH) is a congenital malformation affecting the proximal femurs and acetabulum that are subluxatable, dislocatable, and dislocated. Early diagnosis and treatment is important because failure to diagnose and improper treatment can result in significant morbidity. In this paper, we designed and implemented a computer aided system for the diagnosis and treatment planning of this disease. With the design, the patient received CT (computed tomography) or MRI (magnetic resonance imaging) scan first. A mixture-based PV partial-volume algorithm was applied to perform bone segmentation on CT image, followed by three-dimensional (3D) reconstruction and display of the segmented image, demonstrating the special relationship between the acetabulum and femurs for visual judgment. Several standard procedures, such as Salter procedure, Pemberton procedure and Femoral Shortening osteotomy, were simulated on the screen to rehearse a virtual treatment plan. Quantitative measurement of Acetabular Index (AI) and Femoral Neck Anteversion (FNA) were performed on the 3D image for evaluation of DDH and treatment plans. PC graphics-card GPU architecture was exploited to accelerate the 3D rendering and geometric manipulation. The prototype system was implemented on PC/Windows environment and is currently under clinical trial on patient datasets.

Computer-aided diagnosis of melanoma using border and wavelet-based texture analysis.

PubMed

Garnavi, Rahil; Aldeen, Mohammad; Bailey, James

2012-11-01

This paper presents a novel computer-aided diagnosis system for melanoma. The novelty lies in the optimised selection and integration of features derived from textural, borderbased and geometrical properties of the melanoma lesion. The texture features are derived from using wavelet-decomposition, the border features are derived from constructing a boundaryseries model of the lesion border and analysing it in spatial and frequency domains, and the geometry features are derived from shape indexes. The optimised selection of features is achieved by using the Gain-Ratio method, which is shown to be computationally efficient for melanoma diagnosis application. Classification is done through the use of four classifiers; namely, Support Vector Machine, Random Forest, Logistic Model Tree and Hidden Naive Bayes. The proposed diagnostic system is applied on a set of 289 dermoscopy images (114 malignant, 175 benign) partitioned into train, validation and test image sets. The system achieves and accuracy of 91.26% and AUC value of 0.937, when 23 features are used. Other important findings include (i) the clear advantage gained in complementing texture with border and geometry features, compared to using texture information only, and (ii) higher contribution of texture features than border-based features in the optimised feature set.

Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer's disease.

PubMed

Bhateja, Vikrant; Moin, Aisha; Srivastava, Anuja; Bao, Le Nguyen; Lay-Ekuakille, Aimé; Le, Dac-Nhuong

2016-07-01

Computer based diagnosis of Alzheimer's disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer's disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).

Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer’s disease

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bhateja, Vikrant, E-mail: bhateja.vikrant@gmail.com, E-mail: nhuongld@hus.edu.vn; Moin, Aisha; Srivastava, Anuja

Computer based diagnosis of Alzheimer’s disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer’s disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Componentmore » Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).« less

Human problem solving performance in a fault diagnosis task

NASA Technical Reports Server (NTRS)

Rouse, W. B.

1978-01-01

It is proposed that humans in automated systems will be asked to assume the role of troubleshooter or problem solver and that the problems which they will be asked to solve in such systems will not be amenable to rote solution. The design of visual displays for problem solving in such situations is considered, and the results of two experimental investigations of human problem solving performance in the diagnosis of faults in graphically displayed network problems are discussed. The effects of problem size, forced-pacing, computer aiding, and training are considered. Results indicate that human performance deviates from optimality as problem size increases. Forced-pacing appears to cause the human to adopt fairly brute force strategies, as compared to those adopted in self-paced situations. Computer aiding substantially lessens the number of mistaken diagnoses by performing the bookkeeping portions of the task.

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

PubMed Central

Masood, Ammara; Al-Jumaily, Adel Ali

2013-01-01

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided. PMID:24575126

Abdominal hernias: Radiological features

PubMed Central

Lassandro, Francesco; Iasiello, Francesca; Pizza, Nunzia Luisa; Valente, Tullio; Stefano, Maria Luisa Mangoni di Santo; Grassi, Roberto; Muto, Roberto

2011-01-01

Abdominal wall hernias are common diseases of the abdomen with a global incidence approximately 4%-5%. They are distinguished in external, diaphragmatic and internal hernias on the basis of their localisation. Groin hernias are the most common with a prevalence of 75%, followed by femoral (15%) and umbilical (8%). There is a higher prevalence in males (M:F, 8:1). Diagnosis is usually made on physical examination. However, clinical diagnosis may be difficult, especially in patients with obesity, pain or abdominal wall scarring. In these cases, abdominal imaging may be the first clue to the correct diagnosis and to confirm suspected complications. Different imaging modalities are used: conventional radiographs or barium studies, ultrasonography and Computed Tomography. Imaging modalities can aid in the differential diagnosis of palpable abdominal wall masses and can help to define hernial contents such as fatty tissue, bowel, other organs or fluid. This work focuses on the main radiological findings of abdominal herniations. PMID:21860678

Letter to the Editor: Use of Publicly Available Image Resources

DOE PAGES

Armato, Samuel G.; Drukker, Karen; Li, Feng; ...

2017-05-11

Here we write with regard to the Academic Radiology article entitled, “Computer-aided Diagnosis for Lung Cancer: Usefulness of Nodule Heterogeneity” by Drs. Nishio and Nagashima (1). The authors also report on a computerized method to classify as benign or malignant lung nodules present in computed tomography (CT) scans.

Computer-aided diagnosis of prostate cancer using multi-parametric MRI: comparison between PUN and Tofts models

NASA Astrophysics Data System (ADS)

Mazzetti, S.; Giannini, V.; Russo, F.; Regge, D.

2018-05-01

Computer-aided diagnosis (CAD) systems are increasingly being used in clinical settings to report multi-parametric magnetic resonance imaging (mp-MRI) of the prostate. Usually, CAD systems automatically highlight cancer-suspicious regions to the radiologist, reducing reader variability and interpretation errors. Nevertheless, implementing this software requires the selection of which mp-MRI parameters can best discriminate between malignant and non-malignant regions. To exploit functional information, some parameters are derived from dynamic contrast-enhanced (DCE) acquisitions. In particular, much CAD software employs pharmacokinetic features, such as K trans and k ep, derived from the Tofts model, to estimate a likelihood map of malignancy. However, non-pharmacokinetic models can be also used to describe DCE-MRI curves, without any requirement for prior knowledge or measurement of the arterial input function, which could potentially lead to large errors in parameter estimation. In this work, we implemented an empirical function derived from the phenomenological universalities (PUN) class to fit DCE-MRI. The parameters of the PUN model are used in combination with T2-weighted and diffusion-weighted acquisitions to feed a support vector machine classifier to produce a voxel-wise malignancy likelihood map of the prostate. The results were all compared to those for a CAD system based on Tofts pharmacokinetic features to describe DCE-MRI curves, using different quality aspects of image segmentation, while also evaluating the number and size of false positive (FP) candidate regions. This study included 61 patients with 70 biopsy-proven prostate cancers (PCa). The metrics used to evaluate segmentation quality between the two CAD systems were not statistically different, although the PUN-based CAD reported a lower number of FP, with reduced size compared to the Tofts-based CAD. In conclusion, the CAD software based on PUN parameters is a feasible means with which to detect PCa, without affecting segmentation quality, and hence it could be successfully applied in clinical settings, improving the automated diagnosis process and reducing computational complexity.

Comprehensive Modeling and Visualization of Cardiac Anatomy and Physiology from CT Imaging and Computer Simulations

PubMed Central

Sun, Peng; Zhou, Haoyin; Ha, Seongmin; Hartaigh, Bríain ó; Truong, Quynh A.; Min, James K.

2016-01-01

In clinical cardiology, both anatomy and physiology are needed to diagnose cardiac pathologies. CT imaging and computer simulations provide valuable and complementary data for this purpose. However, it remains challenging to gain useful information from the large amount of high-dimensional diverse data. The current tools are not adequately integrated to visualize anatomic and physiologic data from a complete yet focused perspective. We introduce a new computer-aided diagnosis framework, which allows for comprehensive modeling and visualization of cardiac anatomy and physiology from CT imaging data and computer simulations, with a primary focus on ischemic heart disease. The following visual information is presented: (1) Anatomy from CT imaging: geometric modeling and visualization of cardiac anatomy, including four heart chambers, left and right ventricular outflow tracts, and coronary arteries; (2) Function from CT imaging: motion modeling, strain calculation, and visualization of four heart chambers; (3) Physiology from CT imaging: quantification and visualization of myocardial perfusion and contextual integration with coronary artery anatomy; (4) Physiology from computer simulation: computation and visualization of hemodynamics (e.g., coronary blood velocity, pressure, shear stress, and fluid forces on the vessel wall). Substantially, feedback from cardiologists have confirmed the practical utility of integrating these features for the purpose of computer-aided diagnosis of ischemic heart disease. PMID:26863663

Diagnosis of toxoplasmic encephalitis in patients with acquired immunodeficiency syndrome by using a new serologic method.

PubMed Central

Suzuki, Y; Israelski, D M; Dannemann, B R; Stepick-Biek, P; Thulliez, P; Remington, J S

1988-01-01

The present study was performed to develop a serological method for diagnosing toxoplasmic encephalitis in patients with acquired immunodeficiency syndrome (AIDS). The trophozoite form of Toxoplasma gondii, fixed with either Formalin or acetone, was used in a modification of an agglutination method previously shown to differentiate between the acute and the chronic (latent) stages of infection with toxoplasma in immunologically normal persons. By using these antigens in separate tests and evaluating the data for statistical significance, 70% of patients with AIDS with biopsy-proven toxoplasmic encephalitis were distinguished from control, ambulatory patients with AIDS with toxoplasma antibodies but without signs or symptoms of central nervous system involvement. In a separate study, the agglutination tests identified from controls 84% of patients with AIDS with two or more brain lesions detected by computed-tomographic or magnetic-resonance-imaging scans and suspected of having toxoplasmic encephalitis. Thus, these agglutination tests should prove valuable for the noninvasive diagnosis of toxoplasmic encephalitis in patients with AIDS. PMID:3230132

Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis

PubMed Central

Cárdenas-Peña, David; Collazos-Huertas, Diego; Castellanos-Dominguez, German

2016-01-01

Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014 CADDementia challenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing. PMID:27148392

Evaluation of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector row computed tomography (MDCT): JAFROC study for the improvement in radiologists' diagnostic accuracy.

PubMed

Hirose, Tomohiro; Nitta, Norihisa; Shiraishi, Junji; Nagatani, Yukihiro; Takahashi, Masashi; Murata, Kiyoshi

2008-12-01

The aim of this study was to evaluate the usefulness of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector-row computed tomography (MDCT) in terms of improvement in radiologists' diagnostic accuracy in detecting lung nodules, using jackknife free-response receiver-operating characteristic (JAFROC) analysis. Twenty-one patients (6 without and 15 with lung nodules) were selected randomly from 120 consecutive thoracic computed tomographic examinations. The gold standard for the presence or absence of nodules in the observer study was determined by consensus of two radiologists. Six expert radiologists participated in a free-response receiver operating characteristic study for the detection of lung nodules on MDCT, in which cases were interpreted first without and then with the output of CAD software. Radiologists were asked to indicate the locations of lung nodule candidates on the monitor with their confidence ratings for the presence of lung nodules. The performance of the CAD software indicated that the sensitivity in detecting lung nodules was 71.4%, with 0.95 false-positive results per case. When radiologists used the CAD software, the average sensitivity improved from 39.5% to 81.0%, with an increase in the average number of false-positive results from 0.14 to 0.89 per case. The average figure-of-merit values for the six radiologists were 0.390 without and 0.845 with the output of the CAD software, and there was a statistically significant difference (P < .0001) using the JAFROC analysis. The CAD software for the detection of lung nodules on MDCT has the potential to assist radiologists by increasing their accuracy.

Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images: A Retrospective Survey and Critical Analysis

PubMed Central

Drew, Mark S.

2016-01-01

Cutaneous melanoma is the most life-threatening form of skin cancer. Although advanced melanoma is often considered as incurable, if detected and excised early, the prognosis is promising. Today, clinicians use computer vision in an increasing number of applications to aid early detection of melanoma through dermatological image analysis (dermoscopy images, in particular). Colour assessment is essential for the clinical diagnosis of skin cancers. Due to this diagnostic importance, many studies have either focused on or employed colour features as a constituent part of their skin lesion analysis systems. These studies range from using low-level colour features, such as simple statistical measures of colours occurring in the lesion, to availing themselves of high-level semantic features such as the presence of blue-white veil, globules, or colour variegation in the lesion. This paper provides a retrospective survey and critical analysis of contributions in this research direction. PMID:28096807

Bridging the integration gap between imaging and information systems: a uniform data concept for content-based image retrieval in computer-aided diagnosis.

PubMed

Welter, Petra; Riesmeier, Jörg; Fischer, Benedikt; Grouls, Christoph; Kuhl, Christiane; Deserno, Thomas M

2011-01-01

It is widely accepted that content-based image retrieval (CBIR) can be extremely useful for computer-aided diagnosis (CAD). However, CBIR has not been established in clinical practice yet. As a widely unattended gap of integration, a unified data concept for CBIR-based CAD results and reporting is lacking. Picture archiving and communication systems and the workflow of radiologists must be considered for successful data integration to be achieved. We suggest that CBIR systems applied to CAD should integrate their results in a picture archiving and communication systems environment such as Digital Imaging and Communications in Medicine (DICOM) structured reporting documents. A sample DICOM structured reporting template adaptable to CBIR and an appropriate integration scheme is presented. The proposed CBIR data concept may foster the promulgation of CBIR systems in clinical environments and, thereby, improve the diagnostic process.

Bridging the integration gap between imaging and information systems: a uniform data concept for content-based image retrieval in computer-aided diagnosis

PubMed Central

Riesmeier, Jörg; Fischer, Benedikt; Grouls, Christoph; Kuhl, Christiane; Deserno (né Lehmann), Thomas M

2011-01-01

It is widely accepted that content-based image retrieval (CBIR) can be extremely useful for computer-aided diagnosis (CAD). However, CBIR has not been established in clinical practice yet. As a widely unattended gap of integration, a unified data concept for CBIR-based CAD results and reporting is lacking. Picture archiving and communication systems and the workflow of radiologists must be considered for successful data integration to be achieved. We suggest that CBIR systems applied to CAD should integrate their results in a picture archiving and communication systems environment such as Digital Imaging and Communications in Medicine (DICOM) structured reporting documents. A sample DICOM structured reporting template adaptable to CBIR and an appropriate integration scheme is presented. The proposed CBIR data concept may foster the promulgation of CBIR systems in clinical environments and, thereby, improve the diagnostic process. PMID:21672913

Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform.

PubMed

Jian, Wushuai; Sun, Xueyan; Luo, Shuqian

2012-12-19

Digital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading. Firstly, 25 abnormal ROIs were extracted according to the center and diameter of the lesions manually and 25 normal ROIs were selected randomly. Then micro-calcifications were segmented by combining space and frequency domain techniques. We extracted three texture features based on wavelet (Haar, DB4, DT-CWT) transform. Totally 14 descriptors were introduced to define the characteristics of the suspicious micro-calcifications. Principal Component Analysis (PCA) was used to transform these descriptors to a compact and efficient vector expression. Support Vector Machine (SVM) classifier was used to classify potential micro-calcifications. Finally, we used the receiver operating characteristic (ROC) curve and free-response operating characteristic (FROC) curve to evaluate the performance of the CAD system. The results of SVM classifications based on different wavelets shows DT-CWT has a better performance. Compared with other results, DT-CWT method achieved an accuracy of 96% and 100% for the classification of normal and abnormal ROIs, and the classification of benign and malignant micro-calcifications respectively. In FROC analysis, our CAD system for clinical dataset detection achieved a sensitivity of 83.5% at a false positive per image of 1.85. Compared with general wavelets, DT-CWT could describe the features more effectively, and our CAD system had a competitive performance.

Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform

PubMed Central

2012-01-01

Background Digital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading. Methods Firstly, 25 abnormal ROIs were extracted according to the center and diameter of the lesions manually and 25 normal ROIs were selected randomly. Then micro-calcifications were segmented by combining space and frequency domain techniques. We extracted three texture features based on wavelet (Haar, DB4, DT-CWT) transform. Totally 14 descriptors were introduced to define the characteristics of the suspicious micro-calcifications. Principal Component Analysis (PCA) was used to transform these descriptors to a compact and efficient vector expression. Support Vector Machine (SVM) classifier was used to classify potential micro-calcifications. Finally, we used the receiver operating characteristic (ROC) curve and free-response operating characteristic (FROC) curve to evaluate the performance of the CAD system. Results The results of SVM classifications based on different wavelets shows DT-CWT has a better performance. Compared with other results, DT-CWT method achieved an accuracy of 96% and 100% for the classification of normal and abnormal ROIs, and the classification of benign and malignant micro-calcifications respectively. In FROC analysis, our CAD system for clinical dataset detection achieved a sensitivity of 83.5% at a false positive per image of 1.85. Conclusions Compared with general wavelets, DT-CWT could describe the features more effectively, and our CAD system had a competitive performance. PMID:23253202

Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM.

PubMed

Sharma, Shubhi; Khanna, Pritee

2015-02-01

This work is directed toward the development of a computer-aided diagnosis (CAD) system to detect abnormalities or suspicious areas in digital mammograms and classify them as malignant or nonmalignant. Original mammogram is preprocessed to separate the breast region from its background. To work on the suspicious area of the breast, region of interest (ROI) patches of a fixed size of 128×128 are extracted from the original large-sized digital mammograms. For training, patches are extracted manually from a preprocessed mammogram. For testing, patches are extracted from a highly dense area identified by clustering technique. For all extracted patches corresponding to a mammogram, Zernike moments of different orders are computed and stored as a feature vector. A support vector machine (SVM) is used to classify extracted ROI patches. The experimental study shows that the use of Zernike moments with order 20 and SVM classifier gives better results among other studies. The proposed system is tested on Image Retrieval In Medical Application (IRMA) reference dataset and Digital Database for Screening Mammography (DDSM) mammogram database. On IRMA reference dataset, it attains 99% sensitivity and 99% specificity, and on DDSM mammogram database, it obtained 97% sensitivity and 96% specificity. To verify the applicability of Zernike moments as a fitting texture descriptor, the performance of the proposed CAD system is compared with the other well-known texture descriptors namely gray-level co-occurrence matrix (GLCM) and discrete cosine transform (DCT).

Can computed tomography aid in diagnosis of intramural hematomas of the intestinal wall?

PubMed

Ulusan, Serife; Pekoz, Burcak; Sariturk, Cagla

2015-12-01

We sought to use computed tomography (CT) data to support the correct differential diagnosis of patients with spontaneous intramural hematomas of the gastrointestinal tract, to aid in the clinical management of those using oral anticoagulants. Patient data were retrospectively analyzed and patients were divided into two groups. The first group contained 10 patients (5 females, 5 males, median age 65 years [range 35-79 years]) who had been diagnosed with spontaneous intramural hematomas of the gastrointestinal tract. The second group contained nine patients (5 females, 4 males, median age 41 years [range 24-56 years]) who exhibited intestinal wall thickening on CT, and who had been diagnosed with ulcerative colitis, Crohn's disease, ameboma, and lymphoma. The enhancement patterns in the CT images of the two groups were compared by an experienced and inexperienced radiologist. The differences in values were subjected to ROC analysis. Inter-observer variability was excellent (0.84) when post-contrast CT images were evaluated, as were the subtraction values (0.89). The subtracted values differed significantly between the two groups (p=0.0001). A cutoff of +31.5 HU was optimal in determining whether a hematoma was or was not present. Contrast enhancement of an intestinal wall hematoma is less than that of other intestinal wall pathologies associated with increased wall thickness. If the post-contrast enhancement of a thickened intestinal wall is less than +31.5 HU, a wall hematoma is possible. © Acta Gastro-Enterologica Belgica.

Explanation-aware computing of the prognosis for breast cancer supported by IK-DCBRC: Technical innovation.

PubMed

Khelassi, Abdeldjalil

2014-01-01

Active research is being conducted to determine the prognosis for breast cancer. However, the uncertainty is a major obstacle in this domain of medical research. In that context, explanation-aware computing has the potential for providing meaningful interactions between complex medical applications and users, which would ensure a significant reduction of uncertainty and risks. This paper presents an explanation-aware agent, supported by Intensive Knowledge-Distributed Case-Based Reasoning Classifier (IK-DCBRC), to reduce the uncertainty and risks associated with the diagnosis of breast cancer. A meaningful explanation is generated by inferring from a rule-based system according to the level of abstraction and the reasoning traces. The computer-aided detection is conducted by IK-DCBRC, which is a multi-agent system that applies the case-based reasoning paradigm and a fuzzy similarity function for the automatic prognosis by the class of breast tumors, i.e. malignant or benign, from a pattern of cytological images. A meaningful interaction between the physician and the computer-aided diagnosis system, IK-DCBRC, is achieved via an intelligent agent. The physician can observe the trace of reasoning, terms, justifications, and the strategy to be used to decrease the risks and doubts associated with the automatic diagnosis. The capability of the system we have developed was proven by an example in which conflicts were clarified and transparency was ensured. The explanation agent ensures the transparency of the automatic diagnosis of breast cancer supported by IK-DCBRC, which decreases uncertainty and risks and detects some conflicts.

An open access thyroid ultrasound image database

NASA Astrophysics Data System (ADS)

Pedraza, Lina; Vargas, Carlos; Narváez, Fabián.; Durán, Oscar; Muñoz, Emma; Romero, Eduardo

2015-01-01

Computer aided diagnosis systems (CAD) have been developed to assist radiologists in the detection and diagnosis of abnormalities and a large number of pattern recognition techniques have been proposed to obtain a second opinion. Most of these strategies have been evaluated using different datasets making their performance incomparable. In this work, an open access database of thyroid ultrasound images is presented. The dataset consists of a set of B-mode Ultrasound images, including a complete annotation and diagnostic description of suspicious thyroid lesions by expert radiologists. Several types of lesions as thyroiditis, cystic nodules, adenomas and thyroid cancers were included while an accurate lesion delineation is provided in XML format. The diagnostic description of malignant lesions was confirmed by biopsy. The proposed new database is expected to be a resource for the community to assess different CAD systems.

Influence of Computer-Aided Detection on Performance of Screening Mammography

PubMed Central

Fenton, Joshua J.; Taplin, Stephen H.; Carney, Patricia A.; Abraham, Linn; Sickles, Edward A.; D'Orsi, Carl; Berns, Eric A.; Cutter, Gary; Hendrick, R. Edward; Barlow, William E.; Elmore, Joann G.

2011-01-01

Background Computer-aided detection identifies suspicious findings on mammograms to assist radiologists. Since the Food and Drug Administration approved the technology in 1998, it has been disseminated into practice, but its effect on the accuracy of interpretation is unclear. Methods We determined the association between the use of computer-aided detection at mammography facilities and the performance of screening mammography from 1998 through 2002 at 43 facilities in three states. We had complete data for 222,135 women (a total of 429,345 mammograms), including 2351 women who received a diagnosis of breast cancer within 1 year after screening. We calculated the specificity, sensitivity, and positive predictive value of screening mammography with and without computer-aided detection, as well as the rates of biopsy and breast-cancer detection and the overall accuracy, measured as the area under the receiver-operating-characteristic (ROC) curve. Results Seven facilities (16%) implemented computer-aided detection during the study period. Diagnostic specificity decreased from 90.2% before implementation to 87.2% after implementation (P<0.001), the positive predictive value decreased from 4.1% to 3.2% (P = 0.01), and the rate of biopsy increased by 19.7% (P<0.001). The increase in sensitivity from 80.4% before implementation of computer-aided detection to 84.0% after implementation was not significant (P = 0.32). The change in the cancer-detection rate (including invasive breast cancers and ductal carcinomas in situ) was not significant (4.15 cases per 1000 screening mammograms before implementation and 4.20 cases after implementation, P = 0.90). Analyses of data from all 43 facilities showed that the use of computer-aided detection was associated with significantly lower overall accuracy than was nonuse (area under the ROC curve, 0.871 vs. 0.919; P = 0.005). Conclusions The use of computer-aided detection is associated with reduced accuracy of interpretation of screening mammograms. The increased rate of biopsy with the use of computer-aided detection is not clearly associated with improved detection of invasive breast cancer. PMID:17409321

Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and Future Potential

PubMed Central

Doi, Kunio

2007-01-01

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. In this article, the motivation and philosophy for early development of CAD schemes are presented together with the current status and future potential of CAD in a PACS environment. With CAD, radiologists use the computer output as a “second opinion” and make the final decisions. CAD is a concept established by taking into account equally the roles of physicians and computers, whereas automated computer diagnosis is a concept based on computer algorithms only. With CAD, the performance by computers does not have to be comparable to or better than that by physicians, but needs to be complementary to that by physicians. In fact, a large number of CAD systems have been employed for assisting physicians in the early detection of breast cancers on mammograms. A CAD scheme that makes use of lateral chest images has the potential to improve the overall performance in the detection of lung nodules when combined with another CAD scheme for PA chest images. Because vertebral fractures can be detected reliably by computer on lateral chest radiographs, radiologists’ accuracy in the detection of vertebral fractures would be improved by the use of CAD, and thus early diagnosis of osteoporosis would become possible. In MRA, a CAD system has been developed for assisting radiologists in the detection of intracranial aneurysms. On successive bone scan images, a CAD scheme for detection of interval changes has been developed by use of temporal subtraction images. In the future, many CAD schemes could be assembled as packages and implemented as a part of PACS. For example, the package for chest CAD may include the computerized detection of lung nodules, interstitial opacities, cardiomegaly, vertebral fractures, and interval changes in chest radiographs as well as the computerized classification of benign and malignant nodules and the differential diagnosis of interstitial lung diseases. In order to assist in the differential diagnosis, it would be possible to search for and retrieve images (or lesions) with known pathology, which would be very similar to a new unknown case, from PACS when a reliable and useful method has been developed for quantifying the similarity of a pair of images for visual comparison by radiologists. PMID:17349778

[Accuracy of computer aided measurement for detecting dental proximal caries lesions in images of cone-beam computed tomography].

PubMed

Zhang, Z L; Li, J P; Li, G; Ma, X C

2017-02-09

Objective: To establish and validate a computer program used to aid the detection of dental proximal caries in the images cone beam computed tomography (CBCT) images. Methods: According to the characteristics of caries lesions in X-ray images, a computer aided detection program for proximal caries was established with Matlab and Visual C++. The whole process for caries lesion detection included image import and preprocessing, measuring average gray value of air area, choosing region of interest and calculating gray value, defining the caries areas. The program was used to examine 90 proximal surfaces from 45 extracted human teeth collected from Peking University School and Hospital of Stomatology. The teeth were then scanned with a CBCT scanner (Promax 3D). The proximal surfaces of the teeth were respectively detected by caries detection program and scored by human observer for the extent of lesions with 6-level-scale. With histologic examination serving as the reference standard, the caries detection program and the human observer performances were assessed with receiver operating characteristic (ROC) curves. Student t -test was used to analyze the areas under the ROC curves (AUC) for the differences between caries detection program and human observer. Spearman correlation coefficient was used to analyze the detection accuracy of caries depth. Results: For the diagnosis of proximal caries in CBCT images, the AUC values of human observers and caries detection program were 0.632 and 0.703, respectively. There was a statistically significant difference between the AUC values ( P= 0.023). The correlation between program performance and gold standard (correlation coefficient r (s)=0.525) was higher than that of observer performance and gold standard ( r (s)=0.457) and there was a statistically significant difference between the correlation coefficients ( P= 0.000). Conclusions: The program that automatically detects dental proximal caries lesions could improve the diagnostic value of CBCT images.

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

PubMed Central

Xing, Fuyong; Yang, Lin

2016-01-01

Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to inter-observer variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literatures. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast (DIC), fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation. PMID:26742143

Computer-aided detection of prostate cancer in T2-weighted MRI within the peripheral zone

NASA Astrophysics Data System (ADS)

Rampun, Andrik; Zheng, Ling; Malcolm, Paul; Tiddeman, Bernie; Zwiggelaar, Reyer

2016-07-01

In this paper we propose a prostate cancer computer-aided diagnosis (CAD) system and suggest a set of discriminant texture descriptors extracted from T2-weighted MRI data which can be used as a good basis for a multimodality system. For this purpose, 215 texture descriptors were extracted and eleven different classifiers were employed to achieve the best possible results. The proposed method was tested based on 418 T2-weighted MR images taken from 45 patients and evaluated using 9-fold cross validation with five patients in each fold. The results demonstrated comparable results to existing CAD systems using multimodality MRI. We achieved an area under the receiver operating curve (A z ) values equal to 90.0%+/- 7.6% , 89.5%+/- 8.9% , 87.9%+/- 9.3% and 87.4%+/- 9.2% for Bayesian networks, ADTree, random forest and multilayer perceptron classifiers, respectively, while a meta-voting classifier using average probability as a combination rule achieved 92.7%+/- 7.4% .

Computer-aided detection in musculoskeletal projection radiography: A systematic review.

PubMed

Gundry, M; Knapp, K; Meertens, R; Meakin, J R

2018-05-01

To investigated the accuracy of computer-aided detection (CAD) software in musculoskeletal projection radiography via a systematic review. Following selection screening, eligible studies were assessed for bias, and had their study characteristics extracted resulting in 22 studies being included. Of these 22 three studies had tested their CAD software in a clinical setting; the first study investigated vertebral fractures, reporting a sensitivity score of 69.3% with CAD, compared to 59.8% sensitivity without CAD. The second study tested dental caries diagnosis producing a sensitivity score of 68.8% and specificity of 94.1% with CAD, compared to sensitivity of 39.3% and specificity of 96.7% without CAD. The third indicated osteoporotic cases based on CAD, resulting in 100% sensitivity and 81.3% specificity. The current evidence reported shows a lack of development into the clinical testing phase; however the research does show future promise in the variation of different CAD systems. Copyright © 2017 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.

Ultrasound introscopic image quantitative characteristics for medical diagnosis

NASA Astrophysics Data System (ADS)

Novoselets, Mikhail K.; Sarkisov, Sergey S.; Gridko, Alexander N.; Tcheban, Anatoliy K.

1993-09-01

The results on computer aided extraction of quantitative characteristics (QC) of ultrasound introscopic images for medical diagnosis are presented. Thyroid gland (TG) images of Chernobil Accident sufferers are considered. It is shown that TG diseases can be associated with some values of selected QCs of random echo distribution in the image. The possibility of these QCs usage for TG diseases recognition in accordance with calculated values is analyzed. The role of speckle noise elimination in the solution of the problem on TG diagnosis is considered too.

A Computer-Aided Analysis Method of SPECT Brain Images for Quantitative Treatment Monitoring: Performance Evaluations and Clinical Applications.

PubMed

Zheng, Xiujuan; Wei, Wentao; Huang, Qiu; Song, Shaoli; Wan, Jieqing; Huang, Gang

2017-01-01

The objective and quantitative analysis of longitudinal single photon emission computed tomography (SPECT) images are significant for the treatment monitoring of brain disorders. Therefore, a computer aided analysis (CAA) method is introduced to extract a change-rate map (CRM) as a parametric image for quantifying the changes of regional cerebral blood flow (rCBF) in longitudinal SPECT brain images. The performances of the CAA-CRM approach in treatment monitoring are evaluated by the computer simulations and clinical applications. The results of computer simulations show that the derived CRMs have high similarities with their ground truths when the lesion size is larger than system spatial resolution and the change rate is higher than 20%. In clinical applications, the CAA-CRM approach is used to assess the treatment of 50 patients with brain ischemia. The results demonstrate that CAA-CRM approach has a 93.4% accuracy of recovered region's localization. Moreover, the quantitative indexes of recovered regions derived from CRM are all significantly different among the groups and highly correlated with the experienced clinical diagnosis. In conclusion, the proposed CAA-CRM approach provides a convenient solution to generate a parametric image and derive the quantitative indexes from the longitudinal SPECT brain images for treatment monitoring.

Dr. Lindberg's Legacy : Charting A New Course | NIH MedlinePlus the Magazine

MedlinePlus

... technology, artificial intelligence, computer-aided medical diagnosis, and electronic health records. As the first President of the ... about it—when Don began, NLM had no electronic journals in its collection, few people owned personal ...

AIDS: The Role of Imaging Modalities and Infection Control Policies

PubMed Central

Moore-Stovall, Joyce

1988-01-01

The availability of imaging modalities, such as the chest radiograph, gallium scan, double-contrast barium enema, computed tomography, and nuclear magnetic resonance, are very helpful in the diagnosis, treatment, and follow-up evaluation of patients with acquired immunodeficiency syndrome (AIDS). Because this syndrome causes irreversible destruction of the immune system, patients are susceptible to a multitude of opportunistic infections and malignancies. Health care professionals and the general public would be less fearful and apprehensive of AIDS victims if properly informed about the communicability of this syndrome. PMID:3047412

Applicability of mathematical modeling to problems of environmental physiology

NASA Technical Reports Server (NTRS)

White, Ronald J.; Lujan, Barbara F.; Leonard, Joel I.; Srinivasan, R. Srini

1988-01-01

The paper traces the evolution of mathematical modeling and systems analysis from terrestrial research to research related to space biomedicine and back again to terrestrial research. Topics covered include: power spectral analysis of physiological signals; pattern recognition models for detection of disease processes; and, computer-aided diagnosis programs used in conjunction with a special on-line biomedical computer library.

Operational Assessment of Color Vision

DTIC Science & Technology

2016-06-20

evaluated in this study. 15. SUBJECT TERMS Color vision, aviation, cone contrast test, Colour Assessment & Diagnosis , color Dx, OBVA 16. SECURITY...symbologies are frequently used to aid or direct critical activities such as aircraft landing approaches or railroad right-of-way designations...computer-generated display systems have facilitated the development of computer-based, automated tests of color vision [14,15]. The United Kingdom’s

Clinical feasibility and efficacy of using virtual surgical planning in bimaxillary orthognathic surgery without intermediate splint.

PubMed

Li, Yunfeng; Jiang, Yangmei; Zhang, Nan; Xu, Rui; Hu, Jing; Zhu, Songsong

2015-03-01

Computer-aided jaw surgery has been extensively studied recently. The purpose of this study was to determine the clinical feasibility of performing bimaxillary orthognathic surgery without intermediate splint using virtual surgical planning and rapid prototyping technology. Twelve consecutive patients who underwent bimaxillary orthognathic surgery were included. The presented treatment plan here mainly consists of 6 procedures: (1) data acquisition from computed tomography (CT) of the skull and laser scanning of the dentition; (2) reconstruction and fusion of a virtual skull model with accurate dentition; (3) virtual surgery simulation including osteotomy and movement and repositioning of bony segments; (4) final surgical splint fabrication (no intermediate splint) using computer-aided design and rapid prototyping technology; (5) transfer of the virtual surgical plan to the operating room; and (6) comparison of the actual surgical outcome to the virtual surgical plan. All procedures of the treatment were successfully performed on all 12 patients. In quantification of differences between simulated and actual postoperative outcome, we found that the mean linear difference was less than 1.8 mm, and the mean angular difference was less than 2.5 degrees in all evaluated patients. Results from this study suggested that it was feasible to perform bimaxillary orthognathic surgery without intermediate splint. Virtual surgical planning and the guiding splints facilitated the diagnosis, treatment planning, accurate osteotomy, and bony segments repositioning in orthognathic surgery.

[An integrated segmentation method for 3D ultrasound carotid artery].

PubMed

Yang, Xin; Wu, Huihui; Liu, Yang; Xu, Hongwei; Liang, Huageng; Cai, Wenjuan; Fang, Mengjie; Wang, Yujie

2013-07-01

An integrated segmentation method for 3D ultrasound carotid artery was proposed. 3D ultrasound image was sliced into transverse, coronal and sagittal 2D images on the carotid bifurcation point. Then, the three images were processed respectively, and the carotid artery contours and thickness were obtained finally. This paper tries to overcome the disadvantages of current computer aided diagnosis method, such as high computational complexity, easily introduced subjective errors et al. The proposed method could get the carotid artery overall information rapidly, accurately and completely. It could be transplanted into clinical usage for atherosclerosis diagnosis and prevention.

Novel Fluorescein Angiography-Based Computer-Aided Algorithm for Assessment of Retinal Vessel Permeability

PubMed Central

Chassidim, Yoash; Parmet, Yisrael; Tomkins, Oren; Knyazer, Boris; Friedman, Alon; Levy, Jaime

2013-01-01

Purpose To present a novel method for quantitative assessment of retinal vessel permeability using a fluorescein angiography-based computer algorithm. Methods Twenty-one subjects (13 with diabetic retinopathy, 8 healthy volunteers) underwent fluorescein angiography (FA). Image pre-processing included removal of non-retinal and noisy images and registration to achieve spatial and temporal pixel-based analysis. Permeability was assessed for each pixel by computing intensity kinetics normalized to arterial values. A linear curve was fitted and the slope value was assigned, color-coded and displayed. The initial FA studies and the computed permeability maps were interpreted in a masked and randomized manner by three experienced ophthalmologists for statistical validation of diagnosis accuracy and efficacy. Results Permeability maps were successfully generated for all subjects. For healthy volunteers permeability values showed a normal distribution with a comparable range between subjects. Based on the mean cumulative histogram for the healthy population a threshold (99.5%) for pathological permeability was determined. Clear differences were found between patients and healthy subjects in the number and spatial distribution of pixels with pathological vascular leakage. The computed maps improved the discrimination between patients and healthy subjects, achieved sensitivity and specificity of 0.974 and 0.833 respectively, and significantly improved the consensus among raters for the localization of pathological regions. Conclusion The new algorithm allows quantification of retinal vessel permeability and provides objective, more sensitive and accurate evaluation than the present subjective clinical diagnosis. Future studies with a larger patients’ cohort and different retinal pathologies are awaited to further validate this new approach and its role in diagnosis and treatment follow-up. Successful evaluation of vasculature permeability may be used for the early diagnosis of brain microvascular pathology and potentially predict associated neurological sequelae. Finally, the algorithm could be implemented for intraoperative evaluation of micovascular integrity in other organs or during animal experiments. PMID:23626701

Computer-aided diagnostic detection system of venous beading in retinal images

NASA Astrophysics Data System (ADS)

Yang, Ching-Wen; Ma, DyeJyun; Chao, ShuennChing; Wang, ChuinMu; Wen, Chia-Hsien; Lo, ChienShun; Chung, Pau-Choo; Chang, Chein-I.

2000-05-01

The detection of venous beading in retinal images provides an early sign of diabetic retinopathy and plays an important role as a preprocessing step in diagnosing ocular diseases. We present a computer-aided diagnostic system to automatically detect venous beading of blood vessels. It comprises of two modules, referred to as the blood vessel extraction module and the venus beading detection module. The former uses a bell-shaped Gaussian kernel with 12 azimuths to extract blood vessels while the latter applies a neural network-based shape cognitron to detect venous beading among the extracted blood vessels for diagnosis. Both modules are fully computer-automated. To evaluate the proposed system, 61 retinal images (32 beaded and 29 normal images) are used for performance evaluation.

Computer-aided diagnosis of focal liver lesions by use of physicians' subjective classification of echogenic patterns in baseline and contrast-enhanced ultrasonography.

PubMed

Sugimoto, Katsutoshi; Shiraishi, Junji; Moriyasu, Fuminori; Doi, Kunio

2009-04-01

To develop a computer-aided diagnostic (CAD) scheme for classifying focal liver lesions (FLLs) by use of physicians' subjective classification of echogenic patterns of FLLs on baseline and contrast-enhanced ultrasonography (US). A total of 137 hepatic lesions in 137 patients were evaluated with B-mode and NC100100 (Sonazoid)-enhanced pulse-inversion US; lesions included 74 hepatocellular carcinomas (HCCs) (23: well-differentiated, 36: moderately differentiated, 15: poorly differentiated HCCs), 33 liver metastases, and 30 liver hemangiomas. Three physicians evaluated single images at B-mode and arterial phases with a cine mode. Physicians were asked to classify each lesion into one of eight B-mode and one of eight enhancement patterns, but did not make a diagnosis. To classify five types of FLLs, we employed a decision tree model with four decision nodes and four artificial neural networks (ANNs). The results of the physicians' pattern classifications were used successively for four different ANNs in making decisions at each of the decision nodes in the decision tree model. The classification accuracies for the 137 FLLs were 84.8% for metastasis, 93.3% for hemangioma, and 98.6% for all HCCs. In addition, the classification accuracies for histological differentiation types of HCCs were 65.2% for well-differentiated HCC, 41.7% for moderately differentiated HCC, and 80.0% for poorly differentiated HCC. This CAD scheme has the potential to improve the diagnostic accuracy of liver lesions. However, the accuracy in the histologic differential diagnosis of HCC based on baseline and contrast-enhanced US is still limited.

Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy.

PubMed

Akram, Usman M; Khan, Shoab A

2012-10-01

There is an ever-increasing interest in the development of automatic medical diagnosis systems due to the advancement in computing technology and also to improve the service by medical community. The knowledge about health and disease is required for reliable and accurate medical diagnosis. Diabetic Retinopathy (DR) is one of the most common causes of blindness and it can be prevented if detected and treated early. DR has different signs and the most distinctive are microaneurysm and haemorrhage which are dark lesions and hard exudates and cotton wool spots which are bright lesions. Location and structure of blood vessels and optic disk play important role in accurate detection and classification of dark and bright lesions for early detection of DR. In this article, we propose a computer aided system for the early detection of DR. The article presents algorithms for retinal image preprocessing, blood vessel enhancement and segmentation and optic disk localization and detection which eventually lead to detection of different DR lesions using proposed hybrid fuzzy classifier. The developed methods are tested on four different publicly available databases. The presented methods are compared with recently published methods and the results show that presented methods outperform all others.

Application of infrared thermography in computer aided diagnosis

NASA Astrophysics Data System (ADS)

Faust, Oliver; Rajendra Acharya, U.; Ng, E. Y. K.; Hong, Tan Jen; Yu, Wenwei

2014-09-01

The invention of thermography, in the 1950s, posed a formidable problem to the research community: What is the relationship between disease and heat radiation captured with Infrared (IR) cameras? The research community responded with a continuous effort to find this crucial relationship. This effort was aided by advances in processing techniques, improved sensitivity and spatial resolution of thermal sensors. However, despite this progress fundamental issues with this imaging modality still remain. The main problem is that the link between disease and heat radiation is complex and in many cases even non-linear. Furthermore, the change in heat radiation as well as the change in radiation pattern, which indicate disease, is minute. On a technical level, this poses high requirements on image capturing and processing. On a more abstract level, these problems lead to inter-observer variability and on an even more abstract level they lead to a lack of trust in this imaging modality. In this review, we adopt the position that these problems can only be solved through a strict application of scientific principles and objective performance assessment. Computing machinery is inherently objective; this helps us to apply scientific principles in a transparent way and to assess the performance results. As a consequence, we aim to promote thermography based Computer-Aided Diagnosis (CAD) systems. Another benefit of CAD systems comes from the fact that the diagnostic accuracy is linked to the capability of the computing machinery and, in general, computers become ever more potent. We predict that a pervasive application of computers and networking technology in medicine will help us to overcome the shortcomings of any single imaging modality and this will pave the way for integrated health care systems which maximize the quality of patient care.

ICADx: interpretable computer aided diagnosis of breast masses

NASA Astrophysics Data System (ADS)

Kim, Seong Tae; Lee, Hakmin; Kim, Hak Gu; Ro, Yong Man

2018-02-01

In this study, a novel computer aided diagnosis (CADx) framework is devised to investigate interpretability for classifying breast masses. Recently, a deep learning technology has been successfully applied to medical image analysis including CADx. Existing deep learning based CADx approaches, however, have a limitation in explaining the diagnostic decision. In real clinical practice, clinical decisions could be made with reasonable explanation. So current deep learning approaches in CADx are limited in real world deployment. In this paper, we investigate interpretability in CADx with the proposed interpretable CADx (ICADx) framework. The proposed framework is devised with a generative adversarial network, which consists of interpretable diagnosis network and synthetic lesion generative network to learn the relationship between malignancy and a standardized description (BI-RADS). The lesion generative network and the interpretable diagnosis network compete in an adversarial learning so that the two networks are improved. The effectiveness of the proposed method was validated on public mammogram database. Experimental results showed that the proposed ICADx framework could provide the interpretability of mass as well as mass classification. It was mainly attributed to the fact that the proposed method was effectively trained to find the relationship between malignancy and interpretations via the adversarial learning. These results imply that the proposed ICADx framework could be a promising approach to develop the CADx system.

A review of critical in-flight events research methodology

NASA Technical Reports Server (NTRS)

Giffin, W. C.; Rockwell, T. H.; Smith, P. E.

1985-01-01

Pilot's cognitive responses to critical in-flight events (CIFE's) were investigated, using pilots, who had on the average about 2540 flight hours each, in four experiments: (1) full-mission simulation in a general aviation trainer, (2) paper and pencil CIFE tests, (3) interactive computer-aided scenario testing, and (4) verbal protocols in fault diagnosis tasks. The results of both computer and paper and pencil tests showed only 50 percent efficiency in correct diagnosis of critical events. The efficiency in arriving at a diagnosis was also low: over 20 inquiries were made for 21 percent of the scenarios diagnosed. The information-seeking pattern was random, with frequent retracing over old inquiries. The measures for developing improved cognitive skills for CIFE's are discussed.

Image calibration and registration in cone-beam computed tomogram for measuring the accuracy of computer-aided implant surgery

NASA Astrophysics Data System (ADS)

Lam, Walter Y. H.; Ngan, Henry Y. T.; Wat, Peter Y. P.; Luk, Henry W. K.; Goto, Tazuko K.; Pow, Edmond H. N.

2015-02-01

Medical radiography is the use of radiation to "see through" a human body without breaching its integrity (surface). With computed tomography (CT)/cone beam computed tomography (CBCT), three-dimensional (3D) imaging can be produced. These imagings not only facilitate disease diagnosis but also enable computer-aided surgical planning/navigation. In dentistry, the common method for transfer of the virtual surgical planning to the patient (reality) is the use of surgical stent either with a preloaded planning (static) like a channel or a real time surgical navigation (dynamic) after registration with fiducial markers (RF). This paper describes using the corner of a cube as a radiopaque fiducial marker on an acrylic (plastic) stent, this RF allows robust calibration and registration of Cartesian (x, y, z)- coordinates for linking up the patient (reality) and the imaging (virtuality) and hence the surgical planning can be transferred in either static or dynamic way. The accuracy of computer-aided implant surgery was measured with reference to coordinates. In our preliminary model surgery, a dental implant was planned virtually and placed with preloaded surgical guide. The deviation of the placed implant apex from the planning was x=+0.56mm [more right], y=- 0.05mm [deeper], z=-0.26mm [more lingual]) which was within clinically 2mm safety range. For comparison with the virtual planning, the physically placed implant was CT/CBCT scanned and errors may be introduced. The difference of the actual implant apex to the virtual apex was x=0.00mm, y=+0.21mm [shallower], z=-1.35mm [more lingual] and this should be brought in mind when interpret the results.

Accuracy evaluation of metal copings fabricated by computer-aided milling and direct metal laser sintering systems

PubMed Central

Lee, Wan-Sun; Kim, Woong-Chul

2015-01-01

PURPOSE To assess the marginal and internal gaps of the copings fabricated by computer-aided milling and direct metal laser sintering (DMLS) systems in comparison to casting method. MATERIALS AND METHODS Ten metal copings were fabricated by casting, computer-aided milling, and DMLS. Seven mesiodistal and labiolingual positions were then measured, and each of these were divided into the categories; marginal gap (MG), cervical gap (CG), axial wall at internal gap (AG), and incisal edge at internal gap (IG). Evaluation was performed by a silicone replica technique. A digital microscope was used for measurement of silicone layer. Statistical analyses included one-way and repeated measure ANOVA to test the difference between the fabrication methods and categories of measured points (α=.05), respectively. RESULTS The mean gap differed significantly with fabrication methods (P<.001). Casting produced the narrowest gap in each of the four measured positions, whereas CG, AG, and IG proved narrower in computer-aided milling than in DMLS. Thus, with the exception of MG, all positions exhibited a significant difference between computer-aided milling and DMLS (P<.05). CONCLUSION Although the gap was found to vary with fabrication methods, the marginal and internal gaps of the copings fabricated by computer-aided milling and DMLS fell within the range of clinical acceptance (<120 µm). However, the statistically significant difference to conventional casting indicates that the gaps in computer-aided milling and DMLS fabricated restorations still need to be further reduced. PMID:25932310

Accuracy evaluation of metal copings fabricated by computer-aided milling and direct metal laser sintering systems.

PubMed

Park, Jong-Kyoung; Lee, Wan-Sun; Kim, Hae-Young; Kim, Woong-Chul; Kim, Ji-Hwan

2015-04-01

To assess the marginal and internal gaps of the copings fabricated by computer-aided milling and direct metal laser sintering (DMLS) systems in comparison to casting method. Ten metal copings were fabricated by casting, computer-aided milling, and DMLS. Seven mesiodistal and labiolingual positions were then measured, and each of these were divided into the categories; marginal gap (MG), cervical gap (CG), axial wall at internal gap (AG), and incisal edge at internal gap (IG). Evaluation was performed by a silicone replica technique. A digital microscope was used for measurement of silicone layer. Statistical analyses included one-way and repeated measure ANOVA to test the difference between the fabrication methods and categories of measured points (α=.05), respectively. The mean gap differed significantly with fabrication methods (P<.001). Casting produced the narrowest gap in each of the four measured positions, whereas CG, AG, and IG proved narrower in computer-aided milling than in DMLS. Thus, with the exception of MG, all positions exhibited a significant difference between computer-aided milling and DMLS (P<.05). Although the gap was found to vary with fabrication methods, the marginal and internal gaps of the copings fabricated by computer-aided milling and DMLS fell within the range of clinical acceptance (<120 µm). However, the statistically significant difference to conventional casting indicates that the gaps in computer-aided milling and DMLS fabricated restorations still need to be further reduced.

Clinical MR-mammography: are computer-assisted methods superior to visual or manual measurements for curve type analysis? A systematic approach.

PubMed

Baltzer, Pascal Andreas Thomas; Freiberg, Christian; Beger, Sebastian; Vag, Tibor; Dietzel, Matthias; Herzog, Aimee B; Gajda, Mieczyslaw; Camara, Oumar; Kaiser, Werner A

2009-09-01

Enhancement characteristics after administration of a contrast agent are regarded as a major criterion for differential diagnosis in magnetic resonance mammography (MRM). However, no consensus exists about the best measurement method to assess contrast enhancement kinetics. This systematic investigation was performed to compare visual estimation with manual region of interest (ROI) and computer-aided diagnosis (CAD) analysis for time curve measurements in MRM. A total of 329 patients undergoing surgery after MRM (1.5 T) were analyzed prospectively. Dynamic data were measured using visual estimation, including ROI as well as CAD methods, and classified depending on initial signal increase and delayed enhancement. Pathology revealed 469 lesions (279 malignant, 190 benign). Kappa agreement between the methods ranged from 0.78 to 0.81. Diagnostic accuracies of 74.4% (visual), 75.7% (ROI), and 76.6% (CAD) were found without statistical significant differences. According to our results, curve type measurements are useful as a diagnostic criterion in breast lesions irrespective of the method used.

Does positron emission tomography/computed tomography aid the diagnosis of prosthetic valve infective endocarditis?

PubMed

Balmforth, Damian; Chacko, Jacob; Uppal, Rakesh

2016-10-01

A best evidence topic was constructed according to a structured protocol. The question addressed was whether (18)F-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) aids the diagnosis of prosthetic valve endocarditis (PVE)? A total of 107 publications were found using the reported search, of which 6 represented the best evidence to answer the clinical question. The authors, journal, date and country of publication, patient group studied, study type, relevant outcomes and results of these papers are tabulated. The reported outcome of all studies was a final diagnosis of confirmed endocarditis on follow-up. All the six studies were non-randomized, single-centre, observational studies and thus represented level 3 evidence. The diagnostic capability of PET/CT for PVE was compared with that of the modified Duke Criteria and echocardiography, and reported in terms of sensitivity, specificity and positive and negative predictive values. All studies demonstrated an increased sensitivity for the diagnosis of PVE when PET/CT was combined with the modified Duke Criteria on admission. A higher SUVmax on PET was found to be significantly associated with a confirmed diagnosis of endocarditis and an additional diagnostic benefit of PET/CT angiography over conventional PET/non-enhanced CT is reported due to improved anatomical resolution. However, PET/CT was found to be unreliable in the early postoperative period due to its inability to distinguish between infection and residual postoperative inflammatory changes. PET/CT was also found to be poor at diagnosing cases of native valve endocarditis. We conclude that PET/CT aids in the diagnosis of PVE when combined with the modified Duke Criteria on admission by increasing the diagnostic sensitivity. The diagnostic ability of PET/CT can be potentiated by the use of PET/CTA; however, its use may be unreliable in the early postoperative period or in native valve endocarditis. © The Author 2016. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Computer-aided diagnosis of breast cancer via Gabor wavelet bank and binary-class SVM in mammographic images

NASA Astrophysics Data System (ADS)

Torrents-Barrena, Jordina; Puig, Domenec; Melendez, Jaime; Valls, Aida

2016-03-01

Breast cancer is one of the most dangerous diseases that attack women in their 40s worldwide. Due to this fact, it is estimated that one in eight women will develop a malignant carcinoma during their life. In addition, the carelessness of performing regular screenings is an important reason for the increase of mortality. However, computer-aided diagnosis systems attempt to enhance the quality of mammograms as well as the detection of early signs related to the disease. In this paper we propose a bank of Gabor filters to calculate the mean, standard deviation, skewness and kurtosis features by four-sized evaluation windows. Therefore, an active strategy is used to select the most relevant pixels. Finally, a supervised classification stage using two-class support vector machines is utilised through an accurate estimation of kernel parameters. In order to show the development of our methodology based on mammographic image analysis, two main experiments are fulfilled: abnormal/normal breast tissue classification and the ability to detect the different breast cancer types. Moreover, the public screen-film mini-MIAS database is compared with a digitised breast cancer database to evaluate the method robustness. The area under the receiver operating characteristic curve is used to measure the performance of the method. Furthermore, both confusion matrix and accuracy are calculated to assess the results of the proposed algorithm.

Digital ocular fundus imaging: a review.

PubMed

Bernardes, Rui; Serranho, Pedro; Lobo, Conceição

2011-01-01

Ocular fundus imaging plays a key role in monitoring the health status of the human eye. Currently, a large number of imaging modalities allow the assessment and/or quantification of ocular changes from a healthy status. This review focuses on the main digital fundus imaging modality, color fundus photography, with a brief overview of complementary techniques, such as fluorescein angiography. While focusing on two-dimensional color fundus photography, the authors address the evolution from nondigital to digital imaging and its impact on diagnosis. They also compare several studies performed along the transitional path of this technology. Retinal image processing and analysis, automated disease detection and identification of the stage of diabetic retinopathy (DR) are addressed as well. The authors emphasize the problems of image segmentation, focusing on the major landmark structures of the ocular fundus: the vascular network, optic disk and the fovea. Several proposed approaches for the automatic detection of signs of disease onset and progression, such as microaneurysms, are surveyed. A thorough comparison is conducted among different studies with regard to the number of eyes/subjects, imaging modality, fundus camera used, field of view and image resolution to identify the large variation in characteristics from one study to another. Similarly, the main features of the proposed classifications and algorithms for the automatic detection of DR are compared, thereby addressing computer-aided diagnosis and computer-aided detection for use in screening programs. Copyright © 2011 S. Karger AG, Basel.

Academic consortium for the evaluation of computer-aided diagnosis (CADx) in mammography

NASA Astrophysics Data System (ADS)

Mun, Seong K.; Freedman, Matthew T.; Wu, Chris Y.; Lo, Shih-Chung B.; Floyd, Carey E., Jr.; Lo, Joseph Y.; Chan, Heang-Ping; Helvie, Mark A.; Petrick, Nicholas; Sahiner, Berkman; Wei, Datong; Chakraborty, Dev P.; Clarke, Laurence P.; Kallergi, Maria; Clark, Bob; Kim, Yongmin

1995-04-01

Computer aided diagnosis (CADx) is a promising technology for the detection of breast cancer in screening mammography. A number of different approaches have been developed for CADx research that have achieved significant levels of performance. Research teams now recognize the need for a careful and detailed evaluation study of approaches to accelerate the development of CADx, to make CADx more clinically relevant and to optimize the CADx algorithms based on unbiased evaluations. The results of such a comparative study may provide each of the participating teams with new insights into the optimization of their individual CADx algorithms. This consortium of experienced CADx researchers is working as a group to compare results of the algorithms and to optimize the performance of CADx algorithms by learning from each other. Each institution will be contributing an equal number of cases that will be collected under a standard protocol for case selection, truth determination, and data acquisition to establish a common and unbiased database for the evaluation study. An evaluation procedure for the comparison studies are being developed to analyze the results of individual algorithms for each of the test cases in the common database. Optimization of individual CADx algorithms can be made based on the comparison studies. The consortium effort is expected to accelerate the eventual clinical implementation of CADx algorithms at participating institutions.

Diagnosis of Diabetes Mellitus by Extraction of Morphological Features of Red Blood Cells Using an Artificial Neural Network.

PubMed

Palanisamy, Vinupritha; Mariamichael, Anburajan

2016-10-01

Background and Aim: Diabetes mellitus is a metabolic disorder characterized by varying hyperglycemias either due to insufficient secretion of insulin by the pancreas or improper utilization of glucose. The study was aimed to investigate the association of morphological features of erythrocytes among normal and diabetic subjects and its gender-based changes and thereby to develop a computer aided tool to diagnose diabetes using features extracted from RBC. Materials and Methods: The study involved 138 normal and 144 diabetic subjects. The blood was drawn from the subjects and the blood smear prepared was digitized using Zeiss fluorescent microscope. The digitized images were pre-processed and texture segmentation was performed to extract the various morphological features. The Pearson correlation test was performed and subsequently, classification of subjects as normal and diabetes was carried out by a neural network classifier based on the features that demonstrated significance at the level of P <0.05. Result: The proposed system demonstrated an overall accuracy, sensitivity, specificity, positive predictive value and negative predictive value of 93.3, 93.71, 92.8, 93.1 and 93.5% respectively. Conclusion: The morphological features exhibited a statistically significant difference (P<0.01) between the normal and diabetic cells, suggesting that it could be helpful in the diagnosis of Diabetes mellitus using a computer aided system. © Georg Thieme Verlag KG Stuttgart · New York.

Computer-aided diagnosis with textural features for breast lesions in sonograms.

PubMed

Chen, Dar-Ren; Huang, Yu-Len; Lin, Sheng-Hsiung

2011-04-01

Computer-aided diagnosis (CAD) systems provided second beneficial support reference and enhance the diagnostic accuracy. This paper was aimed to develop and evaluate a CAD with texture analysis in the classification of breast tumors for ultrasound images. The ultrasound (US) dataset evaluated in this study composed of 1020 sonograms of region of interest (ROI) subimages from 255 patients. Two-view sonogram (longitudinal and transverse views) and four different rectangular regions were utilized to analyze each tumor. Six practical textural features from the US images were performed to classify breast tumors as benign or malignant. However, the textural features always perform as a high dimensional vector; high dimensional vector is unfavorable to differentiate breast tumors in practice. The principal component analysis (PCA) was used to reduce the dimension of textural feature vector and then the image retrieval technique was performed to differentiate between benign and malignant tumors. In the experiments, all the cases were sampled with k-fold cross-validation (k=10) to evaluate the performance with receiver operating characteristic (ROC) curve. The area (A(Z)) under the ROC curve for the proposed CAD system with the specific textural features was 0.925±0.019. The classification ability for breast tumor with textural information is satisfactory. This system differentiates benign from malignant breast tumors with a good result and is therefore clinically useful to provide a second opinion. Copyright © 2010 Elsevier Ltd. All rights reserved.

"Tennis elbow". A challenging call for computation and medicine

NASA Astrophysics Data System (ADS)

Sfetsioris, D.; Bontioti, E. N.

2014-10-01

An attempt to give an insight on the features composing this musculotendinous disorder. We address the issues of definition, pathophysiology and the mechanism underlying the onset and the occurrence of the disease, diagnosis and diagnostic tools as well as the methods of treatment. We focus mostly on conservative treatment protocols and we recognize the need for a more thorough investigation with the aid of computation.

Estimation of the failure risk of a maxillary premolar with different crack depths with endodontic treatment by computer-aided design/computer-aided manufacturing ceramic restorations.

PubMed

Lin, Chun-Li; Chang, Yen-Hsiang; Hsieh, Shih-Kai; Chang, Wen-Jen

2013-03-01

This study evaluated the risk of failure for an endodontically treated premolar with different crack depths, which was shearing toward the pulp chamber and was restored by using 3 different computer-aided design/computer-aided manufacturing ceramic restoration configurations. Three 3-dimensional finite element models designed with computer-aided design/computer-aided manufacturing ceramic onlay, endocrown, and conventional crown restorations were constructed to perform simulations. The Weibull function was incorporated with finite element analysis to calculate the long-term failure probability relative to different load conditions. The results indicated that the stress values on the enamel, dentin, and luting cement for endocrown restorations exhibited the lowest values relative to the other 2 restoration methods. Weibull analysis revealed that the overall failure probabilities in a shallow cracked premolar were 27%, 2%, and 1% for the onlay, endocrown, and conventional crown restorations, respectively, in the normal occlusal condition. The corresponding values were 70%, 10%, and 2% for the depth cracked premolar. This numeric investigation suggests that the endocrown provides sufficient fracture resistance only in a shallow cracked premolar with endodontic treatment. The conventional crown treatment can immobilize the premolar for different cracked depths with lower failure risk. Copyright © 2013 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Computer-aided diagnosis of alcoholism-related EEG signals.

PubMed

Acharya, U Rajendra; S, Vidya; Bhat, Shreya; Adeli, Hojjat; Adeli, Amir

2014-12-01

Alcoholism is a severe disorder that affects the functionality of neurons in the central nervous system (CNS) and alters the behavior of the affected person. Electroencephalogram (EEG) signals can be used as a diagnostic tool in the evaluation of subjects with alcoholism. The neurophysiological interpretation of EEG signals in persons with alcoholism (PWA) is based on observation and interpretation of the frequency and power in their EEGs compared to EEG signals from persons without alcoholism. This paper presents a review of the known features of EEGs obtained from PWA and proposes that the impact of alcoholism on the brain can be determined by computer-aided analysis of EEGs through extracting the minute variations in the EEG signals that can differentiate the EEGs of PWA from those of nonaffected persons. The authors advance the idea of automated computer-aided diagnosis (CAD) of alcoholism by employing the EEG signals. This is achieved through judicious combination of signal processing techniques such as wavelet, nonlinear dynamics, and chaos theory and pattern recognition and classification techniques. A CAD system is cost-effective and efficient and can be used as a decision support system by physicians in the diagnosis and treatment of alcoholism especially those who do not specialize in alcoholism or neurophysiology. It can also be of great value to rehabilitation centers to assess PWA over time and to monitor the impact of treatment aimed at minimizing or reversing the effects of the disease on the brain. A CAD system can be used to determine the extent of alcoholism-related changes in EEG signals (low, medium, high) and the effectiveness of therapeutic plans. Copyright © 2014 Elsevier Inc. All rights reserved.

Computer-Aided Diagnosis Of Leukemic Blood Cells

NASA Astrophysics Data System (ADS)

Gunter, U.; Harms, H.; Haucke, M.; Aus, H. M.; ter Meulen, V.

1982-11-01

In a first clinical test, computer programs are being used to diagnose leukemias. The data collected include blood samples from patients suffering from acute myelomonocytic-, acute monocytic- and acute promyelocytic, myeloblastic, prolymphocytic, chronic lymphocytic leukemias and leukemic transformed immunocytoma. The proper differentiation of the leukemic cells is essential because the therapy depends on the type of leukemia. The algorithms analyse the fine chromatin texture and distribution in the nuclei as well as size and shape parameters from the cells and nuclei. Cells with similar nuclei from different leukemias can be distinguished from each other by analyzing the cell cytoplasm images. Recognition of these subtle differences in the cells require an image sampling rate of 15-30 pixel/micron. The results for the entire data set correlate directly to established hematological parameters and support the previously published initial training set .

Post-traumatic Stress Disorder Symptoms Among People Living with HIV/AIDS in Rural China.

PubMed

Luo, Sitong; Lin, Chunqing; Ji, Guoping; Li, Li

2017-11-01

Among people living with HIV/AIDS (PLHA), the occurrence of post-traumatic stress disorder (PTSD) symptoms associated with HIV diagnosis is a common problem. This study examined HIV diagnosis-related PTSD symptoms and its associated factors among PLHA in rural China. We used baseline data from a randomized controlled trial conducted in Anhui Province, China. Surveys of 522 PLHA were conducted via computer-assisted personal interview method. PTSD symptoms were measured based on re-experiencing, avoidance and arousal of the day of HIV diagnosis. Association between PTSD symptoms and demographic characteristics, physical and social functioning were assessed by multiple regression analysis and structural equation modeling. Social functioning exhibited a direct association with HIV diagnosis-related PTSD symptoms, and also mediated the association between PTSD symptoms and age, family income, and physical functioning. The study findings underscore the importance of developing interventions that alleviate PTSD symptoms and improve social functioning among PLHA in rural China.

Computer-aided diagnosis (CAD) for colonoscopy

NASA Astrophysics Data System (ADS)

Gu, Jia; Poirson, Allen

2007-03-01

Colorectal cancer is the second leading cause of cancer deaths, and ranks third for new cancer cases and cancer mortality for both men and women. However, its death rate can be dramatically reduced by appropriate treatment when early detection is available. The purpose of colonoscopy is to identify and assess the severity of lesions, which may be flat or protruding. Due to the subjective nature of the examination, colonoscopic proficiency is highly variable and dependent upon the colonoscopist's knowledge and experience. An automated image processing system providing an objective, rapid, and inexpensive analysis of video from a standard colonoscope could provide a valuable tool for screening and diagnosis. In this paper, we present the design, functionality and preliminary results of its Computer-Aided-Diagnosis (CAD) system for colonoscopy - ColonoCAD TM. ColonoCAD is a complex multi-sensor, multi-data and multi-algorithm image processing system, incorporating data management and visualization, video quality assessment and enhancement, calibration, multiple view based reconstruction, feature extraction and classification. As this is a new field in medical image processing, our hope is that this paper will provide the framework to encourage and facilitate collaboration and discussion between industry, academia, and medical practitioners.

Computer-aided diagnosis of cavernous malformations in brain MR images.

PubMed

Wang, Huiquan; Ahmed, S Nizam; Mandal, Mrinal

2018-06-01

Cavernous malformation or cavernoma is one of the most common epileptogenic lesions. It is a type of brain vessel abnormality that can cause serious symptoms such as seizures, intracerebral hemorrhage, and various neurological disorders. Manual detection of cavernomas by physicians in a large set of brain MRI slices is a time-consuming and labor-intensive task and often delays diagnosis. In this paper, we propose a computer-aided diagnosis (CAD) system for cavernomas based on T2-weighted axial plane MRI image analysis. The proposed technique first extracts the brain area based on atlas registration and active contour model, and then performs template matching to obtain candidate cavernoma regions. Texture, the histogram of oriented gradients and local binary pattern features of each candidate region are calculated, and principal component analysis is applied to reduce the feature dimensionality. Support vector machines (SVMs) are finally used to classify each region into cavernoma or non-cavernoma so that most of the false positives (obtained by template matching) are eliminated. The performance of the proposed CAD system is evaluated and experimental results show that it provides superior performance in cavernoma detection compared to existing techniques. Copyright © 2018 Elsevier Ltd. All rights reserved.

Broadening the Scope of Dental Education.

ERIC Educational Resources Information Center

Loe, Harald

1992-01-01

Scientific and technological advances affecting dental education in the near future are examined, including the growing role of saliva in diagnosis, direct imaging methods, biomaterials research, computer-aided design and manufacturing, molecular biology, and new restorative dentistry. It is argued that dentistry should be a fully recognized…

Bi-model processing for early detection of breast tumor in CAD system

NASA Astrophysics Data System (ADS)

Mughal, Bushra; Sharif, Muhammad; Muhammad, Nazeer

2017-06-01

Early screening of skeptical masses in mammograms may reduce mortality rate among women. This rate can be further reduced upon developing the computer-aided diagnosis system with decrease in false assumptions in medical informatics. This method highlights the early tumor detection in digitized mammograms. For improving the performance of this system, a novel bi-model processing algorithm is introduced. It divides the region of interest into two parts, the first one is called pre-segmented region (breast parenchyma) and other is the post-segmented region (suspicious region). This system follows the scheme of the preprocessing technique of contrast enhancement that can be utilized to segment and extract the desired feature of the given mammogram. In the next phase, a hybrid feature block is presented to show the effective performance of computer-aided diagnosis. In order to assess the effectiveness of the proposed method, a database provided by the society of mammographic images is tested. Our experimental outcomes on this database exhibit the usefulness and robustness of the proposed method.

An integrated classifier for computer-aided diagnosis of colorectal polyps based on random forest and location index strategies

NASA Astrophysics Data System (ADS)

Hu, Yifan; Han, Hao; Zhu, Wei; Li, Lihong; Pickhardt, Perry J.; Liang, Zhengrong

2016-03-01

Feature classification plays an important role in differentiation or computer-aided diagnosis (CADx) of suspicious lesions. As a widely used ensemble learning algorithm for classification, random forest (RF) has a distinguished performance for CADx. Our recent study has shown that the location index (LI), which is derived from the well-known kNN (k nearest neighbor) and wkNN (weighted k nearest neighbor) classifier [1], has also a distinguished role in the classification for CADx. Therefore, in this paper, based on the property that the LI will achieve a very high accuracy, we design an algorithm to integrate the LI into RF for improved or higher value of AUC (area under the curve of receiver operating characteristics -- ROC). Experiments were performed by the use of a database of 153 lesions (polyps), including 116 neoplastic lesions and 37 hyperplastic lesions, with comparison to the existing classifiers of RF and wkNN, respectively. A noticeable gain by the proposed integrated classifier was quantified by the AUC measure.

An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image.

PubMed

Xu, Xiayu; Ding, Wenxiang; Abràmoff, Michael D; Cao, Ruofan

2017-04-01

Retinal artery and vein classification is an important task for the automatic computer-aided diagnosis of various eye diseases and systemic diseases. This paper presents an improved supervised artery and vein classification method in retinal image. Intra-image regularization and inter-subject normalization is applied to reduce the differences in feature space. Novel features, including first-order and second-order texture features, are utilized to capture the discriminating characteristics of arteries and veins. The proposed method was tested on the DRIVE dataset and achieved an overall accuracy of 0.923. This retinal artery and vein classification algorithm serves as a potentially important tool for the early diagnosis of various diseases, including diabetic retinopathy and cardiovascular diseases. Copyright © 2017 Elsevier B.V. All rights reserved.

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection.

PubMed

Jalalian, Afsaneh; Mashohor, Syamsiah; Mahmud, Rozi; Karasfi, Babak; Saripan, M Iqbal B; Ramli, Abdul Rahman B

2017-01-01

Breast cancer is the most prevalent cancer that affects women all over the world. Early detection and treatment of breast cancer could decline the mortality rate. Some issues such as technical reasons, which related to imaging quality and human error, increase misdiagnosis of breast cancer by radiologists. Computer-aided detection systems (CADs) are developed to overcome these restrictions and have been studied in many imaging modalities for breast cancer detection in recent years. The CAD systems improve radiologists' performance in finding and discriminating between the normal and abnormal tissues. These procedures are performed only as a double reader but the absolute decisions are still made by the radiologist. In this study, the recent CAD systems for breast cancer detection on different modalities such as mammography, ultrasound, MRI, and biopsy histopathological images are introduced. The foundation of CAD systems generally consist of four stages: Pre-processing, Segmentation, Feature extraction, and Classification. The approaches which applied to design different stages of CAD system are summarised. Advantages and disadvantages of different segmentation, feature extraction and classification techniques are listed. In addition, the impact of imbalanced datasets in classification outcomes and appropriate methods to solve these issues are discussed. As well as, performance evaluation metrics for various stages of breast cancer detection CAD systems are reviewed.

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

PubMed Central

Jalalian, Afsaneh; Mashohor, Syamsiah; Mahmud, Rozi; Karasfi, Babak; Saripan, M. Iqbal B.; Ramli, Abdul Rahman B.

2017-01-01

Breast cancer is the most prevalent cancer that affects women all over the world. Early detection and treatment of breast cancer could decline the mortality rate. Some issues such as technical reasons, which related to imaging quality and human error, increase misdiagnosis of breast cancer by radiologists. Computer-aided detection systems (CADs) are developed to overcome these restrictions and have been studied in many imaging modalities for breast cancer detection in recent years. The CAD systems improve radiologists' performance in finding and discriminating between the normal and abnormal tissues. These procedures are performed only as a double reader but the absolute decisions are still made by the radiologist. In this study, the recent CAD systems for breast cancer detection on different modalities such as mammography, ultrasound, MRI, and biopsy histopathological images are introduced. The foundation of CAD systems generally consist of four stages: Pre-processing, Segmentation, Feature extraction, and Classification. The approaches which applied to design different stages of CAD system are summarised. Advantages and disadvantages of different segmentation, feature extraction and classification techniques are listed. In addition, the impact of imbalanced datasets in classification outcomes and appropriate methods to solve these issues are discussed. As well as, performance evaluation metrics for various stages of breast cancer detection CAD systems are reviewed. PMID:28435432

Computer-aided system for diabetes care in Berlin, G.D.R.

PubMed

Thoelke, H; Meusel, K; Ratzmann, K P

1990-01-01

In the Centre of Diabetes and Metabolic Disorders of Berlin, G.D.R., a computer-aided care system has been used since 1974, aiming at relieving physicians and medical staff from routine tasks and rendering possible epidemiological research on an unselected diabetes population of a defined area. The basis of the system is the data bank on diabetics (DB), where at present data from approximately 55,000 patients are stored. DB is used as a diabetes register of Berlin. On the basis of standardised criteria of diagnosis and therapy of diabetes mellitus in our dispensary care system, DB facilitates representative epidemiological analyses of the diabetic population, e.g. prevalence, incidence, duration of diabetes, and modes of treatment. The availability of general data on the population or the selection of specified groups of patients serves the management of the care system. Also, it supports the computer-aided recall of type II diabetics, treated either with diet alone or with diet and oral drugs. In this way, the standardised evaluation of treatment strategies in large populations of diabetics is possible on the basis of uniform metabolic criteria (blood glucose plus urinary glucose). The system consists of a main computer in the data processing unit and of personal computers in the diabetes centre which can be used either individually or as terminals to the main computer. During 14 years of experience, the computer-aided out-patient care of type II diabetics has proved efficient in a big-city area with a large population.

Integrating hinge axis approximation and the virtual facial simulation of prosthetic outcomes for treatment with CAD-CAM immediate dentures: A clinical report of a patient with microstomia.

PubMed

Kuric, Katelyn M; Harris, Bryan T; Morton, Dean; Azevedo, Bruno; Lin, Wei-Shao

2017-09-29

This clinical report describes a digital workflow using extraoral digital photographs and volumetric datasets from cone beam computed tomography (CBCT) imaging to create a 3-dimensional (3D), virtual patient with photorealistic appearance. In a patient with microstomia, hinge axis approximation, diagnostic casts simulating postextraction alveolar ridge profile, and facial simulation of prosthetic treatment outcome were completed in a 3D, virtual environment. The approach facilitated the diagnosis, communication, and patient acceptance of the treatment of maxillary and mandibular computer-aided design and computer-aided manufacturing (CAD-CAM) of immediate dentures at increased occlusal vertical dimension. Copyright © 2017 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

PubMed

Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat

2017-09-27

An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

Image Processing and Computer Aided Diagnosis in Computed Tomography of the Breast

DTIC Science & Technology

2007-03-01

TERMS breast imaging, breast CT, scatter compensation, denoising, CAD , Cone-beam CT 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF...clinical projection images. The CAD tool based on signal known exactly (SKE) scenario is under development. Task 6: Test and compare the...performances of the CAD developed in Task 5 applied to processed projection data from Task 1 with the CAD performance on the projection data without Bayesian

Computer-aided diagnosis of lung cancer: definition and detection of ground-glass opacity type of nodules by high-resolution computed tomography.

PubMed

Okada, Tohru; Iwano, Shingo; Ishigaki, Takeo; Kitasaka, Takayuki; Hirano, Yasushi; Mori, Kensaku; Suenaga, Yasuhito; Naganawa, Shinji

2009-02-01

The ground-glass opacity (GGO) of lung cancer is identified only subjectively on computed tomography (CT) images as no quantitative characteristic has been defined for GGOs. We sought to define GGOs quantitatively and to differentiate between GGOs and solid-type lung cancers semiautomatically with a computer-aided diagnosis (CAD). High-resolution CT images of 100 pulmonary nodules (all peripheral lung cancers) were collected from our clinical records. Two radiologists traced the contours of nodules and distinguished GGOs from solid areas. The CT attenuation value of each area was measured. Differentiation between cancer types was assessed by a receiver-operating characteristic (ROC) analysis. The mean CT attenuation of the GGO areas was -618.4 +/- 212.2 HU, whereas that of solid areas was -68.1 +/- 230.3 HU. CAD differentiated between solidand GGO-type lung cancers with a sensitivity of 86.0% and specificity of 96.5% when the threshold value was -370 HU. Four nodules of mixed GGOs were incorrectly classified as the solid type. CAD detected 96.3% of GGO areas when the threshold between GGO and solid areas was 194 HU. Objective definition of GGO area by CT attenuation is feasible. This method is useful for semiautomatic differentiation between GGOs and solid types of lung cancer.

Detection of longitudinal ulcer using roughness value for computer aided diagnosis of Crohn's disease

NASA Astrophysics Data System (ADS)

Oda, Masahiro; Kitasaka, Takayuki; Furukawa, Kazuhiro; Watanabe, Osamu; Ando, Takafumi; Goto, Hidemi; Mori, Kensaku

2011-03-01

The purpose of this paper is to present a new method to detect ulcers, which is one of the symptoms of Crohn's disease, from CT images. Crohn's disease is an inflammatory disease of the digestive tract. Crohn's disease commonly affects the small intestine. An optical or a capsule endoscope is used for small intestine examinations. However, these endoscopes cannot pass through intestinal stenosis parts in some cases. A CT image based diagnosis allows a physician to observe whole intestine even if intestinal stenosis exists. However, because of the complicated shape of the small and large intestines, understanding of shapes of the intestines and lesion positions are difficult in the CT image based diagnosis. Computer-aided diagnosis system for Crohn's disease having automated lesion detection is required for efficient diagnosis. We propose an automated method to detect ulcers from CT images. Longitudinal ulcers make rough surface of the small and large intestinal wall. The rough surface consists of combination of convex and concave parts on the intestinal wall. We detect convex and concave parts on the intestinal wall by a blob and an inverse-blob structure enhancement filters. A lot of convex and concave parts concentrate on roughed parts. We introduce a roughness value to differentiate convex and concave parts concentrated on the roughed parts from the other on the intestinal wall. The roughness value effectively reduces false positives of ulcer detection. Experimental results showed that the proposed method can detect convex and concave parts on the ulcers.

Anniversary Paper: History and status of CAD and quantitative image analysis: The role of Medical Physics and AAPM

PubMed Central

Giger, Maryellen L.; Chan, Heang-Ping; Boone, John

2008-01-01

The roles of physicists in medical imaging have expanded over the years, from the study of imaging systems (sources and detectors) and dose to the assessment of image quality and perception, the development of image processing techniques, and the development of image analysis methods to assist in detection and diagnosis. The latter is a natural extension of medical physicists’ goals in developing imaging techniques to help physicians acquire diagnostic information and improve clinical decisions. Studies indicate that radiologists do not detect all abnormalities on images that are visible on retrospective review, and they do not always correctly characterize abnormalities that are found. Since the 1950s, the potential use of computers had been considered for analysis of radiographic abnormalities. In the mid-1980s, however, medical physicists and radiologists began major research efforts for computer-aided detection or computer-aided diagnosis (CAD), that is, using the computer output as an aid to radiologists—as opposed to a completely automatic computer interpretation—focusing initially on methods for the detection of lesions on chest radiographs and mammograms. Since then, extensive investigations of computerized image analysis for detection or diagnosis of abnormalities in a variety of 2D and 3D medical images have been conducted. The growth of CAD over the past 20 years has been tremendous—from the early days of time-consuming film digitization and CPU-intensive computations on a limited number of cases to its current status in which developed CAD approaches are evaluated rigorously on large clinically relevant databases. CAD research by medical physicists includes many aspects—collecting relevant normal and pathological cases; developing computer algorithms appropriate for the medical interpretation task including those for segmentation, feature extraction, and classifier design; developing methodology for assessing CAD performance; validating the algorithms using appropriate cases to measure performance and robustness; conducting observer studies with which to evaluate radiologists in the diagnostic task without and with the use of the computer aid; and ultimately assessing performance with a clinical trial. Medical physicists also have an important role in quantitative imaging, by validating the quantitative integrity of scanners and developing imaging techniques, and image analysis tools that extract quantitative data in a more accurate and automated fashion. As imaging systems become more complex and the need for better quantitative information from images grows, the future includes the combined research efforts from physicists working in CAD with those working on quantitative imaging systems to readily yield information on morphology, function, molecular structure, and more—from animal imaging research to clinical patient care. A historical review of CAD and a discussion of challenges for the future are presented here, along with the extension to quantitative image analysis. PMID:19175137

Anniversary Paper: History and status of CAD and quantitative image analysis: The role of Medical Physics and AAPM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Giger, Maryellen L.; Chan, Heang-Ping; Boone, John

2008-12-15

The roles of physicists in medical imaging have expanded over the years, from the study of imaging systems (sources and detectors) and dose to the assessment of image quality and perception, the development of image processing techniques, and the development of image analysis methods to assist in detection and diagnosis. The latter is a natural extension of medical physicists' goals in developing imaging techniques to help physicians acquire diagnostic information and improve clinical decisions. Studies indicate that radiologists do not detect all abnormalities on images that are visible on retrospective review, and they do not always correctly characterize abnormalities thatmore » are found. Since the 1950s, the potential use of computers had been considered for analysis of radiographic abnormalities. In the mid-1980s, however, medical physicists and radiologists began major research efforts for computer-aided detection or computer-aided diagnosis (CAD), that is, using the computer output as an aid to radiologists--as opposed to a completely automatic computer interpretation--focusing initially on methods for the detection of lesions on chest radiographs and mammograms. Since then, extensive investigations of computerized image analysis for detection or diagnosis of abnormalities in a variety of 2D and 3D medical images have been conducted. The growth of CAD over the past 20 years has been tremendous--from the early days of time-consuming film digitization and CPU-intensive computations on a limited number of cases to its current status in which developed CAD approaches are evaluated rigorously on large clinically relevant databases. CAD research by medical physicists includes many aspects--collecting relevant normal and pathological cases; developing computer algorithms appropriate for the medical interpretation task including those for segmentation, feature extraction, and classifier design; developing methodology for assessing CAD performance; validating the algorithms using appropriate cases to measure performance and robustness; conducting observer studies with which to evaluate radiologists in the diagnostic task without and with the use of the computer aid; and ultimately assessing performance with a clinical trial. Medical physicists also have an important role in quantitative imaging, by validating the quantitative integrity of scanners and developing imaging techniques, and image analysis tools that extract quantitative data in a more accurate and automated fashion. As imaging systems become more complex and the need for better quantitative information from images grows, the future includes the combined research efforts from physicists working in CAD with those working on quantitative imaging systems to readily yield information on morphology, function, molecular structure, and more--from animal imaging research to clinical patient care. A historical review of CAD and a discussion of challenges for the future are presented here, along with the extension to quantitative image analysis.« less

Bronchoscopic modalities to diagnose sarcoidosis.

PubMed

Benzaquen, Sadia; Aragaki-Nakahodo, Alejandro Adolfo

2017-09-01

Several studies have investigated different bronchoscopic techniques to obtain tissue diagnosis in patients with suspected sarcoidosis when the diagnosis cannot be based on clinicoradiographic findings alone. In this review, we will describe the most recent and relevant evidence from different bronchoscopic modalities to diagnose sarcoidosis. Despite multiple available bronchoscopic modalities to procure tissue samples to diagnose sarcoidosis, the vast majority of evidence favors endobronchial ultrasound transbronchial needle aspiration to diagnose Scadding stages 1 and 2 sarcoidosis. Transbronchial lung cryobiopsy is a new technique that is mainly used to aid in the diagnosis of undifferentiated interstitial lung disease; however, we will discuss its potential use in sarcoidosis. This review illustrates the limited information about the different bronchoscopic techniques to aid in the diagnosis of pulmonary sarcoidosis. However, it demonstrates that the combination of available bronchoscopic techniques increases the diagnostic yield for suspected sarcoidosis.

3D Surgical Simulation

PubMed Central

Cevidanes, Lucia; Tucker, Scott; Styner, Martin; Kim, Hyungmin; Chapuis, Jonas; Reyes, Mauricio; Proffit, William; Turvey, Timothy; Jaskolka, Michael

2009-01-01

This paper discusses the development of methods for computer-aided jaw surgery. Computer-aided jaw surgery allows us to incorporate the high level of precision necessary for transferring virtual plans into the operating room. We also present a complete computer-aided surgery (CAS) system developed in close collaboration with surgeons. Surgery planning and simulation include construction of 3D surface models from Cone-beam CT (CBCT), dynamic cephalometry, semi-automatic mirroring, interactive cutting of bone and bony segment repositioning. A virtual setup can be used to manufacture positioning splints for intra-operative guidance. The system provides further intra-operative assistance with the help of a computer display showing jaw positions and 3D positioning guides updated in real-time during the surgical procedure. The CAS system aids in dealing with complex cases with benefits for the patient, with surgical practice, and for orthodontic finishing. Advanced software tools for diagnosis and treatment planning allow preparation of detailed operative plans, osteotomy repositioning, bone reconstructions, surgical resident training and assessing the difficulties of the surgical procedures prior to the surgery. CAS has the potential to make the elaboration of the surgical plan a more flexible process, increase the level of detail and accuracy of the plan, yield higher operative precision and control, and enhance documentation of cases. Supported by NIDCR DE017727, and DE018962 PMID:20816308

A cloud platform for remote diagnosis of breast cancer in mammography by fusion of machine and human intelligence

NASA Astrophysics Data System (ADS)

Jiang, Guodong; Fan, Ming; Li, Lihua

2016-03-01

Mammography is the gold standard for breast cancer screening, reducing mortality by about 30%. The application of a computer-aided detection (CAD) system to assist a single radiologist is important to further improve mammographic sensitivity for breast cancer detection. In this study, a design and realization of the prototype for remote diagnosis system in mammography based on cloud platform were proposed. To build this system, technologies were utilized including medical image information construction, cloud infrastructure and human-machine diagnosis model. Specifically, on one hand, web platform for remote diagnosis was established by J2EE web technology. Moreover, background design was realized through Hadoop open-source framework. On the other hand, storage system was built up with Hadoop distributed file system (HDFS) technology which enables users to easily develop and run on massive data application, and give full play to the advantages of cloud computing which is characterized by high efficiency, scalability and low cost. In addition, the CAD system was realized through MapReduce frame. The diagnosis module in this system implemented the algorithms of fusion of machine and human intelligence. Specifically, we combined results of diagnoses from doctors' experience and traditional CAD by using the man-machine intelligent fusion model based on Alpha-Integration and multi-agent algorithm. Finally, the applications on different levels of this system in the platform were also discussed. This diagnosis system will have great importance for the balanced health resource, lower medical expense and improvement of accuracy of diagnosis in basic medical institutes.

Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks.

PubMed

Wu, Miao; Yan, Chuanbo; Liu, Huiqiang; Liu, Qian

2018-06-29

Ovarian cancer is one of the most common gynecologic malignancies. Accurate classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid carcinoma, transparent cell carcinoma) is an essential part in the different diagnosis. Computer-aided diagnosis (CADx) can provide useful advice for pathologists to determine the diagnosis correctly. In our study, we employed a Deep Convolutional Neural Networks (DCNN) based on AlexNet to automatically classify the different types of ovarian cancers from cytological images. The DCNN consists of five convolutional layers, three max pooling layers, and two full reconnect layers. Then we trained the model by two group input data separately, one was original image data and the other one was augmented image data including image enhancement and image rotation. The testing results are obtained by the method of 10-fold cross-validation, showing that the accuracy of classification models has been improved from 72.76 to 78.20% by using augmented images as training data. The developed scheme was useful for classifying ovarian cancers from cytological images. © 2018 The Author(s).

Pulmonary embolism detection using localized vessel-based features in dual energy CT

NASA Astrophysics Data System (ADS)

Dicente Cid, Yashin; Depeursinge, Adrien; Foncubierta Rodríguez, Antonio; Platon, Alexandra; Poletti, Pierre-Alexandre; Müller, Henning

2015-03-01

Pulmonary embolism (PE) affects up to 600,000 patients and contributes to at least 100,000 deaths every year in the United States alone. Diagnosis of PE can be difficult as most symptoms are unspecific and early diagnosis is essential for successful treatment. Computed Tomography (CT) images can show morphological anomalies that suggest the existence of PE. Various image-based procedures have been proposed for improving computer-aided diagnosis of PE. We propose a novel method for detecting PE based on localized vessel-based features computed in Dual Energy CT (DECT) images. DECT provides 4D data indexed by the three spatial coordinates and the energy level. The proposed features encode the variation of the Hounsfield Units across the different levels and the CT attenuation related to the amount of iodine contrast in each vessel. A local classification of the vessels is obtained through the classification of these features. Moreover, the localization of the vessel in the lung provides better comparison between patients. Results show that the simple features designed are able to classify pulmonary embolism patients with an AUC (area under the receiver operating curve) of 0.71 on a lobe basis. Prior segmentation of the lung lobes is not necessary because an automatic atlas-based segmentation obtains similar AUC levels (0.65) for the same dataset. The automatic atlas reaches 0.80 AUC in a larger dataset with more control cases.

Computer aided diagnosis system for Alzheimer disease using brain diffusion tensor imaging features selected by Pearson's correlation.

PubMed

Graña, M; Termenon, M; Savio, A; Gonzalez-Pinto, A; Echeveste, J; Pérez, J M; Besga, A

2011-09-20

The aim of this paper is to obtain discriminant features from two scalar measures of Diffusion Tensor Imaging (DTI) data, Fractional Anisotropy (FA) and Mean Diffusivity (MD), and to train and test classifiers able to discriminate Alzheimer's Disease (AD) patients from controls on the basis of features extracted from the FA or MD volumes. In this study, support vector machine (SVM) classifier was trained and tested on FA and MD data. Feature selection is done computing the Pearson's correlation between FA or MD values at voxel site across subjects and the indicative variable specifying the subject class. Voxel sites with high absolute correlation are selected for feature extraction. Results are obtained over an on-going study in Hospital de Santiago Apostol collecting anatomical T1-weighted MRI volumes and DTI data from healthy control subjects and AD patients. FA features and a linear SVM classifier achieve perfect accuracy, sensitivity and specificity in several cross-validation studies, supporting the usefulness of DTI-derived features as an image-marker for AD and to the feasibility of building Computer Aided Diagnosis systems for AD based on them. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Optical coherence tomography and computer-aided diagnosis of a murine model of chronic kidney disease

NASA Astrophysics Data System (ADS)

Wang, Bohan; Wang, Hsing-Wen; Guo, Hengchang; Anderson, Erik; Tang, Qinggong; Wu, Tongtong; Falola, Reuben; Smith, Tikina; Andrews, Peter M.; Chen, Yu

2017-12-01

Chronic kidney disease (CKD) is characterized by a progressive loss of renal function over time. Histopathological analysis of the condition of glomeruli and the proximal convolutional tubules over time can provide valuable insights into the progression of CKD. Optical coherence tomography (OCT) is a technology that can analyze the microscopic structures of a kidney in a nondestructive manner. Recently, we have shown that OCT can provide real-time imaging of kidney microstructures in vivo without administering exogenous contrast agents. A murine model of CKD induced by intravenous Adriamycin (ADR) injection is evaluated by OCT. OCT images of the rat kidneys have been captured every week up to eight weeks. Tubular diameter and hypertrophic tubule population of the kidneys at multiple time points after ADR injection have been evaluated through a fully automated computer-vision system. Results revealed that mean tubular diameter and hypertrophic tubule population increase with time in post-ADR injection period. The results suggest that OCT images of the kidney contain abundant information about kidney histopathology. Fully automated computer-aided diagnosis based on OCT has the potential for clinical evaluation of CKD conditions.

Survival after diagnosis of AIDS: a prospective observational study of 2625 patients. Royal Free/Chelsea and Westminster Hospitals Collaborative Group.

PubMed Central

Mocroft, A.; Youle, M.; Morcinek, J.; Sabin, C. A.; Gazzard, B.; Johnson, M. A.; Phillips, A. N.

1997-01-01

OBJECTIVE: To estimate median survival and changes in survival in patients diagnosed as having AIDS. DESIGN: Prospective observational study. SETTING: Clinics in two large London hospitals. SUBJECTS: 2625 patients with AIDS seen between 1982 and July 1995. MAIN OUTCOME MEASURES: Survival, estimated using lifetable analyses, and factors associated with survival, identified from Cox proportional hazards models. RESULTS: Median survival (20 months) was longer than previous estimates. The CD4 lymphocyte count at or before initial AIDS defining illness decreased significantly over time from 90 x 10(6)/1 during 1987 or earlier to 40 x 10(6)/1 during 1994 and 1995 (P < 0.0001). In the first three months after diagnosis, patients in whom AIDS was diagnosed after 1987 had a much lower risk of death (relative risk 0.44, 95% confidence interval 0.22 to 0.86; P = 0.017) than patients diagnosed before 1987. When the diagnosis was based on oesophageal candidiasis or Kaposi's sarcoma, patients had a lower risk of death than when the diagnosis was based on Pneumocystis carinii pneumonia (0.21 (0.07 to 0.59). P = 0.0030 and 0.37 (0.16 to 0.83), P = 0.016). Three months after AIDS diagnosis, the risk of death was similar in patients whose diagnosis was made after and before 1987 (1.02 (0.79 to 1.31), P = 0.91). There were no differences in survival between patients diagnosed during 1988-90, 1991-3, or 1994-5. CONCLUSIONS: In later years, patients were much more likely to survive their initial illness, but long term survival has remained poor. The decrease in CD4 lymphocyte count at AIDS diagnosis indicates that patients are being diagnosed as having AIDS at ever more advanced stages of immunodeficiency. PMID:9040386

Computer-aided diagnosis of lung cancer: the effect of training data sets on classification accuracy of lung nodules.

PubMed

Gong, Jing; Liu, Ji-Yu; Sun, Xi-Wen; Zheng, Bin; Nie, Sheng-Dong

2018-02-05

This study aims to develop a computer-aided diagnosis (CADx) scheme for classification between malignant and benign lung nodules, and also assess whether CADx performance changes in detecting nodules associated with early and advanced stage lung cancer. The study involves 243 biopsy-confirmed pulmonary nodules. Among them, 76 are benign, 81 are stage I and 86 are stage III malignant nodules. The cases are separated into three data sets involving: (1) all nodules, (2) benign and stage I malignant nodules, and (3) benign and stage III malignant nodules. A CADx scheme is applied to segment lung nodules depicted on computed tomography images and we initially computed 66 3D image features. Then, three machine learning models namely, a support vector machine, naïve Bayes classifier and linear discriminant analysis, are separately trained and tested by using three data sets and a leave-one-case-out cross-validation method embedded with a Relief-F feature selection algorithm. When separately using three data sets to train and test three classifiers, the average areas under receiver operating characteristic curves (AUC) are 0.94, 0.90 and 0.99, respectively. When using the classifiers trained using data sets with all nodules, average AUC values are 0.88 and 0.99 for detecting early and advanced stage nodules, respectively. AUC values computed from three classifiers trained using the same data set are consistent without statistically significant difference (p  >  0.05). This study demonstrates (1) the feasibility of applying a CADx scheme to accurately distinguish between benign and malignant lung nodules, and (2) a positive trend between CADx performance and cancer progression stage. Thus, in order to increase CADx performance in detecting subtle and early cancer, training data sets should include more diverse early stage cancer cases.

Computer-aided diagnosis of lung cancer: the effect of training data sets on classification accuracy of lung nodules

NASA Astrophysics Data System (ADS)

Gong, Jing; Liu, Ji-Yu; Sun, Xi-Wen; Zheng, Bin; Nie, Sheng-Dong

2018-02-01

This study aims to develop a computer-aided diagnosis (CADx) scheme for classification between malignant and benign lung nodules, and also assess whether CADx performance changes in detecting nodules associated with early and advanced stage lung cancer. The study involves 243 biopsy-confirmed pulmonary nodules. Among them, 76 are benign, 81 are stage I and 86 are stage III malignant nodules. The cases are separated into three data sets involving: (1) all nodules, (2) benign and stage I malignant nodules, and (3) benign and stage III malignant nodules. A CADx scheme is applied to segment lung nodules depicted on computed tomography images and we initially computed 66 3D image features. Then, three machine learning models namely, a support vector machine, naïve Bayes classifier and linear discriminant analysis, are separately trained and tested by using three data sets and a leave-one-case-out cross-validation method embedded with a Relief-F feature selection algorithm. When separately using three data sets to train and test three classifiers, the average areas under receiver operating characteristic curves (AUC) are 0.94, 0.90 and 0.99, respectively. When using the classifiers trained using data sets with all nodules, average AUC values are 0.88 and 0.99 for detecting early and advanced stage nodules, respectively. AUC values computed from three classifiers trained using the same data set are consistent without statistically significant difference (p  >  0.05). This study demonstrates (1) the feasibility of applying a CADx scheme to accurately distinguish between benign and malignant lung nodules, and (2) a positive trend between CADx performance and cancer progression stage. Thus, in order to increase CADx performance in detecting subtle and early cancer, training data sets should include more diverse early stage cancer cases.

Survival Outcomes and Effect of Early vs. Deferred cART Among HIV-Infected Patients Diagnosed at the Time of an AIDS-Defining Event: A Cohort Analysis

PubMed Central

Mussini, Cristina; Johnson, Margaret; d'Arminio Monforte, Antonella; Antinori, Andrea; Gill, M. John; Sighinolfi, Laura; Uberti-Foppa, Caterina; Borghi, Vanni; Sabin, Caroline

2011-01-01

Objectives We analyzed clinical progression among persons diagnosed with HIV at the time of an AIDS-defining event, and assessed the impact on outcome of timing of combined antiretroviral treatment (cART). Methods Retrospective, European and Canadian multicohort study.. Patients were diagnosed with HIV from 1997–2004 and had clinical AIDS from 30 days before to 14 days after diagnosis. Clinical progression (new AIDS event, death) was described using Kaplan-Meier analysis stratifying by type of AIDS event. Factors associated with progression were identified with multivariable Cox regression. Progression rates were compared between those starting early (<30 days after AIDS event) or deferred (30–270 days after AIDS event) cART. Results The median (interquartile range) CD4 count and viral load (VL) at diagnosis of the 584 patients were 42 (16, 119) cells/µL and 5.2 (4.5, 5.7) log10 copies/mL. Clinical progression was observed in 165 (28.3%) patients. Older age, a higher VL at diagnosis, and a diagnosis of non-Hodgkin lymphoma (NHL) (vs. other AIDS events) were independently associated with disease progression. Of 366 patients with an opportunistic infection, 178 (48.6%) received early cART. There was no significant difference in clinical progression between those initiating cART early and those deferring treatment (adjusted hazard ratio 1.32 [95% confidence interval 0.87, 2.00], p = 0.20). Conclusions Older patients and patients with high VL or NHL at diagnosis had a worse outcome. Our data suggest that earlier initiation of cART may be beneficial among HIV-infected patients diagnosed with clinical AIDS in our setting. PMID:22043301

Breast Ultrasound: Computer-Aided Diagnosis Approach to Improving Specificity and Decreasing Observer Variability

DTIC Science & Technology

1998-01-01

the conduct of research involving hazardous organisms, the investigator(s) adhered to the CDC-NIH Guide for Biosafetv in Microbiological and...Misinterpretation and misuse of the kappa statistic. American Journal of Epidemiology, 1987. 126: p. 161-169. 23. Soeken. K.L. and P.A. Prescott

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge.

PubMed

Bron, Esther E; Smits, Marion; van der Flier, Wiesje M; Vrenken, Hugo; Barkhof, Frederik; Scheltens, Philip; Papma, Janne M; Steketee, Rebecca M E; Méndez Orellana, Carolina; Meijboom, Rozanna; Pinto, Madalena; Meireles, Joana R; Garrett, Carolina; Bastos-Leite, António J; Abdulkadir, Ahmed; Ronneberger, Olaf; Amoroso, Nicola; Bellotti, Roberto; Cárdenas-Peña, David; Álvarez-Meza, Andrés M; Dolph, Chester V; Iftekharuddin, Khan M; Eskildsen, Simon F; Coupé, Pierrick; Fonov, Vladimir S; Franke, Katja; Gaser, Christian; Ledig, Christian; Guerrero, Ricardo; Tong, Tong; Gray, Katherine R; Moradi, Elaheh; Tohka, Jussi; Routier, Alexandre; Durrleman, Stanley; Sarica, Alessia; Di Fatta, Giuseppe; Sensi, Francesco; Chincarini, Andrea; Smith, Garry M; Stoyanov, Zhivko V; Sørensen, Lauge; Nielsen, Mads; Tangaro, Sabina; Inglese, Paolo; Wachinger, Christian; Reuter, Martin; van Swieten, John C; Niessen, Wiro J; Klein, Stefan

2015-05-01

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org. Copyright © 2015 Elsevier Inc. All rights reserved.

Health Insurance Portability and Accountability Act-Compliant Ocular Telehealth Network for the Remote Diagnosis and Management of Diabetic Retinopathy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Yaquin; Karnowski, Thomas Paul; Tobin Jr, Kenneth William

2011-01-01

In this article, we present the design and implementation of a regional ocular telehealth network for remote assessment and management of diabetic retinopathy (DR), including the design requirements, network topology, protocol design, system work flow, graphics user interfaces, and performance evaluation. The Telemedical Retinal Image Analysis and Diagnosis Network is a computer-aided, image analysis telehealth paradigm for the diagnosis of DR and other retinal diseases using fundus images acquired from primary care end users delivering care to underserved patient populations in the mid-South and southeastern United States.

A health insurance portability and accountability act-compliant ocular telehealth network for the remote diagnosis and management of diabetic retinopathy.

PubMed

Li, Yaqin; Karnowski, Thomas P; Tobin, Kenneth W; Giancardo, Luca; Morris, Scott; Sparrow, Sylvia E; Garg, Seema; Fox, Karen; Chaum, Edward

2011-10-01

In this article, we present the design and implementation of a regional ocular telehealth network for remote assessment and management of diabetic retinopathy (DR), including the design requirements, network topology, protocol design, system work flow, graphics user interfaces, and performance evaluation. The Telemedical Retinal Image Analysis and Diagnosis Network is a computer-aided, image analysis telehealth paradigm for the diagnosis of DR and other retinal diseases using fundus images acquired from primary care end users delivering care to underserved patient populations in the mid-South and southeastern United States.

Computer-Aided Recognition of Facial Attributes for Fetal Alcohol Spectrum Disorders.

PubMed

Valentine, Matthew; Bihm, Dustin C J; Wolf, Lior; Hoyme, H Eugene; May, Philip A; Buckley, David; Kalberg, Wendy; Abdul-Rahman, Omar A

2017-12-01

To compare the detection of facial attributes by computer-based facial recognition software of 2-D images against standard, manual examination in fetal alcohol spectrum disorders (FASD). Participants were gathered from the Fetal Alcohol Syndrome Epidemiology Research database. Standard frontal and oblique photographs of children were obtained during a manual, in-person dysmorphology assessment. Images were submitted for facial analysis conducted by the facial dysmorphology novel analysis technology (an automated system), which assesses ratios of measurements between various facial landmarks to determine the presence of dysmorphic features. Manual blinded dysmorphology assessments were compared with those obtained via the computer-aided system. Areas under the curve values for individual receiver-operating characteristic curves revealed the computer-aided system (0.88 ± 0.02) to be comparable to the manual method (0.86 ± 0.03) in detecting patients with FASD. Interestingly, cases of alcohol-related neurodevelopmental disorder (ARND) were identified more efficiently by the computer-aided system (0.84 ± 0.07) in comparison to the manual method (0.74 ± 0.04). A facial gestalt analysis of patients with ARND also identified more generalized facial findings compared to the cardinal facial features seen in more severe forms of FASD. We found there was an increased diagnostic accuracy for ARND via our computer-aided method. As this category has been historically difficult to diagnose, we believe our experiment demonstrates that facial dysmorphology novel analysis technology can potentially improve ARND diagnosis by introducing a standardized metric for recognizing FASD-associated facial anomalies. Earlier recognition of these patients will lead to earlier intervention with improved patient outcomes. Copyright © 2017 by the American Academy of Pediatrics.

Development of a Reference Image Collection Library for Histopathology Image Processing, Analysis and Decision Support Systems Research.

PubMed

Kostopoulos, Spiros; Ravazoula, Panagiota; Asvestas, Pantelis; Kalatzis, Ioannis; Xenogiannopoulos, George; Cavouras, Dionisis; Glotsos, Dimitris

2017-06-01

Histopathology image processing, analysis and computer-aided diagnosis have been shown as effective assisting tools towards reliable and intra-/inter-observer invariant decisions in traditional pathology. Especially for cancer patients, decisions need to be as accurate as possible in order to increase the probability of optimal treatment planning. In this study, we propose a new image collection library (HICL-Histology Image Collection Library) comprising 3831 histological images of three different diseases, for fostering research in histopathology image processing, analysis and computer-aided diagnosis. Raw data comprised 93, 116 and 55 cases of brain, breast and laryngeal cancer respectively collected from the archives of the University Hospital of Patras, Greece. The 3831 images were generated from the most representative regions of the pathology, specified by an experienced histopathologist. The HICL Image Collection is free for access under an academic license at http://medisp.bme.teiath.gr/hicl/ . Potential exploitations of the proposed library may span over a board spectrum, such as in image processing to improve visualization, in segmentation for nuclei detection, in decision support systems for second opinion consultations, in statistical analysis for investigation of potential correlations between clinical annotations and imaging findings and, generally, in fostering research on histopathology image processing and analysis. To the best of our knowledge, the HICL constitutes the first attempt towards creation of a reference image collection library in the field of traditional histopathology, publicly and freely available to the scientific community.

Soft-Tissue Infections and Their Imaging Mimics: From Cellulitis to Necrotizing Fasciitis.

PubMed

Hayeri, Mohammad Reza; Ziai, Pouya; Shehata, Monda L; Teytelboym, Oleg M; Huang, Brady K

2016-10-01

Infection of the musculoskeletal system can be associated with high mortality and morbidity if not promptly and accurately diagnosed. These infections are generally diagnosed and managed clinically; however, clinical and laboratory findings sometimes lack sensitivity and specificity, and a definite diagnosis may not be possible. In uncertain situations, imaging is frequently performed to confirm the diagnosis, evaluate the extent of the disease, and aid in treatment planning. In particular, cross-sectional imaging, including computed tomography and magnetic resonance imaging, provides detailed anatomic information in the evaluation of soft tissues due to their inherent high spatial and contrast resolution. Imaging findings of soft-tissue infections can be nonspecific and can have different appearances depending on the depth and anatomic extent of tissue involvement. Although many imaging features of infectious disease can overlap with noninfectious processes, imaging can help establish the diagnosis when combined with the clinical history and laboratory findings. Radiologists should be familiar with the spectrum of imaging findings of soft-tissue infections to better aid the referring physician in managing these patients. The aim of this article is to review the spectrum of soft-tissue infections using a systematic anatomic compartment approach. We discuss the clinical features of soft-tissue infections, their imaging findings with emphasis on cross-sectional imaging, their potential mimics, and clinical management. © RSNA, 2016.

Machine Learning in Medical Imaging.

PubMed

Giger, Maryellen L

2018-03-01

Advances in both imaging and computers have synergistically led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks, such as risk assessment, detection, diagnosis, prognosis, and therapy response, as well as in multi-omics disease discovery. A brief overview of the field is given here, allowing the reader to recognize the terminology, the various subfields, and components of machine learning, as well as the clinical potential. Radiomics, an expansion of computer-aided diagnosis, has been defined as the conversion of images to minable data. The ultimate benefit of quantitative radiomics is to (1) yield predictive image-based phenotypes of disease for precision medicine or (2) yield quantitative image-based phenotypes for data mining with other -omics for discovery (ie, imaging genomics). For deep learning in radiology to succeed, note that well-annotated large data sets are needed since deep networks are complex, computer software and hardware are evolving constantly, and subtle differences in disease states are more difficult to perceive than differences in everyday objects. In the future, machine learning in radiology is expected to have a substantial clinical impact with imaging examinations being routinely obtained in clinical practice, providing an opportunity to improve decision support in medical image interpretation. The term of note is decision support, indicating that computers will augment human decision making, making it more effective and efficient. The clinical impact of having computers in the routine clinical practice may allow radiologists to further integrate their knowledge with their clinical colleagues in other medical specialties and allow for precision medicine. Copyright © 2018. Published by Elsevier Inc.

Diagnosis and management of solitary pulmonary nodules.

PubMed

Jeong, Yeon Joo; Lee, Kyung Soo; Kwon, O Jung

2008-12-01

The advent of computed tomography (CT) screening with or without the help of computer-aided detection systems has increased the detection rate of solitary pulmonary nodules (SPNs), including that of early peripheral lung cancer. Helical dynamic (HD)CT, providing the information on morphologic and hemodynamic characteristics with high specificity and reasonably high accuracy, can be used for the initial assessment of SPNs. (18)F-fluorodeoxyglucose PET/CT is more sensitive at detecting malignancy than HDCT. Therefore, PET/CT may be selectively performed to characterize SPNs when HDCT gives an inconclusive diagnosis. Serial volume measurements are currently the most reliable methods for the tissue characterization of subcentimeter nodules. When malignant nodule is highly suspected for subcentimeter nodules, video-assisted thoracoscopic surgery nodule removal after nodule localization using the pulmonary nodule-marker system may be performed for diagnosis and treatment.

Computational Psychiatry

PubMed Central

Wang, Xiao-Jing; Krystal, John H.

2014-01-01

Psychiatric disorders such as autism and schizophrenia arise from abnormalities in brain systems that underlie cognitive, emotional and social functions. The brain is enormously complex and its abundant feedback loops on multiple scales preclude intuitive explication of circuit functions. In close interplay with experiments, theory and computational modeling are essential for understanding how, precisely, neural circuits generate flexible behaviors and their impairments give rise to psychiatric symptoms. This Perspective highlights recent progress in applying computational neuroscience to the study of mental disorders. We outline basic approaches, including identification of core deficits that cut across disease categories, biologically-realistic modeling bridging cellular and synaptic mechanisms with behavior, model-aided diagnosis. The need for new research strategies in psychiatry is urgent. Computational psychiatry potentially provides powerful tools for elucidating pathophysiology that may inform both diagnosis and treatment. To achieve this promise will require investment in cross-disciplinary training and research in this nascent field. PMID:25442941

Applying Data Mining Techniques to Improve Breast Cancer Diagnosis.

PubMed

Diz, Joana; Marreiros, Goreti; Freitas, Alberto

2016-09-01

In the field of breast cancer research, and more than ever, new computer aided diagnosis based systems have been developed aiming to reduce diagnostic tests false-positives. Within this work, we present a data mining based approach which might support oncologists in the process of breast cancer classification and diagnosis. The present study aims to compare two breast cancer datasets and find the best methods in predicting benign/malignant lesions, breast density classification, and even for finding identification (mass / microcalcification distinction). To carry out these tasks, two matrices of texture features extraction were implemented using Matlab, and classified using data mining algorithms, on WEKA. Results revealed good percentages of accuracy for each class: 89.3 to 64.7 % - benign/malignant; 75.8 to 78.3 % - dense/fatty tissue; 71.0 to 83.1 % - finding identification. Among the different tests classifiers, Naive Bayes was the best to identify masses texture, and Random Forests was the first or second best classifier for the majority of tested groups.

A computer-aided diagnosis system of nuclear cataract.

PubMed

Li, Huiqi; Lim, Joo Hwee; Liu, Jiang; Mitchell, Paul; Tan, Ava Grace; Wang, Jie Jin; Wong, Tien Yin

2010-07-01

Cataracts are the leading cause of blindness worldwide, and nuclear cataract is the most common form of cataract. An algorithm for automatic diagnosis of nuclear cataract is investigated in this paper. Nuclear cataract is graded according to the severity of opacity using slit lamp lens images. Anatomical structure in the lens image is detected using a modified active shape model. On the basis of the anatomical landmark, local features are extracted according to clinical grading protocol. Support vector machine regression is employed for grade prediction. This is the first time that the nucleus region can be detected automatically in slit lamp images. The system is validated using clinical images and clinical ground truth on >5000 images. The success rate of structure detection is 95% and the average grading difference is 0.36 on a 5.0 scale. The automatic diagnosis system can improve the grading objectivity and potentially be used in clinics and population studies to save the workload of ophthalmologists.

Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer

NASA Astrophysics Data System (ADS)

Vandenberghe, Michel E.; Scott, Marietta L. J.; Scorer, Paul W.; Söderberg, Magnus; Balcerzak, Denis; Barker, Craig

2017-04-01

Tissue biomarker scoring by pathologists is central to defining the appropriate therapy for patients with cancer. Yet, inter-pathologist variability in the interpretation of ambiguous cases can affect diagnostic accuracy. Modern artificial intelligence methods such as deep learning have the potential to supplement pathologist expertise to ensure constant diagnostic accuracy. We developed a computational approach based on deep learning that automatically scores HER2, a biomarker that defines patient eligibility for anti-HER2 targeted therapies in breast cancer. In a cohort of 71 breast tumour resection samples, automated scoring showed a concordance of 83% with a pathologist. The twelve discordant cases were then independently reviewed, leading to a modification of diagnosis from initial pathologist assessment for eight cases. Diagnostic discordance was found to be largely caused by perceptual differences in assessing HER2 expression due to high HER2 staining heterogeneity. This study provides evidence that deep learning aided diagnosis can facilitate clinical decision making in breast cancer by identifying cases at high risk of misdiagnosis.

B cell-stimulatory cytokines and markers of immune activation are elevated several years prior to the diagnosis of systemic AIDS-associated non-Hodgkin B cell lymphoma

PubMed Central

Breen, Elizabeth Crabb; Hussain, Shehnaz K.; Magpantay, Larry; Jacobson, Lisa P.; Detels, Roger; Rabkin, Charles S.; Kaslow, Richard A.; Variakojis, Daina; Bream, Jay H.; Rinaldo, Charles R.; Ambinder, Richard F.; Martínez-Maza, Otoniel

2011-01-01

Background The risk of developing non-Hodgkin lymphoma (NHL) is greatly increased in HIV infection. The aim of this study was to determine if elevated serum levels of molecules associated with B cell activation precede the diagnosis of AIDS-associated NHL. Methods Serum levels of B cell activation-associated molecules, interleukin-6 (IL6), interleukin-10 (IL10), soluble CD23 (sCD23), soluble CD27 (sCD27), soluble CD30 (sCD30), C-reactive protein (CRP), and IgE were determined in 179 NHL cases and HIV+ controls in the Multicenter AIDS Cohort Study, collected at up to three time points per subject, 0–5 years prior to AIDS-NHL diagnosis. Results Serum IL6, IL10, CRP, sCD23, sCD27, and sCD30 levels were all significantly elevated in the AIDS-NHL group, when compared to HIV+ controls or to AIDS controls, after adjusting for CD4 T cell number. Elevated serum levels of B cell activation-associated molecules were seen to be associated with the development of systemic (non-CNS) NHL, but not with the development of primary CNS lymphoma. Conclusions Levels of certain B cell stimulatory cytokines and molecules associated with immune activation are elevated for several years preceding the diagnosis of systemic AIDS-NHL. This observation is consistent with the hypothesis that chronic B cell activation contributes to the development of these hematologic malignancies. Impact Marked differences in serum levels of several molecules are seen for several years pre-diagnosis in those who eventually develop AIDS-NHL. Some of these molecules may serve as candidate biomarkers and provide valuable information to better define the etiology of NHL. PMID:21527584

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

NASA Astrophysics Data System (ADS)

Cheng, Jie-Zhi; Ni, Dong; Chou, Yi-Hong; Qin, Jing; Tiu, Chui-Mei; Chang, Yeun-Chung; Huang, Chiun-Sheng; Shen, Dinggang; Chen, Chung-Ming

2016-04-01

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans.

PubMed

Cheng, Jie-Zhi; Ni, Dong; Chou, Yi-Hong; Qin, Jing; Tiu, Chui-Mei; Chang, Yeun-Chung; Huang, Chiun-Sheng; Shen, Dinggang; Chen, Chung-Ming

2016-04-15

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.

A computer-aided detection (CAD) system with a 3D algorithm for small acute intracranial hemorrhage

NASA Astrophysics Data System (ADS)

Wang, Ximing; Fernandez, James; Deshpande, Ruchi; Lee, Joon K.; Chan, Tao; Liu, Brent

2012-02-01

Acute Intracranial hemorrhage (AIH) requires urgent diagnosis in the emergency setting to mitigate eventual sequelae. However, experienced radiologists may not always be available to make a timely diagnosis. This is especially true for small AIH, defined as lesion smaller than 10 mm in size. A computer-aided detection (CAD) system for the detection of small AIH would facilitate timely diagnosis. A previously developed 2D algorithm shows high false positive rates in the evaluation based on LAC/USC cases, due to the limitation of setting up correct coordinate system for the knowledge-based classification system. To achieve a higher sensitivity and specificity, a new 3D algorithm is developed. The algorithm utilizes a top-hat transformation and dynamic threshold map to detect small AIH lesions. Several key structures of brain are detected and are used to set up a 3D anatomical coordinate system. A rule-based classification of the lesion detected is applied based on the anatomical coordinate system. For convenient evaluation in clinical environment, the CAD module is integrated with a stand-alone system. The CAD is evaluated by small AIH cases and matched normal collected in LAC/USC. The result of 3D CAD and the previous 2D CAD has been compared.

Call for a Computer-Aided Cancer Detection and Classification Research Initiative in Oman.

PubMed

Mirzal, Andri; Chaudhry, Shafique Ahmad

2016-01-01

Cancer is a major health problem in Oman. It is reported that cancer incidence in Oman is the second highest after Saudi Arabia among Gulf Cooperation Council countries. Based on GLOBOCAN estimates, Oman is predicted to face an almost two-fold increase in cancer incidence in the period 2008-2020. However, cancer research in Oman is still in its infancy. This is due to the fact that medical institutions and infrastructure that play central roles in data collection and analysis are relatively new developments in Oman. We believe the country requires an organized plan and efforts to promote local cancer research. In this paper, we discuss current research progress in cancer diagnosis using machine learning techniques to optimize computer aided cancer detection and classification (CAD). We specifically discuss CAD using two major medical data, i.e., medical imaging and microarray gene expression profiling, because medical imaging like mammography, MRI, and PET have been widely used in Oman for assisting radiologists in early cancer diagnosis and microarray data have been proven to be a reliable source for differential diagnosis. We also discuss future cancer research directions and benefits to Oman economy for entering the cancer research and treatment business as it is a multi-billion dollar industry worldwide.

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

PubMed Central

Cheng, Jie-Zhi; Ni, Dong; Chou, Yi-Hong; Qin, Jing; Tiu, Chui-Mei; Chang, Yeun-Chung; Huang, Chiun-Sheng; Shen, Dinggang; Chen, Chung-Ming

2016-01-01

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features. PMID:27079888

Development of a computer aided diagnosis model for prostate cancer classification on multi-parametric MRI

NASA Astrophysics Data System (ADS)

Alfano, R.; Soetemans, D.; Bauman, G. S.; Gibson, E.; Gaed, M.; Moussa, M.; Gomez, J. A.; Chin, J. L.; Pautler, S.; Ward, A. D.

2018-02-01

Multi-parametric MRI (mp-MRI) is becoming a standard in contemporary prostate cancer screening and diagnosis, and has shown to aid physicians in cancer detection. It offers many advantages over traditional systematic biopsy, which has shown to have very high clinical false-negative rates of up to 23% at all stages of the disease. However beneficial, mp-MRI is relatively complex to interpret and suffers from inter-observer variability in lesion localization and grading. Computer-aided diagnosis (CAD) systems have been developed as a solution as they have the power to perform deterministic quantitative image analysis. We measured the accuracy of such a system validated using accurately co-registered whole-mount digitized histology. We trained a logistic linear classifier (LOGLC), support vector machine (SVC), k-nearest neighbour (KNN) and random forest classifier (RFC) in a four part ROI based experiment against: 1) cancer vs. non-cancer, 2) high-grade (Gleason score ≥4+3) vs. low-grade cancer (Gleason score <4+3), 3) high-grade vs. other tissue components and 4) high-grade vs. benign tissue by selecting the classifier with the highest AUC using 1-10 features from forward feature selection. The CAD model was able to classify malignant vs. benign tissue and detect high-grade cancer with high accuracy. Once fully validated, this work will form the basis for a tool that enhances the radiologist's ability to detect malignancies, potentially improving biopsy guidance, treatment selection, and focal therapy for prostate cancer patients, maximizing the potential for cure and increasing quality of life.

Computer Skill Acquisition and Retention: The Effects of Computer-Aided Self-Explanation

ERIC Educational Resources Information Center

Chi, Tai-Yin

2016-01-01

This research presents an experimental study to determine to what extent computer skill learners can benefit from generating self-explanation with the aid of different computer-based visualization technologies. Self-explanation was stimulated with dynamic visualization (Screencast), static visualization (Screenshot), or verbal instructions only,…

Computer-aided classification of breast masses using contrast-enhanced digital mammograms

NASA Astrophysics Data System (ADS)

Danala, Gopichandh; Aghaei, Faranak; Heidari, Morteza; Wu, Teresa; Patel, Bhavika; Zheng, Bin

2018-02-01

By taking advantages of both mammography and breast MRI, contrast-enhanced digital mammography (CEDM) has emerged as a new promising imaging modality to improve efficacy of breast cancer screening and diagnosis. The primary objective of study is to develop and evaluate a new computer-aided detection and diagnosis (CAD) scheme of CEDM images to classify between malignant and benign breast masses. A CEDM dataset consisting of 111 patients (33 benign and 78 malignant) was retrospectively assembled. Each case includes two types of images namely, low-energy (LE) and dual-energy subtracted (DES) images. First, CAD scheme applied a hybrid segmentation method to automatically segment masses depicting on LE and DES images separately. Optimal segmentation results from DES images were also mapped to LE images and vice versa. Next, a set of 109 quantitative image features related to mass shape and density heterogeneity was initially computed. Last, four multilayer perceptron-based machine learning classifiers integrated with correlationbased feature subset evaluator and leave-one-case-out cross-validation method was built to classify mass regions depicting on LE and DES images, respectively. Initially, when CAD scheme was applied to original segmentation of DES and LE images, the areas under ROC curves were 0.7585+/-0.0526 and 0.7534+/-0.0470, respectively. After optimal segmentation mapping from DES to LE images, AUC value of CAD scheme significantly increased to 0.8477+/-0.0376 (p<0.01). Since DES images eliminate overlapping effect of dense breast tissue on lesions, segmentation accuracy was significantly improved as compared to regular mammograms, the study demonstrated that computer-aided classification of breast masses using CEDM images yielded higher performance.

Classification of clinical significance of MRI prostate findings using 3D convolutional neural networks

NASA Astrophysics Data System (ADS)

Mehrtash, Alireza; Sedghi, Alireza; Ghafoorian, Mohsen; Taghipour, Mehdi; Tempany, Clare M.; Wells, William M.; Kapur, Tina; Mousavi, Parvin; Abolmaesumi, Purang; Fedorov, Andriy

2017-03-01

Prostate cancer (PCa) remains a leading cause of cancer mortality among American men. Multi-parametric magnetic resonance imaging (mpMRI) is widely used to assist with detection of PCa and characterization of its aggressiveness. Computer-aided diagnosis (CADx) of PCa in MRI can be used as clinical decision support system to aid radiologists in interpretation and reporting of mpMRI. We report on the development of a convolution neural network (CNN) model to support CADx in PCa based on the appearance of prostate tissue in mpMRI, conducted as part of the SPIE-AAPM-NCI PROSTATEx challenge. The performance of different combinations of mpMRI inputs to CNN was assessed and the best result was achieved using DWI and DCE-MRI modalities together with the zonal information of the finding. On the test set, the model achieved an area under the receiver operating characteristic curve of 0.80.

A GPU-based computer-assisted microscopy system for assessing the importance of different families of histological characteristics in cancer diagnosis

NASA Astrophysics Data System (ADS)

Glotsos, Dimitris; Kostopoulos, Spiros; Sidiropoulos, Konstantinos; Ravazoula, Panagiota; Kalatzis, Ioannis; Asvestas, Pantelis; Cavouras, Dionisis

2014-01-01

In this study a Computer-Aided Microscopy (CAM) system is proposed for investigating the importance of the histological criteria involved in diagnosing of cancers in microscopy in order to suggest the more informative features for discriminating low from high-grade brain tumours. Four families of criteria have been examined, involving the greylevel variations (i.e. texture), the morphology (i.e. roundness), the architecture (i.e. cellularity) and the overall tumour qualities (expert's ordinal scale). The proposed CAM system was constructed using a modified Seeded Region Growing algorithm for image segmentation, and the Probabilistic Neural Network classifier for image classification. The implementation was designed on a commercial Graphics Processing Unit card using parallel programming. The system's performance using textural, morphological, architectural and ordinal information was 90.8%, 87.0%, 81.2% and 88.9% respectively. Results indicate that nuclei texture is the most important family of features regarding the degree of malignancy, and, thus, may guide more accurate predictions for discriminating low from high grade gliomas. Considering that nuclei texture is almost impractical to be encoded by visual observation, the need to incorporate computer-aided diagnostic tools as second opinion in daily clinical practice of diagnosing rare brain tumours may be justified.

Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy.

PubMed

Kominami, Yoko; Yoshida, Shigeto; Tanaka, Shinji; Sanomura, Yoji; Hirakawa, Tsubasa; Raytchev, Bisser; Tamaki, Toru; Koide, Tetsusi; Kaneda, Kazufumi; Chayama, Kazuaki

2016-03-01

It is necessary to establish cost-effective examinations and treatments for diminutive colorectal tumors that consider the treatment risk and surveillance interval after treatment. The Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI) committee of the American Society for Gastrointestinal Endoscopy published a statement recommending the establishment of endoscopic techniques that practice the resect and discard strategy. The aims of this study were to evaluate whether our newly developed real-time image recognition system can predict histologic diagnoses of colorectal lesions depicted on narrow-band imaging and to satisfy some problems with the PIVI recommendations. We enrolled 41 patients who had undergone endoscopic resection of 118 colorectal lesions (45 nonneoplastic lesions and 73 neoplastic lesions). We compared the results of real-time image recognition system analysis with that of narrow-band imaging diagnosis and evaluated the correlation between image analysis and the pathological results. Concordance between the endoscopic diagnosis and diagnosis by a real-time image recognition system with a support vector machine output value was 97.5% (115/118). Accuracy between the histologic findings of diminutive colorectal lesions (polyps) and diagnosis by a real-time image recognition system with a support vector machine output value was 93.2% (sensitivity, 93.0%; specificity, 93.3%; positive predictive value (PPV), 93.0%; and negative predictive value, 93.3%). Although further investigation is necessary to establish our computer-aided diagnosis system, this real-time image recognition system may satisfy the PIVI recommendations and be useful for predicting the histology of colorectal tumors. Copyright © 2016 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

A Computer-Aided Diagnosis System Using Artificial Intelligence for the Diagnosis and Characterization of Thyroid Nodules on Ultrasound: Initial Clinical Assessment.

PubMed

Choi, Young Jun; Baek, Jung Hwan; Park, Hye Sun; Shim, Woo Hyun; Kim, Tae Yong; Shong, Young Kee; Lee, Jeong Hyun

2017-04-01

An initial clinical assessment is described of a new, commercially available, computer-aided diagnosis (CAD) system using artificial intelligence (AI) for thyroid ultrasound, and its performance is evaluated in the diagnosis of malignant thyroid nodules and categorization of nodule characteristics. Patients with thyroid nodules with decisive diagnosis, whether benign or malignant, were consecutively enrolled from November 2015 to February 2016. An experienced radiologist reviewed the ultrasound image characteristics of the thyroid nodules, while another radiologist assessed the same thyroid nodules using the CAD system, providing ultrasound characteristics and a diagnosis of whether nodules were benign or malignant. The diagnostic performance and agreement of US characteristics between the experienced radiologist and the CAD system were compared. In total, 102 thyroid nodules from 89 patients were included; 59 (57.8%) were benign and 43 (42.2%) were malignant. The CAD system showed a similar sensitivity as the experienced radiologist (90.7% vs. 88.4%, p > 0.99), but a lower specificity and a lower area under the receiver operating characteristic (AUROC) curve (specificity: 74.6% vs. 94.9%, p = 0.002; AUROC: 0.83 vs. 0.92, p = 0.021). Classifications of the ultrasound characteristics (composition, orientation, echogenicity, and spongiform) between radiologist and CAD system were in substantial agreement (κ = 0.659, 0.740, 0.733, and 0.658, respectively), while the margin showed a fair agreement (κ = 0.239). The sensitivity of the CAD system using AI for malignant thyroid nodules was as good as that of the experienced radiologist, while specificity and accuracy were lower than those of the experienced radiologist. The CAD system showed an acceptable agreement with the experienced radiologist for characterization of thyroid nodules.

Effectiveness of computer-aided diagnosis of colorectal lesions using novel software for magnifying narrow-band imaging: a pilot study.

PubMed

Tamai, Naoto; Saito, Yutaka; Sakamoto, Taku; Nakajima, Takeshi; Matsuda, Takahisa; Sumiyama, Kazuki; Tajiri, Hisao; Koyama, Ryosuke; Kido, Shoji

2017-08-01

 Magnifying narrow-band imaging (M-NBI) enables detailed observation of microvascular architecture and can be used in endoscopic diagnosis of colorectal lesion. However, in clinical practice, differential diagnosis and estimation of invasion depth of colorectal lesions based on M-NBI findings require experience. Therefore, developing computer-aided diagnosis (CAD) for M-NBI would be beneficial for clinical practice. The aim of this study was to evaluate the effectiveness of software for CAD of colorectal lesions. In collaboration with Yamaguchi University, we developed novel software that enables CAD of colorectal lesions using M-NBI images. This software for CAD further specifically divides original Sano's colorectal M-NBI classification into 3 groups (group A, capillary pattern [CP] type I; group B, CP type II + CP type IIIA; group C, CP type IIIB), which describe hyperplastic polyps (HPs), adenoma/adenocarcinoma (intramucosal [IM] to submucosal [SM]-superficial) lesions, and SM-deep lesions, respectively. We retrospectively reviewed 121 lesions evaluated using M-NBI. The 121 reviewed lesions included 21 HP, 80 adenoma/adenocarcinoma (IM to SM-superficial), and 20 SM-deep lesions. The concordance rate between the CAD and the diagnosis of the experienced endoscopists was 90.9 %. The sensitivity, specificity, positive and negative predictive values, and accuracy of the CAD for neoplastic lesions were 83.9 %, 82.6 %, 53.1 %, 95.6 %, and 82.8 %, respectively. The values for SM-deep lesions were 83.9 %, 82.6 %, 53.1 %, 95.6 %, and 82.8 %, respectively.  Relatively high diagnostic values were obtained using CAD. This software for CAD could possibly lead to a wider use of M-NBI in the endoscopic diagnosis of colorectal lesions.

Is AIDS a floating point between HIV seroconversion and death? Insights from the Tricontinental Seroconverter Study.

PubMed

van Benthem, B H; Veugelers, P J; Cornelisse, P G; Strathdee, S A; Kaldor, J M; Shafer, K A; Coutinho, R A; van Griensven, G J

1998-06-18

To investigate the significance of the time from seroconversion to AIDS (incubation time) and other covariates for survival from AIDS to death. In survival analysis, survival from AIDS to death was compared for different categories of length of incubation time adjusted and unadjusted for other covariates, and significant predictors for survival from AIDS to death were investigated. Survival after AIDS was not affected by the incubation time in univariate as well as in multivariate analyses. Predictive factors for progression from AIDS to death were age at seroconversion, type of AIDS diagnosis, and CD4 cell count at AIDS. The relative hazard for age at seroconversion increased 1.38-fold over 10 years. Men with a CD4 cell count at AIDS of <130 x 10(6)/l had a twofold higher risk in progression to death than men with higher CD4 cell counts. Persons diagnosed with lymphoma had a sixfold higher risk of progression to death than persons with Kaposi's sarcoma or opportunistic infections. The incubation time as well as other factors before AIDS did not affect survival after AIDS. Survival from AIDS to death can be predicted by data obtained at the time of AIDS diagnosis, such as type of diagnosis, age and CD4 cell count. AIDS seems to be a significant point in progression to death, and not just a floating point between infection and death affected by prior factors for persons who did not receive effective therapy and did not have long incubation times.

Computer-Aided Detection of Prostate Cancer with MRI: Technology and Applications

PubMed Central

Liu, Lizhi; Tian, Zhiqiang; Zhang, Zhenfeng; Fei, Baowei

2016-01-01

One in six men will develop prostate cancer in his life time. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs. Recently, imaging of prostate cancer has greatly advanced since the introduction of multi-parametric magnetic resonance imaging (mp-MRI). Mp-MRI consists of T2-weighted sequences combined with functional sequences including dynamic contrast-enhanced MRI, diffusion-weighted MRI, and MR spectroscopy imaging. Due to the big data and variations in imaging sequences, detection can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In order to improve quantitative assessment of the disease, various computer-aided detection systems have been designed to help radiologists in their clinical practice. This review paper presents an overview of literatures on computer-aided detection of prostate cancer with mp-MRI, which include the technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. PMID:27133005

Applying Learning Diagnosis Diagram in Computer Aided Instructions: Research, Practice and Evaluation

ERIC Educational Resources Information Center

Wu, YuLung

2010-01-01

In Taiwan, when students learn in experiment-related courses, they are often grouped into several teams. The familiar method of grouping learning is "Cooperative Learning". A well-organized grouping strategy improves cooperative learning and increases the number of activities. This study proposes a novel pedagogical method by adopting…

Incorporating Computer-Aided Language Sample Analysis into Clinical Practice

ERIC Educational Resources Information Center

Price, Lisa Hammett; Hendricks, Sean; Cook, Colleen

2010-01-01

Purpose: During the evaluation of language abilities, the needs of the child are best served when multiple types and sources of data are included in the evaluation process. Current educational policies and practice guidelines further dictate the use of authentic assessment data to inform diagnosis and treatment planning. Language sampling and…

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

PubMed Central

Zhang, Fan; Li, Xuelong

2018-01-01

The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system. PMID:29687000

Computer-aided diagnosis: A survey with bibliometric analysis.

PubMed

Takahashi, Ryohei; Kajikawa, Yuya

2017-05-01

Computer-aided diagnosis (CAD) has been a promising area of research over the last two decades. However, CAD is a very complicated subject because it involves a number of medicine and engineering-related fields. To develop a research overview of CAD, we conducted a literature survey with bibliometric analysis, which we report here. Our study determined that CAD research has been classified and categorized according to disease type and imaging modality. This classification began with the CAD of mammograms and eventually progressed to that of brain disease. Furthermore, based on our results, we discuss future directions and opportunities for CAD research. First, in contrast to the typical hypothetical approach, the data-driven approach has shown promise. Second, the normalization of the test datasets and an evaluation method is necessary when adopting an algorithm and a system. Third, we discuss opportunities for the co-evolution of CAD research and imaging instruments-for example, the CAD of bones and pancreatic cancer. Fourth, the potential of synergy with CAD and clinical decision support systems is also discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey.

PubMed

Huang, Qinghua; Zhang, Fan; Li, Xuelong

2018-01-01

The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system.

Analysis of the impact of digital watermarking on computer-aided diagnosis in medical imaging.

PubMed

Garcia-Hernandez, Jose Juan; Gomez-Flores, Wilfrido; Rubio-Loyola, Javier

2016-01-01

Medical images (MI) are relevant sources of information for detecting and diagnosing a large number of illnesses and abnormalities. Due to their importance, this study is focused on breast ultrasound (BUS), which is the main adjunct for mammography to detect common breast lesions among women worldwide. On the other hand, aiming to enhance data security, image fidelity, authenticity, and content verification in e-health environments, MI watermarking has been widely used, whose main goal is to embed patient meta-data into MI so that the resulting image keeps its original quality. In this sense, this paper deals with the comparison of two watermarking approaches, namely spread spectrum based on the discrete cosine transform (SS-DCT) and the high-capacity data-hiding (HCDH) algorithm, so that the watermarked BUS images are guaranteed to be adequate for a computer-aided diagnosis (CADx) system, whose two principal outcomes are lesion segmentation and classification. Experimental results show that HCDH algorithm is highly recommended for watermarking medical images, maintaining the image quality and without introducing distortion into the output of CADx. Copyright © 2015 Elsevier Ltd. All rights reserved.

Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images.

PubMed

Barbosa, Daniel C; Roupar, Dalila B; Ramos, Jaime C; Tavares, Adriano C; Lima, Carlos S

2012-01-11

Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.

Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration.

PubMed

Pycinski, Bartlomiej; Czajkowska, Joanna; Badura, Pawel; Juszczyk, Jan; Pietka, Ewa

2016-01-01

A growing number of medical applications, including minimal invasive surgery, depends on multi-modal or multi-sensors data processing. Fast and accurate 3D scene analysis, comprising data registration, seems to be crucial for the development of computer aided diagnosis and therapy. The advancement of surface tracking system based on optical trackers already plays an important role in surgical procedures planning. However, new modalities, like the time-of-flight (ToF) sensors, widely explored in non-medical fields are powerful and have the potential to become a part of computer aided surgery set-up. Connection of different acquisition systems promises to provide a valuable support for operating room procedures. Therefore, the detailed analysis of the accuracy of such multi-sensors positioning systems is needed. We present the system combining pre-operative CT series with intra-operative ToF-sensor and optical tracker point clouds. The methodology contains: optical sensor set-up and the ToF-camera calibration procedures, data pre-processing algorithms, and registration technique. The data pre-processing yields a surface, in case of CT, and point clouds for ToF-sensor and marker-driven optical tracker representation of an object of interest. An applied registration technique is based on Iterative Closest Point algorithm. The experiments validate the registration of each pair of modalities/sensors involving phantoms of four various human organs in terms of Hausdorff distance and mean absolute distance metrics. The best surface alignment was obtained for CT and optical tracker combination, whereas the worst for experiments involving ToF-camera. The obtained accuracies encourage to further develop the multi-sensors systems. The presented substantive discussion concerning the system limitations and possible improvements mainly related to the depth information produced by the ToF-sensor is useful for computer aided surgery developers.

A dimension reduction strategy for improving the efficiency of computer-aided detection for CT colonography

NASA Astrophysics Data System (ADS)

Song, Bowen; Zhang, Guopeng; Wang, Huafeng; Zhu, Wei; Liang, Zhengrong

2013-02-01

Various types of features, e.g., geometric features, texture features, projection features etc., have been introduced for polyp detection and differentiation tasks via computer aided detection and diagnosis (CAD) for computed tomography colonography (CTC). Although these features together cover more information of the data, some of them are statistically highly-related to others, which made the feature set redundant and burdened the computation task of CAD. In this paper, we proposed a new dimension reduction method which combines hierarchical clustering and principal component analysis (PCA) for false positives (FPs) reduction task. First, we group all the features based on their similarity using hierarchical clustering, and then PCA is employed within each group. Different numbers of principal components are selected from each group to form the final feature set. Support vector machine is used to perform the classification. The results show that when three principal components were chosen from each group we can achieve an area under the curve of receiver operating characteristics of 0.905, which is as high as the original dataset. Meanwhile, the computation time is reduced by 70% and the feature set size is reduce by 77%. It can be concluded that the proposed method captures the most important information of the feature set and the classification accuracy is not affected after the dimension reduction. The result is promising and further investigation, such as automatically threshold setting, are worthwhile and are under progress.

Bilateral symmetry aspects in computer-aided Alzheimer's disease diagnosis by single-photon emission-computed tomography imaging.

PubMed

Illán, Ignacio Alvarez; Górriz, Juan Manuel; Ramírez, Javier; Lang, Elmar W; Salas-Gonzalez, Diego; Puntonet, Carlos G

2012-11-01

This paper explores the importance of the latent symmetry of the brain in computer-aided systems for diagnosing Alzheimer's disease (AD). Symmetry and asymmetry are studied from two points of view: (i) the development of an effective classifier within the scope of machine learning techniques, and (ii) the assessment of its relevance to the AD diagnosis in the early stages of the disease. The proposed methodology is based on eigenimage decomposition of single-photon emission-computed tomography images, using an eigenspace extension to accommodate odd and even eigenvectors separately. This feature extraction technique allows for support-vector-machine classification and image analysis. Identification of AD patterns is improved when the latent symmetry of the brain is considered, with an estimated 92.78% accuracy (92.86% sensitivity, 92.68% specificity) using a linear kernel and a leave-one-out cross validation strategy. Also, asymmetries may be used to define a test for AD that is very specific (90.24% specificity) but not especially sensitive. Two main conclusions are derived from the analysis of the eigenimage spectrum. Firstly, the recognition of AD patterns is improved when considering only the symmetric part of the spectrum. Secondly, asymmetries in the hypo-metabolic patterns, when present, are more pronounced in subjects with AD. Copyright © 2012 Elsevier B.V. All rights reserved.

Computer-assisted initial diagnosis of rare diseases

PubMed Central

Piñol, Marc; Vilaplana, Jordi; Teixidó, Ivan; Cruz, Joaquim; Comas, Jorge; Vilaprinyo, Ester; Sorribas, Albert

2016-01-01

Introduction. Most documented rare diseases have genetic origin. Because of their low individual frequency, an initial diagnosis based on phenotypic symptoms is not always easy, as practitioners might never have been exposed to patients suffering from the relevant disease. It is thus important to develop tools that facilitate symptom-based initial diagnosis of rare diseases by clinicians. In this work we aimed at developing a computational approach to aid in that initial diagnosis. We also aimed at implementing this approach in a user friendly web prototype. We call this tool Rare Disease Discovery. Finally, we also aimed at testing the performance of the prototype. Methods. Rare Disease Discovery uses the publicly available ORPHANET data set of association between rare diseases and their symptoms to automatically predict the most likely rare diseases based on a patient’s symptoms. We apply the method to retrospectively diagnose a cohort of 187 rare disease patients with confirmed diagnosis. Subsequently we test the precision, sensitivity, and global performance of the system under different scenarios by running large scale Monte Carlo simulations. All settings account for situations where absent and/or unrelated symptoms are considered in the diagnosis. Results. We find that this expert system has high diagnostic precision (≥80%) and sensitivity (≥99%), and is robust to both absent and unrelated symptoms. Discussion. The Rare Disease Discovery prediction engine appears to provide a fast and robust method for initial assisted differential diagnosis of rare diseases. We coupled this engine with a user-friendly web interface and it can be freely accessed at http://disease-discovery.udl.cat/. The code and most current database for the whole project can be downloaded from https://github.com/Wrrzag/DiseaseDiscovery/tree/no_classifiers. PMID:27547534

Fault diagnosis

NASA Technical Reports Server (NTRS)

Abbott, Kathy

1990-01-01

The objective of the research in this area of fault management is to develop and implement a decision aiding concept for diagnosing faults, especially faults which are difficult for pilots to identify, and to develop methods for presenting the diagnosis information to the flight crew in a timely and comprehensible manner. The requirements for the diagnosis concept were identified by interviewing pilots, analyzing actual incident and accident cases, and examining psychology literature on how humans perform diagnosis. The diagnosis decision aiding concept developed based on those requirements takes abnormal sensor readings as input, as identified by a fault monitor. Based on these abnormal sensor readings, the diagnosis concept identifies the cause or source of the fault and all components affected by the fault. This concept was implemented for diagnosis of aircraft propulsion and hydraulic subsystems in a computer program called Draphys (Diagnostic Reasoning About Physical Systems). Draphys is unique in two important ways. First, it uses models of both functional and physical relationships in the subsystems. Using both models enables the diagnostic reasoning to identify the fault propagation as the faulted system continues to operate, and to diagnose physical damage. Draphys also reasons about behavior of the faulted system over time, to eliminate possibilities as more information becomes available, and to update the system status as more components are affected by the fault. The crew interface research is examining display issues associated with presenting diagnosis information to the flight crew. One study examined issues for presenting system status information. One lesson learned from that study was that pilots found fault situations to be more complex if they involved multiple subsystems. Another was pilots could identify the faulted systems more quickly if the system status was presented in pictorial or text format. Another study is currently under way to examine pilot mental models of the aircraft subsystems and their use in diagnosis tasks. Future research plans include piloted simulation evaluation of the diagnosis decision aiding concepts and crew interface issues. Information is given in viewgraph form.

Enhancing an appointment diary on a pocket computer for use by people after brain injury.

PubMed

Wright, P; Rogers, N; Hall, C; Wilson, B; Evans, J; Emslie, H

2001-12-01

People with memory loss resulting from brain injury benefit from purpose-designed memory aids such as appointment diaries on pocket computers. The present study explores the effects of extending the range of memory aids and including games. For 2 months, 12 people who had sustained brain injury were loaned a pocket computer containing three purpose-designed memory aids: diary, notebook and to-do list. A month later they were given another computer with the same memory aids but a different method of text entry (physical keyboard or touch-screen keyboard). Machine order was counterbalanced across participants. Assessment was by interviews during the loan periods, rating scales, performance tests and computer log files. All participants could use the memory aids and ten people (83%) found them very useful. Correlations among the three memory aids were not significant, suggesting individual variation in how they were used. Games did not increase use of the memory aids, nor did loan of the preferred pocket computer (with physical keyboard). Significantly more diary entries were made by people who had previously used other memory aids, suggesting that a better understanding of how to use a range of memory aids could benefit some people with brain injury.

Assessment methodologies and statistical issues for computer-aided diagnosis of lung nodules in computed tomography: contemporary research topics relevant to the lung image database consortium.

PubMed

Dodd, Lori E; Wagner, Robert F; Armato, Samuel G; McNitt-Gray, Michael F; Beiden, Sergey; Chan, Heang-Ping; Gur, David; McLennan, Geoffrey; Metz, Charles E; Petrick, Nicholas; Sahiner, Berkman; Sayre, Jim

2004-04-01

Cancer of the lung and bronchus is the leading fatal malignancy in the United States. Five-year survival is low, but treatment of early stage disease considerably improves chances of survival. Advances in multidetector-row computed tomography technology provide detection of smaller lung nodules and offer a potentially effective screening tool. The large number of images per exam, however, requires considerable radiologist time for interpretation and is an impediment to clinical throughput. Thus, computer-aided diagnosis (CAD) methods are needed to assist radiologists with their decision making. To promote the development of CAD methods, the National Cancer Institute formed the Lung Image Database Consortium (LIDC). The LIDC is charged with developing the consensus and standards necessary to create an image database of multidetector-row computed tomography lung images as a resource for CAD researchers. To develop such a prospective database, its potential uses must be anticipated. The ultimate applications will influence the information that must be included along with the images, the relevant measures of algorithm performance, and the number of required images. In this article we outline assessment methodologies and statistical issues as they relate to several potential uses of the LIDC database. We review methods for performance assessment and discuss issues of defining "truth" as well as the complications that arise when truth information is not available. We also discuss issues about sizing and populating a database.

Prosthetically guided maxillofacial surgery: evaluation of the accuracy of a surgical guide and custom-made bone plate in oncology patients after mandibular reconstruction.

PubMed

Mazzoni, Simona; Marchetti, Claudio; Sgarzani, Rossella; Cipriani, Riccardo; Scotti, Roberto; Ciocca, Leonardo

2013-06-01

The aim of the present study was to evaluate the accuracy of prosthetically guided maxillofacial surgery in reconstructing the mandible with a free vascularized flap using custom-made bone plates and a surgical guide to cut the mandible and fibula. The surgical protocol was applied in a study group of seven consecutive mandibular-reconstructed patients who were compared with a control group treated using the standard preplating technique on stereolithographic models (indirect computer-aided design/computer-aided manufacturing method). The precision of both surgical techniques (prosthetically guided maxillofacial surgery and indirect computer-aided design/computer-aided manufacturing procedure) was evaluated by comparing preoperative and postoperative computed tomographic data and assessment of specific landmarks. With regard to midline deviation, no significant difference was documented between the test and control groups. With regard to mandibular angle shift, only one left angle shift on the lateral plane showed a statistically significant difference between the groups. With regard to angular deviation of the body axis, the data showed a significant difference in the arch deviation. All patients in the control group registered greater than 8 degrees of deviation, determining a facial contracture of the external profile at the lower margin of the mandible. With regard to condylar position, the postoperative condylar position was better in the test group than in the control group, although no significant difference was detected. The new protocol for mandibular reconstruction using computer-aided design/computer-aided manufacturing prosthetically guided maxillofacial surgery to construct custom-made guides and plates may represent a viable method of reproducing the patient's anatomical contour, giving the surgeon better procedural control and reducing procedure time. Therapeutic, III.

Artificial Intelligence and Computer Assisted Instruction. CITE Report No. 4.

ERIC Educational Resources Information Center

Elsom-Cook, Mark

The purpose of the paper is to outline some of the major ways in which artificial intelligence research and techniques can affect usage of computers in an educational environment. The role of artificial intelligence is defined, and the difference between Computer Aided Instruction (CAI) and Intelligent Computer Aided Instruction (ICAI) is…

Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network.

PubMed

Jiang, Jiewei; Liu, Xiyang; Zhang, Kai; Long, Erping; Wang, Liming; Li, Wangting; Liu, Lin; Wang, Shuai; Zhu, Mingmin; Cui, Jiangtao; Liu, Zhenzhen; Lin, Zhuoling; Li, Xiaoyan; Chen, Jingjing; Cao, Qianzhong; Li, Jing; Wu, Xiaohang; Wang, Dongni; Wang, Jinghui; Lin, Haotian

2017-11-21

Ocular images play an essential role in ophthalmological diagnoses. Having an imbalanced dataset is an inevitable issue in automated ocular diseases diagnosis; the scarcity of positive samples always tends to result in the misdiagnosis of severe patients during the classification task. Exploring an effective computer-aided diagnostic method to deal with imbalanced ophthalmological dataset is crucial. In this paper, we develop an effective cost-sensitive deep residual convolutional neural network (CS-ResCNN) classifier to diagnose ophthalmic diseases using retro-illumination images. First, the regions of interest (crystalline lens) are automatically identified via twice-applied Canny detection and Hough transformation. Then, the localized zones are fed into the CS-ResCNN to extract high-level features for subsequent use in automatic diagnosis. Second, the impacts of cost factors on the CS-ResCNN are further analyzed using a grid-search procedure to verify that our proposed system is robust and efficient. Qualitative analyses and quantitative experimental results demonstrate that our proposed method outperforms other conventional approaches and offers exceptional mean accuracy (92.24%), specificity (93.19%), sensitivity (89.66%) and AUC (97.11%) results. Moreover, the sensitivity of the CS-ResCNN is enhanced by over 13.6% compared to the native CNN method. Our study provides a practical strategy for addressing imbalanced ophthalmological datasets and has the potential to be applied to other medical images. The developed and deployed CS-ResCNN could serve as computer-aided diagnosis software for ophthalmologists in clinical application.

A deep-learning based automatic pulmonary nodule detection system

NASA Astrophysics Data System (ADS)

Zhao, Yiyuan; Zhao, Liang; Yan, Zhennan; Wolf, Matthias; Zhan, Yiqiang

2018-02-01

Lung cancer is the deadliest cancer worldwide. Early detection of lung cancer is a promising way to lower the risk of dying. Accurate pulmonary nodule detection in computed tomography (CT) images is crucial for early diagnosis of lung cancer. The development of computer-aided detection (CAD) system of pulmonary nodules contributes to making the CT analysis more accurate and with more efficiency. Recent studies from other groups have been focusing on lung cancer diagnosis CAD system by detecting medium to large nodules. However, to fully investigate the relevance between nodule features and cancer diagnosis, a CAD that is capable of detecting nodules with all sizes is needed. In this paper, we present a deep-learning based automatic all size pulmonary nodule detection system by cascading two artificial neural networks. We firstly use a U-net like 3D network to generate nodule candidates from CT images. Then, we use another 3D neural network to refine the locations of the nodule candidates generated from the previous subsystem. With the second sub-system, we bring the nodule candidates closer to the center of the ground truth nodule locations. We evaluate our system on a public CT dataset provided by the Lung Nodule Analysis (LUNA) 2016 grand challenge. The performance on the testing dataset shows that our system achieves 90% sensitivity with an average of 4 false positives per scan. This indicates that our system can be an aid for automatic nodule detection, which is beneficial for lung cancer diagnosis.

Variations in measured performance of CAD schemes due to database composition and scoring protocol

NASA Astrophysics Data System (ADS)

Nishikawa, Robert M.; Yarusso, Laura M.

1998-06-01

There is now a large effort towards developing computer- aided diagnosis (CAD) techniques. It is important to be able to compare performance of different approaches to be able to determine which ones are the most efficacious. There are currently a number of barriers preventing meaningful (statistical) comparisons, two of which are discussed in this paper: database composition and scoring protocol. We have examined how the choice of cases used to test a CAD scheme can affect its performance. We found that our computer scheme varied between a sensitivity of 100% to 77%, at a false-positive rate of 1.0 per image, with only 100% change in the composition of the database. To evaluate the performance of a CAD scheme the output of the computer must be graded. There are a number of different criteria that are being used by different investigators. We have found that for the same set of detection results, the measured sensitivity can be between 40 - 90% depending on the scoring methodology. Clearly consensus must be reached on these two issues in order for the field to make rapid progress. As it stands now, it is not possible to make meaningful comparisons of different techniques.

Computer-aided diagnosis workstation and teleradiology network system for chest diagnosis using the web medical image conference system with a new information security solution

NASA Astrophysics Data System (ADS)

Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Ohmatsu, Hironobu; Kaneko, Masahiro; Kakinuma, Ryutaro; Moriyama, Noriyuki

2010-03-01

Diagnostic MDCT imaging requires a considerable number of images to be read. Moreover, the doctor who diagnoses a medical image is insufficient in Japan. Because of such a background, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis. We also have developed the teleradiology network system by using web medical image conference system. In the teleradiology network system, the security of information network is very important subjects. Our teleradiology network system can perform Web medical image conference in the medical institutions of a remote place using the web medical image conference system. We completed the basic proof experiment of the web medical image conference system with information security solution. We can share the screen of web medical image conference system from two or more web conference terminals at the same time. An opinion can be exchanged mutually by using a camera and a microphone that are connected with the workstation that builds in some diagnostic assistance methods. Biometric face authentication used on site of teleradiology makes "Encryption of file" and "Success in login" effective. Our Privacy and information security technology of information security solution ensures compliance with Japanese regulations. As a result, patients' private information is protected. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new teleradiology network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our teleradiology network system can increase diagnostic speed, diagnostic accuracy and security improvement of medical information.

A Method for Extracting Suspected Parotid Lesions in CT Images using Feature-based Segmentation and Active Contours based on Stationary Wavelet Transform

NASA Astrophysics Data System (ADS)

Wu, T. Y.; Lin, S. F.

2013-10-01

Automatic suspected lesion extraction is an important application in computer-aided diagnosis (CAD). In this paper, we propose a method to automatically extract the suspected parotid regions for clinical evaluation in head and neck CT images. The suspected lesion tissues in low contrast tissue regions can be localized with feature-based segmentation (FBS) based on local texture features, and can be delineated with accuracy by modified active contour models (ACM). At first, stationary wavelet transform (SWT) is introduced. The derived wavelet coefficients are applied to derive the local features for FBS, and to generate enhanced energy maps for ACM computation. Geometric shape features (GSFs) are proposed to analyze each soft tissue region segmented by FBS; the regions with higher similarity GSFs with the lesions are extracted and the information is also applied as the initial conditions for fine delineation computation. Consequently, the suspected lesions can be automatically localized and accurately delineated for aiding clinical diagnosis. The performance of the proposed method is evaluated by comparing with the results outlined by clinical experts. The experiments on 20 pathological CT data sets show that the true-positive (TP) rate on recognizing parotid lesions is about 94%, and the dimension accuracy of delineation results can also approach over 93%.

A Health Insurance Portability and Accountability Act–Compliant Ocular Telehealth Network for the Remote Diagnosis and Management of Diabetic Retinopathy

PubMed Central

Li, Yaqin; Karnowski, Thomas P.; Tobin, Kenneth W.; Giancardo, Luca; Morris, Scott; Sparrow, Sylvia E.; Garg, Seema; Fox, Karen

2011-01-01

Abstract In this article, we present the design and implementation of a regional ocular telehealth network for remote assessment and management of diabetic retinopathy (DR), including the design requirements, network topology, protocol design, system work flow, graphics user interfaces, and performance evaluation. The Telemedical Retinal Image Analysis and Diagnosis Network is a computer-aided, image analysis telehealth paradigm for the diagnosis of DR and other retinal diseases using fundus images acquired from primary care end users delivering care to underserved patient populations in the mid-South and southeastern United States. PMID:21819244

Computer aided detection system for lung cancer using computer tomography scans

NASA Astrophysics Data System (ADS)

Mahesh, Shanthi; Rakesh, Spoorthi; Patil, Vidya C.

2018-04-01

Lung Cancer is a disease can be defined as uncontrolled cell growth in tissues of the lung. If we detect the Lung Cancer in its early stage, then that could be the key of its cure. In this work the non-invasive methods are studied for assisting in nodule detection. It supplies a Computer Aided Diagnosis System (CAD) for early detection of lung cancer nodules from the Computer Tomography (CT) images. CAD system is the one which helps to improve the diagnostic performance of radiologists in their image interpretations. The main aim of this technique is to develop a CAD system for finding the lung cancer using the lung CT images and classify the nodule as Benign or Malignant. For classifying cancer cells, SVM classifier is used. Here, image processing techniques have been used to de-noise, to enhance, for segmentation and edge detection of an image is used to extract the area, perimeter and shape of nodule. The core factors of this research are Image quality and accuracy.

Lung sound analysis for wheeze episode detection.

PubMed

Jain, Abhishek; Vepa, Jithendra

2008-01-01

Listening and interpreting lung sounds by a stethoscope had been an important component of screening and diagnosing lung diseases. However this practice has always been vulnerable to poor audibility, inter-observer variations (between different physicians) and poor reproducibility. Thus computerized analysis of lung sounds for objective diagnosis of lung diseases is seen as a probable aid. In this paper we aim at automatic analysis of lung sounds for wheeze episode detection and quantification. The proposed algorithm integrates and analyses the set of parameters based on ATS (American Thoracic Society) definition of wheezes. It is very robust, computationally simple and yielded sensitivity of 84% and specificity of 86%.

Analysis of adventitious lung sounds originating from pulmonary tuberculosis.

PubMed

Becker, K W; Scheffer, C; Blanckenberg, M M; Diacon, A H

2013-01-01

Tuberculosis is a common and potentially deadly infectious disease, usually affecting the respiratory system and causing the sound properties of symptomatic infected lungs to differ from non-infected lungs. Auscultation is often ruled out as a reliable diagnostic technique for TB due to the random distribution of the infection and the varying severity of damage to the lungs. However, advancements in signal processing techniques for respiratory sounds can improve the potential of auscultation far beyond the capabilities of the conventional mechanical stethoscope. Though computer-based signal analysis of respiratory sounds has produced a significant body of research, there have not been any recent investigations into the computer-aided analysis of lung sounds associated with pulmonary Tuberculosis (TB), despite the severity of the disease in many countries. In this paper, respiratory sounds were recorded from 14 locations around the posterior and anterior chest walls of healthy volunteers and patients infected with pulmonary TB. The most significant signal features in both the time and frequency domains associated with the presence of TB, were identified by using the statistical overlap factor (SOF). These features were then employed to train a neural network to automatically classify the auscultation recordings into their respective healthy or TB-origin categories. The neural network yielded a diagnostic accuracy of 73%, but it is believed that automated filtering of the noise in the clinics, more training samples and perhaps other signal processing methods can improve the results of future studies. This work demonstrates the potential of computer-aided auscultation as an aid for the diagnosis and treatment of TB.

Computed tomography-assisted laparoscopic removal of intraabdominally migrated levonorgestrel-releasing intrauterine systems.

PubMed

Mahmoud, Mohamad S; Merhi, Zaher O

2010-04-01

To report three cases of migrated levonorgestrel intrauterine device (LNG-IUS) into the pelvic/abdominal cavity removed laparoscopically with the aid of preoperative computed tomography (CT) scan imaging. Three patients presenting with a missing LNG-IUS on examination and pelvic ultrasound are presented. A preoperative CT scan was performed, what helped in a successful removal of the LNG-IUS. The patients were discharged home the same day of the procedure. Our cases reinforce, besides the diagnosis of a migrated LNG-IUS by ultrasound, the fact that preoperative CT scan imaging assists in the diagnosis of the precise location of a migrated LNG-IUS into the pelvic/abdominal cavity and helps the physician in the prediction of the difficulty of the laparoscopic removal.

Automatic detection of blood vessels in retinal images for diabetic retinopathy diagnosis.

PubMed

Raja, D Siva Sundhara; Vasuki, S

2015-01-01

Diabetic retinopathy (DR) is a leading cause of vision loss in diabetic patients. DR is mainly caused due to the damage of retinal blood vessels in the diabetic patients. It is essential to detect and segment the retinal blood vessels for DR detection and diagnosis, which prevents earlier vision loss in diabetic patients. The computer aided automatic detection and segmentation of blood vessels through the elimination of optic disc (OD) region in retina are proposed in this paper. The OD region is segmented using anisotropic diffusion filter and subsequentially the retinal blood vessels are detected using mathematical binary morphological operations. The proposed methodology is tested on two different publicly available datasets and achieved 93.99% sensitivity, 98.37% specificity, 98.08% accuracy in DRIVE dataset and 93.6% sensitivity, 98.96% specificity, and 95.94% accuracy in STARE dataset, respectively.

Nurses' Attitudes toward Gay and Hemophiliac Patients with AIDS.

ERIC Educational Resources Information Center

Strasser, Judith A.; Damrosch, Shirley

A sample of nurses (N=183) enrolled in a School of Nursing's master degree program was randomly assigned to read one of six vignettes about a patient who differed only in terms of diagnosis and lifestyle. Possible diagnoses were Acquired Immune Deficiency Syndrome (AIDS), AIDS acquired by a hemophiliac through blood therapy, and leukemia; possible…

Adhesive Bonding to Computer-aided Design/ Computer-aided Manufacturing Esthetic Dental Materials: An Overview.

PubMed

Awad, Mohamed Moustafa; Alqahtani, H; Al-Mudahi, A; Murayshed, M S; Alrahlah, A; Bhandi, Shilpa H

2017-07-01

To review the adhesive bonding to different computer-aided design/computer-aided manufacturing (CAD/CAM) esthetic restorative materials. The use of CAD/CAM esthetic restorative materials has gained popularity in recent years. Several CAD/ CAM esthetic restorative materials are commercially available. Adhesive bonding is a major determinant of success of CAD/ CAM restorations. Review result: An account of the currently available bonding strategies are discussed with their rationale in various CAD/ CAM materials. Different surface treatment methods as well as adhesion promoters can be used to achieve reliable bonding of CAD/CAM restorative materials. Selection of bonding strategy to such material is determined based on its composition. Further evidence is required to evaluate the effect of new surface treatment methods, such as nonthermal atmospheric plasma and self-etching ceramic primer on bonding to different dental ceramics. An understanding of the currently available bonding strategies to CA/CAM materials can help the clinician to select the most indicated system for each category of materials.

Image Processing and Computer Aided Diagnosis in Computed Tomography of the Breast

DTIC Science & Technology

2007-10-01

Brian Harrawood, Ronald Pedroni, Alexander Crowell, Robert Macri, Mathew Kiser, Richard Walter ,Werner 111 Tornow , Neutron Stimulated Emission...1( kkkk k nn kkk n k n k w PBbywbb σσσ += +−⋅+=+ , (2) MLE estimate is known to increase high frequency image noise. To overcome this, some...contrast to noise ratio results for the three images shown in Figure 5. With grid w /o grid w /o grid; scatter reduction RSF 11% 45% 10% CNR 7.04 6.99

Automated Quantification of Pneumothorax in CT

PubMed Central

Do, Synho; Salvaggio, Kristen; Gupta, Supriya; Kalra, Mannudeep; Ali, Nabeel U.; Pien, Homer

2012-01-01

An automated, computer-aided diagnosis (CAD) algorithm for the quantification of pneumothoraces from Multidetector Computed Tomography (MDCT) images has been developed. Algorithm performance was evaluated through comparison to manual segmentation by expert radiologists. A combination of two-dimensional and three-dimensional processing techniques was incorporated to reduce required processing time by two-thirds (as compared to similar techniques). Volumetric measurements on relative pneumothorax size were obtained and the overall performance of the automated method shows an average error of just below 1%. PMID:23082091

Correlative Feature Analysis for Multimodality Breast CAD

DTIC Science & Technology

2009-09-01

Imaging 20, 1275–1284 2001. 22V. Caselles, R . Kimmel, and G. Sapiro, “Geodesic active contours,” Int. J. Comput. Vis. 22, 61–79 1997. 23R. Malladi , J...A. R . Jamieson, C. A. Sennett, and S. A. Jensen, “Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset...Academic Radiology, 15, 1437-1445 (2008). Conference Proceeding Papers [1] Y. Yuan, M. L. Giger, K. Suzuki, H. Li, and A. R . Jamieson, “A

Texture classification of lung computed tomography images

NASA Astrophysics Data System (ADS)

Pheng, Hang See; Shamsuddin, Siti M.

2013-03-01

Current development of algorithms in computer-aided diagnosis (CAD) scheme is growing rapidly to assist the radiologist in medical image interpretation. Texture analysis of computed tomography (CT) scans is one of important preliminary stage in the computerized detection system and classification for lung cancer. Among different types of images features analysis, Haralick texture with variety of statistical measures has been used widely in image texture description. The extraction of texture feature values is essential to be used by a CAD especially in classification of the normal and abnormal tissue on the cross sectional CT images. This paper aims to compare experimental results using texture extraction and different machine leaning methods in the classification normal and abnormal tissues through lung CT images. The machine learning methods involve in this assessment are Artificial Immune Recognition System (AIRS), Naive Bayes, Decision Tree (J48) and Backpropagation Neural Network. AIRS is found to provide high accuracy (99.2%) and sensitivity (98.0%) in the assessment. For experiments and testing purpose, publicly available datasets in the Reference Image Database to Evaluate Therapy Response (RIDER) are used as study cases.

Estimation of risk of cancers before occurrence of acquired immunodeficiency syndrome in persons infected with human immunodeficiency virus.

PubMed

Li, Yueming; Law, Matthew; McDonald, Ann; Correll, Patty; Kaldor, John M; Grulich, Andrew E

2002-01-15

There is methodological debate as to whether cohorts defined by acquired immunodeficiency syndrome (AIDS) diagnosis can be used to estimate risks of cancer in persons with human immunodeficiency virus (HIV) before AIDS. The authors compared risks of non-AIDS-defining cancers before AIDS in persons with HIV using a cohort based on AIDS diagnosis and a second cohort based on HIV diagnosis. National population-based registries of AIDS and HIV diagnoses to August 1999 were matched separately with the National Cancer Registry in Australia. Four analyses were performed. In analysis 1, follow-up was from 5 years before AIDS registration in 8,118 persons with AIDS. Analysis 2 was similar but adjusted expected numbers of cancers for decreased survival. Analysis 3 was based on 7,061 persons registered with HIV, with follow-up from the reported date of diagnosis. Analysis 4 was based on 2,112 AIDS cases previously reported with HIV, with follow-up from 5 years before AIDS diagnosis. In all analyses, follow-up ended at cancer diagnosis, death, 6 months before AIDS, or the end of available cancer data, whichever occurred first. For 10 types of cancer there were at least three cases in any one of the analyses. For these cancers there was no systematic pattern such that one analysis produced consistently higher or lower estimates than the others. These analyses suggest that cancer risk in persons with HIV before AIDS diagnosis may be estimated reliably based on cancer experience 5 years before AIDS.

An automatically generated texture-based atlas of the lungs

NASA Astrophysics Data System (ADS)

Dicente Cid, Yashin; Puonti, Oula; Platon, Alexandra; Van Leemput, Koen; Müller, Henning; Poletti, Pierre-Alexandre

2018-02-01

Many pulmonary diseases can be characterized by visual abnormalities on lung CT scans. Some diseases manifest similar defects but require completely different treatments, as is the case for Pulmonary Hypertension (PH) and Pulmonary Embolism (PE): both present hypo- and hyper-perfused regions but with different distribution across the lung and require different treatment protocols. Finding these distributions by visual inspection is not trivial even for trained radiologists who currently use invasive catheterism to diagnose PH. A Computer-Aided Diagnosis (CAD) tool that could facilitate the non-invasive diagnosis of these diseases can benefit both the radiologists and the patients. Most of the visual differences in the parenchyma can be characterized using texture descriptors. Current CAD systems often use texture information but the texture is either computed in a patch-based fashion, or based on an anatomical division of the lung. The difficulty of precisely finding these divisions in abnormal lungs calls for new tools for obtaining new meaningful divisions of the lungs. In this paper we present a method for unsupervised segmentation of lung CT scans into subregions that are similar in terms of texture and spatial proximity. To this extent, we combine a previously validated Riesz-wavelet texture descriptor with a well-known superpixel segmentation approach that we extend to 3D. We demonstrate the feasibility and accuracy of our approach on a simulated texture dataset, and show preliminary results for CT scans of the lung comparing subjects suffering either from PH or PE. The resulting texture-based atlas of individual lungs can potentially help physicians in diagnosis or be used for studying common texture distributions related to other diseases.

Characteristics of lip-mouth region in smiling position from 80 persons with acceptable faces and individual normal occlusions.

PubMed

Zhang, Jiangheng; Chen, Yangxi; Zhou, Xiukun

2002-09-01

The characteristics of lip-mouth region including the soft and hard tissues in smiling position with frontal fixed position photographic computer-aided analysis were studied. The subjects were 80 persons (40 male and 40 females, age range: 17 to approximately 25 years) with acceptable faces and individual normal occlusions. The subjects were asked to take maximum smiling position to accept photographic measurement with computer-aided analysis. The maximum smile line could be divided into 3 categories: low smile line (16.25%), average smile line (68.75%), and high smile line (15%). The method adopting maximum smiling position to study the lip-month region is reproducible and comparable. This study would be helpful to provide a quantitative reference for clinical investigation, diagnosis, treatment and efficacy appraisal.

Program Helps Generate And Manage Graphics

NASA Technical Reports Server (NTRS)

Truong, L. V.

1994-01-01

Living Color Frame Maker (LCFM) computer program generates computer-graphics frames. Graphical frames saved as text files, in readable and disclosed format, easily retrieved and manipulated by user programs for wide range of real-time visual information applications. LCFM implemented in frame-based expert system for visual aids in management of systems. Monitoring, diagnosis, and/or control, diagrams of circuits or systems brought to "life" by use of designated video colors and intensities to symbolize status of hardware components (via real-time feedback from sensors). Status of systems can be displayed. Written in C++ using Borland C++ 2.0 compiler for IBM PC-series computers and compatible computers running MS-DOS.

[The new concept of osteoporosis. Early diagnosis, prevention and therapy are possible today].

PubMed

Hesch, R D; Harms, H; Rittinghaus, E F; Brabant, G

1990-04-15

A paradigma of osteoporosis pathology is discussed, at the center of which is the hormone-related disturbance of the osteoblast/osteoclast functional unit. A liberal replacement of estrogen-gestagen in post-menopausal women is advocated. Early diagnosis with the aid of quantitative computed tomography makes it possible to establish the indication for timely hormonal treatment in the future, which can result in a measureable increase in bone mass. Late therapy, that is, treatment initiated after the occurrence of fractures, has proven largely ineffective.

Automatic detection of pulmonary nodules at spiral CT: first clinical experience with a computer-aided diagnosis system

NASA Astrophysics Data System (ADS)

Wormanns, Dag; Fiebich, Martin; Wietholt, Christian; Diederich, Stefan; Heindel, Walter

2000-06-01

We evaluated the practical application of a Computer-Aided Diagnosis (CAD) system for viewing spiral computed tomography (CT) of the chest low-dose screening examinations which includes an automatic detection of pulmonary nodules. A UNIX- based CAD system was developed including a detection algorithm for pulmonary nodules and a user interface providing an original axial image, the same image with nodules highlighted, a thin-slab MIP, and a cine mode. As yet, 26 CT examinations with 1625 images were reviewed in a clinical setting and reported by an experienced radiologist using both the CAD system and hardcopies. The CT studies exhibited 19 nodules found on the hardcopies in consensus reporting of 2 experienced radiologists. Viewing with the CAD system was more time consuming than using hardcopies (4.16 vs. 2.92 min) due to analyzing MIP and cine mode. The algorithm detected 49% (18/37) pulmonary nodules larger than 5 mm and 30% (21/70) of all nodules. It produced an average of 6.3 false positive findings per CT study. Most of the missed nodules were adjacent to the pleura. However, the program detected 6 nodules missed by the radiologists. Automatic nodule detection increases the radiologists's awareness of pulmonary lesions. Simultaneous display of axial image and thin-slab MIP makes the radiologist more confident in diagnosis of smaller pulmonary nodules. The CAD system improves the detection of pulmonary nodules at spiral CT. Lack of sensitivity and specificity is still an issue to be addressed but does not prevent practical use.

Computer-Based Technologies in Dentistry: Types and Applications

PubMed Central

Albuha Al-Mussawi, Raja’a M.; Farid, Farzaneh

2016-01-01

During dental education, dental students learn how to examine patients, make diagnosis, plan treatment and perform dental procedures perfectly and efficiently. However, progresses in computer-based technologies including virtual reality (VR) simulators, augmented reality (AR) and computer aided design/computer aided manufacturing (CAD/CAM) systems have resulted in new modalities for instruction and practice of dentistry. Virtual reality dental simulators enable repeated, objective and assessable practice in various controlled situations. Superimposition of three-dimensional (3D) virtual images on actual images in AR allows surgeons to simultaneously visualize the surgical site and superimpose informative 3D images of invisible regions on the surgical site to serve as a guide. The use of CAD/CAM systems for designing and manufacturing of dental appliances and prostheses has been well established. This article reviews computer-based technologies, their application in dentistry and their potentials and limitations in promoting dental education, training and practice. Practitioners will be able to choose from a broader spectrum of options in their field of practice by becoming familiar with new modalities of training and practice. PMID:28392819

Computer-Based Technologies in Dentistry: Types and Applications.

PubMed

Albuha Al-Mussawi, Raja'a M; Farid, Farzaneh

2016-06-01

During dental education, dental students learn how to examine patients, make diagnosis, plan treatment and perform dental procedures perfectly and efficiently. However, progresses in computer-based technologies including virtual reality (VR) simulators, augmented reality (AR) and computer aided design/computer aided manufacturing (CAD/CAM) systems have resulted in new modalities for instruction and practice of dentistry. Virtual reality dental simulators enable repeated, objective and assessable practice in various controlled situations. Superimposition of three-dimensional (3D) virtual images on actual images in AR allows surgeons to simultaneously visualize the surgical site and superimpose informative 3D images of invisible regions on the surgical site to serve as a guide. The use of CAD/CAM systems for designing and manufacturing of dental appliances and prostheses has been well established. This article reviews computer-based technologies, their application in dentistry and their potentials and limitations in promoting dental education, training and practice. Practitioners will be able to choose from a broader spectrum of options in their field of practice by becoming familiar with new modalities of training and practice.

A novel method to acquire 3D data from serial 2D images of a dental cast

NASA Astrophysics Data System (ADS)

Yi, Yaxing; Li, Zhongke; Chen, Qi; Shao, Jun; Li, Xinshe; Liu, Zhiqin

2007-05-01

This paper introduced a newly developed method to acquire three-dimensional data from serial two-dimensional images of a dental cast. The system consists of a computer and a set of data acquiring device. The data acquiring device is used to take serial pictures of the a dental cast; an artificial neural network works to translate two-dimensional pictures to three-dimensional data; then three-dimensional image can reconstruct by the computer. The three-dimensional data acquiring of dental casts is the foundation of computer-aided diagnosis and treatment planning in orthodontics.

Ultrasound based computer-aided-diagnosis of kidneys for pediatric hydronephrosis

NASA Astrophysics Data System (ADS)

Cerrolaza, Juan J.; Peters, Craig A.; Martin, Aaron D.; Myers, Emmarie; Safdar, Nabile; Linguraru, Marius G.

2014-03-01

Ultrasound is the mainstay of imaging for pediatric hydronephrosis, though its potential as diagnostic tool is limited by its subjective assessment, and lack of correlation with renal function. Therefore, all cases showing signs of hydronephrosis undergo further invasive studies, like diuretic renogram, in order to assess the actual renal function. Under the hypothesis that renal morphology is correlated with renal function, a new ultrasound based computer-aided diagnosis (CAD) tool for pediatric hydronephrosis is presented. From 2D ultrasound, a novel set of morphological features of the renal collecting systems and the parenchyma, is automatically extracted using image analysis techniques. From the original set of features, including size, geometric and curvature descriptors, a subset of ten features are selected as predictive variables, combining a feature selection technique and area under the curve filtering. Using the washout half time (T1/2) as indicative of renal obstruction, two groups are defined. Those cases whose T1/2 is above 30 minutes are considered to be severe, while the rest would be in the safety zone, where diuretic renography could be avoided. Two different classification techniques are evaluated (logistic regression, and support vector machines). Adjusting the probability decision thresholds to operate at the point of maximum sensitivity, i.e., preventing any severe case be misclassified, specificities of 53%, and 75% are achieved, for the logistic regression and the support vector machine classifier, respectively. The proposed CAD system allows to establish a link between non-invasive non-ionizing imaging techniques and renal function, limiting the need for invasive and ionizing diuretic renography.

A new computer aided diagnosis system for evaluation of chronic liver disease with ultrasound shear wave elastography imaging.

PubMed

Gatos, Ilias; Tsantis, Stavros; Spiliopoulos, Stavros; Karnabatidis, Dimitris; Theotokas, Ioannis; Zoumpoulis, Pavlos; Loupas, Thanasis; Hazle, John D; Kagadis, George C

2016-03-01

Classify chronic liver disease (CLD) from ultrasound shear-wave elastography (SWE) imaging by means of a computer aided diagnosis (CAD) system. The proposed algorithm employs an inverse mapping technique (red-green-blue to stiffness) to quantify 85 SWE images (54 healthy and 31 with CLD). Texture analysis is then applied involving the automatic calculation of 330 first and second order textural features from every transformed stiffness value map to determine functional features that characterize liver elasticity and describe liver condition for all available stages. Consequently, a stepwise regression analysis feature selection procedure is utilized toward a reduced feature subset that is fed into the support vector machines (SVMs) classification algorithm in the design of the CAD system. With regard to the mapping procedure accuracy, the stiffness map values had an average difference of 0.01 ± 0.001 kPa compared to the quantification results derived from the color-box provided by the built-in software of the ultrasound system. Highest classification accuracy from the SVM model was 87.0% with sensitivity and specificity values of 83.3% and 89.1%, respectively. Receiver operating characteristic curves analysis gave an area under the curve value of 0.85 with [0.77-0.89] confidence interval. The proposed CAD system employing color to stiffness mapping and classification algorithms offered superior results, comparing the already published clinical studies. It could prove to be of value to physicians improving the diagnostic accuracy of CLD and can be employed as a second opinion tool for avoiding unnecessary invasive procedures.

Automated diagnosis of autism: in search of a mathematical marker.

PubMed

Bhat, Shreya; Acharya, U Rajendra; Adeli, Hojjat; Bairy, G Muralidhar; Adeli, Amir

2014-01-01

Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. An early detection of the abnormality, due to irregular processing in the brain, can be achieved using electroencephalograms (EEG). The variations in the EEG signals cannot be deciphered by mere visual inspection. Computer-aided diagnostic tools can be used to recognize the subtle and invisible information present in the irregular EEG pattern and diagnose autism. This paper presents a state-of-the-art review of automated EEG-based diagnosis of autism. Various time domain, frequency domain, time-frequency domain, and nonlinear dynamics for the analysis of autistic EEG signals are described briefly. A focus of the review is the use of nonlinear dynamics and chaos theory to discover the mathematical biomarkers for the diagnosis of the autism analogous to biological markers. A combination of the time-frequency and nonlinear dynamic analysis is the most effective approach to characterize the nonstationary and chaotic physiological signals for the automated EEG-based diagnosis of autism spectrum disorder (ASD). The features extracted using these nonlinear methods can be used as mathematical markers to detect the early stage of autism and aid the clinicians in their diagnosis. This will expedite the administration of appropriate therapies to treat the disorder.

Computer aided segmentation of kidneys using locally shape constrained deformable models on CT images

NASA Astrophysics Data System (ADS)

Erdt, Marius; Sakas, Georgios

2010-03-01

This work presents a novel approach for model based segmentation of the kidney in images acquired by Computed Tomography (CT). The developed computer aided segmentation system is expected to support computer aided diagnosis and operation planning. We have developed a deformable model based approach based on local shape constraints that prevents the model from deforming into neighboring structures while allowing the global shape to adapt freely to the data. Those local constraints are derived from the anatomical structure of the kidney and the presence and appearance of neighboring organs. The adaptation process is guided by a rule-based deformation logic in order to improve the robustness of the segmentation in areas of diffuse organ boundaries. Our work flow consists of two steps: 1.) a user guided positioning and 2.) an automatic model adaptation using affine and free form deformation in order to robustly extract the kidney. In cases which show pronounced pathologies, the system also offers real time mesh editing tools for a quick refinement of the segmentation result. Evaluation results based on 30 clinical cases using CT data sets show an average dice correlation coefficient of 93% compared to the ground truth. The results are therefore in most cases comparable to manual delineation. Computation times of the automatic adaptation step are lower than 6 seconds which makes the proposed system suitable for an application in clinical practice.

[Usefulness of imaging examinations in preoperative diagnosis of acute appendicitis].

PubMed

Nitoń, Tomasz; Górecka-Nitoń, Aleksandra

2014-01-01

Acute appendicitis (AA) is the cause one of most operations perform in department of general surgery on emergency ward. Frequency of acute appendicitis range from 6-8% of population. Clinical presentation is frequently unspecified and despite common occurence leads to many difficulties in diagnosis. Diagnosis of acute appendicitis includes clinical examination, laboratory tests, diagnostic scoring systems, computer programs as physisian aids and imaging examinations. About 30-45% patients suspected of acute appendicitis have untypical clinical presentation and here use of US or CT is very helpful. Longstanding use of US resulted in high AA evaluation accuracy with high sensitivity (75-90%) and specificity (84-100%). CT demonstrates above 95% ratio of correct diagnoses, reduces negative appendectomy rates and perforation rates as well as unnecessary observations. CT sensitivity and specificity CT is estimated between 83-100% among different authors. Expedited AA diagnosis, surgery and reduced hospitalization time are possible advantages of imaging tests. Additionally these tests can detect alternative deseases imitating acute appnedicitis. Use of imaging tests especially CT is beneficial in fertile women because of frequent genito-urinary disorders leading to the most diagnostic errors. However thera are contraindications in use of CT, for example it can not be performed in early pregnancy etc...

Computer-aided detection of renal calculi from noncontrast CT images using TV-flow and MSER features.

PubMed

Liu, Jianfei; Wang, Shijun; Turkbey, Evrim B; Linguraru, Marius George; Yao, Jianhua; Summers, Ronald M

2015-01-01

Renal calculi are common extracolonic incidental findings on computed tomographic colonography (CTC). This work aims to develop a fully automated computer-aided diagnosis system to accurately detect renal calculi on CTC images. The authors developed a total variation (TV) flow method to reduce image noise within the kidneys while maintaining the characteristic appearance of renal calculi. Maximally stable extremal region (MSER) features were then calculated to robustly identify calculi candidates. Finally, the authors computed texture and shape features that were imported to support vector machines for calculus classification. The method was validated on a dataset of 192 patients and compared to a baseline approach that detects calculi by thresholding. The authors also compared their method with the detection approaches using anisotropic diffusion and nonsmoothing. At a false positive rate of 8 per patient, the sensitivities of the new method and the baseline thresholding approach were 69% and 35% (p < 1e - 3) on all calculi from 1 to 433 mm(3) in the testing dataset. The sensitivities of the detection methods using anisotropic diffusion and nonsmoothing were 36% and 0%, respectively. The sensitivity of the new method increased to 90% if only larger and more clinically relevant calculi were considered. Experimental results demonstrated that TV-flow and MSER features are efficient means to robustly and accurately detect renal calculi on low-dose, high noise CTC images. Thus, the proposed method can potentially improve diagnosis.

Computer-aided detection of renal calculi from noncontrast CT images using TV-flow and MSER features

PubMed Central

Liu, Jianfei; Wang, Shijun; Turkbey, Evrim B.; Linguraru, Marius George; Yao, Jianhua; Summers, Ronald M.

2015-01-01

Purpose: Renal calculi are common extracolonic incidental findings on computed tomographic colonography (CTC). This work aims to develop a fully automated computer-aided diagnosis system to accurately detect renal calculi on CTC images. Methods: The authors developed a total variation (TV) flow method to reduce image noise within the kidneys while maintaining the characteristic appearance of renal calculi. Maximally stable extremal region (MSER) features were then calculated to robustly identify calculi candidates. Finally, the authors computed texture and shape features that were imported to support vector machines for calculus classification. The method was validated on a dataset of 192 patients and compared to a baseline approach that detects calculi by thresholding. The authors also compared their method with the detection approaches using anisotropic diffusion and nonsmoothing. Results: At a false positive rate of 8 per patient, the sensitivities of the new method and the baseline thresholding approach were 69% and 35% (p < 1e − 3) on all calculi from 1 to 433 mm3 in the testing dataset. The sensitivities of the detection methods using anisotropic diffusion and nonsmoothing were 36% and 0%, respectively. The sensitivity of the new method increased to 90% if only larger and more clinically relevant calculi were considered. Conclusions: Experimental results demonstrated that TV-flow and MSER features are efficient means to robustly and accurately detect renal calculi on low-dose, high noise CTC images. Thus, the proposed method can potentially improve diagnosis. PMID:25563255

Computer-aided detection of basal cell carcinoma through blood content analysis in dermoscopy images

NASA Astrophysics Data System (ADS)

Kharazmi, Pegah; Kalia, Sunil; Lui, Harvey; Wang, Z. Jane; Lee, Tim K.

2018-02-01

Basal cell carcinoma (BCC) is the most common type of skin cancer, which is highly damaging to the skin at its advanced stages and causes huge costs on the healthcare system. However, most types of BCC are easily curable if detected at early stage. Due to limited access to dermatologists and expert physicians, non-invasive computer-aided diagnosis is a viable option for skin cancer screening. A clinical biomarker of cancerous tumors is increased vascularization and excess blood flow. In this paper, we present a computer-aided technique to differentiate cancerous skin tumors from benign lesions based on vascular characteristics of the lesions. Dermoscopy image of the lesion is first decomposed using independent component analysis of the RGB channels to derive melanin and hemoglobin maps. A novel set of clinically inspired features and ratiometric measurements are then extracted from each map to characterize the vascular properties and blood content of the lesion. The feature set is then fed into a random forest classifier. Over a dataset of 664 skin lesions, the proposed method achieved an area under ROC curve of 0.832 in a 10-fold cross validation for differentiating basal cell carcinomas from benign lesions.

Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications.

PubMed

Liu, Lizhi; Tian, Zhiqiang; Zhang, Zhenfeng; Fei, Baowei

2016-08-01

One in six men will develop prostate cancer in his lifetime. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs. Recently, imaging of prostate cancer has greatly advanced since the introduction of multiparametric magnetic resonance imaging (mp-MRI). Mp-MRI consists of T2-weighted sequences combined with functional sequences including dynamic contrast-enhanced MRI, diffusion-weighted MRI, and magnetic resonance spectroscopy imaging. Because of the big data and variations in imaging sequences, detection can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. To improve quantitative assessment of the disease, various computer-aided detection systems have been designed to help radiologists in their clinical practice. This review paper presents an overview of literatures on computer-aided detection of prostate cancer with mp-MRI, which include the technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Hepatitis Diagnosis Using Facial Color Image

NASA Astrophysics Data System (ADS)

Liu, Mingjia; Guo, Zhenhua

Facial color diagnosis is an important diagnostic method in traditional Chinese medicine (TCM). However, due to its qualitative, subjective and experi-ence-based nature, traditional facial color diagnosis has a very limited application in clinical medicine. To circumvent the subjective and qualitative problems of facial color diagnosis of Traditional Chinese Medicine, in this paper, we present a novel computer aided facial color diagnosis method (CAFCDM). The method has three parts: face Image Database, Image Preprocessing Module and Diagnosis Engine. Face Image Database is carried out on a group of 116 patients affected by 2 kinds of liver diseases and 29 healthy volunteers. The quantitative color feature is extracted from facial images by using popular digital image processing techni-ques. Then, KNN classifier is employed to model the relationship between the quantitative color feature and diseases. The results show that the method can properly identify three groups: healthy, severe hepatitis with jaundice and severe hepatitis without jaundice with accuracy higher than 73%.

Improved biliary detection and diagnosis through intelligent machine analysis.

PubMed

Logeswaran, Rajasvaran

2012-09-01

This paper reports on work undertaken to improve automated detection of bile ducts in magnetic resonance cholangiopancreatography (MRCP) images, with the objective of conducting preliminary classification of the images for diagnosis. The proposed I-BDeDIMA (Improved Biliary Detection and Diagnosis through Intelligent Machine Analysis) scheme is a multi-stage framework consisting of successive phases of image normalization, denoising, structure identification, object labeling, feature selection and disease classification. A combination of multiresolution wavelet, dynamic intensity thresholding, segment-based region growing, region elimination, statistical analysis and neural networks, is used in this framework to achieve good structure detection and preliminary diagnosis. Tests conducted on over 200 clinical images with known diagnosis have shown promising results of over 90% accuracy. The scheme outperforms related work in the literature, making it a viable framework for computer-aided diagnosis of biliary diseases. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

The use of infrared thermal imaging in the diagnosis of deep vein thrombosis

NASA Astrophysics Data System (ADS)

Kacmaz, Seydi; Ercelebi, Ergun; Zengin, Suat; Cindoruk, Sener

2017-11-01

The diagnosis of Deep Vein Thrombosis is of vital importance, especially in emergency situations where there is a lack of time and the patient's condition is critical. Late diagnosis causes cost increase, long waiting time, and improper treatment. Today, with the rapidly developing technology, the cost of thermal cameras is gradually decreasing day by day. Studies have shown that many diseases are associated with heat. As a result, infrared images are thought to be a tool for diagnosing various diseases. In this study, it has been shown that infrared thermal imaging can be used as a pre-screening test in the diagnosis of Deep Vein Thrombosis with the developed computer aided software. In addition, a sample combination is shown for applications that utilize emergency services to perform diagnosis and treatment of Deep Vein Thrombosis as soon as possible.

[123I]N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane single-photon emission computed tomography brain imaging in the diagnosis of dementia with Lewy bodies.

PubMed

Walker, Zuzana; Cummings, Jeffrey L

2012-01-01

Early, accurate diagnosis of dementia with Lewy bodies (DLB), in particular its differentiation from Alzheimer's disease, is important for optimal management, providing patients/carers with information about the likely symptomatology and illness course, allowing initiation of effective pharmacotherapy, and avoiding the consequences of neuroleptic sensitivity. Clinical diagnosis of DLB has high specificity but low sensitivity. Clinical trials of [(123)I]N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane single-photon emission computed tomography ([(123)I]FP-CIT SPECT) indicate high positive and negative percent agreement with reference to clinical diagnosis, and high sensitivity and specificity in patients with neuropathologically confirmed diagnoses of DLB. An abnormal [(123)I]FP-CIT SPECT image in patients fulfilling criteria for possible DLB advances the certainty of a diagnosis to probable DLB. [(123)I]FP-CIT SPECT, by identifying the striatal dopaminergic deficit, can be a valuable diagnostic aid and can provide support to a clinical diagnosis of DLB in patients with dementia. The technique is likely to be of particular utility in patients with dementia with an uncertain diagnosis. Copyright © 2012 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Visualization of suspicious lesions in breast MRI based on intelligent neural systems

NASA Astrophysics Data System (ADS)

Twellmann, Thorsten; Lange, Oliver; Nattkemper, Tim Wilhelm; Meyer-Bäse, Anke

2006-05-01

Intelligent medical systems based on supervised and unsupervised artificial neural networks are applied to the automatic visualization and classification of suspicious lesions in breast MRI. These systems represent an important component of future sophisticated computer-aided diagnosis systems and enable the extraction of spatial and temporal features of dynamic MRI data stemming from patients with confirmed lesion diagnosis. By taking into account the heterogenity of the cancerous tissue, these techniques reveal the malignant, benign and normal kinetic signals and and provide a regional subclassification of pathological breast tissue. Intelligent medical systems are expected to have substantial implications in healthcare politics by contributing to the diagnosis of indeterminate breast lesions by non-invasive imaging.

Study of Aided Diagnosis of Hepatic Carcinoma Based on Artificial Neural Network Combined with Tumor Marker Group

NASA Astrophysics Data System (ADS)

Tan, Shanjuan; Feng, Feifei; Wu, Yongjun; Wu, Yiming

To develop a computer-aided diagnostic scheme by using an artificial neural network (ANN) combined with tumor markers for diagnosis of hepatic carcinoma (HCC) as a clinical assistant method. 140 serum samples (50 malignant, 40 benign and 50 normal) were analyzed for α-fetoprotein (AFP), carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), sialic acid (SA) and calcium (Ca). The five tumor marker values were then used as ANN inputs data. The result of ANN was compared with that of discriminant analysis by receiver operating characteristic (ROC) curve (AUC) analysis. The diagnostic accuracy of ANN and discriminant analysis among all samples of the test group was 95.5% and 79.3%, respectively. Analysis of multiple tumor markers based on ANN may be a better choice than the traditional statistical methods for differentiating HCC from benign or normal.

Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer

PubMed Central

Vandenberghe, Michel E.; Scott, Marietta L. J.; Scorer, Paul W.; Söderberg, Magnus; Balcerzak, Denis; Barker, Craig

2017-01-01

Tissue biomarker scoring by pathologists is central to defining the appropriate therapy for patients with cancer. Yet, inter-pathologist variability in the interpretation of ambiguous cases can affect diagnostic accuracy. Modern artificial intelligence methods such as deep learning have the potential to supplement pathologist expertise to ensure constant diagnostic accuracy. We developed a computational approach based on deep learning that automatically scores HER2, a biomarker that defines patient eligibility for anti-HER2 targeted therapies in breast cancer. In a cohort of 71 breast tumour resection samples, automated scoring showed a concordance of 83% with a pathologist. The twelve discordant cases were then independently reviewed, leading to a modification of diagnosis from initial pathologist assessment for eight cases. Diagnostic discordance was found to be largely caused by perceptual differences in assessing HER2 expression due to high HER2 staining heterogeneity. This study provides evidence that deep learning aided diagnosis can facilitate clinical decision making in breast cancer by identifying cases at high risk of misdiagnosis. PMID:28378829

Pressure ulcer image segmentation technique through synthetic frequencies generation and contrast variation using toroidal geometry.

PubMed

David, Ortiz P; Sierra-Sosa, Daniel; Zapirain, Begoña García

2017-01-06

Pressure ulcers have become subject of study in recent years due to the treatment high costs and decreased life quality from patients. These chronic wounds are related to the global life expectancy increment, being the geriatric and physical disable patients the principal affected by this condition. Injuries diagnosis and treatment usually takes weeks or even months by medical personel. Using non-invasive techniques, such as image processing techniques, it is possible to conduct an analysis from ulcers and aid in its diagnosis. This paper proposes a novel technique for image segmentation based on contrast changes by using synthetic frequencies obtained from the grayscale value available in each pixel of the image. These synthetic frequencies are calculated using the model of energy density over an electric field to describe a relation between a constant density and the image amplitude in a pixel. A toroidal geometry is used to decompose the image into different contrast levels by variating the synthetic frequencies. Then, the decomposed image is binarized applying Otsu's threshold allowing for obtaining the contours that describe the contrast variations. Morphological operations are used to obtain the desired segment of the image. The proposed technique is evaluated by synthesizing a Data Base with 51 images of pressure ulcers, provided by the Centre IGURCO. With the segmentation of these pressure ulcer images it is possible to aid in its diagnosis and treatment. To provide evidences of technique performance, digital image correlation was used as a measure, where the segments obtained using the methodology are compared with the real segments. The proposed technique is compared with two benchmarked algorithms. The results over the technique present an average correlation of 0.89 with a variation of ±0.1 and a computational time of 9.04 seconds. The methodology presents better segmentation results than the benchmarked algorithms using less computational time and without the need of an initial condition.

Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics.

PubMed

Kuru, Kaya; Niranjan, Mahesan; Tunca, Yusuf; Osvank, Erhan; Azim, Tayyaba

2014-10-01

In general, medical geneticists aim to pre-diagnose underlying syndromes based on facial features before performing cytological or molecular analyses where a genotype-phenotype interrelation is possible. However, determining correct genotype-phenotype interrelationships among many syndromes is tedious and labor-intensive, especially for extremely rare syndromes. Thus, a computer-aided system for pre-diagnosis can facilitate effective and efficient decision support, particularly when few similar cases are available, or in remote rural districts where diagnostic knowledge of syndromes is not readily available. The proposed methodology, visual diagnostic decision support system (visual diagnostic DSS), employs machine learning (ML) algorithms and digital image processing techniques in a hybrid approach for automated diagnosis in medical genetics. This approach uses facial features in reference images of disorders to identify visual genotype-phenotype interrelationships. Our statistical method describes facial image data as principal component features and diagnoses syndromes using these features. The proposed system was trained using a real dataset of previously published face images of subjects with syndromes, which provided accurate diagnostic information. The method was tested using a leave-one-out cross-validation scheme with 15 different syndromes, each of comprised 5-9 cases, i.e., 92 cases in total. An accuracy rate of 83% was achieved using this automated diagnosis technique, which was statistically significant (p<0.01). Furthermore, the sensitivity and specificity values were 0.857 and 0.870, respectively. Our results show that the accurate classification of syndromes is feasible using ML techniques. Thus, a large number of syndromes with characteristic facial anomaly patterns could be diagnosed with similar diagnostic DSSs to that described in the present study, i.e., visual diagnostic DSS, thereby demonstrating the benefits of using hybrid image processing and ML-based computer-aided diagnostics for identifying facial phenotypes. Copyright © 2014. Published by Elsevier B.V.

Significant Increase in Factual Knowledge with Web-Assisted Problem-Based Learning as Part of an Undergraduate Cardio-Respiratory Curriculum

ERIC Educational Resources Information Center

Raupach, T.; Munscher, C.; Pukrop, T.; Anders, S.; Harendza, S.

2010-01-01

In recent years, increasing attention has been paid to web-based learning although the advantages of computer-aided instruction over traditional teaching formats still need to be confirmed. This study examined whether participation in an online module on the differential diagnosis of dyspnoea impacts on student performance in a multiple choice…

Twelve years' experience of computer-aided diagnosis in a district general hospital.

PubMed Central

McAdam, W. A.; Brock, B. M.; Armitage, T.; Davenport, P.; Chan, M.; de Dombal, F. T.

1990-01-01

This paper describes experience in a modern district general hospital with a small desktop system for computer-aided diagnosis of acute abdominal pain, over a 12-year period involving 5512 cases. When compared with a baseline year (1973) in which unaided performance was monitored, during an initial study period (1974-76) the diagnostic accuracy of junior staff rose by between 10 and 15%. This higher performance level was then maintained for a decade (1976-86) despite changes in staff. The perforation rate among appendicitis cases fell from 27% to 12.5%, accompanied by a smaller fall in negative laparotomy rates. The saving in surgical bednights devoted to acute abdominal pain was approximately 15%, and the notional cost of resources saved during the first 6 years of operation was 120,000 pounds. Other hospitals have shown--in the short term--benefits similar to those obtained at Airedale District General Hospital. The long-term benefits of the system at Airedale reinforce the conclusions of the earlier short-term trials that a comparable system should probably be offered to all DGHs in the UK, not as an exercise in 'artificial intelligence' but as an effective continuing stimulus to good clinical practice. PMID:2185682

A ROC-based feature selection method for computer-aided detection and diagnosis

NASA Astrophysics Data System (ADS)

Wang, Songyuan; Zhang, Guopeng; Liao, Qimei; Zhang, Junying; Jiao, Chun; Lu, Hongbing

2014-03-01

Image-based computer-aided detection and diagnosis (CAD) has been a very active research topic aiming to assist physicians to detect lesions and distinguish them from benign to malignant. However, the datasets fed into a classifier usually suffer from small number of samples, as well as significantly less samples available in one class (have a disease) than the other, resulting in the classifier's suboptimal performance. How to identifying the most characterizing features of the observed data for lesion detection is critical to improve the sensitivity and minimize false positives of a CAD system. In this study, we propose a novel feature selection method mR-FAST that combines the minimal-redundancymaximal relevance (mRMR) framework with a selection metric FAST (feature assessment by sliding thresholds) based on the area under a ROC curve (AUC) generated on optimal simple linear discriminants. With three feature datasets extracted from CAD systems for colon polyps and bladder cancer, we show that the space of candidate features selected by mR-FAST is more characterizing for lesion detection with higher AUC, enabling to find a compact subset of superior features at low cost.

Computer-aided diagnosis of mammographic masses using geometric verification-based image retrieval

NASA Astrophysics Data System (ADS)

Li, Qingliang; Shi, Weili; Yang, Huamin; Zhang, Huimao; Li, Guoxin; Chen, Tao; Mori, Kensaku; Jiang, Zhengang

2017-03-01

Computer-Aided Diagnosis of masses in mammograms is an important indicator of breast cancer. The use of retrieval systems in breast examination is increasing gradually. In this respect, the method of exploiting the vocabulary tree framework and the inverted file in the mammographic masse retrieval have been proved high accuracy and excellent scalability. However it just considered the features in each image as a visual word and had ignored the spatial configurations of features. It greatly affect the retrieval performance. To overcome this drawback, we introduce the geometric verification method to retrieval in mammographic masses. First of all, we obtain corresponding match features based on the vocabulary tree framework and the inverted file. After that, we grasps the main point of local similarity characteristic of deformations in the local regions by constructing the circle regions of corresponding pairs. Meanwhile we segment the circle to express the geometric relationship of local matches in the area and generate the spatial encoding strictly. Finally we judge whether the matched features are correct or not, based on verifying the all spatial encoding are whether satisfied the geometric consistency. Experiments show the promising results of our approach.

Superpixel-based segmentation of glottal area from videolaryngoscopy images

NASA Astrophysics Data System (ADS)

Turkmen, H. Irem; Albayrak, Abdulkadir; Karsligil, M. Elif; Kocak, Ismail

2017-11-01

Segmentation of the glottal area with high accuracy is one of the major challenges for the development of systems for computer-aided diagnosis of vocal-fold disorders. We propose a hybrid model combining conventional methods with a superpixel-based segmentation approach. We first employed a superpixel algorithm to reveal the glottal area by eliminating the local variances of pixels caused by bleedings, blood vessels, and light reflections from mucosa. Then, the glottal area was detected by exploiting a seeded region-growing algorithm in a fully automatic manner. The experiments were conducted on videolaryngoscopy images obtained from both patients having pathologic vocal folds as well as healthy subjects. Finally, the proposed hybrid approach was compared with conventional region-growing and active-contour model-based glottal area segmentation algorithms. The performance of the proposed method was evaluated in terms of segmentation accuracy and elapsed time. The F-measure, true negative rate, and dice coefficients of the hybrid method were calculated as 82%, 93%, and 82%, respectively, which are superior to the state-of-art glottal-area segmentation methods. The proposed hybrid model achieved high success rates and robustness, making it suitable for developing a computer-aided diagnosis system that can be used in clinical routines.

Initial development of a computer-aided diagnosis tool for solitary pulmonary nodules

NASA Astrophysics Data System (ADS)

Catarious, David M., Jr.; Baydush, Alan H.; Floyd, Carey E., Jr.

2001-07-01

This paper describes the development of a computer-aided diagnosis (CAD) tool for solitary pulmonary nodules. This CAD tool is built upon physically meaningful features that were selected because of their relevance to shape and texture. These features included a modified version of the Hotelling statistic (HS), a channelized HS, three measures of fractal properties, two measures of spicularity, and three manually measured shape features. These features were measured from a difficult database consisting of 237 regions of interest (ROIs) extracted from digitized chest radiographs. The center of each 256x256 pixel ROI contained a suspicious lesion which was sent to follow-up by a radiologist and whose nature was later clinically determined. Linear discriminant analysis (LDA) was used to search the feature space via sequential forward search using percentage correct as the performance metric. An optimized feature subset, selected for the highest accuracy, was then fed into a three layer artificial neural network (ANN). The ANN's performance was assessed by receiver operating characteristic (ROC) analysis. A leave-one-out testing/training methodology was employed for the ROC analysis. The performance of this system is competitive with that of three radiologists on the same database.

An interactive system for computer-aided diagnosis of breast masses.

PubMed

Wang, Xingwei; Li, Lihua; Liu, Wei; Xu, Weidong; Lederman, Dror; Zheng, Bin

2012-10-01

Although mammography is the only clinically accepted imaging modality for screening the general population to detect breast cancer, interpreting mammograms is difficult with lower sensitivity and specificity. To provide radiologists "a visual aid" in interpreting mammograms, we developed and tested an interactive system for computer-aided detection and diagnosis (CAD) of mass-like cancers. Using this system, an observer can view CAD-cued mass regions depicted on one image and then query any suspicious regions (either cued or not cued by CAD). CAD scheme automatically segments the suspicious region or accepts manually defined region and computes a set of image features. Using content-based image retrieval (CBIR) algorithm, CAD searches for a set of reference images depicting "abnormalities" similar to the queried region. Based on image retrieval results and a decision algorithm, a classification score is assigned to the queried region. In this study, a reference database with 1,800 malignant mass regions and 1,800 benign and CAD-generated false-positive regions was used. A modified CBIR algorithm with a new function of stretching the attributes in the multi-dimensional space and decision scheme was optimized using a genetic algorithm. Using a leave-one-out testing method to classify suspicious mass regions, we compared the classification performance using two CBIR algorithms with either equally weighted or optimally stretched attributes. Using the modified CBIR algorithm, the area under receiver operating characteristic curve was significantly increased from 0.865 ± 0.006 to 0.897 ± 0.005 (p < 0.001). This study demonstrated the feasibility of developing an interactive CAD system with a large reference database and achieving improved performance.

Computer-aided classification of lung nodules on computed tomography images via deep learning technique

PubMed Central

Hua, Kai-Lung; Hsu, Che-Hao; Hidayati, Shintami Chusnul; Cheng, Wen-Huang; Chen, Yu-Jen

2015-01-01

Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD) scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain. PMID:26346558

Computer-aided classification of lung nodules on computed tomography images via deep learning technique.

PubMed

Hua, Kai-Lung; Hsu, Che-Hao; Hidayati, Shintami Chusnul; Cheng, Wen-Huang; Chen, Yu-Jen

2015-01-01

Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD) scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain.

Computed tomography or necropsy diagnosis of multiple bullae and the treatment of pneumothorax in rhesus macaques (Macaca mulatta).

PubMed

Kim, Jong-Min; Han, Sungyoung; Shin, Jun-Seop; Min, Byoung-Hoon; Jeong, Won Young; Lee, Ga Eul; Kim, Min Sun; Kim, Ju Eun; Chung, Hyunwoo; Park, Chung-Gyu

2017-10-01

Pulmonary bullae and pneumothorax have various etiologies in veterinary medicine. We diagnosed multiple pulmonary bullae combined with or without pneumothorax by computed tomography (CT) or necropsy in seven rhesus macaques (Macaca mulatta) imported from China. Two of seven rhesus macaques accompanied by pneumothorax were cured by fixation of ruptured lung through left or right 3rd intercostal thoracotomy. Pneumonyssus simicola, one of the etiologies of pulmonary bullae, was not detected from tracheobronchiolar lavage. To the best of our knowledge, this is the first case report on the CT-aided diagnosis of pulmonary bullae and the successful treatment of combined pneumothorax by thoracotomy in non-human primates (NHPs). © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Brain tumor classification of microscopy images using deep residual learning

NASA Astrophysics Data System (ADS)

Ishikawa, Yota; Washiya, Kiyotada; Aoki, Kota; Nagahashi, Hiroshi

2016-12-01

The crisis rate of brain tumor is about one point four in ten thousands. In general, cytotechnologists take charge of cytologic diagnosis. However, the number of cytotechnologists who can diagnose brain tumors is not sufficient, because of the necessity of highly specialized skill. Computer-Aided Diagnosis by computational image analysis may dissolve the shortage of experts and support objective pathological examinations. Our purpose is to support a diagnosis from a microscopy image of brain cortex and to identify brain tumor by medical image processing. In this study, we analyze Astrocytes that is a type of glia cell of central nerve system. It is not easy for an expert to discriminate brain tumor correctly since the difference between astrocytes and low grade astrocytoma (tumors formed from Astrocyte) is very slight. In this study, we present a novel method to segment cell regions robustly using BING objectness estimation and to classify brain tumors using deep convolutional neural networks (CNNs) constructed by deep residual learning. BING is a fast object detection method and we use pretrained BING model to detect brain cells. After that, we apply a sequence of post-processing like Voronoi diagram, binarization, watershed transform to obtain fine segmentation. For classification using CNNs, a usual way of data argumentation is applied to brain cells database. Experimental results showed 98.5% accuracy of classification and 98.2% accuracy of segmentation.

Machine learning for the assessment of Alzheimer's disease through DTI

NASA Astrophysics Data System (ADS)

Lella, Eufemia; Amoroso, Nicola; Bellotti, Roberto; Diacono, Domenico; La Rocca, Marianna; Maggipinto, Tommaso; Monaco, Alfonso; Tangaro, Sabina

2017-09-01

Digital imaging techniques have found several medical applications in the development of computer aided detection systems, especially in neuroimaging. Recent advances in Diffusion Tensor Imaging (DTI) aim to discover biological markers for the early diagnosis of Alzheimer's disease (AD), one of the most widespread neurodegenerative disorders. We explore here how different supervised classification models provide a robust support to the diagnosis of AD patients. We use DTI measures, assessing the structural integrity of white matter (WM) fiber tracts, to reveal patterns of disrupted brain connectivity. In particular, we provide a voxel-wise measure of fractional anisotropy (FA) and mean diffusivity (MD), thus identifying the regions of the brain mostly affected by neurodegeneration, and then computing intensity features to feed supervised classification algorithms. In particular, we evaluate the accuracy of discrimination of AD patients from healthy controls (HC) with a dataset of 80 subjects (40 HC, 40 AD), from the Alzheimer's Disease Neurodegenerative Initiative (ADNI). In this study, we compare three state-of-the-art classification models: Random Forests, Naive Bayes and Support Vector Machines (SVMs). We use a repeated five-fold cross validation framework with nested feature selection to perform a fair comparison between these algorithms and evaluate the information content they provide. Results show that AD patterns are well localized within the brain, thus DTI features can support the AD diagnosis.

Training Aids for Online Instruction: An Analysis.

ERIC Educational Resources Information Center

Guy, Robin Frederick

This paper describes a number of different types of training aids currently employed in online training: non-interactive audiovisual presentations; interactive computer-based aids; partially interactive aids based on recorded searches; print-based materials; and kits. The advantages and disadvantages of each type of aid are noted, and a table…

Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification.

PubMed

Ramírez, J; Górriz, J M; Segovia, F; Chaves, R; Salas-Gonzalez, D; López, M; Alvarez, I; Padilla, P

2010-03-19

This letter shows a computer aided diagnosis (CAD) technique for the early detection of the Alzheimer's disease (AD) by means of single photon emission computed tomography (SPECT) image classification. The proposed method is based on partial least squares (PLS) regression model and a random forest (RF) predictor. The challenge of the curse of dimensionality is addressed by reducing the large dimensionality of the input data by downscaling the SPECT images and extracting score features using PLS. A RF predictor then forms an ensemble of classification and regression tree (CART)-like classifiers being its output determined by a majority vote of the trees in the forest. A baseline principal component analysis (PCA) system is also developed for reference. The experimental results show that the combined PLS-RF system yields a generalization error that converges to a limit when increasing the number of trees in the forest. Thus, the generalization error is reduced when using PLS and depends on the strength of the individual trees in the forest and the correlation between them. Moreover, PLS feature extraction is found to be more effective for extracting discriminative information from the data than PCA yielding peak sensitivity, specificity and accuracy values of 100%, 92.7%, and 96.9%, respectively. Moreover, the proposed CAD system outperformed several other recently developed AD CAD systems. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.

Classification of Computer-Aided Design-Computer-Aided Manufacturing Applications for the Reconstruction of Cranio-Maxillo-Facial Defects.

PubMed

Wauters, Lauri D J; Miguel-Moragas, Joan San; Mommaerts, Maurice Y

2015-11-01

To gain insight into the methodology of different computer-aided design-computer-aided manufacturing (CAD-CAM) applications for the reconstruction of cranio-maxillo-facial (CMF) defects. We reviewed and analyzed the available literature pertaining to CAD-CAM for use in CMF reconstruction. We proposed a classification system of the techniques of implant and cutting, drilling, and/or guiding template design and manufacturing. The system consisted of 4 classes (I-IV). These classes combine techniques used for both the implant and template to most accurately describe the methodology used. Our classification system can be widely applied. It should facilitate communication and immediate understanding of the methodology of CAD-CAM applications for the reconstruction of CMF defects.

Segmentation of touching mycobacterium tuberculosis from Ziehl-Neelsen stained sputum smear images

NASA Astrophysics Data System (ADS)

Xu, Chao; Zhou, Dongxiang; Liu, Yunhui

2015-12-01

Touching Mycobacterium tuberculosis objects in the Ziehl-Neelsen stained sputum smear images present different shapes and invisible boundaries in the adhesion areas, which increases the difficulty in objects recognition and counting. In this paper, we present a segmentation method of combining the hierarchy tree analysis with gradient vector flow snake to address this problem. The skeletons of the objects are used for structure analysis based on the hierarchy tree. The gradient vector flow snake is used to estimate the object edge. Experimental results show that the single objects composing the touching objects are successfully segmented by the proposed method. This work will improve the accuracy and practicability of the computer-aided diagnosis of tuberculosis.

Medical diagnosis and treatment using high-resolution manometry with computer-aided system

NASA Astrophysics Data System (ADS)

Pedowski, Tomasz; Wasiewicz, Piotr; Maciejewski, Ryszard; Wallner, Grzegorz

2010-09-01

Nowadays computers analyze medical data almost in every diagnosis and treatment steps. We develop new technology which gives us better and more precise diagnosis. We chose esophageal high resolution manometry with impedance (HRMI) which has been considered as a "gold standard" test for esophageal motility. HRMI is the next generation of manometry explanation which is more sensitive and accurate to EFT. Examination allows physicians to ger information about esophageal peristalsis, amplitude and duration of the esophageal contraction and liquid/viscous bolus transit time from mouth through stomach. In 2008 we examined 80 patients using "old" EFT manometry and 80 patients in 2009 using high resolution manometry (HRMI). Everybody got manometry, endoscopy and x-ray examination. We asked about symptoms which we correlate and connect with data from EFT and HRMI. We tried to find a good algorithm for this purpose in order to do a simple and helpful tool for physician to make righta diagnosis and treatment decision. Connection between data and symptoms seems to be right and clear, but finding a good algorithm for given data is the main problem.

Computer aided diagnosis of diabetic peripheral neuropathy

NASA Astrophysics Data System (ADS)

Chekh, Viktor; Soliz, Peter; McGrew, Elizabeth; Barriga, Simon; Burge, Mark; Luan, Shuang

2014-03-01

Diabetic peripheral neuropathy (DPN) refers to the nerve damage that can occur in diabetes patients. It most often affects the extremities, such as the feet, and can lead to peripheral vascular disease, deformity, infection, ulceration, and even amputation. The key to managing diabetic foot is prevention and early detection. Unfortunately, current existing diagnostic techniques are mostly based on patient sensations and exhibit significant inter- and intra-observer differences. We have developed a computer aided diagnostic (CAD) system for diabetic peripheral neuropathy. The thermal response of the feet of diabetic patients following cold stimulus is captured using an infrared camera. The plantar foot in the images from a thermal video are segmented and registered for tracking points or specific regions. The temperature recovery of each point on the plantar foot is extracted using our bio-thermal model and analyzed. The regions that exhibit abnormal ability to recover are automatically identified to aid the physicians to recognize problematic areas. The key to our CAD system is the segmentation of infrared video. The main challenges for segmenting infrared video compared to normal digital video are (1) as the foot warms up, it also warms up the surrounding, creating an ever changing contrast; and (2) there may be significant motion during imaging. To overcome this, a hybrid segmentation algorithm was developed based on a number of techniques such as continuous max-flow, model based segmentation, shape preservation, convex hull, and temperature normalization. Verifications of the automatic segmentation and registration using manual segmentation and markers show good agreement.

Computer-aided drug discovery.

PubMed

Bajorath, Jürgen

2015-01-01

Computational approaches are an integral part of interdisciplinary drug discovery research. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true impact on drug discovery at different levels. If applied in a scientifically meaningful way, computational methods improve the ability to identify and evaluate potential drug molecules, but there remain weaknesses in the methods that preclude naïve applications. Herein, current trends in computer-aided drug discovery are reviewed, and selected computational areas are discussed. Approaches are highlighted that aid in the identification and optimization of new drug candidates. Emphasis is put on the presentation and discussion of computational concepts and methods, rather than case studies or application examples. As such, this contribution aims to provide an overview of the current methodological spectrum of computational drug discovery for a broad audience.

[Crusted scabies in HIV/AIDS infected patients. Report of 15 cases].

PubMed

Tirado-Sánchez, Andrés; Bonifaz, Alexandro; Montes de Oca-Sánchez, Griselda; Araiza-Santibañez, Javier; Ponce-Olivera, Rosa María

2016-01-01

Crusted (Norwegian) scabies is a rare disease that occurs in patients with compromised immune system like patients with HIV/AIDS. We report 15 cases of crusted scabies in patients with HIV/AIDS successfully treated with oral ivermectin. The mean age of the patients was 43.7±8.06 and the diagnosis was made at a median of 5 months. All patients were diagnosed with HIV/AIDS treatment with antiretroviral therapy. Patients were treated with repeated doses of oral ivermectin with different schemes with good tolerance and efficacy with full resolution and without recurrence. Ivermectin is the treatment of choice for crusted scabies; it is tolerable and accessible to the patient. Immunosuppressed patients are those with the highest risk of acquiring that disease; we highlight the importance of lesion scraping to perform a correct and early diagnosis.

HIV impact on women: gender difference among late testers and advanced HIV infection

NASA Astrophysics Data System (ADS)

Sukmawati, N. M. D. D.; Merati, T. P.; Somia, A.; Utama, S.; Gayatri, Y.

2018-03-01

This study reported the effect of gender difference on HIV seropositive late testers or advanced infection. A retrospective cohort study of newly diagnosed HIV seropositive based on adatabase in the main referral hospital in Denpasar, Bali, Indonesia from 2004 – 2016. Women and man were categorized as late testers (CD4 ≤ 200 cells/uL and/or AIDS diagnosis ≤ 12 months from first HIV test date). Non-late testers (CD4 > 200 cells/uL and/or no AIDS diagnosis during study period or diagnosis of AIDS >12 months from HIV diagnosis), of reproductive age (13 – 49 years old), and not of reproductive age (>49 years old). Logistic regression was used to estimate risk and its statistical significance. The model consists of gender and age correctly classified 83.5% of cases. Women were almost two times more likely to present as non-late testers compared to men, and reproductive age of 15 – 49 years were 1.5 times more likely to present as non-late testers compared to those with age > 49 years. Women affected by HIV almost in equal as for men. Women and those within reproductive age were more likely to present before the advanced stage compared to men and those aged > 49 years.

Orthodontics: computer-aided diagnosis and treatment planning

NASA Astrophysics Data System (ADS)

Yi, Yaxing; Li, Zhongke; Wei, Suyuan; Deng, Fanglin; Yao, Sen

2000-10-01

The purpose of this article is to introduce the outline of our newly developed computer-aided 3D dental cast analyzing system with laser scanning, and its preliminary clinical applications. The system is composed of a scanning device and a personal computer as a scanning controller and post processor. The scanning device is composed of a laser beam emitter, two sets of linear CCD cameras and a table which is rotatable by two-degree-of-freedom. The rotating is controlled precisely by a personal computer. The dental cast is projected and scanned with a laser beam. Triangulation is applied to determine the location of each point. Generation of 3D graphics of the dental cast takes approximately 40 minutes. About 170,000 sets of X,Y,Z coordinates are store for one dental cast. Besides the conventional linear and angular measurements of the dental cast, we are also able to demonstrate the size of the top surface area of each molar. The advantage of this system is that it facilitates the otherwise complicated and time- consuming mock surgery necessary for treatment planning in orthognathic surgery.

Tracheomalacia and recurrent exacerbations of chronic obstructive pulmonary disease: a case report and review of the literature

PubMed Central

Kerolus, Ghaly; Ikladios, Ossama

2016-01-01

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of disability and death worldwide. COPD exacerbation is usually treated with antibiotics, systemic corticosteroids, and inhaled bronchodilators. We present a case of recurrent COPD exacerbation that was treated repeatedly with standard therapy. Dynamic expiratory computed tomography of the chest was done, which revealed concomitant tracheomalacia. COPD and tracheomalacia may coexist during recurrent exacerbations of COPD, and delayed diagnosis can be associated with severe comorbidities. Ordering the appropriate imaging may aid in the correct diagnosis and facilitate appropriate management. PMID:27987292

Imaging of vascular lesions of the head and neck.

PubMed

Griauzde, Julius; Srinivasan, Ashok

2015-01-01

The diagnosis of vascular lesions of the head and neck should be directed by classifying the lesions as tumors or malformations and by determining their flow characteristics. Location of the lesion is key when differentiating between vascular neoplasms. Ultrasonography is an appropriate screening tool; MRI is often used to confirm the diagnosis. Computed tomography can be used for further characterization of the lesion, particularly when there is bony involvement. In many cases, vascular lesions grow to be extensive. In these cases, percutaneous sclerotherapy or embolization therapy can be employed to aid in surgical resection. Copyright © 2015 Elsevier Inc. All rights reserved.

Vertebra identification using template matching modelmp and K-means clustering.

PubMed

Larhmam, Mohamed Amine; Benjelloun, Mohammed; Mahmoudi, Saïd

2014-03-01

Accurate vertebra detection and segmentation are essential steps for automating the diagnosis of spinal disorders. This study is dedicated to vertebra alignment measurement, the first step in a computer-aided diagnosis tool for cervical spine trauma. Automated vertebral segment alignment determination is a challenging task due to low contrast imaging and noise. A software tool for segmenting vertebrae and detecting subluxations has clinical significance. A robust method was developed and tested for cervical vertebra identification and segmentation that extracts parameters used for vertebra alignment measurement. Our contribution involves a novel combination of a template matching method and an unsupervised clustering algorithm. In this method, we build a geometric vertebra mean model. To achieve vertebra detection, manual selection of the region of interest is performed initially on the input image. Subsequent preprocessing is done to enhance image contrast and detect edges. Candidate vertebra localization is then carried out by using a modified generalized Hough transform (GHT). Next, an adapted cost function is used to compute local voted centers and filter boundary data. Thereafter, a K-means clustering algorithm is applied to obtain clusters distribution corresponding to the targeted vertebrae. These clusters are combined with the vote parameters to detect vertebra centers. Rigid segmentation is then carried out by using GHT parameters. Finally, cervical spine curves are extracted to measure vertebra alignment. The proposed approach was successfully applied to a set of 66 high-resolution X-ray images. Robust detection was achieved in 97.5 % of the 330 tested cervical vertebrae. An automated vertebral identification method was developed and demonstrated to be robust to noise and occlusion. This work presents a first step toward an automated computer-aided diagnosis system for cervical spine trauma detection.

A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography.

PubMed

Timp, Sheila; Karssemeijer, Nico

2004-05-01

Mass segmentation plays a crucial role in computer-aided diagnosis (CAD) systems for classification of suspicious regions as normal, benign, or malignant. In this article we present a robust and automated segmentation technique--based on dynamic programming--to segment mass lesions from surrounding tissue. In addition, we propose an efficient algorithm to guarantee resulting contours to be closed. The segmentation method based on dynamic programming was quantitatively compared with two other automated segmentation methods (region growing and the discrete contour model) on a dataset of 1210 masses. For each mass an overlap criterion was calculated to determine the similarity with manual segmentation. The mean overlap percentage for dynamic programming was 0.69, for the other two methods 0.60 and 0.59, respectively. The difference in overlap percentage was statistically significant. To study the influence of the segmentation method on the performance of a CAD system two additional experiments were carried out. The first experiment studied the detection performance of the CAD system for the different segmentation methods. Free-response receiver operating characteristics analysis showed that the detection performance was nearly identical for the three segmentation methods. In the second experiment the ability of the classifier to discriminate between malignant and benign lesions was studied. For region based evaluation the area Az under the receiver operating characteristics curve was 0.74 for dynamic programming, 0.72 for the discrete contour model, and 0.67 for region growing. The difference in Az values obtained by the dynamic programming method and region growing was statistically significant. The differences between other methods were not significant.

[Construction and analysis of questionnaires on AIDS cough in traditional Chinese medicine diagnosis and treatment procedures].

PubMed

Zhang, Ying; Xue, Liu-Hua; Chen, Yu-Xia; Huang, Shi-Jing; Pan, Ju-Hua; Wang, Jie

2013-08-01

To norm the behavior of AIDS cough in traditional Chinese medicine diagnosis and treatment and improve the clinical level of cough treatment for HIV/AIDS, and build AIDS cough diagnosis and treatment procedures in traditional Chinese medicine. Combined with clinical practice,to formulate questionnaire on AIDS cough in traditional Chinese medicine diagnosis and treatment by both English and Chinese literature research to expertise consultation and verify the results of the questionnaires on the statistics using the Delphi method. Questionnaire contents consist of overview, pathogeny, diagnosis standard, dialectical medication (phlegm heat resistance pulmonary lung and kidney Yin deficiency lung spleen-deficiency), treating spleen-deficiency (lung), moxibustion treatment and aftercare care and diet and mental, average (2.93-3.00), full mark rate (93.10%-100%) ranks average (9.91-10.67) and (287.50-309.50) of which are the most high value, and the variation coefficient is 0.00, the Kendall coefficient (Kendalls W) is 0.049 which is statistical significance, the questionnaire reliability value of alpha was 0.788. Preliminary standarded concept, etiology and pathogenesis, diagnosis and syndrome differentiation treatment of AIDS cough, basically recognised by the experts in this field, and laid the foundation of traditional Chinese medicine diagnosis and treatment on develop the AIDS cough specifications.

Usability Studies in Virtual and Traditional Computer Aided Design Environments for Fault Identification

DTIC Science & Technology

2017-08-08

Usability Studies In Virtual And Traditional Computer Aided Design Environments For Fault Identification Dr. Syed Adeel Ahmed, Xavier University...virtual environment with wand interfaces compared directly with a workstation non-stereoscopic traditional CAD interface with keyboard and mouse. In...the differences in interaction when compared with traditional human computer interfaces. This paper provides analysis via usability study methods

Study on computer-aided diagnosis of hepatic MR imaging and mammography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang Xuejun

2005-04-01

It is well known that the liver is an organ easily attacked by diseases. The purpose of this study is to develop a computer-aided diagnosis (CAD) scheme for helping radiologists to differentiate hepatic diseases more efficiently. Our software named LIVERANN integrated the magnetic resonance (MR) imaging findings with different pulse sequences to classify the five categories of hepatic diseases by using the artificial neural network (ANN) method. The intensity and homogeneity within the region of interest (ROI) delineated by a radiologist were automatically calculated to obtain numerical data by the program for input signals to the ANN. Outputs were themore » five pathological categories of hepatic diseases (hepatic cyst, hepatocellular carcinoma, dysplasia in cirrhosis, cavernous hemangioma, and metastasis). The experiment demonstrated a testing accuracy of 93% from 80 patients. In order to differentiate the cirrhosis from normal liver, the volume ratio of left to whole (LTW) was proposed to quantify the degree of cirrhosis by three-dimensional (3D) volume analysis. The liver region was firstly extracted from computed tomography (CT) or MR slices based on edge detection algorithms, and then separated into left lobe and right lobe by the hepatic umbilical fissure. The volume ratio of these two parts showed that the LTW ratio in the liver was significantly improved in the differentiation performance, with (25.6%{+-}4.3%) in cirrhosis versus the normal liver (16.4%{+-}5.4%). In addition, the application of the ANN method for detecting clustered microcalcifications in masses on mammograms was described here as well. A new structural ANN, so-called a shift-invariant artificial neural network (SIANN), was integrated with our triple-ring filter (TRF) method in our CAD system. As the result, the sensitivity of detecting clusters was improved from 90% by our previous TRF method to 95% by using both SIANN and TRF.« less

Post-traumatic stress disorder among recently diagnosed patients with HIV/AIDS in South Africa.

PubMed

Olley, B O; Zeier, M D; Seedat, S; Stein, D J

2005-07-01

This study examined the prevalence of and factors associated with post-traumatic stress disorder in recently diagnosed HIV/AIDS patients in South Africa. One hundred and forty-nine (44 male, 105 female) recently diagnosed HIV/AIDS patients (mean duration since diagnosis = 5.8 months, SD = 4.1) were evaluated. Subjects were assessed using the MINI International Neuropsychiatric Interview (MINI), the Carver Brief COPE coping scale and the Sheehan Disability Scale. In addition, previous exposures to trauma and past risk behaviours were assessed. Twenty-two patients (14.8%) met criteria for PTSD. Current psychiatric conditions more likely to be associated with PTSD included major depressive disorder (29% in PTSD patients versus 7% in non-PTSD patients, p = 0.004), suicidality (54% versus 11%, p = 0.001) and social anxiety disorder (40% versus 13%, p = 0.04). Further patients with PTSD reported significantly more work impairment and demonstrated a trend towards higher usage of alcohol as a means of coping. Discriminant function analysis indicated that female gender and a history of sexual violation in the past year were significantly associated with a diagnosis of PTSD. Patients whose PTSD was a direct result of an HIV/AIDS diagnosis (8/22) did not differ from other patients with PTSD on demographic or clinical features. In the South African context, PTSD is not an uncommon disorder in patients with HIV/AIDS. In some cases, PTSD is secondary to the diagnosis of HIV/AIDS but in most cases it is seen after other traumas, with sexual violation and intimate partner violence in women being particularly important.

JPRS Report, Science & Technology, USSR: Life Sciences.

DTIC Science & Technology

1987-09-25

Ministry of Health, Moscow] iAbstract] Assessment of the specificity of Virognostika diagnosticum (produced by Organum, the Netherlands) in AIDS diagnosis...was determined in studies of school children and in patients with diseases posing different levels of risk of infection by the AIDS virus. Positive...KLINICHESKAYA KHIRURGIYA in Russian No 10 Oct 86 p 67 [Article by V, K, Minachenko, of the Inter-Oblast Brigade for Specialized Aid to Patients

A Structure for Creating Quality Software.

ERIC Educational Resources Information Center

Christensen, Larry C.; Bodey, Michael R.

1990-01-01

Addresses the issue of assuring quality software for use in computer-aided instruction and presents a structure by which developers can create quality courseware. Differences between courseware and computer-aided instruction software are discussed, methods for testing software are described, and human factors issues as well as instructional design…

An expert support system for breast cancer diagnosis using color wavelet features.

PubMed

Issac Niwas, S; Palanisamy, P; Chibbar, Rajni; Zhang, W J

2012-10-01

Breast cancer diagnosis can be done through the pathologic assessments of breast tissue samples such as core needle biopsy technique. The result of analysis on this sample by pathologist is crucial for breast cancer patient. In this paper, nucleus of tissue samples are investigated after decomposition by means of the Log-Gabor wavelet on HSV color domain and an algorithm is developed to compute the color wavelet features. These features are used for breast cancer diagnosis using Support Vector Machine (SVM) classifier algorithm. The ability of properly trained SVM is to correctly classify patterns and make them particularly suitable for use in an expert system that aids in the diagnosis of cancer tissue samples. The results are compared with other multivariate classifiers such as Naïves Bayes classifier and Artificial Neural Network. The overall accuracy of the proposed method using SVM classifier will be further useful for automation in cancer diagnosis.

Lesion classification using clinical and visual data fusion by multiple kernel learning

NASA Astrophysics Data System (ADS)

Kisilev, Pavel; Hashoul, Sharbell; Walach, Eugene; Tzadok, Asaf

2014-03-01

To overcome operator dependency and to increase diagnosis accuracy in breast ultrasound (US), a lot of effort has been devoted to developing computer-aided diagnosis (CAD) systems for breast cancer detection and classification. Unfortunately, the efficacy of such CAD systems is limited since they rely on correct automatic lesions detection and localization, and on robustness of features computed based on the detected areas. In this paper we propose a new approach to boost the performance of a Machine Learning based CAD system, by combining visual and clinical data from patient files. We compute a set of visual features from breast ultrasound images, and construct the textual descriptor of patients by extracting relevant keywords from patients' clinical data files. We then use the Multiple Kernel Learning (MKL) framework to train SVM based classifier to discriminate between benign and malignant cases. We investigate different types of data fusion methods, namely, early, late, and intermediate (MKL-based) fusion. Our database consists of 408 patient cases, each containing US images, textual description of complaints and symptoms filled by physicians, and confirmed diagnoses. We show experimentally that the proposed MKL-based approach is superior to other classification methods. Even though the clinical data is very sparse and noisy, its MKL-based fusion with visual features yields significant improvement of the classification accuracy, as compared to the image features only based classifier.

Computer-Aided Diagnosis of Solid Breast Lesions Using an Ultrasonic Multi-Feature Analysis Procedure

DTIC Science & Technology

2011-01-01

areas. We quantified morphometric features by geometric and fractal analysis of traced lesion boundaries. Although no single parameter can reliably...These include acoustic descriptors (“echogenicity,” “heterogeneity,” “shadowing”) and morphometric descriptors (“area,” “aspect ratio,” “border...quantitative descriptors; some morphometric features (such as border irregularity) also were particularly effective in lesion classification. Our

Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI.

PubMed

Song, Yang; Zhang, Yu-Dong; Yan, Xu; Liu, Hui; Zhou, Minxiong; Hu, Bingwen; Yang, Guang

2018-04-16

Deep learning is the most promising methodology for automatic computer-aided diagnosis of prostate cancer (PCa) with multiparametric MRI (mp-MRI). To develop an automatic approach based on deep convolutional neural network (DCNN) to classify PCa and noncancerous tissues (NC) with mp-MRI. Retrospective. In all, 195 patients with localized PCa were collected from a PROSTATEx database. In total, 159/17/19 patients with 444/48/55 observations (215/23/23 PCas and 229/25/32 NCs) were randomly selected for training/validation/testing, respectively. T 2 -weighted, diffusion-weighted, and apparent diffusion coefficient images. A radiologist manually labeled the regions of interest of PCas and NCs and estimated the Prostate Imaging Reporting and Data System (PI-RADS) scores for each region. Inspired by VGG-Net, we designed a patch-based DCNN model to distinguish between PCa and NCs based on a combination of mp-MRI data. Additionally, an enhanced prediction method was used to improve the prediction accuracy. The performance of DCNN prediction was tested using a receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Moreover, the predicted result was compared with the PI-RADS score to evaluate its clinical value using decision curve analysis. Two-sided Wilcoxon signed-rank test with statistical significance set at 0.05. The DCNN produced excellent diagnostic performance in distinguishing between PCa and NC for testing datasets with an AUC of 0.944 (95% confidence interval: 0.876-0.994), sensitivity of 87.0%, specificity of 90.6%, PPV of 87.0%, and NPV of 90.6%. The decision curve analysis revealed that the joint model of PI-RADS and DCNN provided additional net benefits compared with the DCNN model and the PI-RADS scheme. The proposed DCNN-based model with enhanced prediction yielded high performance in statistical analysis, suggesting that DCNN could be used in computer-aided diagnosis (CAD) for PCa classification. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Psychiatric considerations in the diagnosis, treatment, and prevention of HIV/AIDS.

PubMed

Ruiz, P; Guynn, R W; Matorin, A A

2000-05-01

HIV/AIDS has the unfortunate distinction of being one of the most devastating epidemics of the twentieth century. By the end of June, 1999, 420,201 deaths in persons with AIDS had been reported in the United States. While HIV/AIDS patients are currently living longer as a result of more effective and complex treatments, no vaccination or cure has yet been discovered. Over the years, the HIV/AIDS epidemic has become multifactorial and currently affects several different special population groups. Individuals who are at high risk for becoming infected with HIV or who already suffer from HIV/AIDS can benefit greatly from the interventions of psychiatrists or other mental health professionals. It is important that psychiatrists collaborate very closely with infectious disease specialists in the management of HIV/AIDS and its psychological sequelae. The authors describe the psychiatric conditions that most often occur in association with HIV/AIDS: mood disorders, anxiety disorders, substance-related disorders, psychotic disorders, insomnia and sleep disorders, delirium, dementia, and pain syndromes. We present guidelines for diagnosis and psychopharmacological and psychotherapeutic treatment of these disorders in patients with HIV/AIDS. The article concludes with a discussion of prevention strategies that can be used in a mental health treatment setting and special issues related to treating HIV/AIDS in certain special population groups.

Computer-aided diagnosis of periapical cyst and keratocystic odontogenic tumor on cone beam computed tomography.

PubMed

Yilmaz, E; Kayikcioglu, T; Kayipmaz, S

2017-07-01

In this article, we propose a decision support system for effective classification of dental periapical cyst and keratocystic odontogenic tumor (KCOT) lesions obtained via cone beam computed tomography (CBCT). CBCT has been effectively used in recent years for diagnosing dental pathologies and determining their boundaries and content. Unlike other imaging techniques, CBCT provides detailed and distinctive information about the pathologies by enabling a three-dimensional (3D) image of the region to be displayed. We employed 50 CBCT 3D image dataset files as the full dataset of our study. These datasets were identified by experts as periapical cyst and KCOT lesions according to the clinical, radiographic and histopathologic features. Segmentation operations were performed on the CBCT images using viewer software that we developed. Using the tools of this software, we marked the lesional volume of interest and calculated and applied the order statistics and 3D gray-level co-occurrence matrix for each CBCT dataset. A feature vector of the lesional region, including 636 different feature items, was created from those statistics. Six classifiers were used for the classification experiments. The Support Vector Machine (SVM) classifier achieved the best classification performance with 100% accuracy, and 100% F-score (F1) scores as a result of the experiments in which a ten-fold cross validation method was used with a forward feature selection algorithm. SVM achieved the best classification performance with 96.00% accuracy, and 96.00% F1 scores in the experiments in which a split sample validation method was used with a forward feature selection algorithm. SVM additionally achieved the best performance of 94.00% accuracy, and 93.88% F1 in which a leave-one-out (LOOCV) method was used with a forward feature selection algorithm. Based on the results, we determined that periapical cyst and KCOT lesions can be classified with a high accuracy with the models that we built using the new dataset selected for this study. The studies mentioned in this article, along with the selected 3D dataset, 3D statistics calculated from the dataset, and performance results of the different classifiers, comprise an important contribution to the field of computer-aided diagnosis of dental apical lesions. Copyright © 2017 Elsevier B.V. All rights reserved.

Multiphasic Health Testing in the Clinic Setting

PubMed Central

LaDou, Joseph

1971-01-01

The economy of automated multiphasic health testing (amht) activities patterned after the high-volume Kaiser program can be realized in low-volume settings. amht units have been operated at daily volumes of 20 patients in three separate clinical environments. These programs have displayed economics entirely compatible with cost figures published by the established high-volume centers. This experience, plus the expanding capability of small, general purpose, digital computers (minicomputers) indicates that a group of six or more physicians generating 20 laboratory appraisals per day can economically justify a completely automated multiphasic health testing facility. This system would reside in the clinic or hospital where it is used and can be configured to do analyses such as electrocardiography and generate laboratory reports, and communicate with large computer systems in university medical centers. Experience indicates that the most effective means of implementing these benefits of automation is to make them directly available to the medical community with the physician playing the central role. Economic justification of a dedicated computer through low-volume health testing then allows, as a side benefit, automation of administrative as well as other diagnostic activities—for example, patient billing, computer-aided diagnosis, and computer-aided therapeutics. PMID:4935771

Application of computer-aided diagnosis (CAD) in MR-mammography (MRM): do we really need whole lesion time curve distribution analysis?

PubMed

Baltzer, Pascal Andreas Thomas; Renz, Diane M; Kullnig, Petra E; Gajda, Mieczyslaw; Camara, Oumar; Kaiser, Werner A

2009-04-01

The identification of the most suspect enhancing part of a lesion is regarded as a major diagnostic criterion in dynamic magnetic resonance mammography. Computer-aided diagnosis (CAD) software allows the semi-automatic analysis of the kinetic characteristics of complete enhancing lesions, providing additional information about lesion vasculature. The diagnostic value of this information has not yet been quantified. Consecutive patients from routine diagnostic studies (1.5 T, 0.1 mmol gadopentetate dimeglumine, dynamic gradient-echo sequences at 1-minute intervals) were analyzed prospectively using CAD. Dynamic sequences were processed and reduced to a parametric map. Curve types were classified by initial signal increase (not significant, intermediate, and strong) and the delayed time course of signal intensity (continuous, plateau, and washout). Lesion enhancement was measured using CAD. The most suspect curve, the curve-type distribution percentage, and combined dynamic data were compared. Statistical analysis included logistic regression analysis and receiver-operating characteristic analysis. Fifty-one patients with 46 malignant and 44 benign lesions were enrolled. On receiver-operating characteristic analysis, the most suspect curve showed diagnostic accuracy of 76.7 +/- 5%. In comparison, the curve-type distribution percentage demonstrated accuracy of 80.2 +/- 4.9%. Combined dynamic data had the highest diagnostic accuracy (84.3 +/- 4.2%). These differences did not achieve statistical significance. With appropriate cutoff values, sensitivity and specificity, respectively, were found to be 80.4% and 72.7% for the most suspect curve, 76.1% and 83.6% for the curve-type distribution percentage, and 78.3% and 84.5% for both parameters. The integration of whole-lesion dynamic data tends to improve specificity. However, no statistical significance backs up this finding.

The feasibility of using manual segmentation in a multifeature computer-aided diagnosis system for classification of skin lesions: a retrospective comparative study.

PubMed

Chang, Wen-Yu; Huang, Adam; Chen, Yin-Chun; Lin, Chi-Wei; Tsai, John; Yang, Chung-Kai; Huang, Yin-Tseng; Wu, Yi-Fan; Chen, Gwo-Shing

2015-05-03

To investigate the feasibility of manual segmentation by users of different backgrounds in a previously developed multifeature computer-aided diagnosis (CADx) system to classify melanocytic and non-melanocytic skin lesions based on conventional digital photographic images. In total, 347 conventional photographs of melanocytic and non-melanocytic skin lesions were retrospectively reviewed, and manually segmented by two groups of physicians, dermatologists and general practitioners, as well as by an automated segmentation software program, JSEG. The performance of CADx based on inputs from these two groups of physicians and that of the JSEG program was compared using feature agreement analysis. The estimated area under the receiver operating characteristic curve for classification of benign or malignant skin lesions based were comparable on individual segmentation by the gold standard (0.893, 95% CI 0.856 to 0.930), dermatologists (0.886, 95% CI 0.863 to 0.908), general practitioners (0.883, 95% CI 0.864 to 0.903) and JSEG (0.856, 95% CI 0.812 to 0.899). The agreement in the malignancy probability scores among the physicians was excellent (intraclass correlation coefficient: 0.91). By selecting an optimal cut-off value of malignancy probability score, the sensitivity and specificity were 80.07% and 81.47% for dermatologists and 79.90% and 80.20% for general practitioners. This study suggests that manual segmentation by general practitioners is feasible in the described CADx system for classifying benign and malignant skin lesions. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

A method to improve visual similarity of breast masses for an interactive computer-aided diagnosis environment.

PubMed

Zheng, Bin; Lu, Amy; Hardesty, Lara A; Sumkin, Jules H; Hakim, Christiane M; Ganott, Marie A; Gur, David

2006-01-01

The purpose of this study was to develop and test a method for selecting "visually similar" regions of interest depicting breast masses from a reference library to be used in an interactive computer-aided diagnosis (CAD) environment. A reference library including 1000 malignant mass regions and 2000 benign and CAD-generated false-positive regions was established. When a suspicious mass region is identified, the scheme segments the region and searches for similar regions from the reference library using a multifeature based k-nearest neighbor (KNN) algorithm. To improve selection of reference images, we added an interactive step. All actual masses in the reference library were subjectively rated on a scale from 1 to 9 as to their "visual margins speculations". When an observer identifies a suspected mass region during a case interpretation he/she first rates the margins and the computerized search is then limited only to regions rated as having similar levels of spiculation (within +/-1 scale difference). In an observer preference study including 85 test regions, two sets of the six "similar" reference regions selected by the KNN with and without the interactive step were displayed side by side with each test region. Four radiologists and five nonclinician observers selected the more appropriate ("similar") reference set in a two alternative forced choice preference experiment. All four radiologists and five nonclinician observers preferred the sets of regions selected by the interactive method with an average frequency of 76.8% and 74.6%, respectively. The overall preference for the interactive method was highly significant (p < 0.001). The study demonstrated that a simple interactive approach that includes subjectively perceived ratings of one feature alone namely, a rating of margin "spiculation," could substantially improve the selection of "visually similar" reference images.

Self-care of elderly people after the diagnosis of acquired immunodeficiency syndrome.

PubMed

Araujo, Graciela Machado de; Leite, Marinês Tambara; Hildebrandt, Leila Mariza; Oliveski, Cinthia Cristina; Beuter, Margrid

2018-01-01

to characterize the seropositive elderly for the Human Immunodeficiency Virus and Acquired Immunodeficiency Syndrome (HIV/AIDS) in their socio-demographic aspects; to understand how the elderly take care of themselves from the diagnosis of HIV/AIDS. Qualitative, descriptive, exploratory research conducted at a Voluntary Counseling and Testing Center with 10 elderly people receiving treatment for HIV/AIDS. The data were analyzed according to the content analysis. Data show the elderly people's lack of knowledge about HIV/AIDS transmission, the experience of being elderly and having HIV/AIDS, caring for oneself and life after diagnosis of HIV/AIDS in their daily lives. Final considerations: The diagnosis of HIV/AIDS seropositivity in the elderly generates a blend of feelings and fears that lead to food changes, adherence to treatment and the renunciation of daily and social habits, manifested as ways of self-care.

Computer-aided diagnosis of liver tumors on computed tomography images.

PubMed

Chang, Chin-Chen; Chen, Hong-Hao; Chang, Yeun-Chung; Yang, Ming-Yang; Lo, Chung-Ming; Ko, Wei-Chun; Lee, Yee-Fan; Liu, Kao-Lang; Chang, Ruey-Feng

2017-07-01

Liver cancer is the tenth most common cancer in the USA, and its incidence has been increasing for several decades. Early detection, diagnosis, and treatment of the disease are very important. Computed tomography (CT) is one of the most common and robust imaging techniques for the detection of liver cancer. CT scanners can provide multiple-phase sequential scans of the whole liver. In this study, we proposed a computer-aided diagnosis (CAD) system to diagnose liver cancer using the features of tumors obtained from multiphase CT images. A total of 71 histologically-proven liver tumors including 49 benign and 22 malignant lesions were evaluated with the proposed CAD system to evaluate its performance. Tumors were identified by the user and then segmented using a region growing algorithm. After tumor segmentation, three kinds of features were obtained for each tumor, including texture, shape, and kinetic curve. The texture was quantified using 3 dimensional (3-D) texture data of the tumor based on the grey level co-occurrence matrix (GLCM). Compactness, margin, and an elliptic model were used to describe the 3-D shape of the tumor. The kinetic curve was established from each phase of tumor and represented as variations in density between each phase. Backward elimination was used to select the best combination of features, and binary logistic regression analysis was used to classify the tumors with leave-one-out cross validation. The accuracy and sensitivity for the texture were 71.82% and 68.18%, respectively, which were better than for the shape and kinetic curve under closed specificity. Combining all of the features achieved the highest accuracy (58/71, 81.69%), sensitivity (18/22, 81.82%), and specificity (40/49, 81.63%). The Az value of combining all features was 0.8713. Combining texture, shape, and kinetic curve features may be able to differentiate benign from malignant tumors in the liver using our proposed CAD system. Copyright © 2017 Elsevier B.V. All rights reserved.

A supervised 'lesion-enhancement' filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD).

PubMed

Suzuki, Kenji

2009-09-21

Computer-aided diagnosis (CAD) has been an active area of study in medical image analysis. A filter for the enhancement of lesions plays an important role for improving the sensitivity and specificity in CAD schemes. The filter enhances objects similar to a model employed in the filter; e.g. a blob-enhancement filter based on the Hessian matrix enhances sphere-like objects. Actual lesions, however, often differ from a simple model; e.g. a lung nodule is generally modeled as a solid sphere, but there are nodules of various shapes and with internal inhomogeneities such as a nodule with spiculations and ground-glass opacity. Thus, conventional filters often fail to enhance actual lesions. Our purpose in this study was to develop a supervised filter for the enhancement of actual lesions (as opposed to a lesion model) by use of a massive-training artificial neural network (MTANN) in a CAD scheme for detection of lung nodules in CT. The MTANN filter was trained with actual nodules in CT images to enhance actual patterns of nodules. By use of the MTANN filter, the sensitivity and specificity of our CAD scheme were improved substantially. With a database of 69 lung cancers, nodule candidate detection by the MTANN filter achieved a 97% sensitivity with 6.7 false positives (FPs) per section, whereas nodule candidate detection by a difference-image technique achieved a 96% sensitivity with 19.3 FPs per section. Classification-MTANNs were applied for further reduction of the FPs. The classification-MTANNs removed 60% of the FPs with a loss of one true positive; thus, it achieved a 96% sensitivity with 2.7 FPs per section. Overall, with our CAD scheme based on the MTANN filter and classification-MTANNs, an 84% sensitivity with 0.5 FPs per section was achieved.

Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration

PubMed Central

Badura, Pawel; Juszczyk, Jan; Pietka, Ewa

2016-01-01

Purpose A growing number of medical applications, including minimal invasive surgery, depends on multi-modal or multi-sensors data processing. Fast and accurate 3D scene analysis, comprising data registration, seems to be crucial for the development of computer aided diagnosis and therapy. The advancement of surface tracking system based on optical trackers already plays an important role in surgical procedures planning. However, new modalities, like the time-of-flight (ToF) sensors, widely explored in non-medical fields are powerful and have the potential to become a part of computer aided surgery set-up. Connection of different acquisition systems promises to provide a valuable support for operating room procedures. Therefore, the detailed analysis of the accuracy of such multi-sensors positioning systems is needed. Methods We present the system combining pre-operative CT series with intra-operative ToF-sensor and optical tracker point clouds. The methodology contains: optical sensor set-up and the ToF-camera calibration procedures, data pre-processing algorithms, and registration technique. The data pre-processing yields a surface, in case of CT, and point clouds for ToF-sensor and marker-driven optical tracker representation of an object of interest. An applied registration technique is based on Iterative Closest Point algorithm. Results The experiments validate the registration of each pair of modalities/sensors involving phantoms of four various human organs in terms of Hausdorff distance and mean absolute distance metrics. The best surface alignment was obtained for CT and optical tracker combination, whereas the worst for experiments involving ToF-camera. Conclusion The obtained accuracies encourage to further develop the multi-sensors systems. The presented substantive discussion concerning the system limitations and possible improvements mainly related to the depth information produced by the ToF-sensor is useful for computer aided surgery developers. PMID:27434396

Use of pattern recognition and neural networks for non-metric sex diagnosis from lateral shape of calvarium: an innovative model for computer-aided diagnosis in forensic and physical anthropology.

PubMed

Cavalli, Fabio; Lusnig, Luca; Trentin, Edmondo

2017-05-01

Sex determination on skeletal remains is one of the most important diagnosis in forensic cases and in demographic studies on ancient populations. Our purpose is to realize an automatic operator-independent method to determine the sex from the bone shape and to test an intelligent, automatic pattern recognition system in an anthropological domain. Our multiple-classifier system is based exclusively on the morphological variants of a curve that represents the sagittal profile of the calvarium, modeled via artificial neural networks, and yields an accuracy higher than 80 %. The application of this system to other bone profiles is expected to further improve the sensibility of the methodology.

Differentiation of several interstitial lung disease patterns in HRCT images using support vector machine: role of databases on performance

NASA Astrophysics Data System (ADS)

Kale, Mandar; Mukhopadhyay, Sudipta; Dash, Jatindra K.; Garg, Mandeep; Khandelwal, Niranjan

2016-03-01

Interstitial lung disease (ILD) is complicated group of pulmonary disorders. High Resolution Computed Tomography (HRCT) considered to be best imaging technique for analysis of different pulmonary disorders. HRCT findings can be categorised in several patterns viz. Consolidation, Emphysema, Ground Glass Opacity, Nodular, Normal etc. based on their texture like appearance. Clinician often find it difficult to diagnosis these pattern because of their complex nature. In such scenario computer-aided diagnosis system could help clinician to identify patterns. Several approaches had been proposed for classification of ILD patterns. This includes computation of textural feature and training /testing of classifier such as artificial neural network (ANN), support vector machine (SVM) etc. In this paper, wavelet features are calculated from two different ILD database, publically available MedGIFT ILD database and private ILD database, followed by performance evaluation of ANN and SVM classifiers in terms of average accuracy. It is found that average classification accuracy by SVM is greater than ANN where trained and tested on same database. Investigation continued further to test variation in accuracy of classifier when training and testing is performed with alternate database and training and testing of classifier with database formed by merging samples from same class from two individual databases. The average classification accuracy drops when two independent databases used for training and testing respectively. There is significant improvement in average accuracy when classifiers are trained and tested with merged database. It infers dependency of classification accuracy on training data. It is observed that SVM outperforms ANN when same database is used for training and testing.

Histogram-based adaptive gray level scaling for texture feature classification of colorectal polyps

NASA Astrophysics Data System (ADS)

Pomeroy, Marc; Lu, Hongbing; Pickhardt, Perry J.; Liang, Zhengrong

2018-02-01

Texture features have played an ever increasing role in computer aided detection (CADe) and diagnosis (CADx) methods since their inception. Texture features are often used as a method of false positive reduction for CADe packages, especially for detecting colorectal polyps and distinguishing them from falsely tagged residual stool and healthy colon wall folds. While texture features have shown great success there, the performance of texture features for CADx have lagged behind primarily because of the more similar features among different polyps types. In this paper, we present an adaptive gray level scaling and compare it to the conventional equal-spacing of gray level bins. We use a dataset taken from computed tomography colonography patients, with 392 polyp regions of interest (ROIs) identified and have a confirmed diagnosis through pathology. Using the histogram information from the entire ROI dataset, we generate the gray level bins such that each bin contains roughly the same number of voxels Each image ROI is the scaled down to two different numbers of gray levels, using both an equal spacing of Hounsfield units for each bin, and our adaptive method. We compute a set of texture features from the scaled images including 30 gray level co-occurrence matrix (GLCM) features and 11 gray level run length matrix (GLRLM) features. Using a random forest classifier to distinguish between hyperplastic polyps and all others (adenomas and adenocarcinomas), we find that the adaptive gray level scaling can improve performance based on the area under the receiver operating characteristic curve by up to 4.6%.

A Study of the Use of Ontologies for Building Computer-Aided Control Engineering Self-Learning Educational Software

ERIC Educational Resources Information Center

García, Isaías; Benavides, Carmen; Alaiz, Héctor; Alonso, Angel

2013-01-01

This paper describes research on the use of knowledge models (ontologies) for building computer-aided educational software in the field of control engineering. Ontologies are able to represent in the computer a very rich conceptual model of a given domain. This model can be used later for a number of purposes in different software applications. In…

Accuracy of computer-aided diagnosis based on narrow-band imaging endocytoscopy for diagnosing colorectal lesions: comparison with experts.

PubMed

Misawa, Masashi; Kudo, Shin-Ei; Mori, Yuichi; Takeda, Kenichi; Maeda, Yasuharu; Kataoka, Shinichi; Nakamura, Hiroki; Kudo, Toyoki; Wakamura, Kunihiko; Hayashi, Takemasa; Katagiri, Atsushi; Baba, Toshiyuki; Ishida, Fumio; Inoue, Haruhiro; Nimura, Yukitaka; Oda, Msahiro; Mori, Kensaku

2017-05-01

Real-time characterization of colorectal lesions during colonoscopy is important for reducing medical costs, given that the need for a pathological diagnosis can be omitted if the accuracy of the diagnostic modality is sufficiently high. However, it is sometimes difficult for community-based gastroenterologists to achieve the required level of diagnostic accuracy. In this regard, we developed a computer-aided diagnosis (CAD) system based on endocytoscopy (EC) to evaluate cellular, glandular, and vessel structure atypia in vivo. The purpose of this study was to compare the diagnostic ability and efficacy of this CAD system with the performances of human expert and trainee endoscopists. We developed a CAD system based on EC with narrow-band imaging that allowed microvascular evaluation without dye (ECV-CAD). The CAD algorithm was programmed based on texture analysis and provided a two-class diagnosis of neoplastic or non-neoplastic, with probabilities. We validated the diagnostic ability of the ECV-CAD system using 173 randomly selected EC images (49 non-neoplasms, 124 neoplasms). The images were evaluated by the CAD and by four expert endoscopists and three trainees. The diagnostic accuracies for distinguishing between neoplasms and non-neoplasms were calculated. ECV-CAD had higher overall diagnostic accuracy than trainees (87.8 vs 63.4%; [Formula: see text]), but similar to experts (87.8 vs 84.2%; [Formula: see text]). With regard to high-confidence cases, the overall accuracy of ECV-CAD was also higher than trainees (93.5 vs 71.7%; [Formula: see text]) and comparable to experts (93.5 vs 90.8%; [Formula: see text]). ECV-CAD showed better diagnostic accuracy than trainee endoscopists and was comparable to that of experts. ECV-CAD could thus be a powerful decision-making tool for less-experienced endoscopists.

[Medical computer-aided detection method based on deep learning].

PubMed

Tao, Pan; Fu, Zhongliang; Zhu, Kai; Wang, Lili

2018-03-01

This paper performs a comprehensive study on the computer-aided detection for the medical diagnosis with deep learning. Based on the region convolution neural network and the prior knowledge of target, this algorithm uses the region proposal network, the region of interest pooling strategy, introduces the multi-task loss function: classification loss, bounding box localization loss and object rotation loss, and optimizes it by end-to-end. For medical image it locates the target automatically, and provides the localization result for the next stage task of segmentation. For the detection of left ventricular in echocardiography, proposed additional landmarks such as mitral annulus, endocardial pad and apical position, were used to estimate the left ventricular posture effectively. In order to verify the robustness and effectiveness of the algorithm, the experimental data of ultrasonic and nuclear magnetic resonance images are selected. Experimental results show that the algorithm is fast, accurate and effective.

Towards a computer-aided diagnosis system for vocal cord diseases.

PubMed

Verikas, A; Gelzinis, A; Bacauskiene, M; Uloza, V

2006-01-01

The objective of this work is to investigate a possibility of creating a computer-aided decision support system for an automated analysis of vocal cord images aiming to categorize diseases of vocal cords. The problem is treated as a pattern recognition task. To obtain a concise and informative representation of a vocal cord image, colour, texture, and geometrical features are used. The representation is further analyzed by a pattern classifier categorizing the image into healthy, diffuse, and nodular classes. The approach developed was tested on 785 vocal cord images collected at the Department of Otolaryngology, Kaunas University of Medicine, Lithuania. A correct classification rate of over 87% was obtained when categorizing a set of unseen images into the aforementioned three classes. Bearing in mind the high similarity of the decision classes, the results obtained are rather encouraging and the developed tools could be very helpful for assuring objective analysis of the images of laryngeal diseases.

From Phonomecanocardiography to Phonocardiography computer aided

NASA Astrophysics Data System (ADS)

Granados, J.; Tavera, F.; López, G.; Velázquez, J. M.; Hernández, R. T.; López, G. A.

2017-01-01

Due to lack of training doctors to identify many of the disorders in the heart by conventional listening, it is necessary to add an objective and methodological analysis to support this technique. In order to obtain information of the performance of the heart to be able to diagnose heart disease through a simple, cost-effective procedure by means of a data acquisition system, we have obtained Phonocardiograms (PCG), which are images of the sounds emitted by the heart. A program of acoustic, visual and artificial vision recognition was elaborated to interpret them. Based on the results of previous research of cardiologists a code of interpretation of PCG and associated diseases was elaborated. Also a site, within the university campus, of experimental sampling of cardiac data was created. Phonocardiography computer-aided is a viable and low cost procedure which provides additional medical information to make a diagnosis of complex heart diseases. We show some previous results.

Features of Computer-Based Decision Aids: Systematic Review, Thematic Synthesis, and Meta-Analyses.

PubMed

Syrowatka, Ania; Krömker, Dörthe; Meguerditchian, Ari N; Tamblyn, Robyn

2016-01-26

Patient information and education, such as decision aids, are gradually moving toward online, computer-based environments. Considerable research has been conducted to guide content and presentation of decision aids. However, given the relatively new shift to computer-based support, little attention has been given to how multimedia and interactivity can improve upon paper-based decision aids. The first objective of this review was to summarize published literature into a proposed classification of features that have been integrated into computer-based decision aids. Building on this classification, the second objective was to assess whether integration of specific features was associated with higher-quality decision making. Relevant studies were located by searching MEDLINE, Embase, CINAHL, and CENTRAL databases. The review identified studies that evaluated computer-based decision aids for adults faced with preference-sensitive medical decisions and reported quality of decision-making outcomes. A thematic synthesis was conducted to develop the classification of features. Subsequently, meta-analyses were conducted based on standardized mean differences (SMD) from randomized controlled trials (RCTs) that reported knowledge or decisional conflict. Further subgroup analyses compared pooled SMDs for decision aids that incorporated a specific feature to other computer-based decision aids that did not incorporate the feature, to assess whether specific features improved quality of decision making. Of 3541 unique publications, 58 studies met the target criteria and were included in the thematic synthesis. The synthesis identified six features: content control, tailoring, patient narratives, explicit values clarification, feedback, and social support. A subset of 26 RCTs from the thematic synthesis was used to conduct the meta-analyses. As expected, computer-based decision aids performed better than usual care or alternative aids; however, some features performed better than others. Integration of content control improved quality of decision making (SMD 0.59 vs 0.23 for knowledge; SMD 0.39 vs 0.29 for decisional conflict). In contrast, tailoring reduced quality of decision making (SMD 0.40 vs 0.71 for knowledge; SMD 0.25 vs 0.52 for decisional conflict). Similarly, patient narratives also reduced quality of decision making (SMD 0.43 vs 0.65 for knowledge; SMD 0.17 vs 0.46 for decisional conflict). Results were varied for different types of explicit values clarification, feedback, and social support. Integration of media rich or interactive features into computer-based decision aids can improve quality of preference-sensitive decision making. However, this is an emerging field with limited evidence to guide use. The systematic review and thematic synthesis identified features that have been integrated into available computer-based decision aids, in an effort to facilitate reporting of these features and to promote integration of such features into decision aids. The meta-analyses and associated subgroup analyses provide preliminary evidence to support integration of specific features into future decision aids. Further research can focus on clarifying independent contributions of specific features through experimental designs and refining the designs of features to improve effectiveness.

Impact of accelerated progression to AIDS on public health monitoring of late HIV diagnosis.

PubMed

Sabharwal, Charulata J; Sepkowitz, Kent; Mehta, Reshma; Shepard, Colin; Bodach, Sara; Torian, Lucia; Begier, Elizabeth M

2011-03-01

Some patients develop AIDS within a year of HIV infection ("accelerated progression"). Classifying such cases as late HIV diagnosis may lead to inaccurate evaluation of HIV testing efforts. We sought to determine this group's contribution to overall late diagnosis rates. To identify cases of accelerated progression (development of AIDS within 12 months of a negative HIV test), we reviewed published HIV seroconverter cohort studies and used New York City's (NYC) HIV/AIDS surveillance registry. From the literature review, three seroconverter cohort studies revealed that 1.0-3.6% of participants had accelerated progression to AIDS. Applying this frequency estimate to the number of new infections in NYC (4762) for 2006 calculated by the Centers for Diseases Control and Prevention's incidence formula, we estimated that 3.6-13.0% of 1317 NYC HIV cases who are diagnosed with AIDS within 12 months of HIV diagnosis are accelerated progressors, not persons HIV infected for many years who did not test and present with AIDS (i.e., delayed diagnosis). In addition, our analysis of the 2006 NYC surveillance registry confirmed the occurrence of accelerated progression in a population-based setting; 67 accelerated progressors were reported and 9 (13%) could be confirmed through follow-up medical record review. With increased HIV testing initiatives, the irreducible proportion of AIDS cases with accelerated progression must be considered when interpreting late diagnosis data.

Semi-automated detection of anterior cruciate ligament injury from MRI.

PubMed

Štajduhar, Ivan; Mamula, Mihaela; Miletić, Damir; Ünal, Gözde

2017-03-01

A radiologist's work in detecting various injuries or pathologies from radiological scans can be tiresome, time consuming and prone to errors. The field of computer-aided diagnosis aims to reduce these factors by introducing a level of automation in the process. In this paper, we deal with the problem of detecting the presence of anterior cruciate ligament (ACL) injury in a human knee. We examine the possibility of aiding the diagnosis process by building a decision-support model for detecting the presence of milder ACL injuries (not requiring operative treatment) and complete ACL ruptures (requiring operative treatment) from sagittal plane magnetic resonance (MR) volumes of human knees. Histogram of oriented gradient (HOG) descriptors and gist descriptors are extracted from manually selected rectangular regions of interest enveloping the wider cruciate ligament area. Performance of two machine-learning models is explored, coupled with both feature extraction methods: support vector machine (SVM) and random forests model. Model generalisation properties were determined by performing multiple iterations of stratified 10-fold cross validation whilst observing the area under the curve (AUC) score. Sagittal plane knee joint MR data was retrospectively gathered at the Clinical Hospital Centre Rijeka, Croatia, from 2007 until 2014. Type of ACL injury was established in a double-blind fashion by comparing the retrospectively set diagnosis against the prospective opinion of another radiologist. After clean up, the resulting dataset consisted of 917 usable labelled exam sequences of left or right knees. Experimental results suggest that a linear-kernel SVM learned from HOG descriptors has the best generalisation properties among the experimental models compared, having an area under the curve of 0.894 for the injury-detection problem and 0.943 for the complete-rupture-detection problem. Although the problem of performing semi-automated ACL-injury diagnosis by observing knee-joint MR volumes alone is a difficult one, experimental results suggest potential clinical application of computer-aided decision making, both for detecting milder injuries and detecting complete ruptures. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Recent development on computer aided tissue engineering--a review.

PubMed

Sun, Wei; Lal, Pallavi

2002-02-01

The utilization of computer-aided technologies in tissue engineering has evolved in the development of a new field of computer-aided tissue engineering (CATE). This article reviews recent development and application of enabling computer technology, imaging technology, computer-aided design and computer-aided manufacturing (CAD and CAM), and rapid prototyping (RP) technology in tissue engineering, particularly, in computer-aided tissue anatomical modeling, three-dimensional (3-D) anatomy visualization and 3-D reconstruction, CAD-based anatomical modeling, computer-aided tissue classification, computer-aided tissue implantation and prototype modeling assisted surgical planning and reconstruction.

An Efficient Computational Framework for the Analysis of Whole Slide Images: Application to Follicular Lymphoma Immunohistochemistry

PubMed Central

Samsi, Siddharth; Krishnamurthy, Ashok K.; Gurcan, Metin N.

2012-01-01

Follicular Lymphoma (FL) is one of the most common non-Hodgkin Lymphoma in the United States. Diagnosis and grading of FL is based on the review of histopathological tissue sections under a microscope and is influenced by human factors such as fatigue and reader bias. Computer-aided image analysis tools can help improve the accuracy of diagnosis and grading and act as another tool at the pathologist’s disposal. Our group has been developing algorithms for identifying follicles in immunohistochemical images. These algorithms have been tested and validated on small images extracted from whole slide images. However, the use of these algorithms for analyzing the entire whole slide image requires significant changes to the processing methodology since the images are relatively large (on the order of 100k × 100k pixels). In this paper we discuss the challenges involved in analyzing whole slide images and propose potential computational methodologies for addressing these challenges. We discuss the use of parallel computing tools on commodity clusters and compare performance of the serial and parallel implementations of our approach. PMID:22962572

Computer-aided Classification of Mammographic Masses Using Visually Sensitive Image Features

PubMed Central

Wang, Yunzhi; Aghaei, Faranak; Zarafshani, Ali; Qiu, Yuchen; Qian, Wei; Zheng, Bin

2017-01-01

Purpose To develop a new computer-aided diagnosis (CAD) scheme that computes visually sensitive image features routinely used by radiologists to develop a machine learning classifier and distinguish between the malignant and benign breast masses detected from digital mammograms. Methods An image dataset including 301 breast masses was retrospectively selected. From each segmented mass region, we computed image features that mimic five categories of visually sensitive features routinely used by radiologists in reading mammograms. We then selected five optimal features in the five feature categories and applied logistic regression models for classification. A new CAD interface was also designed to show lesion segmentation, computed feature values and classification score. Results Areas under ROC curves (AUC) were 0.786±0.026 and 0.758±0.027 when to classify mass regions depicting on two view images, respectively. By fusing classification scores computed from two regions, AUC increased to 0.806±0.025. Conclusion This study demonstrated a new approach to develop CAD scheme based on 5 visually sensitive image features. Combining with a “visual aid” interface, CAD results may be much more easily explainable to the observers and increase their confidence to consider CAD generated classification results than using other conventional CAD approaches, which involve many complicated and visually insensitive texture features. PMID:27911353

HIV / AIDS: Symptoms, Diagnosis, Prevention and Treatment

MedlinePlus

Skip Navigation Bar Home Current Issue Past Issues HIV / AIDS HIV / AIDS: Symptoms , Diagnosis, Prevention and Treatment Past Issues / ... Most people who have become recently infected with HIV will not have any symptoms. They may, however, ...

Computer Aided Learning of Mathematics: Software Evaluation

ERIC Educational Resources Information Center

Yushau, B.; Bokhari, M. A.; Wessels, D. C. J.

2004-01-01

Computer Aided Learning of Mathematics (CALM) has been in use for some time in the Prep-Year Mathematics Program at King Fahd University of Petroleum & Minerals. Different kinds of software (both locally designed and imported) have been used in the quest of optimizing the recitation/problem session hour of the mathematics classes. This paper…

APPLICATION OF COMPUTER-AIDED TOMOGRAPHY TO VISUALIZE AND QUANTIFY BIOGENIC STRUCTURES IN MARINE SEDIMENTS

EPA Science Inventory

We used computer-aided tomography (CT) for 3D visualization and 2D analysis of

marine sediment cores from 3 stations (at 10, 75 and 118 m depths) with different environmental

impact. Biogenic structures such as tubes and burrows were quantified and compared among st...

3D reconstruction of the optic nerve head using stereo fundus images for computer-aided diagnosis of glaucoma

NASA Astrophysics Data System (ADS)

Tang, Li; Kwon, Young H.; Alward, Wallace L. M.; Greenlee, Emily C.; Lee, Kyungmoo; Garvin, Mona K.; Abràmoff, Michael D.

2010-03-01

The shape of the optic nerve head (ONH) is reconstructed automatically using stereo fundus color images by a robust stereo matching algorithm, which is needed for a quantitative estimate of the amount of nerve fiber loss for patients with glaucoma. Compared to natural scene stereo, fundus images are noisy because of the limits on illumination conditions and imperfections of the optics of the eye, posing challenges to conventional stereo matching approaches. In this paper, multi scale pixel feature vectors which are robust to noise are formulated using a combination of both pixel intensity and gradient features in scale space. Feature vectors associated with potential correspondences are compared with a disparity based matching score. The deep structures of the optic disc are reconstructed with a stack of disparity estimates in scale space. Optical coherence tomography (OCT) data was collected at the same time, and depth information from 3D segmentation was registered with the stereo fundus images to provide the ground truth for performance evaluation. In experiments, the proposed algorithm produces estimates for the shape of the ONH that are close to the OCT based shape, and it shows great potential to help computer-aided diagnosis of glaucoma and other related retinal diseases.

Segmentation of White Blood Cells From Microscopic Images Using a Novel Combination of K-Means Clustering and Modified Watershed Algorithm.

PubMed

Ghane, Narjes; Vard, Alireza; Talebi, Ardeshir; Nematollahy, Pardis

2017-01-01

Recognition of white blood cells (WBCs) is the first step to diagnose some particular diseases such as acquired immune deficiency syndrome, leukemia, and other blood-related diseases that are usually done by pathologists using an optical microscope. This process is time-consuming, extremely tedious, and expensive and needs experienced experts in this field. Thus, a computer-aided diagnosis system that assists pathologists in the diagnostic process can be so effective. Segmentation of WBCs is usually a first step in developing a computer-aided diagnosis system. The main purpose of this paper is to segment WBCs from microscopic images. For this purpose, we present a novel combination of thresholding, k-means clustering, and modified watershed algorithms in three stages including (1) segmentation of WBCs from a microscopic image, (2) extraction of nuclei from cell's image, and (3) separation of overlapping cells and nuclei. The evaluation results of the proposed method show that similarity measures, precision, and sensitivity respectively were 92.07, 96.07, and 94.30% for nucleus segmentation and 92.93, 97.41, and 93.78% for cell segmentation. In addition, statistical analysis presents high similarity between manual segmentation and the results obtained by the proposed method.

Computer-aided diagnosis of retinopathy in retinal fundus images of preterm infants via quantification of vascular tortuosity

PubMed Central

Oloumi, Faraz; Rangayyan, Rangaraj M.; Ells, Anna L.

2016-01-01

Abstract. Retinopathy of prematurity (ROP), a disorder of the retina occurring in preterm infants, is the leading cause of preventable childhood blindness. An active phase of ROP that requires treatment is associated with the presence of plus disease, which is diagnosed clinically in a qualitative manner by visual assessment of the existence of a certain level of increase in the thickness and tortuosity of retinal vessels. The present study performs computer-aided diagnosis (CAD) of plus disease via quantitative measurement of tortuosity in retinal fundus images of preterm infants. Digital image processing techniques were developed for the detection of retinal vessels and measurement of their tortuosity. The total lengths of abnormally tortuous vessels in each quadrant and the entire image were then computed. A minimum-length diagnostic-decision-making criterion was developed to assess the diagnostic sensitivity and specificity of the values obtained. The area (Az) under the receiver operating characteristic curve was used to assess the overall diagnostic accuracy of the methods. Using a set of 19 retinal fundus images of preterm infants with plus disease and 91 without plus disease, the proposed methods provided an overall diagnostic accuracy of Az=0.98. Using the total length of all abnormally tortuous vessel segments in an image, our techniques are capable of CAD of plus disease with high accuracy without the need for manual selection of vessels to analyze. The proposed methods may be used in a clinical or teleophthalmological setting. PMID:28018938

Content Based Image Retrieval based on Wavelet Transform coefficients distribution

PubMed Central

Lamard, Mathieu; Cazuguel, Guy; Quellec, Gwénolé; Bekri, Lynda; Roux, Christian; Cochener, Béatrice

2007-01-01

In this paper we propose a content based image retrieval method for diagnosis aid in medical fields. We characterize images without extracting significant features by using distribution of coefficients obtained by building signatures from the distribution of wavelet transform. The research is carried out by computing signature distances between the query and database images. Several signatures are proposed; they use a model of wavelet coefficient distribution. To enhance results, a weighted distance between signatures is used and an adapted wavelet base is proposed. Retrieval efficiency is given for different databases including a diabetic retinopathy, a mammography and a face database. Results are promising: the retrieval efficiency is higher than 95% for some cases using an optimization process. PMID:18003013

Computer-assisted cytologic diagnosis in pancreatic FNA: An application of neural networks to image analysis.

PubMed

Momeni-Boroujeni, Amir; Yousefi, Elham; Somma, Jonathan

2017-12-01

Fine-needle aspiration (FNA) biopsy is an accurate method for the diagnosis of solid pancreatic masses. However, a significant number of cases still pose a diagnostic challenge. The authors have attempted to design a computer model to aid in the diagnosis of these biopsies. Images were captured of cell clusters on ThinPrep slides from 75 pancreatic FNA cases (20 malignant, 24 benign, and 31 atypical). A K-means clustering algorithm was used to segment the cell clusters into separable regions of interest before extracting features similar to those used for cytomorphologic assessment. A multilayer perceptron neural network (MNN) was trained and then tested for its ability to distinguish benign from malignant cases. A total of 277 images of cell clusters were obtained. K-means clustering identified 68,301 possible regions of interest overall. Features such as contour, perimeter, and area were found to be significantly different between malignant and benign images (P <.05). The MNN was 100% accurate for benign and malignant categories. The model's predictions from the atypical data set were 77% accurate. The results of the current study demonstrate that computer models can be used successfully to distinguish benign from malignant pancreatic cytology. The fact that the model can categorize atypical cases into benign or malignant with 77% accuracy highlights the great potential of this technology. Although further study is warranted to validate its clinical applications in pancreatic and perhaps other areas of cytology as well, the potential for improved patient outcomes using MNN for image analysis in pathology is significant. Cancer Cytopathol 2017;125:926-33. © 2017 American Cancer Society. © 2017 American Cancer Society.

Computer-aided detection system for lung cancer in computed tomography scans: Review and future prospects

PubMed Central

2014-01-01

Introduction The goal of this paper is to present a critical review of major Computer-Aided Detection systems (CADe) for lung cancer in order to identify challenges for future research. CADe systems must meet the following requirements: improve the performance of radiologists providing high sensitivity in the diagnosis, a low number of false positives (FP), have high processing speed, present high level of automation, low cost (of implementation, training, support and maintenance), the ability to detect different types and shapes of nodules, and software security assurance. Methods The relevant literature related to “CADe for lung cancer” was obtained from PubMed, IEEEXplore and Science Direct database. Articles published from 2009 to 2013, and some articles previously published, were used. A systemic analysis was made on these articles and the results were summarized. Discussion Based on literature search, it was observed that many if not all systems described in this survey have the potential to be important in clinical practice. However, no significant improvement was observed in sensitivity, number of false positives, level of automation and ability to detect different types and shapes of nodules in the studied period. Challenges were presented for future research. Conclusions Further research is needed to improve existing systems and propose new solutions. For this, we believe that collaborative efforts through the creation of open source software communities are necessary to develop a CADe system with all the requirements mentioned and with a short development cycle. In addition, future CADe systems should improve the level of automation, through integration with picture archiving and communication systems (PACS) and the electronic record of the patient, decrease the number of false positives, measure the evolution of tumors, evaluate the evolution of the oncological treatment, and its possible prognosis. PMID:24713067

Computer-aided classification of breast microcalcification clusters: merging of features from image processing and radiologists

NASA Astrophysics Data System (ADS)

Lo, Joseph Y.; Gavrielides, Marios A.; Markey, Mia K.; Jesneck, Jonathan L.

2003-05-01

We developed an ensemble classifier for the task of computer-aided diagnosis of breast microcalcification clusters,which are very challenging to characterize for radiologists and computer models alike. The purpose of this study is to help radiologists identify whether suspicious calcification clusters are benign vs. malignant, such that they may potentially recommend fewer unnecessary biopsies for actually benign lesions. The data consists of mammographic features extracted by automated image processing algorithms as well as manually interpreted by radiologists according to a standardized lexicon. We used 292 cases from a publicly available mammography database. From each cases, we extracted 22 image processing features pertaining to lesion morphology, 5 radiologist features also pertaining to morphology, and the patient age. Linear discriminant analysis (LDA) models were designed using each of the three data types. Each local model performed poorly; the best was one based upon image processing features which yielded ROC area index AZ of 0.59 +/- 0.03 and partial AZ above 90% sensitivity of 0.08 +/- 0.03. We then developed ensemble models using different combinations of those data types, and these models all improved performance compared to the local models. The final ensemble model was based upon 5 features selected by stepwise LDA from all 28 available features. This ensemble performed with AZ of 0.69 +/- 0.03 and partial AZ of 0.21 +/- 0.04, which was statistically significantly better than the model based on the image processing features alone (p<0.001 and p=0.01 for full and partial AZ respectively). This demonstrated the value of the radiologist-extracted features as a source of information for this task. It also suggested there is potential for improved performance using this ensemble classifier approach to combine different sources of currently available data.

Computer-aided detection system for lung cancer in computed tomography scans: review and future prospects.

PubMed

Firmino, Macedo; Morais, Antônio H; Mendoça, Roberto M; Dantas, Marcel R; Hekis, Helio R; Valentim, Ricardo

2014-04-08

The goal of this paper is to present a critical review of major Computer-Aided Detection systems (CADe) for lung cancer in order to identify challenges for future research. CADe systems must meet the following requirements: improve the performance of radiologists providing high sensitivity in the diagnosis, a low number of false positives (FP), have high processing speed, present high level of automation, low cost (of implementation, training, support and maintenance), the ability to detect different types and shapes of nodules, and software security assurance. The relevant literature related to "CADe for lung cancer" was obtained from PubMed, IEEEXplore and Science Direct database. Articles published from 2009 to 2013, and some articles previously published, were used. A systemic analysis was made on these articles and the results were summarized. Based on literature search, it was observed that many if not all systems described in this survey have the potential to be important in clinical practice. However, no significant improvement was observed in sensitivity, number of false positives, level of automation and ability to detect different types and shapes of nodules in the studied period. Challenges were presented for future research. Further research is needed to improve existing systems and propose new solutions. For this, we believe that collaborative efforts through the creation of open source software communities are necessary to develop a CADe system with all the requirements mentioned and with a short development cycle. In addition, future CADe systems should improve the level of automation, through integration with picture archiving and communication systems (PACS) and the electronic record of the patient, decrease the number of false positives, measure the evolution of tumors, evaluate the evolution of the oncological treatment, and its possible prognosis.

A paradigm shift in orthognathic surgery? A comparison of navigation, computer-aided designed/computer-aided manufactured splints, and "classic" intermaxillary splints to surgical transfer of virtual orthognathic planning.

PubMed

Zinser, Max J; Sailer, Hermann F; Ritter, Lutz; Braumann, Bert; Maegele, Marc; Zöller, Joachim E

2013-12-01

Advances in computers and imaging have permitted the adoption of 3-dimensional (3D) virtual planning protocols in orthognathic surgery, which may allow a paradigm shift when the virtual planning can be transferred properly. The purpose of this investigation was to compare the versatility and precision of innovative computer-aided designed and computer-aided manufactured (CAD/CAM) surgical splints, intraoperative navigation, and "classic" intermaxillary occlusal splints for surgical transfer of virtual orthognathic planning. The protocols consisted of maxillofacial imaging, diagnosis, virtual orthognathic planning, and surgical planning transfer using newly designed CAD/CAM splints (approach A), navigation (approach B), and intermaxillary occlusal splints (approach C). In this prospective observational study, all patients underwent bimaxillary osteotomy. Eight patients were treated using approach A, 10 using approach B, and 12 using approach C. These techniques were evaluated by applying 13 hard and 7 soft tissue parameters to compare the virtual orthognathic planning (T0) with the postoperative result (T1) using 3D cephalometry and image fusion (ΔT1 vs T0). The highest precision (ΔT1 vs T0) for the maxillary planning transfer was observed with CAD/CAM splints (<0.23 mm; P > .05) followed by surgical "waferless" navigation (<0.61 mm, P < .05) and classic intermaxillary occlusal splints (<1.1 mm; P < .05). Only the innovative CAD/CAM splints kept the condyles in their central position in the temporomandibular joint. However, no technique enables a precise prediction of the mandible and soft tissue. CAD/CAM splints and surgical navigation provide a reliable, innovative, and precise approach for the transfer of virtual orthognathic planning. These computer-assisted techniques may offer an alternate approach to the use of classic intermaxillary occlusal splints. Copyright © 2013 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

Faultfinder: A diagnostic expert system with graceful degradation for onboard aircraft applications

NASA Technical Reports Server (NTRS)

Abbott, Kathy H.; Schutte, Paul C.; Palmer, Michael T.; Ricks, Wendell R.

1988-01-01

A research effort was conducted to explore the application of artificial intelligence technology to automation of fault monitoring and diagnosis as an aid to the flight crew. Human diagnostic reasoning was analyzed and actual accident and incident cases were reconstructed. Based on this analysis and reconstruction, diagnostic concepts were conceived and implemented for an aircraft's engine and hydraulic subsystems. These concepts are embedded within a multistage approach to diagnosis that reasons about time-based, causal, and qualitative information, and enables a certain amount of graceful degradation. The diagnostic concepts are implemented in a computer program called Faultfinder that serves as a research prototype.

PyEEG: an open source Python module for EEG/MEG feature extraction.

PubMed

Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

2011-01-01

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction.

[Automated identification, interpretation and classification of focal changes in the lungs on the images obtained at computed tomography for lung cancer screening].

PubMed

Barchuk, A A; Podolsky, M D; Tarakanov, S A; Kotsyuba, I Yu; Gaidukov, V S; Kuznetsov, V I; Merabishvili, V M; Barchuk, A S; Levchenko, E V; Filochkina, A V; Arseniev, A I

2015-01-01

This review article analyzes data of literature devoted to the description, interpretation and classification of focal (nodal) changes in the lungs detected by computed tomography of the chest cavity. There are discussed possible criteria for determining the most likely of their character--primary and metastatic tumor processes, inflammation, scarring, and autoimmune changes, tuberculosis and others. Identification of the most characteristic, reliable and statistically significant evidences of a variety of pathological processes in the lungs including the use of modern computer-aided detection and diagnosis of sites will optimize the diagnostic measures and ensure processing of a large volume of medical data in a short time.

UNSEDATED COMPUTED TOMOGRAPHY FOR DIAGNOSIS OF PELVIC CANAL OBSTRUCTION IN A LEOPARD GECKO (EUBLEPHARIS MACULARIUS).

PubMed

DeCourcy, Kelly; Hostnik, Eric T; Lorbach, Josh; Knoblaugh, Sue

2016-12-01

An adult leopard gecko ( Eublepharis macularius ) presented for lethargy, hyporexia, weight loss, decreased passage of waste, and a palpable caudal coelomic mass. Computed tomography showed a heterogeneous hyperattenuating (∼143 Hounsfield units) structure within the right caudal coelom. The distal colon-coprodeum lumen or urinary bladder was hypothesized as the most likely location for the heterogeneous structure. Medical support consisted of warm water and lubricant enema, as well as a heated environment. Medical intervention aided the passage of a plug comprised centrally of cholesterol and urates with peripheral stratified layers of fibrin, macrophages, heterophils, and bacteria. Within 24 hr, a follow-up computed tomography scan showed resolution of the pelvic canal plug.

PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction

PubMed Central

Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

2011-01-01

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction. PMID:21512582

Computer-aided diagnosis with potential application to rapid detection of disease outbreaks.

PubMed

Burr, Tom; Koster, Frederick; Picard, Rick; Forslund, Dave; Wokoun, Doug; Joyce, Ed; Brillman, Judith; Froman, Phil; Lee, Jack

2007-04-15

Our objectives are to quickly interpret symptoms of emergency patients to identify likely syndromes and to improve population-wide disease outbreak detection. We constructed a database of 248 syndromes, each syndrome having an estimated probability of producing any of 85 symptoms, with some two-way, three-way, and five-way probabilities reflecting correlations among symptoms. Using these multi-way probabilities in conjunction with an iterative proportional fitting algorithm allows estimation of full conditional probabilities. Combining these conditional probabilities with misdiagnosis error rates and incidence rates via Bayes theorem, the probability of each syndrome is estimated. We tested a prototype of computer-aided differential diagnosis (CADDY) on simulated data and on more than 100 real cases, including West Nile Virus, Q fever, SARS, anthrax, plague, tularaemia and toxic shock cases. We conclude that: (1) it is important to determine whether the unrecorded positive status of a symptom means that the status is negative or that the status is unknown; (2) inclusion of misdiagnosis error rates produces more realistic results; (3) the naive Bayes classifier, which assumes all symptoms behave independently, is slightly outperformed by CADDY, which includes available multi-symptom information on correlations; as more information regarding symptom correlations becomes available, the advantage of CADDY over the naive Bayes classifier should increase; (4) overlooking low-probability, high-consequence events is less likely if the standard output summary is augmented with a list of rare syndromes that are consistent with observed symptoms, and (5) accumulating patient-level probabilities across a larger population can aid in biosurveillance for disease outbreaks. c 2007 John Wiley & Sons, Ltd.

Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience.

PubMed

Komeda, Yoriaki; Handa, Hisashi; Watanabe, Tomohiro; Nomura, Takanobu; Kitahashi, Misaki; Sakurai, Toshiharu; Okamoto, Ayana; Minami, Tomohiro; Kono, Masashi; Arizumi, Tadaaki; Takenaka, Mamoru; Hagiwara, Satoru; Matsui, Shigenaga; Nishida, Naoshi; Kashida, Hiroshi; Kudo, Masatoshi

2017-01-01

Computer-aided diagnosis (CAD) is becoming a next-generation tool for the diagnosis of human disease. CAD for colon polyps has been suggested as a particularly useful tool for trainee colonoscopists, as the use of a CAD system avoids the complications associated with endoscopic resections. In addition to conventional CAD, a convolutional neural network (CNN) system utilizing artificial intelligence (AI) has been developing rapidly over the past 5 years. We attempted to generate a unique CNN-CAD system with an AI function that studied endoscopic images extracted from movies obtained with colonoscopes used in routine examinations. Here, we report our preliminary results of this novel CNN-CAD system for the diagnosis of colon polyps. A total of 1,200 images from cases of colonoscopy performed between January 2010 and December 2016 at Kindai University Hospital were used. These images were extracted from the video of actual endoscopic examinations. Additional video images from 10 cases of unlearned processes were retrospectively assessed in a pilot study. They were simply diagnosed as either an adenomatous or nonadenomatous polyp. The number of images used by AI to learn to distinguish adenomatous from nonadenomatous was 1,200:600. These images were extracted from the videos of actual endoscopic examinations. The size of each image was adjusted to 256 × 256 pixels. A 10-hold cross-validation was carried out. The accuracy of the 10-hold cross-validation is 0.751, where the accuracy is the ratio of the number of correct answers over the number of all the answers produced by the CNN. The decisions by the CNN were correct in 7 of 10 cases. A CNN-CAD system using routine colonoscopy might be useful for the rapid diagnosis of colorectal polyp classification. Further prospective studies in an in vivo setting are required to confirm the effectiveness of a CNN-CAD system in routine colonoscopy. © 2017 S. Karger AG, Basel.

Computer-aided-diagnosis (CAD) for colposcopy

NASA Astrophysics Data System (ADS)

Lange, Holger; Ferris, Daron G.

2005-04-01

Uterine cervical cancer is the second most common cancer among women worldwide. Colposcopy is a diagnostic method, whereby a physician (colposcopist) visually inspects the lower genital tract (cervix, vulva and vagina), with special emphasis on the subjective appearance of metaplastic epithelium comprising the transformation zone on the cervix. Cervical cancer precursor lesions and invasive cancer exhibit certain distinctly abnormal morphologic features. Lesion characteristics such as margin; color or opacity; blood vessel caliber, intercapillary spacing and distribution; and contour are considered by colposcopists to derive a clinical diagnosis. Clinicians and academia have suggested and shown proof of concept that automated image analysis of cervical imagery can be used for cervical cancer screening and diagnosis, having the potential to have a direct impact on improving women"s health care and reducing associated costs. STI Medical Systems is developing a Computer-Aided-Diagnosis (CAD) system for colposcopy -- ColpoCAD. At the heart of ColpoCAD is a complex multi-sensor, multi-data and multi-feature image analysis system. A functional description is presented of the envisioned ColpoCAD system, broken down into: Modality Data Management System, Image Enhancement, Feature Extraction, Reference Database, and Diagnosis and directed Biopsies. The system design and development process of the image analysis system is outlined. The system design provides a modular and open architecture built on feature based processing. The core feature set includes the visual features used by colposcopists. This feature set can be extended to include new features introduced by new instrument technologies, like fluorescence and impedance, and any other plausible feature that can be extracted from the cervical data. Preliminary results of our research on detecting the three most important features: blood vessel structures, acetowhite regions and lesion margins are shown. As this is a new and very complex field in medical image processing, the hope is that this paper can provide a framework and basis to encourage and facilitate collaboration and discussion between industry, academia, and medical practitioners.

Role of Gist and PHOG Features in Computer-Aided Diagnosis of Tuberculosis without Segmentation

PubMed Central

Chauhan, Arun; Chauhan, Devesh; Rout, Chittaranjan

2014-01-01

Purpose Effective diagnosis of tuberculosis (TB) relies on accurate interpretation of radiological patterns found in a chest radiograph (CXR). Lack of skilled radiologists and other resources, especially in developing countries, hinders its efficient diagnosis. Computer-aided diagnosis (CAD) methods provide second opinion to the radiologists for their findings and thereby assist in better diagnosis of cancer and other diseases including TB. However, existing CAD methods for TB are based on the extraction of textural features from manually or semi-automatically segmented CXRs. These methods are prone to errors and cannot be implemented in X-ray machines for automated classification. Methods Gabor, Gist, histogram of oriented gradients (HOG), and pyramid histogram of oriented gradients (PHOG) features extracted from the whole image can be implemented into existing X-ray machines to discriminate between TB and non-TB CXRs in an automated manner. Localized features were extracted for the above methods using various parameters, such as frequency range, blocks and region of interest. The performance of these features was evaluated against textural features. Two digital CXR image datasets (8-bit DA and 14-bit DB) were used for evaluating the performance of these features. Results Gist (accuracy 94.2% for DA, 86.0% for DB) and PHOG (accuracy 92.3% for DA, 92.0% for DB) features provided better results for both the datasets. These features were implemented to develop a MATLAB toolbox, TB-Xpredict, which is freely available for academic use at http://sourceforge.net/projects/tbxpredict/. This toolbox provides both automated training and prediction modules and does not require expertise in image processing for operation. Conclusion Since the features used in TB-Xpredict do not require segmentation, the toolbox can easily be implemented in X-ray machines. This toolbox can effectively be used for the mass screening of TB in high-burden areas with improved efficiency. PMID:25390291

Modeling, simulation, and analysis at Sandia National Laboratories for health care systems

NASA Astrophysics Data System (ADS)

Polito, Joseph

1994-12-01

Modeling, Simulation, and Analysis are special competencies of the Department of Energy (DOE) National Laboratories which have been developed and refined through years of national defense work. Today, many of these skills are being applied to the problem of understanding the performance of medical devices and treatments. At Sandia National Laboratories we are developing models at all three levels of health care delivery: (1) phenomenology models for Observation and Test, (2) model-based outcomes simulations for Diagnosis and Prescription, and (3) model-based design and control simulations for the Administration of Treatment. A sampling of specific applications include non-invasive sensors for blood glucose, ultrasonic scanning for development of prosthetics, automated breast cancer diagnosis, laser burn debridement, surgical staple deformation, minimally invasive control for administration of a photodynamic drug, and human-friendly decision support aids for computer-aided diagnosis. These and other projects are being performed at Sandia with support from the DOE and in cooperation with medical research centers and private companies. Our objective is to leverage government engineering, modeling, and simulation skills with the biotechnical expertise of the health care community to create a more knowledge-rich environment for decision making and treatment.

Levels of Death Anxiety in Terminally Ill Men: A Pilot Study.

ERIC Educational Resources Information Center

Hayslip, Bert, Jr.; And Others

1992-01-01

Administered measures of overt and covert fear of death to 20 healthy men and 13 men diagnosed with Acquired Immune Deficiency Syndrome (AIDS). Groups did not differ significantly on overt measure; AIDS group had higher total scores on covert measure. Findings suggest that one's life trajectory is redefined when the diagnosis of terminal illness…

A tutorial on the use of ROC analysis for computer-aided diagnostic systems.

PubMed

Scheipers, Ulrich; Perrey, Christian; Siebers, Stefan; Hansen, Christian; Ermert, Helmut

2005-07-01

The application of the receiver operating characteristic (ROC) curve for computer-aided diagnostic systems is reviewed. A statistical framework is presented and different methods of evaluating the classification performance of computer-aided diagnostic systems, and, in particular, systems for ultrasonic tissue characterization, are derived. Most classifiers that are used today are dependent on a separation threshold, which can be chosen freely in many cases. The separation threshold separates the range of output values of the classification system into different target groups, thus conducting the actual classification process. In the first part of this paper, threshold specific performance measures, e.g., sensitivity and specificity, are presented. In the second part, a threshold-independent performance measure, the area under the ROC curve, is reviewed. Only the use of separation threshold-independent performance measures provides classification results that are overall representative for computer-aided diagnostic systems. The following text was motivated by the lack of a complete and definite discussion of the underlying subject in available textbooks, references and publications. Most manuscripts published so far address the theme of performance evaluation using ROC analysis in a manner too general to be practical for everyday use in the development of computer-aided diagnostic systems. Nowadays, the user of computer-aided diagnostic systems typically handles huge amounts of numerical data, not always distributed normally. Many assumptions made in more or less theoretical works on ROC analysis are no longer valid for real-life data. The paper aims at closing the gap between theoretical works and real-life data. The review provides the interested scientist with information needed to conduct ROC analysis and to integrate algorithms performing ROC analysis into classification systems while understanding the basic principles of classification.

SVM feature selection based rotation forest ensemble classifiers to improve computer-aided diagnosis of Parkinson disease.

PubMed

Ozcift, Akin

2012-08-01

Parkinson disease (PD) is an age-related deterioration of certain nerve systems, which affects movement, balance, and muscle control of clients. PD is one of the common diseases which affect 1% of people older than 60 years. A new classification scheme based on support vector machine (SVM) selected features to train rotation forest (RF) ensemble classifiers is presented for improving diagnosis of PD. The dataset contains records of voice measurements from 31 people, 23 with PD and each record in the dataset is defined with 22 features. The diagnosis model first makes use of a linear SVM to select ten most relevant features from 22. As a second step of the classification model, six different classifiers are trained with the subset of features. Subsequently, at the third step, the accuracies of classifiers are improved by the utilization of RF ensemble classification strategy. The results of the experiments are evaluated using three metrics; classification accuracy (ACC), Kappa Error (KE) and Area under the Receiver Operating Characteristic (ROC) Curve (AUC). Performance measures of two base classifiers, i.e. KStar and IBk, demonstrated an apparent increase in PD diagnosis accuracy compared to similar studies in literature. After all, application of RF ensemble classification scheme improved PD diagnosis in 5 of 6 classifiers significantly. We, numerically, obtained about 97% accuracy in RF ensemble of IBk (a K-Nearest Neighbor variant) algorithm, which is a quite high performance for Parkinson disease diagnosis.

Texture Feature Extraction and Classification for Iris Diagnosis

NASA Astrophysics Data System (ADS)

Ma, Lin; Li, Naimin

Appling computer aided techniques in iris image processing, and combining occidental iridology with the traditional Chinese medicine is a challenging research area in digital image processing and artificial intelligence. This paper proposes an iridology model that consists the iris image pre-processing, texture feature analysis and disease classification. To the pre-processing, a 2-step iris localization approach is proposed; a 2-D Gabor filter based texture analysis and a texture fractal dimension estimation method are proposed for pathological feature extraction; and at last support vector machines are constructed to recognize 2 typical diseases such as the alimentary canal disease and the nerve system disease. Experimental results show that the proposed iridology diagnosis model is quite effective and promising for medical diagnosis and health surveillance for both hospital and public use.

Functional tremor.

PubMed

Schwingenschuh, P; Deuschl, G

2016-01-01

Functional tremor is the commonest reported functional movement disorder. A confident clinical diagnosis of functional tremor is often possible based on the following "positive" criteria: a sudden tremor onset, unusual disease course, often with fluctuations or remissions, distractibility of the tremor if attention is removed from the affected body part, tremor entrainment, tremor variability, and a coactivation sign. Many patients show excessive exhaustion during examination. Other somatizations may be revealed in the medical history and patients may show additional functional neurologic symptoms and signs. In cases where the clinical diagnosis remains challenging, providing a "laboratory-supported" level of certainty aids an early positive diagnosis. In rare cases, in which the distinction from Parkinson's disease is difficult, dopamine transporter single-photon emission computed tomography (DAT-SPECT) can be indicated. © 2016 Elsevier B.V. All rights reserved.

Computer-aided diagnosis for osteoporosis using chest 3D CT images

NASA Astrophysics Data System (ADS)

Yoneda, K.; Matsuhiro, M.; Suzuki, H.; Kawata, Y.; Niki, N.; Nakano, Y.; Ohmatsu, H.; Kusumoto, M.; Tsuchida, T.; Eguchi, K.; Kaneko, M.

2016-03-01

The patients of osteoporosis comprised of about 13 million people in Japan and it is one of the problems the aging society has. In order to prevent the osteoporosis, it is necessary to do early detection and treatment. Multi-slice CT technology has been improving the three dimensional (3-D) image analysis with higher body axis resolution and shorter scan time. The 3-D image analysis using multi-slice CT images of thoracic vertebra can be used as a support to diagnose osteoporosis and at the same time can be used for lung cancer diagnosis which may lead to early detection. We develop automatic extraction and partitioning algorithm for spinal column by analyzing vertebral body structure, and the analysis algorithm of the vertebral body using shape analysis and a bone density measurement for the diagnosis of osteoporosis. Osteoporosis diagnosis support system obtained high extraction rate of the thoracic vertebral in both normal and low doses.

The effect of feature selection methods on computer-aided detection of masses in mammograms

NASA Astrophysics Data System (ADS)

Hupse, Rianne; Karssemeijer, Nico

2010-05-01

In computer-aided diagnosis (CAD) research, feature selection methods are often used to improve generalization performance of classifiers and shorten computation times. In an application that detects malignant masses in mammograms, we investigated the effect of using a selection criterion that is similar to the final performance measure we are optimizing, namely the mean sensitivity of the system in a predefined range of the free-response receiver operating characteristics (FROC). To obtain the generalization performance of the selected feature subsets, a cross validation procedure was performed on a dataset containing 351 abnormal and 7879 normal regions, each region providing a set of 71 mass features. The same number of noise features, not containing any information, were added to investigate the ability of the feature selection algorithms to distinguish between useful and non-useful features. It was found that significantly higher performances were obtained using feature sets selected by the general test statistic Wilks' lambda than using feature sets selected by the more specific FROC measure. Feature selection leads to better performance when compared to a system in which all features were used.

Features of Computer-Based Decision Aids: Systematic Review, Thematic Synthesis, and Meta-Analyses

PubMed Central

Krömker, Dörthe; Meguerditchian, Ari N; Tamblyn, Robyn

2016-01-01

Background Patient information and education, such as decision aids, are gradually moving toward online, computer-based environments. Considerable research has been conducted to guide content and presentation of decision aids. However, given the relatively new shift to computer-based support, little attention has been given to how multimedia and interactivity can improve upon paper-based decision aids. Objective The first objective of this review was to summarize published literature into a proposed classification of features that have been integrated into computer-based decision aids. Building on this classification, the second objective was to assess whether integration of specific features was associated with higher-quality decision making. Methods Relevant studies were located by searching MEDLINE, Embase, CINAHL, and CENTRAL databases. The review identified studies that evaluated computer-based decision aids for adults faced with preference-sensitive medical decisions and reported quality of decision-making outcomes. A thematic synthesis was conducted to develop the classification of features. Subsequently, meta-analyses were conducted based on standardized mean differences (SMD) from randomized controlled trials (RCTs) that reported knowledge or decisional conflict. Further subgroup analyses compared pooled SMDs for decision aids that incorporated a specific feature to other computer-based decision aids that did not incorporate the feature, to assess whether specific features improved quality of decision making. Results Of 3541 unique publications, 58 studies met the target criteria and were included in the thematic synthesis. The synthesis identified six features: content control, tailoring, patient narratives, explicit values clarification, feedback, and social support. A subset of 26 RCTs from the thematic synthesis was used to conduct the meta-analyses. As expected, computer-based decision aids performed better than usual care or alternative aids; however, some features performed better than others. Integration of content control improved quality of decision making (SMD 0.59 vs 0.23 for knowledge; SMD 0.39 vs 0.29 for decisional conflict). In contrast, tailoring reduced quality of decision making (SMD 0.40 vs 0.71 for knowledge; SMD 0.25 vs 0.52 for decisional conflict). Similarly, patient narratives also reduced quality of decision making (SMD 0.43 vs 0.65 for knowledge; SMD 0.17 vs 0.46 for decisional conflict). Results were varied for different types of explicit values clarification, feedback, and social support. Conclusions Integration of media rich or interactive features into computer-based decision aids can improve quality of preference-sensitive decision making. However, this is an emerging field with limited evidence to guide use. The systematic review and thematic synthesis identified features that have been integrated into available computer-based decision aids, in an effort to facilitate reporting of these features and to promote integration of such features into decision aids. The meta-analyses and associated subgroup analyses provide preliminary evidence to support integration of specific features into future decision aids. Further research can focus on clarifying independent contributions of specific features through experimental designs and refining the designs of features to improve effectiveness. PMID:26813512

Computer-aided diagnosis based on enhancement of degraded fundus photographs.

PubMed

Jin, Kai; Zhou, Mei; Wang, Shaoze; Lou, Lixia; Xu, Yufeng; Ye, Juan; Qian, Dahong

2018-05-01

Retinal imaging is an important and effective tool for detecting retinal diseases. However, degraded images caused by the aberrations of the eye can disguise lesions, so that a diseased eye can be mistakenly diagnosed as normal. In this work, we propose a new image enhancement method to improve the quality of degraded images. A new method is used to enhance degraded-quality fundus images. In this method, the image is converted from the input RGB colour space to LAB colour space and then each normalized component is enhanced using contrast-limited adaptive histogram equalization. Human visual system (HVS)-based fundus image quality assessment, combined with diagnosis by experts, is used to evaluate the enhancement. The study included 191 degraded-quality fundus photographs of 143 subjects with optic media opacity. Objective quality assessment of image enhancement (range: 0-1) indicated that our method improved colour retinal image quality from an average of 0.0773 (variance 0.0801) to an average of 0.3973 (variance 0.0756). Following enhancement, area under curves (AUC) were 0.996 for the glaucoma classifier, 0.989 for the diabetic retinopathy (DR) classifier, 0.975 for the age-related macular degeneration (AMD) classifier and 0.979 for the other retinal diseases classifier. The relatively simple method for enhancing degraded-quality fundus images achieves superior image enhancement, as demonstrated in a qualitative HVS-based image quality assessment. This retinal image enhancement may, therefore, be employed to assist ophthalmologists in more efficient screening of retinal diseases and the development of computer-aided diagnosis. © 2017 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

A new 3D texture feature based computer-aided diagnosis approach to differentiate pulmonary nodules

NASA Astrophysics Data System (ADS)

Han, Fangfang; Wang, Huafeng; Song, Bowen; Zhang, Guopeng; Lu, Hongbing; Moore, William; Zhao, Hong; Liang, Zhengrong

2013-02-01

To distinguish malignant pulmonary nodules from benign ones is of much importance in computer-aided diagnosis of lung diseases. Compared to many previous methods which are based on shape or growth assessing of nodules, this proposed three-dimensional (3D) texture feature based approach extracted fifty kinds of 3D textural features from gray level, gradient and curvature co-occurrence matrix, and more derivatives of the volume data of the nodules. To evaluate the presented approach, the Lung Image Database Consortium public database was downloaded. Each case of the database contains an annotation file, which indicates the diagnosis results from up to four radiologists. In order to relieve partial-volume effect, interpolation process was carried out to those volume data with image slice thickness more than 1mm, and thus we had categorized the downloaded datasets to five groups to validate the proposed approach, one group of thickness less than 1mm, two types of thickness range from 1mm to 1.25mm and greater than 1.25mm (each type contains two groups, one with interpolation and the other without). Since support vector machine is based on statistical learning theory and aims to learn for predicting future data, so it was chosen as the classifier to perform the differentiation task. The measure on the performance was based on the area under the curve (AUC) of Receiver Operating Characteristics. From 284 nodules (122 malignant and 162 benign ones), the validation experiments reported a mean of 0.9051 and standard deviation of 0.0397 for the AUC value on average over 100 randomizations.

Modeling and Analysis of Power Processing Systems (MAPPS). Volume 1: Technical report

NASA Technical Reports Server (NTRS)

Lee, F. C.; Rahman, S.; Carter, R. A.; Wu, C. H.; Yu, Y.; Chang, R.

1980-01-01

Computer aided design and analysis techniques were applied to power processing equipment. Topics covered include: (1) discrete time domain analysis of switching regulators for performance analysis; (2) design optimization of power converters using augmented Lagrangian penalty function technique; (3) investigation of current-injected multiloop controlled switching regulators; and (4) application of optimization for Navy VSTOL energy power system. The generation of the mathematical models and the development and application of computer aided design techniques to solve the different mathematical models are discussed. Recommendations are made for future work that would enhance the application of the computer aided design techniques for power processing systems.

Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence

PubMed Central

Jaworek-Korjakowska, Joanna; Kłeczek, Paweł

2016-01-01

Background. Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis (CAD) systems have been proposed to increase the specificity and sensitivity of melanoma detection. Although such computer programs are developed for different diagnostic algorithms, to the best of our knowledge, a system to classify different melanocytic lesions has not been proposed yet. Method. In this research we present a new approach to the classification of melanocytic lesions. This work is focused not only on categorization of skin lesions as benign or malignant but also on specifying the exact type of a skin lesion including melanoma, Clark nevus, Spitz/Reed nevus, and blue nevus. The proposed automatic algorithm contains the following steps: image enhancement, lesion segmentation, feature extraction, and selection as well as classification. Results. The algorithm has been tested on 300 dermoscopic images and achieved accuracy of 92% indicating that the proposed approach classified most of the melanocytic lesions correctly. Conclusions. A proposed system can not only help to precisely diagnose the type of the skin mole but also decrease the amount of biopsies and reduce the morbidity related to skin lesion excision. PMID:26885520

Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence.

PubMed

Jaworek-Korjakowska, Joanna; Kłeczek, Paweł

2016-01-01

Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis (CAD) systems have been proposed to increase the specificity and sensitivity of melanoma detection. Although such computer programs are developed for different diagnostic algorithms, to the best of our knowledge, a system to classify different melanocytic lesions has not been proposed yet. In this research we present a new approach to the classification of melanocytic lesions. This work is focused not only on categorization of skin lesions as benign or malignant but also on specifying the exact type of a skin lesion including melanoma, Clark nevus, Spitz/Reed nevus, and blue nevus. The proposed automatic algorithm contains the following steps: image enhancement, lesion segmentation, feature extraction, and selection as well as classification. The algorithm has been tested on 300 dermoscopic images and achieved accuracy of 92% indicating that the proposed approach classified most of the melanocytic lesions correctly. A proposed system can not only help to precisely diagnose the type of the skin mole but also decrease the amount of biopsies and reduce the morbidity related to skin lesion excision.

A Comparative Study of Computer-Aided Clinical Diagnosis of Birth Defects.

DTIC Science & Technology

1981-01-21

SUPPRESSIBLE HYPERPARATHYROIDISM TYPE NEONATAL FAMILIAL HYPOCHONDROPLASIA HYPOGLYCEMIA TYPE FAMILIAL NEONATAL HYPOGLYCEMIA TYPE LEUCINE INDUCED...disease. There are also causal links between disease nodes. These are unidirectional links indicating that one disease can cause the other. These links...contains the finding and has no superior which does.4 If f5 was then entered, D8 and D9 would quality to be evoked. This would cause D1 to be deactivated

A Computer-Aided Diagnosis System for Breast Cancer Combining Mammography and Proteomics

DTIC Science & Technology

2007-05-01

findings in both Data sets C and M. The likelihood ratio is the probability of the features un- der the malignant case divided by the probability of...likelihood ratio value as a classification decision variable, the probabilities of detection and false alarm are calculated as follows: Pdfusion...lowered the fused classifier’s performance to near chance levels. A genetic algorithm searched over the likelihood- ratio thresh- old values for each

Automated detection of pulmonary nodules in CT images with support vector machines

NASA Astrophysics Data System (ADS)

Liu, Lu; Liu, Wanyu; Sun, Xiaoming

2008-10-01

Many methods have been proposed to avoid radiologists fail to diagnose small pulmonary nodules. Recently, support vector machines (SVMs) had received an increasing attention for pattern recognition. In this paper, we present a computerized system aimed at pulmonary nodules detection; it identifies the lung field, extracts a set of candidate regions with a high sensitivity ratio and then classifies candidates by the use of SVMs. The Computer Aided Diagnosis (CAD) system presented in this paper supports the diagnosis of pulmonary nodules from Computed Tomography (CT) images as inflammation, tuberculoma, granuloma..sclerosing hemangioma, and malignant tumor. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of SVMs classifiers. The achieved classification performance was 100%, 92.75% and 90.23% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers.

PubMed

Mougiakakou, Stavroula G; Valavanis, Ioannis K; Nikita, Alexandra; Nikita, Konstantina S

2007-09-01

The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed. Number of regions of interests (ROIs) corresponding to C1-C4 have been defined by experienced radiologists in non-enhanced liver CT images. For each ROI, five distinct sets of texture features were extracted using first order statistics, spatial gray level dependence matrix, gray level difference method, Laws' texture energy measures, and fractal dimension measurements. Two different ECs were constructed and compared. The first one consists of five multilayer perceptron neural networks (NNs), each using as input one of the computed texture feature sets or its reduced version after genetic algorithm-based feature selection. The second EC comprised five different primary classifiers, namely one multilayer perceptron NN, one probabilistic NN, and three k-nearest neighbor classifiers, each fed with the combination of the five texture feature sets or their reduced versions. The final decision of each EC was extracted by using appropriate voting schemes, while bootstrap re-sampling was utilized in order to estimate the generalization ability of the CAD architectures based on the available relatively small-sized data set. The best mean classification accuracy (84.96%) is achieved by the second EC using a fused feature set, and the weighted voting scheme. The fused feature set was obtained after appropriate feature selection applied to specific subsets of the original feature set. The comparative assessment of the various CAD architectures shows that combining three types of classifiers with a voting scheme, fed with identical feature sets obtained after appropriate feature selection and fusion, may result in an accurate system able to assist differential diagnosis of focal liver lesions from non-enhanced CT images.

geneCBR: a translational tool for multiple-microarray analysis and integrative information retrieval for aiding diagnosis in cancer research.

PubMed

Glez-Peña, Daniel; Díaz, Fernando; Hernández, Jesús M; Corchado, Juan M; Fdez-Riverola, Florentino

2009-06-18

Bioinformatics and medical informatics are two research fields that serve the needs of different but related communities. Both domains share the common goal of providing new algorithms, methods and technological solutions to biomedical research, and contributing to the treatment and cure of diseases. Although different microarray techniques have been successfully used to investigate useful information for cancer diagnosis at the gene expression level, the true integration of existing methods into day-to-day clinical practice is still a long way off. Within this context, case-based reasoning emerges as a suitable paradigm specially intended for the development of biomedical informatics applications and decision support systems, given the support and collaboration involved in such a translational development. With the goals of removing barriers against multi-disciplinary collaboration and facilitating the dissemination and transfer of knowledge to real practice, case-based reasoning systems have the potential to be applied to translational research mainly because their computational reasoning paradigm is similar to the way clinicians gather, analyze and process information in their own practice of clinical medicine. In addressing the issue of bridging the existing gap between biomedical researchers and clinicians who work in the domain of cancer diagnosis, prognosis and treatment, we have developed and made accessible a common interactive framework. Our geneCBR system implements a freely available software tool that allows the use of combined techniques that can be applied to gene selection, clustering, knowledge extraction and prediction for aiding diagnosis in cancer research. For biomedical researches, geneCBR expert mode offers a core workbench for designing and testing new techniques and experiments. For pathologists or oncologists, geneCBR diagnostic mode implements an effective and reliable system that can diagnose cancer subtypes based on the analysis of microarray data using a CBR architecture. For programmers, geneCBR programming mode includes an advanced edition module for run-time modification of previous coded techniques. geneCBR is a new translational tool that can effectively support the integrative work of programmers, biomedical researches and clinicians working together in a common framework. The code is freely available under the GPL license and can be obtained at http://www.genecbr.org.

Deep Learning in Gastrointestinal Endoscopy.

PubMed

Patel, Vivek; Armstrong, David; Ganguli, Malika; Roopra, Sandeep; Kantipudi, Neha; Albashir, Siwar; Kamath, Markad V

2016-01-01

Gastrointestinal (GI) endoscopy is used to inspect the lumen or interior of the GI tract for several purposes, including, (1) making a clinical diagnosis, in real time, based on the visual appearances; (2) taking targeted tissue samples for subsequent histopathological examination; and (3) in some cases, performing therapeutic interventions targeted at specific lesions. GI endoscopy is therefore predicated on the assumption that the operator-the endoscopist-is able to identify and characterize abnormalities or lesions accurately and reproducibly. However, as in other areas of clinical medicine, such as histopathology and radiology, many studies have documented marked interobserver and intraobserver variability in lesion recognition. Thus, there is a clear need and opportunity for techniques or methodologies that will enhance the quality of lesion recognition and diagnosis and improve the outcomes of GI endoscopy. Deep learning models provide a basis to make better clinical decisions in medical image analysis. Biomedical image segmentation, classification, and registration can be improved with deep learning. Recent evidence suggests that the application of deep learning methods to medical image analysis can contribute significantly to computer-aided diagnosis. Deep learning models are usually considered to be more flexible and provide reliable solutions for image analysis problems compared to conventional computer vision models. The use of fast computers offers the possibility of real-time support that is important for endoscopic diagnosis, which has to be made in real time. Advanced graphics processing units and cloud computing have also favored the use of machine learning, and more particularly, deep learning for patient care. This paper reviews the rapidly evolving literature on the feasibility of applying deep learning algorithms to endoscopic imaging.

Colour vision in AIDS patients without HIV retinopathy.

PubMed

Sommerhalder, J; Baglivo, E; Barbey, C; Hirschel, B; Roth, A; Pelizzone, M

1998-11-01

Patients suffering from AIDS develop ocular complications, the most frequent being HIV retinopathy. It is however not clear, if functional visual impairments can be observed as early indicators of ocular complications, before clinical diagnosis of HIV retinopathy is made at fundus examination. To address this issue, we measured colour vision in a group of 49 AIDS subjects with normal clinical fundi using the 'two equation method'. This method, combining red-green Rayleigh and the blue-green Moreland metameric matches, enables more complete and quantitative assessments of colour vision than those based on pigmentary tests. Data were collected on our computer controlled colorimeter and compared to those of normal subjects. While most AIDS subjects without HIV retinopathy demonstrated normal colour vision, a significant portion of them had wider matches than normal subjects (11% for the Rayleigh equation and 16% for the Moreland equation). Furthermore, matching ranges of the Moreland equation were significantly correlated with CD4 lymphocyte counts. Patients with low CD4 values tended to produce larger matching ranges than the patients with high CD4 values. A within subject study on 17 patients confirmed this trend and showed that the patients who increased/decreased their CD4 blood counts generally improved/impaired their colour discrimination in the Moreland match. No such correlation was found between the matching ranges of the Rayleigh equation and the CD4 counts. These results show that colour discrimination is slightly reduced in some AIDS subjects, although there are no detectable ocular complications. They also suggest two different types of colour vision impairments in AIDS patients without retinopathy: one reversible process affecting colour discrimination in the blue-green range; and another irreversible process affecting colour discrimination in the red-green range.

[Computer-assisted phonetography as a diagnostic aid in functional dysphonia].

PubMed

Airainer, R; Klingholz, F

1991-07-01

A total of 160 voice-trained and untrained subjects with functional dysphonia were given a "clinical rating" according to their clinical findings. This was a certain value on a scale that recorded the degree of functional voice disorder ranging from a marked hypofunction to an extreme hyperfunction. The phonetograms of these patients were approximated by ellipses, whereby the definition and quantitative recording of several phonetogram parameters were rendered possible. By means of a linear combination of phonetogram parameters, a "calculated assessment" was obtained for each patient that was expected to tally with the "clinical rating". This paper demonstrates that a graduation of the dysphonic clinical picture with regard to the presence of hypofunctional or hyperfunctional components is possible via computerised phonetogram evaluation. In this case, the "calculated assessments" for both male and female singers and non-singers must be computed using different linear combinations. The method can be introduced as a supplementary diagnostic procedure in the diagnosis of functional dysphonia.

Needs assessment for next generation computer-aided mammography reference image databases and evaluation studies.

PubMed

Horsch, Alexander; Hapfelmeier, Alexander; Elter, Matthias

2011-11-01

Breast cancer is globally a major threat for women's health. Screening and adequate follow-up can significantly reduce the mortality from breast cancer. Human second reading of screening mammograms can increase breast cancer detection rates, whereas this has not been proven for current computer-aided detection systems as "second reader". Critical factors include the detection accuracy of the systems and the screening experience and training of the radiologist with the system. When assessing the performance of systems and system components, the choice of evaluation methods is particularly critical. Core assets herein are reference image databases and statistical methods. We have analyzed characteristics and usage of the currently largest publicly available mammography database, the Digital Database for Screening Mammography (DDSM) from the University of South Florida, in literature indexed in Medline, IEEE Xplore, SpringerLink, and SPIE, with respect to type of computer-aided diagnosis (CAD) (detection, CADe, or diagnostics, CADx), selection of database subsets, choice of evaluation method, and quality of descriptions. 59 publications presenting 106 evaluation studies met our selection criteria. In 54 studies (50.9%), the selection of test items (cases, images, regions of interest) extracted from the DDSM was not reproducible. Only 2 CADx studies, not any CADe studies, used the entire DDSM. The number of test items varies from 100 to 6000. Different statistical evaluation methods are chosen. Most common are train/test (34.9% of the studies), leave-one-out (23.6%), and N-fold cross-validation (18.9%). Database-related terminology tends to be imprecise or ambiguous, especially regarding the term "case". Overall, both the use of the DDSM as data source for evaluation of mammography CAD systems, and the application of statistical evaluation methods were found highly diverse. Results reported from different studies are therefore hardly comparable. Drawbacks of the DDSM (e.g. varying quality of lesion annotations) may contribute to the reasons. But larger bias seems to be caused by authors' own decisions upon study design. RECOMMENDATIONS/CONCLUSION: For future evaluation studies, we derive a set of 13 recommendations concerning the construction and usage of a test database, as well as the application of statistical evaluation methods.

Methodology for Benefit Analysis of CAD/CAM (Computer-Aided Design/Computer-Aided Manufacturing) in USN Shipyards.

DTIC Science & Technology

1984-03-01

difference calcula- tion, would result in erroneously lower productivity ratios. Only two topics are not adequately addressed with the method --the first...for determination of this term. 3. CAIDQS P.22li y §easuI~gZ._ Jelbo The next method differs significantly from the previous two in that it deals with...Chasen’s Method (as applied by Lonq Beach M.S.) . . . . . . . o . o . . . . . 31 2. Shah G Yans Method . . . . . . . . . . . 34 3. CARDOS Productivity

Evaluation of Computer-Aided Instruction in a Gross Anatomy Course: A Six-Year Study

ERIC Educational Resources Information Center

McNulty, John A.; Sonntag, Beth; Sinacore, James M.

2009-01-01

Web-based computer-aided instruction (CAI) has become increasingly important to medical curricula. This multi-year study investigated the effectiveness of CAI and the factors affecting level of individual use. Three CAI were tested that differed in specificity of applicability to the curriculum and in the level of student interaction with the CAI.…

A Computer-Aided Technique for Stylistic Discrimination; The Authorship of "Greene's Groatsworth of Wit." Final Report.

ERIC Educational Resources Information Center

Austin, Warren B.

Who wrote "The Groatsworth of Wit?" Was it Greene, as hitherto believed, or Chettle? To distinguish between the two writers' styles, and thereby determine the authorship of a 16th Century literary work of particular interest to Shakespearean scholars, computer-aided techniques were employed. The two authors' differing practices in word choice and…

Exploration of a physiologically-inspired hearing-aid algorithm using a computer model mimicking impaired hearing.

PubMed

Jürgens, Tim; Clark, Nicholas R; Lecluyse, Wendy; Meddis, Ray

2016-01-01

To use a computer model of impaired hearing to explore the effects of a physiologically-inspired hearing-aid algorithm on a range of psychoacoustic measures. A computer model of a hypothetical impaired listener's hearing was constructed by adjusting parameters of a computer model of normal hearing. Absolute thresholds, estimates of compression, and frequency selectivity (summarized to a hearing profile) were assessed using this model with and without pre-processing the stimuli by a hearing-aid algorithm. The influence of different settings of the algorithm on the impaired profile was investigated. To validate the model predictions, the effect of the algorithm on hearing profiles of human impaired listeners was measured. A computer model simulating impaired hearing (total absence of basilar membrane compression) was used, and three hearing-impaired listeners participated. The hearing profiles of the model and the listeners showed substantial changes when the test stimuli were pre-processed by the hearing-aid algorithm. These changes consisted of lower absolute thresholds, steeper temporal masking curves, and sharper psychophysical tuning curves. The hearing-aid algorithm affected the impaired hearing profile of the model to approximate a normal hearing profile. Qualitatively similar results were found with the impaired listeners' hearing profiles.

Assessment of Itakura Distance as a valuable feature for computer-aided classification of sleep stages.

PubMed

Ebrahimi, F; Mikaili, M; Estrada, E; Nazeran, H

2007-01-01

Staging and detection of various states of sleep derived from EEG and other biomedical signals have proven to be very helpful in diagnosis, prognosis and remedy of various sleep related disorders. The time consuming and costly process of visual scoring of sleep stages by a specialist has always motivated researchers to develop an automatic sleep scoring system and the first step toward achieving this task is finding discriminating characteristics (or features) for each stage. A vast variety of these features and methods have been investigated in the sleep literature with different degrees of success. In this study, we investigated the performance of a newly introduced measure: the Itakura Distance (ID), as a similarity measure between EEG and EOG signals. This work demonstrated and further confirmed the outcomes of our previous research that the Itakura Distance serves as a valuable similarity measure to differentiate between different sleep stages.

A transfer learning approach for classification of clinical significant prostate cancers from mpMRI scans

NASA Astrophysics Data System (ADS)

Chen, Quan; Xu, Xiang; Hu, Shiliang; Li, Xiao; Zou, Qing; Li, Yunpeng

2017-03-01

Deep learning has shown a great potential in computer aided diagnosis. However, in many applications, large dataset is not available. This makes the training of a sophisticated deep learning neural network (DNN) difficult. In this study, we demonstrated that with transfer learning, we can quickly retrain start-of-the-art DNN models with limited data provided by the prostateX challenge. The training data consists of 330 lesions, only 78 were clinical significant. Efforts were made to balance the data during training. We used ImageNet pre-trained inceptionV3 and Vgg-16 model and obtained AUC of 0.81 and 0.83 respectively on the prostateX test data, good for a 4th place finish. We noticed that models trained for different prostate zone has different sensitivity. Applying scaling factors before merging the result improves the AUC for the final result.

Accuracy of different types of computer-aided design/computer-aided manufacturing surgical guides for dental implant placement

PubMed Central

Geng, Wei; Liu, Changying; Su, Yucheng; Li, Jun; Zhou, Yanmin

2015-01-01

Purpose: To evaluate the clinical outcomes of implants placed using different types of computer-aided design/computer-aided manufacturing (CAD/CAM) surgical guides, including partially guided and totally guided templates, and determine the accuracy of these guides Materials and methods: In total, 111 implants were placed in 24 patients using CAD/CAM surgical guides. After implant insertion, the positions and angulations of the placed implants relative to those of the planned ones were determined using special software that matched pre- and postoperative computed tomography (CT) images, and deviations were calculated and compared between the different guides and templates. Results: The mean angular deviations were 1.72 ± 1.67 and 2.71 ± 2.58, the mean deviations in position at the neck were 0.27 ± 0.24 and 0.69 ± 0.66 mm, the mean deviations in position at the apex were 0.37 ± 0.35 and 0.94 ± 0.75 mm, and the mean depth deviations were 0.32 ± 0.32 and 0.51 ± 0.48 mm with tooth- and mucosa-supported stereolithographic guides, respectively (P < .05 for all). The mean distance deviations when partially guided (29 implants) and totally guided templates (30 implants) were used were 0.54 ± 0.50 mm and 0.89 ± 0.78 mm, respectively, at the neck and 1.10 ± 0.85 mm and 0.81 ± 0.64 mm, respectively, at the apex, with corresponding mean angular deviations of 2.56 ± 2.23° and 2.90 ± 3.0° (P > .05 for all). Conclusions: Tooth-supported surgical guides may be more accurate than mucosa-supported guides, while both partially and totally guided templates can simplify surgery and aid in optimal implant placement. PMID:26309497

A human visual based binarization technique for histological images

NASA Astrophysics Data System (ADS)

Shreyas, Kamath K. M.; Rajendran, Rahul; Panetta, Karen; Agaian, Sos

2017-05-01

In the field of vision-based systems for object detection and classification, thresholding is a key pre-processing step. Thresholding is a well-known technique for image segmentation. Segmentation of medical images, such as Computed Axial Tomography (CAT), Magnetic Resonance Imaging (MRI), X-Ray, Phase Contrast Microscopy, and Histological images, present problems like high variability in terms of the human anatomy and variation in modalities. Recent advances made in computer-aided diagnosis of histological images help facilitate detection and classification of diseases. Since most pathology diagnosis depends on the expertise and ability of the pathologist, there is clearly a need for an automated assessment system. Histological images are stained to a specific color to differentiate each component in the tissue. Segmentation and analysis of such images is problematic, as they present high variability in terms of color and cell clusters. This paper presents an adaptive thresholding technique that aims at segmenting cell structures from Haematoxylin and Eosin stained images. The thresholded result can further be used by pathologists to perform effective diagnosis. The effectiveness of the proposed method is analyzed by visually comparing the results to the state of art thresholding methods such as Otsu, Niblack, Sauvola, Bernsen, and Wolf. Computer simulations demonstrate the efficiency of the proposed method in segmenting critical information.

Effects of Response to 2014-2015 Ebola Outbreak on Deaths from Malaria, HIV/AIDS, and Tuberculosis, West Africa.

PubMed

Parpia, Alyssa S; Ndeffo-Mbah, Martial L; Wenzel, Natasha S; Galvani, Alison P

2016-03-01

Response to the 2014-2015 Ebola outbreak in West Africa overwhelmed the healthcare systems of Guinea, Liberia, and Sierra Leone, reducing access to health services for diagnosis and treatment for the major diseases that are endemic to the region: malaria, HIV/AIDS, and tuberculosis. To estimate the repercussions of the Ebola outbreak on the populations at risk for these diseases, we developed computational models for disease transmission and infection progression. We estimated that a 50% reduction in access to healthcare services during the Ebola outbreak exacerbated malaria, HIV/AIDS, and tuberculosis mortality rates by additional death counts of 6,269 (2,564-12,407) in Guinea; 1,535 (522-2,8780) in Liberia; and 2,819 (844-4,844) in Sierra Leone. The 2014-2015 Ebola outbreak was catastrophic in these countries, and its indirect impact of increasing the mortality rates of other diseases was also substantial.

Novel Treatment Planning of Hemimandibular Hyperplasia by the Use of Three-Dimensional Computer-Aided-Design and Computer-Aided-Manufacturing Technologies.

PubMed

Hatamleh, Muhanad M; Yeung, Elizabeth; Osher, Jonas; Huppa, Chrisopher

2017-05-01

Hemimandibular hyperplasia is characterized by an obvious overgrowth in the size of the mandible on one side, which can extend up to the midline causing facial asymmetry. Surgical resection of the overgrowth depends heavily on the skill and experience of the surgeon. This report describes a novel methodology of applying three-dimensional computer-aided-design and computer-aided-manufacturing principles in improving the outcome of surgery in 2 mandibular hyperplasia patients. Both patients had their cone beam computer tomography (CBCT) scan performed. CMF Pro Plan software (v. 2.1) was used to process the scan data into virtual 3-dimensional models of the maxilla and mandible. Head tilt was adjusted manually by following horizontal reference. Facial asymmetry secondary to mandibular hypertrophy was obvious on frontal and lateral views. Simulation functions were followed including mirror imaging of the unaffected mandibular side into the hyperplastic side and position was optimized by translation and orientation functions. Reconstruction of virtual symmetry was assessed and checked by running 3-dimensional measurements. Then, subtraction functions were used to create a 3-dimensional template defining the outline of the lower mandibular osteotomy needed. Precision of mandibular teeth was enhanced by amalgamating the CBCT scan with e-cast scan of the patient lower teeth. 3-Matic software (v. 10.0) was used in designing cutting guide(s) that define the amount of overgrowth to be resected. The top section of the guide was resting on the teeth hence ensuring stability and accuracy while positioning it. The guide design was exported as an .stl file and printed using in-house 3-dimensional printer in biocompatible resin. Three-dimensional technologies of both softwares (CMF Pro Plan and 3-Matic) are accurate and reliable methods in the diagnosis, treatment planning, and designing of cutting guides that optimize surgical correction of hemimandibular hyperplasia at timely and cost-effect manner.

Economic Comparison of Processes Using Spreadsheet Programs

NASA Technical Reports Server (NTRS)

Ferrall, J. F.; Pappano, A. W.; Jennings, C. N.

1986-01-01

Inexpensive approach aids plant-design decisions. Commercially available electronic spreadsheet programs aid economic comparison of different processes for producing particular end products. Facilitates plantdesign decisions without requiring large expenditures for powerful mainframe computers.

Evaluation of a computer-aided detection algorithm for timely diagnosis of small acute intracranial hemorrhage on computed tomography in a critical care environment

NASA Astrophysics Data System (ADS)

Lee, Joon K.; Chan, Tao; Liu, Brent J.; Huang, H. K.

2009-02-01

Detection of acute intracranial hemorrhage (AIH) is a primary task in the interpretation of computed tomography (CT) brain scans of patients suffering from acute neurological disturbances or after head trauma. Interpretation can be difficult especially when the lesion is inconspicuous or the reader is inexperienced. We have previously developed a computeraided detection (CAD) algorithm to detect small AIH. One hundred and thirty five small AIH CT studies from the Los Angeles County (LAC) + USC Hospital were identified and matched by age and sex with one hundred and thirty five normal studies. These cases were then processed using our AIH CAD system to evaluate the efficacy and constraints of the algorithm.

Two methods of Haustral fold detection from computed tomographic virtual colonoscopy images

NASA Astrophysics Data System (ADS)

Chowdhury, Ananda S.; Tan, Sovira; Yao, Jianhua; Linguraru, Marius G.; Summers, Ronald M.

2009-02-01

Virtual colonoscopy (VC) has gained popularity as a new colon diagnostic method over the last decade. VC is a new, less invasive alternative to the usually practiced optical colonoscopy for colorectal polyp and cancer screening, the second major cause of cancer related deaths in industrial nations. Haustral (colonic) folds serve as important landmarks for virtual endoscopic navigation in the existing computer-aided-diagnosis (CAD) system. In this paper, we propose and compare two different methods of haustral fold detection from volumetric computed tomographic virtual colonoscopy images. The colon lumen is segmented from the input using modified region growing and fuzzy connectedness. The first method for fold detection uses a level set that evolves on a mesh representation of the colon surface. The colon surface is obtained from the segmented colon lumen using the Marching Cubes algorithm. The second method for fold detection, based on a combination of heat diffusion and fuzzy c-means algorithm, is employed on the segmented colon volume. Folds obtained on the colon volume using this method are then transferred to the corresponding colon surface. After experimentation with different datasets, results are found to be promising. The results also demonstrate that the first method has a tendency of slight under-segmentation while the second method tends to slightly over-segment the folds.

Effects of response bias and judgment framing on operator use of an automated aid in a target detection task.

PubMed

Rice, Stephen; McCarley, Jason S

2011-12-01

Automated diagnostic aids prone to false alarms often produce poorer human performance in signal detection tasks than equally reliable miss-prone aids. However, it is not yet clear whether this is attributable to differences in the perceptual salience of the automated aids' misses and false alarms or is the result of inherent differences in operators' cognitive responses to different forms of automation error. The present experiments therefore examined the effects of automation false alarms and misses on human performance under conditions in which the different forms of error were matched in their perceptual characteristics. Young adult participants performed a simulated baggage x-ray screening task while assisted by an automated diagnostic aid. Judgments from the aid were rendered as text messages presented at the onset of each trial, and every trial was followed by a second text message providing response feedback. Thus, misses and false alarms from the aid were matched for their perceptual salience. Experiment 1 found that even under these conditions, false alarms from the aid produced poorer human performance and engendered lower automation use than misses from the aid. Experiment 2, however, found that the asymmetry between misses and false alarms was reduced when the aid's false alarms were framed as neutral messages rather than explicit misjudgments. Results suggest that automation false alarms and misses differ in their inherent cognitive salience and imply that changes in diagnosis framing may allow designers to encourage better use of imperfectly reliable automated aids.

Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms.

PubMed

Adabi, Saba; Hosseinzadeh, Matin; Noei, Shahryar; Conforto, Silvia; Daveluy, Steven; Clayton, Anne; Mehregan, Darius; Nasiriavanaki, Mohammadreza

2017-12-20

Currently, diagnosis of skin diseases is based primarily on the visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography (OCT) has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural characteristics in OCT images. There is, therefore, a critical need to systematically analyze OCT images from different body sites, to identify their significant qualitative and quantitative differences. Sixty-three optical and textural features extracted from OCT images of healthy and diseased skin are analyzed and, in conjunction with decision-theoretic approaches, used to create computational models of the diseases. We demonstrate that these models provide objective information to the clinician to assist in the diagnosis of abnormalities of cutaneous microstructure, and hence, aid in the determination of treatment. Specifically, we demonstrate the performance of this methodology on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) from healthy tissue.

[HIV infection in different age groups: Potential implications for prevention. CoRIS Cohort, Spain, 2004-2008].

PubMed

Caro-Murillo, Ana María; Gil Luciano, Ana; Navarro Rubio, Gemma; Leal Noval, Manuel; Blanco Ramos, José Ramón

2010-04-24

To describe the characteristics of HIV infected adults according to their age at recruitment in CoRIS. Analysis of an open, prospective, multicentric cohort of HIV+ adults without previous antiretroviral treatment, attended for the first time from January/2004 to November/2008, in 28 Spanish hospitals (CoRIS). We analyzed their characteristics at recruitment and the distribution of AIDS defining illnesses (ADI) prior to cohort entry and during follow up, according to their age at recruitment. Delayed diagnosis was defined as a patient with AIDS diagnosis and/or CD4+ cell count lower than 200 cells/microl within the first year after HIV diagnosis. Of 4,418 patients included, 30.4% were < or =30 years old, 60.6% between 31 and 50 and 8.9% older than 50 at cohort entry; 31.6% of patients were immigrants (44.1% in the youngest group), 79.6% had been sexually transmitted and 15.2% had an AIDS diagnosis at cohort entry (28.1% between those older than 50). In 34.6% of cases there was a late diagnosis (53.3% in the oldest group). The ADIs varied according to age; tuberculosis was more frequent in the youngest. Pneumocystis jiroveci pneumonia, progressive multifocal leukoencephalopathy, HIV related encephalopathy, recurrent pneumonia and primary lymphoma of brain were more frequent among the oldest. The immunological characteristics and the distribution of ADIs varied according to age. The proportion of late diagnosis was unacceptably high, suggesting the need of specific interventions designed to promote earlier diagnosis. 2009 Elsevier España, S.L. All rights reserved.

Image segmentation of pyramid style identifier based on Support Vector Machine for colorectal endoscopic images.

PubMed

Okamoto, Takumi; Koide, Tetsushi; Sugi, Koki; Shimizu, Tatsuya; Anh-Tuan Hoang; Tamaki, Toru; Raytchev, Bisser; Kaneda, Kazufumi; Kominami, Yoko; Yoshida, Shigeto; Mieno, Hiroshi; Tanaka, Shinji

2015-08-01

With the increase of colorectal cancer patients in recent years, the needs of quantitative evaluation of colorectal cancer are increased, and the computer-aided diagnosis (CAD) system which supports doctor's diagnosis is essential. In this paper, a hardware design of type identification module in CAD system for colorectal endoscopic images with narrow band imaging (NBI) magnification is proposed for real-time processing of full high definition image (1920 × 1080 pixel). A pyramid style image segmentation with SVMs for multi-size scan windows, which can be implemented on an FPGA with small circuit area and achieve high accuracy, is proposed for actual complex colorectal endoscopic images.

Foreign body aspiration in adult airways: therapeutic approach

PubMed Central

Hewlett, Justin C.; Rickman, Otis B.; Lentz, Robert J.; Prakash, Udaya B.

2017-01-01

Tracheobronchial foreign body (FB) aspiration is an uncommon but potentially life-threatening event in adults. Symptoms typically consist of a choking event followed by cough and dyspnea, however, these findings are inconsistent and symptoms may mimic more chronic lung diseases such as asthma or chronic obstructive pulmonary disease. Chest radiography and computed tomography can provide information regarding the location and characteristics of foreign bodies and aid in diagnosis. Bronchoscopy remains the gold standard for diagnosis and management of FB aspiration. The authors describe the typical clinical presentation, diagnostic evaluation, and bronchoscopic management of foreign bodies in adult airways with a focus on bronchoscopic techniques and potential complications of FB extraction. PMID:29221325

An Approach towards Ultrasound Kidney Cysts Detection using Vector Graphic Image Analysis

NASA Astrophysics Data System (ADS)

Mahmud, Wan Mahani Hafizah Wan; Supriyanto, Eko

2017-08-01

This study develops new approach towards detection of kidney ultrasound image for both with single cyst as well as multiple cysts. 50 single cyst images and 25 multiple cysts images were used to test the developed algorithm. Steps involved in developing this algorithm were vector graphic image formation and analysis, thresholding, binarization, filtering as well as roundness test. Performance evaluation to 50 single cyst images gave accuracy of 92%, while for multiple cysts images, the accuracy was about 86.89% when tested to 25 multiple cysts images. This developed algorithm may be used in developing a computerized system such as computer aided diagnosis system to help medical experts in diagnosis of kidney cysts.

Remote Diagnosis of Nitrogen Status in Winter Oilseed Rape

NASA Astrophysics Data System (ADS)

Liu, S.

2016-12-01

Winter oilseed rape is one of the most important oilseed crops in the world. Compared with cereal crops, it requires high amount of nitrogen (N) supplies, but it is also characterized by low N use efficiency. The N nutrition index (NNI), defined as the ratio of the actual plant N concentration (PNC) to the critical PNC at a given biomass level, has been widely used to diagnose plant N status and to aid optimizing N fertilization. But traditional techniques to determine NNI in the lab are time-consuming and expensive. Remote sensing provides a promising approach for large-scale and rapid monitoring and diagnosis of crop N status. In this study, we conducted the experiment in the winter oilseed rape field with eight fertilization treatments in the growing season of 2014 and 2015. PNC, dry mass, and canopy spectra were measured during the different growth stages of winter oilseed rape. The N dilution curve was developed with measurements, and NNI was computed and analyzed for different treatments and different growth stage. For the same treatment, NNI decreased as more leaves were developing. Two methods were applied to remotely estimating NNI for winter oilseed rape: (1) NNI was estimated directly with vegetation indices (VIs) derived from canopy spectra; (2) the actual PNC and the critical PNC at the given biomass level were estimated separately with different types of VIs, and NNI was then computed with the two parts of the estimations. We found that VIs based solely on bands in the visible region provided the most accurate estimates of PNC. Estimating NNI directly with VIs had better performance than estimating the actual PNC and the critical PNC separately.

Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study from 104 lesions with histological ground truth.

PubMed

Thon, Anika; Teichgräber, Ulf; Tennstedt-Schenk, Cornelia; Hadjidemetriou, Stathis; Winzler, Sven; Malich, Ansgar; Papageorgiou, Ismini

2017-01-01

Prostate cancer (PCa) diagnosis by means of multiparametric magnetic resonance imaging (mpMRI) is a current challenge for the development of computer-aided detection (CAD) tools. An innovative CAD-software (Watson Elementary™) was proposed to achieve high sensitivity and specificity, as well as to allege a correlate to Gleason grade. To assess the performance of Watson Elementary™ in automated PCa diagnosis in our hospital´s database of MRI-guided prostate biopsies. The evaluation was retrospective for 104 lesions (47 PCa, 57 benign) from 79, 64.61±6.64 year old patients using 3T T2-weighted imaging, Apparent Diffusion Coefficient (ADC) maps and dynamic contrast enhancement series. Watson Elementary™ utilizes signal intensity, diffusion properties and kinetic profile to compute a proportional Gleason grade predictor, termed Malignancy Attention Index (MAI). The analysis focused on (i) the CAD sensitivity and specificity to classify suspect lesions and (ii) the MAI correlation with the histopathological ground truth. The software revealed a sensitivity of 46.80% for PCa classification. The specificity for PCa was found to be 75.43% with a positive predictive value of 61.11%, a negative predictive value of 63.23% and a false discovery rate of 38.89%. CAD classified PCa and benign lesions with equal probability (P 0.06, χ2 test). Accordingly, receiver operating characteristic analysis suggests a poor predictive value for MAI with an area under curve of 0.65 (P 0.02), which is not superior to the performance of board certified observers. Moreover, MAI revealed no significant correlation with Gleason grade (P 0.60, Pearson´s correlation). The tested CAD software for mpMRI analysis was a weak PCa biomarker in this dataset. Targeted prostate biopsy and histology remains the gold standard for prostate cancer diagnosis.

Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study from 104 lesions with histological ground truth

PubMed Central

Thon, Anika; Teichgräber, Ulf; Tennstedt-Schenk, Cornelia; Hadjidemetriou, Stathis; Winzler, Sven; Malich, Ansgar

2017-01-01

Background Prostate cancer (PCa) diagnosis by means of multiparametric magnetic resonance imaging (mpMRI) is a current challenge for the development of computer-aided detection (CAD) tools. An innovative CAD-software (Watson Elementary™) was proposed to achieve high sensitivity and specificity, as well as to allege a correlate to Gleason grade. Aim/Objective To assess the performance of Watson Elementary™ in automated PCa diagnosis in our hospital´s database of MRI-guided prostate biopsies. Methods The evaluation was retrospective for 104 lesions (47 PCa, 57 benign) from 79, 64.61±6.64 year old patients using 3T T2-weighted imaging, Apparent Diffusion Coefficient (ADC) maps and dynamic contrast enhancement series. Watson Elementary™ utilizes signal intensity, diffusion properties and kinetic profile to compute a proportional Gleason grade predictor, termed Malignancy Attention Index (MAI). The analysis focused on (i) the CAD sensitivity and specificity to classify suspect lesions and (ii) the MAI correlation with the histopathological ground truth. Results The software revealed a sensitivity of 46.80% for PCa classification. The specificity for PCa was found to be 75.43% with a positive predictive value of 61.11%, a negative predictive value of 63.23% and a false discovery rate of 38.89%. CAD classified PCa and benign lesions with equal probability (P 0.06, χ2 test). Accordingly, receiver operating characteristic analysis suggests a poor predictive value for MAI with an area under curve of 0.65 (P 0.02), which is not superior to the performance of board certified observers. Moreover, MAI revealed no significant correlation with Gleason grade (P 0.60, Pearson´s correlation). Conclusion The tested CAD software for mpMRI analysis was a weak PCa biomarker in this dataset. Targeted prostate biopsy and histology remains the gold standard for prostate cancer diagnosis. PMID:29023572

Automated quantitative muscle biopsy analysis system

NASA Technical Reports Server (NTRS)

Castleman, Kenneth R. (Inventor)

1980-01-01

An automated system to aid the diagnosis of neuromuscular diseases by producing fiber size histograms utilizing histochemically stained muscle biopsy tissue. Televised images of the microscopic fibers are processed electronically by a multi-microprocessor computer, which isolates, measures, and classifies the fibers and displays the fiber size distribution. The architecture of the multi-microprocessor computer, which is iterated to any required degree of complexity, features a series of individual microprocessors P.sub.n each receiving data from a shared memory M.sub.n-1 and outputing processed data to a separate shared memory M.sub.n+1 under control of a program stored in dedicated memory M.sub.n.

Computer-based diagnostic decisionmaking.

PubMed

Miller, R A

1987-12-01

The three decisionmaking aids described by the authors attack the generic problem of "see no evil, hear no evil, speak no evil"--improving the detection, diagnosis, and therapy of psychiatric disorders in the primary care setting. The three systems represent interventions at different steps in the process of providing appropriate care to psychiatric patients. The DSPW system of Robins and Marcus offers the potential of increasing the recognition of psychiatric disease in the physician's office. Politser's IDS program is representative of the sort of sophisticated microcomputer-based decisionmaking support tools that will become available to physicians in the not-too-distant future. Erdman's study of the impact of explanation capabilities on the acceptability of therapy recommending systems points out the need for careful scientific evaluations of features added to diagnostic and therapeutic systems.

Use of Parallel Micro-Platform for the Simulation the Space Exploration

NASA Astrophysics Data System (ADS)

Velasco Herrera, Victor Manuel; Velasco Herrera, Graciela; Rosano, Felipe Lara; Rodriguez Lozano, Salvador; Lucero Roldan Serrato, Karen

The purpose of this work is to create a parallel micro-platform, that simulates the virtual movements of a space exploration in 3D. One of the innovations presented in this design consists of the application of a lever mechanism for the transmission of the movement. The development of such a robot is a challenging task very different of the industrial manipulators due to a totally different target system of requirements. This work presents the study and simulation, aided by computer, of the movement of this parallel manipulator. The development of this model has been developed using the platform of computer aided design Unigraphics, in which it was done the geometric modeled of each one of the components and end assembly (CAD), the generation of files for the computer aided manufacture (CAM) of each one of the pieces and the kinematics simulation of the system evaluating different driving schemes. We used the toolbox (MATLAB) of aerospace and create an adaptive control module to simulate the system.

Computer-aided drug design for AMP-activated protein kinase activators.

PubMed

Wang, Zhanli; Huo, Jianxin; Sun, Lidan; Wang, Yongfu; Jin, Hongwei; Yu, Hui; Zhang, Liangren; Zhou, Lishe

2011-09-01

AMP-activated protein kinase (AMPK) is an important therapeutic target for the potential treatment of metabolic disorders, cardiovascular disease and cancer. Recently, various classes of compounds that activate AMPK by direct or indirect interactions have been reported. The importance of computer-aided drug design approaches in the search for potent activators of AMPK is now established, including structure-based design, ligand-based design, fragment-based design, as well as structural analysis. This review article highlights the computer-aided drug design approaches utilized to discover of activators targeting AMPK. The principles, advantages or limitation of the different methods are also being discussed together with examples of applications taken from the literatures.

Computer-aided diagnosis of pulmonary diseases using x-ray darkfield radiography

NASA Astrophysics Data System (ADS)

Einarsdóttir, Hildur; Yaroshenko, Andre; Velroyen, Astrid; Bech, Martin; Hellbach, Katharina; Auweter, Sigrid; Yildirim, Önder; Meinel, Felix G.; Eickelberg, Oliver; Reiser, Maximilian; Larsen, Rasmus; Kjær Ersbøll, Bjarne; Pfeiffer, Franz

2015-12-01

In this work we develop a computer-aided diagnosis (CAD) scheme for classification of pulmonary disease for grating-based x-ray radiography. In addition to conventional transmission radiography, the grating-based technique provides a dark-field imaging modality, which utilizes the scattering properties of the x-rays. This modality has shown great potential for diagnosing early stage emphysema and fibrosis in mouse lungs in vivo. The CAD scheme is developed to assist radiologists and other medical experts to develop new diagnostic methods when evaluating grating-based images. The scheme consists of three stages: (i) automatic lung segmentation; (ii) feature extraction from lung shape and dark-field image intensities; (iii) classification between healthy, emphysema and fibrosis lungs. A study of 102 mice was conducted with 34 healthy, 52 emphysema and 16 fibrosis subjects. Each image was manually annotated to build an experimental dataset. System performance was assessed by: (i) determining the quality of the segmentations; (ii) validating emphysema and fibrosis recognition by a linear support vector machine using leave-one-out cross-validation. In terms of segmentation quality, we obtained an overlap percentage (Ω) 92.63  ±  3.65%, Dice Similarity Coefficient (DSC) 89.74  ±  8.84% and Jaccard Similarity Coefficient 82.39  ±  12.62%. For classification, the accuracy, sensitivity and specificity of diseased lung recognition was 100%. Classification between emphysema and fibrosis resulted in an accuracy of 93%, whilst the sensitivity was 94% and specificity 88%. In addition to the automatic classification of lungs, deviation maps created by the CAD scheme provide a visual aid for medical experts to further assess the severity of pulmonary disease in the lung, and highlights regions affected.

Current and emerging technologies in melanoma diagnosis: the state of the art

PubMed Central

Psaty, Estee L.; Halpern, Allan C.

2017-01-01

Relative to other specialties, dermatologists have been slow to adopt advanced technologic diagnostic aids. After all, most skin disease can be diagnosed by simple visual inspection, and the skin is readily accessible for a diagnostic biopsy. Diagnostic aids such as total body photography and dermoscopy improve the clinician's ability to diagnose melanoma beyond unaided visual inspection, however, and are now considered mainstream methods for early detection. Emerging technologies such as in vivo reflectance confocal microscopy are currently being investigated to determine their utility for noninvasive diagnosis of melanoma. This review summarizes the currently available cutaneous imaging devices and new frontiers in noninvasive diagnosis of skin disease. We anticipate that multimodal systems that combine different imaging technologies will further improve our ability to detect, at the bedside, melanoma at an earlier stage. PMID:19095152

Trends in Computer-Aided Manufacturing in Prosthodontics: A Review of the Available Streams

PubMed Central

Bennamoun, Mohammed

2014-01-01

In prosthodontics, conventional methods of fabrication of oral and facial prostheses have been considered the gold standard for many years. The development of computer-aided manufacturing and the medical application of this industrial technology have provided an alternative way of fabricating oral and facial prostheses. This narrative review aims to evaluate the different streams of computer-aided manufacturing in prosthodontics. To date, there are two streams: the subtractive and the additive approaches. The differences reside in the processing protocols, materials used, and their respective accuracy. In general, there is a tendency for the subtractive method to provide more homogeneous objects with acceptable accuracy that may be more suitable for the production of intraoral prostheses where high occlusal forces are anticipated. Additive manufacturing methods have the ability to produce large workpieces with significant surface variation and competitive accuracy. Such advantages make them ideal for the fabrication of facial prostheses. PMID:24817888

Evaluation of image compression for computer-aided diagnosis of breast tumors in 3D sonography

NASA Astrophysics Data System (ADS)

Chen, We-Min; Huang, Yu-Len; Tao, Chi-Chuan; Chen, Dar-Ren; Moon, Woo-Kyung

2006-03-01

Medical imaging examinations form the basis for physicians diagnosing diseases, as evidenced by the increasing use of digital medical images for picture archiving and communications systems (PACS). However, with enlarged medical image databases and rapid growth of patients' case reports, PACS requires image compression to accelerate the image transmission rate and conserve disk space for diminishing implementation costs. For this purpose, JPEG and JPEG2000 have been accepted as legal formats for the digital imaging and communications in medicine (DICOM). The high compression ratio is felt to be useful for medical imagery. Therefore, this study evaluates the compression ratios of JPEG and JPEG2000 standards for computer-aided diagnosis (CAD) of breast tumors in 3-D medical ultrasound (US) images. The 3-D US data sets with various compression ratios are compressed using the two efficacious image compression standards. The reconstructed data sets are then diagnosed by a previous proposed CAD system. The diagnostic accuracy is measured based on receiver operating characteristic (ROC) analysis. Namely, the ROC curves are used to compare the diagnostic performance of two or more reconstructed images. Analysis results ensure a comparison of the compression ratios by using JPEG and JPEG2000 for 3-D US images. Results of this study provide the possible bit rates using JPEG and JPEG2000 for 3-D breast US images.

Web-based computer-aided-diagnosis (CAD) system for bone age assessment (BAA) of children

NASA Astrophysics Data System (ADS)

Zhang, Aifeng; Uyeda, Joshua; Tsao, Sinchai; Ma, Kevin; Vachon, Linda A.; Liu, Brent J.; Huang, H. K.

2008-03-01

Bone age assessment (BAA) of children is a clinical procedure frequently performed in pediatric radiology to evaluate the stage of skeletal maturation based on a left hand and wrist radiograph. The most commonly used standard: Greulich and Pyle (G&P) Hand Atlas was developed 50 years ago and exclusively based on Caucasian population. Moreover, inter- & intra-observer discrepancies using this method create a need of an objective and automatic BAA method. A digital hand atlas (DHA) has been collected with 1,400 hand images of normal children from Asian, African American, Caucasian and Hispanic descends. Based on DHA, a fully automatic, objective computer-aided-diagnosis (CAD) method was developed and it was adapted to specific population. To bring DHA and CAD method to the clinical environment as a useful tool in assisting radiologist to achieve higher accuracy in BAA, a web-based system with direct connection to a clinical site is designed as a novel clinical implementation approach for online and real time BAA. The core of the system, a CAD server receives the image from clinical site, processes it by the CAD method and finally, generates report. A web service publishes the results and radiologists at the clinical site can review it online within minutes. This prototype can be easily extended to multiple clinical sites and will provide the foundation for broader use of the CAD system for BAA.

Computer-aided diagnosis workstation and network system for chest diagnosis based on multislice CT images

NASA Astrophysics Data System (ADS)

Satoh, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Kakinuma, Ryutarou; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru

2007-03-01

Multislice CT scanner advanced remarkably at the speed at which the chest CT images were acquired for mass screening. Mass screening based on multislice CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. Moreover, we have provided diagnostic assistance methods to medical screening specialists by using a lung cancer screening algorithm built into mobile helical CT scanner for the lung cancer mass screening done in the region without the hospital. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router and Biometric fingerprint authentication system and Biometric face authentication system for safety of medical information. Based on these diagnostic assistance methods, we have now developed a new computer-aided workstation and database that can display suspected lesions three-dimensionally in a short time. This paper describes basic studies that have been conducted to evaluate this new system.

Computer-aided system for diagnosing thyroid nodules on ultrasound: A comparison with radiologist-based clinical assessments.

PubMed

Gao, Luying; Liu, Ruyu; Jiang, Yuxin; Song, Wenfeng; Wang, Ying; Liu, Jia; Wang, Juanjuan; Wu, Dongqian; Li, Shuai; Hao, Aimin; Zhang, Bo

2018-04-01

The purpose of this study was to compare the diagnostic efficiency of a thyroid ultrasound computer-aided diagnosis (CAD) system with that of 1 radiologist. This study retrospectively reviewed 342 surgically resected thyroid nodules from July 2013 to December 2013 at our center. The nodules were assessed on typical ultrasound images using the CAD system and reviewed by 1 experienced radiologist. The radiologist stratified the risk of malignancy using the Thyroid Imaging Reporting and Data Systems (TIRADS) and the American Thyroid Association (ATA) guidelines. The radiologist, using TI-RADS and ATA guidelines, performed better than the CAD system (P < .01). The sensitivity of the CAD system was similar to that of an experienced radiologist (P > .05; P < .01; and P > .05). However, we found that the CAD system had lower specificity (P < .01). The sensitivity of a thyroid ultrasound CAD system in differentiating nodules was similar to that of an experienced radiologist. However, the CAD system had lower specificity. © 2017 Wiley Periodicals, Inc.

Histological Image Feature Mining Reveals Emergent Diagnostic Properties for Renal Cancer

PubMed Central

Kothari, Sonal; Phan, John H.; Young, Andrew N.; Wang, May D.

2016-01-01

Computer-aided histological image classification systems are important for making objective and timely cancer diagnostic decisions. These systems use combinations of image features that quantify a variety of image properties. Because researchers tend to validate their diagnostic systems on specific cancer endpoints, it is difficult to predict which image features will perform well given a new cancer endpoint. In this paper, we define a comprehensive set of common image features (consisting of 12 distinct feature subsets) that quantify a variety of image properties. We use a data-mining approach to determine which feature subsets and image properties emerge as part of an “optimal” diagnostic model when applied to specific cancer endpoints. Our goal is to assess the performance of such comprehensive image feature sets for application to a wide variety of diagnostic problems. We perform this study on 12 endpoints including 6 renal tumor subtype endpoints and 6 renal cancer grade endpoints. Keywords-histology, image mining, computer-aided diagnosis PMID:28163980

Computed-aided diagnosis (CAD) in the detection of breast cancer.

PubMed

Dromain, C; Boyer, B; Ferré, R; Canale, S; Delaloge, S; Balleyguier, C

2013-03-01

Computer-aided detection (CAD) systems have been developed for interpretation to improve mammographic detection of breast cancer at screening by reducing the number of false-negative interpretation that can be caused by subtle findings, radiologist distraction and complex architecture. They use a digitized mammographic image that can be obtained from both screen-film mammography and full field digital mammography. Its performance in breast cancer detection is dependent on the performance of the CAD itself, the population to which it is applied and the radiologists who use it. There is a clear benefit to the use of CAD in less experienced radiologist and in detecting breast carcinomas presenting as microcalcifications. This review gives a detailed description CAD systems used in mammography and their performance in assistance of reading in screening mammography and as an alternative to double reading. Other CAD systems developed for MRI and ultrasound are also presented and discussed. Copyright © 2012. Published by Elsevier Ireland Ltd.

Lung lobe segmentation based on statistical atlas and graph cuts

NASA Astrophysics Data System (ADS)

Nimura, Yukitaka; Kitasaka, Takayuki; Honma, Hirotoshi; Takabatake, Hirotsugu; Mori, Masaki; Natori, Hiroshi; Mori, Kensaku

2012-03-01

This paper presents a novel method that can extract lung lobes by utilizing probability atlas and multilabel graph cuts. Information about pulmonary structures plays very important role for decision of the treatment strategy and surgical planning. The human lungs are divided into five anatomical regions, the lung lobes. Precise segmentation and recognition of lung lobes are indispensable tasks in computer aided diagnosis systems and computer aided surgery systems. A lot of methods for lung lobe segmentation are proposed. However, these methods only target the normal cases. Therefore, these methods cannot extract the lung lobes in abnormal cases, such as COPD cases. To extract lung lobes in abnormal cases, this paper propose a lung lobe segmentation method based on probability atlas of lobe location and multilabel graph cuts. The process consists of three components; normalization based on the patient's physique, probability atlas generation, and segmentation based on graph cuts. We apply this method to six cases of chest CT images including COPD cases. Jaccard index was 79.1%.

Efficient method for events detection in phonocardiographic signals

NASA Astrophysics Data System (ADS)

Martinez-Alajarin, Juan; Ruiz-Merino, Ramon

2005-06-01

The auscultation of the heart is still the first basic analysis tool used to evaluate the functional state of the heart, as well as the first indicator used to submit the patient to a cardiologist. In order to improve the diagnosis capabilities of auscultation, signal processing algorithms are currently being developed to assist the physician at primary care centers for adult and pediatric population. A basic task for the diagnosis from the phonocardiogram is to detect the events (main and additional sounds, murmurs and clicks) present in the cardiac cycle. This is usually made by applying a threshold and detecting the events that are bigger than the threshold. However, this method usually does not allow the detection of the main sounds when additional sounds and murmurs exist, or it may join several events into a unique one. In this paper we present a reliable method to detect the events present in the phonocardiogram, even in the presence of heart murmurs or additional sounds. The method detects relative maxima peaks in the amplitude envelope of the phonocardiogram, and computes a set of parameters associated with each event. Finally, a set of characteristics is extracted from each event to aid in the identification of the events. Besides, the morphology of the murmurs is also detected, which aids in the differentiation of different diseases that can occur in the same temporal localization. The algorithms have been applied to real normal heart sounds and murmurs, achieving satisfactory results.

In vitro Fracture strength and hardness of different computer-aided design/computer-aided manufacturing inlays.

PubMed

Sagsoz, O; Yildiz, M; Hojjat Ghahramanzadeh, A S L; Alsaran, A

2018-03-01

The purpose of this study was to examine the fracture strength and surface microhardness of computer-aided design/computer-aided manufacturing (CAD/CAM) materials in vitro. Mesial-occlusal-distal inlays were made from five different CAD/CAM materials (feldspathic ceramic, CEREC blocs; leucite-reinforced ceramic, IPS Empress CAD; resin nano ceramic, 3M ESPE Lava Ultimate; hybrid ceramic, VITA Enamic; and lithium disilicate ceramic, IPS e.max CAD) using CEREC 4 CAD/CAM system. Samples were adhesively cemented to metal analogs with a resin cement (3M ESPE, U200). The fracture tests were carried out with a universal testing machine. Furthermore, five samples were prepared from each CAD/CAM material for micro-Vickers hardness test. Data were analyzed with statistics software SPSS 20 (IBM Corp., New York, USA). Fracture strength of lithium disilicate inlays (3949 N) was found to be higher than other ceramic inlays (P < 0.05). There was no difference between other inlays statistically (P > 0.05). The highest micro-Vickers hardness was measured in lithium disilicate samples, and the lowest was in resin nano ceramic samples. Fracture strength results demonstrate that inlays can withstand the forces in the mouth. Statistical results showed that fracture strength and micro-Vickers hardness of feldspathic ceramic, leucite-reinforced ceramic, and lithium disilicate ceramic materials had a positive correlation.

Is computer-aided interpretation of 99Tcm-HMPAO leukocyte scans better than the naked eye?

PubMed

Almer, S; Peters, A M; Ekberg, S; Franzén, L; Granerus, G; Ström, M

1995-04-01

In order to compare visual interpretation of inflammation detected by leukocyte scintigraphy with that of different computer-aided quantification methods, 34 patients (25 with ulcerative colitis and 9 with endoscopically verified non-inflamed colonic mucosa), were investigated using 99Tcm-hexamethylpropyleneamine oxime (99Tcm-HMPAO) leukocyte scintigraphy and colonoscopy with biopsies. Scintigrams were obtained 45 min and 4 h after the injection of labelled cells. Computer-generated grading of seven colon segments using four different methods was performed on each scintigram for each patient. The same segments were graded independently using a 4-point visual scale. Endoscopic and histological inflammation were scored on 4-point scales. At 45 min, a positive correlation was found between endoscopic and scan gradings in individual colon segments when using visual grading and three of the four computer-aided methods (Spearman's rs = 0.30-0.64, P < 0.001). Histological grading correlated with visual grading and with two of the four computer-aided methods at 45 min (rs = 0.42-0.54, P < 0.001). At 4 h, all grading methods correlated positively with both endoscopic and histological assessment. The correlation coefficients were, in all but one instance, highest for the visual grading. As an inter-observer comparison to assess agreement between the visual gradings of two nuclear physicians, 14 additional patients (9 ulcerative colitis, 5 infectious enterocolitis) underwent leukocyte scintigraphy. Agreement assessed using kappa statistics was 0.54 at 45 min (P < 0.001). Separate data concerning the presence/absence of active inflammation showed a high kappa value (0.74, P < 0.001). Our results showed that a simple scintigraphic scoring system based on assessment using the human eye reflects colonic inflammation at least as well as computer-aided grading, and that highly correlated results can be achieved between different investigators.

Racial-ethnic differences in all-cause and HIV mortality, Florida, 2000–2011

PubMed Central

Trepka, Mary Jo; Fennie, Kristopher P.; Sheehan, Diana M.; Niyonsenga, Theophile; Lieb, Spencer; Maddox, Lorene M.

2016-01-01

Purpose We compared all-cause and human immunodeficiency virus (HIV) mortality in a population-based, HIV-infected cohort. Methods Using records of people diagnosed with HIV during 2000–2009 from the Florida Enhanced HIV/Acquired Immunodeficiency Syndrome (AIDS) Reporting System, we conducted a proportional hazards analysis for all-cause mortality and a competing risk analysis for HIV mortality through 2011 controlling for individual level factors, neighborhood poverty, and rural/urban status and stratifying by concurrent AIDS status (AIDS within 3 months of HIV diagnosis). Results Of 59,880 HIV-infected people, 32.2% had concurrent AIDS, and 19.3% died. Adjusting for period of diagnosis, age group, sex, country of birth, HIV transmission mode, area level poverty and rural/urban status, non-Hispanic Black (NHB) and Hispanic people had an elevated adjusted hazards ratio (aHR) for HIV mortality relative to non-Hispanic whites (NHB concurrent AIDS: aHR 1.34, 95% CI 1.23–1.47; NHB without concurrent AIDS: aHR 1.41, 95% CI 1.26–1.57; Hispanic concurrent AIDS: aHR 1.18, 95% CI 1.05–1.32; Hispanic without concurrent AIDS: aHR 1.18, 95% CI 1.03–1.36). Conclusions Considering competing causes of death, NHB and Hispanic people had a higher risk of HIV mortality even among those without concurrent AIDS, indicating a need to identify and address barriers to HIV care in these populations. PMID:26948103

[30 years since the first AIDS cases were reported: history and the present part III].

PubMed

Brůčková, Marie

2012-12-01

The end of the article features the development of HIV/AIDS diagnosis and its implementation in the Czech Republic. The establishment of the National Reference Laboratory for AIDS (NRL AIDS) at the National Institute of Public Health late in 1985 is mentioned and its responsibilities as the methodology centre in the areas of HIV/AIDS laboratory diagnosis and epidemiology are specified. In cooperation with the respective experts, a pilot HIV/AIDS prevalence study was conducted in the Czech Republic. The general criteria for HIV/AIDS laboratory diagnosis were set for both blood transfusion service and microbiology laboratories. Early in 1987, mass screening of blood donors was introduced in blood transfusion centres and in the second half of the same year, the HIV screening program was extended to selected microbiology laboratories. The NRL AIDS established a unified data reporting system, analyzed the results at the national level, and since 1989, has been reporting the outcomes to the international AIDS, and later HIV/AIDS, reporting system. The NRL AIDS also participated in a number of international projects in the areas of the research and development of laboratory techniques and epidemiological surveillance.

Evaluation of MTANNs for eliminating false-positive with different computer aided pulmonary nodules detection software.

PubMed

Shi, Zhenghao; Ma, Jiejue; Feng, Yaning; He, Lifeng; Suzuki, Kenji

2015-11-01

MTANN (Massive Training Artificial Neural Network) is a promising tool, which applied to eliminate false-positive for thoracic CT in recent years. In order to evaluate whether this method is feasible to eliminate false-positive of different CAD schemes, especially, when it is applied to commercial CAD software, this paper evaluate the performance of the method for eliminating false-positives produced by three different versions of commercial CAD software for lung nodules detection in chest radiographs. Experimental results demonstrate that the approach is useful in reducing FPs for different computer aided lung nodules detection software in chest radiographs.

Automatic glaucoma diagnosis through medical imaging informatics.

PubMed

Liu, Jiang; Zhang, Zhuo; Wong, Damon Wing Kee; Xu, Yanwu; Yin, Fengshou; Cheng, Jun; Tan, Ngan Meng; Kwoh, Chee Keong; Xu, Dong; Tham, Yih Chung; Aung, Tin; Wong, Tien Yin

2013-01-01

Computer-aided diagnosis for screening utilizes computer-based analytical methodologies to process patient information. Glaucoma is the leading irreversible cause of blindness. Due to the lack of an effective and standard screening practice, more than 50% of the cases are undiagnosed, which prevents the early treatment of the disease. To design an automatic glaucoma diagnosis architecture automatic glaucoma diagnosis through medical imaging informatics (AGLAIA-MII) that combines patient personal data, medical retinal fundus image, and patient's genome information for screening. 2258 cases from a population study were used to evaluate the screening software. These cases were attributed with patient personal data, retinal images and quality controlled genome data. Utilizing the multiple kernel learning-based classifier, AGLAIA-MII, combined patient personal data, major image features, and important genome single nucleotide polymorphism (SNP) features. Receiver operating characteristic curves were plotted to compare AGLAIA-MII's performance with classifiers using patient personal data, images, and genome SNP separately. AGLAIA-MII was able to achieve an area under curve value of 0.866, better than 0.551, 0.722 and 0.810 by the individual personal data, image and genome information components, respectively. AGLAIA-MII also demonstrated a substantial improvement over the current glaucoma screening approach based on intraocular pressure. AGLAIA-MII demonstrates for the first time the capability of integrating patients' personal data, medical retinal image and genome information for automatic glaucoma diagnosis and screening in a large dataset from a population study. It paves the way for a holistic approach for automatic objective glaucoma diagnosis and screening.

Analysis of framelets for breast cancer diagnosis.

PubMed

Thivya, K S; Sakthivel, P; Venkata Sai, P M

2016-01-01

Breast cancer is the second threatening tumor among the women. The effective way of reducing breast cancer is its early detection which helps to improve the diagnosing process. Digital mammography plays a significant role in mammogram screening at earlier stage of breast carcinoma. Even though, it is very difficult to find accurate abnormality in prevalent screening by radiologists. But the possibility of precise breast cancer screening is encouraged by predicting the accurate type of abnormality through Computer Aided Diagnosis (CAD) systems. The two most important indicators of breast malignancy are microcalcifications and masses. In this study, framelet transform, a multiresolutional analysis is investigated for the classification of the above mentioned two indicators. The statistical and co-occurrence features are extracted from the framelet decomposed mammograms with different resolution levels and support vector machine is employed for classification with k-fold cross validation. This system achieves 94.82% and 100% accuracy in normal/abnormal classification (stage I) and benign/malignant classification (stage II) of mass classification system and 98.57% and 100% for microcalcification system when using the MIAS database.

Automatic cerebrospinal fluid segmentation in non-contrast CT images using a 3D convolutional network

NASA Astrophysics Data System (ADS)

Patel, Ajay; van de Leemput, Sil C.; Prokop, Mathias; van Ginneken, Bram; Manniesing, Rashindra

2017-03-01

Segmentation of anatomical structures is fundamental in the development of computer aided diagnosis systems for cerebral pathologies. Manual annotations are laborious, time consuming and subject to human error and observer variability. Accurate quantification of cerebrospinal fluid (CSF) can be employed as a morphometric measure for diagnosis and patient outcome prediction. However, segmenting CSF in non-contrast CT images is complicated by low soft tissue contrast and image noise. In this paper we propose a state-of-the-art method using a multi-scale three-dimensional (3D) fully convolutional neural network (CNN) to automatically segment all CSF within the cranial cavity. The method is trained on a small dataset comprised of four manually annotated cerebral CT images. Quantitative evaluation of a separate test dataset of four images shows a mean Dice similarity coefficient of 0.87 +/- 0.01 and mean absolute volume difference of 4.77 +/- 2.70 %. The average prediction time was 68 seconds. Our method allows for fast and fully automated 3D segmentation of cerebral CSF in non-contrast CT, and shows promising results despite a limited amount of training data.

Is AIDS a Biasing Factor in Teacher Judgment?

ERIC Educational Resources Information Center

Walker, David W.; Hulecki, Mary B.

1989-01-01

Regular-education, third-grade teachers (n=91) in Indiana reviewed one of two psychological reports, identical except that one reported a diagnosis of Acquired Immune Deficiency Syndrome (AIDS) and one reported a diagnosis of rheumatic fever. AIDS was not found to be a biasing factor in teachers' judgments regarding special education placement.…

Computer-Aided Design/Computer-Assisted Manufacture Monolithic Restorations for Severely Worn Dentition: A Case History Report.

PubMed

Abou-Ayash, Samir; Boldt, Johannes; Vuck, Alexander

Full-arch rehabilitation of patients with severe tooth wear due to parafunctional behavior is a challenge for dentists and dental technicians, especially when a highly esthetic outcome is desired. A variety of different treatment options and prosthetic materials are available for such a clinical undertaking. The ongoing progress of computer-aided design/computer-assisted manufacture technologies in combination with all-ceramic materials provides a predictable workflow for these complex cases. This case history report describes a comprehensive, step-by-step treatment protocol leading to an optimally predictable treatment outcome for an esthetically compromised patient.

Continuous measurements of mandibular cortical width on dental panoramic radiographs for computer-aided diagnosis of osteoporosis

NASA Astrophysics Data System (ADS)

Kavitha, M. S.; Asano, Akira; Taguchi, Akira

2011-03-01

The aim of this study is to develop a computer-aided osteoporosis diagnosis system that automatically determines the inferior cortical width of the mandible continuously on dental panoramic radiographs to realize statistically more robust measurements than the conventional one-point measurements. The cortical width was continuously measured on dental panoramic radiographs by enhancing the original image, determining cortical boundaries, and finally evaluating the distance between boundaries continuously throughout the region of interest. The diagnostic performance using the average width calculated from the continuous measurement was compared with BMD at lumbar spine and femoral neck in 100 postmenopausal women of whom 50 to the development of the tool and 50 to its validation with no history of osteoporosis was evaluated. We experimentally showed the superiority of our method with improved sensitivity and specificity of identifying the development subjects were 90.0% and 75.0% in women with low spinal BMD and 81.8% and 69.2% in those with low femoral BMD, respectively. The corresponding values in the validation subjects were 93.3% and 82.9% at the lumbar spine and 92.3% and 75.7% at the femoral neck, respectively in terms of efficacy for diagnosing osteoporosis. We also assessed the diagnosis and classification of women with osteoporosis using support vector machine employing the average and variance of the continuous measurements gave excellent discrimination ability. It yields sensitivity and specificity of 90.9% and 83.8%, respectively with lumbar spine and 90.0% and 69.1%, respectively with femoral neck BMD. Performance comparison and simplicity of this method indicate that our computeraided system is readily applicable to clinical practice.

Breast Histopathological Image Retrieval Based on Latent Dirichlet Allocation.

PubMed

Ma, Yibing; Jiang, Zhiguo; Zhang, Haopeng; Xie, Fengying; Zheng, Yushan; Shi, Huaqiang; Zhao, Yu

2017-07-01

In the field of pathology, whole slide image (WSI) has become the major carrier of visual and diagnostic information. Content-based image retrieval among WSIs can aid the diagnosis of an unknown pathological image by finding its similar regions in WSIs with diagnostic information. However, the huge size and complex content of WSI pose several challenges for retrieval. In this paper, we propose an unsupervised, accurate, and fast retrieval method for a breast histopathological image. Specifically, the method presents a local statistical feature of nuclei for morphology and distribution of nuclei, and employs the Gabor feature to describe the texture information. The latent Dirichlet allocation model is utilized for high-level semantic mining. Locality-sensitive hashing is used to speed up the search. Experiments on a WSI database with more than 8000 images from 15 types of breast histopathology demonstrate that our method achieves about 0.9 retrieval precision as well as promising efficiency. Based on the proposed framework, we are developing a search engine for an online digital slide browsing and retrieval platform, which can be applied in computer-aided diagnosis, pathology education, and WSI archiving and management.

Computer-aided diagnosis system: a Bayesian hybrid classification method.

PubMed

Calle-Alonso, F; Pérez, C J; Arias-Nicolás, J P; Martín, J

2013-10-01

A novel method to classify multi-class biomedical objects is presented. The method is based on a hybrid approach which combines pairwise comparison, Bayesian regression and the k-nearest neighbor technique. It can be applied in a fully automatic way or in a relevance feedback framework. In the latter case, the information obtained from both an expert and the automatic classification is iteratively used to improve the results until a certain accuracy level is achieved, then, the learning process is finished and new classifications can be automatically performed. The method has been applied in two biomedical contexts by following the same cross-validation schemes as in the original studies. The first one refers to cancer diagnosis, leading to an accuracy of 77.35% versus 66.37%, originally obtained. The second one considers the diagnosis of pathologies of the vertebral column. The original method achieves accuracies ranging from 76.5% to 96.7%, and from 82.3% to 97.1% in two different cross-validation schemes. Even with no supervision, the proposed method reaches 96.71% and 97.32% in these two cases. By using a supervised framework the achieved accuracy is 97.74%. Furthermore, all abnormal cases were correctly classified. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach.

PubMed

Salvatore, Christian; Cerasa, Antonio; Battista, Petronilla; Gilardi, Maria C; Quattrone, Aldo; Castiglioni, Isabella

2015-01-01

Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as to lessen the time and cost of clinical trials. Magnetic Resonance (MR)-related biomarkers have been recently identified by the use of machine learning methods for the in vivo differential diagnosis of AD. However, the vast majority of neuroimaging papers investigating this topic are focused on the difference between AD and patients with mild cognitive impairment (MCI), not considering the impact of MCI patients who will (MCIc) or not convert (MCInc) to AD. Morphological T1-weighted MRIs of 137 AD, 76 MCIc, 134 MCInc, and 162 healthy controls (CN) selected from the Alzheimer's disease neuroimaging initiative (ADNI) cohort, were used by an optimized machine learning algorithm. Voxels influencing the classification between these AD-related pre-clinical phases involved hippocampus, entorhinal cortex, basal ganglia, gyrus rectus, precuneus, and cerebellum, all critical regions known to be strongly involved in the pathophysiological mechanisms of AD. Classification accuracy was 76% AD vs. CN, 72% MCIc vs. CN, 66% MCIc vs. MCInc (nested 20-fold cross validation). Our data encourage the application of computer-based diagnosis in clinical practice of AD opening new prospective in the early management of AD patients.

How Do Social Capital and HIV/AIDS Outcomes Geographically Cluster and Which Sociocontextual Mechanisms Predict Differences Across Clusters?

PubMed

Ransome, Yusuf; Dean, Lorraine T; Crawford, Natalie D; Metzger, David S; Blank, Michael B; Nunn, Amy S

2017-09-01

Place of residence has been associated with HIV transmission risks. Social capital, defined as features of social organization that improve efficiency of society by facilitating coordinated actions, often varies by neighborhood, and hypothesized to have protective effects on HIV care continuum outcomes. We examined whether the association between social capital and 2 HIV care continuum outcomes clustered geographically and whether sociocontextual mechanisms predict differences across clusters. Bivariate Local Moran's I evaluated geographical clustering in the association between social capital (participation in civic and social organizations, 2006, 2008, 2010) and [5-year (2007-2011) prevalence of late HIV diagnosis and linkage to HIV care] across Philadelphia, PA, census tracts (N = 378). Maps documented the clusters and multinomial regression assessed which sociocontextual mechanisms (eg, racial composition) predict differences across clusters. We identified 4 significant clusters (high social capital-high HIV/AIDS, low social capital-low HIV/AIDS, low social capital-high HIV/AIDS, and high social capital-low HIV/AIDS). Moran's I between social capital and late HIV diagnosis was (I = 0.19, z = 9.54, P < 0.001) and linkage to HIV care (I = 0.06, z = 3.274, P = 0.002). In multivariable analysis, median household income predicted differences across clusters, particularly where social capital was lowest and HIV burden the highest, compared with clusters with high social capital and lowest HIV burden. The association between social participation and HIV care continuum outcomes cluster geographically in Philadelphia, PA. HIV prevention interventions should account for this phenomenon. Reducing geographic disparities will require interventions tailored to each continuum step and that address socioeconomic factors such as neighborhood median income.

Comparative Proteomic Profiling and Biomarker Identification of Traditional Chinese Medicine-Based HIV/AIDS Syndromes.

PubMed

Wen, Li; Liu, Ye-Fang; Jiang, Cen; Zeng, Shao-Qian; Su, Yue; Wu, Wen-Jun; Liu, Xi-Yang; Wang, Jian; Liu, Ying; Su, Chen; Li, Bai-Xue; Feng, Quan-Sheng

2018-03-08

Given the challenges in exploring lifelong therapy with little side effect for human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV/AIDS) cases, there is increasing interest in developing traditional Chinese medicine (TCM) treatments based on specific TCM syndrome. However, there are few objective and biological evidences for classification and diagnosis of HIV/AIDS TCM syndromes to date. In this study, iTRAQ-2DLC-MS/MS coupled with bioinformatics were firstly employed for comparative proteomic profiling of top popular TCM syndromes of HIV/AIDS: accumulation of heat-toxicity (AHT) and Yang deficiency of spleen and kidney (YDSK). It was found that for the two TCM syndromes, the identified differential expressed proteins (DEPs) as well as their biological function distributions and participation in signaling pathways were significantly different, providing biological evidence for the classification of HIV/AIDS TCM syndromes. Furthermore, the TCM syndrome-specific DEPs were confirmed as biomarkers based on western blot analyses, including FN1, GPX3, KRT10 for AHT and RBP4, ApoE, KNG1 for YDSK. These biomarkers also biologically linked with the specific TCM syndrome closely. Thus the clinical and biological basis for differentiation and diagnosis of HIV/AIDs TCM syndromes were provided for the first time, providing more opportunities for stable exertion and better application of TCM efficacy and superiority in HIV/AIDS treatment.

Computer-aided diagnosis of contrast-enhanced spectral mammography: A feasibility study.

PubMed

Patel, Bhavika K; Ranjbar, Sara; Wu, Teresa; Pockaj, Barbara A; Li, Jing; Zhang, Nan; Lobbes, Mark; Zhang, Bin; Mitchell, J Ross

2018-01-01

To evaluate whether the use of a computer-aided diagnosis-contrast-enhanced spectral mammography (CAD-CESM) tool can further increase the diagnostic performance of CESM compared with that of experienced radiologists. This IRB-approved retrospective study analyzed 50 lesions described on CESM from August 2014 to December 2015. Histopathologic analyses, used as the criterion standard, revealed 24 benign and 26 malignant lesions. An expert breast radiologist manually outlined lesion boundaries on the different views. A set of morphologic and textural features were then extracted from the low-energy and recombined images. Machine-learning algorithms with feature selection were used along with statistical analysis to reduce, select, and combine features. Selected features were then used to construct a predictive model using a support vector machine (SVM) classification method in a leave-one-out-cross-validation approach. The classification performance was compared against the diagnostic predictions of 2 breast radiologists with access to the same CESM cases. Based on the SVM classification, CAD-CESM correctly identified 45 of 50 lesions in the cohort, resulting in an overall accuracy of 90%. The detection rate for the malignant group was 88% (3 false-negative cases) and 92% for the benign group (2 false-positive cases). Compared with the model, radiologist 1 had an overall accuracy of 78% and a detection rate of 92% (2 false-negative cases) for the malignant group and 62% (10 false-positive cases) for the benign group. Radiologist 2 had an overall accuracy of 86% and a detection rate of 100% for the malignant group and 71% (8 false-positive cases) for the benign group. The results of our feasibility study suggest that a CAD-CESM tool can provide complementary information to radiologists, mainly by reducing the number of false-positive findings. Copyright © 2017 Elsevier B.V. All rights reserved.

Coping and perception of women with HIV infection

PubMed Central

Renesto, Helana Maria Ferreira; Falbo, Ana Rodrigues; Souza, Edvaldo; Vasconcelos, Maria Gorete

2014-01-01

OBJECTIVE To analyze women’s perceptions and coping regarding the discovery of an HIV infection. METHODS A qualitative study in an HIV/AIDS Specialist Helpdesk in Recife, PE, Northeastern Brazil, from January to September 2010, involving eight women living with asymptomatic HIV aged between 27 and 37 years, without criteria for diagnosis of AIDS infected through intercourse and monitored by the service for at least one year. Forms were used to characterize the clinical situation and semi-structured interviews to understand perceptions and feelings related to personal trajectory after diagnosis and different ways of facing the diagnosis in family and social life. Content analysis was performed as suggested by Bardin. RESULTS The thematic category that emerged was stigma and discrimination. The women had life trajectories marked by stigma, which was perceived as discrimination after the diagnosis and in the experiences of everyday life. The revelation of the infection was perceived as limiting to a normal life, leading to the need to conceal the diagnosis. The discriminatory attitudes of some health care professionals, non-specialist in HIV/AIDS, had a negative impact on future experiences in other health services. Besides the effects of institutional stigma, the perception of women was that the service did not include dedicated space for the expression of other needs beyond the disease, which could help in fighting the infection. CONCLUSIONS Living with HIV was strongly linked to stigma. The results show the importance of strengthening educational approaches and emotional support at the time of diagnosis in order to facilitate coping with the condition of seropositivity. PMID:24789635

Analysis of Interval Changes on Mammograms for Computer Aided Diagnosis

DTIC Science & Technology

2000-05-01

tizer was calibrated so that the gray values were linearly and erage pixel values in the template and ROI, respectively. The inversely proportional to the...earlier for linearly and inversely proportional to the OD within the alignment of the breast regions, except that the regions to be range 0-4 OD...results versely proportional to the radial distance r from the nipple. in a decrease in the value of (to 20 mm. This decrease helps For the data set

Obstetrical emergencies.

PubMed

Biddle, D; Macintire, D K

2000-05-01

This article discusses different techniques that can be used in the diagnosis and treatment of obstetrical emergencies. Female reproductive emergencies commonly encountered by small animal practitioners include pyometra, dystocia, cesarean section, mastitis, eclampsia, uterine torsion, and uterine prolapse. A thorough knowledge of normal and abnormal reproductive behavior will aid the emergency veterinarian in successfully managing such cases. Timely diagnosis and treatment of these emergencies will often give a good outcome.

High-Throughput Histopathological Image Analysis via Robust Cell Segmentation and Hashing

PubMed Central

Zhang, Xiaofan; Xing, Fuyong; Su, Hai; Yang, Lin; Zhang, Shaoting

2015-01-01

Computer-aided diagnosis of histopathological images usually requires to examine all cells for accurate diagnosis. Traditional computational methods may have efficiency issues when performing cell-level analysis. In this paper, we propose a robust and scalable solution to enable such analysis in a real-time fashion. Specifically, a robust segmentation method is developed to delineate cells accurately using Gaussian-based hierarchical voting and repulsive balloon model. A large-scale image retrieval approach is also designed to examine and classify each cell of a testing image by comparing it with a massive database, e.g., half-million cells extracted from the training dataset. We evaluate this proposed framework on a challenging and important clinical use case, i.e., differentiation of two types of lung cancers (the adenocarcinoma and squamous carcinoma), using thousands of lung microscopic tissue images extracted from hundreds of patients. Our method has achieved promising accuracy and running time by searching among half-million cells. PMID:26599156

The role of magnetic resonance imaging in the diagnosis of Parkinson's disease: a review.

PubMed

Al-Radaideh, Ali M; Rababah, Eman M

2016-01-01

Parkinson's disease (PD) is the second most common neurodegenerative disease after Alzheimer's in elderly people. Different structural and functional neuroimaging methods play a great role in the early diagnosis of neurodegenerative diseases. This review discusses the role of magnetic resonance imaging (MRI) in the diagnosis of PD. MRI provides clinicians with structural and functional information of human brain noninvasively. Advanced quantitative MRI techniques have shown promise for detecting pathological changes related to different stages of PD. Collectively, advanced MRI techniques at high and ultrahigh magnetic fields aid in better understanding of the nature and progression of PD. Copyright © 2016 Elsevier Inc. All rights reserved.

Computer-Aided Diagnosis of Micro-Malignant Melanoma Lesions Applying Support Vector Machines.

PubMed

Jaworek-Korjakowska, Joanna

2016-01-01

Background. One of the fatal disorders causing death is malignant melanoma, the deadliest form of skin cancer. The aim of the modern dermatology is the early detection of skin cancer, which usually results in reducing the mortality rate and less extensive treatment. This paper presents a study on classification of melanoma in the early stage of development using SVMs as a useful technique for data classification. Method. In this paper an automatic algorithm for the classification of melanomas in their early stage, with a diameter under 5 mm, has been presented. The system contains the following steps: image enhancement, lesion segmentation, feature calculation and selection, and classification stage using SVMs. Results. The algorithm has been tested on 200 images including 70 melanomas and 130 benign lesions. The SVM classifier achieved sensitivity of 90% and specificity of 96%. The results indicate that the proposed approach captured most of the malignant cases and could provide reliable information for effective skin mole examination. Conclusions. Micro-melanomas due to the small size and low advancement of development create enormous difficulties during the diagnosis even for experts. The use of advanced equipment and sophisticated computer systems can help in the early diagnosis of skin lesions.

Estimation of salient regions related to chronic gastritis using gastric X-ray images.

PubMed

Togo, Ren; Ishihara, Kenta; Ogawa, Takahiro; Haseyama, Miki

2016-10-01

Since technical knowledge and a high degree of experience are necessary for diagnosis of chronic gastritis, computer-aided diagnosis (CAD) systems that analyze gastric X-ray images are desirable in the field of medicine. Therefore, a new method that estimates salient regions related to chronic gastritis/non-gastritis for supporting diagnosis is presented in this paper. In order to estimate salient regions related to chronic gastritis/non-gastritis, the proposed method monitors the distance between a target image feature and Support Vector Machine (SVM)-based hyperplane for its classification. Furthermore, our method realizes removal of the influence of regions outside the stomach by using positional relationships between the stomach and other organs. Consequently, since the proposed method successfully estimates salient regions of gastric X-ray images for which chronic gastritis and non-gastritis are unknown, visual support for inexperienced clinicians becomes feasible. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mixture model-based clustering and logistic regression for automatic detection of microaneurysms in retinal images

NASA Astrophysics Data System (ADS)

Sánchez, Clara I.; Hornero, Roberto; Mayo, Agustín; García, María

2009-02-01

Diabetic Retinopathy is one of the leading causes of blindness and vision defects in developed countries. An early detection and diagnosis is crucial to avoid visual complication. Microaneurysms are the first ocular signs of the presence of this ocular disease. Their detection is of paramount importance for the development of a computer-aided diagnosis technique which permits a prompt diagnosis of the disease. However, the detection of microaneurysms in retinal images is a difficult task due to the wide variability that these images usually present in screening programs. We propose a statistical approach based on mixture model-based clustering and logistic regression which is robust to the changes in the appearance of retinal fundus images. The method is evaluated on the public database proposed by the Retinal Online Challenge in order to obtain an objective performance measure and to allow a comparative study with other proposed algorithms.

Computer-assisted versus conventional free fibula flap technique for craniofacial reconstruction: an outcomes comparison.

PubMed

Seruya, Mitchel; Fisher, Mark; Rodriguez, Eduardo D

2013-11-01

There has been rising interest in computer-aided design/computer-aided manufacturing for preoperative planning and execution of osseous free flap reconstruction. The purpose of this study was to compare outcomes between computer-assisted and conventional fibula free flap techniques for craniofacial reconstruction. A two-center, retrospective review was carried out on patients who underwent fibula free flap surgery for craniofacial reconstruction from 2003 to 2012. Patients were categorized by the type of reconstructive technique: conventional (between 2003 and 2009) or computer-aided design/computer-aided manufacturing (from 2010 to 2012). Demographics, surgical factors, and perioperative and long-term outcomes were compared. A total of 68 patients underwent microsurgical craniofacial reconstruction: 58 conventional and 10 computer-aided design and manufacturing fibula free flaps. By demographics, patients undergoing the computer-aided design/computer-aided manufacturing method were significantly older and had a higher rate of radiotherapy exposure compared with conventional patients. Intraoperatively, the median number of osteotomies was significantly higher (2.0 versus 1.0, p=0.002) and the median ischemia time was significantly shorter (120 minutes versus 170 minutes, p=0.004) for the computer-aided design/computer-aided manufacturing technique compared with conventional techniques; operative times were shorter for patients undergoing the computer-aided design/computer-aided manufacturing technique, although this did not reach statistical significance. Perioperative and long-term outcomes were equivalent for the two groups, notably, hospital length of stay, recipient-site infection, partial and total flap loss, and rate of soft-tissue and bony tissue revisions. Microsurgical craniofacial reconstruction using a computer-assisted fibula flap technique yielded significantly shorter ischemia times amidst a higher number of osteotomies compared with conventional techniques. Therapeutic, III.

Human identification based on cranial computed tomography scan — a case report

PubMed Central

Silva, RF; Botelho, TL; Prado, FB; Kawagushi, JT; Daruge Júnior, E; Bérzin, F

2011-01-01

Today, there is increasing use of CT scanning on a clinical basis, aiding in the diagnosis of diseases or injuries. This exam also provides important information that allows identification of individuals. This paper reports the use of a CT scan on the skull, taken when the victim was alive, for the positive identification of a victim of a traffic accident in which the fingerprint analysis was impossible. The authors emphasize that the CT scan is a tool primarily used in clinical diagnosis and may contribute significantly to forensic purpose, allowing the exploration of virtual corpses before the classic autopsy. The use of CT scans might increase the quantity and quality of information involved in the death of the person examined. PMID:21493883

Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images

PubMed Central

Wang, Yu; Zhang, Yaonan; Yao, Zhaomin; Zhao, Ruixue; Zhou, Fengfeng

2016-01-01

Non-lethal macular diseases greatly impact patients’ life quality, and will cause vision loss at the late stages. Visual inspection of the optical coherence tomography (OCT) images by the experienced clinicians is the main diagnosis technique. We proposed a computer-aided diagnosis (CAD) model to discriminate age-related macular degeneration (AMD), diabetic macular edema (DME) and healthy macula. The linear configuration pattern (LCP) based features of the OCT images were screened by the Correlation-based Feature Subset (CFS) selection algorithm. And the best model based on the sequential minimal optimization (SMO) algorithm achieved 99.3% in the overall accuracy for the three classes of samples. PMID:28018716

Cytokine signaling pathway polymorphisms and AIDS-related non-Hodgkin lymphoma risk in the Multicenter AIDS Cohort Study

PubMed Central

Wong, Hui-Lee; Breen, Elizabeth C.; Pfeiffer, Ruth M.; Aissani, Brahim; Martinson, Jeremy J.; Margolick, Joseph B.; Kaslow, Richard A.; Jacobson, Lisa P.; Ambinder, Richard F.; Chanock, Stephen; Martínez-Maza, Otoniel; Rabkin, Charles S.

2014-01-01

Cytokine stimulation of B-cell proliferation may be an important etiologic mechanism for acquired immunodeficiency syndrome (AIDS)-related non-Hodgkin lymphoma (NHL). The Epstein-Barr virus may be a co-factor, particularly for primary central nervous system (CNS) tumors, which are uniformly EBV-positive in the setting of AIDS. Thus, we examined associations of genetic variation in IL10 and related cytokine signaling molecules (IL10RA, CXCL12, IL13, IL4, IL4R, CCL5 and BCL6) with AIDS-related NHL risk and evaluated differences between primary CNS and systemic tumors. We compared 160 Multicenter AIDS Cohort Study (MACS) participants with incident lymphomas, of which 90 followed another AIDS diagnosis, to HIV-1-seropositive controls matched on duration of lymphoma-free survival post-HIV-1 infection (N=160) or post-AIDS diagnosis (N=90). We fit conditional logistic regression models to estimate odds ratios (ORs) and 95 percent confidence intervals (95%CIs). Carriage of at least one copy of the T allele for the IL10 rs1800871 (as compared to no copies) was associated with decreased AIDS-NHL risk specific to lymphomas arising from the CNS (CC vs. CT/TT: OR=0.3; 95%CI: 0.1, 0.7) but not systemically (CC vs. CT/TT: OR=1.0; 95%CI: 0.5, 1.9) (Pheterogeneity=0.03). Carriage of two copies of the “low IL10” haplotype rs1800896_A/rs1800871_T/rs1800872_A was associated with decreased lymphoma risk that varied by number of copies (Ptrend=0.02). None of the ORs for the other studied polymorphisms was significantly different from 1.0. Excessive IL10 response to HIV-1 infection may be associated with increased risk of NHL, particularly in the CNS. IL10 dysregulation may be an important etiologic pathway for EBV-related lymphomagenesis. PMID:20299965

Automatic grade classification of Barretts Esophagus through feature enhancement

NASA Astrophysics Data System (ADS)

Ghatwary, Noha; Ahmed, Amr; Ye, Xujiong; Jalab, Hamid

2017-03-01

Barretts Esophagus (BE) is a precancerous condition that affects the esophagus tube and has the risk of developing esophageal adenocarcinoma. BE is the process of developing metaplastic intestinal epithelium and replacing the normal cells in the esophageal area. The detection of BE is considered difficult due to its appearance and properties. The diagnosis is usually done through both endoscopy and biopsy. Recently, Computer Aided Diagnosis systems have been developed to support physicians opinion when facing difficulty in detection/classification in different types of diseases. In this paper, an automatic classification of Barretts Esophagus condition is introduced. The presented method enhances the internal features of a Confocal Laser Endomicroscopy (CLE) image by utilizing a proposed enhancement filter. This filter depends on fractional differentiation and integration that improve the features in the discrete wavelet transform of an image. Later on, various features are extracted from each enhanced image on different levels for the multi-classification process. Our approach is validated on a dataset that consists of a group of 32 patients with 262 images with different histology grades. The experimental results demonstrated the efficiency of the proposed technique. Our method helps clinicians for more accurate classification. This potentially helps to reduce the need for biopsies needed for diagnosis, facilitate the regular monitoring of treatment/development of the patients case and can help train doctors with the new endoscopy technology. The accurate automatic classification is particularly important for the Intestinal Metaplasia (IM) type, which could turn into deadly cancerous. Hence, this work contributes to automatic classification that facilitates early intervention/treatment and decreasing biopsy samples needed.

Shear bond strength of computer-aided design and computer-aided manufacturing feldspathic and nano resin ceramics blocks cemented with three different generations of resin cement.

PubMed

Ab-Ghani, Zuryati; Jaafar, Wahyuni; Foo, Siew Fon; Ariffin, Zaihan; Mohamad, Dasmawati

2015-01-01

To evaluate the shear bond strength between the dentin substrate and computer-aided design and computer-aided manufacturing feldspathic ceramic and nano resin ceramics blocks cemented with resin cement. Sixty cuboidal blocks (5 mm × 5 mm × 5 mm) were fabricated in equal numbers from feldspathic ceramic CEREC(®) Blocs PC and nano resin ceramic Lava™ Ultimate, and randomly divided into six groups (n = 10). Each block was cemented to the dentin of 60 extracted human premolar using Variolink(®) II/Syntac Classic (multi-steps etch-and-rinse adhesive bonding), NX3 Nexus(®) (two-steps etch-and-rinse adhesive bonding) and RelyX™ U200 self-adhesive cement. All specimens were thermocycled, and shear bond strength testing was done using the universal testing machine at a crosshead speed of 1.0 mm/min. Data were analyzed using one-way ANOVA. Combination of CEREC(®) Blocs PC and Variolink(®) II showed the highest mean shear bond strength (8.71 Mpa), while the lowest of 2.06 Mpa were observed in Lava™ Ultimate and RelyX™ U200. There was no significant difference in the mean shear bond strength between different blocks. Variolink(®) II cement using multi-steps etch-and-rinse adhesive bonding provided a higher shear bond strength than the self-adhesive cement RelyX U200. The shear bond strength was not affected by the type of blocks used.

Evaluation of marginal/internal fit of chrome-cobalt crowns: Direct laser metal sintering versus computer-aided design and computer-aided manufacturing.

PubMed

Gunsoy, S; Ulusoy, M

2016-01-01

The purpose of this study was to evaluate the internal and marginal fit of chrome cobalt (Co-Cr) crowns were fabricated with laser sintering, computer-aided design (CAD) and computer-aided manufacturing, and conventional methods. Polyamide master and working models were designed and fabricated. The models were initially designed with a software application for three-dimensional (3D) CAD (Maya, Autodesk Inc.). All models were fabricated models were produced by a 3D printer (EOSINT P380 SLS, EOS). 128 1-unit Co-Cr fixed dental prostheses were fabricated with four different techniques: Conventional lost wax method, milled wax with lost-wax method (MWLW), direct laser metal sintering (DLMS), and milled Co-Cr (MCo-Cr). The cement film thickness of the marginal and internal gaps was measured by an observer using a stereomicroscope after taking digital photos in ×24. Best fit rates according to mean and standard deviations of all measurements was in DLMS both in premolar (65.84) and molar (58.38) models in μm. A significant difference was found DLMS and the rest of fabrication techniques (P < 0.05). No significant difference was found between MCo-CR and MWLW in all fabrication techniques both in premolar and molar models (P > 0.05). DMLS was best fitting fabrication techniques for single crown based on the results.The best fit was found in marginal; the larger gap was found in occlusal.All groups were within the clinically acceptable misfit range.

Computer-aided design of peptide near infrared fluorescent probe for tumor diagnosis

NASA Astrophysics Data System (ADS)

Zhang, Congying; Gu, Yueqing

2014-09-01

Integrin αvβ3 receptors are expressed on activated endothelial cells during neovascularization to maintain tumor growth, so they become hot research tagets in cancer diagnosis. Peptides possess several attractive features when compared to protein and small molecule, such as small size and high structural compatibility with target proteins. Efficient design of high-affinity peptide ligands to Integrin αvβ3 receptors has been an important problem. Designed peptides in silico provide a valuable and high-selectivity peptide, meanwhile decrease the time of drug screening. In this study, we design peptide which can bind with integrin αvβ3 via computer, and then synthesis near infrared fluorescent probe. The characterization of this near infrared fluorescent probe was detected by UV. To investigate the tumor cell targeting of this probe, it was labeled with visible fluorescent dye Rhodamine B (RhB) for microscopy. To evaluate the targeting capability of this near infrared fluorescent probe, mice bearing integrin αvβ3 positive tumor xenografts were used. In vitro cellular experiments indicated that this probe have a clear binding affinity to αvβ3-positive tumor cells. In vivo experiments confirmed the receptor binding specificity of this probe. The peptide of computational design can bind with integrin αvβ3. Combined peptide near-infrared fluorescent probe with imaging technology use for clinical and tumor diagnosis have a greater development in future.

A New Approach to Implant-Based Midface Reconstruction Following Subtotal Maxillectomy.

PubMed

Dawood, Andrew; Kalavrezos, Nicholas; Tanner, Susan

2016-01-01

This case presentation describes the reconstruction of an extensive maxillary-orbital defect following subtotal resection of the maxilla en bloc with orbital exenteration in a young adult following the diagnosis of chondrosarcoma. A new approach to composite midface reconstruction with dental implants is described, in which computer-guided surgery (CGS) was used to obliquely position dental implants interradicularly in the residual maxilla, such that the implant tips lie in close proximity to the root apices of the remaining teeth. The implants were then used to fixate a milled-titanium bar, fabricated using computer-aided design and manufacture (CAD/CAM), and provided with attachments for the stabilization and retention of a maxillary obturator.

Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images.

PubMed

Rajaraman, Sivaramakrishnan; Antani, Sameer K; Poostchi, Mahdieh; Silamut, Kamolrat; Hossain, Md A; Maude, Richard J; Jaeger, Stefan; Thoma, George R

2018-01-01

Malaria is a blood disease caused by the Plasmodium parasites transmitted through the bite of female Anopheles mosquito. Microscopists commonly examine thick and thin blood smears to diagnose disease and compute parasitemia. However, their accuracy depends on smear quality and expertise in classifying and counting parasitized and uninfected cells. Such an examination could be arduous for large-scale diagnoses resulting in poor quality. State-of-the-art image-analysis based computer-aided diagnosis (CADx) methods using machine learning (ML) techniques, applied to microscopic images of the smears using hand-engineered features demand expertise in analyzing morphological, textural, and positional variations of the region of interest (ROI). In contrast, Convolutional Neural Networks (CNN), a class of deep learning (DL) models promise highly scalable and superior results with end-to-end feature extraction and classification. Automated malaria screening using DL techniques could, therefore, serve as an effective diagnostic aid. In this study, we evaluate the performance of pre-trained CNN based DL models as feature extractors toward classifying parasitized and uninfected cells to aid in improved disease screening. We experimentally determine the optimal model layers for feature extraction from the underlying data. Statistical validation of the results demonstrates the use of pre-trained CNNs as a promising tool for feature extraction for this purpose.

Computer-Aided Facilities Management Systems (CAFM).

ERIC Educational Resources Information Center

Cyros, Kreon L.

Computer-aided facilities management (CAFM) refers to a collection of software used with increasing frequency by facilities managers. The six major CAFM components are discussed with respect to their usefulness and popularity in facilities management applications: (1) computer-aided design; (2) computer-aided engineering; (3) decision support…

[Late diagnosis and vulnerabilities of the elderly living with HIV/AIDS].

PubMed

Alencar, Rúbia Aguiar; Ciosak, Suely Itsuko

2015-04-01

To identify vulnerabilities of elderly people with HIV/AIDS and the trajectory that they follow until reaching the diagnosis of the disease. Qualitative research conducted in specialized clinics in the state of São Paulo, from January to June 2011. Semi-structured interviews were conducted with 11 elderly people who were found to be infected with the virus at the age of 60 years or older. The interviews were analyzed using content analysis. In this process four categories emerged, then analyzed with reference to the theoretical framework of vulnerability. Late diagnosis of HIV infection or AIDS among the elderly happens in the secondary or tertiary service. Issues related to sexual life of the elderly are only questioned by health professionals after the diagnosis, also the time that condom use becomes absolute. It is believed that the investigation of the vulnerability of the elderly to HIV/AIDS allows for carrying out appropriate interventions for this population.

A New Approach to Develop Computer-aided Diagnosis Scheme of Breast Mass Classification Using Deep Learning Technology

PubMed Central

Qiu, Yuchen; Yan, Shiju; Gundreddy, Rohith Reddy; Wang, Yunzhi; Cheng, Samuel; Liu, Hong; Zheng, Bin

2017-01-01

PURPOSE To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. METHODS An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. RESULTS The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. CONCLUSIONS This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process. PMID:28436410

A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.

PubMed

Qiu, Yuchen; Yan, Shiju; Gundreddy, Rohith Reddy; Wang, Yunzhi; Cheng, Samuel; Liu, Hong; Zheng, Bin

2017-01-01

To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process.

Soil, water, and vegetation conditions in south Texas

NASA Technical Reports Server (NTRS)

Wiegand, C. L.; Gausman, H. W.; Leamer, R. W.; Richardson, A. J.; Everitt, J. H.; Gerbermann, A. H. (Principal Investigator)

1976-01-01

The author has identified the following significant results. Software development for a computer-aided crop and soil survey system is nearing completion. Computer-aided variety classification accuracies using LANDSAT-1 MSS data for a 600 hectare citrus farm were 83% for Redblush grapefruit and 91% for oranges. These accuracies indicate that there is good potential for computer-aided inventories of grapefruit and orange citrus orchards with LANDSAT-type MSS data. Mean digital values of clouds differed statistically from those for crop, soil, and water entities, and those for cloud shadows were enough lower than sunlit crop and soil to be distinguishable. The standard errors of estimate for the calibration of computer compatible tape coordinate system (pixel and record) to earth coordinate system (longitude and latitude) for 6 LANDSAT scenes ranged from 0.72 to 1.50 pixels and from 0.58 to 1.75 records.

Clinical and genetic characterization of the autoinflammatory diseases diagnosed in an adult reference center.

PubMed

Hernández-Rodríguez, José; Ruíz-Ortiz, Estíbaliz; Tomé, Adrià; Espinosa, Gerard; González-Roca, Eva; Mensa-Vilaró, Anna; Prieto-González, Sergio; Espígol-Frigolé, Georgina; Mensa, Josep; Cardellach, Francesc; Grau, Josep M; Cid, Maria C; Yagüe, Jordi; Aróstegui, Juan I; Cervera, Ricard

2016-01-01

Autoinflammatory diseases (AID) are usually diagnosed during the pediatric age. However, adult-onset disease or diagnosis during adulthood has been occasionally described. To assess the clinical and genetic characteristics of adult patients diagnosed with an AID in an adult referral center for AID. We retrospectively evaluated clinical and genetic features of adult patients (≥16 years) diagnosed with an AID or referred after AID diagnosis to the Clinical Unit of AID, at the Department of Autoimmune Diseases, Hospital Clínic of Barcelona, from 2008 to 2014. During the study period, a genetic study for suspected AID was requested to 90 patients at the Department of Autoimmune Diseases. A final diagnosis of monogenic AID was achieved in 17 patients (19% of patients tested). Five additional cases were diagnosed with periodic fever, aphthous stomatitis, pharyngitis and cervical adenitis (PFAPA) syndrome and 10 patients with AID were referred from other adult departments. Finally, a total of 32 patients with AID were finally diagnosed or monitored in our Clinical Unit. These included 12 (37.5%) familial Mediterranean fever, 6 (18.8%) tumour necrosis factor-receptor associated periodic syndrome, 8 (25%) cryopirin-associated periodic syndromes (Muckle-Wells syndrome [MWS] or overlap familial cold-associated periodic syndrome/MWS), 1 (3.1%) mevalonate kinase deficiency, and 5 (15.6%) PFAPA. Clinical evidence of disease-onset during childhood and adulthood was observed in 15 (47%) and 17 (53%) patients, respectively. Overall, the final diagnosis was obtained after a delay of a mean of 12 years (range 0-47 years). Compared to children, adult patients with AID in our series presented more frequently with non-severe manifestations and none of them developed amyloidosis during follow-up. Adult patients also carried higher proportion of low-penetrance mutations or polymorphisms and all genetic variants were presented in heterozygosis or as heterozygous compounds. Adult disease-onset or delayed diagnosis of AID during adulthood is associated with milder disease phenotypes, and seem to be driven by mild genotypes, with predominant presence of low-penetrance mutations or polymorphisms. Copyright © 2015 Elsevier B.V. All rights reserved.

A Fast Approach to Automatic Detection of Brain Lesions

PubMed Central

Koley, Subhranil; Chakraborty, Chandan; Mainero, Caterina; Fischl, Bruce; Aganj, Iman

2017-01-01

Template matching is a popular approach to computer-aided detection of brain lesions from magnetic resonance (MR) images. The outcomes are often sufficient for localizing lesions and assisting clinicians in diagnosis. However, processing large MR volumes with three-dimensional (3D) templates is demanding in terms of computational resources, hence the importance of the reduction of computational complexity of template matching, particularly in situations in which time is crucial (e.g. emergent stroke). In view of this, we make use of 3D Gaussian templates with varying radii and propose a new method to compute the normalized cross-correlation coefficient as a similarity metric between the MR volume and the template to detect brain lesions. Contrary to the conventional fast Fourier transform (FFT) based approach, whose runtime grows as O(N logN) with the number of voxels, the proposed method computes the cross-correlation in O(N). We show through our experiments that the proposed method outperforms the FFT approach in terms of computational time, and retains comparable accuracy. PMID:29082383

Framework for cognitive analysis of dynamic perfusion computed tomography with visualization of large volumetric data

NASA Astrophysics Data System (ADS)

Hachaj, Tomasz; Ogiela, Marek R.

2012-10-01

The proposed framework for cognitive analysis of perfusion computed tomography images is a fusion of image processing, pattern recognition, and image analysis procedures. The output data of the algorithm consists of: regions of perfusion abnormalities, anatomy atlas description of brain tissues, measures of perfusion parameters, and prognosis for infracted tissues. That information is superimposed onto volumetric computed tomography data and displayed to radiologists. Our rendering algorithm enables rendering large volumes on off-the-shelf hardware. This portability of rendering solution is very important because our framework can be run without using expensive dedicated hardware. The other important factors are theoretically unlimited size of rendered volume and possibility of trading of image quality for rendering speed. Such rendered, high quality visualizations may be further used for intelligent brain perfusion abnormality identification, and computer aided-diagnosis of selected types of pathologies.

On the Value of Computer-aided Instruction: Thoughts after Teaching Sales Writing in a Computer Classroom.

ERIC Educational Resources Information Center

Hagge, John

1986-01-01

Focuses on problems encountered with computer-aided writing instruction. Discusses conflicts caused by the computer classroom concept, some general paradoxes and ethical implications of computer-aided instruction. (EL)

Computer vision for microscopy diagnosis of malaria.

PubMed

Tek, F Boray; Dempster, Andrew G; Kale, Izzet

2009-07-13

This paper reviews computer vision and image analysis studies aiming at automated diagnosis or screening of malaria infection in microscope images of thin blood film smears. Existing works interpret the diagnosis problem differently or propose partial solutions to the problem. A critique of these works is furnished. In addition, a general pattern recognition framework to perform diagnosis, which includes image acquisition, pre-processing, segmentation, and pattern classification components, is described. The open problems are addressed and a perspective of the future work for realization of automated microscopy diagnosis of malaria is provided.

Project-Based Teaching-Learning Computer-Aided Engineering Tools

ERIC Educational Resources Information Center

Simoes, J. A.; Relvas, C.; Moreira, R.

2004-01-01

Computer-aided design, computer-aided manufacturing, computer-aided analysis, reverse engineering and rapid prototyping are tools that play an important key role within product design. These are areas of technical knowledge that must be part of engineering and industrial design courses' curricula. This paper describes our teaching experience of…

Topology optimization aided structural design: Interpretation, computational aspects and 3D printing.

PubMed

Kazakis, Georgios; Kanellopoulos, Ioannis; Sotiropoulos, Stefanos; Lagaros, Nikos D

2017-10-01

Construction industry has a major impact on the environment that we spend most of our life. Therefore, it is important that the outcome of architectural intuition performs well and complies with the design requirements. Architects usually describe as "optimal design" their choice among a rather limited set of design alternatives, dictated by their experience and intuition. However, modern design of structures requires accounting for a great number of criteria derived from multiple disciplines, often of conflicting nature. Such criteria derived from structural engineering, eco-design, bioclimatic and acoustic performance. The resulting vast number of alternatives enhances the need for computer-aided architecture in order to increase the possibility of arriving at a more preferable solution. Therefore, the incorporation of smart, automatic tools in the design process, able to further guide designer's intuition becomes even more indispensable. The principal aim of this study is to present possibilities to integrate automatic computational techniques related to topology optimization in the phase of intuition of civil structures as part of computer aided architectural design. In this direction, different aspects of a new computer aided architectural era related to the interpretation of the optimized designs, difficulties resulted from the increased computational effort and 3D printing capabilities are covered here in.

Innovative telecommunications for law enforcement

NASA Technical Reports Server (NTRS)

Sohn, R. L.

1976-01-01

The operation of computer-aided dispatch, mobile digital communications, and automatic vehicle location systems used in law enforcement is discussed, and characteristics of systems used by different agencies are compared. With reference to computer-aided dispatch systems, the data base components, dispatcher work load, extent of usage, and design trends are surveyed. The capabilities, levels of communication, and traffic load of mobile digital communications systems are examined. Different automatic vehicle location systems are distinguished, and two systems are evaluated. Other aspects of the application of innovative technology to operational command, control, and communications systems for law enforcement agencies are described.

Endodontic management of mesiobuccal-2 canal in four-rooted and five-canalled mandibular third molar.

PubMed

Garg, Amit Kumar; Bhardwaj, Anuj; Mantri, Vijay R; Agrawal, Neha

2014-05-01

A case of unusual Root morphology is presented to demonstrate anatomic variations in mandibular third molar. The most common configuration of mandibular third molar is two Roots and three canals; however they may have many different combinations. Endodontic treatment was performed in mandibular third molar having aberrant anatomy. Four Root canal orifices were located with the aid of dental operating microscope (DOM) and three separate Roots were diagnosed with radiographs. Spiral computed tomography (SCT) showed the presence of an extra canal and extra Root, indicating a rare anatomic configuration. Looking for additional canals and Roots are important part of successful endodontics, as the knowledge of their existence enable clinicians to treat a case successfully that otherwise might end in failure. The use of DOM and SCT in this case greatly contributed toward making a confirmatory diagnosis and successful endodontic treatment of four-rooted and five-canalled mandibular third molar. Variation in Root canal anatomy is very common. Knowledge of these variations is very essential for successful Root canal outcome, inability to do so can lead to missed canals and failures. Hence, thorough knowledge of Root canal anatomy and advances in diagnostic aids are essential.

[The use of computer-aided colorimeter in porcelain-fused-to-metal (PFM) crowns among patients with special colored teeth].

PubMed

Fu, Yuan-fei; Weng, Wei-min

2004-02-01

To evaluate the roll of ShadeEye-NCC, a computer-aided colorimeter, in Porcelain-Fused-to-Metal crowns among patients with special colored Teeth. The first step was to choose the proper patients. The next was to use the colorimeter to measure the base shade of tooth and fabricate the PFM crowns according to the recipe given by the colorimeter. At last, the effects of the PFM crowns were evaluated subjectively by patients and doctor. The satisfaction rates of patients and doctor were 83.7% and 81.4% respectively, there was no significant difference between the two rates. The computer-aided colorimeter can offer good base shade recipe for fabricating PFM crowns of patients with special colored teeth.

Cerebrospinal fluid leaks and encephaloceles of temporal bone origin: nuances to diagnosis and management.

PubMed

Jeevan, Dhruve S; Ormond, D Ryan; Kim, Ana H; Meiteles, Lawrence Z; Stidham, Katrina R; Linstrom, Christopher; Murali, Raj

2015-04-01

Temporal bone encephalocele has become less common as the incidence of chronic mastoid infection and surgery for this condition has decreased. As a result, the diagnosis is often delayed, and the encephalocele is often an incidental finding. This situation can result in serious neurologic complications with patients presenting with cerebrospinal fluid leak and meningitis. We review the occurrence of, characteristics of, and repair experience with temporal encephaloceles from 2000-2012. We conducted a retrospective review of 32 patients undergoing combined mastoidectomy and middle cranial fossa craniotomy for the treatment of temporal encephalocele. The diagnosis of temporal encephalocele was made in all patients using high-resolution temporal bone computed tomography and magnetic resonance imaging. At the time of diagnosis, 12 patients had confirmed cerebrospinal fluid leak; other common presenting symptoms included hearing loss and ear fullness. Tegmen defect was most commonly due to chronic otitis media (n = 14). Of these patients, 8 had undergone prior mastoidectomy, suggesting an iatrogenic cause. Other etiologies included radiation exposure, congenital defects, and spontaneous defects. Additionally, 2 patients presented with meningitis; 1 patient had serious neurologic deficits resulting from venous infarction. The risk of severe neurologic complications after the herniation of intracranial contents through a tegmen defect necessitates prompt recognition and appropriate management. Computed tomography and magnetic resonance imaging aid in definitive diagnosis. A combined mastoid/middle fossa approach allows for sustainable repair with adequate exposure of defects and support of intracranial contents. Copyright © 2015 Elsevier Inc. All rights reserved.

Software Infrastructure for Computer-aided Drug Discovery and Development, a Practical Example with Guidelines.

PubMed

Moretti, Loris; Sartori, Luca

2016-09-01

In the field of Computer-Aided Drug Discovery and Development (CADDD) the proper software infrastructure is essential for everyday investigations. The creation of such an environment should be carefully planned and implemented with certain features in order to be productive and efficient. Here we describe a solution to integrate standard computational services into a functional unit that empowers modelling applications for drug discovery. This system allows users with various level of expertise to run in silico experiments automatically and without the burden of file formatting for different software, managing the actual computation, keeping track of the activities and graphical rendering of the structural outcomes. To showcase the potential of this approach, performances of five different docking programs on an Hiv-1 protease test set are presented. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Pulse oximetry: a potential aid in endodontic diagnosis?

PubMed

Caplan, Dan

2010-06-01

Pulse oximetry: review of a potential aid in endodontic diagnosis. Jafarzadeh H, Rosenberg PA. J Endod 2009;35(3):329-33. Dan Caplan, DDS, PhD. This article provided a description of pulse oximetry, its use in patient care settings, and its potential for use in endodontic diagnosis. Information not available. Comprehensive literature review. Level 3: Other evidence. Not applicable.

Fused man-machine classification schemes to enhance diagnosis of breast microcalcifications

NASA Astrophysics Data System (ADS)

Andreadis, Ioannis; Sevastianos, Chatzistergos; George, Spyrou; Konstantina, Nikita

2017-11-01

Computer aided diagnosis (CAD x ) approaches are developed towards the effective discrimination between benign and malignant clusters of microcalcifications. Different sources of information are exploited, such as features extracted from the image analysis of the region of interest, features related to the location of the cluster inside the breast, age of the patient and descriptors provided by the radiologists while performing their diagnostic task. A series of different CAD x schemes are implemented, each of which uses a different category of features and adopts a variety of machine learning algorithms and alternative image processing techniques. A novel framework is introduced where these independent diagnostic components are properly combined according to features critical to a radiologist in an attempt to identify the most appropriate CAD x schemes for the case under consideration. An open access database (Digital Database of Screening Mammography (DDSM)) has been elaborated to construct a large dataset with cases of varying subtlety, in order to ensure the development of schemes with high generalization ability, as well as extensive evaluation of their performance. The obtained results indicate that the proposed framework succeeds in improving the diagnostic procedure, as the achieved overall classification performance outperforms all the independent single diagnostic components, as well as the radiologists that assessed the same cases, in terms of accuracy, sensitivity, specificity and area under the curve following receiver operating characteristic analysis.

Structural analysis of paintings based on brush strokes

NASA Astrophysics Data System (ADS)

Sablatnig, Robert; Kammerer, Paul; Zolda, Ernestine

1998-05-01

The origin of works of art can often not be attributed to a certain artist. Likewise it is difficult to say whether paintings or drawings are originals or forgeries. In various fields of art new technical methods are used to examine the age, the state of preservation and the origin of the materials used. For the examination of paintings, radiological methods like X-ray and infra-red diagnosis, digital radiography, computer-tomography, etc. and color analyzes are employed to authenticate art. But all these methods do not relate certain characteristics in art work to a specific artist -- the artist's personal style. In order to study this personal style of a painter, experts in art history and image processing try to examine the 'structural signature' based on brush strokes within paintings, in particular in portrait miniatures. A computer-aided classification and recognition system for portrait miniatures is developed, which enables a semi- automatic classification and forgery detection based on content, color, and brush strokes. A hierarchically structured classification scheme is introduced which separates the classification into three different levels of information: color, shape of region, and structure of brush strokes.

An application to pulmonary emphysema classification based on model of texton learning by sparse representation

NASA Astrophysics Data System (ADS)

Zhang, Min; Zhou, Xiangrong; Goshima, Satoshi; Chen, Huayue; Muramatsu, Chisako; Hara, Takeshi; Yokoyama, Ryojiro; Kanematsu, Masayuki; Fujita, Hiroshi

2012-03-01

We aim at using a new texton based texture classification method in the classification of pulmonary emphysema in computed tomography (CT) images of the lungs. Different from conventional computer-aided diagnosis (CAD) pulmonary emphysema classification methods, in this paper, firstly, the dictionary of texton is learned via applying sparse representation(SR) to image patches in the training dataset. Then the SR coefficients of the test images over the dictionary are used to construct the histograms for texture presentations. Finally, classification is performed by using a nearest neighbor classifier with a histogram dissimilarity measure as distance. The proposed approach is tested on 3840 annotated regions of interest consisting of normal tissue and mild, moderate and severe pulmonary emphysema of three subtypes. The performance of the proposed system, with an accuracy of about 88%, is comparably higher than state of the art method based on the basic rotation invariant local binary pattern histograms and the texture classification method based on texton learning by k-means, which performs almost the best among other approaches in the literature.

Domain Adaptation for Alzheimer’s Disease Diagnostics

PubMed Central

Wachinger, Christian; Reuter, Martin

2016-01-01

With the increasing prevalence of Alzheimer’s disease, research focuses on the early computer-aided diagnosis of dementia with the goal to understand the disease process, determine risk and preserving factors, and explore preventive therapies. By now, large amounts of data from multi-site studies have been made available for developing, training, and evaluating automated classifiers. Yet, their translation to the clinic remains challenging, in part due to their limited generalizability across different datasets. In this work, we describe a compact classification approach that mitigates overfitting by regularizing the multinomial regression with the mixed ℓ1/ℓ2 norm. We combine volume, thickness, and anatomical shape features from MRI scans to characterize neuroanatomy for the three-class classification of Alzheimer’s disease, mild cognitive impairment and healthy controls. We demonstrate high classification accuracy via independent evaluation within the scope of the CADDementia challenge. We, furthermore, demonstrate that variations between source and target datasets can substantially influence classification accuracy. The main contribution of this work addresses this problem by proposing an approach for supervised domain adaptation based on instance weighting. Integration of this method into our classifier allows us to assess different strategies for domain adaptation. Our results demonstrate (i) that training on only the target training set yields better results than the naïve combination (union) of source and target training sets, and (ii) that domain adaptation with instance weighting yields the best classification results, especially if only a small training component of the target dataset is available. These insights imply that successful deployment of systems for computer-aided diagnostics to the clinic depends not only on accurate classifiers that avoid overfitting, but also on a dedicated domain adaptation strategy. PMID:27262241

[Medical expert systems and clinical needs].

PubMed

Buscher, H P

1991-10-18

The rapid expansion of computer-based systems for problem solving or decision making in medicine, the so-called medical expert systems, emphasize the need for reappraisal of their indication and value. Where specialist knowledge is required, in particular where medical decisions are susceptible to error these systems will probably serve as a valuable support. In the near future computer-based systems should be able to aid the interpretation of findings of technical investigations and the control of treatment, especially where rapid reactions are necessary despite the need of complex analysis of investigated parameters. In the distant future complete support of diagnostic procedures from the history to final diagnosis is possible. It promises to be particularly attractive for the diagnosis of seldom diseases, for difficult differential diagnoses, and in the decision making in the case of expensive, risky or new diagnostic or therapeutic methods. The physician needs to be aware of certain dangers, ranging from misleading information up to abuse. Patient information depends often on subjective reports and error-prone observations. Although basing on problematic knowledge computer-born decisions may have an imperative effect on medical decision making. Also it must be born in mind that medical decisions should always combine the rational with a consideration of human motives.

Visual and computer software-aided estimates of Dupuytren's contractures: correlation with clinical goniometric measurements.

PubMed

Smith, R P; Dias, J J; Ullah, A; Bhowal, B

2009-05-01

Corrective surgery for Dupuytren's disease represents a significant proportion of a hand surgeon's workload. The decision to go ahead with surgery and the success of surgery requires measuring the degree of contracture of the diseased finger(s). This is performed in clinic with a goniometer, pre- and postoperatively. Monitoring the recurrence of the contracture can inform on surgical outcome, research and audit. We compared visual and computer software-aided estimation of Dupuytren's contractures to clinical goniometric measurements in 60 patients with Dupuytren's disease. Patients' hands were digitally photographed. There were 76 contracted finger joints--70 proximal interphalangeal joints and six distal interphalangeal joints. The degrees of contracture of these images were visually assessed by six orthopaedic staff of differing seniority and re-assessed with computer software. Across assessors, the Pearson correlation between the goniometric measurements and the visual estimations was 0.83 and this significantly improved to 0.88 with computer software. Reliability with intra-class correlations achieved 0.78 and 0.92 for the visual and computer-aided estimations, respectively, and with test-retest analysis, 0.92 for visual estimation and 0.95 for computer-aided measurements. Visual estimations of Dupuytren's contractures correlate well with actual clinical goniometric measurements and improve further if measured with computer software. Digital images permit monitoring of contracture after surgery and may facilitate research into disease progression and auditing of surgical technique.

A SINDA thermal model using CAD/CAE technologies

NASA Technical Reports Server (NTRS)

Rodriguez, Jose A.; Spencer, Steve

1992-01-01

The approach to thermal analysis described by this paper is a technique that incorporates Computer Aided Design (CAD) and Computer Aided Engineering (CAE) to develop a thermal model that has the advantages of Finite Element Methods (FEM) without abandoning the unique advantages of Finite Difference Methods (FDM) in the analysis of thermal systems. The incorporation of existing CAD geometry, the powerful use of a pre and post processor and the ability to do interdisciplinary analysis, will be described.

Genome-Wide Association Scan in HIV-1-Infected Individuals Identifying Variants Influencing Disease Course

PubMed Central

van Manen, Daniëlle; Delaneau, Olivier; Kootstra, Neeltje A.; Boeser-Nunnink, Brigitte D.; Limou, Sophie; Bol, Sebastiaan M.; Burger, Judith A.; Zwinderman, Aeilko H.; Moerland, Perry D.; van 't Slot, Ruben; Zagury, Jean-François; van 't Wout, Angélique B.; Schuitemaker, Hanneke

2011-01-01

Background AIDS develops typically after 7–11 years of untreated HIV-1 infection, with extremes of very rapid disease progression (<2 years) and long-term non-progression (>15 years). To reveal additional host genetic factors that may impact on the clinical course of HIV-1 infection, we designed a genome-wide association study (GWAS) in 404 participants of the Amsterdam Cohort Studies on HIV-1 infection and AIDS. Methods The association of SNP genotypes with the clinical course of HIV-1 infection was tested in Cox regression survival analyses using AIDS-diagnosis and AIDS-related death as endpoints. Results Multiple, not previously identified SNPs, were identified to be strongly associated with disease progression after HIV-1 infection, albeit not genome-wide significant. However, three independent SNPs in the top ten associations between SNP genotypes and time between seroconversion and AIDS-diagnosis, and one from the top ten associations between SNP genotypes and time between seroconversion and AIDS-related death, had P-values smaller than 0.05 in the French Genomics of Resistance to Immunodeficiency Virus cohort on disease progression. Conclusions Our study emphasizes that the use of different phenotypes in GWAS may be useful to unravel the full spectrum of host genetic factors that may be associated with the clinical course of HIV-1 infection. PMID:21811574

Effectiveness of a federal Healthy Start Program on HIV/AIDS risk reduction among women in Hillsborough County, Florida.

PubMed

August, Euna; Aliyu, Muktar H; Mbah, Alfred; Okwechime, Ifechukwude; Adegoke, Korede K; de la Cruz, Cara; Berry, Estrellita Lo; Salihu, Hamisu M

2015-04-01

To examine the impact of the Central Hillsborough Healthy Start Project (CHHS) on human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) diagnosis rates in women in Hillsborough County, Florida. Project records were linked to hospital discharge data and vital statistics (Florida, 1998-2007; N = 1,696,221). The χ(2) test was used to compare rates for HIV/AIDS and pregnancy-related complications for mothers within the CHHS service area with mothers in Hillsborough County and the rest of Florida. During a 10-year period, HIV/AIDS diagnosis rates among women in the CHHS service area declined by 56.3% (P = 0.01). The observed decline was most evident among black women. HIV/AIDS diagnosis rates in the rest of Hillsborough County and Florida remained unchanged (P = 0.48). Lessons learned from the CHHS Project can be used to develop effective and comprehensive models for addressing the HIV epidemic.

Interactive content-based image retrieval (CBIR) computer-aided diagnosis (CADx) system for ultrasound breast masses using relevance feedback

NASA Astrophysics Data System (ADS)

Cho, Hyun-chong; Hadjiiski, Lubomir; Sahiner, Berkman; Chan, Heang-Ping; Paramagul, Chintana; Helvie, Mark; Nees, Alexis V.

2012-03-01

We designed a Content-Based Image Retrieval (CBIR) Computer-Aided Diagnosis (CADx) system to assist radiologists in characterizing masses on ultrasound images. The CADx system retrieves masses that are similar to a query mass from a reference library based on computer-extracted features that describe texture, width-to-height ratio, and posterior shadowing of a mass. Retrieval is performed with k nearest neighbor (k-NN) method using Euclidean distance similarity measure and Rocchio relevance feedback algorithm (RRF). In this study, we evaluated the similarity between the query and the retrieved masses with relevance feedback using our interactive CBIR CADx system. The similarity assessment and feedback were provided by experienced radiologists' visual judgment. For training the RRF parameters, similarities of 1891 image pairs obtained from 62 masses were rated by 3 MQSA radiologists using a 9-point scale (9=most similar). A leave-one-out method was used in training. For each query mass, 5 most similar masses were retrieved from the reference library using radiologists' similarity ratings, which were then used by RRF to retrieve another 5 masses for the same query. The best RRF parameters were chosen based on three simulated observer experiments, each of which used one of the radiologists' ratings for retrieval and relevance feedback. For testing, 100 independent query masses on 100 images and 121 reference masses on 230 images were collected. Three radiologists rated the similarity between the query and the computer-retrieved masses. Average similarity ratings without and with RRF were 5.39 and 5.64 on the training set and 5.78 and 6.02 on the test set, respectively. The average Az values without and with RRF were 0.86+/-0.03 and 0.87+/-0.03 on the training set and 0.91+/-0.03 and 0.90+/-0.03 on the test set, respectively. This study demonstrated that RRF improved the similarity of the retrieved masses.

Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach.

PubMed

Liu, Shuo; Zeng, Jinshu; Gong, Huizhou; Yang, Hongqin; Zhai, Jia; Cao, Yi; Liu, Junxiu; Luo, Yuling; Li, Yuhua; Maguire, Liam; Ding, Xuemei

2018-01-01

Breast cancer is the most prevalent cancer in women in most countries of the world. Many computer-aided diagnostic methods have been proposed, but there are few studies on quantitative discovery of probabilistic dependencies among breast cancer data features and identification of the contribution of each feature to breast cancer diagnosis. This study aims to fill this void by utilizing a Bayesian network (BN) modelling approach. A K2 learning algorithm and statistical computation methods are used to construct BN structure and assess the obtained BN model. The data used in this study were collected from a clinical ultrasound dataset derived from a Chinese local hospital and a fine-needle aspiration cytology (FNAC) dataset from UCI machine learning repository. Our study suggested that, in terms of ultrasound data, cell shape is the most significant feature for breast cancer diagnosis, and the resistance index presents a strong probabilistic dependency on blood signals. With respect to FNAC data, bare nuclei are the most important discriminating feature of malignant and benign breast tumours, and uniformity of both cell size and cell shape are tightly interdependent. The BN modelling approach can support clinicians in making diagnostic decisions based on the significant features identified by the model, especially when some other features are missing for specific patients. The approach is also applicable to other healthcare data analytics and data modelling for disease diagnosis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Improvement of a Clinical Score for Necrotizing Fasciitis: ‘Pain Out of Proportion’ and High CRP Levels Aid the Diagnosis

PubMed Central

Siegel, Ekkehard; Hanke, Eric; von Stebut, Esther

2015-01-01

Necrotizing fasciitis (NF) is a rare mono-/polymicrobial skin infection that spreads to underlying tissues. NF is quickly progressing and leads to life threatening situations. Immediate surgical debridement together with i.v. antibiotic administration is required to avoid fatal outcome. Early diagnosis is often delayed due to underestimation or confusion with cellulitis. We now compared the initial clinical and laboratory presentation of NF and cellulitis in detail to assess if a typical pattern can be identified that aids timely diagnosis of NF and avoidance of fatal outcome. 138 different clinical and laboratory features of 29 NF patients were compared to those of 59 age- and gender matched patients with severe erysipelas requiring a subsequent hospitalization time of ≥10 days. Differences in clinical presentation were not obvious; however, NF patients suffered significantly more often from strong pain. NF patients exhibited dramatically elevated CRP levels (5-fold, p>0.001). The overall laboratory risk indicator for necrotizing fasciitis (LRINEC) score was significantly higher in NF patients as compared to cellulitis. However, a modification of the score (alteration of laboratory parameters, addition of clinical parameters) led to a clear improvement of the score with a higher positive predictive value without losing specificity. In summary, clinical differentiation of NF from cellulitis appears to be hard. ‘Pain out of proportion’ may be an early sign for NF. An improvement of the LRINEC score emphasizing only relevant laboratory and clinical findings as suggested may aid the early diagnosis of NF in the future leading to improvement of disease outcome by enabling rapid adequate therapy. PMID:26196941

Evaluation of the fracture resistance of computer-aided design/computer-aided manufacturing monolithic crowns prepared in different cement thicknesses.

PubMed

Sagsoz, N Polat; Yanıkoglu, N

2018-04-01

The purpose of this study was to evaluate the fracture resistance of monolithic computer-aided design/computer-aided manufacturing (CAD/CAM) crowns that are prepared with different cement thickness. For this investigation, a human maxillary premolar tooth was selected. Master model preparation was performed with a demand bur under water spray. Master die was taken to fabricate 105 epoxy resin replicas. The crowns were milled using a CEREC 4 CAD/CAM system (Software Version, 4.2.0.57192). CAD/CAM crowns were made using resin nanoceramic, feldspathic glass ceramic, lithium disilicate, and leucite-reinforced ceramics. Each group was subdivided into three groups in accordance with three different cement thicknesses (30, 90, and 150 μm). Crowns milled out. Then RelyX ™ U200 was used as a luting agent to bond the crowns to the prepared samples. After one hour cementations, the specimens were stored in water bath at 37°C for 1 week before testing. Seven unprepared and unrestored teeth were kept and tested as a control group. A universal test machine was used to assume the fracture resistance of all specimens. The compressive load (N) that caused fracture was recorded for each specimen. Fracture resistance data were statistically analyzed by one-way ANOVA and two-factor interaction modeling test (α = 0.001). There are statistically significant differences between fracture resistances of CAD/CAM monolithic crown materials (P < 0.001). It is seen that cement thickness is not statistically significant for fracture resistance of CAD/CAM monolithic crowns (P > 0.001). CAD/CAM monolithic crown materials affected fracture resistance. Cement thickness (30, 90, and 150 μm) was not effective on fracture resistance of CAD/CAM monolithic crowns.

A novel strategy for load balancing of distributed medical applications.

PubMed

Logeswaran, Rajasvaran; Chen, Li-Choo

2012-04-01

Current trends in medicine, specifically in the electronic handling of medical applications, ranging from digital imaging, paperless hospital administration and electronic medical records, telemedicine, to computer-aided diagnosis, creates a burden on the network. Distributed Service Architectures, such as Intelligent Network (IN), Telecommunication Information Networking Architecture (TINA) and Open Service Access (OSA), are able to meet this new challenge. Distribution enables computational tasks to be spread among multiple processors; hence, performance is an important issue. This paper proposes a novel approach in load balancing, the Random Sender Initiated Algorithm, for distribution of tasks among several nodes sharing the same computational object (CO) instances in Distributed Service Architectures. Simulations illustrate that the proposed algorithm produces better network performance than the benchmark load balancing algorithms-the Random Node Selection Algorithm and the Shortest Queue Algorithm, especially under medium and heavily loaded conditions.