A Computer-Aided Distinction Method of Borderline Grades of Oral Cancer

NASA Astrophysics Data System (ADS)

Sami, Mustafa M.; Saito, Masahisa; Muramatsu, Shogo; Kikuchi, Hisakazu; Saku, Takashi

We have developed a new computer-aided diagnostic system for differentiating oral borderline malignancies in hematoxylin-eosin stained microscopic images. Epithelial dysplasia and carcinoma in-situ (CIS) of oral mucosa are two different borderline grades similar to each other, and it is difficult to distinguish between them. A new image processing and analysis method has been applied to a variety of histopathological features and shows the possibility for differentiating the oral cancer borderline grades automatically. The method is based on comparing the drop-shape similarity level in a particular manually selected pair of neighboring rete ridges. It was found that the considered similarity level in dysplasia was higher than those in epithelial CIS, of which pathological diagnoses were conventionally made by pathologists. The developed image processing method showed a good promise for the computer-aided pathological assessment of oral borderline malignancy differentiation in clinical practice.

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.

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.

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

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

Cloud-Based NoSQL Open Database of Pulmonary Nodules for Computer-Aided Lung Cancer Diagnosis and Reproducible Research.

PubMed

Ferreira Junior, José Raniery; Oliveira, Marcelo Costa; de Azevedo-Marques, Paulo Mazzoncini

2016-12-01

Lung cancer is the leading cause of cancer-related deaths in the world, and its main manifestation is pulmonary nodules. Detection and classification of pulmonary nodules are challenging tasks that must be done by qualified specialists, but image interpretation errors make those tasks difficult. In order to aid radiologists on those hard tasks, it is important to integrate the computer-based tools with the lesion detection, pathology diagnosis, and image interpretation processes. However, computer-aided diagnosis research faces the problem of not having enough shared medical reference data for the development, testing, and evaluation of computational methods for diagnosis. In order to minimize this problem, this paper presents a public nonrelational document-oriented cloud-based database of pulmonary nodules characterized by 3D texture attributes, identified by experienced radiologists and classified in nine different subjective characteristics by the same specialists. Our goal with the development of this database is to improve computer-aided lung cancer diagnosis and pulmonary nodule detection and classification research through the deployment of this database in a cloud Database as a Service framework. Pulmonary nodule data was provided by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), image descriptors were acquired by a volumetric texture analysis, and database schema was developed using a document-oriented Not only Structured Query Language (NoSQL) approach. The proposed database is now with 379 exams, 838 nodules, and 8237 images, 4029 of them are CT scans and 4208 manually segmented nodules, and it is allocated in a MongoDB instance on a cloud infrastructure.

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.

Comparative study viruses with computer-aided phase microscope AIRYSCAN

NASA Astrophysics Data System (ADS)

Tychinsky, Vladimir P.; Koufal, Georgy E.; Perevedentseva, Elena V.; Vyshenskaia, Tatiana V.

1996-12-01

Traditionally viruses are studied with scanning electron microscopy (SEM) after complicated procedure of sample preparation without the possibility to study it under natural conditions. We obtained images of viruses (Vaccinia virus, Rotavirus) and rickettsias (Rickettsia provazekii, Coxiella burnetti) in native state with computer-aided phase microscope airyscan -- the interference microscope of Linnik layout with phase modulation of the reference wave with dissector image tube as coordinate-sensitive photodetector and computer processing of phase image. A light source was the He-Ne laser. The main result is coincidence of dimensions and shape of phase images with available information concerning their morphology obtained with SEM and other methods. The fine structure of surface and nuclei is observed. This method may be applied for virus recognition and express identification, investigation of virus structure and the analysis of cell-virus interaction.

Optimising the measurement of bruises in children across conventional and cross polarized images using segmentation analysis techniques in Image J, Photoshop and circle diameter measurements.

PubMed

Harris, C; Alcock, A; Trefan, L; Nuttall, D; Evans, S T; Maguire, S; Kemp, A M

2018-02-01

Bruising is a common abusive injury in children, and it is standard practice to image and measure them, yet there is no current standard for measuring bruise size consistently. We aim to identify the optimal method of measuring photographic images of bruises, including computerised measurement techniques. 24 children aged <11 years (mean age of 6.9, range 2.5-10 years) with a bruise were recruited from the community. Demographics and bruise details were recorded. Each bruise was measured in vivo using a paper measuring tape. Standardised conventional and cross polarized digital images were obtained. The diameter of bruise images were measured by three computer aided measurement techniques: Image J (segmentation with Simple Interactive Object Extraction (maximum Feret diameter), 'Circular Selection Tool' (Circle diameter), & the Photoshop 'ruler' software (Photoshop diameter)). Inter and intra-observer effects were determined by two individuals repeating 11 electronic measurements, and relevant Intraclass Correlation Coefficient's (ICC's) were used to establish reliability. Spearman's rank correlation was used to compare in vivo with computerised measurements; a comparison of measurement techniques across imaging modalities was conducted using Kolmogorov-Smirnov tests. Significance was set at p < 0.05 for all tests. Images were available for 38 bruises in vivo, with 48 bruises visible on cross polarized imaging and 46 on conventional imaging (some bruises interpreted as being single in vivo appeared to be multiple in digital images). Correlation coefficients were >0.5 for all techniques, with maximum Feret diameter and maximum Photoshop diameter on conventional images having the strongest correlation with in vivo measurements. There were significant differences between in vivo and computer-aided measurements, but none between different computer-aided measurement techniques. Overall, computer aided measurements appeared larger than in vivo. Inter- and intra-observer agreement was high for all maximum diameter measurements (ICC's > 0.7). Whilst there are minimal differences between measurements of images obtained, the most consistent results were obtained when conventional images, segmented by Image J Software, were measured with a Feret diameter. This is therefore proposed as a standard for future research, and forensic practice, with the proviso that all computer aided measurements appear larger than in vivo. Copyright © 2018 Elsevier Ltd and Faculty of Forensic and Legal Medicine. 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.

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.

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.

The microcomputer in the dental office: a new diagnostic aid.

PubMed

van der Stelt, P F

1985-06-01

The first computer applications in the dental office were based upon standard accountancy procedures. Recently, more and more computer applications have become available to meet the specific requirements of dental practice. This implies not only business procedures, but also facilities to store patient records in the system and retrieve them easily. Another development concerns the automatic calculation of diagnostic data such as those provided in cephalometric analysis. Furthermore, growth and surgical results in the craniofacial area can be predicted by computerized extrapolation. Computers have been useful in obtaining the patient's anamnestic data objectively and for the making of decisions based on such data. Computer-aided instruction systems have been developed for undergraduate students to bridge the gap between textbook and patient interaction without the risks inherent in the latter. Radiology will undergo substantial changes as a result of the application of electronic imaging devices instead of the conventional radiographic films. Computer-assisted electronic imaging will enable image processing, image enhancement, pattern recognition and data transmission for consultation and storage purposes. Image processing techniques will increase image quality whilst still allowing low-dose systems. Standardization of software and system configuration and the development of 'user friendly' programs is the major concern for the near future.

The ERTS-1 investigation (ER-600). Volume 2: ERTS-1 coastal/estuarine analysis. [Galveston Bay, Texas

NASA Technical Reports Server (NTRS)

Erb, R. B.

1974-01-01

The Coastal Analysis Team of the Johnson Space Center conducted a 1-year investigation of ERTS-1 MSS data to determine its usefulness in coastal zone management. Galveston Bay, Texas, was the study area for evaluating both conventional image interpretation and computer-aided techniques. There was limited success in detecting, identifying and measuring areal extent of water bodies, turbidity zones, phytoplankton blooms, salt marshes, grasslands, swamps, and low wetlands using image interpretation techniques. Computer-aided techniques were generally successful in identifying these features. Aerial measurement of salt marshes accuracies ranged from 89 to 99 percent. Overall classification accuracy of all study sites was 89 percent for Level 1 and 75 percent for Level 2.

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.

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.

Multicenter study of quantitative computed tomography analysis using a computer-aided three-dimensional system in patients with idiopathic pulmonary fibrosis.

PubMed

Iwasawa, Tae; Kanauchi, Tetsu; Hoshi, Toshiko; Ogura, Takashi; Baba, Tomohisa; Gotoh, Toshiyuki; Oba, Mari S

2016-01-01

To evaluate the feasibility of automated quantitative analysis with a three-dimensional (3D) computer-aided system (i.e., Gaussian histogram normalized correlation, GHNC) of computed tomography (CT) images from different scanners. Each institution's review board approved the research protocol. Informed patient consent was not required. The participants in this multicenter prospective study were 80 patients (65 men, 15 women) with idiopathic pulmonary fibrosis. Their mean age was 70.6 years. Computed tomography (CT) images were obtained by four different scanners set at different exposures. We measured the extent of fibrosis using GHNC, and used Pearson's correlation analysis, Bland-Altman plots, and kappa analysis to directly compare the GHNC results with manual scoring by radiologists. Multiple linear regression analysis was performed to determine the association between the CT data and forced vital capacity (FVC). For each scanner, the extent of fibrosis as determined by GHNC was significantly correlated with the radiologists' score. In multivariate analysis, the extent of fibrosis as determined by GHNC was significantly correlated with FVC (p < 0.001). There was no significant difference between the results obtained using different CT scanners. Gaussian histogram normalized correlation was feasible, irrespective of the type of CT scanner used.

Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: preliminary experiments

NASA Astrophysics Data System (ADS)

Mazurowski, Maciej A.; Zhang, Jing; Lo, Joseph Y.; Kuzmiak, Cherie M.; Ghate, Sujata V.; Yoon, Sora

2014-03-01

Providing high quality mammography education to radiology trainees is essential, as good interpretation skills potentially ensure the highest benefit of screening mammography for patients. We have previously proposed a computer-aided education system that utilizes trainee models, which relate human-assessed image characteristics to interpretation error. We proposed that these models be used to identify the most difficult and therefore the most educationally useful cases for each trainee. In this study, as a next step in our research, we propose to build trainee models that utilize features that are automatically extracted from images using computer vision algorithms. To predict error, we used a logistic regression which accepts imaging features as input and returns error as output. Reader data from 3 experts and 3 trainees were used. Receiver operating characteristic analysis was applied to evaluate the proposed trainee models. Our experiments showed that, for three trainees, our models were able to predict error better than chance. This is an important step in the development of adaptive computer-aided education systems since computer-extracted features will allow for faster and more extensive search of imaging databases in order to identify the most educationally beneficial cases.

Automated analysis and classification of melanocytic tumor on skin whole slide images.

PubMed

Xu, Hongming; Lu, Cheng; Berendt, Richard; Jha, Naresh; Mandal, Mrinal

2018-06-01

This paper presents a computer-aided technique for automated analysis and classification of melanocytic tumor on skin whole slide biopsy images. The proposed technique consists of four main modules. First, skin epidermis and dermis regions are segmented by a multi-resolution framework. Next, epidermis analysis is performed, where a set of epidermis features reflecting nuclear morphologies and spatial distributions is computed. In parallel with epidermis analysis, dermis analysis is also performed, where dermal cell nuclei are segmented and a set of textural and cytological features are computed. Finally, the skin melanocytic image is classified into different categories such as melanoma, nevus or normal tissue by using a multi-class support vector machine (mSVM) with extracted epidermis and dermis features. Experimental results on 66 skin whole slide images indicate that the proposed technique achieves more than 95% classification accuracy, which suggests that the technique has the potential to be used for assisting pathologists on skin biopsy image analysis and classification. Copyright © 2018 Elsevier Ltd. All rights reserved.

Computer-Aided Diagnosis of Anterior Segment Eye Abnormalities using Visible Wavelength Image Analysis Based Machine Learning.

PubMed

S V, Mahesh Kumar; R, Gunasundari

2018-06-02

Eye disease is a major health problem among the elderly people. Cataract and corneal arcus are the major abnormalities that exist in the anterior segment eye region of aged people. Hence, computer-aided diagnosis of anterior segment eye abnormalities will be helpful for mass screening and grading in ophthalmology. In this paper, we propose a multiclass computer-aided diagnosis (CAD) system using visible wavelength (VW) eye images to diagnose anterior segment eye abnormalities. In the proposed method, the input VW eye images are pre-processed for specular reflection removal and the iris circle region is segmented using a circular Hough Transform (CHT)-based approach. The first-order statistical features and wavelet-based features are extracted from the segmented iris circle and used for classification. The Support Vector Machine (SVM) by Sequential Minimal Optimization (SMO) algorithm was used for the classification. In experiments, we used 228 VW eye images that belong to three different classes of anterior segment eye abnormalities. The proposed method achieved a predictive accuracy of 96.96% with 97% sensitivity and 99% specificity. The experimental results show that the proposed method has significant potential for use in clinical applications.

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.

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.

Ultrasonic image analysis and image-guided interventions.

PubMed

Noble, J Alison; Navab, Nassir; Becher, H

2011-08-06

The fields of medical image analysis and computer-aided interventions deal with reducing the large volume of digital images (X-ray, computed tomography, magnetic resonance imaging (MRI), positron emission tomography and ultrasound (US)) to more meaningful clinical information using software algorithms. US is a core imaging modality employed in these areas, both in its own right and used in conjunction with the other imaging modalities. It is receiving increased interest owing to the recent introduction of three-dimensional US, significant improvements in US image quality, and better understanding of how to design algorithms which exploit the unique strengths and properties of this real-time imaging modality. This article reviews the current state of art in US image analysis and its application in image-guided interventions. The article concludes by giving a perspective from clinical cardiology which is one of the most advanced areas of clinical application of US image analysis and describing some probable future trends in this important area of ultrasonic imaging research.

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.

Deep Learning in Medical Image Analysis.

PubMed

Shen, Dinggang; Wu, Guorong; Suk, Heung-Il

2017-06-21

This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.

The ERTS-1 investigation (ER-600). Volume 4: ERTS-1 range analysis

NASA Technical Reports Server (NTRS)

Erb, R. B.

1974-01-01

The Range Analysis Team conducted an investigation to determine the utility of using LANDSAT 1 data for mapping vegetation-type information on range and related grazing lands. Two study areas within the Houston Area Test Site (HATS) were mapped to the highest classification level possible using manual image interpretation and computer aided classification techniques. Rangeland was distinguished from nonrangeland (water, urban area, and cropland) and was further classified as woodland versus nonwoodland. Finer classification of coastal features was attempted with some success in differentiating the lowland zone from the drier upland zone. Computer aided temporal analysis techniques enhanced discrimination among nearly all the vegetation types found in this investigation.

Comparative performance analysis for computer aided lung nodule detection and segmentation on ultra-low-dose vs. standard-dose CT

NASA Astrophysics Data System (ADS)

Wiemker, Rafael; Rogalla, Patrik; Opfer, Roland; Ekin, Ahmet; Romano, Valentina; Bülow, Thomas

2006-03-01

The performance of computer aided lung nodule detection (CAD) and computer aided nodule volumetry is compared between standard-dose (70-100 mAs) and ultra-low-dose CT images (5-10 mAs). A direct quantitative performance comparison was possible, since for each patient both an ultra-low-dose and a standard-dose CT scan were acquired within the same examination session. The data sets were recorded with a multi-slice CT scanner at the Charite university hospital Berlin with 1 mm slice thickness. Our computer aided nodule detection and segmentation algorithms were deployed on both ultra-low-dose and standard-dose CT data without any dose-specific fine-tuning or preprocessing. As a reference standard 292 nodules from 20 patients were visually identified, each nodule both in ultra-low-dose and standard-dose data sets. The CAD performance was analyzed by virtue of multiple FROC curves for different lower thresholds of the nodule diameter. For nodules with a volume-equivalent diameter equal or larger than 4 mm (149 nodules pairs), we observed a detection rate of 88% at a median false positive rate of 2 per patient in standard-dose images, and 86% detection rate in ultra-low-dose images, also at 2 FPs per patient. Including even smaller nodules equal or larger than 2 mm (272 nodules pairs), we observed a detection rate of 86% in standard-dose images, and 84% detection rate in ultra-low-dose images, both at a rate of 5 FPs per patient. Moreover, we observed a correlation of 94% between the volume-equivalent nodule diameter as automatically measured on ultra-low-dose versus on standard-dose images, indicating that ultra-low-dose CT is also feasible for growth-rate assessment in follow-up examinations. The comparable performance of lung nodule CAD in ultra-low-dose and standard-dose images is of particular interest with respect to lung cancer screening of asymptomatic patients.

Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review.

PubMed

Chen, Jia-Mei; Li, Yan; Xu, Jun; Gong, Lei; Wang, Lin-Wei; Liu, Wen-Lou; Liu, Juan

2017-03-01

With the advance of digital pathology, image analysis has begun to show its advantages in information analysis of hematoxylin and eosin histopathology images. Generally, histological features in hematoxylin and eosin images are measured to evaluate tumor grade and prognosis for breast cancer. This review summarized recent works in image analysis of hematoxylin and eosin histopathology images for breast cancer prognosis. First, prognostic factors for breast cancer based on hematoxylin and eosin histopathology images were summarized. Then, usual procedures of image analysis for breast cancer prognosis were systematically reviewed, including image acquisition, image preprocessing, image detection and segmentation, and feature extraction. Finally, the prognostic value of image features and image feature-based prognostic models was evaluated. Moreover, we discussed the issues of current analysis, and some directions for future research.

Political leaders and the media. Can we measure political leadership images in newspapers using computer-assisted content analysis?

PubMed

Aaldering, Loes; Vliegenthart, Rens

Despite the large amount of research into both media coverage of politics as well as political leadership, surprisingly little research has been devoted to the ways political leaders are discussed in the media. This paper studies whether computer-aided content analysis can be applied in examining political leadership images in Dutch newspaper articles. It, firstly, provides a conceptualization of political leader character traits that integrates different perspectives in the literature. Moreover, this paper measures twelve political leadership images in media coverage, based on a large-scale computer-assisted content analysis of Dutch media coverage (including almost 150.000 newspaper articles), and systematically tests the quality of the employed measurement instrument by assessing the relationship between the images, the variance in the measurement, the over-time development of images for two party leaders and by comparing the computer results with manual coding. We conclude that the computerized content analysis provides a valid measurement for the leadership images in Dutch newspapers. Moreover, we find that the dimensions political craftsmanship, vigorousness, integrity, communicative performances and consistency are regularly applied in discussing party leaders, but that portrayal of party leaders in terms of responsiveness is almost completely absent in Dutch newspapers.

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 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.

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.

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

Digital pathology imaging as a novel platform for standardization and globalization of quantitative nephropathology

PubMed Central

Gimpel, Charlotte; Kain, Renate; Laurinavicius, Arvydas; Bueno, Gloria; Zeng, Caihong; Liu, Zhihong; Schaefer, Franz; Kretzler, Matthias; Holzman, Lawrence B.; Hewitt, Stephen M.

2017-01-01

Abstract The introduction of digital pathology to nephrology provides a platform for the development of new methodologies and protocols for visual, morphometric and computer-aided assessment of renal biopsies. Application of digital imaging to pathology made substantial progress over the past decade; it is now in use for education, clinical trials and translational research. Digital pathology evolved as a valuable tool to generate comprehensive structural information in digital form, a key prerequisite for achieving precision pathology for computational biology. The application of this new technology on an international scale is driving novel methods for collaborations, providing unique opportunities but also challenges. Standardization of methods needs to be rigorously evaluated and applied at each step, from specimen processing to scanning, uploading into digital repositories, morphologic, morphometric and computer-aided assessment, data collection and analysis. In this review, we discuss the status and opportunities created by the application of digital imaging to precision nephropathology, and present a vision for the near future. PMID:28584625

Digital pathology imaging as a novel platform for standardization and globalization of quantitative nephropathology.

PubMed

Barisoni, Laura; Gimpel, Charlotte; Kain, Renate; Laurinavicius, Arvydas; Bueno, Gloria; Zeng, Caihong; Liu, Zhihong; Schaefer, Franz; Kretzler, Matthias; Holzman, Lawrence B; Hewitt, Stephen M

2017-04-01

The introduction of digital pathology to nephrology provides a platform for the development of new methodologies and protocols for visual, morphometric and computer-aided assessment of renal biopsies. Application of digital imaging to pathology made substantial progress over the past decade; it is now in use for education, clinical trials and translational research. Digital pathology evolved as a valuable tool to generate comprehensive structural information in digital form, a key prerequisite for achieving precision pathology for computational biology. The application of this new technology on an international scale is driving novel methods for collaborations, providing unique opportunities but also challenges. Standardization of methods needs to be rigorously evaluated and applied at each step, from specimen processing to scanning, uploading into digital repositories, morphologic, morphometric and computer-aided assessment, data collection and analysis. In this review, we discuss the status and opportunities created by the application of digital imaging to precision nephropathology, and present a vision for the near future.

Computer-aided design/computer-aided manufacturing skull base drill.

PubMed

Couldwell, William T; MacDonald, Joel D; Thomas, Charles L; Hansen, Bradley C; Lapalikar, Aniruddha; Thakkar, Bharat; Balaji, Alagar K

2017-05-01

The authors have developed a simple device for computer-aided design/computer-aided manufacturing (CAD-CAM) that uses an image-guided system to define a cutting tool path that is shared with a surgical machining system for drilling bone. Information from 2D images (obtained via CT and MRI) is transmitted to a processor that produces a 3D image. The processor generates code defining an optimized cutting tool path, which is sent to a surgical machining system that can drill the desired portion of bone. This tool has applications for bone removal in both cranial and spine neurosurgical approaches. Such applications have the potential to reduce surgical time and associated complications such as infection or blood loss. The device enables rapid removal of bone within 1 mm of vital structures. The validity of such a machining tool is exemplified in the rapid (< 3 minutes machining time) and accurate removal of bone for transtemporal (for example, translabyrinthine) approaches.

New breast cancer prognostic factors identified by computer-aided image analysis of HE stained histopathology images

PubMed Central

Chen, Jia-Mei; Qu, Ai-Ping; Wang, Lin-Wei; Yuan, Jing-Ping; Yang, Fang; Xiang, Qing-Ming; Maskey, Ninu; Yang, Gui-Fang; Liu, Juan; Li, Yan

2015-01-01

Computer-aided image analysis (CAI) can help objectively quantify morphologic features of hematoxylin-eosin (HE) histopathology images and provide potentially useful prognostic information on breast cancer. We performed a CAI workflow on 1,150 HE images from 230 patients with invasive ductal carcinoma (IDC) of the breast. We used a pixel-wise support vector machine classifier for tumor nests (TNs)-stroma segmentation, and a marker-controlled watershed algorithm for nuclei segmentation. 730 morphologic parameters were extracted after segmentation, and 12 parameters identified by Kaplan-Meier analysis were significantly associated with 8-year disease free survival (P < 0.05 for all). Moreover, four image features including TNs feature (HR 1.327, 95%CI [1.001 - 1.759], P = 0.049), TNs cell nuclei feature (HR 0.729, 95%CI [0.537 - 0.989], P = 0.042), TNs cell density (HR 1.625, 95%CI [1.177 - 2.244], P = 0.003), and stromal cell structure feature (HR 1.596, 95%CI [1.142 - 2.229], P = 0.006) were identified by multivariate Cox proportional hazards model to be new independent prognostic factors. The results indicated that CAI can assist the pathologist in extracting prognostic information from HE histopathology images for IDC. The TNs feature, TNs cell nuclei feature, TNs cell density, and stromal cell structure feature could be new prognostic factors. PMID:26022540

Computer vision applications for coronagraphic optical alignment and image processing.

PubMed

Savransky, Dmitry; Thomas, Sandrine J; Poyneer, Lisa A; Macintosh, Bruce A

2013-05-10

Modern coronagraphic systems require very precise alignment between optical components and can benefit greatly from automated image processing. We discuss three techniques commonly employed in the fields of computer vision and image analysis as applied to the Gemini Planet Imager, a new facility instrument for the Gemini South Observatory. We describe how feature extraction and clustering methods can be used to aid in automated system alignment tasks, and also present a search algorithm for finding regular features in science images used for calibration and data processing. Along with discussions of each technique, we present our specific implementation and show results of each one in operation.

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.

Computerized analysis of sonograms for the detection of breast lesions

NASA Astrophysics Data System (ADS)

Drukker, Karen; Giger, Maryellen L.; Horsch, Karla; Vyborny, Carl J.

2002-05-01

With a renewed interest in using non-ionizing radiation for the screening of high risk women, there is a clear role for a computerized detection aid in ultrasound. Thus, we are developing a computerized detection method for the localization of lesions on breast ultrasound images. The computerized detection scheme utilizes two methods. Firstly, a radial gradient index analysis is used to distinguish potential lesions from normal parenchyma. Secondly, an image skewness analysis is performed to identify posterior acoustic shadowing. We analyzed 400 cases (757 images) consisting of complex cysts, solid benign lesions, and malignant lesions. The detection method yielded an overall sensitivity of 95% by image, and 99% by case at a false-positive rate of 0.94 per image. In 51% of all images, only the lesion itself was detected, while in 5% of the images only the shadowing was identified. For malignant lesions these numbers were 37% and 9%, respectively. In summary, we have developed a computer detection method for lesions on ultrasound images of the breast, which may ultimately aid in breast cancer screening.

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.

Imperceptible watermarking for security of fundus images in tele-ophthalmology applications and computer-aided diagnosis of retina diseases.

PubMed

Singh, Anushikha; Dutta, Malay Kishore

2017-12-01

The authentication and integrity verification of medical images is a critical and growing issue for patients in e-health services. Accurate identification of medical images and patient verification is an essential requirement to prevent error in medical diagnosis. The proposed work presents an imperceptible watermarking system to address the security issue of medical fundus images for tele-ophthalmology applications and computer aided automated diagnosis of retinal diseases. In the proposed work, patient identity is embedded in fundus image in singular value decomposition domain with adaptive quantization parameter to maintain perceptual transparency for variety of fundus images like healthy fundus or disease affected image. In the proposed method insertion of watermark in fundus image does not affect the automatic image processing diagnosis of retinal objects & pathologies which ensure uncompromised computer-based diagnosis associated with fundus image. Patient ID is correctly recovered from watermarked fundus image for integrity verification of fundus image at the diagnosis centre. The proposed watermarking system is tested in a comprehensive database of fundus images and results are convincing. results indicate that proposed watermarking method is imperceptible and it does not affect computer vision based automated diagnosis of retinal diseases. Correct recovery of patient ID from watermarked fundus image makes the proposed watermarking system applicable for authentication of fundus images for computer aided diagnosis and Tele-ophthalmology applications. Copyright © 2017 Elsevier B.V. All rights reserved.

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.

PubMed

Shin, Hoo-Chang; Roth, Holger R; Gao, Mingchen; Lu, Le; Xu, Ziyue; Nogues, Isabella; Yao, Jianhua; Mollura, Daniel; Summers, Ronald M

2016-05-01

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.

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.

Do pre-trained deep learning models improve computer-aided classification of digital mammograms?

NASA Astrophysics Data System (ADS)

Aboutalib, Sarah S.; Mohamed, Aly A.; Zuley, Margarita L.; Berg, Wendie A.; Luo, Yahong; Wu, Shandong

2018-02-01

Digital mammography screening is an important exam for the early detection of breast cancer and reduction in mortality. False positives leading to high recall rates, however, results in unnecessary negative consequences to patients and health care systems. In order to better aid radiologists, computer-aided tools can be utilized to improve distinction between image classifications and thus potentially reduce false recalls. The emergence of deep learning has shown promising results in the area of biomedical imaging data analysis. This study aimed to investigate deep learning and transfer learning methods that can improve digital mammography classification performance. In particular, we evaluated the effect of pre-training deep learning models with other imaging datasets in order to boost classification performance on a digital mammography dataset. Two types of datasets were used for pre-training: (1) a digitized film mammography dataset, and (2) a very large non-medical imaging dataset. By using either of these datasets to pre-train the network initially, and then fine-tuning with the digital mammography dataset, we found an increase in overall classification performance in comparison to a model without pre-training, with the very large non-medical dataset performing the best in improving the classification accuracy.

Computer-aided assessment of pulmonary disease in novel swine-origin H1N1 influenza on CT

NASA Astrophysics Data System (ADS)

Yao, Jianhua; Dwyer, Andrew J.; Summers, Ronald M.; Mollura, Daniel J.

2011-03-01

The 2009 pandemic is a global outbreak of novel H1N1 influenza. Radiologic images can be used to assess the presence and severity of pulmonary infection. We develop a computer-aided assessment system to analyze the CT images from Swine-Origin Influenza A virus (S-OIV) novel H1N1 cases. The technique is based on the analysis of lung texture patterns and classification using a support vector machine (SVM). Pixel-wise tissue classification is computed from the SVM value. The method was validated on four H1N1 cases and ten normal cases. We demonstrated that the technique can detect regions of pulmonary abnormality in novel H1N1 patients and differentiate these regions from visually normal lung (area under the ROC curve is 0.993). This technique can also be applied to differentiate regions infected by different pulmonary diseases.

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.

ACSYNT - A standards-based system for parametric, computer aided conceptual design of aircraft

NASA Technical Reports Server (NTRS)

Jayaram, S.; Myklebust, A.; Gelhausen, P.

1992-01-01

A group of eight US aerospace companies together with several NASA and NAVY centers, led by NASA Ames Systems Analysis Branch, and Virginia Tech's CAD Laboratory agreed, through the assistance of Americal Technology Initiative, in 1990 to form the ACSYNT (Aircraft Synthesis) Institute. The Institute is supported by a Joint Sponsored Research Agreement to continue the research and development in computer aided conceptual design of aircraft initiated by NASA Ames Research Center and Virginia Tech's CAD Laboratory. The result of this collaboration, a feature-based, parametric computer aided aircraft conceptual design code called ACSYNT, is described. The code is based on analysis routines begun at NASA Ames in the early 1970's. ACSYNT's CAD system is based entirely on the ISO standard Programmer's Hierarchical Interactive Graphics System and is graphics-device independent. The code includes a highly interactive graphical user interface, automatically generated Hermite and B-Spline surface models, and shaded image displays. Numerous features to enhance aircraft conceptual design are described.

Use of Computer-Aided Tomography (CT) Imaging for Quantifying Coarse Roots, Rhizomes, Peat, and Particle Densities in Marsh Soils

EPA Science Inventory

Computer-aided Tomography (CT) imaging was utilized to quantify wet mass of coarse roots, rhizomes, and peat in cores collected from organic-rich (Jamaica Bay, NY) and mineral (North Inlet, SC) Spartina alterniflora soils. Calibration rods composed of materials with standard dens...

Synthesizing parallel imaging applications using the CAP (computer-aided parallelization) tool

NASA Astrophysics Data System (ADS)

Gennart, Benoit A.; Mazzariol, Marc; Messerli, Vincent; Hersch, Roger D.

1997-12-01

Imaging applications such as filtering, image transforms and compression/decompression require vast amounts of computing power when applied to large data sets. These applications would potentially benefit from the use of parallel processing. However, dedicated parallel computers are expensive and their processing power per node lags behind that of the most recent commodity components. Furthermore, developing parallel applications remains a difficult task: writing and debugging the application is difficult (deadlocks), programs may not be portable from one parallel architecture to the other, and performance often comes short of expectations. In order to facilitate the development of parallel applications, we propose the CAP computer-aided parallelization tool which enables application programmers to specify at a high-level of abstraction the flow of data between pipelined-parallel operations. In addition, the CAP tool supports the programmer in developing parallel imaging and storage operations. CAP enables combining efficiently parallel storage access routines and image processing sequential operations. This paper shows how processing and I/O intensive imaging applications must be implemented to take advantage of parallelism and pipelining between data access and processing. This paper's contribution is (1) to show how such implementations can be compactly specified in CAP, and (2) to demonstrate that CAP specified applications achieve the performance of custom parallel code. The paper analyzes theoretically the performance of CAP specified applications and demonstrates the accuracy of the theoretical analysis through experimental measurements.

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.

Making Ceramic/Polymer Parts By Extrusion Stereolithography

NASA Technical Reports Server (NTRS)

Stuffle, Kevin; Mulligan, A.; Creegan, P.; Boulton, J. M.; Lombardi, J. L.; Calvert, P. D.

1996-01-01

Extrusion stereolithography developmental method of computer-controlled manufacturing of objects out of ceramic/polymer composite materials. Computer-aided design/computer-aided manufacturing (CAD/CAM) software used to create image of desired part and translate image into motion commands for combination of mechanisms moving resin dispenser. Extrusion performed in coordination with motion of dispenser so buildup of extruded material takes on size and shape of desired part. Part thermally cured after deposition.

Computer-aided global breast MR image feature analysis for prediction of tumor response to chemotherapy: performance assessment

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Tan, Maxine; Hollingsworth, Alan B.; Zheng, Bin; Cheng, Samuel

2016-03-01

Dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) has been used increasingly in breast cancer diagnosis and assessment of cancer treatment efficacy. In this study, we applied a computer-aided detection (CAD) scheme to automatically segment breast regions depicting on MR images and used the kinetic image features computed from the global breast MR images acquired before neoadjuvant chemotherapy to build a new quantitative model to predict response of the breast cancer patients to the chemotherapy. To assess performance and robustness of this new prediction model, an image dataset involving breast MR images acquired from 151 cancer patients before undergoing neoadjuvant chemotherapy was retrospectively assembled and used. Among them, 63 patients had "complete response" (CR) to chemotherapy in which the enhanced contrast levels inside the tumor volume (pre-treatment) was reduced to the level as the normal enhanced background parenchymal tissues (post-treatment), while 88 patients had "partially response" (PR) in which the high contrast enhancement remain in the tumor regions after treatment. We performed the studies to analyze the correlation among the 22 global kinetic image features and then select a set of 4 optimal features. Applying an artificial neural network trained with the fusion of these 4 kinetic image features, the prediction model yielded an area under ROC curve (AUC) of 0.83+/-0.04. This study demonstrated that by avoiding tumor segmentation, which is often difficult and unreliable, fusion of kinetic image features computed from global breast MR images without tumor segmentation can also generate a useful clinical marker in predicting efficacy of chemotherapy.

3D surface rendered MR images of the brain and its vasculature.

PubMed

Cline, H E; Lorensen, W E; Souza, S P; Jolesz, F A; Kikinis, R; Gerig, G; Kennedy, T E

1991-01-01

Both time-of-flight and phase contrast magnetic resonance angiography images are combined with stationary tissue images to provide data depicting two contrast relationships yielding intrinsic discrimination of brain matter and flowing blood. A computer analysis is based on nearest neighbor segmentation and the connection between anatomical structures to partition the images into different tissue categories: from which, high resolution brain parenchymal and vascular surfaces are constructed and rendered in juxtaposition, aiding in surgical planning.

[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.

Computer-aided decision support systems for endoscopy in the gastrointestinal tract: a review.

PubMed

Liedlgruber, Michael; Uhl, Andreas

2011-01-01

Today, medical endoscopy is a widely used procedure to inspect the inner cavities of the human body. The advent of endoscopic imaging techniques-allowing the acquisition of images or videos-created the possibility for the development of the whole new branch of computer-aided decision support systems. Such systems aim at helping physicians to identify possibly malignant abnormalities more accurately. At the beginning of this paper, we give a brief introduction to the history of endoscopy, followed by introducing the main types of endoscopes which emerged so far (flexible endoscope, wireless capsule endoscope, and confocal laser endomicroscope). We then give a brief introduction to computer-aided decision support systems specifically targeted at endoscopy in the gastrointestinal tract. Then we present general facts and figures concerning computer-aided decision support systems and summarize work specifically targeted at computer-aided decision support in the gastrointestinal tract. This summary is followed by a discussion of some common issues concerning the approaches reviewed and suggestions of possible ways to resolve them.

Modelling terahertz radiation absorption and reflection with computational phantoms of skin and associated appendages

NASA Astrophysics Data System (ADS)

Vilagosh, Zoltan; Lajevardipour, Alireza; Wood, Andrew

2018-01-01

Finite-difference time-domain (FDTD) computational phantoms aid the analysis of THz radiation interaction with human skin. The presented computational phantoms have accurate anatomical layering and electromagnetic properties. A novel "large sheet" simulation technique is used allowing for a realistic representation of lateral absorption and reflection of in-vivo measurements. Simulations carried out to date have indicated that hair follicles act as THz propagation channels and confirms the possible role of melanin, both in nevi and skin pigmentation, to act as a significant absorber of THz radiation. A novel freezing technique has promise in increasing the depth of skin penetration of THz radiation to aid diagnostic imaging.

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.

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.

Deep Learning in Medical Image Analysis

PubMed Central

Shen, Dinggang; Wu, Guorong; Suk, Heung-Il

2016-01-01

The computer-assisted analysis for better interpreting images have been longstanding issues in the medical imaging field. On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images. Specifically, exploiting hierarchical feature representations learned solely from data, instead of handcrafted features mostly designed based on domain-specific knowledge, lies at the core of the advances. In that way, deep learning is rapidly proving to be the state-of-the-art foundation, achieving enhanced performances in various medical applications. In this article, we introduce the fundamentals of deep learning methods; review their successes to image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease diagnosis or prognosis, and so on. We conclude by raising research issues and suggesting future directions for further improvements. PMID:28301734

Comparison of digital intraoral scanners by single-image capture system and full-color movie system.

PubMed

Yamamoto, Meguru; Kataoka, Yu; Manabe, Atsufumi

2017-01-01

The use of dental computer-aided design/computer-aided manufacturing (CAD/CAM) restoration is rapidly increasing. This study was performed to evaluate the marginal and internal cement thickness and the adhesive gap of internal cavities comprising CAD/CAM materials using two digital impression acquisition methods and micro-computed tomography. Images obtained by a single-image acquisition system (Bluecam Ver. 4.0) and a full-color video acquisition system (Omnicam Ver. 4.2) were divided into the BL and OM groups, respectively. Silicone impressions were prepared from an ISO-standard metal mold, and CEREC Stone BC and New Fuji Rock IMP were used to create working models (n=20) in the BL and OM groups (n=10 per group), respectively. Individual inlays were designed in a conventional manner using designated software, and all restorations were prepared using CEREC inLab MC XL. These were assembled with the corresponding working models used for measurement, and the level of fit was examined by three-dimensional analysis based on micro-computed tomography. Significant differences in the marginal and internal cement thickness and adhesive gap spacing were found between the OM and BL groups. The full-color movie capture system appears to be a more optimal restoration system than the single-image capture system.

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.

Digital image processing for information extraction.

NASA Technical Reports Server (NTRS)

Billingsley, F. C.

1973-01-01

The modern digital computer has made practical image processing techniques for handling nonlinear operations in both the geometrical and the intensity domains, various types of nonuniform noise cleanup, and the numerical analysis of pictures. An initial requirement is that a number of anomalies caused by the camera (e.g., geometric distortion, MTF roll-off, vignetting, and nonuniform intensity response) must be taken into account or removed to avoid their interference with the information extraction process. Examples illustrating these operations are discussed along with computer techniques used to emphasize details, perform analyses, classify materials by multivariate analysis, detect temporal differences, and aid in human interpretation of photos.

[The automatic iris map overlap technology in computer-aided iridiagnosis].

PubMed

He, Jia-feng; Ye, Hu-nian; Ye, Miao-yuan

2002-11-01

In the paper, iridology and computer-aided iridiagnosis technologies are briefly introduced and the extraction method of the collarette contour is then investigated. The iris map can be overlapped on the original iris image based on collarette contour extraction. The research on collarette contour extraction and iris map overlap is of great importance to computer-aided iridiagnosis technologies.

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.

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.

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.

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.

BioImg.org: A Catalog of Virtual Machine Images for the Life Sciences

PubMed Central

Dahlö, Martin; Haziza, Frédéric; Kallio, Aleksi; Korpelainen, Eija; Bongcam-Rudloff, Erik; Spjuth, Ola

2015-01-01

Virtualization is becoming increasingly important in bioscience, enabling assembly and provisioning of complete computer setups, including operating system, data, software, and services packaged as virtual machine images (VMIs). We present an open catalog of VMIs for the life sciences, where scientists can share information about images and optionally upload them to a server equipped with a large file system and fast Internet connection. Other scientists can then search for and download images that can be run on the local computer or in a cloud computing environment, providing easy access to bioinformatics environments. We also describe applications where VMIs aid life science research, including distributing tools and data, supporting reproducible analysis, and facilitating education. BioImg.org is freely available at: https://bioimg.org. PMID:26401099

BioImg.org: A Catalog of Virtual Machine Images for the Life Sciences.

PubMed

Dahlö, Martin; Haziza, Frédéric; Kallio, Aleksi; Korpelainen, Eija; Bongcam-Rudloff, Erik; Spjuth, Ola

2015-01-01

Virtualization is becoming increasingly important in bioscience, enabling assembly and provisioning of complete computer setups, including operating system, data, software, and services packaged as virtual machine images (VMIs). We present an open catalog of VMIs for the life sciences, where scientists can share information about images and optionally upload them to a server equipped with a large file system and fast Internet connection. Other scientists can then search for and download images that can be run on the local computer or in a cloud computing environment, providing easy access to bioinformatics environments. We also describe applications where VMIs aid life science research, including distributing tools and data, supporting reproducible analysis, and facilitating education. BioImg.org is freely available at: https://bioimg.org.

The ERTS-1 investigation (ER-600): A compendium of analysis results of the utility of ERTS-1 data for land resources management

NASA Technical Reports Server (NTRS)

Erb, R. B.

1974-01-01

The results of the ERTS-1 investigations conducted by the Earth Observations Division at the NASA Lyndon B. Johnson Space Center are summarized in this report, which is an overview of documents detailing individual investigations. Conventional image interpretation and computer-aided classification procedures were the two basic techniques used in analyzing the data for detecting, identifying, locating, and measuring surface features related to earth resources. Data from the ERTS-1 multispectral scanner system were useful for all applications studied, which included agriculture, coastal and estuarine analysis, forestry, range, land use and urban land use, and signature extension. Percentage classification accuracies are cited for the conventional and computer-aided techniques.

A novel computer system for the evaluation of nasolabial morphology, symmetry and aesthetics after cleft lip and palate treatment. Part 1: General concept and validation.

PubMed

Pietruski, Piotr; Majak, Marcin; Debski, Tomasz; Antoszewski, Boguslaw

2017-04-01

The need for a widely accepted method suitable for a multicentre quantitative evaluation of facial aesthetics after surgical treatment of cleft lip and palate (CLP) has been emphasized for years. The aim of this study was to validate a novel computer system 'Analyse It Doc' (A.I.D.) as a tool for objective anthropometric analysis of the nasolabial region. An indirect anthropometric analysis of facial photographs was conducted with the A.I.D. system and Adobe Photoshop/ImageJ software. Intra-rater and inter-rater reliability and the time required for the analysis were estimated separately for each method and compared. Analysis with A.I.D. system was nearly 10-fold faster than that with the reference evaluation method. The A.I.D. system provided strong inter-rater and intra-rater correlations for linear, angular and area measurements of the nasolabial region, as well as a significantly higher accuracy and reproducibility of angular measurements in submental view. No statistically significant inter-method differences were found for other measurements. The hereby presented novel computer system is suitable for simple, time-efficient and reliable multicenter photogrammetric analyses of the nasolabial region in CLP patients and healthy subjects. Copyright © 2017 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

Evaluation of computer-aided detection of lesions in mammograms obtained with a digital phase-contrast mammography system.

PubMed

Tanaka, Toyohiko; Nitta, Norihisa; Ohta, Shinichi; Kobayashi, Tsuyoshi; Kano, Akiko; Tsuchiya, Keiko; Murakami, Yoko; Kitahara, Sawako; Wakamiya, Makoto; Furukawa, Akira; Takahashi, Masashi; Murata, Kiyoshi

2009-12-01

A computer-aided detection (CAD) system was evaluated for its ability to detect microcalcifications and masses on images obtained with a digital phase-contrast mammography (PCM) system, a system characterised by the sharp images provided by phase contrast and by the high resolution of 25-μm-pixel mammograms. Fifty abnormal and 50 normal mammograms were collected from about 3,500 mammograms and printed on film for reading on a light box. Seven qualified radiologists participated in an observer study based on receiver operating characteristic (ROC) analysis. The average of the areas under ROC curve (AUC) values for the ROC analysis with and without CAD were 0.927 and 0.897 respectively (P = 0.015). The AUC values improved from 0.840 to 0.888 for microcalcifications (P = 0.034) and from 0.947 to 0.962 for masses (P = 0.025) respectively. The application of CAD to the PCM system is a promising approach for the detection of breast cancer in its early stages.

What Is A Picture Archiving And Communication System (PACS)?

NASA Astrophysics Data System (ADS)

Marceau, Carla

1982-01-01

A PACS is a digital system for acquiring, storing, moving and displaying picture or image information. It is an alternative to film jackets that has been made possible by recent breakthroughs in computer technology: telecommunications, local area nets and optical disks. The fundamental concept of the digital representation of image information is introduced. It is shown that freeing images from a material representation on film or paper leads to a dramatic increase in flexibility in our use of the images. The ultimate goal of a medical PACS system is a radiology department without film jackets. The inherent nature of digital images and the power of the computer allow instant free "copies" of images to be made and thrown away. These copies can be transmitted to distant sites in seconds, without the "original" ever leaving the archives of the radiology department. The result is a radiology department with much freer access to patient images and greater protection against lost or misplaced image information. Finally, images in digital form can be treated as data for the computer in image processing, which includes enhancement, reconstruction and even computer-aided analysis.

Creation of Anatomically Accurate Computer-Aided Design (CAD) Solid Models from Medical Images

NASA Technical Reports Server (NTRS)

Stewart, John E.; Graham, R. Scott; Samareh, Jamshid A.; Oberlander, Eric J.; Broaddus, William C.

1999-01-01

Most surgical instrumentation and implants used in the world today are designed with sophisticated Computer-Aided Design (CAD)/Computer-Aided Manufacturing (CAM) software. This software automates the mechanical development of a product from its conceptual design through manufacturing. CAD software also provides a means of manipulating solid models prior to Finite Element Modeling (FEM). Few surgical products are designed in conjunction with accurate CAD models of human anatomy because of the difficulty with which these models are created. We have developed a novel technique that creates anatomically accurate, patient specific CAD solids from medical images in a matter of minutes.

Lytro camera technology: theory, algorithms, performance analysis

NASA Astrophysics Data System (ADS)

Georgiev, Todor; Yu, Zhan; Lumsdaine, Andrew; Goma, Sergio

2013-03-01

The Lytro camera is the first implementation of a plenoptic camera for the consumer market. We consider it a successful example of the miniaturization aided by the increase in computational power characterizing mobile computational photography. The plenoptic camera approach to radiance capture uses a microlens array as an imaging system focused on the focal plane of the main camera lens. This paper analyzes the performance of Lytro camera from a system level perspective, considering the Lytro camera as a black box, and uses our interpretation of Lytro image data saved by the camera. We present our findings based on our interpretation of Lytro camera file structure, image calibration and image rendering; in this context, artifacts and final image resolution are discussed.

Quantification of Hepatorenal Index for Computer-Aided Fatty Liver Classification with Self-Organizing Map and Fuzzy Stretching from Ultrasonography.

PubMed

Kim, Kwang Baek; Kim, Chang Won

2015-01-01

Accurate measures of liver fat content are essential for investigating hepatic steatosis. For a noninvasive inexpensive ultrasonographic analysis, it is necessary to validate the quantitative assessment of liver fat content so that fully automated reliable computer-aided software can assist medical practitioners without any operator subjectivity. In this study, we attempt to quantify the hepatorenal index difference between the liver and the kidney with respect to the multiple severity status of hepatic steatosis. In order to do this, a series of carefully designed image processing techniques, including fuzzy stretching and edge tracking, are applied to extract regions of interest. Then, an unsupervised neural learning algorithm, the self-organizing map, is designed to establish characteristic clusters from the image, and the distribution of the hepatorenal index values with respect to the different levels of the fatty liver status is experimentally verified to estimate the differences in the distribution of the hepatorenal index. Such findings will be useful in building reliable computer-aided diagnostic software if combined with a good set of other characteristic feature sets and powerful machine learning classifiers in the future.

Quantification of Hepatorenal Index for Computer-Aided Fatty Liver Classification with Self-Organizing Map and Fuzzy Stretching from Ultrasonography

PubMed Central

Kim, Kwang Baek

2015-01-01

Accurate measures of liver fat content are essential for investigating hepatic steatosis. For a noninvasive inexpensive ultrasonographic analysis, it is necessary to validate the quantitative assessment of liver fat content so that fully automated reliable computer-aided software can assist medical practitioners without any operator subjectivity. In this study, we attempt to quantify the hepatorenal index difference between the liver and the kidney with respect to the multiple severity status of hepatic steatosis. In order to do this, a series of carefully designed image processing techniques, including fuzzy stretching and edge tracking, are applied to extract regions of interest. Then, an unsupervised neural learning algorithm, the self-organizing map, is designed to establish characteristic clusters from the image, and the distribution of the hepatorenal index values with respect to the different levels of the fatty liver status is experimentally verified to estimate the differences in the distribution of the hepatorenal index. Such findings will be useful in building reliable computer-aided diagnostic software if combined with a good set of other characteristic feature sets and powerful machine learning classifiers in the future. PMID:26247023

Comparison of 3D computer-aided with manual cerebral aneurysm measurements in different imaging modalities.

PubMed

Groth, M; Forkert, N D; Buhk, J H; Schoenfeld, M; Goebell, E; Fiehler, J

2013-02-01

To compare intra- and inter-observer reliability of aneurysm measurements obtained by a 3D computer-aided technique with standard manual aneurysm measurements in different imaging modalities. A total of 21 patients with 29 cerebral aneurysms were studied. All patients underwent digital subtraction angiography (DSA), contrast-enhanced (CE-MRA) and time-of-flight magnetic resonance angiography (TOF-MRA). Aneurysm neck and depth diameters were manually measured by two observers in each modality. Additionally, semi-automatic computer-aided diameter measurements were performed using 3D vessel surface models derived from CE- (CE-com) and TOF-MRA (TOF-com) datasets. Bland-Altman analysis (BA) and intra-class correlation coefficient (ICC) were used to evaluate intra- and inter-observer agreement. BA revealed the narrowest relative limits of intra- and inter-observer agreement for aneurysm neck and depth diameters obtained by TOF-com (ranging between ±5.3 % and ±28.3 %) and CE-com (ranging between ±23.3 % and ±38.1 %). Direct measurements in DSA, TOF-MRA and CE-MRA showed considerably wider limits of agreement. The highest ICCs were observed for TOF-com and CE-com (ICC values, 0.92 or higher for intra- as well as inter-observer reliability). Computer-aided aneurysm measurement in 3D offers improved intra- and inter-observer reliability and a reproducible parameter extraction, which may be used in clinical routine and as objective surrogate end-points in clinical trials.

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

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.

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 Remote Driving

NASA Technical Reports Server (NTRS)

Wilcox, Brian H.

1994-01-01

System for remote control of robotic land vehicle requires only small radio-communication bandwidth. Twin video cameras on vehicle create stereoscopic images. Operator views cross-polarized images on two cathode-ray tubes through correspondingly polarized spectacles. By use of cursor on frozen image, remote operator designates path. Vehicle proceeds to follow path, by use of limited degree of autonomous control to cope with unexpected conditions. System concept, called "computer-aided remote driving" (CARD), potentially useful in exploration of other planets, military surveillance, firefighting, and clean-up of hazardous materials.

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

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 analysis of digital dental impressions obtained from intraoral and extraoral scanners.

PubMed

Bohner, Lauren Oliveira Lima; De Luca Canto, Graziela; Marció, Bruno Silva; Laganá, Dalva Cruz; Sesma, Newton; Tortamano Neto, Pedro

2017-11-01

The internal and marginal adaptation of a computer-aided design and computer-aided manufacturing (CAD-CAM) prosthesis relies on the quality of the 3-dimensional image. The quality of imaging systems requires evaluation. The purpose of this in vitro study was to evaluate and compare the trueness of intraoral and extraoral scanners in scanning prepared teeth. Ten acrylic resin teeth to be used as a reference dataset were prepared according to standard guidelines and scanned with an industrial computed tomography system. Data were acquired with 4 scanner devices (n=10): the Trios intraoral scanner (TIS), the D250 extraoral scanner (DES), the Cerec Bluecam intraoral scanner (CBIS), and the Cerec InEosX5 extraoral scanner (CIES). For intraoral scanners, each tooth was digitized individually. Extraoral scanning was obtained from dental casts of each prepared tooth. The discrepancy between each scan and its respective reference model was obtained by deviation analysis (μm) and volume/area difference (μm). Statistical analysis was performed using linear models for repeated measurement factors test and 1-way ANOVA (α=.05). No significant differences in deviation values were found among scanners. For CBIS and CIES, the deviation was significantly higher (P<.05) for occlusal and cervical surfaces. With regard to volume differences, no statistically significant differences were found (TIS=340 ±230 μm; DES=380 ±360 μm; CBIS=780 ±770 μm; CIES=340 ±300 μm). Intraoral and extraoral scanners showed similar trueness in scanning prepared teeth. Higher discrepancies are expected to occur in the cervical region and on the occlusal surface. Copyright © 2017 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

Accuracy and efficiency of computer-aided anatomical analysis using 3D visualization software based on semi-automated and automated segmentations.

PubMed

An, Gao; Hong, Li; Zhou, Xiao-Bing; Yang, Qiong; Li, Mei-Qing; Tang, Xiang-Yang

2017-03-01

We investigated and compared the functionality of two 3D visualization software provided by a CT vendor and a third-party vendor, respectively. Using surgical anatomical measurement as baseline, we evaluated the accuracy of 3D visualization and verified their utility in computer-aided anatomical analysis. The study cohort consisted of 50 adult cadavers fixed with the classical formaldehyde method. The computer-aided anatomical analysis was based on CT images (in DICOM format) acquired by helical scan with contrast enhancement, using a CT vendor provided 3D visualization workstation (Syngo) and a third-party 3D visualization software (Mimics) that was installed on a PC. Automated and semi-automated segmentations were utilized in the 3D visualization workstation and software, respectively. The functionality and efficiency of automated and semi-automated segmentation methods were compared. Using surgical anatomical measurement as a baseline, the accuracy of 3D visualization based on automated and semi-automated segmentations was quantitatively compared. In semi-automated segmentation, the Mimics 3D visualization software outperformed the Syngo 3D visualization workstation. No significant difference was observed in anatomical data measurement by the Syngo 3D visualization workstation and the Mimics 3D visualization software (P>0.05). Both the Syngo 3D visualization workstation provided by a CT vendor and the Mimics 3D visualization software by a third-party vendor possessed the needed functionality, efficiency and accuracy for computer-aided anatomical analysis. Copyright © 2016 Elsevier GmbH. 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.

Regional Lung Ventilation Analysis Using Temporally Resolved Magnetic Resonance Imaging.

PubMed

Kolb, Christoph; Wetscherek, Andreas; Buzan, Maria Teodora; Werner, René; Rank, Christopher M; Kachelrie, Marc; Kreuter, Michael; Dinkel, Julien; Heuel, Claus Peter; Maier-Hein, Klaus

We propose a computer-aided method for regional ventilation analysis and observation of lung diseases in temporally resolved magnetic resonance imaging (4D MRI). A shape model-based segmentation and registration workflow was used to create an atlas-derived reference system in which regional tissue motion can be quantified and multimodal image data can be compared regionally. Model-based temporal registration of the lung surfaces in 4D MRI data was compared with the registration of 4D computed tomography (CT) images. A ventilation analysis was performed on 4D MR images of patients with lung fibrosis; 4D MR ventilation maps were compared with corresponding diagnostic 3D CT images of the patients and 4D CT maps of subjects without impaired lung function (serving as reference). Comparison between the computed patient-specific 4D MR regional ventilation maps and diagnostic CT images shows good correlation in conspicuous regions. Comparison to 4D CT-derived ventilation maps supports the plausibility of the 4D MR maps. Dynamic MRI-based flow-volume loops and spirograms further visualize the free-breathing behavior. The proposed methods allow for 4D MR-based regional analysis of tissue dynamics and ventilation in spontaneous breathing and comparison of patient data. The proposed atlas-based reference coordinate system provides an automated manner of annotating and comparing multimodal lung image data.

A Review of Developments in Computer-Based Systems to Image Teeth and Produce Dental Restorations

PubMed Central

Rekow, E. Dianne; Erdman, Arthur G.; Speidel, T. Michael

1987-01-01

Computer-aided design and manufacturing (CAD/CAM) make it possible to automate the creation of dental restorations. Currently practiced techniques are described. Three automated systems currently under development are described and compared. Advances in computer-aided design and computer-aided manufacturing (CAD/CAM) provide a new option for dentistry, creating an alternative technique for producing dental restorations. It is possible to create dental restorations that are automatically produced and meet or exceed current requirements for fit and occlusion.

Computer-Aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging: A Review.

PubMed

Bharti, Puja; Mittal, Deepti; Ananthasivan, Rupa

2016-04-19

Diffuse liver diseases, such as hepatitis, fatty liver, and cirrhosis, are becoming a leading cause of fatality and disability all over the world. Early detection and diagnosis of these diseases is extremely important to save lives and improve effectiveness of treatment. Ultrasound imaging, a noninvasive diagnostic technique, is the most commonly used modality for examining liver abnormalities. However, the accuracy of ultrasound-based diagnosis depends highly on expertise of radiologists. Computer-aided diagnosis systems based on ultrasound imaging assist in fast diagnosis, provide a reliable "second opinion" for experts, and act as an effective tool to measure response of treatment on patients undergoing clinical trials. In this review, we first describe appearance of liver abnormalities in ultrasound images and state the practical issues encountered in characterization of diffuse liver diseases that can be addressed by software algorithms. We then discuss computer-aided diagnosis in general with features and classifiers relevant to diffuse liver diseases. In later sections of this paper, we review the published studies and describe the key findings of those studies. A concise tabular summary comparing image database, features extraction, feature selection, and classification algorithms presented in the published studies is also exhibited. Finally, we conclude with a summary of key findings and directions for further improvements in the areas of accuracy and objectiveness of computer-aided diagnosis. © The Author(s) 2016.

Pulmonary lobar volumetry using novel volumetric computer-aided diagnosis and computed tomography

PubMed Central

Iwano, Shingo; Kitano, Mariko; Matsuo, Keiji; Kawakami, Kenichi; Koike, Wataru; Kishimoto, Mariko; Inoue, Tsutomu; Li, Yuanzhong; Naganawa, Shinji

2013-01-01

OBJECTIVES To compare the accuracy of pulmonary lobar volumetry using the conventional number of segments method and novel volumetric computer-aided diagnosis using 3D computed tomography images. METHODS We acquired 50 consecutive preoperative 3D computed tomography examinations for lung tumours reconstructed at 1-mm slice thicknesses. We calculated the lobar volume and the emphysematous lobar volume < −950 HU of each lobe using (i) the slice-by-slice method (reference standard), (ii) number of segments method, and (iii) semi-automatic and (iv) automatic computer-aided diagnosis. We determined Pearson correlation coefficients between the reference standard and the three other methods for lobar volumes and emphysematous lobar volumes. We also compared the relative errors among the three measurement methods. RESULTS Both semi-automatic and automatic computer-aided diagnosis results were more strongly correlated with the reference standard than the number of segments method. The correlation coefficients for automatic computer-aided diagnosis were slightly lower than those for semi-automatic computer-aided diagnosis because there was one outlier among 50 cases (2%) in the right upper lobe and two outliers among 50 cases (4%) in the other lobes. The number of segments method relative error was significantly greater than those for semi-automatic and automatic computer-aided diagnosis (P < 0.001). The computational time for automatic computer-aided diagnosis was 1/2 to 2/3 than that of semi-automatic computer-aided diagnosis. CONCLUSIONS A novel lobar volumetry computer-aided diagnosis system could more precisely measure lobar volumes than the conventional number of segments method. Because semi-automatic computer-aided diagnosis and automatic computer-aided diagnosis were complementary, in clinical use, it would be more practical to first measure volumes by automatic computer-aided diagnosis, and then use semi-automatic measurements if automatic computer-aided diagnosis failed. PMID:23526418

Microscopic medical image classification framework via deep learning and shearlet transform.

PubMed

Rezaeilouyeh, Hadi; Mollahosseini, Ali; Mahoor, Mohammad H

2016-10-01

Cancer is the second leading cause of death in US after cardiovascular disease. Image-based computer-aided diagnosis can assist physicians to efficiently diagnose cancers in early stages. Existing computer-aided algorithms use hand-crafted features such as wavelet coefficients, co-occurrence matrix features, and recently, histogram of shearlet coefficients for classification of cancerous tissues and cells in images. These hand-crafted features often lack generalizability since every cancerous tissue and cell has a specific texture, structure, and shape. An alternative approach is to use convolutional neural networks (CNNs) to learn the most appropriate feature abstractions directly from the data and handle the limitations of hand-crafted features. A framework for breast cancer detection and prostate Gleason grading using CNN trained on images along with the magnitude and phase of shearlet coefficients is presented. Particularly, we apply shearlet transform on images and extract the magnitude and phase of shearlet coefficients. Then we feed shearlet features along with the original images to our CNN consisting of multiple layers of convolution, max pooling, and fully connected layers. Our experiments show that using the magnitude and phase of shearlet coefficients as extra information to the network can improve the accuracy of detection and generalize better compared to the state-of-the-art methods that rely on hand-crafted features. This study expands the application of deep neural networks into the field of medical image analysis, which is a difficult domain considering the limited medical data available for such analysis.

Dimensionality of visual complexity in computer graphics scenes

NASA Astrophysics Data System (ADS)

Ramanarayanan, Ganesh; Bala, Kavita; Ferwerda, James A.; Walter, Bruce

2008-02-01

How do human observers perceive visual complexity in images? This problem is especially relevant for computer graphics, where a better understanding of visual complexity can aid in the development of more advanced rendering algorithms. In this paper, we describe a study of the dimensionality of visual complexity in computer graphics scenes. We conducted an experiment where subjects judged the relative complexity of 21 high-resolution scenes, rendered with photorealistic methods. Scenes were gathered from web archives and varied in theme, number and layout of objects, material properties, and lighting. We analyzed the subject responses using multidimensional scaling of pooled subject responses. This analysis embedded the stimulus images in a two-dimensional space, with axes that roughly corresponded to "numerosity" and "material / lighting complexity". In a follow-up analysis, we derived a one-dimensional complexity ordering of the stimulus images. We compared this ordering with several computable complexity metrics, such as scene polygon count and JPEG compression size, and did not find them to be very correlated. Understanding the differences between these measures can lead to the design of more efficient rendering algorithms in computer graphics.

Automated metastatic brain lesion detection: a computer aided diagnostic and clinical research tool

NASA Astrophysics Data System (ADS)

Devine, Jeremy; Sahgal, Arjun; Karam, Irene; Martel, Anne L.

2016-03-01

The accurate localization of brain metastases in magnetic resonance (MR) images is crucial for patients undergoing stereotactic radiosurgery (SRS) to ensure that all neoplastic foci are targeted. Computer automated tumor localization and analysis can improve both of these tasks by eliminating inter and intra-observer variations during the MR image reading process. Lesion localization is accomplished using adaptive thresholding to extract enhancing objects. Each enhancing object is represented as a vector of features which includes information on object size, symmetry, position, shape, and context. These vectors are then used to train a random forest classifier. We trained and tested the image analysis pipeline on 3D axial contrast-enhanced MR images with the intention of localizing the brain metastases. In our cross validation study and at the most effective algorithm operating point, we were able to identify 90% of the lesions at a precision rate of 60%.

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.

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.

Mutual information-based template matching scheme for detection of breast masses: from mammography to digital breast tomosynthesis

PubMed Central

Mazurowski, Maciej A; Lo, Joseph Y; Harrawood, Brian P; Tourassi, Georgia D

2011-01-01

Development of a computational decision aid for a new medical imaging modality typically is a long and complicated process. It consists of collecting data in the form of images and annotations, development of image processing and pattern recognition algorithms for analysis of the new images and finally testing of the resulting system. Since new imaging modalities are developed more rapidly than ever before, any effort for decreasing the time and cost of this development process could result in maximizing the benefit of the new imaging modality to patients by making the computer aids quickly available to radiologists that interpret the images. In this paper, we make a step in this direction and investigate the possibility of translating the knowledge about the detection problem from one imaging modality to another. Specifically, we present a computer-aided detection (CAD) system for mammographic masses that uses a mutual information-based template matching scheme with intelligently selected templates. We presented principles of template matching with mutual information for mammography before. In this paper, we present an implementation of those principles in a complete computer-aided detection system. The proposed system, through an automatic optimization process, chooses the most useful templates (mammographic regions of interest) using a large database of previously collected and annotated mammograms. Through this process, the knowledge about the task of detecting masses in mammograms is incorporated in the system. Then we evaluate whether our system developed for screen-film mammograms can be successfully applied not only to other mammograms but also to digital breast tomosynthesis (DBT) reconstructed slices without adding any DBT cases for training. Our rationale is that since mutual information is known to be a robust intermodality image similarity measure, it has high potential of transferring knowledge between modalities in the context of the mass detection task. Experimental evaluation of the system on mammograms showed competitive performance compared to other mammography CAD systems recently published in the literature. When the system was applied “as-is” to DBT, its performance was notably worse than that for mammograms. However, with a simple additional preprocessing step, the performance of the system reached levels similar to that obtained for mammograms. In conclusion, the presented CAD system not only performed competitively on screen-film mammograms but it also performed robustly on DBT showing that direct transfer of knowledge across breast imaging modalities for mass detection is in fact possible. PMID:21554985

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 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.

APPLICATION OF COMPUTER AIDED TOMOGRAPHY (CAT) TO THE STUDY OF MARINE BENTIC COMMUNITIES

EPA Science Inventory

Sediment cores were imaged using a Computer-Aided Tomography (CT) scanner at Massachusetts General Hospital, Boston, Massachusetts, United States. Procedures were developed, using the attenuation of X-rays, to differentiate between sediment and the water contained in macrobenthic...

3D Texture Features Mining for MRI Brain Tumor Identification

NASA Astrophysics Data System (ADS)

Rahim, Mohd Shafry Mohd; Saba, Tanzila; Nayer, Fatima; Syed, Afraz Zahra

2014-03-01

Medical image segmentation is a process to extract region of interest and to divide an image into its individual meaningful, homogeneous components. Actually, these components will have a strong relationship with the objects of interest in an image. For computer-aided diagnosis and therapy process, medical image segmentation is an initial mandatory step. Medical image segmentation is a sophisticated and challenging task because of the sophisticated nature of the medical images. Indeed, successful medical image analysis heavily dependent on the segmentation accuracy. Texture is one of the major features to identify region of interests in an image or to classify an object. 2D textures features yields poor classification results. Hence, this paper represents 3D features extraction using texture analysis and SVM as segmentation technique in the testing methodologies.

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 method and rapid prototyping for the personalized fabrication of a silicone bandage digital prosthesis].

PubMed

Ventura Ferreira, Nuno; Leal, Nuno; Correia Sá, Inês; Reis, Ana; Marques, Marisa

2014-01-01

The fabrication of digital prostheses has acquired growing importance not only for the possibility for the patient to overcome psychosocial trauma but also to promote grip functionality. An application method of three dimensional-computer-aided design technologies for the production of passive prostheses is presented by means of a fifth finger amputee clinical case following bilateral hand replantation.Three-dimensional-computerized tomography was used for the collection of anthropometric images of the hands. Computer-aided design techniques were used to develop the digital file-based prosthesis from the reconstruction images by inversion and superimposing the contra-lateral finger images. The rapid prototyping manufacturing method was used for the production of a silicone bandage prosthesis prototype. This approach replaces the traditional manual method by a virtual method that is basis for the optimization of a high speed, accurate and innovative process.

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.

ADP of multispectral scanner data for land use mapping

NASA Technical Reports Server (NTRS)

Hoffer, R. M.

1971-01-01

The advantages and disadvantages of various remote sensing instrumentation and analysis techniques are reviewed. The use of multispectral scanner data and the automatic data processing techniques are considered. A computer-aided analysis system for remote sensor data is described with emphasis on the image display, statistics processor, wavelength band selection, classification processor, and results display. Advanced techniques in using spectral and temporal data are also considered.

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.

The Use of Images in Intelligent Advisor Systems.

ERIC Educational Resources Information Center

Boulet, Marie-Michele

This paper describes the intelligent advisor system, named CODAMA, used in teaching a university-level systems analysis and design course. The paper discusses: (1) the use of CODAMA to assist students to transfer theoretical knowledge to the practical; (2) details of how CODAMA is applied in conjunction with a computer-aided software engineering…

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.

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.

Toward a standard reference database for computer-aided mammography

NASA Astrophysics Data System (ADS)

Oliveira, Júlia E. E.; Gueld, Mark O.; de A. Araújo, Arnaldo; Ott, Bastian; Deserno, Thomas M.

2008-03-01

Because of the lack of mammography databases with a large amount of codified images and identified characteristics like pathology, type of breast tissue, and abnormality, there is a problem for the development of robust systems for computer-aided diagnosis. Integrated to the Image Retrieval in Medical Applications (IRMA) project, we present an available mammography database developed from the union of: The Mammographic Image Analysis Society Digital Mammogram Database (MIAS), The Digital Database for Screening Mammography (DDSM), the Lawrence Livermore National Laboratory (LLNL), and routine images from the Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen. Using the IRMA code, standardized coding of tissue type, tumor staging, and lesion description was developed according to the American College of Radiology (ACR) tissue codes and the ACR breast imaging reporting and data system (BI-RADS). The import was done automatically using scripts for image download, file format conversion, file name, web page and information file browsing. Disregarding the resolution, this resulted in a total of 10,509 reference images, and 6,767 images are associated with an IRMA contour information feature file. In accordance to the respective license agreements, the database will be made freely available for research purposes, and may be used for image based evaluation campaigns such as the Cross Language Evaluation Forum (CLEF). We have also shown that it can be extended easily with further cases imported from a picture archiving and communication system (PACS).

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

Medical image analysis with artificial neural networks.

PubMed

Jiang, J; Trundle, P; Ren, J

2010-12-01

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. Copyright © 2010 Elsevier Ltd. All rights reserved.

Space Spurred Computer Graphics

NASA Technical Reports Server (NTRS)

1983-01-01

Dicomed Corporation was asked by NASA in the early 1970s to develop processing capabilities for recording images sent from Mars by Viking spacecraft. The company produced a film recorder which increased the intensity levels and the capability for color recording. This development led to a strong technology base resulting in sophisticated computer graphics equipment. Dicomed systems are used to record CAD (computer aided design) and CAM (computer aided manufacturing) equipment, to update maps and produce computer generated animation.

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

Electronic Circuit Analysis Language (ECAL)

NASA Astrophysics Data System (ADS)

Chenghang, C.

1983-03-01

The computer aided design technique is an important development in computer applications and it is an important component of computer science. The special language for electronic circuit analysis is the foundation of computer aided design or computer aided circuit analysis (abbreviated as CACD and CACA) of simulated circuits. Electronic circuit analysis language (ECAL) is a comparatively simple and easy to use circuit analysis special language which uses the FORTRAN language to carry out the explanatory executions. It is capable of conducting dc analysis, ac analysis, and transient analysis of a circuit. Futhermore, the results of the dc analysis can be used directly as the initial conditions for the ac and transient analyses.

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.

Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques

PubMed Central

Bayır, Şafak

2016-01-01

With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC. PMID:27110272

Estimation of Fine-Scale Histologic Features at Low Magnification.

PubMed

Zarella, Mark D; Quaschnick, Matthew R; Breen, David E; Garcia, Fernando U

2018-06-18

- Whole-slide imaging has ushered in a new era of technology that has fostered the use of computational image analysis for diagnostic support and has begun to transfer the act of analyzing a slide to computer monitors. Due to the overwhelming amount of detail available in whole-slide images, analytic procedures-whether computational or visual-often operate at magnifications lower than the magnification at which the image was acquired. As a result, a corresponding reduction in image resolution occurs. It is unclear how much information is lost when magnification is reduced, and whether the rich color attributes of histologic slides can aid in reconstructing some of that information. - To examine the correspondence between the color and spatial properties of whole-slide images to elucidate the impact of resolution reduction on the histologic attributes of the slide. - We simulated image resolution reduction and modeled its effect on classification of the underlying histologic structure. By harnessing measured histologic features and the intrinsic spatial relationships between histologic structures, we developed a predictive model to estimate the histologic composition of tissue in a manner that exceeds the resolution of the image. - Reduction in resolution resulted in a significant loss of the ability to accurately characterize histologic components at magnifications less than ×10. By utilizing pixel color, this ability was improved at all magnifications. - Multiscale analysis of histologic images requires an adequate understanding of the limitations imposed by image resolution. Our findings suggest that some of these limitations may be overcome with computational modeling.

Visidep (TM): A Three-Dimensional Imaging System For The Unaided Eye

NASA Astrophysics Data System (ADS)

McLaurin, A. Porter; Jones, Edwin R.; Cathey, LeConte

1984-05-01

The VISIDEP process for creating images in three dimensions on flat screens is suitable for photographic, electrographic and computer generated imaging systems. Procedures for generating these images vary from medium to medium due to the specific requirements of each technology. Imaging requirements for photographic and electrographic media are more directly tied to the hardware than are computer based systems. Applications of these technologies are not limited to entertainment, but have implications for training, interactive computer/video systems, medical imaging, and inspection equipment. Through minor modification the system can provide three-dimensional images with accurately measureable relationships for robotics and adds this factor for future developments in artificial intelligence. In almost any area requiring image analysis or critical review, VISIDEP provides the added advantage of three-dimensionality. All of this is readily accomplished without aids to the human eye. The system can be viewed in full color, false-color infra-red, and monochromatic modalities from any angle and is also viewable with a single eye. Thus, the potential of application for this developing system is extensive and covers the broad spectrum of human endeavor from entertainment to scientific study.

Simple computer method provides contours for radiological images

NASA Technical Reports Server (NTRS)

Newell, J. D.; Keller, R. A.; Baily, N. A.

1975-01-01

Computer is provided with information concerning boundaries in total image. Gradient of each point in digitized image is calculated with aid of threshold technique; then there is invoked set of algorithms designed to reduce number of gradient elements and to retain only major ones for definition of contour.

Computer-aided Prognosis of Neuroblastoma on Whole-slide Images: Classification of Stromal Development

PubMed Central

Sertel, O.; Kong, J.; Shimada, H.; Catalyurek, U.V.; Saltz, J.H.; Gurcan, M.N.

2009-01-01

We are developing a computer-aided prognosis system for neuroblastoma (NB), a cancer of the nervous system and one of the most malignant tumors affecting children. Histopathological examination is an important stage for further treatment planning in routine clinical diagnosis of NB. According to the International Neuroblastoma Pathology Classification (the Shimada system), NB patients are classified into favorable and unfavorable histology based on the tissue morphology. In this study, we propose an image analysis system that operates on digitized H&E stained whole-slide NB tissue samples and classifies each slide as either stroma-rich or stroma-poor based on the degree of Schwannian stromal development. Our statistical framework performs the classification based on texture features extracted using co-occurrence statistics and local binary patterns. Due to the high resolution of digitized whole-slide images, we propose a multi-resolution approach that mimics the evaluation of a pathologist such that the image analysis starts from the lowest resolution and switches to higher resolutions when necessary. We employ an offine feature selection step, which determines the most discriminative features at each resolution level during the training step. A modified k-nearest neighbor classifier is used to determine the confidence level of the classification to make the decision at a particular resolution level. The proposed approach was independently tested on 43 whole-slide samples and provided an overall classification accuracy of 88.4%. PMID:20161324

Improving image segmentation performance and quantitative analysis via a computer-aided grading methodology for optical coherence tomography retinal image analysis.

PubMed

Debuc, Delia Cabrera; Salinas, Harry M; Ranganathan, Sudarshan; Tátrai, Erika; Gao, Wei; Shen, Meixiao; Wang, Jianhua; Somfai, Gábor M; Puliafito, Carmen A

2010-01-01

We demonstrate quantitative analysis and error correction of optical coherence tomography (OCT) retinal images by using a custom-built, computer-aided grading methodology. A total of 60 Stratus OCT (Carl Zeiss Meditec, Dublin, California) B-scans collected from ten normal healthy eyes are analyzed by two independent graders. The average retinal thickness per macular region is compared with the automated Stratus OCT results. Intergrader and intragrader reproducibility is calculated by Bland-Altman plots of the mean difference between both gradings and by Pearson correlation coefficients. In addition, the correlation between Stratus OCT and our methodology-derived thickness is also presented. The mean thickness difference between Stratus OCT and our methodology is 6.53 microm and 26.71 microm when using the inner segment/outer segment (IS/OS) junction and outer segment/retinal pigment epithelium (OS/RPE) junction as the outer retinal border, respectively. Overall, the median of the thickness differences as a percentage of the mean thickness is less than 1% and 2% for the intragrader and intergrader reproducibility test, respectively. The measurement accuracy range of the OCT retinal image analysis (OCTRIMA) algorithm is between 0.27 and 1.47 microm and 0.6 and 1.76 microm for the intragrader and intergrader reproducibility tests, respectively. Pearson correlation coefficients demonstrate R(2)>0.98 for all Early Treatment Diabetic Retinopathy Study (ETDRS) regions. Our methodology facilitates a more robust and localized quantification of the retinal structure in normal healthy controls and patients with clinically significant intraretinal features.

Improving image segmentation performance and quantitative analysis via a computer-aided grading methodology for optical coherence tomography retinal image analysis

NASA Astrophysics Data System (ADS)

Cabrera Debuc, Delia; Salinas, Harry M.; Ranganathan, Sudarshan; Tátrai, Erika; Gao, Wei; Shen, Meixiao; Wang, Jianhua; Somfai, Gábor M.; Puliafito, Carmen A.

2010-07-01

We demonstrate quantitative analysis and error correction of optical coherence tomography (OCT) retinal images by using a custom-built, computer-aided grading methodology. A total of 60 Stratus OCT (Carl Zeiss Meditec, Dublin, California) B-scans collected from ten normal healthy eyes are analyzed by two independent graders. The average retinal thickness per macular region is compared with the automated Stratus OCT results. Intergrader and intragrader reproducibility is calculated by Bland-Altman plots of the mean difference between both gradings and by Pearson correlation coefficients. In addition, the correlation between Stratus OCT and our methodology-derived thickness is also presented. The mean thickness difference between Stratus OCT and our methodology is 6.53 μm and 26.71 μm when using the inner segment/outer segment (IS/OS) junction and outer segment/retinal pigment epithelium (OS/RPE) junction as the outer retinal border, respectively. Overall, the median of the thickness differences as a percentage of the mean thickness is less than 1% and 2% for the intragrader and intergrader reproducibility test, respectively. The measurement accuracy range of the OCT retinal image analysis (OCTRIMA) algorithm is between 0.27 and 1.47 μm and 0.6 and 1.76 μm for the intragrader and intergrader reproducibility tests, respectively. Pearson correlation coefficients demonstrate R2>0.98 for all Early Treatment Diabetic Retinopathy Study (ETDRS) regions. Our methodology facilitates a more robust and localized quantification of the retinal structure in normal healthy controls and patients with clinically significant intraretinal features.

Machine Learning Applications to Resting-State Functional MR Imaging Analysis.

PubMed

Billings, John M; Eder, Maxwell; Flood, William C; Dhami, Devendra Singh; Natarajan, Sriraam; Whitlow, Christopher T

2017-11-01

Machine learning is one of the most exciting and rapidly expanding fields within computer science. Academic and commercial research entities are investing in machine learning methods, especially in personalized medicine via patient-level classification. There is great promise that machine learning methods combined with resting state functional MR imaging will aid in diagnosis of disease and guide potential treatment for conditions thought to be impossible to identify based on imaging alone, such as psychiatric disorders. We discuss machine learning methods and explore recent advances. Copyright © 2017 Elsevier Inc. All rights reserved.

Integrated computer-aided forensic case analysis, presentation, and documentation based on multimodal 3D data.

PubMed

Bornik, Alexander; Urschler, Martin; Schmalstieg, Dieter; Bischof, Horst; Krauskopf, Astrid; Schwark, Thorsten; Scheurer, Eva; Yen, Kathrin

2018-06-01

Three-dimensional (3D) crime scene documentation using 3D scanners and medical imaging modalities like computed tomography (CT) and magnetic resonance imaging (MRI) are increasingly applied in forensic casework. Together with digital photography, these modalities enable comprehensive and non-invasive recording of forensically relevant information regarding injuries/pathologies inside the body and on its surface. Furthermore, it is possible to capture traces and items at crime scenes. Such digitally secured evidence has the potential to similarly increase case understanding by forensic experts and non-experts in court. Unlike photographs and 3D surface models, images from CT and MRI are not self-explanatory. Their interpretation and understanding requires radiological knowledge. Findings in tomography data must not only be revealed, but should also be jointly studied with all the 2D and 3D data available in order to clarify spatial interrelations and to optimally exploit the data at hand. This is technically challenging due to the heterogeneous data representations including volumetric data, polygonal 3D models, and images. This paper presents a novel computer-aided forensic toolbox providing tools to support the analysis, documentation, annotation, and illustration of forensic cases using heterogeneous digital data. Conjoint visualization of data from different modalities in their native form and efficient tools to visually extract and emphasize findings help experts to reveal unrecognized correlations and thereby enhance their case understanding. Moreover, the 3D case illustrations created for case analysis represent an efficient means to convey the insights gained from case analysis to forensic non-experts involved in court proceedings like jurists and laymen. The capability of the presented approach in the context of case analysis, its potential to speed up legal procedures and to ultimately enhance legal certainty is demonstrated by introducing a number of representative forensic cases. Copyright © 2018 The Author(s). Published by Elsevier B.V. All rights reserved.

Application of CT-PSF-based computer-simulated lung nodules for evaluating the accuracy of computer-aided volumetry.

PubMed

Funaki, Ayumu; Ohkubo, Masaki; Wada, Shinichi; Murao, Kohei; Matsumoto, Toru; Niizuma, Shinji

2012-07-01

With the wide dissemination of computed tomography (CT) screening for lung cancer, measuring the nodule volume accurately with computer-aided volumetry software is increasingly important. Many studies for determining the accuracy of volumetry software have been performed using a phantom with artificial nodules. These phantom studies are limited, however, in their ability to reproduce the nodules both accurately and in the variety of sizes and densities required. Therefore, we propose a new approach of using computer-simulated nodules based on the point spread function measured in a CT system. The validity of the proposed method was confirmed by the excellent agreement obtained between computer-simulated nodules and phantom nodules regarding the volume measurements. A practical clinical evaluation of the accuracy of volumetry software was achieved by adding simulated nodules onto clinical lung images, including noise and artifacts. The tested volumetry software was revealed to be accurate within an error of 20 % for nodules >5 mm and with the difference between nodule density and background (lung) (CT value) being 400-600 HU. Such a detailed analysis can provide clinically useful information on the use of volumetry software in CT screening for lung cancer. We concluded that the proposed method is effective for evaluating the performance of computer-aided volumetry software.

A review of computer aided interpretation technology for the evaluation of radiographs of aluminum welds

NASA Technical Reports Server (NTRS)

Lloyd, J. F., Sr.

1987-01-01

Industrial radiography is a well established, reliable means of providing nondestructive structural integrity information. The majority of industrial radiographs are interpreted by trained human eyes using transmitted light and various visual aids. Hundreds of miles of radiographic information are evaluated, documented and archived annually. In many instances, there are serious considerations in terms of interpreter fatigue, subjectivity and limited archival space. Quite often it is difficult to quickly retrieve radiographic information for further analysis or investigation. Methods of improving the quality and efficiency of the radiographic process are being explored, developed and incorporated whenever feasible. High resolution cameras, digital image processing, and mass digital data storage offer interesting possibilities for improving the industrial radiographic process. A review is presented of computer aided radiographic interpretation technology in terms of how it could be used to enhance the radiographic interpretation process in evaluating radiographs of aluminum welds.

Computer-based analysis of microvascular alterations in a mouse model for Alzheimer's disease

NASA Astrophysics Data System (ADS)

Heinzer, Stefan; Müller, Ralph; Stampanoni, Marco; Abela, Rafael; Meyer, Eric P.; Ulmann-Schuler, Alexandra; Krucker, Thomas

2007-03-01

Vascular factors associated with Alzheimer's disease (AD) have recently gained increased attention. To investigate changes in vascular, particularly microvascular architecture, we developed a hierarchical imaging framework to obtain large-volume, high-resolution 3D images from brains of transgenic mice modeling AD. In this paper, we present imaging and data analysis methods which allow compiling unique characteristics from several hundred gigabytes of image data. Image acquisition is based on desktop micro-computed tomography (µCT) and local synchrotron-radiation µCT (SRµCT) scanning with a nominal voxel size of 16 µm and 1.4 µm, respectively. Two visualization approaches were implemented: stacks of Z-buffer projections for fast data browsing, and progressive-mesh based surface rendering for detailed 3D visualization of the large datasets. In a first step, image data was assessed visually via a Java client connected to a central database. Identified characteristics of interest were subsequently quantified using global morphometry software. To obtain even deeper insight into microvascular alterations, tree analysis software was developed providing local morphometric parameters such as number of vessel segments or vessel tortuosity. In the context of ever increasing image resolution and large datasets, computer-aided analysis has proven both powerful and indispensable. The hierarchical approach maintains the context of local phenomena, while proper visualization and morphometry provide the basis for detailed analysis of the pathology related to structure. Beyond analysis of microvascular changes in AD this framework will have significant impact considering that vascular changes are involved in other neurodegenerative diseases as well as in cancer, cardiovascular disease, asthma, and arthritis.

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.

Computer-aided pulmonary image analysis in small animal models

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

Xu, Ziyue; Mansoor, Awais; Mollura, Daniel J.

Purpose: To develop an automated pulmonary image analysis framework for infectious lung diseases in small animal models. Methods: The authors describe a novel pathological lung and airway segmentation method for small animals. The proposed framework includes identification of abnormal imaging patterns pertaining to infectious lung diseases. First, the authors’ system estimates an expected lung volume by utilizing a regression function between total lung capacity and approximated rib cage volume. A significant difference between the expected lung volume and the initial lung segmentation indicates the presence of severe pathology, and invokes a machine learning based abnormal imaging pattern detection system next.more » The final stage of the proposed framework is the automatic extraction of airway tree for which new affinity relationships within the fuzzy connectedness image segmentation framework are proposed by combining Hessian and gray-scale morphological reconstruction filters. Results: 133 CT scans were collected from four different studies encompassing a wide spectrum of pulmonary abnormalities pertaining to two commonly used small animal models (ferret and rabbit). Sensitivity and specificity were greater than 90% for pathological lung segmentation (average dice similarity coefficient > 0.9). While qualitative visual assessments of airway tree extraction were performed by the participating expert radiologists, for quantitative evaluation the authors validated the proposed airway extraction method by using publicly available EXACT’09 data set. Conclusions: The authors developed a comprehensive computer-aided pulmonary image analysis framework for preclinical research applications. The proposed framework consists of automatic pathological lung segmentation and accurate airway tree extraction. The framework has high sensitivity and specificity; therefore, it can contribute advances in preclinical research in pulmonary diseases.« less

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.

Progressive data transmission for anatomical landmark detection in a cloud.

PubMed

Sofka, M; Ralovich, K; Zhang, J; Zhou, S K; Comaniciu, D

2012-01-01

In the concept of cloud-computing-based systems, various authorized users have secure access to patient records from a number of care delivery organizations from any location. This creates a growing need for remote visualization, advanced image processing, state-of-the-art image analysis, and computer aided diagnosis. This paper proposes a system of algorithms for automatic detection of anatomical landmarks in 3D volumes in the cloud computing environment. The system addresses the inherent problem of limited bandwidth between a (thin) client, data center, and data analysis server. The problem of limited bandwidth is solved by a hierarchical sequential detection algorithm that obtains data by progressively transmitting only image regions required for processing. The client sends a request to detect a set of landmarks for region visualization or further analysis. The algorithm running on the data analysis server obtains a coarse level image from the data center and generates landmark location candidates. The candidates are then used to obtain image neighborhood regions at a finer resolution level for further detection. This way, the landmark locations are hierarchically and sequentially detected and refined. Only image regions surrounding landmark location candidates need to be trans- mitted during detection. Furthermore, the image regions are lossy compressed with JPEG 2000. Together, these properties amount to at least 30 times bandwidth reduction while achieving similar accuracy when compared to an algorithm using the original data. The hierarchical sequential algorithm with progressive data transmission considerably reduces bandwidth requirements in cloud-based detection systems.

Trends in radiology and experimental research.

PubMed

Sardanelli, Francesco

2017-01-01

European Radiology Experimental , the new journal launched by the European Society of Radiology, is placed in the context of three general and seven radiology-specific trends. After describing the impact of population aging, personalized/precision medicine, and information technology development, the article considers the following trends: the tension between subspecialties and the unity of the discipline; attention to patient safety; the challenge of reproducibility for quantitative imaging; standardized and structured reporting; search for higher levels of evidence in radiology (from diagnostic performance to patient outcome); the increasing relevance of interventional radiology; and continuous technological evolution. The new journal will publish not only studies on phantoms, cells, or animal models but also those describing development steps of imaging biomarkers or those exploring secondary end-points of large clinical trials. Moreover, consideration will be given to studies regarding: computer modelling and computer aided detection and diagnosis; contrast materials, tracers, and theranostics; advanced image analysis; optical, molecular, hybrid and fusion imaging; radiomics and radiogenomics; three-dimensional printing, information technology, image reconstruction and post-processing, big data analysis, teleradiology, clinical decision support systems; radiobiology; radioprotection; and physics in radiology. The journal aims to establish a forum for basic science, computer and information technology, radiology, and other medical subspecialties.

Spatial Statistics for Tumor Cell Counting and Classification

NASA Astrophysics Data System (ADS)

Wirjadi, Oliver; Kim, Yoo-Jin; Breuel, Thomas

To count and classify cells in histological sections is a standard task in histology. One example is the grading of meningiomas, benign tumors of the meninges, which requires to assess the fraction of proliferating cells in an image. As this process is very time consuming when performed manually, automation is required. To address such problems, we propose a novel application of Markov point process methods in computer vision, leading to algorithms for computing the locations of circular objects in images. In contrast to previous algorithms using such spatial statistics methods in image analysis, the present one is fully trainable. This is achieved by combining point process methods with statistical classifiers. Using simulated data, the method proposed in this paper will be shown to be more accurate and more robust to noise than standard image processing methods. On the publicly available SIMCEP benchmark for cell image analysis algorithms, the cell count performance of the present paper is significantly more accurate than results published elsewhere, especially when cells form dense clusters. Furthermore, the proposed system performs as well as a state-of-the-art algorithm for the computer-aided histological grading of meningiomas when combined with a simple k-nearest neighbor classifier for identifying proliferating cells.

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

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…

Breast cancer histopathology image analysis: a review.

PubMed

Veta, Mitko; Pluim, Josien P W; van Diest, Paul J; Viergever, Max A

2014-05-01

This paper presents an overview of methods that have been proposed for the analysis of breast cancer histopathology images. This research area has become particularly relevant with the advent of whole slide imaging (WSI) scanners, which can perform cost-effective and high-throughput histopathology slide digitization, and which aim at replacing the optical microscope as the primary tool used by pathologist. Breast cancer is the most prevalent form of cancers among women, and image analysis methods that target this disease have a huge potential to reduce the workload in a typical pathology lab and to improve the quality of the interpretation. This paper is meant as an introduction for nonexperts. It starts with an overview of the tissue preparation, staining and slide digitization processes followed by a discussion of the different image processing techniques and applications, ranging from analysis of tissue staining to computer-aided diagnosis, and prognosis of breast cancer patients.

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

Accurate computer-aided quantification of left ventricular parameters: experience in 1555 cardiac magnetic resonance studies from the Framingham Heart Study.

PubMed

Hautvast, Gilion L T F; Salton, Carol J; Chuang, Michael L; Breeuwer, Marcel; O'Donnell, Christopher J; Manning, Warren J

2012-05-01

Quantitative analysis of short-axis functional cardiac magnetic resonance images can be performed using automatic contour detection methods. The resulting myocardial contours must be reviewed and possibly corrected, which can be time-consuming, particularly when performed across all cardiac phases. We quantified the impact of manual contour corrections on both analysis time and quantitative measurements obtained from left ventricular short-axis cine images acquired from 1555 participants of the Framingham Heart Study Offspring cohort using computer-aided contour detection methods. The total analysis time for a single case was 7.6 ± 1.7 min for an average of 221 ± 36 myocardial contours per participant. This included 4.8 ± 1.6 min for manual contour correction of 2% of all automatically detected endocardial contours and 8% of all automatically detected epicardial contours. However, the impact of these corrections on global left ventricular parameters was limited, introducing differences of 0.4 ± 4.1 mL for end-diastolic volume, -0.3 ± 2.9 mL for end-systolic volume, 0.7 ± 3.1 mL for stroke volume, and 0.3 ± 1.8% for ejection fraction. We conclude that left ventricular functional parameters can be obtained under 5 min from short-axis functional cardiac magnetic resonance images using automatic contour detection methods. Manual correction more than doubles analysis time, with minimal impact on left ventricular volumes and ejection fraction. Copyright © 2011 Wiley Periodicals, Inc.

New layer-based imaging and rapid prototyping techniques for computer-aided design and manufacture of custom dental restoration.

PubMed

Lee, M-Y; Chang, C-C; Ku, Y C

2008-01-01

Fixed dental restoration by conventional methods greatly relies on the skill and experience of the dental technician. The quality and accuracy of the final product depends mostly on the technician's subjective judgment. In addition, the traditional manual operation involves many complex procedures, and is a time-consuming and labour-intensive job. Most importantly, no quantitative design and manufacturing information is preserved for future retrieval. In this paper, a new device for scanning the dental profile and reconstructing 3D digital information of a dental model based on a layer-based imaging technique, called abrasive computer tomography (ACT) was designed in-house and proposed for the design of custom dental restoration. The fixed partial dental restoration was then produced by rapid prototyping (RP) and computer numerical control (CNC) machining methods based on the ACT scanned digital information. A force feedback sculptor (FreeForm system, Sensible Technologies, Inc., Cambridge MA, USA), which comprises 3D Touch technology, was applied to modify the morphology and design of the fixed dental restoration. In addition, a comparison of conventional manual operation and digital manufacture using both RP and CNC machining technologies for fixed dental restoration production is presented. Finally, a digital custom fixed restoration manufacturing protocol integrating proposed layer-based dental profile scanning, computer-aided design, 3D force feedback feature modification and advanced fixed restoration manufacturing techniques is illustrated. The proposed method provides solid evidence that computer-aided design and manufacturing technologies may become a new avenue for custom-made fixed restoration design, analysis, and production in the 21st century.

An interactive method based on the live wire for segmentation of the breast in mammography images.

PubMed

Zewei, Zhang; Tianyue, Wang; Li, Guo; Tingting, Wang; Lu, Xu

2014-01-01

In order to improve accuracy of computer-aided diagnosis of breast lumps, the authors introduce an improved interactive segmentation method based on Live Wire. This paper presents the Gabor filters and FCM clustering algorithm is introduced to the Live Wire cost function definition. According to the image FCM analysis for image edge enhancement, we eliminate the interference of weak edge and access external features clear segmentation results of breast lumps through improving Live Wire on two cases of breast segmentation data. Compared with the traditional method of image segmentation, experimental results show that the method achieves more accurate segmentation of breast lumps and provides more accurate objective basis on quantitative and qualitative analysis of breast lumps.

Computer-Aided Methodology for Syndromic Strabismus Diagnosis.

PubMed

Sousa de Almeida, João Dallyson; Silva, Aristófanes Corrêa; Teixeira, Jorge Antonio Meireles; Paiva, Anselmo Cardoso; Gattass, Marcelo

2015-08-01

Strabismus is a pathology that affects approximately 4 % of the population, causing aesthetic problems reversible at any age and irreversible sensory alterations that modify the vision mechanism. The Hirschberg test is one type of examination for detecting this pathology. Computer-aided detection/diagnosis is being used with relative success to aid health professionals. Nevertheless, the routine use of high-tech devices for aiding ophthalmological diagnosis and therapy is not a reality within the subspecialty of strabismus. Thus, this work presents a methodology to aid in diagnosis of syndromic strabismus through digital imaging. Two hundred images belonging to 40 patients previously diagnosed by an specialist were tested. The method was demonstrated to be 88 % accurate in esotropias identification (ET), 100 % for exotropias (XT), 80.33 % for hypertropias (HT), and 83.33 % for hypotropias (HoT). The overall average error was 5.6Δ and 3.83Δ for horizontal and vertical deviations, respectively, against the measures presented by the specialist.

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

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

Production and characterization of pure cryogenic inertial fusion targets

NASA Astrophysics Data System (ADS)

Boyd, B. A.; Kamerman, G. W.

An experimental cryogenic inertial fusion target generator and two optical techniques for automated target inspection are described. The generator produces 100 microns diameter solid hydrogen spheres at a rate compatible with fueling requirements of conceptual inertial fusion power plants. A jet of liquified hydrogen is disrupted into droplets by an ultrasonically excited nozzle. The droplets solidify into microspheres while falling through a chamber maintained below the hydrogen triple point pressure. Stable operation of the generator has been demonstrated for up to three hours. The optical inspection techniques are computer aided photomicrography and coarse diffraction pattern analysis (CDPA). The photomicrography system uses a conventional microscope coupled to a computer by a solid state camera and digital image memory. The computer enhances the stored image and performs feature extraction to determine pellet parameters. The CDPA technique uses Fourier transform optics and a special detector array to perform optical processing of a target image.

Semivariogram Analysis of Bone Images Implemented on FPGA Architectures.

PubMed

Shirvaikar, Mukul; Lagadapati, Yamuna; Dong, Xuanliang

2017-03-01

Osteoporotic fractures are a major concern for the healthcare of elderly and female populations. Early diagnosis of patients with a high risk of osteoporotic fractures can be enhanced by introducing second-order statistical analysis of bone image data using techniques such as variogram analysis. Such analysis is computationally intensive thereby creating an impediment for introduction into imaging machines found in common clinical settings. This paper investigates the fast implementation of the semivariogram algorithm, which has been proven to be effective in modeling bone strength, and should be of interest to readers in the areas of computer-aided diagnosis and quantitative image analysis. The semivariogram is a statistical measure of the spatial distribution of data, and is based on Markov Random Fields (MRFs). Semivariogram analysis is a computationally intensive algorithm that has typically seen applications in the geosciences and remote sensing areas. Recently, applications in the area of medical imaging have been investigated, resulting in the need for efficient real time implementation of the algorithm. A semi-variance, γ ( h ), is defined as the half of the expected squared differences of pixel values between any two data locations with a lag distance of h . Due to the need to examine each pair of pixels in the image or sub-image being processed, the base algorithm complexity for an image window with n pixels is O ( n 2 ) Field Programmable Gate Arrays (FPGAs) are an attractive solution for such demanding applications due to their parallel processing capability. FPGAs also tend to operate at relatively modest clock rates measured in a few hundreds of megahertz. This paper presents a technique for the fast computation of the semivariogram using two custom FPGA architectures. A modular architecture approach is chosen to allow for replication of processing units. This allows for high throughput due to concurrent processing of pixel pairs. The current implementation is focused on isotropic semivariogram computations only. The algorithm is benchmarked using VHDL on a Xilinx XUPV5-LX110T development Kit, which utilizes the Virtex5 FPGA. Medical image data from DXA scans are utilized for the experiments. Implementation results show that a significant advantage in computational speed is attained by the architectures with respect to implementation on a personal computer with an Intel i7 multi-core processor.

Semivariogram Analysis of Bone Images Implemented on FPGA Architectures

PubMed Central

Shirvaikar, Mukul; Lagadapati, Yamuna; Dong, Xuanliang

2016-01-01

Osteoporotic fractures are a major concern for the healthcare of elderly and female populations. Early diagnosis of patients with a high risk of osteoporotic fractures can be enhanced by introducing second-order statistical analysis of bone image data using techniques such as variogram analysis. Such analysis is computationally intensive thereby creating an impediment for introduction into imaging machines found in common clinical settings. This paper investigates the fast implementation of the semivariogram algorithm, which has been proven to be effective in modeling bone strength, and should be of interest to readers in the areas of computer-aided diagnosis and quantitative image analysis. The semivariogram is a statistical measure of the spatial distribution of data, and is based on Markov Random Fields (MRFs). Semivariogram analysis is a computationally intensive algorithm that has typically seen applications in the geosciences and remote sensing areas. Recently, applications in the area of medical imaging have been investigated, resulting in the need for efficient real time implementation of the algorithm. A semi-variance, γ(h), is defined as the half of the expected squared differences of pixel values between any two data locations with a lag distance of h. Due to the need to examine each pair of pixels in the image or sub-image being processed, the base algorithm complexity for an image window with n pixels is O (n2) Field Programmable Gate Arrays (FPGAs) are an attractive solution for such demanding applications due to their parallel processing capability. FPGAs also tend to operate at relatively modest clock rates measured in a few hundreds of megahertz. This paper presents a technique for the fast computation of the semivariogram using two custom FPGA architectures. A modular architecture approach is chosen to allow for replication of processing units. This allows for high throughput due to concurrent processing of pixel pairs. The current implementation is focused on isotropic semivariogram computations only. The algorithm is benchmarked using VHDL on a Xilinx XUPV5-LX110T development Kit, which utilizes the Virtex5 FPGA. Medical image data from DXA scans are utilized for the experiments. Implementation results show that a significant advantage in computational speed is attained by the architectures with respect to implementation on a personal computer with an Intel i7 multi-core processor. PMID:28428829

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.

FFDM image quality assessment using computerized image texture analysis

NASA Astrophysics Data System (ADS)

Berger, Rachelle; Carton, Ann-Katherine; Maidment, Andrew D. A.; Kontos, Despina

2010-04-01

Quantitative measures of image quality (IQ) are routinely obtained during the evaluation of imaging systems. These measures, however, do not necessarily correlate with the IQ of the actual clinical images, which can also be affected by factors such as patient positioning. No quantitative method currently exists to evaluate clinical IQ. Therefore, we investigated the potential of using computerized image texture analysis to quantitatively assess IQ. Our hypothesis is that image texture features can be used to assess IQ as a measure of the image signal-to-noise ratio (SNR). To test feasibility, the "Rachel" anthropomorphic breast phantom (Model 169, Gammex RMI) was imaged with a Senographe 2000D FFDM system (GE Healthcare) using 220 unique exposure settings (target/filter, kVs, and mAs combinations). The mAs were varied from 10%-300% of that required for an average glandular dose (AGD) of 1.8 mGy. A 2.5cm2 retroareolar region of interest (ROI) was segmented from each image. The SNR was computed from the ROIs segmented from images linear with dose (i.e., raw images) after flat-field and off-set correction. Image texture features of skewness, coarseness, contrast, energy, homogeneity, and fractal dimension were computed from the Premium ViewTM postprocessed image ROIs. Multiple linear regression demonstrated a strong association between the computed image texture features and SNR (R2=0.92, p<=0.001). When including kV, target and filter as additional predictor variables, a stronger association with SNR was observed (R2=0.95, p<=0.001). The strong associations indicate that computerized image texture analysis can be used to measure image SNR and potentially aid in automating IQ assessment as a component of the clinical workflow. Further work is underway to validate our findings in larger clinical datasets.

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

Histology image analysis for carcinoma detection and grading

PubMed Central

He, Lei; Long, L. Rodney; Antani, Sameer; Thoma, George R.

2012-01-01

This paper presents an overview of the image analysis techniques in the domain of histopathology, specifically, for the objective of automated carcinoma detection and classification. As in other biomedical imaging areas such as radiology, many computer assisted diagnosis (CAD) systems have been implemented to aid histopathologists and clinicians in cancer diagnosis and research, which have been attempted to significantly reduce the labor and subjectivity of traditional manual intervention with histology images. The task of automated histology image analysis is usually not simple due to the unique characteristics of histology imaging, including the variability in image preparation techniques, clinical interpretation protocols, and the complex structures and very large size of the images themselves. In this paper we discuss those characteristics, provide relevant background information about slide preparation and interpretation, and review the application of digital image processing techniques to the field of histology image analysis. In particular, emphasis is given to state-of-the-art image segmentation methods for feature extraction and disease classification. Four major carcinomas of cervix, prostate, breast, and lung are selected to illustrate the functions and capabilities of existing CAD systems. PMID:22436890

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.

A new approach to develop computer-aided detection schemes of digital mammograms

NASA Astrophysics Data System (ADS)

Tan, Maxine; Qian, Wei; Pu, Jiantao; Liu, Hong; Zheng, Bin

2015-06-01

The purpose of this study is to develop a new global mammographic image feature analysis based computer-aided detection (CAD) scheme and evaluate its performance in detecting positive screening mammography examinations. A dataset that includes images acquired from 1896 full-field digital mammography (FFDM) screening examinations was used in this study. Among them, 812 cases were positive for cancer and 1084 were negative or benign. After segmenting the breast area, a computerized scheme was applied to compute 92 global mammographic tissue density based features on each of four mammograms of the craniocaudal (CC) and mediolateral oblique (MLO) views. After adding three existing popular risk factors (woman’s age, subjectively rated mammographic density, and family breast cancer history) into the initial feature pool, we applied a sequential forward floating selection feature selection algorithm to select relevant features from the bilateral CC and MLO view images separately. The selected CC and MLO view image features were used to train two artificial neural networks (ANNs). The results were then fused by a third ANN to build a two-stage classifier to predict the likelihood of the FFDM screening examination being positive. CAD performance was tested using a ten-fold cross-validation method. The computed area under the receiver operating characteristic curve was AUC = 0.779   ±   0.025 and the odds ratio monotonically increased from 1 to 31.55 as CAD-generated detection scores increased. The study demonstrated that this new global image feature based CAD scheme had a relatively higher discriminatory power to cue the FFDM examinations with high risk of being positive, which may provide a new CAD-cueing method to assist radiologists in reading and interpreting screening mammograms.

An ICA-based method for the segmentation of pigmented skin lesions in macroscopic images.

PubMed

Cavalcanti, Pablo G; Scharcanski, Jacob; Di Persia, Leandro E; Milone, Diego H

2011-01-01

Segmentation is an important step in computer-aided diagnostic systems for pigmented skin lesions, since that a good definition of the lesion area and its boundary at the image is very important to distinguish benign from malignant cases. In this paper a new skin lesion segmentation method is proposed. This method uses Independent Component Analysis to locate skin lesions in the image, and this location information is further refined by a Level-set segmentation method. Our method was evaluated in 141 images and achieved an average segmentation error of 16.55%, lower than the results for comparable state-of-the-art methods proposed in literature.

Left ventricular fluid mechanics: the long way from theoretical models to clinical applications.

PubMed

Pedrizzetti, Gianni; Domenichini, Federico

2015-01-01

The flow inside the left ventricle is characterized by the formation of vortices that smoothly accompany blood from the mitral inlet to the aortic outlet. Computational fluid dynamics permitted to shed some light on the fundamental processes involved with vortex motion. More recently, patient-specific numerical simulations are becoming an increasingly feasible tool that can be integrated with the developing imaging technologies. The existing computational methods are reviewed in the perspective of their potential role as a novel aid for advanced clinical analysis. The current results obtained by simulation methods either alone or in combination with medical imaging are summarized. Open problems are highlighted and perspective clinical applications are discussed.

Comparison of three aids for teaching lumbar surgical anatomy.

PubMed

Das, S; Mitchell, P

2013-08-01

Reduced surgeons' training time has resulted in a need to increase the speed of learning. Currently, anatomy education involves traditional (textbooks, physical models, cadaveric dissection/prosection) and recent (electronic) techniques. As yet there are no available data comparing their performance. The performance of three anatomical training aids at teaching the surgical anatomy of the lumbar spinal was compared. The aids used were paper-based images, a three-dimensional plastic model and a semitransparent computer model. Fifty one study subjects were recruited from a population of junior doctors, nurses, medical and nursing students. Three study groups were created which differed in the order of presenting the aids. For each subject, spinal anatomy was revised by the investigator, teaching them the anatomy using each aid. They were specifically taught the locations of the intervertebral disc, pedicles and nerve roots in the lateral recesses. They then drew these structures on a response sheet (three response sheets per subject). The computer model was the best at allowing subjects accurately to determine structure location followed by the paper-based images, the plastic model was the worst. Accuracy improved with successive models used but this trend was not significant. Subjects were not versed in spinal anatomy beforehand, so meaningful baseline measures were not available. The educational performance of surgical anatomical training aids can be measured and compared. A computer generated 3 dimensional model gave the best results with paper-based images second and the plastic model third.

Computer aided detection system for Osteoporosis using low dose thoracic 3D CT images

NASA Astrophysics Data System (ADS)

Tsuji, Daisuke; Matsuhiro, Mikio; Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Nakano, Yasutaka; Harada, Masafumi; Kusumoto, Masahiko; Tsuchida, Takaaki; Eguchi, Kenji; Kaneko, Masahiro

2018-02-01

The patient of osteoporosis is about 13 million people in Japan and it is one of healthy life problems in the aging society. It is necessary to do early stage detection and treatment in order to prevent the osteoporosis. Multi-slice CT technology has been improving the three dimensional (3D) image analysis with higher resolution and shorter scan time. The 3D image analysis of thoracic vertebra can be used for supporting to diagnosis of osteoporosis. This analysis can be used for lung cancer detection at the same time. We develop method of shape analysis and CT values of spongy bone for the detection osteoporosis. Osteoporosis and lung cancer screening show high extraction rate by the thoracic vertebral evaluation CT images. In addition, we created standard pattern of CT value per thoracic vertebra for male age group using 298 low dose data.

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.

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.

Identification of lesion images from gastrointestinal endoscope based on feature extraction of combinational methods with and without learning process.

PubMed

Liu, Ding-Yun; Gan, Tao; Rao, Ni-Ni; Xing, Yao-Wen; Zheng, Jie; Li, Sang; Luo, Cheng-Si; Zhou, Zhong-Jun; Wan, Yong-Li

2016-08-01

The gastrointestinal endoscopy in this study refers to conventional gastroscopy and wireless capsule endoscopy (WCE). Both of these techniques produce a large number of images in each diagnosis. The lesion detection done by hand from the images above is time consuming and inaccurate. This study designed a new computer-aided method to detect lesion images. We initially designed an algorithm named joint diagonalisation principal component analysis (JDPCA), in which there are no approximation, iteration or inverting procedures. Thus, JDPCA has a low computational complexity and is suitable for dimension reduction of the gastrointestinal endoscopic images. Then, a novel image feature extraction method was established through combining the algorithm of machine learning based on JDPCA and conventional feature extraction algorithm without learning. Finally, a new computer-aided method is proposed to identify the gastrointestinal endoscopic images containing lesions. The clinical data of gastroscopic images and WCE images containing the lesions of early upper digestive tract cancer and small intestinal bleeding, which consist of 1330 images from 291 patients totally, were used to confirm the validation of the proposed method. The experimental results shows that, for the detection of early oesophageal cancer images, early gastric cancer images and small intestinal bleeding images, the mean values of accuracy of the proposed method were 90.75%, 90.75% and 94.34%, with the standard deviations (SDs) of 0.0426, 0.0334 and 0.0235, respectively. The areas under the curves (AUCs) were 0.9471, 0.9532 and 0.9776, with the SDs of 0.0296, 0.0285 and 0.0172, respectively. Compared with the traditional related methods, our method showed a better performance. It may therefore provide worthwhile guidance for improving the efficiency and accuracy of gastrointestinal disease diagnosis and is a good prospect for clinical application. Copyright © 2016 Elsevier B.V. All rights reserved.

Computational oncology.

PubMed

Lefor, Alan T

2011-08-01

Oncology research has traditionally been conducted using techniques from the biological sciences. The new field of computational oncology has forged a new relationship between the physical sciences and oncology to further advance research. By applying physics and mathematics to oncologic problems, new insights will emerge into the pathogenesis and treatment of malignancies. One major area of investigation in computational oncology centers around the acquisition and analysis of data, using improved computing hardware and software. Large databases of cellular pathways are being analyzed to understand the interrelationship among complex biological processes. Computer-aided detection is being applied to the analysis of routine imaging data including mammography and chest imaging to improve the accuracy and detection rate for population screening. The second major area of investigation uses computers to construct sophisticated mathematical models of individual cancer cells as well as larger systems using partial differential equations. These models are further refined with clinically available information to more accurately reflect living systems. One of the major obstacles in the partnership between physical scientists and the oncology community is communications. Standard ways to convey information must be developed. Future progress in computational oncology will depend on close collaboration between clinicians and investigators to further the understanding of cancer using these new approaches.

Modelling, simulation and computer-aided design (CAD) of gyrotrons for novel applications in the high-power terahertz science and technologies

NASA Astrophysics Data System (ADS)

Sabchevski, S.; Idehara, T.; Damyanova, M.; Zhelyazkov, I.; Balabanova, E.; Vasileva, E.

2018-03-01

Gyrotrons are the most powerful sources of CW coherent radiation in the sub-THz and THz frequency bands. In recent years, they have demonstrated a remarkable potential for bridging the so-called THz-gap in the electromagnetic spectrum and opened the road to many novel applications of the terahertz waves. Among them are various advanced spectroscopic techniques (e.g., ESR and DNP-NMR), plasma physics and fusion research, materials processing and characterization, imaging and inspection, new medical technologies and biological studies. In this paper, we review briefly the current status of the research in this broad field and present our problem-oriented software packages developed recently for numerical analysis, computer-aided design (CAD) and optimization of gyrotrons.

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.

Quantitative CT analysis of honeycombing area in idiopathic pulmonary fibrosis: Correlations with pulmonary function tests.

PubMed

Nakagawa, Hiroaki; Nagatani, Yukihiro; Takahashi, Masashi; Ogawa, Emiko; Tho, Nguyen Van; Ryujin, Yasushi; Nagao, Taishi; Nakano, Yasutaka

2016-01-01

The 2011 official statement of idiopathic pulmonary fibrosis (IPF) mentions that the extent of honeycombing and the worsening of fibrosis on high-resolution computed tomography (HRCT) in IPF are associated with the increased risk of mortality. However, there are few reports about the quantitative computed tomography (CT) analysis of honeycombing area. In this study, we first proposed a computer-aided method for quantitative CT analysis of honeycombing area in patients with IPF. We then evaluated the correlations between honeycombing area measured by the proposed method with that estimated by radiologists or with parameters of PFTs. Chest HRCTs and pulmonary function tests (PFTs) of 36 IPF patients, who were diagnosed using HRCT alone, were retrospectively evaluated. Two thoracic radiologists independently estimated the honeycombing area as Identified Area (IA) and the percentage of honeycombing area to total lung area as Percent Area (PA) on 3 axial CT slices for each patient. We also developed a computer-aided method to measure the honeycombing area on CT images of those patients. The total honeycombing area as CT honeycombing area (HA) and the percentage of honeycombing area to total lung area as CT %honeycombing area (%HA) were derived from the computer-aided method for each patient. HA derived from three CT slices was significantly correlated with IA (ρ=0.65 for Radiologist 1 and ρ=0.68 for Radiologist 2). %HA derived from three CT slices was also significantly correlated with PA (ρ=0.68 for Radiologist 1 and ρ=0.70 for Radiologist 2). HA and %HA derived from all CT slices were significantly correlated with FVC (%pred.), DLCO (%pred.), and the composite physiologic index (CPI) (HA: ρ=-0.43, ρ=-0.56, ρ=0.63 and %HA: ρ=-0.60, ρ=-0.49, ρ=0.69, respectively). The honeycombing area measured by the proposed computer-aided method was correlated with that estimated by expert radiologists and with parameters of PFTs. This quantitative CT analysis of honeycombing area may be useful and reliable in patients with IPF. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

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 classification of Alzheimer's disease based on support vector machine with combination of cerebral image features in MRI

NASA Astrophysics Data System (ADS)

Jongkreangkrai, C.; Vichianin, Y.; Tocharoenchai, C.; Arimura, H.; Alzheimer's Disease Neuroimaging Initiative

2016-03-01

Several studies have differentiated Alzheimer's disease (AD) using cerebral image features derived from MR brain images. In this study, we were interested in combining hippocampus and amygdala volumes and entorhinal cortex thickness to improve the performance of AD differentiation. Thus, our objective was to investigate the useful features obtained from MRI for classification of AD patients using support vector machine (SVM). T1-weighted MR brain images of 100 AD patients and 100 normal subjects were processed using FreeSurfer software to measure hippocampus and amygdala volumes and entorhinal cortex thicknesses in both brain hemispheres. Relative volumes of hippocampus and amygdala were calculated to correct variation in individual head size. SVM was employed with five combinations of features (H: hippocampus relative volumes, A: amygdala relative volumes, E: entorhinal cortex thicknesses, HA: hippocampus and amygdala relative volumes and ALL: all features). Receiver operating characteristic (ROC) analysis was used to evaluate the method. AUC values of five combinations were 0.8575 (H), 0.8374 (A), 0.8422 (E), 0.8631 (HA) and 0.8906 (ALL). Although “ALL” provided the highest AUC, there were no statistically significant differences among them except for “A” feature. Our results showed that all suggested features may be feasible for computer-aided classification of AD patients.

Computational intelligence for target assessment in Parkinson's disease

NASA Astrophysics Data System (ADS)

Micheli-Tzanakou, Evangelia; Hamilton, J. L.; Zheng, J.; Lehman, Richard M.

2001-11-01

Recent advances in image and signal processing have created a new challenging environment for biomedical engineers. Methods that were developed for different fields are now finding a fertile ground in biomedicine, especially in the analysis of bio-signals and in the understanding of images. More and more, these methods are used in the operating room, helping surgeons, and in the physician's office as aids for diagnostic purposes. Neural Network (NN) research on the other hand, has gone a long way in the past decade. NNs now consist of many thousands of highly interconnected processing elements that can encode, store and recall relationships between different patterns by altering the weighting coefficients of inputs in a systematic way. Although they can generate reasonable outputs from unknown input patterns, and can tolerate a great deal of noise, they are very slow when run on a serial machine. We have used advanced signal processing and innovative image processing methods that are used along with computational intelligence for diagnostic purposes and as visualization aids inside and outside the operating room. Applications to be discussed include EEGs and field potentials in Parkinson's disease along with 3D reconstruction of MR or fMR brain images in Parkinson's patients, are currently used in the operating room for Pallidotomies and Deep Brain Stimulation (DBS).

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.

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.

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 detection systems to improve lung cancer early diagnosis: state-of-the-art and challenges

NASA Astrophysics Data System (ADS)

Traverso, A.; Lopez Torres, E.; Fantacci, M. E.; Cerello, P.

2017-05-01

Lung cancer is one of the most lethal types of cancer, because its early diagnosis is not good enough. In fact, the detection of pulmonary nodule, potential lung cancers, in Computed Tomography scans is a very challenging and time-consuming task for radiologists. To support radiologists, researchers have developed Computer-Aided Diagnosis (CAD) systems for the automated detection of pulmonary nodules in chest Computed Tomography scans. Despite the high level of technological developments and the proved benefits on the overall detection performance, the usage of Computer-Aided Diagnosis in clinical practice is far from being a common procedure. In this paper we investigate the causes underlying this discrepancy and present a solution to tackle it: the M5L WEB- and Cloud-based on-demand Computer-Aided Diagnosis. In addition, we prove how the combination of traditional imaging processing techniques with state-of-art advanced classification algorithms allows to build a system whose performance could be much larger than any Computer-Aided Diagnosis developed so far. This outcome opens the possibility to use the CAD as clinical decision support for radiologists.

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.

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.

Automatic recognition of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNNs.

PubMed

Han, Guanghui; Liu, Xiabi; Zheng, Guangyuan; Wang, Murong; Huang, Shan

2018-06-06

Ground-glass opacity (GGO) is a common CT imaging sign on high-resolution CT, which means the lesion is more likely to be malignant compared to common solid lung nodules. The automatic recognition of GGO CT imaging signs is of great importance for early diagnosis and possible cure of lung cancers. The present GGO recognition methods employ traditional low-level features and system performance improves slowly. Considering the high-performance of CNN model in computer vision field, we proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling is performed on multi-views and multi-receptive fields, which reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has the ability to obtain the optimal fine-tuning model. Multi-CNN models fusion strategy obtains better performance than any single trained model. We evaluated our method on the GGO nodule samples in publicly available LIDC-IDRI dataset of chest CT scans. The experimental results show that our method yields excellent results with 96.64% sensitivity, 71.43% specificity, and 0.83 F1 score. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images. Graphical abstract We proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has ability to obtain the optimal fine-tuning model. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images.

Computer Instructional Aids for Undergraduate Control Education.

ERIC Educational Resources Information Center

Volz, Richard A.; And Others

Engineering is coming to rely more and more heavily upon the computer for computations, analyses, and graphic displays which aid the design process. A general purpose simulation system, the Time-shared Automatic Control Laboratory (TACL), and a set of computer-aided design programs, Control Oriented Interactive Graphic Analysis and Design…

Micrometric precision of prosthetic dental crowns obtained by optical scanning and computer-aided designing/computer-aided manufacturing system

NASA Astrophysics Data System (ADS)

das Neves, Flávio Domingues; de Almeida Prado Naves Carneiro, Thiago; do Prado, Célio Jesus; Prudente, Marcel Santana; Zancopé, Karla; Davi, Letícia Resende; Mendonça, Gustavo; Soares, Carlos José

2014-08-01

The current study evaluated prosthetic dental crowns obtained by optical scanning and a computer-aided designing/computer-aided manufacturing system using micro-computed tomography to compare the marginal fit. The virtual models were obtained with four different scanning surfaces: typodont (T), regular impressions (RI), master casts (MC), and powdered master casts (PMC). Five virtual models were obtained for each group. For each model, a crown was designed on the software and milled from feldspathic ceramic blocks. Micro-CT images were obtained for marginal gap measurements and the data were statistically analyzed by one-way analysis of variance followed by Tukey's test. The mean vertical misfit was T=62.6±65.2 μm; MC=60.4±38.4 μm; PMC=58.1±38.0 μm, and RI=89.8±62.8 μm. Considering a percentage of vertical marginal gap of up to 75 μm, the results were T=71.5%, RI=49.2%, MC=69.6%, and PMC=71.2%. The percentages of horizontal overextension were T=8.5%, RI=0%, MC=0.8%, and PMC=3.8%. Based on the results, virtual model acquisition by scanning the typodont (simulated mouth) or MC, with or without powder, showed acceptable values for the marginal gap. The higher result of marginal gap of the RI group suggests that it is preferable to scan this directly from the mouth or from MC.

Image-Based Reverse Engineering and Visual Prototyping of Woven Cloth.

PubMed

Schroder, Kai; Zinke, Arno; Klein, Reinhard

2015-02-01

Realistic visualization of cloth has many applications in computer graphics. An ongoing research problem is how to best represent and capture cloth models, specifically when considering computer aided design of cloth. Previous methods produce highly realistic images, however, they are either difficult to edit or require the measurement of large databases to capture all variations of a cloth sample. We propose a pipeline to reverse engineer cloth and estimate a parametrized cloth model from a single image. We introduce a geometric yarn model, integrating state-of-the-art textile research. We present an automatic analysis approach to estimate yarn paths, yarn widths, their variation and a weave pattern. Several examples demonstrate that we are able to model the appearance of the original cloth sample. Properties derived from the input image give a physically plausible basis that is fully editable using a few intuitive parameters.

Computer-Aided Evaluation of Blood Vessel Geometry From Acoustic Images.

PubMed

Lindström, Stefan B; Uhlin, Fredrik; Bjarnegård, Niclas; Gylling, Micael; Nilsson, Kamilla; Svensson, Christina; Yngman-Uhlin, Pia; Länne, Toste

2018-04-01

A method for computer-aided assessment of blood vessel geometries based on shape-fitting algorithms from metric vision was evaluated. Acoustic images of cross sections of the radial artery and cephalic vein were acquired, and medical practitioners used a computer application to measure the wall thickness and nominal diameter of these blood vessels with a caliper method and the shape-fitting method. The methods performed equally well for wall thickness measurements. The shape-fitting method was preferable for measuring the diameter, since it reduced systematic errors by up to 63% in the case of the cephalic vein because of its eccentricity. © 2017 by the American Institute of Ultrasound in Medicine.

Accuracy of virtual surgical planning of orthognathic surgery with aid of CAD/CAM fabricated surgical splint-A novel 3D analyzing algorithm.

PubMed

Chin, Shih-Jan; Wilde, Frank; Neuhaus, Michael; Schramm, Alexander; Gellrich, Nils-Claudius; Rana, Majeed

2017-12-01

The benefit of computer-assisted planning in orthognathic surgery has been extensively documented over the last decade. This study aims to evaluate the accuracy of a virtual orthognathic surgical plan by a novel three dimensional (3D) analysis method. Ten patients who required orthognathic surgery were included in this study. A virtual surgical plan was achieved by the combination of a 3D skull model acquired from computed tomography (CT) and surface scanning of the upper and lower dental arch respectively and final occlusal position. Osteotomies and movement of maxilla and mandible were simulated by Dolphin Imaging 11.8 Premium ® (Dolphin Imaging and Management Solutions, Chatsworth, CA). The surgical plan was transferred to surgical splints fabricated by means of Computer Aided Design/Computer Aided Manufacturing (CAD/CAM). Differences of three dimensional measurements between the virtual surgical plan and postoperative results were evaluated. The results from all parameters showed that the virtual surgical plans were successfully transferred by the assistance of CAD/CAM fabricated surgical splint. Wilcoxon's signed rank test showed that no statistically significant deviation between surgical plan and post-operational result could be detected. However, deviation of angle U1 axis-HP and distance of A-CP could not fulfill the clinical success criteria. Virtual surgical planning and CAD/CAM fabricated surgical splint are proven to facilitate treatment planning and offer an accurate surgical result in orthognathic surgery. Copyright © 2017 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

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

A Web-Based Computer-Aided Learning Module for an Anatomy Course Using Open Source Image Mapping Software

ERIC Educational Resources Information Center

Carleton, Renee E.

2012-01-01

Computer-aided learning (CAL) is used increasingly to teach anatomy in post-secondary programs. Studies show that augmentation of traditional cadaver dissection and model examination by CAL can be associated with positive student learning outcomes. In order to reduce costs associated with the purchase of skeletons and models and to encourage study…

Recent advances in the reconstruction of cranio-maxillofacial defects using computer-aided design/computer-aided manufacturing.

PubMed

Oh, Ji-Hyeon

2018-12-01

With the development of computer-aided design/computer-aided manufacturing (CAD/CAM) technology, it has been possible to reconstruct the cranio-maxillofacial defect with more accurate preoperative planning, precise patient-specific implants (PSIs), and shorter operation times. The manufacturing processes include subtractive manufacturing and additive manufacturing and should be selected in consideration of the material type, available technology, post-processing, accuracy, lead time, properties, and surface quality. Materials such as titanium, polyethylene, polyetheretherketone (PEEK), hydroxyapatite (HA), poly-DL-lactic acid (PDLLA), polylactide-co-glycolide acid (PLGA), and calcium phosphate are used. Design methods for the reconstruction of cranio-maxillofacial defects include the use of a pre-operative model printed with pre-operative data, printing a cutting guide or template after virtual surgery, a model after virtual surgery printed with reconstructed data using a mirror image, and manufacturing PSIs by directly obtaining PSI data after reconstruction using a mirror image. By selecting the appropriate design method, manufacturing process, and implant material according to the case, it is possible to obtain a more accurate surgical procedure, reduced operation time, the prevention of various complications that can occur using the traditional method, and predictive results compared to the traditional method.

Computer-Aided Diagnostic (CAD) Scheme by Use of Contralateral Subtraction Technique

NASA Astrophysics Data System (ADS)

Nagashima, Hiroyuki; Harakawa, Tetsumi

We developed a computer-aided diagnostic (CAD) scheme for detection of subtle image findings of acute cerebral infarction in brain computed tomography (CT) by using a contralateral subtraction technique. In our computerized scheme, the lateral inclination of image was first corrected automatically by rotating and shifting. The contralateral subtraction image was then derived by subtraction of reversed image from original image. Initial candidates for acute cerebral infarctions were identified using the multiple-thresholding and image filtering techniques. As the 1st step for removing false positive candidates, fourteen image features were extracted in each of the initial candidates. Halfway candidates were detected by applying the rule-based test with these image features. At the 2nd step, five image features were extracted using the overlapping scale with halfway candidates in interest slice and upper/lower slice image. Finally, acute cerebral infarction candidates were detected by applying the rule-based test with five image features. The sensitivity in the detection for 74 training cases was 97.4% with 3.7 false positives per image. The performance of CAD scheme for 44 testing cases had an approximate result to training cases. Our CAD scheme using the contralateral subtraction technique can reveal suspected image findings of acute cerebral infarctions in CT images.

Research and analysis of head-directed area-of-interest visual system concepts

NASA Technical Reports Server (NTRS)

Sinacori, J. B.

1983-01-01

An analysis and survey with conjecture supporting a preliminary data base design is presented. The data base is intended for use in a Computer Image Generator visual subsystem for a rotorcraft flight simulator that is used for rotorcraft systems development, not training. The approach taken was to attempt to identify the visual perception strategies used during terrain flight, survey environmental and image generation factors, and meld these into a preliminary data base design. This design is directed at Data Base developers, and hopefully will stimulate and aid their efforts to evolve such a Base that will support simulation of terrain flight operations.

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.

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.

Computer-Aided Classification of Visual Ventilation Patterns in Patients with Chronic Obstructive Pulmonary Disease at Two-Phase Xenon-Enhanced CT

PubMed Central

Yoon, Soon Ho; Jung, Julip; Hong, Helen; Park, Eun Ah; Lee, Chang Hyun; Lee, Youkyung; Jin, Kwang Nam; Choo, Ji Yung; Lee, Nyoung Keun

2014-01-01

Objective To evaluate the technical feasibility, performance, and interobserver agreement of a computer-aided classification (CAC) system for regional ventilation at two-phase xenon-enhanced CT in patients with chronic obstructive pulmonary disease (COPD). Materials and Methods Thirty-eight patients with COPD underwent two-phase xenon ventilation CT with resulting wash-in (WI) and wash-out (WO) xenon images. The regional ventilation in structural abnormalities was visually categorized into four patterns by consensus of two experienced radiologists who compared the xenon attenuation of structural abnormalities with that of adjacent normal parenchyma in the WI and WO images, and it served as the reference. Two series of image datasets of structural abnormalities were randomly extracted for optimization and validation. The proportion of agreement on a per-lesion basis and receiver operating characteristics on a per-pixel basis between CAC and reference were analyzed for optimization. Thereafter, six readers independently categorized the regional ventilation in structural abnormalities in the validation set without and with a CAC map. Interobserver agreement was also compared between assessments without and with CAC maps using multirater κ statistics. Results Computer-aided classification maps were successfully generated in 31 patients (81.5%). The proportion of agreement and the average area under the curve of optimized CAC maps were 94% (75/80) and 0.994, respectively. Multirater κ value was improved from moderate (κ = 0.59; 95% confidence interval [CI], 0.56-0.62) at the initial assessment to excellent (κ = 0.82; 95% CI, 0.79-0.85) with the CAC map. Conclusion Our proposed CAC system demonstrated the potential for regional ventilation pattern analysis and enhanced interobserver agreement on visual classification of regional ventilation. PMID:24843245

Computer-aided classification of visual ventilation patterns in patients with chronic obstructive pulmonary disease at two-phase xenon-enhanced CT.

PubMed

Yoon, Soon Ho; Goo, Jin Mo; Jung, Julip; Hong, Helen; Park, Eun Ah; Lee, Chang Hyun; Lee, Youkyung; Jin, Kwang Nam; Choo, Ji Yung; Lee, Nyoung Keun

2014-01-01

To evaluate the technical feasibility, performance, and interobserver agreement of a computer-aided classification (CAC) system for regional ventilation at two-phase xenon-enhanced CT in patients with chronic obstructive pulmonary disease (COPD). Thirty-eight patients with COPD underwent two-phase xenon ventilation CT with resulting wash-in (WI) and wash-out (WO) xenon images. The regional ventilation in structural abnormalities was visually categorized into four patterns by consensus of two experienced radiologists who compared the xenon attenuation of structural abnormalities with that of adjacent normal parenchyma in the WI and WO images, and it served as the reference. Two series of image datasets of structural abnormalities were randomly extracted for optimization and validation. The proportion of agreement on a per-lesion basis and receiver operating characteristics on a per-pixel basis between CAC and reference were analyzed for optimization. Thereafter, six readers independently categorized the regional ventilation in structural abnormalities in the validation set without and with a CAC map. Interobserver agreement was also compared between assessments without and with CAC maps using multirater κ statistics. Computer-aided classification maps were successfully generated in 31 patients (81.5%). The proportion of agreement and the average area under the curve of optimized CAC maps were 94% (75/80) and 0.994, respectively. Multirater κ value was improved from moderate (κ = 0.59; 95% confidence interval [CI], 0.56-0.62) at the initial assessment to excellent (κ = 0.82; 95% CI, 0.79-0.85) with the CAC map. Our proposed CAC system demonstrated the potential for regional ventilation pattern analysis and enhanced interobserver agreement on visual classification of regional ventilation.

Gap-free segmentation of vascular networks with automatic image processing pipeline.

PubMed

Hsu, Chih-Yang; Ghaffari, Mahsa; Alaraj, Ali; Flannery, Michael; Zhou, Xiaohong Joe; Linninger, Andreas

2017-03-01

Current image processing techniques capture large vessels reliably but often fail to preserve connectivity in bifurcations and small vessels. Imaging artifacts and noise can create gaps and discontinuity of intensity that hinders segmentation of vascular trees. However, topological analysis of vascular trees require proper connectivity without gaps, loops or dangling segments. Proper tree connectivity is also important for high quality rendering of surface meshes for scientific visualization or 3D printing. We present a fully automated vessel enhancement pipeline with automated parameter settings for vessel enhancement of tree-like structures from customary imaging sources, including 3D rotational angiography, magnetic resonance angiography, magnetic resonance venography, and computed tomography angiography. The output of the filter pipeline is a vessel-enhanced image which is ideal for generating anatomical consistent network representations of the cerebral angioarchitecture for further topological or statistical analysis. The filter pipeline combined with computational modeling can potentially improve computer-aided diagnosis of cerebrovascular diseases by delivering biometrics and anatomy of the vasculature. It may serve as the first step in fully automatic epidemiological analysis of large clinical datasets. The automatic analysis would enable rigorous statistical comparison of biometrics in subject-specific vascular trees. The robust and accurate image segmentation using a validated filter pipeline would also eliminate operator dependency that has been observed in manual segmentation. Moreover, manual segmentation is time prohibitive given that vascular trees have more than thousands of segments and bifurcations so that interactive segmentation consumes excessive human resources. Subject-specific trees are a first step toward patient-specific hemodynamic simulations for assessing treatment outcomes. Copyright © 2017 Elsevier Ltd. All rights reserved.

A web-based computer aided system for liver surgery planning: initial implementation on RayPlus

NASA Astrophysics Data System (ADS)

Luo, Ming; Yuan, Rong; Sun, Zhi; Li, Tianhong; Xie, Qingguo

2016-03-01

At present, computer aided systems for liver surgery design and risk evaluation are widely used in clinical all over the world. However, most systems are local applications that run on high-performance workstations, and the images have to processed offline. Compared with local applications, a web-based system is accessible anywhere and for a range of regardless of relative processing power or operating system. RayPlus (http://rayplus.life.hust.edu.cn), a B/S platform for medical image processing, was developed to give a jump start on web-based medical image processing. In this paper, we implement a computer aided system for liver surgery planning on the architecture of RayPlus. The system consists of a series of processing to CT images including filtering, segmentation, visualization and analyzing. Each processing is packaged into an executable program and runs on the server side. CT images in DICOM format are processed step by to interactive modeling on browser with zero-installation and server-side computing. The system supports users to semi-automatically segment the liver, intrahepatic vessel and tumor from the pre-processed images. Then, surface and volume models are built to analyze the vessel structure and the relative position between adjacent organs. The results show that the initial implementation meets satisfactorily its first-order objectives and provide an accurate 3D delineation of the liver anatomy. Vessel labeling and resection simulation are planned to add in the future. The system is available on Internet at the link mentioned above and an open username for testing is offered.

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.

Computer aided stress analysis of long bones utilizing computer tomography

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

Marom, S.A.

1986-01-01

A computer aided analysis method, utilizing computed tomography (CT) has been developed, which together with a finite element program determines the stress-displacement pattern in a long bone section. The CT data file provides the geometry, the density and the material properties for the generated finite element model. A three-dimensional finite element model of a tibial shaft is automatically generated from the CT file by a pre-processing procedure for a finite element program. The developed pre-processor includes an edge detection algorithm which determines the boundaries of the reconstructed cross-sectional images of the scanned bone. A mesh generation procedure than automatically generatesmore » a three-dimensional mesh of a user-selected refinement. The elastic properties needed for the stress analysis are individually determined for each model element using the radiographic density (CT number) of each pixel with the elemental borders. The elastic modulus is determined from the CT radiographic density by using an empirical relationship from the literature. The generated finite element model, together with applied loads, determined from existing gait analysis and initial displacements, comprise a formatted input for the SAP IV finite element program. The output of this program, stresses and displacements at the model elements and nodes, are sorted and displayed by a developed post-processor to provide maximum and minimum values at selected locations in the model.« less

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.

Award-Winning Animation Helps Scientists See Nature at Work | News | NREL

Science.gov Websites

Scientists See Nature at Work August 8, 2008 A computer-aided image combines a photo of a man with a three -dimensional, computer-generated image. The man has long brown hair and a long beard. He is wearing a blue - simultaneously. "It is very difficult to parallelize the process to run even on a huge computer,"

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.

Paediatric x-ray radiation dose reduction and image quality analysis.

PubMed

Martin, L; Ruddlesden, R; Makepeace, C; Robinson, L; Mistry, T; Starritt, H

2013-09-01

Collaboration of multiple staff groups has resulted in significant reduction in the risk of radiation-induced cancer from radiographic x-ray exposure during childhood. In this study at an acute NHS hospital trust, a preliminary audit identified initial exposure factors. These were compared with European and UK guidance, leading to the introduction of new factors that were in compliance with European guidance on x-ray tube potentials. Image quality was assessed using standard anatomical criteria scoring, and visual grading characteristics analysis assessed the impact on image quality of changes in exposure factors. This analysis determined the acceptability of gradual radiation dose reduction below the European and UK guidance levels. Chest and pelvis exposures were optimised, achieving dose reduction for each age group, with 7%-55% decrease in critical organ dose. Clinicians confirmed diagnostic image quality throughout the iterative process. Analysis of images acquired with preliminary and final exposure factors indicated an average visual grading analysis result of 0.5, demonstrating equivalent image quality. The optimisation process and final radiation doses are reported for Carestream computed radiography to aid other hospitals in minimising radiation risks to children.

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.

Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology.

PubMed

Sharma, Harshita; Zerbe, Norman; Klempert, Iris; Hellwich, Olaf; Hufnagl, Peter

2017-11-01

Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue. Classification performance of the developed deep learning approach is quantitatively compared with traditional image analysis methods in digital histopathology requiring prior computation of handcrafted features, such as statistical measures using gray level co-occurrence matrix, Gabor filter-bank responses, LBP histograms, gray histograms, HSV histograms and RGB histograms, followed by random forest machine learning. Additionally, the widely known AlexNet deep convolutional framework is comparatively analyzed for the corresponding classification problems. The proposed convolutional neural network architecture reports favorable results, with an overall classification accuracy of 0.6990 for cancer classification and 0.8144 for necrosis detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

The robot's eyes - Stereo vision system for automated scene analysis

NASA Technical Reports Server (NTRS)

Williams, D. S.

1977-01-01

Attention is given to the robot stereo vision system which maintains the image produced by solid-state detector television cameras in a dynamic random access memory called RAPID. The imaging hardware consists of sensors (two solid-state image arrays using a charge injection technique), a video-rate analog-to-digital converter, the RAPID memory, and various types of computer-controlled displays, and preprocessing equipment (for reflexive actions, processing aids, and object detection). The software is aimed at locating objects and transversibility. An object-tracking algorithm is discussed and it is noted that tracking speed is in the 50-75 pixels/s range.

Evaluation of Microcomputer-Based Operation and Maintenance Management Systems for Army Water/Wastewater Treatment Plant Operation.

DTIC Science & Technology

1986-07-01

COMPUTER-AIDED OPERATION MANAGEMENT SYSTEM ................. 29 Functions of an Off-Line Computer-Aided Operation Management System Applications of...System Comparisons 85 DISTRIBUTION 5V J. • 0. FIGURES Number Page 1 Hardware Components 21 2 Basic Functions of a Computer-Aided Operation Management System...Plant Visits 26 4 Computer-Aided Operation Management Systems Reviewed for Analysis of Basic Functions 29 5 Progress of Software System Installation and

[Application of computer-assisted 3D imaging simulation for surgery].

PubMed

Matsushita, S; Suzuki, N

1994-03-01

This article describes trends in application of various imaging technology in surgical planning, navigation, and computer aided surgery. Imaging information is essential factor for simulation in medicine. It includes three dimensional (3D) image reconstruction, neuro-surgical navigation, creating substantial model based on 3D imaging data and etc. These developments depend mostly on 3D imaging technique, which is much contributed by recent computer technology. 3D imaging can offer new intuitive information to physician and surgeon, and this method is suitable for mechanical control. By utilizing simulated results, we can obtain more precise surgical orientation, estimation, and operation. For more advancement, automatic and high speed recognition of medical imaging is being developed.

Chemistry by Computer.

ERIC Educational Resources Information Center

Garmon, Linda

1981-01-01

Describes the features of various computer chemistry programs. Utilization of computer graphics, color, digital imaging, and other innovations are discussed in programs including those which aid in the identification of unknowns, predict whether chemical reactions are feasible, and predict the biological activity of xenobiotic compounds. (CS)

Digital hand atlas and computer-aided bone age assessment via the Web

NASA Astrophysics Data System (ADS)

Cao, Fei; Huang, H. K.; Pietka, Ewa; Gilsanz, Vicente

1999-07-01

A frequently used assessment method of bone age is atlas matching by a radiological examination of a hand image against a reference set of atlas patterns of normal standards. We are in a process of developing a digital hand atlas with a large standard set of normal hand and wrist images that reflect the skeletal maturity, race and sex difference, and current child development. The digital hand atlas will be used for a computer-aided bone age assessment via Web. We have designed and partially implemented a computer-aided diagnostic (CAD) system for Web-based bone age assessment. The system consists of a digital hand atlas, a relational image database and a Web-based user interface. The digital atlas is based on a large standard set of normal hand an wrist images with extracted bone objects and quantitative features. The image database uses a content- based indexing to organize the hand images and their attributes and present to users in a structured way. The Web-based user interface allows users to interact with the hand image database from browsers. Users can use a Web browser to push a clinical hand image to the CAD server for a bone age assessment. Quantitative features on the examined image, which reflect the skeletal maturity, will be extracted and compared with patterns from the atlas database to assess the bone age. The relevant reference imags and the final assessment report will be sent back to the user's browser via Web. The digital atlas will remove the disadvantages of the currently out-of-date one and allow the bone age assessment to be computerized and done conveniently via Web. In this paper, we present the system design and Web-based client-server model for computer-assisted bone age assessment and our initial implementation of the digital atlas database.

Roles of universal three-dimensional image analysis devices that assist surgical operations.

PubMed

Sakamoto, Tsuyoshi

2014-04-01

The circumstances surrounding medical image analysis have undergone rapid evolution. In such a situation, it can be said that "imaging" obtained through medical imaging modality and the "analysis" that we employ have become amalgamated. Recently, we feel the distance between "imaging" and "analysis" has become closer regarding the imaging analysis of any organ system, as if both terms mentioned above have become integrated. The history of medical image analysis started with the appearance of the computer. The invention of multi-planar reconstruction (MPR) used in the helical scan had a significant impact and became the basis for recent image analysis. Subsequently, curbed MPR (CPR) and other methods were developed, and the 3D diagnostic imaging and image analysis of the human body have started on a full scale. Volume rendering: the development of a new rendering algorithm and the significant improvement of memory and CPUs contributed to the development of "volume rendering," which allows 3D views with retained internal information. A new value was created by this development; computed tomography (CT) images that used to be for "diagnosis" before that time have become "applicable to treatment." In the past, before the development of volume rendering, a clinician had to mentally reconstruct an image reconfigured for diagnosis into a 3D image, but these developments have allowed the depiction of a 3D image on a monitor. Current technology: Currently, in Japan, the estimation of the liver volume and the perfusion area of the portal vein and hepatic vein are vigorously being adopted during preoperative planning for hepatectomy. Such a circumstance seems to be brought by the substantial improvement of said basic techniques and by upgrading the user interface, allowing doctors easy manipulation by themselves. The following describes the specific techniques. Future of post-processing technology: It is expected, in terms of the role of image analysis, for better or worse, that computer-aided diagnosis (CAD) will develop to a highly advanced level in every diagnostic field. Further, it is also expected in the treatment field that a technique coordinating various devices will be strongly required as a surgery navigator. Actually, surgery using an image navigator is being widely studied, and coordination with hardware, including robots, will also be developed. © 2014 Japanese Society of Hepato-Biliary-Pancreatic Surgery.

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

Automated Dermoscopy Image Analysis of Pigmented Skin Lesions

PubMed Central

Baldi, Alfonso; Quartulli, Marco; Murace, Raffaele; Dragonetti, Emanuele; Manganaro, Mario; Guerra, Oscar; Bizzi, Stefano

2010-01-01

Dermoscopy (dermatoscopy, epiluminescence microscopy) is a non-invasive diagnostic technique for the in vivo observation of pigmented skin lesions (PSLs), allowing a better visualization of surface and subsurface structures (from the epidermis to the papillary dermis). This diagnostic tool permits the recognition of morphologic structures not visible by the naked eye, thus opening a new dimension in the analysis of the clinical morphologic features of PSLs. In order to reduce the learning-curve of non-expert clinicians and to mitigate problems inherent in the reliability and reproducibility of the diagnostic criteria used in pattern analysis, several indicative methods based on diagnostic algorithms have been introduced in the last few years. Recently, numerous systems designed to provide computer-aided analysis of digital images obtained by dermoscopy have been reported in the literature. The goal of this article is to review these systems, focusing on the most recent approaches based on content-based image retrieval systems (CBIR). PMID:24281070

Evaluation of SLAR and simulated thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

NASA Technical Reports Server (NTRS)

Hoffer, R. M.; Dean, M. E.; Knowlton, D. J.; Latty, R. S.

1982-01-01

Kershaw County, South Carolina was selected as the study site for analyzing simulated thematic mapper MSS data and dual-polarized X-band synthetic aperture radar (SAR) data. The impact of the improved spatial and spectral characteristics of the LANDSAT D thematic mapper data on computer aided analysis for forest cover type mapping was examined as well as the value of synthetic aperture radar data for differentiating forest and other cover types. The utility of pattern recognition techniques for analyzing SAR data was assessed. Topics covered include: (1) collection and of TMS and reference data; (2) reformatting, geometric and radiometric rectification, and spatial resolution degradation of TMS data; (3) development of training statistics and test data sets; (4) evaluation of different numbers and combinations of wavelength bands on classification performance; (5) comparison among three classification algorithms; and (6) the effectiveness of the principal component transformation in data analysis. The collection, digitization, reformatting, and geometric adjustment of SAR data are also discussed. Image interpretation results and classification results are presented.

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

Automated assessment of joint synovitis activity from medical ultrasound and power doppler examinations using image processing and machine learning methods.

PubMed

Cupek, Rafal; Ziębiński, Adam

2016-01-01

Rheumatoid arthritis is the most common rheumatic disease with arthritis, and causes substantial functional disability in approximately 50% patients after 10 years. Accurate measurement of the disease activity is crucial to provide an adequate treatment and care to the patients. The aim of this study is focused on a computer aided diagnostic system that supports an assessment of synovitis severity. This paper focus on a computer aided diagnostic system that was developed within joint Polish-Norwegian research project related to the automated assessment of the severity of synovitis. Semiquantitative ultrasound with power Doppler is a reliable and widely used method of assessing synovitis. Synovitis is estimated by ultrasound examiner using the scoring system graded from 0 to 3. Activity score is estimated on the basis of the examiner's experience or standardized ultrasound atlases. The method needs trained medical personnel and the result can be affected by a human error. The porotype of a computer-aided diagnostic system and algorithms essential for an analysis of ultrasonic images of finger joints are main scientific output of the MEDUSA project. Medusa Evaluation System prototype uses bone, skin, joint and synovitis area detectors for mutual structural model based evaluation of synovitis. Finally, several algorithms that support the semi-automatic or automatic detection of the bone region were prepared as well as a system that uses the statistical data processing approach in order to automatically localize the regions of interest. Semiquantitative ultrasound with power Doppler is a reliable and widely used method of assessing synovitis. Activity score is estimated on the basis of the examiner's experience and the result can be affected by a human error. In this paper we presented the MEDUSA project which is focused on a computer aided diagnostic system that supports an assessment of synovitis severity.

The future of computer-aided sperm analysis

PubMed Central

Mortimer, Sharon T; van der Horst, Gerhard; Mortimer, David

2015-01-01

Computer-aided sperm analysis (CASA) technology was developed in the late 1980s for analyzing sperm movement characteristics or kinematics and has been highly successful in enabling this field of research. CASA has also been used with great success for measuring semen characteristics such as sperm concentration and proportions of progressive motility in many animal species, including wide application in domesticated animal production laboratories and reproductive toxicology. However, attempts to use CASA for human clinical semen analysis have largely met with poor success due to the inherent difficulties presented by many human semen samples caused by sperm clumping and heavy background debris that, until now, have precluded accurate digital image analysis. The authors review the improved capabilities of two modern CASA platforms (Hamilton Thorne CASA-II and Microptic SCA6) and consider their current and future applications with particular reference to directing our focus towards using this technology to assess functional rather than simple descriptive characteristics of spermatozoa. Specific requirements for validating CASA technology as a semi-automated system for human semen analysis are also provided, with particular reference to the accuracy and uncertainty of measurement expected of a robust medical laboratory test for implementation in clinical laboratories operating according to modern accreditation standards. PMID:25926614

Computer-aided diagnostics of screening mammography using content-based image retrieval

NASA Astrophysics Data System (ADS)

Deserno, Thomas M.; Soiron, Michael; de Oliveira, Júlia E. E.; de A. Araújo, Arnaldo

2012-03-01

Breast cancer is one of the main causes of death among women in occidental countries. In the last years, screening mammography has been established worldwide for early detection of breast cancer, and computer-aided diagnostics (CAD) is being developed to assist physicians reading mammograms. A promising method for CAD is content-based image retrieval (CBIR). Recently, we have developed a classification scheme of suspicious tissue pattern based on the support vector machine (SVM). In this paper, we continue moving towards automatic CAD of screening mammography. The experiments are based on in total 10,509 radiographs that have been collected from different sources. From this, 3,375 images are provided with one and 430 radiographs with more than one chain code annotation of cancerous regions. In different experiments, this data is divided into 12 and 20 classes, distinguishing between four categories of tissue density, three categories of pathology and in the 20 class problem two categories of different types of lesions. Balancing the number of images in each class yields 233 and 45 images remaining in each of the 12 and 20 classes, respectively. Using a two-dimensional principal component analysis, features are extracted from small patches of 128 x 128 pixels and classified by means of a SVM. Overall, the accuracy of the raw classification was 61.6 % and 52.1 % for the 12 and the 20 class problem, respectively. The confusion matrices are assessed for detailed analysis. Furthermore, an implementation of a SVM-based CBIR system for CADx in screening mammography is presented. In conclusion, with a smarter patch extraction, the CBIR approach might reach precision rates that are helpful for the physicians. This, however, needs more comprehensive evaluation on clinical data.

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.

[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.

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.

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 detection and quantification of endolymphatic hydrops within the mouse cochlea in vivo using optical coherence tomography

NASA Astrophysics Data System (ADS)

Liu, George S.; Kim, Jinkyung; Applegate, Brian E.; Oghalai, John S.

2017-07-01

Diseases that cause hearing loss and/or vertigo in humans such as Meniere's disease are often studied using animal models. The volume of endolymph within the inner ear varies with these diseases. Here, we used a mouse model of increased endolymph volume, endolymphatic hydrops, to develop a computer-aided objective approach to measure endolymph volume from images collected in vivo using optical coherence tomography. The displacement of Reissner's membrane from its normal position was measured in cochlear cross sections. We validated our computer-aided measurements with manual measurements and with trained observer labels. This approach allows for computer-aided detection of endolymphatic hydrops in mice, with test performance showing sensitivity of 91% and specificity of 87% using a running average of five measurements. These findings indicate that this approach is accurate and reliable for classifying endolymphatic hydrops and quantifying endolymph volume.

Rapid development of medical imaging tools with open-source libraries.

PubMed

Caban, Jesus J; Joshi, Alark; Nagy, Paul

2007-11-01

Rapid prototyping is an important element in researching new imaging analysis techniques and developing custom medical applications. In the last ten years, the open source community and the number of open source libraries and freely available frameworks for biomedical research have grown significantly. What they offer are now considered standards in medical image analysis, computer-aided diagnosis, and medical visualization. A cursory review of the peer-reviewed literature in imaging informatics (indeed, in almost any information technology-dependent scientific discipline) indicates the current reliance on open source libraries to accelerate development and validation of processes and techniques. In this survey paper, we review and compare a few of the most successful open source libraries and frameworks for medical application development. Our dual intentions are to provide evidence that these approaches already constitute a vital and essential part of medical image analysis, diagnosis, and visualization and to motivate the reader to use open source libraries and software for rapid prototyping of medical applications and tools.

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.

APPLICATION OF COMPUTER-AIDED TOMOGRAPHY (CAT) AS A POTENTIAL INDICATOR OF MARINE MARCO BENTHIC ACTIVITY ALONG POLLUTION GRADIENTS

EPA Science Inventory

Sediment cores were imaged using a local hospital CAT scanner. These image data were transferred to a personal computer at our laboratory using specially developed software. Previously, we reported an inverse correlation (r2 = 0.98, P<0.01) between the average sediment x-ray atte...

Computer-aided screening system for cervical precancerous cells based on field emission scanning electron microscopy and energy dispersive x-ray images and spectra

NASA Astrophysics Data System (ADS)

Jusman, Yessi; Ng, Siew-Cheok; Hasikin, Khairunnisa; Kurnia, Rahmadi; Osman, Noor Azuan Bin Abu; Teoh, Kean Hooi

2016-10-01

The capability of field emission scanning electron microscopy and energy dispersive x-ray spectroscopy (FE-SEM/EDX) to scan material structures at the microlevel and characterize the material with its elemental properties has inspired this research, which has developed an FE-SEM/EDX-based cervical cancer screening system. The developed computer-aided screening system consisted of two parts, which were the automatic features of extraction and classification. For the automatic features extraction algorithm, the image and spectra of cervical cells features extraction algorithm for extracting the discriminant features of FE-SEM/EDX data was introduced. The system automatically extracted two types of features based on FE-SEM/EDX images and FE-SEM/EDX spectra. Textural features were extracted from the FE-SEM/EDX image using a gray level co-occurrence matrix technique, while the FE-SEM/EDX spectra features were calculated based on peak heights and corrected area under the peaks using an algorithm. A discriminant analysis technique was employed to predict the cervical precancerous stage into three classes: normal, low-grade intraepithelial squamous lesion (LSIL), and high-grade intraepithelial squamous lesion (HSIL). The capability of the developed screening system was tested using 700 FE-SEM/EDX spectra (300 normal, 200 LSIL, and 200 HSIL cases). The accuracy, sensitivity, and specificity performances were 98.2%, 99.0%, and 98.0%, respectively.

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.

Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme

NASA Astrophysics Data System (ADS)

Tan, Maxine; Pu, Jiantao; Zheng, Bin

2014-08-01

The high false-positive recall rate is one of the major dilemmas that significantly reduce the efficacy of screening mammography, which harms a large fraction of women and increases healthcare cost. This study aims to investigate the feasibility of helping reduce false-positive recalls by developing a new computer-aided diagnosis (CAD) scheme based on the analysis of global mammographic texture and density features computed from four-view images. Our database includes full-field digital mammography (FFDM) images acquired from 1052 recalled women (669 positive for cancer and 383 benign). Each case has four images: two craniocaudal (CC) and two mediolateral oblique (MLO) views. Our CAD scheme first computed global texture features related to the mammographic density distribution on the segmented breast regions of four images. Second, the computed features were given to two artificial neural network (ANN) classifiers that were separately trained and tested in a ten-fold cross-validation scheme on CC and MLO view images, respectively. Finally, two ANN classification scores were combined using a new adaptive scoring fusion method that automatically determined the optimal weights to assign to both views. CAD performance was tested using the area under a receiver operating characteristic curve (AUC). The AUC = 0.793  ±  0.026 was obtained for this four-view CAD scheme, which was significantly higher at the 5% significance level than the AUCs achieved when using only CC (p = 0.025) or MLO (p = 0.0004) view images, respectively. This study demonstrates that a quantitative assessment of global mammographic image texture and density features could provide useful and/or supplementary information to classify between malignant and benign cases among the recalled cases, which may eventually help reduce the false-positive recall rate in screening mammography.

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.

The Research of Computer Aided Farm Machinery Designing Method Based on Ergonomics

NASA Astrophysics Data System (ADS)

Gao, Xiyin; Li, Xinling; Song, Qiang; Zheng, Ying

Along with agricultural economy development, the farm machinery product type Increases gradually, the ergonomics question is also getting more and more prominent. The widespread application of computer aided machinery design makes it possible that farm machinery design is intuitive, flexible and convenient. At present, because the developed computer aided ergonomics software has not suitable human body database, which is needed in view of farm machinery design in China, the farm machinery design have deviation in ergonomics analysis. This article puts forward that using the open database interface procedure in CATIA to establish human body database which aims at the farm machinery design, and reading the human body data to ergonomics module of CATIA can product practical application virtual body, using human posture analysis and human activity analysis module to analysis the ergonomics in farm machinery, thus computer aided farm machinery designing method based on engineering can be realized.

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.

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.

Performance of fusion algorithms for computer-aided detection and classification of mines in very shallow water obtained from testing in navy Fleet Battle Exercise-Hotel 2000

NASA Astrophysics Data System (ADS)

Ciany, Charles M.; Zurawski, William; Kerfoot, Ian

2001-10-01

The performance of Computer Aided Detection/Computer Aided Classification (CAD/CAC) Fusion algorithms on side-scan sonar images was evaluated using data taken at the Navy's's Fleet Battle Exercise-Hotel held in Panama City, Florida, in August 2000. A 2-of-3 binary fusion algorithm is shown to provide robust performance. The algorithm accepts the classification decisions and associated contact locations form three different CAD/CAC algorithms, clusters the contacts based on Euclidian distance, and then declares a valid target when a clustered contact is declared by at least 2 of the 3 individual algorithms. This simple binary fusion provided a 96 percent probability of correct classification at a false alarm rate of 0.14 false alarms per image per side. The performance represented a 3.8:1 reduction in false alarms over the best performing single CAD/CAC algorithm, with no loss in probability of correct classification.

A computer graphics reconstruction and optical analysis of scale anomalies in Caravaggio's Supper at Emmaus

NASA Astrophysics Data System (ADS)

Stork, David G.; Furuichi, Yasuo

2011-03-01

David Hockney has argued that the right hand of the disciple, thrust to the rear in Caravaggio's Supper at Emmaus (1606), is anomalously large as a result of the artist refocusing a putative secret lens-based optical projector and tracing the image it projected onto his canvas. We show through rigorous optical analysis that to achieve such an anomalously large hand image, Caravaggio would have needed to make extremely large, conspicuous and implausible alterations to his studio setup, moving both his purported lens and his canvas nearly two meters between "exposing" the disciple's left hand and then his right hand. Such major disruptions to his studio would have impeded -not aided- Caravaggio in his work. Our optical analysis quantifies these problems and our computer graphics reconstruction of Caravaggio's studio illustrates these problems. In this way we conclude that Caravaggio did not use optical projections in the way claimed by Hockney, but instead most likely set the sizes of these hands "by eye" for artistic reasons.

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.

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.

Reducing false-positive detections by combining two stage-1 computer-aided mass detection algorithms

NASA Astrophysics Data System (ADS)

Bedard, Noah D.; Sampat, Mehul P.; Stokes, Patrick A.; Markey, Mia K.

2006-03-01

In this paper we present a strategy for reducing the number of false-positives in computer-aided mass detection. Our approach is to only mark "consensus" detections from among the suspicious sites identified by different "stage-1" detection algorithms. By "stage-1" we mean that each of the Computer-aided Detection (CADe) algorithms is designed to operate with high sensitivity, allowing for a large number of false positives. In this study, two mass detection methods were used: (1) Heath and Bowyer's algorithm based on the average fraction under the minimum filter (AFUM) and (2) a low-threshold bi-lateral subtraction algorithm. The two methods were applied separately to a set of images from the Digital Database for Screening Mammography (DDSM) to obtain paired sets of mass candidates. The consensus mass candidates for each image were identified by a logical "and" operation of the two CADe algorithms so as to eliminate regions of suspicion that were not independently identified by both techniques. It was shown that by combining the evidence from the AFUM filter method with that obtained from bi-lateral subtraction, the same sensitivity could be reached with fewer false-positives per image relative to using the AFUM filter alone.

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.

Impact of various color LED flashlights and different lighting source to skin distances on the manual and the computer-aided detection of basal cell carcinoma borders.

PubMed

Bakht, Mohamadreza K; Pouladian, Majid; Mofrad, Farshid B; Honarpisheh, Hamid

2014-02-01

Quantitative analysis based on digital skin image has been proven to be helpful in dermatology. Moreover, the borders of the basal cell carcinoma (BCC) lesions have been challenging borders for the automatic detection methods. In this work, a computer-aided dermatoscopy system was proposed to enhance the clinical detection of BCC lesion borders. Fifty cases of BCC were selected and 2000 pictures were taken. The lesion images data were obtained with eight colors of flashlights and in five different lighting source to skin distances (SSDs). Then, the image-processing techniques were used for automatic detection of lesion borders. Further, the dermatologists marked the lesions on the obtained photos. Considerable differences between the obtained values referring to the photographs that were taken at super blue and aqua green color lighting were observed for most of the BCC borders. It was observed that by changing the SSD, an optimum distance could be found where that the accuracy of the detection reaches to a maximum value. This study clearly indicates that by changing SSD and lighting color, manual and automatic detection of BCC lesions borders can be enhanced. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Deep learning in mammography and breast histology, an overview and future trends.

PubMed

Hamidinekoo, Azam; Denton, Erika; Rampun, Andrik; Honnor, Kate; Zwiggelaar, Reyer

2018-07-01

Recent improvements in biomedical image analysis using deep learning based neural networks could be exploited to enhance the performance of Computer Aided Diagnosis (CAD) systems. Considering the importance of breast cancer worldwide and the promising results reported by deep learning based methods in breast imaging, an overview of the recent state-of-the-art deep learning based CAD systems developed for mammography and breast histopathology images is presented. In this study, the relationship between mammography and histopathology phenotypes is described, which takes biological aspects into account. We propose a computer based breast cancer modelling approach: the Mammography-Histology-Phenotype-Linking-Model, which develops a mapping of features/phenotypes between mammographic abnormalities and their histopathological representation. Challenges are discussed along with the potential contribution of such a system to clinical decision making and treatment management. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

Computer-Aided Communication Satellite System Analysis and Optimization.

ERIC Educational Resources Information Center

Stagl, Thomas W.; And Others

Various published computer programs for fixed/broadcast communication satellite system synthesis and optimization are discussed. The rationale for selecting General Dynamics/Convair's Satellite Telecommunication Analysis and Modeling Program (STAMP) in modified form to aid in the system costing and sensitivity analysis work in the Program on…

Computer Aided Phenomenography: The Role of Leximancer Computer Software in Phenomenographic Investigation

ERIC Educational Resources Information Center

Penn-Edwards, Sorrel

2010-01-01

The qualitative research methodology of phenomenography has traditionally required a manual sorting and analysis of interview data. In this paper I explore a potential means of streamlining this procedure by considering a computer aided process not previously reported upon. Two methods of lexicological analysis, manual and automatic, were examined…

Morphometric information to reduce the semantic gap in the characterization of microscopic images of thyroid nodules.

PubMed

Macedo, Alessandra A; Pessotti, Hugo C; Almansa, Luciana F; Felipe, Joaquim C; Kimura, Edna T

2016-07-01

The analyses of several systems for medical-imaging processing typically support the extraction of image attributes, but do not comprise some information that characterizes images. For example, morphometry can be applied to find new information about the visual content of an image. The extension of information may result in knowledge. Subsequently, results of mappings can be applied to recognize exam patterns, thus improving the accuracy of image retrieval and allowing a better interpretation of exam results. Although successfully applied in breast lesion images, the morphometric approach is still poorly explored in thyroid lesions due to the high subjectivity thyroid examinations. This paper presents a theoretical-practical study, considering Computer Aided Diagnosis (CAD) and Morphometry, to reduce the semantic discontinuity between medical image features and human interpretation of image content. The proposed method aggregates the content of microscopic images characterized by morphometric information and other image attributes extracted by traditional object extraction algorithms. This method carries out segmentation, feature extraction, image labeling and classification. Morphometric analysis was included as an object extraction method in order to verify the improvement of its accuracy for automatic classification of microscopic images. To validate this proposal and verify the utility of morphometric information to characterize thyroid images, a CAD system was created to classify real thyroid image-exams into Papillary Cancer, Goiter and Non-Cancer. Results showed that morphometric information can improve the accuracy and precision of image retrieval and the interpretation of results in computer-aided diagnosis. For example, in the scenario where all the extractors are combined with the morphometric information, the CAD system had its best performance (70% of precision in Papillary cases). Results signalized a positive use of morphometric information from images to reduce semantic discontinuity between human interpretation and image characterization. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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 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.

An Interactive Computer Aided Design and Analysis Package.

DTIC Science & Technology

1986-03-01

Al-A167 114 AN INTERACTIVE COMPUTER AIDED DESIGN MUD ANAILYSIS 1/𔃼 PACKAGE(U) NAVAL POSTGRADUATE SCHOOL NONTEREY CA T L EUALD "AR 86 UNCLSSIFIED F... SCHOOL Monterey, California DTIC .LECTE MAYOS THESIS AN INTERACTIVE COMPUTER AIDED DESIGN AND ANALYSIS PACKAGE by Terrence L. Ewald March 1986 jThesis...ORGANIZATION Naval Postgraduate School (if dAp90h81111) Naval Postgraduate School . 62A 6C. ADDRESS (0ty. State, and ZIP Code) 7b. ADDRESS (City State. and

Image quality prediction: an aid to the Viking Lander imaging investigation on Mars.

PubMed

Huck, F O; Wall, S D

1976-07-01

Two Viking spacecraft scheduled to land on Mars in the summer of 1976 will return multispectral panoramas of the Martian surface with resolutions 4 orders of magnitude higher than have been previously obtained and stereo views with resolutions approaching that of the human eye. Mission constraints and uncertainties require a carefully planned imaging investigation that is supported by a computer model of camera response and surface features to aid in diagnosing camera performance, in establishing a preflight imaging strategy, and in rapidly revising this strategy if pictures returned from Mars reveal unfavorable or unanticipated conditions.

Scan-layered reconstructions: A pilot study of a nondestructive dental histoanatomical analysis method and digital workflow to create restorations driven by natural dentin and enamel morphology.

PubMed

Malta Barbosa, João; Tovar, Nick; A Tuesta, Pablo; Hirata, Ronaldo; Guimarães, Nuno; Romanini, José C; Moghadam, Marjan; Coelho, Paulo G; Jahangiri, Leila

2017-07-08

This work aims to present a pilot study of a non-destructive dental histo-anatomical analysis technique as well as to push the boundaries of the presently available restorative workflows for the fabrication of highly customized ceramic restorations. An extracted human maxillary central incisor was subject to a micro computed tomography scan and the acquired data was transferred into a workstation, reconstructed, segmented, evaluated and later imported into a Computer-Aided Design/Computer-Aided Manufacturing software for the fabrication of a ceramic resin-bonded prosthesis. The obtained prosthesis presented an encouraging optical behavior and was used clinically as final restoration. The digitally layered restorative replication of natural tooth morphology presents today as a clear possibility. New clinical and laboratory-fabricated, biologically inspired digital restorative protocols are to be expected in the near future. The digitally layered restorative replication of natural tooth morphology presents today as a clear possibility. This pilot study may represent a stimulus for future research and applications of digital imaging as well as digital restorative workflows in service of esthetic dentistry. © 2017 Wiley Periodicals, Inc.

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.

Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features.

PubMed

Su, Hang; Yin, Zhaozheng; Huh, Seungil; Kanade, Takeo

2013-10-01

Phase-contrast microscopy is one of the most common and convenient imaging modalities to observe long-term multi-cellular processes, which generates images by the interference of lights passing through transparent specimens and background medium with different retarded phases. Despite many years of study, computer-aided phase contrast microscopy analysis on cell behavior is challenged by image qualities and artifacts caused by phase contrast optics. Addressing the unsolved challenges, the authors propose (1) a phase contrast microscopy image restoration method that produces phase retardation features, which are intrinsic features of phase contrast microscopy, and (2) a semi-supervised learning based algorithm for cell segmentation, which is a fundamental task for various cell behavior analysis. Specifically, the image formation process of phase contrast microscopy images is first computationally modeled with a dictionary of diffraction patterns; as a result, each pixel of a phase contrast microscopy image is represented by a linear combination of the bases, which we call phase retardation features. Images are then partitioned into phase-homogeneous atoms by clustering neighboring pixels with similar phase retardation features. Consequently, cell segmentation is performed via a semi-supervised classification technique over the phase-homogeneous atoms. Experiments demonstrate that the proposed approach produces quality segmentation of individual cells and outperforms previous approaches. Copyright © 2013 Elsevier B.V. All rights reserved.

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.

Simplified computer-aided detection scheme of microcalcification clusters in digital breast tomosynthesis images.

PubMed

Ji-Wook Jeong; Seung-Hoon Chae; Eun Young Chae; Hak Hee Kim; Young Wook Choi; Sooyeul Lee

2016-08-01

A computer-aided detection (CADe) algorithm for clustered microcalcifications (MCs) in reconstructed digital breast tomosynthesis (DBT) images is suggested. The MC-like objects were enhanced by a Hessian-based 3D calcification response function, and a signal-to-noise ratio (SNR) enhanced image was also generated to screen the MC clustering seed objects. A connected component segmentation method was used to detect the cluster seed objects, which were considered as potential clustering centers of MCs. Bounding cubes for the accepted clustering seed candidate were generated and the overlapping cubes were combined and examined. After the MC clustering and false-positive (FP) reduction step, the average number of FPs was estimated to be 0.87 per DBT volume with a sensitivity of 90.5%.

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.

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.

Computer-aided boundary delineation of agricultural lands

NASA Technical Reports Server (NTRS)

Cheng, Thomas D.; Angelici, Gary L.; Slye, Robert E.; Ma, Matt

1989-01-01

The National Agricultural Statistics Service of the United States Department of Agriculture (USDA) presently uses labor-intensive aerial photographic interpretation techniques to divide large geographical areas into manageable-sized units for estimating domestic crop and livestock production. Prototype software, the computer-aided stratification (CAS) system, was developed to automate the procedure, and currently runs on a Sun-based image processing system. With a background display of LANDSAT Thematic Mapper and United States Geological Survey Digital Line Graph data, the operator uses a cursor to delineate agricultural areas, called sampling units, which are assigned to strata of land-use and land-cover types. The resultant stratified sampling units are used as input into subsequent USDA sampling procedures. As a test, three counties in Missouri were chosen for application of the CAS procedures. Subsequent analysis indicates that CAS was five times faster in creating sampling units than the manual techniques were.

Image-Based Predictive Modeling of Heart Mechanics.

PubMed

Wang, V Y; Nielsen, P M F; Nash, M P

2015-01-01

Personalized biophysical modeling of the heart is a useful approach for noninvasively analyzing and predicting in vivo cardiac mechanics. Three main developments support this style of analysis: state-of-the-art cardiac imaging technologies, modern computational infrastructure, and advanced mathematical modeling techniques. In vivo measurements of cardiac structure and function can be integrated using sophisticated computational methods to investigate mechanisms of myocardial function and dysfunction, and can aid in clinical diagnosis and developing personalized treatment. In this article, we review the state-of-the-art in cardiac imaging modalities, model-based interpretation of 3D images of cardiac structure and function, and recent advances in modeling that allow personalized predictions of heart mechanics. We discuss how using such image-based modeling frameworks can increase the understanding of the fundamental biophysics behind cardiac mechanics, and assist with diagnosis, surgical guidance, and treatment planning. Addressing the challenges in this field will require a coordinated effort from both the clinical-imaging and modeling communities. We also discuss future directions that can be taken to bridge the gap between basic science and clinical translation.

Wide-field Imaging System and Rapid Direction of Optical Zoom (WOZ)

DTIC Science & Technology

2010-09-25

commercial software packages: SolidWorks, COMSOL Multiphysics, and ZEMAX optical design. SolidWorks is a computer aided design package, which as a live...interface to COMSOL. COMSOL is a finite element analysis/partial differential equation solver. ZEMAX is an optical design package. Both COMSOL and... ZEMAX have live interfaces to MatLab. Our initial investigations have enabled a model in SolidWorks to be updated in COMSOL, an FEA calculation

Enhancing Engineering Computer-Aided Design Education Using Lectures Recorded on the PC

ERIC Educational Resources Information Center

McGrann, Roy T. R.

2006-01-01

Computer-Aided Engineering (CAE) is a course that is required during the third year in the mechanical engineering curriculum at Binghamton University. The primary objective of the course is to educate students in the procedures of computer-aided engineering design. The solid modeling and analysis program Pro/Engineer[TM] (PTC[R]) is used as the…

A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications

PubMed Central

Sechopoulos, Ioannis

2013-01-01

Many important post-acquisition aspects of breast tomosynthesis imaging can impact its clinical performance. Chief among them is the reconstruction algorithm that generates the representation of the three-dimensional breast volume from the acquired projections. But even after reconstruction, additional processes, such as artifact reduction algorithms, computer aided detection and diagnosis, among others, can also impact the performance of breast tomosynthesis in the clinical realm. In this two part paper, a review of breast tomosynthesis research is performed, with an emphasis on its medical physics aspects. In the companion paper, the first part of this review, the research performed relevant to the image acquisition process is examined. This second part will review the research on the post-acquisition aspects, including reconstruction, image processing, and analysis, as well as the advanced applications being investigated for breast tomosynthesis. PMID:23298127

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

Optical tomographic detection of rheumatoid arthritis with computer-aided classification schemes

NASA Astrophysics Data System (ADS)

Klose, Christian D.; Klose, Alexander D.; Netz, Uwe; Beuthan, Jürgen; Hielscher, Andreas H.

2009-02-01

A recent research study has shown that combining multiple parameters, drawn from optical tomographic images, leads to better classification results to identifying human finger joints that are affected or not affected by rheumatic arthritis RA. Building up on the research findings of the previous study, this article presents an advanced computer-aided classification approach for interpreting optical image data to detect RA in finger joints. Additional data are used including, for example, maximum and minimum values of the absorption coefficient as well as their ratios and image variances. Classification performances obtained by the proposed method were evaluated in terms of sensitivity, specificity, Youden index and area under the curve AUC. Results were compared to different benchmarks ("gold standard"): magnet resonance, ultrasound and clinical evaluation. Maximum accuracies (AUC=0.88) were reached when combining minimum/maximum-ratios and image variances and using ultrasound as gold standard.

DeepInfer: open-source deep learning deployment toolkit for image-guided therapy

NASA Astrophysics Data System (ADS)

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A.; Kapur, Tina; Wells, William M.; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-03-01

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research work ows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

DeepInfer: Open-Source Deep Learning Deployment Toolkit for Image-Guided Therapy.

PubMed

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A; Kapur, Tina; Wells, William M; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-02-11

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

DeepInfer: Open-Source Deep Learning Deployment Toolkit for Image-Guided Therapy

PubMed Central

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A.; Kapur, Tina; Wells, William M.; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-01-01

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose “DeepInfer” – an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections. PMID:28615794

Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends

PubMed Central

Mansoor, Awais; Foster, Brent; Xu, Ziyue; Papadakis, Georgios Z.; Folio, Les R.; Udupa, Jayaram K.; Mollura, Daniel J.

2015-01-01

The computer-based process of identifying the boundaries of lung from surrounding thoracic tissue on computed tomographic (CT) images, which is called segmentation, is a vital first step in radiologic pulmonary image analysis. Many algorithms and software platforms provide image segmentation routines for quantification of lung abnormalities; however, nearly all of the current image segmentation approaches apply well only if the lungs exhibit minimal or no pathologic conditions. When moderate to high amounts of disease or abnormalities with a challenging shape or appearance exist in the lungs, computer-aided detection systems may be highly likely to fail to depict those abnormal regions because of inaccurate segmentation methods. In particular, abnormalities such as pleural effusions, consolidations, and masses often cause inaccurate lung segmentation, which greatly limits the use of image processing methods in clinical and research contexts. In this review, a critical summary of the current methods for lung segmentation on CT images is provided, with special emphasis on the accuracy and performance of the methods in cases with abnormalities and cases with exemplary pathologic findings. The currently available segmentation methods can be divided into five major classes: (a) thresholding-based, (b) region-based, (c) shape-based, (d) neighboring anatomy–guided, and (e) machine learning–based methods. The feasibility of each class and its shortcomings are explained and illustrated with the most common lung abnormalities observed on CT images. In an overview, practical applications and evolving technologies combining the presented approaches for the practicing radiologist are detailed. ©RSNA, 2015 PMID:26172351

Deep learning of symmetrical discrepancies for computer-aided detection of mammographic masses

NASA Astrophysics Data System (ADS)

Kooi, Thijs; Karssemeijer, Nico

2017-03-01

When humans identify objects in images, context is an important cue; a cheetah is more likely to be a domestic cat when a television set is recognised in the background. Similar principles apply to the analysis of medical images. The detection of diseases that manifest unilaterally in symmetrical organs or organ pairs can in part be facilitated by a search for symmetrical discrepancies in or between the organs in question. During a mammographic exam, images are recorded of each breast and absence of a certain structure around the same location in the contralateral image will render the area under scrutiny more suspicious and conversely, the presence of similar tissue less so. In this paper, we present a fusion scheme for a deep Convolutional Neural Network (CNN) architecture with the goal to optimally capture such asymmetries. The method is applied to the domain of mammography CAD, but can be relevant to other medical image analysis tasks where symmetry is important such as lung, prostate or brain images.

Computational System For Rapid CFD Analysis In Engineering

NASA Technical Reports Server (NTRS)

Barson, Steven L.; Ascoli, Edward P.; Decroix, Michelle E.; Sindir, Munir M.

1995-01-01

Computational system comprising modular hardware and software sub-systems developed to accelerate and facilitate use of techniques of computational fluid dynamics (CFD) in engineering environment. Addresses integration of all aspects of CFD analysis process, including definition of hardware surfaces, generation of computational grids, CFD flow solution, and postprocessing. Incorporates interfaces for integration of all hardware and software tools needed to perform complete CFD analysis. Includes tools for efficient definition of flow geometry, generation of computational grids, computation of flows on grids, and postprocessing of flow data. System accepts geometric input from any of three basic sources: computer-aided design (CAD), computer-aided engineering (CAE), or definition by user.

Design of a receiver operating characteristic (ROC) study of 10:1 lossy image compression

NASA Astrophysics Data System (ADS)

Collins, Cary A.; Lane, David; Frank, Mark S.; Hardy, Michael E.; Haynor, David R.; Smith, Donald V.; Parker, James E.; Bender, Gregory N.; Kim, Yongmin

1994-04-01

The digital archiving system at Madigan Army Medical Center (MAMC) uses a 10:1 lossy data compression algorithm for most forms of computed radiography. A systematic study on the potential effect of lossy image compression on patient care has been initiated with a series of studies focused on specific diagnostic tasks. The studies are based upon the receiver operating characteristic (ROC) method of analysis for diagnostic systems. The null hypothesis is that observer performance with approximately 10:1 compressed and decompressed images is not different from using original, uncompressed images for detecting subtle pathologic findings seen on computed radiographs of bone, chest, or abdomen, when viewed on a high-resolution monitor. Our design involves collecting cases from eight pathologic categories. Truth is determined by committee using confirmatory studies performed during routine clinical practice whenever possible. Software has been developed to aid in case collection and to allow reading of the cases for the study using stand-alone Siemens Litebox workstations. Data analysis uses two methods, ROC analysis and free-response ROC (FROC) methods. This study will be one of the largest ROC/FROC studies of its kind and could benefit clinical radiology practice using PACS technology. The study design and results from a pilot FROC study are presented.

Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology

PubMed Central

Di Ruberto, Cecilia; Kocher, Michel

2018-01-01

Malaria is an epidemic health disease and a rapid, accurate diagnosis is necessary for proper intervention. Generally, pathologists visually examine blood stained slides for malaria diagnosis. Nevertheless, this kind of visual inspection is subjective, error-prone and time-consuming. In order to overcome the issues, numerous methods of automatic malaria diagnosis have been proposed so far. In particular, many researchers have used mathematical morphology as a powerful tool for computer aided malaria detection and classification. Mathematical morphology is not only a theory for the analysis of spatial structures, but also a very powerful technique widely used for image processing purposes and employed successfully in biomedical image analysis, especially in preprocessing and segmentation tasks. Microscopic image analysis and particularly malaria detection and classification can greatly benefit from the use of morphological operators. The aim of this paper is to present a review of recent mathematical morphology based methods for malaria parasite detection and identification in stained blood smears images. PMID:29419781

Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology.

PubMed

Loddo, Andrea; Di Ruberto, Cecilia; Kocher, Michel

2018-02-08

Malaria is an epidemic health disease and a rapid, accurate diagnosis is necessary for proper intervention. Generally, pathologists visually examine blood stained slides for malaria diagnosis. Nevertheless, this kind of visual inspection is subjective, error-prone and time-consuming. In order to overcome the issues, numerous methods of automatic malaria diagnosis have been proposed so far. In particular, many researchers have used mathematical morphology as a powerful tool for computer aided malaria detection and classification. Mathematical morphology is not only a theory for the analysis of spatial structures, but also a very powerful technique widely used for image processing purposes and employed successfully in biomedical image analysis, especially in preprocessing and segmentation tasks. Microscopic image analysis and particularly malaria detection and classification can greatly benefit from the use of morphological operators. The aim of this paper is to present a review of recent mathematical morphology based methods for malaria parasite detection and identification in stained blood smears images.

Evaluating a Computerized Aid for Conducting a Cognitive Task Analysis

DTIC Science & Technology

2000-01-01

in conducting a cognitive task analysis . The conduct of a cognitive task analysis is costly and labor intensive. As a result, a few computerized aids...evaluation of a computerized aid, specifically CAT-HCI (Cognitive Analysis Tool - Human Computer Interface), for the conduct of a detailed cognitive task analysis . A

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.

Flow visualization V; Proceedings of the 5th International Symposium, Prague, Czechoslovakia, Aug. 21-25, 1989

NASA Astrophysics Data System (ADS)

Reznicek, R.

The present conference on flow visualization encompasses methods exploiting tracing particles, surface tracing methods, methods exploiting the effects of streaming fluid on passing radiation/field, computer-aided flow visualization, and applications to fluid mechanics, aerodynamics, flow devices, shock tubes, and heat/mass transfer. Specific issues include visualizing velocity distribution by stereo photography, dark-field Fourier quasiinterferometry, speckle tomography of an open flame, a fast eye for real-time image analysis, and velocity-field determination based on flow-image analysis. Also addressed are flows around rectangular prisms with oscillating flaps at the leading edges, the tomography of aerodynamic objects, the vapor-screen technique applied to a delta-wing aircraft, flash-lamp planar imaging, IR-thermography applications in convective heat transfer, and the visualization of marangoni effects in evaporating sessile drops.

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.

Dynamic MRI-based computer aided diagnostic systems for early detection of kidney transplant rejection: A survey

NASA Astrophysics Data System (ADS)

Mostapha, Mahmoud; Khalifa, Fahmi; Alansary, Amir; Soliman, Ahmed; Gimel'farb, Georgy; El-Baz, Ayman

2013-10-01

Early detection of renal transplant rejection is important to implement appropriate medical and immune therapy in patients with transplanted kidneys. In literature, a large number of computer-aided diagnostic (CAD) systems using different image modalities, such as ultrasound (US), magnetic resonance imaging (MRI), computed tomography (CT), and radionuclide imaging, have been proposed for early detection of kidney diseases. A typical CAD system for kidney diagnosis consists of a set of processing steps including: motion correction, segmentation of the kidney and/or its internal structures (e.g., cortex, medulla), construction of agent kinetic curves, functional parameter estimation, diagnosis, and assessment of the kidney status. In this paper, we survey the current state-of-the-art CAD systems that have been developed for kidney disease diagnosis using dynamic MRI. In addition, the paper addresses several challenges that researchers face in developing efficient, fast and reliable CAD systems for the early detection of kidney diseases.

Syntactic methods of shape feature description and its application in analysis of medical images

NASA Astrophysics Data System (ADS)

Ogiela, Marek R.; Tadeusiewicz, Ryszard

2000-02-01

The paper presents specialist algorithms of morphologic analysis of shapes of selected organs of abdominal cavity proposed in order to diagnose disease symptoms occurring in the main pancreatic ducts and upper segments of ureters. Analysis of the correct morphology of these structures has been conducted with the use of syntactic methods of pattern recognition. Its main objective is computer-aided support to early diagnosis of neoplastic lesions and pancreatitis based on images taken in the course of examination with the endoscopic retrograde cholangiopancreatography (ERCP) method and a diagnosis of morphological lesions in ureter based on kidney radiogram analysis. In the analysis of ERCP images, the main objective is to recognize morphological lesions in pancreas ducts characteristic for carcinoma and chronic pancreatitis. In the case of kidney radiogram analysis the aim is to diagnose local irregularity of ureter lumen. Diagnosing the above mentioned lesion has been conducted with the use of syntactic methods of pattern recognition, in particular the languages of shape features description and context-free attributed grammars. These methods allow to recognize and describe in a very efficient way the aforementioned lesions on images obtained as a result of initial image processing into diagrams of widths of the examined structures.

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.

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-aided detection of brain metastasis on 3D MR imaging: Observer performance study.

PubMed

Sunwoo, Leonard; Kim, Young Jae; Choi, Seung Hong; Kim, Kwang-Gi; Kang, Ji Hee; Kang, Yeonah; Bae, Yun Jung; Yoo, Roh-Eul; Kim, Jihang; Lee, Kyong Joon; Lee, Seung Hyun; Choi, Byung Se; Jung, Cheolkyu; Sohn, Chul-Ho; Kim, Jae Hyoung

2017-01-01

To assess the effect of computer-aided detection (CAD) of brain metastasis (BM) on radiologists' diagnostic performance in interpreting three-dimensional brain magnetic resonance (MR) imaging using follow-up imaging and consensus as the reference standard. The institutional review board approved this retrospective study. The study cohort consisted of 110 consecutive patients with BM and 30 patients without BM. The training data set included MR images of 80 patients with 450 BM nodules. The test set included MR images of 30 patients with 134 BM nodules and 30 patients without BM. We developed a CAD system for BM detection using template-matching and K-means clustering algorithms for candidate detection and an artificial neural network for false-positive reduction. Four reviewers (two neuroradiologists and two radiology residents) interpreted the test set images before and after the use of CAD in a sequential manner. The sensitivity, false positive (FP) per case, and reading time were analyzed. A jackknife free-response receiver operating characteristic (JAFROC) method was used to determine the improvement in the diagnostic accuracy. The sensitivity of CAD was 87.3% with an FP per case of 302.4. CAD significantly improved the diagnostic performance of the four reviewers with a figure-of-merit (FOM) of 0.874 (without CAD) vs. 0.898 (with CAD) according to JAFROC analysis (p < 0.01). Statistically significant improvement was noted only for less-experienced reviewers (FOM without vs. with CAD, 0.834 vs. 0.877, p < 0.01). The additional time required to review the CAD results was approximately 72 sec (40% of the total review time). CAD as a second reader helps radiologists improve their diagnostic performance in the detection of BM on MR imaging, particularly for less-experienced reviewers.

Cone Beam Computed Tomography-Dawn of A New Imaging Modality in Orthodontics

PubMed Central

Mamatha, J; Chaitra, K R; Paul, Renji K; George, Merin; Anitha, J; Khanna, Bharti

2015-01-01

Today, we are in a world of innovations, and there are various diagnostics aids that help to take a decision regarding treatment in a well-planned way. Cone beam computed tomography (CBCT) has been a vital tool for imaging diagnostic tool in orthodontics. This article reviews case reports during orthodontic treatment and importance of CBCT during the treatment evaluation. PMID:26225116

Machine learning and computer vision approaches for phenotypic profiling.

PubMed

Grys, Ben T; Lo, Dara S; Sahin, Nil; Kraus, Oren Z; Morris, Quaid; Boone, Charles; Andrews, Brenda J

2017-01-02

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach. © 2017 Grys et al.

Machine learning and computer vision approaches for phenotypic profiling

PubMed Central

Morris, Quaid

2017-01-01

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach. PMID:27940887

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

Validation of the Electromagnetic Code FACETS for Numerical Simulation of Radar Target Images

DTIC Science & Technology

2009-12-01

Validation of the electromagnetic code FACETS for numerical simulation of radar target images S. Wong...Validation of the electromagnetic code FACETS for numerical simulation of radar target images S. Wong DRDC Ottawa...for simulating radar images of a target is obtained, through direct simulation-to-measurement comparisons. A 3-dimensional computer-aided design

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.

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.

Automated identification of retained surgical items in radiological images

NASA Astrophysics Data System (ADS)

Agam, Gady; Gan, Lin; Moric, Mario; Gluncic, Vicko

2015-03-01

Retained surgical items (RSIs) in patients is a major operating room (OR) patient safety concern. An RSI is any surgical tool, sponge, needle or other item inadvertently left in a patients body during the course of surgery. If left undetected, RSIs may lead to serious negative health consequences such as sepsis, internal bleeding, and even death. To help physicians efficiently and effectively detect RSIs, we are developing computer-aided detection (CADe) software for X-ray (XR) image analysis, utilizing large amounts of currently available image data to produce a clinically effective RSI detection system. Physician analysis of XRs for the purpose of RSI detection is a relatively lengthy process that may take up to 45 minutes to complete. It is also error prone due to the relatively low acuity of the human eye for RSIs in XR images. The system we are developing is based on computer vision and machine learning algorithms. We address the problem of low incidence by proposing synthesis algorithms. The CADe software we are developing may be integrated into a picture archiving and communication system (PACS), be implemented as a stand-alone software application, or be integrated into portable XR machine software through application programming interfaces. Preliminary experimental results on actual XR images demonstrate the effectiveness of the proposed approach.

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

PubMed Central

Hoo-Chang, Shin; Roth, Holger R.; Gao, Mingchen; Lu, Le; Xu, Ziyue; Nogues, Isabella; Yao, Jianhua; Mollura, Daniel

2016-01-01

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets (i.e. ImageNet) and the revival of deep convolutional neural networks (CNN). CNNs enable learning data-driven, highly representative, layered hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models (supervised) pre-trained from natural image dataset to medical image tasks (although domain transfer between two medical image datasets is also possible). In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computeraided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, with 85% sensitivity at 3 false positive per patient, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks. PMID:26886976

Fracture risk assessment: improved evaluation of vertebral integrity among metastatic cancer patients to aid in surgical decision-making

NASA Astrophysics Data System (ADS)

Augustine, Kurt E.; Camp, Jon J.; Holmes, David R.; Huddleston, Paul M.; Lu, Lichun; Yaszemski, Michael J.; Robb, Richard A.

2012-03-01

Failure of the spine's structural integrity from metastatic disease can lead to both pain and neurologic deficit. Fractures that require treatment occur in over 30% of bony metastases. Our objective is to use computed tomography (CT) in conjunction with analytic techniques that have been previously developed to predict fracture risk in cancer patients with metastatic disease to the spine. Current clinical practice for cancer patients with spine metastasis often requires an empirical decision regarding spinal reconstructive surgery. Early image-based software systems used for CT analysis are time consuming and poorly suited for clinical application. The Biomedical Image Resource (BIR) at Mayo Clinic, Rochester has developed an image analysis computer program that calculates from CT scans, the residual load-bearing capacity in a vertebra with metastatic cancer. The Spine Cancer Assessment (SCA) program is built on a platform designed for clinical practice, with a workflow format that allows for rapid selection of patient CT exams, followed by guided image analysis tasks, resulting in a fracture risk report. The analysis features allow the surgeon to quickly isolate a single vertebra and obtain an immediate pre-surgical multiple parallel section composite beam fracture risk analysis based on algorithms developed at Mayo Clinic. The analysis software is undergoing clinical validation studies. We expect this approach will facilitate patient management and utilization of reliable guidelines for selecting among various treatment option based on fracture risk.

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.

Body Imaging

NASA Technical Reports Server (NTRS)

1990-01-01

Magnetic Resonance Imaging (MRI) and Computer-aided Tomography (CT) images are often complementary. In most cases, MRI is good for viewing soft tissue but not bone, while CT images are good for bone but not always good for soft tissue discrimination. Physicians and engineers in the Department of Radiology at the University of Michigan Hospitals are developing a technique for combining the best features of MRI and CT scans to increase the accuracy of discriminating one type of body tissue from another. One of their research tools is a computer program called HICAP. The program can be used to distinguish between healthy and diseased tissue in body images.

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

Computer-aided diagnostic approach of dermoscopy images acquiring relevant features

NASA Astrophysics Data System (ADS)

Castillejos-Fernández, H.; Franco-Arcega, A.; López-Ortega, O.

2016-09-01

In skin cancer detection, automated analysis of borders, colors, and structures of a lesion relies upon an accurate segmentation process and it is an important first step in any Computer-Aided Diagnosis (CAD) system. However, irregular and disperse lesion borders, low contrast, artifacts in images and variety of colors within the interest region make the problem difficult. In this paper, we propose an efficient approach of automatic classification which considers specific lesion features. First, for the selection of lesion skin we employ the segmentation algorithm W-FCM.1 Then, in the feature extraction stage we consider several aspects: the area of the lesion, which is calculated by correlating axes and we calculate the specific the value of asymmetry in both axes. For color analysis we employ an ensemble of clusterers including K-Means, Fuzzy K-Means and Kohonep maps, all of which estimate the presence of one or more colors defined in ABCD rule and the values for each of the segmented colors. Another aspect to consider is the type of structures that appear in the lesion Those are defined by using the ell-known GLCM method. During the classification stage we compare several methods in order to define if the lesion is benign or malignant. An important contribution of the current approach in segmentation-classification problem resides in the use of information from all color channels together, as well as the measure of each color in the lesion and the axes correlation. The segmentation and classification measures have been performed using sensibility, specificity, accuracy and AUC metric over a set of dermoscopy images from ISDIS data set

Similarity based false-positive reduction for breast cancer using radiographic and pathologic imaging features

NASA Astrophysics Data System (ADS)

Pai, Akshay; Samala, Ravi K.; Zhang, Jianying; Qian, Wei

2010-03-01

Mammography reading by radiologists and breast tissue image interpretation by pathologists often leads to high False Positive (FP) Rates. Similarly, current Computer Aided Diagnosis (CADx) methods tend to concentrate more on sensitivity, thus increasing the FP rates. A novel method is introduced here which employs similarity based method to decrease the FP rate in the diagnosis of microcalcifications. This method employs the Principal Component Analysis (PCA) and the similarity metrics in order to achieve the proposed goal. The training and testing set is divided into generalized (Normal and Abnormal) and more specific (Abnormal, Normal, Benign) classes. The performance of this method as a standalone classification system is evaluated in both the cases (general and specific). In another approach the probability of each case belonging to a particular class is calculated. If the probabilities are too close to classify, the augmented CADx system can be instructed to have a detailed analysis of such cases. In case of normal cases with high probability, no further processing is necessary, thus reducing the computation time. Hence, this novel method can be employed in cascade with CADx to reduce the FP rate and also avoid unnecessary computational time. Using this methodology, a false positive rate of 8% and 11% is achieved for mammography and cellular images respectively.

Artificial neural network-aided image analysis system for cell counting.

PubMed

Sjöström, P J; Frydel, B R; Wahlberg, L U

1999-05-01

In histological preparations containing debris and synthetic materials, it is difficult to automate cell counting using standard image analysis tools, i.e., systems that rely on boundary contours, histogram thresholding, etc. In an attempt to mimic manual cell recognition, an automated cell counter was constructed using a combination of artificial intelligence and standard image analysis methods. Artificial neural network (ANN) methods were applied on digitized microscopy fields without pre-ANN feature extraction. A three-layer feed-forward network with extensive weight sharing in the first hidden layer was employed and trained on 1,830 examples using the error back-propagation algorithm on a Power Macintosh 7300/180 desktop computer. The optimal number of hidden neurons was determined and the trained system was validated by comparison with blinded human counts. System performance at 50x and lO0x magnification was evaluated. The correlation index at 100x magnification neared person-to-person variability, while 50x magnification was not useful. The system was approximately six times faster than an experienced human. ANN-based automated cell counting in noisy histological preparations is feasible. Consistent histology and computer power are crucial for system performance. The system provides several benefits, such as speed of analysis and consistency, and frees up personnel for other tasks.

[Informatics, robotics and medicine].

PubMed

Carpentier, A

1999-01-01

Information technology is becoming common use in Medicine. Among the numerous applications are data processing, image analysis, 3D reconstruction, telemedicine, to mention only few of them. The interest of computers in surgical research and development is lesser known. Two examples are given: computer aided conception and simulation of physiologic systems. Robotics has been introduced more recently. There are three types of robotics corresponding to three types of use: targetting used by neural surgeons to localize tumors or anatomical structures, visualization used by general surgeons to hold and mobilize laparoscopes, instrumentation introduced more recently by cardiac surgeons to perform totally endoscopic cardiac operations. All these techniques open new ways for tomorrow "Instrumental Medicine".

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.

Integrated IMA (Information Mission Areas) IC (Information Center) Guide

DTIC Science & Technology

1989-06-01

COMPUTER AIDED DESIGN / COMPUTER AIDED MANUFACTURE 8-8 8.3.7 LIQUID CRYSTAL DISPLAY PANELS 8-8 8.3.8 ARTIFICIAL INTELLIGENCE APPLIED TO VI 8-9 8.4...2 10.3.1 DESKTOP PUBLISHING 10-3 10.3.2 INTELLIGENT COPIERS 10-5 10.3.3 ELECTRONIC ALTERNATIVES TO PRINTED DOCUMENTS 10-5 10.3.4 ELECTRONIC FORMS...Optical Disk LCD Units Storage Image Scanners Graphics Forms Output Generation Copiers Devices Software Optical Disk Intelligent Storage Copiers Work Group

CT Imaging of Hardwood Logs for Lumber Production

Treesearch

Daniel L. Schmoldt; Pei Li; A. Lynn Abbott

1996-01-01

Hardwood sawmill operators need to improve the conversion of raw material (logs) into lumber. Internal log scanning provides detailed information that can aid log processors in improving lumber recovery. However, scanner data (i.e. tomographic images) need to be analyzed prior to presentation to saw operators. Automatic labeling of computer tomography (CT) images is...

77 FR 21574 - Prospective Grant of Exclusive License: Method for Segmenting Medical Images and Detecting...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-10

... methods help solve imaging problems such as image ``leakage,'' which causes distortion, overloads datasets... enhance detection. This is helpful to identify harmful features such as precancerous polyps or other anomalies. The field of use may be limited to ``computer aided detection in colonography.'' The prospective...

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.

Comparing methods for analysis of biomedical hyperspectral image data

NASA Astrophysics Data System (ADS)

Leavesley, Silas J.; Sweat, Brenner; Abbott, Caitlyn; Favreau, Peter F.; Annamdevula, Naga S.; Rich, Thomas C.

2017-02-01

Over the past 2 decades, hyperspectral imaging technologies have been adapted to address the need for molecule-specific identification in the biomedical imaging field. Applications have ranged from single-cell microscopy to whole-animal in vivo imaging and from basic research to clinical systems. Enabling this growth has been the availability of faster, more effective hyperspectral filtering technologies and more sensitive detectors. Hence, the potential for growth of biomedical hyperspectral imaging is high, and many hyperspectral imaging options are already commercially available. However, despite the growth in hyperspectral technologies for biomedical imaging, little work has been done to aid users of hyperspectral imaging instruments in selecting appropriate analysis algorithms. Here, we present an approach for comparing the effectiveness of spectral analysis algorithms by combining experimental image data with a theoretical "what if" scenario. This approach allows us to quantify several key outcomes that characterize a hyperspectral imaging study: linearity of sensitivity, positive detection cut-off slope, dynamic range, and false positive events. We present results of using this approach for comparing the effectiveness of several common spectral analysis algorithms for detecting weak fluorescent protein emission in the midst of strong tissue autofluorescence. Results indicate that this approach should be applicable to a very wide range of applications, allowing a quantitative assessment of the effectiveness of the combined biology, hardware, and computational analysis for detecting a specific molecular signature.

Coping with Degraded or Denied Environments in the C2 Approach Space

DTIC Science & Technology

2013-06-01

IMAGE (Lizotte, Bernier, Mokhtari , & Boivin, 2013), WISE (Pearce, Robinson, & Wright, 2003), PANOPEA (Bruzzone, Tremori, & Merkuryev, 2011) and three...Bernier, F., Mokhtari , M., & Boivin, E. (2013). IMAGE Final Report: An Interactive Computer-aided Cognition Capability for C4ISR Complexity

Linear Optimization and Image Reconstruction

DTIC Science & Technology

1994-06-01

final example is again a novel one. We formulate the problem of computer assisted tomographic ( CAT ) image reconstruction as a linear optimization...possibility that a patient, Fred, suffers from a brain tumor. Further, the physician opts to make use of the CAT (Computer Aided Tomography) scan device...and examine the inside of Fred’s head without exploratory surgery. The CAT scan machine works by projecting a finite number of X-rays of known

On-line transmission electron microscopic image analysis of chromatin texture for differentiation of thyroid gland tumors.

PubMed

Kriete, A; Schäffer, R; Harms, H; Aus, H M

1987-06-01

Nuclei of the cells from the thyroid gland were analyzed in a transmission electron microscope by direct TV scanning and on-line image processing. The method uses the advantages of a visual-perception model to detect structures in noisy and low-contrast images. The features analyzed include area, a form factor and texture parameters from the second derivative stage. Three tumor-free thyroid tissues, three follicular adenomas, three follicular carcinomas and three papillary carcinomas were studied. The computer-aided cytophotometric method showed that the most significant differences were the statistics of the chromatin texture features of homogeneity and regularity. These findings document the possibility of an automated differentiation of tumors at the ultrastructural level.

Shaded-Color Picture Generation of Computer-Defined Arbitrary Shapes

NASA Technical Reports Server (NTRS)

Cozzolongo, J. V.; Hermstad, D. L.; Mccoy, D. S.; Clark, J.

1986-01-01

SHADE computer program generates realistic color-shaded pictures from computer-defined arbitrary shapes. Objects defined for computer representation displayed as smooth, color-shaded surfaces, including varying degrees of transparency. Results also used for presentation of computational results. By performing color mapping, SHADE colors model surface to display analysis results as pressures, stresses, and temperatures. NASA has used SHADE extensively in sign and analysis of high-performance aircraft. Industry should find applications for SHADE in computer-aided design and computer-aided manufacturing. SHADE written in VAX FORTRAN and MACRO Assembler for either interactive or batch execution.

Three-Dimensional Computational Fluid Dynamics

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

Haworth, D.C.; O'Rourke, P.J.; Ranganathan, R.

1998-09-01

Computational fluid dynamics (CFD) is one discipline falling under the broad heading of computer-aided engineering (CAE). CAE, together with computer-aided design (CAD) and computer-aided manufacturing (CAM), comprise a mathematical-based approach to engineering product and process design, analysis and fabrication. In this overview of CFD for the design engineer, our purposes are three-fold: (1) to define the scope of CFD and motivate its utility for engineering, (2) to provide a basic technical foundation for CFD, and (3) to convey how CFD is incorporated into engineering product and process design.

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.

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

Aghaei, Faranak; Tan, Maxine; Liu, Hong

Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from bothmore » tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy.« less

Image database for digital hand atlas

NASA Astrophysics Data System (ADS)

Cao, Fei; Huang, H. K.; Pietka, Ewa; Gilsanz, Vicente; Dey, Partha S.; Gertych, Arkadiusz; Pospiech-Kurkowska, Sywia

2003-05-01

Bone age assessment is a procedure frequently performed in pediatric patients to evaluate their growth disorder. A commonly used method is atlas matching by a visual comparison of a hand radiograph with a small reference set of old Greulich-Pyle atlas. We have developed a new digital hand atlas with a large set of clinically normal hand images of diverse ethnic groups. In this paper, we will present our system design and implementation of the digital atlas database to support the computer-aided atlas matching for bone age assessment. The system consists of a hand atlas image database, a computer-aided diagnostic (CAD) software module for image processing and atlas matching, and a Web user interface. Users can use a Web browser to push DICOM images, directly or indirectly from PACS, to the CAD server for a bone age assessment. Quantitative features on the examined image, which reflect the skeletal maturity, are then extracted and compared with patterns from the atlas image database to assess the bone age. The digital atlas method built on a large image database and current Internet technology provides an alternative to supplement or replace the traditional one for a quantitative, accurate and cost-effective assessment of bone age.

Computer aided weld defect delineation using statistical parametric active contours in radiographic inspection.

PubMed

Goumeidane, Aicha Baya; Nacereddine, Nafaa; Khamadja, Mohammed

2015-01-01

A perfect knowledge of a defect shape is determinant for the analysis step in automatic radiographic inspection. Image segmentation is carried out on radiographic images and extract defects indications. This paper deals with weld defect delineation in radiographic images. The proposed method is based on a new statistics-based explicit active contour. An association of local and global modeling of the image pixels intensities is used to push the model to the desired boundaries. Furthermore, other strategies are proposed to accelerate its evolution and make the convergence speed depending only on the defect size as selecting a band around the active contour curve. The experimental results are very promising, since experiments on synthetic and radiographic images show the ability of the proposed model to extract a piece-wise homogenous object from very inhomogeneous background, even in a bad quality image.

Image Registration of Cone-Beam Computer Tomography and Preprocedural Computer Tomography Aids in Localization of Adrenal Veins and Decreasing Radiation Dose in Adrenal Vein Sampling

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

Busser, Wendy M. H., E-mail: wendy.busser@radboudumc.nl; Arntz, Mark J.; Jenniskens, Sjoerd F. M.

2015-08-15

PurposeWe assessed whether image registration of cone-beam computed tomography (CT) (CBCT) and contrast-enhanced CT (CE-CT) images indicating the locations of the adrenal veins can aid in increasing the success rate of first-attempts adrenal vein sampling (AVS) and therefore decreasing patient radiation dose.Materials and Methods CBCT scans were acquired in the interventional suite (Philips Allura Xper FD20) and rigidly registered to the vertebra in previously acquired CE-CT. Adrenal vein locations were marked on the CT image and superimposed with live fluoroscopy and digital-subtraction angiography (DSA) to guide the AVS. Seventeen first attempts at AVS were performed with image registration and retrospectivelymore » compared with 15 first attempts without image registration performed earlier by the same 2 interventional radiologists. First-attempt AVS was considered successful when both adrenal vein samples showed representative cortisol levels. Sampling time, dose-area product (DAP), number of DSA runs, fluoroscopy time, and skin dose were recorded.ResultsWithout image registration, the first attempt at sampling was successful in 8 of 15 procedures indicating a success rate of 53.3 %. This increased to 76.5 % (13 of 17) by adding CBCT and CE-CT image registration to AVS procedures (p = 0.266). DAP values (p = 0.001) and DSA runs (p = 0.026) decreased significantly by adding image registration guidance. Sampling and fluoroscopy times and skin dose showed no significant changes.ConclusionGuidance based on registration of CBCT and previously acquired diagnostic CE-CT can aid in enhancing localization of the adrenal veins thereby increasing the success rate of first-attempt AVS with a significant decrease in the number of used DSA runs and, consequently, radiation dose required.« less

Automatic breast tissue density estimation scheme in digital mammography images

NASA Astrophysics Data System (ADS)

Menechelli, Renan C.; Pacheco, Ana Luisa V.; Schiabel, Homero

2017-03-01

Cases of breast cancer have increased substantially each year. However, radiologists are subject to subjectivity and failures of interpretation which may affect the final diagnosis in this examination. The high density features in breast tissue are important factors related to these failures. Thus, among many functions some CADx (Computer-Aided Diagnosis) schemes are classifying breasts according to the predominant density. In order to aid in such a procedure, this work attempts to describe automated software for classification and statistical information on the percentage change in breast tissue density, through analysis of sub regions (ROIs) from the whole mammography image. Once the breast is segmented, the image is divided into regions from which texture features are extracted. Then an artificial neural network MLP was used to categorize ROIs. Experienced radiologists have previously determined the ROIs density classification, which was the reference to the software evaluation. From tests results its average accuracy was 88.7% in ROIs classification, and 83.25% in the classification of the whole breast density in the 4 BI-RADS density classes - taking into account a set of 400 images. Furthermore, when considering only a simplified two classes division (high and low densities) the classifier accuracy reached 93.5%, with AUC = 0.95.

Computer-aided analysis for the Mechanics of Granular Materials (MGM) experiment, part 2

NASA Technical Reports Server (NTRS)

Parker, Joey K.

1987-01-01

Computer vision based analysis for the MGM experiment is continued and expanded into new areas. Volumetric strains of granular material triaxial test specimens have been measured from digitized images. A computer-assisted procedure is used to identify the edges of the specimen, and the edges are used in a 3-D model to estimate specimen volume. The results of this technique compare favorably to conventional measurements. A simplified model of the magnification caused by diffraction of light within the water of the test apparatus was also developed. This model yields good results when the distance between the camera and the test specimen is large compared to the specimen height. An algorithm for a more accurate 3-D magnification correction is also presented. The use of composite and RGB (red-green-blue) color cameras is discussed and potentially significant benefits from using an RGB camera are presented.

Integrated Computer-Aided Drafting Instruction (ICADI).

ERIC Educational Resources Information Center

Chen, C. Y.; McCampbell, David H.

Until recently, computer-aided drafting and design (CAD) systems were almost exclusively operated on mainframes or minicomputers and their cost prohibited many schools from offering CAD instruction. Today, many powerful personal computers are capable of performing the high-speed calculation and analysis required by the CAD application; however,…

Multivariate statistics of the Jacobian matrices in tensor based morphometry and their application to HIV/AIDS.

PubMed

Lepore, Natasha; Brun, Caroline A; Chiang, Ming-Chang; Chou, Yi-Yu; Dutton, Rebecca A; Hayashi, Kiralee M; Lopez, Oscar L; Aizenstein, Howard J; Toga, Arthur W; Becker, James T; Thompson, Paul M

2006-01-01

Tensor-based morphometry (TBM) is widely used in computational anatomy as a means to understand shape variation between structural brain images. A 3D nonlinear registration technique is typically used to align all brain images to a common neuroanatomical template, and the deformation fields are analyzed statistically to identify group differences in anatomy. However, the differences are usually computed solely from the determinants of the Jacobian matrices that are associated with the deformation fields computed by the registration procedure. Thus, much of the information contained within those matrices gets thrown out in the process. Only the magnitude of the expansions or contractions is examined, while the anisotropy and directional components of the changes are ignored. Here we remedy this problem by computing multivariate shape change statistics using the strain matrices. As the latter do not form a vector space, means and covariances are computed on the manifold of positive-definite matrices to which they belong. We study the brain morphology of 26 HIV/AIDS patients and 14 matched healthy control subjects using our method. The images are registered using a high-dimensional 3D fluid registration algorithm, which optimizes the Jensen-Rényi divergence, an information-theoretic measure of image correspondence. The anisotropy of the deformation is then computed. We apply a manifold version of Hotelling's T2 test to the strain matrices. Our results complement those found from the determinants of the Jacobians alone and provide greater power in detecting group differences in brain structure.

Emergency Management Computer-Aided Trainer (EMCAT)

NASA Technical Reports Server (NTRS)

Rodriguez, R. C.; Johnson, R. P.

1986-01-01

The Emergency Management Computer-Aided Trainer (EMCAT) developed by Essex Corporation or NASA and the Federal Emergency Management Administration's (FEMA) National Fire Academy (NFA) is described. It is a computer based training system for fire fighting personnel. A prototype EMCAT system was developed by NASA first using video tape images and then video disk images when the technology became available. The EMCAT system is meant to fill the training needs of the fire fighting community with affordable state-of-the-art technologies. An automated real time simulation of the fire situation was needed to replace the outdated manual training methods currently being used. In order to be successful, this simulator had to provide realism, be user friendly, be affordable, and support multiple scenarios. The EMCAT system meets these requirements and therefore represents an innovative training tool, not only for the fire fighting community, but also for the needs of other disciplines.

A comparative approach to computer aided design model of a dog femur.

PubMed

Turamanlar, O; Verim, O; Karabulut, A

2016-01-01

Computer assisted technologies offer new opportunities in medical imaging and rapid prototyping in biomechanical engineering. Three dimensional (3D) modelling of soft tissues and bones are becoming more important. The accuracy of the analysis in modelling processes depends on the outline of the tissues derived from medical images. The aim of this study is the evaluation of the accuracy of 3D models of a dog femur derived from computed tomography data by using point cloud method and boundary line method on several modelling software. Solidworks, Rapidform and 3DSMax software were used to create 3D models and outcomes were evaluated statistically. The most accurate 3D prototype of the dog femur was created with stereolithography method using rapid prototype device. Furthermore, the linearity of the volumes of models was investigated between software and the constructed models. The difference between the software and real models manifests the sensitivity of the software and the devices used in this manner.

Minnesota Computer Aided Library System (MCALS); University of Minnesota Subsystem Cost/Benefits Analysis.

ERIC Educational Resources Information Center

Lourey, Eugene D., Comp.

The Minnesota Computer Aided Library System (MCALS) provides a basis of unification for library service program development in Minnesota for eventual linkage to the national information network. A prototype plan for communications functions is illustrated. A cost/benefits analysis was made to show the cost/effectiveness potential for MCALS. System…

MOVANAID: An Interactive Aid for Analysis of Movement Capabilities.

ERIC Educational Resources Information Center

Cooper, George E.; And Others

A computer-drive interactive aid for movement analysis, called MOVANAID, has been developed to be of assistance in the performance of certain Army intelligence processing tasks in a tactical environment. It can compute fastest travel times and paths through road networks for military units of various types, as well as fastest times in which…

Optic cup segmentation from fundus images for glaucoma diagnosis.

PubMed

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

2017-01-02

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.

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

Local spatial frequency analysis for computer vision

NASA Technical Reports Server (NTRS)

Krumm, John; Shafer, Steven A.

1990-01-01

A sense of vision is a prerequisite for a robot to function in an unstructured environment. However, real-world scenes contain many interacting phenomena that lead to complex images which are difficult to interpret automatically. Typical computer vision research proceeds by analyzing various effects in isolation (e.g., shading, texture, stereo, defocus), usually on images devoid of realistic complicating factors. This leads to specialized algorithms which fail on real-world images. Part of this failure is due to the dichotomy of useful representations for these phenomena. Some effects are best described in the spatial domain, while others are more naturally expressed in frequency. In order to resolve this dichotomy, we present the combined space/frequency representation which, for each point in an image, shows the spatial frequencies at that point. Within this common representation, we develop a set of simple, natural theories describing phenomena such as texture, shape, aliasing and lens parameters. We show these theories lead to algorithms for shape from texture and for dealiasing image data. The space/frequency representation should be a key aid in untangling the complex interaction of phenomena in images, allowing automatic understanding of real-world scenes.

Computer-aided diagnostic system for detection of Hashimoto thyroiditis on ultrasound images from a Polish population.

PubMed

Acharya, U Rajendra; Sree, S Vinitha; Krishnan, M Muthu Rama; Molinari, Filippo; Zieleźnik, Witold; Bardales, Ricardo H; Witkowska, Agnieszka; Suri, Jasjit S

2014-02-01

Computer-aided diagnostic (CAD) techniques aid physicians in better diagnosis of diseases by extracting objective and accurate diagnostic information from medical data. Hashimoto thyroiditis is the most common type of inflammation of the thyroid gland. The inflammation changes the structure of the thyroid tissue, and these changes are reflected as echogenic changes on ultrasound images. In this work, we propose a novel CAD system (a class of systems called ThyroScan) that extracts textural features from a thyroid sonogram and uses them to aid in the detection of Hashimoto thyroiditis. In this paradigm, we extracted grayscale features based on stationary wavelet transform from 232 normal and 294 Hashimoto thyroiditis-affected thyroid ultrasound images obtained from a Polish population. Significant features were selected using a Student t test. The resulting feature vectors were used to build and evaluate the following 4 classifiers using a 10-fold stratified cross-validation technique: support vector machine, decision tree, fuzzy classifier, and K-nearest neighbor. Using 7 significant features that characterized the textural changes in the images, the fuzzy classifier had the highest classification accuracy of 84.6%, sensitivity of 82.8%, specificity of 87.0%, and a positive predictive value of 88.9%. The proposed ThyroScan CAD system uses novel features to noninvasively detect the presence of Hashimoto thyroiditis on ultrasound images. Compared to manual interpretations of ultrasound images, the CAD system offers a more objective interpretation of the nature of the thyroid. The preliminary results presented in this work indicate the possibility of using such a CAD system in a clinical setting after evaluating it with larger databases in multicenter clinical trials.

Post-processing methods of rendering and visualizing 3-D reconstructed tomographic images

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

Wong, S.T.C.

The purpose of this presentation is to discuss the computer processing techniques of tomographic images, after they have been generated by imaging scanners, for volume visualization. Volume visualization is concerned with the representation, manipulation, and rendering of volumetric data. Since the first digital images were produced from computed tomography (CT) scanners in the mid 1970s, applications of visualization in medicine have expanded dramatically. Today, three-dimensional (3D) medical visualization has expanded from using CT data, the first inherently digital source of 3D medical data, to using data from various medical imaging modalities, including magnetic resonance scanners, positron emission scanners, digital ultrasound,more » electronic and confocal microscopy, and other medical imaging modalities. We have advanced from rendering anatomy to aid diagnosis and visualize complex anatomic structures to planning and assisting surgery and radiation treatment. New, more accurate and cost-effective procedures for clinical services and biomedical research have become possible by integrating computer graphics technology with medical images. This trend is particularly noticeable in current market-driven health care environment. For example, interventional imaging, image-guided surgery, and stereotactic and visualization techniques are now stemming into surgical practice. In this presentation, we discuss only computer-display-based approaches of volumetric medical visualization. That is, we assume that the display device available is two-dimensional (2D) in nature and all analysis of multidimensional image data is to be carried out via the 2D screen of the device. There are technologies such as holography and virtual reality that do provide a {open_quotes}true 3D screen{close_quotes}. To confine the scope, this presentation will not discuss such approaches.« less

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 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.

Computational Virtual Reality (VR) as a human-computer interface in the operation of telerobotic systems

NASA Technical Reports Server (NTRS)

Bejczy, Antal K.

1995-01-01

This presentation focuses on the application of computer graphics or 'virtual reality' (VR) techniques as a human-computer interface tool in the operation of telerobotic systems. VR techniques offer very valuable task realization aids for planning, previewing and predicting robotic actions, operator training, and for visual perception of non-visible events like contact forces in robotic tasks. The utility of computer graphics in telerobotic operation can be significantly enhanced by high-fidelity calibration of virtual reality images to actual TV camera images. This calibration will even permit the creation of artificial (synthetic) views of task scenes for which no TV camera views are available.

A Survey of CAD/CAM Technology Applications in the U.S. Shipbuilding Industry

DTIC Science & Technology

1984-01-01

operation for drafting. Computer Aided Engineering (CAE) analysis is used primarily to determine the validity of design characteristics and produc- tion...include time standard generation, sea trial analysis , and group Systems integration While no systems surveyed Aided Design (CAD) is the technology... analysis . is the largest problem involving software packages. are truly integrated, many are interfaced. Computer most interfaced category with links

Multidisciplinary analysis of actively controlled large flexible spacecraft

NASA Technical Reports Server (NTRS)

Cooper, Paul A.; Young, John W.; Sutter, Thomas R.

1986-01-01

The control of Flexible Structures (COFS) program has supported the development of an analysis capability at the Langley Research Center called the Integrated Multidisciplinary Analysis Tool (IMAT) which provides an efficient data storage and transfer capability among commercial computer codes to aid in the dynamic analysis of actively controlled structures. IMAT is a system of computer programs which transfers Computer-Aided-Design (CAD) configurations, structural finite element models, material property and stress information, structural and rigid-body dynamic model information, and linear system matrices for control law formulation among various commercial applications programs through a common database. Although general in its formulation, IMAT was developed specifically to aid in the evaluation of the structures. A description of the IMAT system and results of an application of the system are given.

Spline function approximation techniques for image geometric distortion representation. [for registration of multitemporal remote sensor imagery

NASA Technical Reports Server (NTRS)

Anuta, P. E.

1975-01-01

Least squares approximation techniques were developed for use in computer aided correction of spatial image distortions for registration of multitemporal remote sensor imagery. Polynomials were first used to define image distortion over the entire two dimensional image space. Spline functions were then investigated to determine if the combination of lower order polynomials could approximate a higher order distortion with less computational difficulty. Algorithms for generating approximating functions were developed and applied to the description of image distortion in aircraft multispectral scanner imagery. Other applications of the techniques were suggested for earth resources data processing areas other than geometric distortion representation.

EUROPattern Suite technology for computer-aided immunofluorescence microscopy in autoantibody diagnostics.

PubMed

Krause, C; Ens, K; Fechner, K; Voigt, J; Fraune, J; Rohwäder, E; Hahn, M; Danckwardt, M; Feirer, C; Barth, E; Martinetz, T; Stöcker, W

2015-04-01

Antinuclear autoantibodies (ANA) are highly informative biomarkers in autoimmune diagnostics. The increasing demand for effective test systems, however, has led to the development of a confusingly large variety of different platforms. One of them, the indirect immunofluorescence (IIF), is regarded as the common gold standard for ANA screening, as described in a position statement by the American College of Rheumatology in 2009. Technological solutions have been developed aimed at standardization and automation of IIF to overcome methodological limitations and subjective bias in IIF interpretation. In this review, we present the EUROPattern Suite, a system for computer-aided immunofluorescence microscopy (CAIFM) including automated acquisition of digital images and evaluation of IIF results. The system was originally designed for ANA diagnostics on human epithelial cells, but its applications have been extended with the latest system update version 1.5 to the analysis of antineutrophil cytoplasmic antibodies (ANCA) and anti-dsDNA antibodies. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist-based assessments.

PubMed

Chang, Yongjun; Paul, Anjan Kumar; Kim, Namkug; Baek, Jung Hwan; Choi, Young Jun; Ha, Eun Ju; Lee, Kang Dae; Lee, Hyoung Shin; Shin, DaeSeock; Kim, Nakyoung

2016-01-01

To develop a semiautomated computer-aided diagnosis (cad) system for thyroid cancer using two-dimensional ultrasound images that can be used to yield a second opinion in the clinic to differentiate malignant and benign lesions. A total of 118 ultrasound images that included axial and longitudinal images from patients with biopsy-confirmed malignant (n = 30) and benign (n = 29) nodules were collected. Thyroid cad software was developed to extract quantitative features from these images based on thyroid nodule segmentation in which adaptive diffusion flow for active contours was used. Various features, including histogram, intensity differences, elliptical fit, gray-level co-occurrence matrixes, and gray-level run-length matrixes, were evaluated for each region imaged. Based on these imaging features, a support vector machine (SVM) classifier was used to differentiate benign and malignant nodules. Leave-one-out cross-validation with sequential forward feature selection was performed to evaluate the overall accuracy of this method. Additionally, analyses with contingency tables and receiver operating characteristic (ROC) curves were performed to compare the performance of cad with visual inspection by expert radiologists based on established gold standards. Most univariate features for this proposed cad system attained accuracies that ranged from 78.0% to 83.1%. When optimal SVM parameters that were established using a grid search method with features that radiologists use for visual inspection were employed, the authors could attain rates of accuracy that ranged from 72.9% to 84.7%. Using leave-one-out cross-validation results in a multivariate analysis of various features, the highest accuracy achieved using the proposed cad system was 98.3%, whereas visual inspection by radiologists reached 94.9% accuracy. To obtain the highest accuracies, "axial ratio" and "max probability" in axial images were most frequently included in the optimal feature sets for the authors' proposed cad system, while "shape" and "calcification" in longitudinal images were most frequently included in the optimal feature sets for visual inspection by radiologists. The computed areas under curves in the ROC analysis were 0.986 and 0.979 for the proposed cad system and visual inspection by radiologists, respectively; no significant difference was detected between these groups. The use of thyroid cad to differentiate malignant from benign lesions shows accuracy similar to that obtained via visual inspection by radiologists. Thyroid cad might be considered a viable way to generate a second opinion for radiologists in clinical practice.

Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist-based assessments

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

Chang, Yongjun; Paul, Anjan Kumar; Kim, Namkug, E-mail: namkugkim@gmail.com

Purpose: To develop a semiautomated computer-aided diagnosis (CAD) system for thyroid cancer using two-dimensional ultrasound images that can be used to yield a second opinion in the clinic to differentiate malignant and benign lesions. Methods: A total of 118 ultrasound images that included axial and longitudinal images from patients with biopsy-confirmed malignant (n = 30) and benign (n = 29) nodules were collected. Thyroid CAD software was developed to extract quantitative features from these images based on thyroid nodule segmentation in which adaptive diffusion flow for active contours was used. Various features, including histogram, intensity differences, elliptical fit, gray-level co-occurrencemore » matrixes, and gray-level run-length matrixes, were evaluated for each region imaged. Based on these imaging features, a support vector machine (SVM) classifier was used to differentiate benign and malignant nodules. Leave-one-out cross-validation with sequential forward feature selection was performed to evaluate the overall accuracy of this method. Additionally, analyses with contingency tables and receiver operating characteristic (ROC) curves were performed to compare the performance of CAD with visual inspection by expert radiologists based on established gold standards. Results: Most univariate features for this proposed CAD system attained accuracies that ranged from 78.0% to 83.1%. When optimal SVM parameters that were established using a grid search method with features that radiologists use for visual inspection were employed, the authors could attain rates of accuracy that ranged from 72.9% to 84.7%. Using leave-one-out cross-validation results in a multivariate analysis of various features, the highest accuracy achieved using the proposed CAD system was 98.3%, whereas visual inspection by radiologists reached 94.9% accuracy. To obtain the highest accuracies, “axial ratio” and “max probability” in axial images were most frequently included in the optimal feature sets for the authors’ proposed CAD system, while “shape” and “calcification” in longitudinal images were most frequently included in the optimal feature sets for visual inspection by radiologists. The computed areas under curves in the ROC analysis were 0.986 and 0.979 for the proposed CAD system and visual inspection by radiologists, respectively; no significant difference was detected between these groups. Conclusions: The use of thyroid CAD to differentiate malignant from benign lesions shows accuracy similar to that obtained via visual inspection by radiologists. Thyroid CAD might be considered a viable way to generate a second opinion for radiologists in clinical practice.« less

Hepatic Kaposi Sarcoma Revisited: An Important but Less Commonly Seen Neoplasm in Patients With Acquired Immunodeficiency Syndrome.

PubMed

Chen, Frank; Gulati, Mittul; Tchelepi, Hisham

2017-03-01

Hepatic Kaposi sarcoma (KS) is the most commonly seen hepatic neoplasm in patients with acquired immunodeficiency syndrome (AIDS), found in 34% of patients in an autopsy series. However, the incidence of hepatic KS has significantly declined since the advent of highly active antiretroviral therapy and is not as commonly seen on imaging. We present a case of hepatic KS in a patient with AIDS, which was initially mistaken for hepatic abscesses on computed tomography. We discuss the computed tomography, grayscale ultrasound, and contrast-enhanced ultrasound appearance of hepatic KS and how to distinguish this hepatic neoplasm from other common hepatic lesions seen in patients with AIDS.

Computer-aided, multi-modal, and compression diffuse optical studies of breast tissue

NASA Astrophysics Data System (ADS)

Busch, David Richard, Jr.

Diffuse Optical Tomography and Spectroscopy permit measurement of important physiological parameters non-invasively through ˜10 cm of tissue. I have applied these techniques in measurements of human breast and breast cancer. My thesis integrates three loosely connected themes in this context: multi-modal breast cancer imaging, automated data analysis of breast cancer images, and microvascular hemodynamics of breast under compression. As per the first theme, I describe construction, testing, and the initial clinical usage of two generations of imaging systems for simultaneous diffuse optical and magnetic resonance imaging. The second project develops a statistical analysis of optical breast data from many spatial locations in a population of cancers to derive a novel optical signature of malignancy; I then apply this data-derived signature for localization of cancer in additional subjects. Finally, I construct and deploy diffuse optical instrumentation to measure blood content and blood flow during breast compression; besides optics, this research has implications for any method employing breast compression, e.g., mammography.

Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application-Part 2: From Clinical Implementation to Enterprise.

PubMed

Shaikh, Faiq; Franc, Benjamin; Allen, Erastus; Sala, Evis; Awan, Omer; Hendrata, Kenneth; Halabi, Safwan; Mohiuddin, Sohaib; Malik, Sana; Hadley, Dexter; Shrestha, Rasu

2018-03-01

Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. More recently, the advancement in the field of quantitative image analysis coupled with machine learning-based data analytics, classification, and integration has ushered in the era of radiomics, a paradigm shift that holds tremendous potential in clinical decision support as well as drug discovery. However, there are important issues to consider to incorporate radiomics into a clinically applicable system and a commercially viable solution. In this two-part series, we offer insights into the development of the translational pipeline for radiomics from methodology to clinical implementation (Part 1) and from that point to enterprise development (Part 2). In Part 2 of this two-part series, we study the components of the strategy pipeline, from clinical implementation to building enterprise solutions. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Strategic Use of Microscrews for Enhancing the Accuracy of Computer-Guided Implant Surgery in Fully Edentulous Arches: A Case History Report.

PubMed

Lee, Du-Hyeong

Implant guide systems can be classified by their supporting structure as tooth-, mucosa-, or bone-supported. Mucosa-supported guides for fully edentulous arches show lower accuracy in implant placement because of errors in image registration and guide positioning. This article introduces the application of a novel microscrew system for computer-aided implant surgery. This technique can markedly improve the accuracy of computer-guided implant surgery in fully edentulous arches by eliminating errors from image fusion and guide positioning.

Segmentation of breast ultrasound images based on active contours using neutrosophic theory.

PubMed

Lotfollahi, Mahsa; Gity, Masoumeh; Ye, Jing Yong; Mahlooji Far, A

2018-04-01

Ultrasound imaging is an effective approach for diagnosing breast cancer, but it is highly operator-dependent. Recent advances in computer-aided diagnosis have suggested that it can assist physicians in diagnosis. Definition of the region of interest before computer analysis is still needed. Since manual outlining of the tumor contour is tedious and time-consuming for a physician, developing an automatic segmentation method is important for clinical application. The present paper represents a novel method to segment breast ultrasound images. It utilizes a combination of region-based active contour and neutrosophic theory to overcome the natural properties of ultrasound images including speckle noise and tissue-related textures. First, due to inherent speckle noise and low contrast of these images, we have utilized a non-local means filter and fuzzy logic method for denoising and image enhancement, respectively. This paper presents an improved weighted region-scalable active contour to segment breast ultrasound images using a new feature derived from neutrosophic theory. This method has been applied to 36 breast ultrasound images. It generates true-positive and false-positive results, and similarity of 95%, 6%, and 90%, respectively. The purposed method indicates clear advantages over other conventional methods of active contour segmentation, i.e., region-scalable fitting energy and weighted region-scalable fitting energy.

Computer-aided analysis with Image J for quantitatively assessing psoriatic lesion area.

PubMed

Sun, Z; Wang, Y; Ji, S; Wang, K; Zhao, Y

2015-11-01

Body surface area is important in determining the severity of psoriasis. However, objective, reliable, and practical method is still in need for this purpose. We performed a computer image analysis (CIA) of psoriatic area using the image J freeware to determine whether this method could be used for objective evaluation of psoriatic area. Fifteen psoriasis patients were randomized to be treated with adalimumab or placebo in a clinical trial. At each visit, the psoriasis area of each body site was estimated by two physicians (E-method), and standard photographs were taken. The psoriasis area in the pictures was assessed with CIA using semi-automatic threshold selection (T-method), or manual selection (M-method, gold standard). The results assessed by the three methods were analyzed with reliability and affecting factors evaluated. Both T- and E-method correlated strongly with M-method, and T-method had a slightly stronger correlation with M-method. Both T- and E-methods had a good consistency between the evaluators. All the three methods were able to detect the change in the psoriatic area after treatment, while the E-method tends to overestimate. The CIA with image J freeware is reliable and practicable in quantitatively assessing the lesional of psoriasis area. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Applications of computer-aided text analysis in natural resources.

Treesearch

David N. Bengston

2000-01-01

Ten contributed papers describe the use of a variety of approaches to computer-aided text analysis and their application to a wide range of research questions related to natural resources and the environment. Taken together, these papers paint a picture of a growing and vital area of research on the human dimensions of natural resource management.

Development of kinetic analysis technique for PACS management and a screening examination in dynamic radiography

NASA Astrophysics Data System (ADS)

Tsuchiya, Yuichiro; Kodera, Yoshie

2005-04-01

The purpose of this study was to develop of kinetic analysis method for PACS management and computer-aided diagnosis. We obtained dynamic chest radiographs (512x512, 8bit, 4fps, and 1344x1344, 12bit, 3fps) of five healthy volunteers during respiration using an I.I. system twice, and one healthy volunteer using dynamic FPD system. Optical flows of images were obtained using customized block matching technique, and were divided into a direction, and transformed into the RGB color. Density was determined by the sum pixel length of movement during respiration phase. The made new static image was defined as the "kinetic map". The evaluation of patient's collation was performed with a template matching to the three colors. The same person's each correlation value and similar-coefficient which is defined in this study were statistically significant high (P<0.01). We used the artificial neural network (ANN) for the judgment of the same person. Five volunteers were divided into two groups, three volunteers and two volunteers became a training signal and unknown signal. Correlation value and similar-coefficient was used for the input signal, and ANN was designed so that the same person's probability might be outputted. The average of the specificity of the unknown signal obtained 98.2%. The kinetic map including the imitation tumor was used for the simulation. The tumor was detected by temporal subtraction of kinetic map, and then the superior sensitivity was obtained. Our analysis method was useful in risk management and computer-aided diagnosis.

ΤND: a thyroid nodule detection system for analysis of ultrasound images and videos.

PubMed

Keramidas, Eystratios G; Maroulis, Dimitris; Iakovidis, Dimitris K

2012-06-01

In this paper, we present a computer-aided-diagnosis (CAD) system prototype, named TND (Thyroid Nodule Detector), for the detection of nodular tissue in ultrasound (US) thyroid images and videos acquired during thyroid US examinations. The proposed system incorporates an original methodology that involves a novel algorithm for automatic definition of the boundaries of the thyroid gland, and a novel approach for the extraction of noise resilient image features effectively representing the textural and the echogenic properties of the thyroid tissue. Through extensive experimental evaluation on real thyroid US data, its accuracy in thyroid nodule detection has been estimated to exceed 95%. These results attest to the feasibility of the clinical application of TND, for the provision of a second more objective opinion to the radiologists by exploiting image evidences.

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

DTIC Science & Technology

2007-06-01

the masses were identified by an experi- enced Mammography Quality Standards Act (MQSA) radiologist. The no-mass data set contained 98 cases. The time...force, and the difference in time between the two acquisitions would cause differ- ences in the subtlety of the masses on the FFDMs and SFMs. However...images," Medical Physics 18, 955-963 (1991). 20A. J. Mendez, P. G. Tahoces, M. J. Lado , M. Souto, and J. J. Vidal, "Computer-aided diagnosis: Automatic

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 interpretation approach for optical tomographic images

NASA Astrophysics Data System (ADS)

Klose, Christian D.; Klose, Alexander D.; Netz, Uwe J.; Scheel, Alexander K.; Beuthan, Jürgen; Hielscher, Andreas H.

2010-11-01

A computer-aided interpretation approach is proposed to detect rheumatic arthritis (RA) in human finger joints using optical tomographic images. The image interpretation method employs a classification algorithm that makes use of a so-called self-organizing mapping scheme to classify fingers as either affected or unaffected by RA. Unlike in previous studies, this allows for combining multiple image features, such as minimum and maximum values of the absorption coefficient for identifying affected and not affected joints. Classification performances obtained by the proposed method were evaluated in terms of sensitivity, specificity, Youden index, and mutual information. Different methods (i.e., clinical diagnostics, ultrasound imaging, magnet resonance imaging, and inspection of optical tomographic images), were used to produce ground truth benchmarks to determine the performance of image interpretations. Using data from 100 finger joints, findings suggest that some parameter combinations lead to higher sensitivities, while others to higher specificities when compared to single parameter classifications employed in previous studies. Maximum performances are reached when combining the minimum/maximum ratio of the absorption coefficient and image variance. In this case, sensitivities and specificities over 0.9 can be achieved. These values are much higher than values obtained when only single parameter classifications were used, where sensitivities and specificities remained well below 0.8.

An application of Chan-Vese method used to determine the ROI area in CT lung screening

NASA Astrophysics Data System (ADS)

Prokop, Paweł; Surtel, Wojciech

2016-09-01

The article presents two approaches of determining the ROI area in CT lung screening. First approach is based on a classic method of framing the image in order to determine the ROI by using a MaZda tool. Second approach is based on segmentation of CT images of the lungs and reducing the redundant information from the image. Of the two approaches of an Active Contour, it was decided to choose the Chan-Vese method. In order to determine the effectiveness of the approach, it was performed an analysis of received ROI texture and extraction of textural features. In order to determine the effectiveness of the method, it was performed an analysis of the received ROI textures and extraction of the texture features, by using a Mazda tool. The results were compared and presented in the form of the radar graphs. The second approach proved to be effective and appropriate and consequently it is used for further analysis of CT images, in the computer-aided diagnosis of sarcoidosis.

Benefits of utilizing CellProfiler as a characterization tool for U-10Mo nuclear fuel

DOE PAGES

Collette, R.; Douglas, J.; Patterson, L.; ...

2015-05-01

Automated image processing techniques have the potential to aid in the performance evaluation of nuclear fuels by eliminating judgment calls that may vary from person-to-person or sample-to-sample. Analysis of in-core fuel performance is required for design and safety evaluations related to almost every aspect of the nuclear fuel cycle. This study presents a methodology for assessing the quality of uranium-molybdenum fuel images and describes image analysis routines designed for the characterization of several important microstructural properties. The analyses are performed in CellProfiler, an open-source program designed to enable biologists without training in computer vision or programming to automatically extract cellularmore » measurements from large image sets. The quality metric scores an image based on three parameters: the illumination gradient across the image, the overall focus of the image, and the fraction of the image that contains scratches. The metric presents the user with the ability to ‘pass’ or ‘fail’ an image based on a reproducible quality score. Passable images may then be characterized through a separate CellProfiler pipeline, which enlists a variety of common image analysis techniques. The results demonstrate the ability to reliably pass or fail images based on the illumination, focus, and scratch fraction of the image, followed by automatic extraction of morphological data with respect to fission gas voids, interaction layers, and grain boundaries.« less

Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis.

PubMed

Chen, Peng-Jen; Lin, Meng-Chiung; Lai, Mei-Ju; Lin, Jung-Chun; Lu, Henry Horng-Shing; Tseng, Vincent S

2018-02-01

Narrow-band imaging is an image-enhanced form of endoscopy used to observed microstructures and capillaries of the mucosal epithelium which allows for real-time prediction of histologic features of colorectal polyps. However, narrow-band imaging expertise is required to differentiate hyperplastic from neoplastic polyps with high levels of accuracy. We developed and tested a system of computer-aided diagnosis with a deep neural network (DNN-CAD) to analyze narrow-band images of diminutive colorectal polyps. We collected 1476 images of neoplastic polyps and 681 images of hyperplastic polyps, obtained from the picture archiving and communications system database in a tertiary hospital in Taiwan. Histologic findings from the polyps were also collected and used as the reference standard. The images and data were used to train the DNN. A test set of images (96 hyperplastic and 188 neoplastic polyps, smaller than 5 mm), obtained from patients who underwent colonoscopies from March 2017 through August 2017, was then used to test the diagnostic ability of the DNN-CAD vs endoscopists (2 expert and 4 novice), who were asked to classify the images of the test set as neoplastic or hyperplastic. Their classifications were compared with findings from histologic analysis. The primary outcome measures were diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic time. The accuracy, sensitivity, specificity, PPV, NPV, and diagnostic time were compared among DNN-CAD, the novice endoscopists, and the expert endoscopists. The study was designed to detect a difference of 10% in accuracy by a 2-sided McNemar test. In the test set, the DNN-CAD identified neoplastic or hyperplastic polyps with 96.3% sensitivity, 78.1% specificity, a PPV of 89.6%, and a NPV of 91.5%. Fewer than half of the novice endoscopists classified polyps with a NPV of 90% (their NPVs ranged from 73.9% to 84.0%). DNN-CAD classified polyps as neoplastic or hyperplastic in 0.45 ± 0.07 seconds-shorter than the time required by experts (1.54 ± 1.30 seconds) and nonexperts (1.77 ± 1.37 seconds) (both P < .001). DNN-CAD classified polyps with perfect intra-observer agreement (kappa score of 1). There was a low level of intra-observer and inter-observer agreement in classification among endoscopists. We developed a system called DNN-CAD to identify neoplastic or hyperplastic colorectal polyps less than 5 mm. The system classified polyps with a PPV of 89.6%, and a NPV of 91.5%, and in a shorter time than endoscopists. This deep-learning model has potential for not only endoscopic image recognition but for other forms of medical image analysis, including sonography, computed tomography, and magnetic resonance images. Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.

Leveraging unsupervised training sets for multi-scale compartmentalization in renal pathology

NASA Astrophysics Data System (ADS)

Lutnick, Brendon; Tomaszewski, John E.; Sarder, Pinaki

2017-03-01

Clinical pathology relies on manual compartmentalization and quantification of biological structures, which is time consuming and often error-prone. Application of computer vision segmentation algorithms to histopathological image analysis, in contrast, can offer fast, reproducible, and accurate quantitative analysis to aid pathologists. Algorithms tunable to different biologically relevant structures can allow accurate, precise, and reproducible estimates of disease states. In this direction, we have developed a fast, unsupervised computational method for simultaneously separating all biologically relevant structures from histopathological images in multi-scale. Segmentation is achieved by solving an energy optimization problem. Representing the image as a graph, nodes (pixels) are grouped by minimizing a Potts model Hamiltonian, adopted from theoretical physics, modeling interacting electron spins. Pixel relationships (modeled as edges) are used to update the energy of the partitioned graph. By iteratively improving the clustering, the optimal number of segments is revealed. To reduce computational time, the graph is simplified using a Cantor pairing function to intelligently reduce the number of included nodes. The classified nodes are then used to train a multiclass support vector machine to apply the segmentation over the full image. Accurate segmentations of images with as many as 106 pixels can be completed only in 5 sec, allowing for attainable multi-scale visualization. To establish clinical potential, we employed our method in renal biopsies to quantitatively visualize for the first time scale variant compartments of heterogeneous intra- and extraglomerular structures simultaneously. Implications of the utility of our method extend to fields such as oncology, genomics, and non-biological problems.

An automated normative-based fluorodeoxyglucose positron emission tomography image-analysis procedure to aid Alzheimer disease diagnosis using statistical parametric mapping and interactive image display

NASA Astrophysics Data System (ADS)

Chen, Kewei; Ge, Xiaolin; Yao, Li; Bandy, Dan; Alexander, Gene E.; Prouty, Anita; Burns, Christine; Zhao, Xiaojie; Wen, Xiaotong; Korn, Ronald; Lawson, Michael; Reiman, Eric M.

2006-03-01

Having approved fluorodeoxyglucose positron emission tomography (FDG PET) for the diagnosis of Alzheimer's disease (AD) in some patients, the Centers for Medicare and Medicaid Services suggested the need to develop and test analysis techniques to optimize diagnostic accuracy. We developed an automated computer package comparing an individual's FDG PET image to those of a group of normal volunteers. The normal control group includes FDG-PET images from 82 cognitively normal subjects, 61.89+/-5.67 years of age, who were characterized demographically, clinically, neuropsychologically, and by their apolipoprotein E genotype (known to be associated with a differential risk for AD). In addition, AD-affected brain regions functionally defined as based on a previous study (Alexander, et al, Am J Psychiatr, 2002) were also incorporated. Our computer package permits the user to optionally select control subjects, matching the individual patient for gender, age, and educational level. It is fully streamlined to require minimal user intervention. With one mouse click, the program runs automatically, normalizing the individual patient image, setting up a design matrix for comparing the single subject to a group of normal controls, performing the statistics, calculating the glucose reduction overlap index of the patient with the AD-affected brain regions, and displaying the findings in reference to the AD regions. In conclusion, the package automatically contrasts a single patient to a normal subject database using sound statistical procedures. With further validation, this computer package could be a valuable tool to assist physicians in decision making and communicating findings with patients and patient families.

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

Mammogram segmentation using maximal cell strength updation in cellular automata.

PubMed

Anitha, J; Peter, J Dinesh

2015-08-01

Breast cancer is the most frequently diagnosed type of cancer among women. Mammogram is one of the most effective tools for early detection of the breast cancer. Various computer-aided systems have been introduced to detect the breast cancer from mammogram images. In a computer-aided diagnosis system, detection and segmentation of breast masses from the background tissues is an important issue. In this paper, an automatic segmentation method is proposed to identify and segment the suspicious mass regions of mammogram using a modified transition rule named maximal cell strength updation in cellular automata (CA). In coarse-level segmentation, the proposed method performs an adaptive global thresholding based on the histogram peak analysis to obtain the rough region of interest. An automatic seed point selection is proposed using gray-level co-occurrence matrix-based sum average feature in the coarse segmented image. Finally, the method utilizes CA with the identified initial seed point and the modified transition rule to segment the mass region. The proposed approach is evaluated over the dataset of 70 mammograms with mass from mini-MIAS database. Experimental results show that the proposed approach yields promising results to segment the mass region in the mammograms with the sensitivity of 92.25% and accuracy of 93.48%.

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.

Enhanced Image-Aided Navigation Algorithm with Automatic Calibration and Affine Distortion Prediction

DTIC Science & Technology

2012-03-01

Lowe, David G. “Distinctive Image Features from Scale-Invariant Keypoints”. International Journal of Computer Vision, 2004. 13. Maybeck, Peter S...Fairfax Drive - 3rd Floor Arlington,VA 22203 Dr. Stefanie Tompkins ; (703)248–1540; Stefanie.Tompkins@darpa.mil DARPA Distribution A. Approved for Public

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

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.

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

COSMIC monthly progress report

NASA Technical Reports Server (NTRS)

1994-01-01

Activities of the Computer Software Management and Information Center (COSMIC) are summarized for the month of April 1994. Tables showing the current inventory of programs available from COSMIC are presented and program processing and evaluation activities are summarized. Five articles were prepared for publication in the NASA Tech Brief Journal. These articles (included in this report) describe the following software items: GAP 1.0 - Groove Analysis Program, Version 1.0; SUBTRANS - Subband/Transform MATLAB Functions for Image Processing; CSDM - COLD-SAT Dynamic Model; CASRE - Computer Aided Software Reliability Estimation; and XOPPS - OEL Project Planner/Scheduler Tool. Activities in the areas of marketing, customer service, benefits identification, maintenance and support, and disseminations are also described along with a budget summary.

Interactive tele-radiological segmentation systems for treatment and diagnosis.

PubMed

Zimeras, S; Gortzis, L G

2012-01-01

Telehealth is the exchange of health information and the provision of health care services through electronic information and communications technology, where participants are separated by geographic, time, social and cultural barriers. The shift of telemedicine from desktop platforms to wireless and mobile technologies is likely to have a significant impact on healthcare in the future. It is therefore crucial to develop a general information exchange e-medical system to enables its users to perform online and offline medical consultations through diagnosis. During the medical diagnosis, image analysis techniques combined with doctor's opinions could be useful for final medical decisions. Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. In medical images, segmentation has traditionally been done by human experts. Even with the aid of image processing software (computer-assisted segmentation tools), manual segmentation of 2D and 3D CT images is tedious, time-consuming, and thus impractical, especially in cases where a large number of objects must be specified. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. The main purpose of this work is to analyze segmentation techniques for the definition of anatomical structures under telemedical systems.

Algorithms for Image Analysis and Combination of Pattern Classifiers with Application to Medical Diagnosis

NASA Astrophysics Data System (ADS)

Georgiou, Harris

2009-10-01

Medical Informatics and the application of modern signal processing in the assistance of the diagnostic process in medical imaging is one of the more recent and active research areas today. This thesis addresses a variety of issues related to the general problem of medical image analysis, specifically in mammography, and presents a series of algorithms and design approaches for all the intermediate levels of a modern system for computer-aided diagnosis (CAD). The diagnostic problem is analyzed with a systematic approach, first defining the imaging characteristics and features that are relevant to probable pathology in mammo-grams. Next, these features are quantified and fused into new, integrated radio-logical systems that exhibit embedded digital signal processing, in order to improve the final result and minimize the radiological dose for the patient. In a higher level, special algorithms are designed for detecting and encoding these clinically interest-ing imaging features, in order to be used as input to advanced pattern classifiers and machine learning models. Finally, these approaches are extended in multi-classifier models under the scope of Game Theory and optimum collective deci-sion, in order to produce efficient solutions for combining classifiers with minimum computational costs for advanced diagnostic systems. The material covered in this thesis is related to a total of 18 published papers, 6 in scientific journals and 12 in international conferences.

Measurement of myocardial perfusion and infarction size using computer-aided diagnosis system for myocardial contrast echocardiography.

PubMed

Du, Guo-Qing; Xue, Jing-Yi; Guo, Yanhui; Chen, Shuang; Du, Pei; Wu, Yan; Wang, Yu-Hang; Zong, Li-Qiu; Tian, Jia-Wei

2015-09-01

Proper evaluation of myocardial microvascular perfusion and assessment of infarct size is critical for clinicians. We have developed a novel computer-aided diagnosis (CAD) approach for myocardial contrast echocardiography (MCE) to measure myocardial perfusion and infarct size. Rabbits underwent 15 min of coronary occlusion followed by reperfusion (group I, n = 15) or 60 min of coronary occlusion followed by reperfusion (group II, n = 15). Myocardial contrast echocardiography was performed before and 7 d after ischemia/reperfusion, and images were analyzed with the CAD system on the basis of eliminating particle swarm optimization clustering analysis. The myocardium was quickly and accurately detected using contrast-enhanced images, myocardial perfusion was quantitatively calibrated and a color-coded map calibrated by contrast intensity and automatically produced by the CAD system was used to outline the infarction region. Calibrated contrast intensity was significantly lower in infarct regions than in non-infarct regions, allowing differentiation of abnormal and normal myocardial perfusion. Receiver operating characteristic curve analysis documented that -54-pixel contrast intensity was an optimal cutoff point for the identification of infarcted myocardium with a sensitivity of 95.45% and specificity of 87.50%. Infarct sizes obtained using myocardial perfusion defect analysis of original contrast images and the contrast intensity-based color-coded map in computerized images were compared with infarct sizes measured using triphenyltetrazolium chloride staining. Use of the proposed CAD approach provided observers with more information. The infarct sizes obtained with myocardial perfusion defect analysis, the contrast intensity-based color-coded map and triphenyltetrazolium chloride staining were 23.72 ± 8.41%, 21.77 ± 7.8% and 18.21 ± 4.40% (% left ventricle) respectively (p > 0.05), indicating that computerized myocardial contrast echocardiography can accurately measure infarct size. On the basis of the results, we believe the CAD method can quickly and automatically measure myocardial perfusion and infarct size and will, it is hoped, be very helpful in clinical therapeutics. Copyright © 2015 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

PET/CT imaging of clear cell renal cell carcinoma with 124I labeled chimeric antibody

PubMed Central

Bahnson, Eamonn E.; Murrey, Douglas A.; Mojzisik, Cathy M.; Hall, Nathan C.; Martinez-Suarez, Humberto J.; Knopp, Michael V.; Martin, Edward W.; Povoski, Stephen P.; Bahnson, Robert R.

2009-01-01

Clear cell renal cell carcinoma (ccRCC) presents problems for urologists in diagnosis, treatment selection, intraoperative surgical margin analysis, and long term monitoring. In this paper we describe the development of a radiolabeled antibody specific to ccRCC (124I-cG250) and its potential to help urologists manage each of these problems. We believe 124I-cG250, in conjunction with perioperative Positron emission tomography/computed tomography imaging and intraoperative handheld gamma probe use, has the potential to diagnose ccRCC, aid in determining a proper course of treatment (operative or otherwise), confirm complete resection of malignant tissue in real time, and monitor patients post-operatively. PMID:21789055

Accurate Inventories Of Irrigated Land

NASA Technical Reports Server (NTRS)

Wall, S.; Thomas, R.; Brown, C.

1992-01-01

System for taking land-use inventories overcomes two problems in estimating extent of irrigated land: only small portion of large state surveyed in given year, and aerial photographs made on 1 day out of year do not provide adequate picture of areas growing more than one crop per year. Developed for state of California as guide to controlling, protecting, conserving, and distributing water within state. Adapted to any large area in which large amounts of irrigation water needed for agriculture. Combination of satellite images, aerial photography, and ground surveys yields data for computer analysis. Analyst also consults agricultural statistics, current farm reports, weather reports, and maps. These information sources aid in interpreting patterns, colors, textures, and shapes on Landsat-images.

Basic research and 12 years of clinical experience in computer-assisted navigation technology: a review.

PubMed

Ewers, R; Schicho, K; Undt, G; Wanschitz, F; Truppe, M; Seemann, R; Wagner, A

2005-01-01

Computer-aided surgical navigation technology is commonly used in craniomaxillofacial surgery. It offers substantial improvement regarding esthetic and functional aspects in a range of surgical procedures. Based on augmented reality principles, where the real operative site is merged with computer generated graphic information, computer-aided navigation systems were employed, among other procedures, in dental implantology, arthroscopy of the temporomandibular joint, osteotomies, distraction osteogenesis, image guided biopsies and removals of foreign bodies. The decision to perform a procedure with or without computer-aided intraoperative navigation depends on the expected benefit to the procedure as well as on the technical expenditure necessary to achieve that goal. This paper comprises the experience gained in 12 years of research, development and routine clinical application. One hundred and fifty-eight operations with successful application of surgical navigation technology--divided into five groups--are evaluated regarding the criteria "medical benefit" and "technical expenditure" necessary to perform these procedures. Our results indicate that the medical benefit is likely to outweight the expenditure of technology with few exceptions (calvaria transplant, resection of the temporal bone, reconstruction of the orbital floor). Especially in dental implantology, specialized software reduces time and additional costs necessary to plan and perform procedures with computer-aided surgical navigation.

Computer-aided diagnosis in phase contrast imaging X-ray computed tomography for quantitative characterization of ex vivo human patellar cartilage.

PubMed

Nagarajan, Mahesh B; Coan, Paola; Huber, Markus B; Diemoz, Paul C; Glaser, Christian; Wismuller, Axel

2013-10-01

Visualization of ex vivo human patellar cartilage matrix through the phase contrast imaging X-ray computed tomography (PCI-CT) has been previously demonstrated. Such studies revealed osteoarthritis-induced changes to chondrocyte organization in the radial zone. This study investigates the application of texture analysis to characterizing such chondrocyte patterns in the presence and absence of osteoarthritic damage. Texture features derived from Minkowski functionals (MF) and gray-level co-occurrence matrices (GLCM) were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These texture features were subsequently used in a machine learning task with support vector regression to classify ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). The best classification performance was observed with the MF features perimeter (AUC: 0.94 ±0.08 ) and "Euler characteristic" (AUC: 0.94 ±0.07 ), and GLCM-derived feature "Correlation" (AUC: 0.93 ±0.07). These results suggest that such texture features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix, enabling classification of cartilage as healthy or osteoarthritic with high accuracy.

COMPUTATIONAL ANALYSIS BASED ON ARTIFICIAL NEURAL NETWORKS FOR AIDING IN DIAGNOSING OSTEOARTHRITIS OF THE LUMBAR SPINE.

PubMed

Veronezi, Carlos Cassiano Denipotti; de Azevedo Simões, Priscyla Waleska Targino; Dos Santos, Robson Luiz; da Rocha, Edroaldo Lummertz; Meláo, Suelen; de Mattos, Merisandra Côrtes; Cechinel, Cristian

2011-01-01

To ascertain the advantages of applying artificial neural networks to recognize patterns on lumbar spine radiographies in order to aid in the process of diagnosing primary osteoarthritis. This was a cross-sectional descriptive analytical study with a quantitative approach and an emphasis on diagnosis. The training set was composed of images collected between January and July 2009 from patients who had undergone lateral-view digital radiographies of the lumbar spine, which were provided by a radiology clinic located in the municipality of Criciúma (SC). Out of the total of 260 images gathered, those with distortions, those presenting pathological conditions that altered the architecture of the lumbar spine and those with patterns that were difficult to characterize were discarded, resulting in 206 images. The image data base (n = 206) was then subdivided, resulting in 68 radiographies for the training stage, 68 images for tests and 70 for validation. A hybrid neural network based on Kohonen self-organizing maps and on Multilayer Perceptron networks was used. After 90 cycles, the validation was carried out on the best results, achieving accuracy of 62.85%, sensitivity of 65.71% and specificity of 60%. Even though the effectiveness shown was moderate, this study is still innovative. The values show that the technique used has a promising future, pointing towards further studies on image and cycle processing methodology with a larger quantity of radiographies.

Quantitative kinetic analysis of lung nodules by temporal subtraction technique in dynamic chest radiography with a flat panel detector

NASA Astrophysics Data System (ADS)

Tsuchiya, Yuichiro; Kodera, Yoshie; Tanaka, Rie; Sanada, Shigeru

2007-03-01

Early detection and treatment of lung cancer is one of the most effective means to reduce cancer mortality; chest X-ray radiography has been widely used as a screening examination or health checkup. The new examination method and the development of computer analysis system allow obtaining respiratory kinetics by the use of flat panel detector (FPD), which is the expanded method of chest X-ray radiography. Through such changes functional evaluation of respiratory kinetics in chest has become available. Its introduction into clinical practice is expected in the future. In this study, we developed the computer analysis algorithm for the purpose of detecting lung nodules and evaluating quantitative kinetics. Breathing chest radiograph obtained by modified FPD was converted into 4 static images drawing the feature, by sequential temporal subtraction processing, morphologic enhancement processing, kinetic visualization processing, and lung region detection processing, after the breath synchronization process utilizing the diaphragmatic analysis of the vector movement. The artificial neural network used to analyze the density patterns detected the true nodules by analyzing these static images, and drew their kinetic tracks. For the algorithm performance and the evaluation of clinical effectiveness with 7 normal patients and simulated nodules, both showed sufficient detecting capability and kinetic imaging function without statistically significant difference. Our technique can quantitatively evaluate the kinetic range of nodules, and is effective in detecting a nodule on a breathing chest radiograph. Moreover, the application of this technique is expected to extend computer-aided diagnosis systems and facilitate the development of an automatic planning system for radiation therapy.

Intelligent Image Based Computer Aided Education (IICAE)

NASA Astrophysics Data System (ADS)

David, Amos A.; Thiery, Odile; Crehange, Marion

1989-03-01

Artificial Intelligence (AI) has found its way into Computer Aided Education (CAE), and there are several systems constructed to put in evidence its interesting advantages. We believe that images (graphic or real) play an important role in learning. However, the use of images, outside their use as illustration, makes it necessary to have applications such as AI. We shall develop the application of AI in an image based CAE and briefly present the system under construction to put in evidence our concept. We shall also elaborate a methodology for constructing such a system. Futhermore we shall briefly present the pedagogical and psychological activities in a learning process. Under the pedagogical and psychological aspect of learning, we shall develop areas such as the importance of image in learning both as pedagogical objects as well as means for obtaining psychological information about the learner. We shall develop the learner's model, its use, what to build into it and how. Under the application of AI in an image based CAE, we shall develop the importance of AI in exploiting the knowledge base in the learning environment and its application as a means of implementing pedagogical strategies.

Techniques for computer-aided analysis of ERTS-1 data, useful in geologic, forest and water resource surveys. [Colorado Rocky Mountains

NASA Technical Reports Server (NTRS)

Hoffer, R. M.

1974-01-01

Forestry, geology, and water resource applications were the focus of this study, which involved the use of computer-implemented pattern-recognition techniques to analyze ERTS-1 data. The results have proven the value of computer-aided analysis techniques, even in areas of mountainous terrain. Several analysis capabilities have been developed during these ERTS-1 investigations. A procedure to rotate, deskew, and geometrically scale the MSS data results in 1:24,000 scale printouts that can be directly overlayed on 7 1/2 minutes U.S.G.S. topographic maps. Several scales of computer-enhanced "false color-infrared" composites of MSS data can be obtained from a digital display unit, and emphasize the tremendous detail present in the ERTS-1 data. A grid can also be superimposed on the displayed data to aid in specifying areas of interest.

On the convergence of nanotechnology and Big Data analysis for computer-aided diagnosis.

PubMed

Rodrigues, Jose F; Paulovich, Fernando V; de Oliveira, Maria Cf; de Oliveira, Osvaldo N

2016-04-01

An overview is provided of the challenges involved in building computer-aided diagnosis systems capable of precise medical diagnostics based on integration and interpretation of data from different sources and formats. The availability of massive amounts of data and computational methods associated with the Big Data paradigm has brought hope that such systems may soon be available in routine clinical practices, which is not the case today. We focus on visual and machine learning analysis of medical data acquired with varied nanotech-based techniques and on methods for Big Data infrastructure. Because diagnosis is essentially a classification task, we address the machine learning techniques with supervised and unsupervised classification, making a critical assessment of the progress already made in the medical field and the prospects for the near future. We also advocate that successful computer-aided diagnosis requires a merge of methods and concepts from nanotechnology and Big Data analysis.

Modeling And Simulation Of Bar Code Scanners Using Computer Aided Design Software

NASA Astrophysics Data System (ADS)

Hellekson, Ron; Campbell, Scott

1988-06-01

Many optical systems have demanding requirements to package the system in a small 3 dimensional space. The use of computer graphic tools can be a tremendous aid to the designer in analyzing the optical problems created by smaller and less costly systems. The Spectra Physics grocery store bar code scanner employs an especially complex 3 dimensional scan pattern to read bar code labels. By using a specially written program which interfaces with a computer aided design system, we have simulated many of the functions of this complex optical system. In this paper we will illustrate how a recent version of the scanner has been designed. We will discuss the use of computer graphics in the design process including interactive tweaking of the scan pattern, analysis of collected light, analysis of the scan pattern density, and analysis of the manufacturing tolerances used to build the scanner.

Improvement of automatic hemorrhage detection methods using brightness correction on fundus images

NASA Astrophysics Data System (ADS)

Hatanaka, Yuji; Nakagawa, Toshiaki; Hayashi, Yoshinori; Kakogawa, Masakatsu; Sawada, Akira; Kawase, Kazuhide; Hara, Takeshi; Fujita, Hiroshi

2008-03-01

We have been developing several automated methods for detecting abnormalities in fundus images. The purpose of this study is to improve our automated hemorrhage detection method to help diagnose diabetic retinopathy. We propose a new method for preprocessing and false positive elimination in the present study. The brightness of the fundus image was changed by the nonlinear curve with brightness values of the hue saturation value (HSV) space. In order to emphasize brown regions, gamma correction was performed on each red, green, and blue-bit image. Subsequently, the histograms of each red, blue, and blue-bit image were extended. After that, the hemorrhage candidates were detected. The brown regions indicated hemorrhages and blood vessels and their candidates were detected using density analysis. We removed the large candidates such as blood vessels. Finally, false positives were removed by using a 45-feature analysis. To evaluate the new method for the detection of hemorrhages, we examined 125 fundus images, including 35 images with hemorrhages and 90 normal images. The sensitivity and specificity for the detection of abnormal cases was were 80% and 88%, respectively. These results indicate that the new method may effectively improve the performance of our computer-aided diagnosis system for hemorrhages.

A Study of Computer-Aided Geometric Optical Design.

DTIC Science & Technology

1982-10-01

short programs on tape. A computer account number and Cyber computer manuals were obtained. A familiarity with the use and maintenance of computer files...in the interpretation of the information. Ray fans, spot diagrams, wavefront variance, Strehl ratio, vignetting .- diagrams Pnd optical transfer...other surface begins to cut off these rays (20:113). This is characterized by a loss of intensity at the outside of the image. A known manual

Image-guided tissue engineering

PubMed Central

Ballyns, Jeffrey J; Bonassar, Lawrence J

2009-01-01

Replication of anatomic shape is a significant challenge in developing implants for regenerative medicine. This has lead to significant interest in using medical imaging techniques such as magnetic resonance imaging and computed tomography to design tissue engineered constructs. Implementation of medical imaging and computer aided design in combination with technologies for rapid prototyping of living implants enables the generation of highly reproducible constructs with spatial resolution up to 25 μm. In this paper, we review the medical imaging modalities available and a paradigm for choosing a particular imaging technique. We also present fabrication techniques and methodologies for producing cellular engineered constructs. Finally, we comment on future challenges involved with image guided tissue engineering and efforts to generate engineered constructs ready for implantation. PMID:19583811

Underwater Inherent Optical Properties Estimation Using a Depth Aided Deep Neural Network.

PubMed

Yu, Zhibin; Wang, Yubo; Zheng, Bing; Zheng, Haiyong; Wang, Nan; Gu, Zhaorui

2017-01-01

Underwater inherent optical properties (IOPs) are the fundamental clues to many research fields such as marine optics, marine biology, and underwater vision. Currently, beam transmissometers and optical sensors are considered as the ideal IOPs measuring methods. But these methods are inflexible and expensive to be deployed. To overcome this problem, we aim to develop a novel measuring method using only a single underwater image with the help of deep artificial neural network. The power of artificial neural network has been proved in image processing and computer vision fields with deep learning technology. However, image-based IOPs estimation is a quite different and challenging task. Unlike the traditional applications such as image classification or localization, IOP estimation looks at the transparency of the water between the camera and the target objects to estimate multiple optical properties simultaneously. In this paper, we propose a novel Depth Aided (DA) deep neural network structure for IOPs estimation based on a single RGB image that is even noisy. The imaging depth information is considered as an aided input to help our model make better decision.

Experimental Design and Data Analysis in Receiver Operating Characteristic Studies: Lessons Learned from Reports in Radiology from 1997 to 20061

PubMed Central

Shiraishi, Junji; Pesce, Lorenzo L.; Metz, Charles E.; Doi, Kunio

2009-01-01

Purpose: To provide a broad perspective concerning the recent use of receiver operating characteristic (ROC) analysis in medical imaging by reviewing ROC studies published in Radiology between 1997 and 2006 for experimental design, imaging modality, medical condition, and ROC paradigm. Materials and Methods: Two hundred ninety-five studies were obtained by conducting a literature search with PubMed with two criteria: publication in Radiology between 1997 and 2006 and occurrence of the phrase “receiver operating characteristic.” Studies returned by the query that were not diagnostic imaging procedure performance evaluations were excluded. Characteristics of the remaining studies were tabulated. Results: Two hundred thirty-three (79.0%) of the 295 studies reported findings based on observers' diagnostic judgments or objective measurements. Forty-three (14.6%) did not include human observers, with most of these reporting an evaluation of a computer-aided diagnosis system or functional data obtained with computed tomography (CT) or magnetic resonance (MR) imaging. The remaining 19 (6.4%) studies were classified as reviews or meta-analyses and were excluded from our subsequent analysis. Among the various imaging modalities, MR imaging (46.0%) and CT (25.7%) were investigated most frequently. Approximately 60% (144 of 233) of ROC studies with human observers published in Radiology included three or fewer observers. Conclusion: ROC analysis is widely used in radiologic research, confirming its fundamental role in assessing diagnostic performance. However, the ROC studies reported in Radiology were not always adequate to support clear and clinically relevant conclusions. © RSNA, 2009 Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.2533081632/-/DC1 PMID:19864510

Towards Building a High Performance Spatial Query System for Large Scale Medical Imaging Data.

PubMed

Aji, Ablimit; Wang, Fusheng; Saltz, Joel H

2012-11-06

Support of high performance queries on large volumes of scientific spatial data is becoming increasingly important in many applications. This growth is driven by not only geospatial problems in numerous fields, but also emerging scientific applications that are increasingly data- and compute-intensive. For example, digital pathology imaging has become an emerging field during the past decade, where examination of high resolution images of human tissue specimens enables more effective diagnosis, prediction and treatment of diseases. Systematic analysis of large-scale pathology images generates tremendous amounts of spatially derived quantifications of micro-anatomic objects, such as nuclei, blood vessels, and tissue regions. Analytical pathology imaging provides high potential to support image based computer aided diagnosis. One major requirement for this is effective querying of such enormous amount of data with fast response, which is faced with two major challenges: the "big data" challenge and the high computation complexity. In this paper, we present our work towards building a high performance spatial query system for querying massive spatial data on MapReduce. Our framework takes an on demand index building approach for processing spatial queries and a partition-merge approach for building parallel spatial query pipelines, which fits nicely with the computing model of MapReduce. We demonstrate our framework on supporting multi-way spatial joins for algorithm evaluation and nearest neighbor queries for microanatomic objects. To reduce query response time, we propose cost based query optimization to mitigate the effect of data skew. Our experiments show that the framework can efficiently support complex analytical spatial queries on MapReduce.

Towards Building a High Performance Spatial Query System for Large Scale Medical Imaging Data

PubMed Central

Aji, Ablimit; Wang, Fusheng; Saltz, Joel H.

2013-01-01

Support of high performance queries on large volumes of scientific spatial data is becoming increasingly important in many applications. This growth is driven by not only geospatial problems in numerous fields, but also emerging scientific applications that are increasingly data- and compute-intensive. For example, digital pathology imaging has become an emerging field during the past decade, where examination of high resolution images of human tissue specimens enables more effective diagnosis, prediction and treatment of diseases. Systematic analysis of large-scale pathology images generates tremendous amounts of spatially derived quantifications of micro-anatomic objects, such as nuclei, blood vessels, and tissue regions. Analytical pathology imaging provides high potential to support image based computer aided diagnosis. One major requirement for this is effective querying of such enormous amount of data with fast response, which is faced with two major challenges: the “big data” challenge and the high computation complexity. In this paper, we present our work towards building a high performance spatial query system for querying massive spatial data on MapReduce. Our framework takes an on demand index building approach for processing spatial queries and a partition-merge approach for building parallel spatial query pipelines, which fits nicely with the computing model of MapReduce. We demonstrate our framework on supporting multi-way spatial joins for algorithm evaluation and nearest neighbor queries for microanatomic objects. To reduce query response time, we propose cost based query optimization to mitigate the effect of data skew. Our experiments show that the framework can efficiently support complex analytical spatial queries on MapReduce. PMID:24501719

Comprehensive automatic assessment of retinal vascular abnormalities for computer-assisted retinopathy grading.

PubMed

Joshi, Vinayak; Agurto, Carla; VanNess, Richard; Nemeth, Sheila; Soliz, Peter; Barriga, Simon

2014-01-01

One of the most important signs of systemic disease that presents on the retina is vascular abnormalities such as in hypertensive retinopathy. Manual analysis of fundus images by human readers is qualitative and lacks in accuracy, consistency and repeatability. Present semi-automatic methods for vascular evaluation are reported to increase accuracy and reduce reader variability, but require extensive reader interaction; thus limiting the software-aided efficiency. Automation thus holds a twofold promise. First, decrease variability while increasing accuracy, and second, increasing the efficiency. In this paper we propose fully automated software as a second reader system for comprehensive assessment of retinal vasculature; which aids the readers in the quantitative characterization of vessel abnormalities in fundus images. This system provides the reader with objective measures of vascular morphology such as tortuosity, branching angles, as well as highlights of areas with abnormalities such as artery-venous nicking, copper and silver wiring, and retinal emboli; in order for the reader to make a final screening decision. To test the efficacy of our system, we evaluated the change in performance of a newly certified retinal reader when grading a set of 40 color fundus images with and without the assistance of the software. The results demonstrated an improvement in reader's performance with the software assistance, in terms of accuracy of detection of vessel abnormalities, determination of retinopathy, and reading time. This system enables the reader in making computer-assisted vasculature assessment with high accuracy and consistency, at a reduced reading time.

Computer-aided meiotic maturation assay (CAMMA) of zebrafish (danio rerio) oocytes in vitro.

PubMed

Lessman, Charles A; Nathani, Ravikanth; Uddin, Rafique; Walker, Jamie; Liu, Jianxiong

2007-01-01

We have developed a new technique called Computer-Aided Meiotic Maturation Assay (CAMMA) for imaging large arrays of zebrafish oocytes and automatically collecting image files at regular intervals during meiotic maturation. This novel method uses a transparency scanner interfaced to a computer with macro programming that automatically scans and archives the image files. Images are stacked and analyzed with ImageJ to quantify changes in optical density characteristic of zebrafish oocyte maturation. Major advantages of CAMMA include (1) ability to image very large arrays of oocytes and follow individual cells over time, (2) simultaneously image many treatment groups, (3) digitized images may be stacked, animated, and analyzed in programs such as ImageJ, NIH-Image, or ScionImage, and (4) CAMMA system is inexpensive, costing less than most microscopes used in traditional assays. We have used CAMMA to determine the dose response and time course of oocyte maturation induced by 17alpha-hydroxyprogesterone (HP). Maximal decrease in optical density occurs around 5 hr after 0.1 micro g/ml HP (28.5 degrees C), approximately 3 hr after germinal vesicle migration (GVM) and dissolution (GVD). In addition to changes in optical density, GVD is accompanied by streaming of ooplasm to the animal pole to form a blastodisc. These dynamic changes are readily visualized by animating image stacks from CAMMA; thus, CAMMA provides a valuable source of time-lapse movies for those studying zebrafish oocyte maturation. The oocyte clearing documented by CAMMA is correlated to changes in size distribution of major yolk proteins upon SDS-PAGE, and, this in turn, is related to increased cyclin B(1) protein.

A semi-automated software tool to study treadmill locomotion in the rat: from experiment videos to statistical gait analysis.

PubMed

Gravel, P; Tremblay, M; Leblond, H; Rossignol, S; de Guise, J A

2010-07-15

A computer-aided method for the tracking of morphological markers in fluoroscopic images of a rat walking on a treadmill is presented and validated. The markers correspond to bone articulations in a hind leg and are used to define the hip, knee, ankle and metatarsophalangeal joints. The method allows a user to identify, using a computer mouse, about 20% of the marker positions in a video and interpolate their trajectories from frame-to-frame. This results in a seven-fold speed improvement in detecting markers. This also eliminates confusion problems due to legs crossing and blurred images. The video images are corrected for geometric distortions from the X-ray camera, wavelet denoised, to preserve the sharpness of minute bone structures, and contrast enhanced. From those images, the marker positions across video frames are extracted, corrected for rat "solid body" motions on the treadmill, and used to compute the positional and angular gait patterns. Robust Bootstrap estimates of those gait patterns and their prediction and confidence bands are finally generated. The gait patterns are invaluable tools to study the locomotion of healthy animals or the complex process of locomotion recovery in animals with injuries. The method could, in principle, be adapted to analyze the locomotion of other animals as long as a fluoroscopic imager and a treadmill are available. Copyright 2010 Elsevier B.V. All rights reserved.

A Clash of Symbols: An Analysis of Competing Images and Arguments in the AIDS Controversy.

ERIC Educational Resources Information Center

Gilder, Eric

Efforts to contain the spread of Acquired Immune Deficiency Syndrome (AIDS) have been slowed by numerous arguing factions, political, religious, and medical, all of which perceive the AIDS epidemic through a different set of symbols. The images can be more easily understood using Kenneth Boulding's Threat, Integry, and Exchange (or TIE) model. The…

Computer-aided analysis of Skylab scanner data for land use mapping, forestry and water resource applications

NASA Technical Reports Server (NTRS)

Hoffer, R. M.

1975-01-01

Skylab data were obtained over a mountainous test site containing a complex association of cover types and rugged topography. The application of computer-aided analysis techniques to the multispectral scanner data produced a number of significant results. Techniques were developed to digitally overlay topographic data (elevation, slope, and aspect) onto the S-192 MSS data to provide a method for increasing the effectiveness and accuracy of computer-aided analysis techniques for cover type mapping. The S-192 MSS data were analyzed using computer techniques developed at Laboratory for Applications of Remote Sensing (LARS), Purdue University. Land use maps, forest cover type maps, snow cover maps, and area tabulations were obtained and evaluated. These results compared very well with information obtained by conventional techniques. Analysis of the spectral characteristics of Skylab data has conclusively proven the value of the middle infrared portion of the spectrum (about 1.3-3.0 micrometers), a wavelength region not previously available in multispectral satellite data.

Secondary Maxillary and Orbital Floor Reconstruction With a Free Scapular Flap Using Cutting and Fixation Guides Created by Computer-Aided Design/Computer-Aided Manufacturing.

PubMed

Morita, Daiki; Numajiri, Toshiaki; Tsujiko, Shoko; Nakamura, Hiroko; Yamochi, Ryo; Sowa, Yoshihiro; Yasuda, Makoto; Hirano, Shigeru

2017-11-01

Computer-aided design/computer-aided manufacturing (CAD/CAM) guides are now widely used in maxillofacial reconstruction. However, there are few reports of CAD/CAM guides being used for scapular flaps. The authors performed the secondary maxillary and orbital floor reconstruction using a free latissimus dorsi muscle, cutaneous tissue, and scapular flap designed using CAD/CAM techniques in a 72-year-old man who had undergone partial maxillectomy four years previously. The patient had diplopia, the vertical dystopia of eye position, and a large oral-nasal-cutaneous fistula. After the operation, the authors confirmed that the deviation between the postoperative and preoperative planning three-dimensional images was less than 2 mm. Because scapular guides require 3 cutting surfaces, the shape of the scapular guide is more complex than that of a conventional fibular guide. In orbital floor reconstruction, the use of a CAM technique such as that used to manufacture the authors' fixation guide is as necessary for accurate, safe, and easy reconstruction as is preoperative CAD planning. The production of a fixation guide as well as a cutting guide is particularly useful because it is difficult to determine the angle for reconstructing the orbital floor by freehand techniques. In this case, the orbital floor was reconstructed based on a mirror image of the healthy side to avoid overcompression of the orbital tissue. Although the patient's vertical dystopia of eye position was improved, diplopia was not improved because, for greater safety, the authors did not plan overcorrection of the orbital volume.

Machine Learning and Radiology

PubMed Central

Wang, Shijun; Summers, Ronald M.

2012-01-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

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…

SP mountain data analysis

NASA Technical Reports Server (NTRS)

Rawson, R. F.; Hamilton, R. E.; Liskow, C. L.; Dias, A. R.; Jackson, P. L.

1981-01-01

An analysis of synthetic aperture radar data of SP Mountain was undertaken to demonstrate the use of digital image processing techniques to aid in geologic interpretation of SAR data. These data were collected with the ERIM X- and L-band airborne SAR using like- and cross-polarizations. The resulting signal films were used to produce computer compatible tapes, from which four-channel imagery was generated. Slant range-to-ground range and range-azimuth-scale corrections were made in order to facilitate image registration; intensity corrections were also made. Manual interpretation of the imagery showed that L-band represented the geology of the area better than X-band. Several differences between the various images were also noted. Further digital analysis of the corrected data was done for enhancement purposes. This analysis included application of an MSS differencing routine and development of a routine for removal of relief displacement. It was found that accurate registration of the SAR channels is critical to the effectiveness of the differencing routine. Use of the relief displacement algorithm on the SP Mountain data demonstrated the feasibility of the technique.

Effect of slice thickness on image noise and diagnostic content of single-source-dual energy computed tomography

NASA Astrophysics Data System (ADS)

Alshipli, Marwan; Kabir, Norlaili A.

2017-05-01

Computed tomography (CT) employs X-ray radiation to create cross-sectional images. Dual-energy CT acquisition includes the images acquired from an alternating voltage of X-ray tube: a low- and a high-peak kilovoltage. The main objective of this study is to determine the best slice thickness that reduces image noise with adequate diagnostic information using dual energy CT head protocol. The study used the ImageJ software and statistical analyses to aid the medical image analysis of dual-energy CT. In this study, ImageJ software and F-test were utilised as the combination methods to analyse DICOM CT images. They were used to investigate the effect of slice thickness on noise and visibility in dual-energy CT head protocol images. Catphan-600 phantom was scanned at different slice thickness values;.6, 1, 2, 3, 4, 5 and 6 mm, then quantitative analyses were carried out. The DECT operated in helical mode with another fixed scan parameter values. Based on F-test statistical analyses, image noise at 0.6, 1, and 2 mm were significantly different compared to the other images acquired at slice thickness of 3, 4, 5, and 6 mm. However, no significant differences of image noise were observed at 3, 4, 5, and 6 mm. As a result, better diagnostic image value, image visibility, and lower image noise in dual-energy CT head protocol was observed at a slice thickness of 3 mm.

Applying a new computer-aided detection scheme generated imaging marker to predict short-term breast cancer risk

NASA Astrophysics Data System (ADS)

Mirniaharikandehei, Seyedehnafiseh; Hollingsworth, Alan B.; Patel, Bhavika; Heidari, Morteza; Liu, Hong; Zheng, Bin

2018-05-01

This study aims to investigate the feasibility of identifying a new quantitative imaging marker based on false-positives generated by a computer-aided detection (CAD) scheme to help predict short-term breast cancer risk. An image dataset including four view mammograms acquired from 1044 women was retrospectively assembled. All mammograms were originally interpreted as negative by radiologists. In the next subsequent mammography screening, 402 women were diagnosed with breast cancer and 642 remained negative. An existing CAD scheme was applied ‘as is’ to process each image. From CAD-generated results, four detection features including the total number of (1) initial detection seeds and (2) the final detected false-positive regions, (3) average and (4) sum of detection scores, were computed from each image. Then, by combining the features computed from two bilateral images of left and right breasts from either craniocaudal or mediolateral oblique view, two logistic regression models were trained and tested using a leave-one-case-out cross-validation method to predict the likelihood of each testing case being positive in the next subsequent screening. The new prediction model yielded the maximum prediction accuracy with an area under a ROC curve of AUC  =  0.65  ±  0.017 and the maximum adjusted odds ratio of 4.49 with a 95% confidence interval of (2.95, 6.83). The results also showed an increasing trend in the adjusted odds ratio and risk prediction scores (p  <  0.01). Thus, this study demonstrated that CAD-generated false-positives might include valuable information, which needs to be further explored for identifying and/or developing more effective imaging markers for predicting short-term breast cancer risk.

Computer-Aided Nodule Assessment and Risk Yield Risk Management of Adenocarcinoma: The Future of Imaging?

PubMed

Foley, Finbar; Rajagopalan, Srinivasan; Raghunath, Sushravya M; Boland, Jennifer M; Karwoski, Ronald A; Maldonado, Fabien; Bartholmai, Brian J; Peikert, Tobias

2016-01-01

Increased clinical use of chest high-resolution computed tomography results in increased identification of lung adenocarcinomas and persistent subsolid opacities. However, these lesions range from very indolent to extremely aggressive tumors. Clinically relevant diagnostic tools to noninvasively risk stratify and guide individualized management of these lesions are lacking. Research efforts investigating semiquantitative measures to decrease interrater and intrarater variability are emerging, and in some cases steps have been taken to automate this process. However, many such methods currently are still suboptimal, require validation and are not yet clinically applicable. The computer-aided nodule assessment and risk yield software application represents a validated tool for the automated, quantitative, and noninvasive tool for risk stratification of adenocarcinoma lung nodules. Computer-aided nodule assessment and risk yield correlates well with consensus histology and postsurgical patient outcomes, and therefore may help to guide individualized patient management, for example, in identification of nodules amenable to radiological surveillance, or in need of adjunctive therapy. Copyright © 2016 Elsevier Inc. All rights reserved.

Development of an autonomous video rendezvous and docking system, phase 2

NASA Technical Reports Server (NTRS)

Tietz, J. C.; Richardson, T. E.

1983-01-01

The critical elements of an autonomous video rendezvous and docking system were built and used successfully in a physical laboratory simulation. The laboratory system demonstrated that a small, inexpensive electronic package and a flight computer of modest size can analyze television images to derive guidance information for spacecraft. In the ultimate application, the system would use a docking aid consisting of three flashing lights mounted on a passive target spacecraft. Television imagery of the docking aid would be processed aboard an active chase vehicle to derive relative positions and attitudes of the two spacecraft. The demonstration system used scale models of the target spacecraft with working docking aids. A television camera mounted on a 6 degree of freedom (DOF) simulator provided imagery of the target to simulate observations from the chase vehicle. A hardware video processor extracted statistics from the imagery, from which a computer quickly computed position and attitude. Computer software known as a Kalman filter derived velocity information from position measurements.

Computer-aided-engineering system for modeling and analysis of ECLSS integration testing

NASA Technical Reports Server (NTRS)

Sepahban, Sonbol

1987-01-01

The accurate modeling and analysis of two-phase fluid networks found in environmental control and life support systems is presently undertaken by computer-aided engineering (CAE) techniques whose generalized fluid dynamics package can solve arbitrary flow networks. The CAE system for integrated test bed modeling and analysis will also furnish interfaces and subsystem/test-article mathematical models. Three-dimensional diagrams of the test bed are generated by the system after performing the requisite simulation and analysis.

Computer-aided design of antenna structures and components

NASA Technical Reports Server (NTRS)

Levy, R.

1976-01-01

This paper discusses computer-aided design procedures for antenna reflector structures and related components. The primary design aid is a computer program that establishes cross sectional sizes of the structural members by an optimality criterion. Alternative types of deflection-dependent objectives can be selected for designs subject to constraints on structure weight. The computer program has a special-purpose formulation to design structures of the type frequently used for antenna construction. These structures, in common with many in other areas of application, are represented by analytical models that employ only the three translational degrees of freedom at each node. The special-purpose construction of the program, however, permits coding and data management simplifications that provide advantages in problem size and execution speed. Size and speed are essentially governed by the requirements of structural analysis and are relatively unaffected by the added requirements of design. Computation times to execute several design/analysis cycles are comparable to the times required by general-purpose programs for a single analysis cycle. Examples in the paper illustrate effective design improvement for structures with several thousand degrees of freedom and within reasonable computing times.

A Computational Model Quantifies the Effect of Anatomical Variability on Velopharyngeal Function

ERIC Educational Resources Information Center

Inouye, Joshua M.; Perry, Jamie L.; Lin, Kant Y.; Blemker, Silvia S.

2015-01-01

Purpose: This study predicted the effects of velopharyngeal (VP) anatomical parameters on VP function to provide a greater understanding of speech mechanics and aid in the treatment of speech disorders. Method: We created a computational model of the VP mechanism using dimensions obtained from magnetic resonance imaging measurements of 10 healthy…

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 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.

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

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.

Inspection of aeronautical mechanical parts with a pan-tilt-zoom camera: an approach guided by the computer-aided design model

NASA Astrophysics Data System (ADS)

Viana, Ilisio; Orteu, Jean-José; Cornille, Nicolas; Bugarin, Florian

2015-11-01

We focus on quality control of mechanical parts in aeronautical context using a single pan-tilt-zoom (PTZ) camera and a computer-aided design (CAD) model of the mechanical part. We use the CAD model to create a theoretical image of the element to be checked, which is further matched with the sensed image of the element to be inspected, using a graph theory-based approach. The matching is carried out in two stages. First, the two images are used to create two attributed graphs representing the primitives (ellipses and line segments) in the images. In the second stage, the graphs are matched using a similarity function built from the primitive parameters. The similarity scores of the matching are injected in the edges of a bipartite graph. A best-match-search procedure in the bipartite graph guarantees the uniqueness of the match solution. The method achieves promising performance in tests with synthetic data including missing elements, displaced elements, size changes, and combinations of these cases. The results open good prospects for using the method with realistic data.

Image formation simulation for computer-aided inspection planning of machine vision systems

NASA Astrophysics Data System (ADS)

Irgenfried, Stephan; Bergmann, Stephan; Mohammadikaji, Mahsa; Beyerer, Jürgen; Dachsbacher, Carsten; Wörn, Heinz

2017-06-01

In this work, a simulation toolset for Computer Aided Inspection Planning (CAIP) of systems for automated optical inspection (AOI) is presented along with a versatile two-robot-setup for verification of simulation and system planning results. The toolset helps to narrow down the large design space of optical inspection systems in interaction with a system expert. The image formation taking place in optical inspection systems is simulated using GPU-based real time graphics and high quality off-line-rendering. The simulation pipeline allows a stepwise optimization of the system, from fast evaluation of surface patch visibility based on real time graphics up to evaluation of image processing results based on off-line global illumination calculation. A focus of this work is on the dependency of simulation quality on measuring, modeling and parameterizing the optical surface properties of the object to be inspected. The applicability to real world problems is demonstrated by taking the example of planning a 3D laser scanner application. Qualitative and quantitative comparison results of synthetic and real images are presented.

Policy Analysis Implications of a Model to Improve the Delivery of Financial Aid to Disadvantaged Students. AIR 1983 Annual Forum Paper.

ERIC Educational Resources Information Center

Fenske, Robert H.; Porter, John D.

The role of institutional research in policy analysis regarding the operation of a computer model for delivery of financial aid to disadvantaged students is considered. A student financial aid model at Arizona State University is designed to develop a profile of late appliers for aid funds and also those who file inaccurate or incomplete…

Just Scan It!-Weapon Reconstruction in Computed Tomography on Historical and Current Swiss Military Guns.

PubMed

Franckenberg, Sabine; Binder, Thomas; Bolliger, Stephan; Thali, Michael J; Ross, Steffen G

2016-09-01

Cross-sectional imaging, such as computed tomography, has been increasingly implemented in both historic and recent postmortem forensic investigations. It aids in determining cause and manner of death as well as in correlating injuries to possible weapons. This study illuminates the feasibility of reconstructing guns in computed tomography and gives a distinct overview of historic and recent Swiss Army guns.

[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.

Implementation of a low-cost mobile devices to support medical diagnosis.

PubMed

García Sánchez, Carlos; Botella Juan, Guillermo; Ayuso Márquez, Fermín; González Rodríguez, Diego; Prieto-Matías, Manuel; Tirado Fernández, Francisco

2013-01-01

Medical imaging has become an absolutely essential diagnostic tool for clinical practices; at present, pathologies can be detected with an earliness never before known. Its use has not only been relegated to the field of radiology but also, increasingly, to computer-based imaging processes prior to surgery. Motion analysis, in particular, plays an important role in analyzing activities or behaviors of live objects in medicine. This short paper presents several low-cost hardware implementation approaches for the new generation of tablets and/or smartphones for estimating motion compensation and segmentation in medical images. These systems have been optimized for breast cancer diagnosis using magnetic resonance imaging technology with several advantages over traditional X-ray mammography, for example, obtaining patient information during a short period. This paper also addresses the challenge of offering a medical tool that runs on widespread portable devices, both on tablets and/or smartphones to aid in patient diagnostics.

CADx Mammography

NASA Astrophysics Data System (ADS)

Costaridou, Lena

Although a wide variety of Computer-Aided Diagnosis (CADx) schemes have been proposed across breast imaging modalities, and especially in mammography, research is still ongoing to meet the high performance CADx requirements. In this chapter, methodological contributions to CADx in mammography and adjunct breast imaging modalities are reviewed, as they play a major role in early detection, diagnosis and clinical management of breast cancer. At first, basic terms and definitions are provided. Then, emphasis is given to lesion content derivation, both anatomical and functional, considering only quantitative image features of micro-calcification clusters and masses across modalities. Additionally, two CADx application examples are provided. The first example investigates the effect of segmentation accuracy on micro-calcification cluster morphology derivation in X-ray mammography. The second one demonstrates the efficiency of texture analysis in quantification of enhancement kinetics, related to vascular heterogeneity, for mass classification in dynamic contrast-enhanced magnetic resonance imaging.

Deep learning approach to bacterial colony classification.

PubMed

Zieliński, Bartosz; Plichta, Anna; Misztal, Krzysztof; Spurek, Przemysław; Brzychczy-Włoch, Monika; Ochońska, Dorota

2017-01-01

In microbiology it is diagnostically useful to recognize various genera and species of bacteria. It can be achieved using computer-aided methods, which make the recognition processes more automatic and thus significantly reduce the time necessary for the classification. Moreover, in case of diagnostic uncertainty (the misleading similarity in shape or structure of bacterial cells), such methods can minimize the risk of incorrect recognition. In this article, we apply the state of the art method for texture analysis to classify genera and species of bacteria. This method uses deep Convolutional Neural Networks to obtain image descriptors, which are then encoded and classified with Support Vector Machine or Random Forest. To evaluate this approach and to make it comparable with other approaches, we provide a new dataset of images. DIBaS dataset (Digital Image of Bacterial Species) contains 660 images with 33 different genera and species of bacteria.

Implementation of a Low-Cost Mobile Devices to Support Medical Diagnosis

PubMed Central

García Sánchez, Carlos; Botella Juan, Guillermo; Ayuso Márquez, Fermín; González Rodríguez, Diego; Prieto-Matías, Manuel; Tirado Fernández, Francisco

2013-01-01

Medical imaging has become an absolutely essential diagnostic tool for clinical practices; at present, pathologies can be detected with an earliness never before known. Its use has not only been relegated to the field of radiology but also, increasingly, to computer-based imaging processes prior to surgery. Motion analysis, in particular, plays an important role in analyzing activities or behaviors of live objects in medicine. This short paper presents several low-cost hardware implementation approaches for the new generation of tablets and/or smartphones for estimating motion compensation and segmentation in medical images. These systems have been optimized for breast cancer diagnosis using magnetic resonance imaging technology with several advantages over traditional X-ray mammography, for example, obtaining patient information during a short period. This paper also addresses the challenge of offering a medical tool that runs on widespread portable devices, both on tablets and/or smartphones to aid in patient diagnostics. PMID:24489600

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

Three-Dimensional Computer-Aided Detection of Microcalcification Clusters in Digital Breast Tomosynthesis.

PubMed

Jeong, Ji-Wook; Chae, Seung-Hoon; Chae, Eun Young; Kim, Hak Hee; Choi, Young-Wook; Lee, Sooyeul

2016-01-01

We propose computer-aided detection (CADe) algorithm for microcalcification (MC) clusters in reconstructed digital breast tomosynthesis (DBT) images. The algorithm consists of prescreening, MC detection, clustering, and false-positive (FP) reduction steps. The DBT images containing the MC-like objects were enhanced by a multiscale Hessian-based three-dimensional (3D) objectness response function and a connected-component segmentation method was applied to extract the cluster seed objects as potential clustering centers of MCs. Secondly, a signal-to-noise ratio (SNR) enhanced image was also generated to detect the individual MC candidates and prescreen the MC-like objects. Each cluster seed candidate was prescreened by counting neighboring individual MC candidates nearby the cluster seed object according to several microcalcification clustering criteria. As a second step, we introduced bounding boxes for the accepted seed candidate, clustered all the overlapping cubes, and examined. After the FP reduction step, the average number of FPs per case was estimated to be 2.47 per DBT volume with a sensitivity of 83.3%.

The virtual mirror: a new interaction paradigm for augmented reality environments.

PubMed

Bichlmeier, Christoph; Heining, Sandro Michael; Feuerstein, Marco; Navab, Nassir

2009-09-01

Medical augmented reality (AR) has been widely discussed within the medical imaging as well as computer aided surgery communities. Different systems for exemplary medical applications have been proposed. Some of them produced promising results. One major issue still hindering AR technology to be regularly used in medical applications is the interaction between physician and the superimposed 3-D virtual data. Classical interaction paradigms, for instance with keyboard and mouse, to interact with visualized medical 3-D imaging data are not adequate for an AR environment. This paper introduces the concept of a tangible/controllable Virtual Mirror for medical AR applications. This concept intuitively augments the direct view of the surgeon with all desired views on volumetric medical imaging data registered with the operation site without moving around the operating table or displacing the patient. We selected two medical procedures to demonstrate and evaluate the potentials of the Virtual Mirror for the surgical workflow. Results confirm the intuitiveness of this new paradigm and its perceptive advantages for AR-based computer aided interventions.

Computer aided system for segmentation and visualization of microcalcifications in digital mammograms.

PubMed

Reljin, Branimir; Milosević, Zorica; Stojić, Tomislav; Reljin, Irini

2009-01-01

Two methods for segmentation and visualization of microcalcifications in digital or digitized mammograms are described. First method is based on modern mathematical morphology, while the second one uses the multifractal approach. In the first method, by using an appropriate combination of some morphological operations, high local contrast enhancement, followed by significant suppression of background tissue, irrespective of its radiology density, is obtained. By iterative procedure, this method highly emphasizes only small bright details, possible microcalcifications. In a multifractal approach, from initial mammogram image, a corresponding multifractal "images" are created, from which a radiologist has a freedom to change the level of segmentation. An appropriate user friendly computer aided visualization (CAV) system with embedded two methods is realized. The interactive approach enables the physician to control the level and the quality of segmentation. Suggested methods were tested through mammograms from MIAS database as a gold standard, and from clinical praxis, using digitized films and digital images from full field digital mammograph.

Interfacing Computer Aided Parallelization and Performance Analysis

NASA Technical Reports Server (NTRS)

Jost, Gabriele; Jin, Haoqiang; Labarta, Jesus; Gimenez, Judit; Biegel, Bryan A. (Technical Monitor)

2003-01-01

When porting sequential applications to parallel computer architectures, the program developer will typically go through several cycles of source code optimization and performance analysis. We have started a project to develop an environment where the user can jointly navigate through program structure and performance data information in order to make efficient optimization decisions. In a prototype implementation we have interfaced the CAPO computer aided parallelization tool with the Paraver performance analysis tool. We describe both tools and their interface and give an example for how the interface helps within the program development cycle of a benchmark code.

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.

Design and fabrication of facial prostheses for cancer patient applying computer aided method and manufacturing (CADCAM)

NASA Astrophysics Data System (ADS)

Din, Tengku Noor Daimah Tengku; Jamayet, Nafij; Rajion, Zainul Ahmad; Luddin, Norhayati; Abdullah, Johari Yap; Abdullah, Abdul Manaf; Yahya, Suzana

2016-12-01

Facial defects are either congenital or caused by trauma or cancer where most of them affect the person appearance. The emotional pressure and low self-esteem are problems commonly related to patient with facial defect. To overcome this problem, silicone prosthesis was designed to cover the defect part. This study describes the techniques in designing and fabrication for facial prosthesis applying computer aided method and manufacturing (CADCAM). The steps of fabricating the facial prosthesis were based on a patient case. The patient was diagnosed for Gorlin Gotz syndrome and came to Hospital Universiti Sains Malaysia (HUSM) for prosthesis. The 3D image of the patient was reconstructed from CT data using MIMICS software. Based on the 3D image, the intercanthal and zygomatic measurements of the patient were compared with available data in the database to find the suitable nose shape. The normal nose shape for the patient was retrieved from the nasal digital library. Mirror imaging technique was used to mirror the facial part. The final design of facial prosthesis including eye, nose and cheek was superimposed to see the result virtually. After the final design was confirmed, the mould design was created. The mould of nasal prosthesis was printed using Objet 3D printer. Silicone casting was done using the 3D print mould. The final prosthesis produced from the computer aided method was acceptable to be used for facial rehabilitation to provide better quality of life.

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

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.

A method for normalizing pathology images to improve feature extraction for quantitative pathology.

PubMed

Tam, Allison; Barker, Jocelyn; Rubin, Daniel

2016-01-01

With the advent of digital slide scanning technologies and the potential proliferation of large repositories of digital pathology images, many research studies can leverage these data for biomedical discovery and to develop clinical applications. However, quantitative analysis of digital pathology images is impeded by batch effects generated by varied staining protocols and staining conditions of pathological slides. To overcome this problem, this paper proposes a novel, fully automated stain normalization method to reduce batch effects and thus aid research in digital pathology applications. Their method, intensity centering and histogram equalization (ICHE), normalizes a diverse set of pathology images by first scaling the centroids of the intensity histograms to a common point and then applying a modified version of contrast-limited adaptive histogram equalization. Normalization was performed on two datasets of digitized hematoxylin and eosin (H&E) slides of different tissue slices from the same lung tumor, and one immunohistochemistry dataset of digitized slides created by restaining one of the H&E datasets. The ICHE method was evaluated based on image intensity values, quantitative features, and the effect on downstream applications, such as a computer aided diagnosis. For comparison, three methods from the literature were reimplemented and evaluated using the same criteria. The authors found that ICHE not only improved performance compared with un-normalized images, but in most cases showed improvement compared with previous methods for correcting batch effects in the literature. ICHE may be a useful preprocessing step a digital pathology image processing pipeline.

A marker-based watershed method for X-ray image segmentation.

PubMed

Zhang, Xiaodong; Jia, Fucang; Luo, Suhuai; Liu, Guiying; Hu, Qingmao

2014-03-01

Digital X-ray images are the most frequent modality for both screening and diagnosis in hospitals. To facilitate subsequent analysis such as quantification and computer aided diagnosis (CAD), it is desirable to exclude image background. A marker-based watershed segmentation method was proposed to segment background of X-ray images. The method consisted of six modules: image preprocessing, gradient computation, marker extraction, watershed segmentation from markers, region merging and background extraction. One hundred clinical direct radiograph X-ray images were used to validate the method. Manual thresholding and multiscale gradient based watershed method were implemented for comparison. The proposed method yielded a dice coefficient of 0.964±0.069, which was better than that of the manual thresholding (0.937±0.119) and that of multiscale gradient based watershed method (0.942±0.098). Special means were adopted to decrease the computational cost, including getting rid of few pixels with highest grayscale via percentile, calculation of gradient magnitude through simple operations, decreasing the number of markers by appropriate thresholding, and merging regions based on simple grayscale statistics. As a result, the processing time was at most 6s even for a 3072×3072 image on a Pentium 4 PC with 2.4GHz CPU (4 cores) and 2G RAM, which was more than one time faster than that of the multiscale gradient based watershed method. The proposed method could be a potential tool for diagnosis and quantification of X-ray images. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Digital Workflow for Computer-Guided Implant Surgery in Edentulous Patients: A Case Report.

PubMed

Oh, Ji-Hyeon; An, Xueyin; Jeong, Seung-Mi; Choi, Byung-Ho

2017-12-01

The purpose of this article was to describe a fully digital workflow used to perform computer-guided flapless implant placement in an edentulous patient without the use of conventional impressions, models, or a radiographic guide. Digital data for the workflow were acquired using an intraoral scanner and cone-beam computed tomography (CBCT). The image fusion of the intraoral scan data and CBCT data was performed by matching resin markers placed in the patient's mouth. The definitive digital data were used to design a prosthetically driven implant position, surgical template, and computer-aided design and computer-aided manufacturing fabricated fixed dental prosthesis. The authors believe this is the first published case describing such a technique in computer-guided flapless implant surgery for edentulous patients. Copyright © 2017 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

Computer-aided design and rapid prototyping-assisted contouring of costal cartilage graft for facial reconstructive surgery.

PubMed

Lee, Shu Jin; Lee, Heow Pueh; Tse, Kwong Ming; Cheong, Ee Cherk; Lim, Siak Piang

2012-06-01

Complex 3-D defects of the facial skeleton are difficult to reconstruct with freehand carving of autogenous bone grafts. Onlay bone grafts are hard to carve and are associated with imprecise graft-bone interface contact and bony resorption. Autologous cartilage is well established in ear reconstruction as it is easy to carve and is associated with minimal resorption. In the present study, we aimed to reconstruct the hypoplastic orbitozygomatic region in a patient with left hemifacial microsomia using computer-aided design and rapid prototyping to facilitate costal cartilage carving and grafting. A three-step process of (1) 3-D reconstruction of the computed tomographic image, (2) mirroring the facial skeleton, and (3) modeling and rapid prototyping of the left orbitozygomaticomalar region and reconstruction template was performed. The template aided in donor site selection and extracorporeal contouring of the rib cartilage graft to allow for an accurate fit of the graft to the bony model prior to final fixation in the patient. We are able to refine the existing computer-aided design and rapid prototyping methods to allow for extracorporeal contouring of grafts and present rib cartilage as a good alternative to bone for autologous reconstruction.

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

Mixture of learners for cancer stem cell detection using CD13 and H and E stained images

NASA Astrophysics Data System (ADS)

Oǧuz, Oǧuzhan; Akbaş, Cem Emre; Mallah, Maen; Taşdemir, Kasım.; Akhan Güzelcan, Ece; Muenzenmayer, Christian; Wittenberg, Thomas; Üner, Ayşegül; Cetin, A. E.; ćetin Atalay, Rengül

2016-03-01

In this article, algorithms for cancer stem cell (CSC) detection in liver cancer tissue images are developed. Conventionally, a pathologist examines of cancer cell morphologies under microscope. Computer aided diagnosis systems (CAD) aims to help pathologists in this tedious and repetitive work. The first algorithm locates CSCs in CD13 stained liver tissue images. The method has also an online learning algorithm to improve the accuracy of detection. The second family of algorithms classify the cancer tissues stained with H and E which is clinically routine and cost effective than immunohistochemistry (IHC) procedure. The algorithms utilize 1D-SIFT and Eigen-analysis based feature sets as descriptors. Normal and cancerous tissues can be classified with 92.1% accuracy in H and E stained images. Classification accuracy of low and high-grade cancerous tissue images is 70.4%. Therefore, this study paves the way for diagnosing the cancerous tissue and grading the level of it using H and E stained microscopic tissue images.

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

A way toward analyzing high-content bioimage data by means of semantic annotation and visual data mining

NASA Astrophysics Data System (ADS)

Herold, Julia; Abouna, Sylvie; Zhou, Luxian; Pelengaris, Stella; Epstein, David B. A.; Khan, Michael; Nattkemper, Tim W.

2009-02-01

In the last years, bioimaging has turned from qualitative measurements towards a high-throughput and highcontent modality, providing multiple variables for each biological sample analyzed. We present a system which combines machine learning based semantic image annotation and visual data mining to analyze such new multivariate bioimage data. Machine learning is employed for automatic semantic annotation of regions of interest. The annotation is the prerequisite for a biological object-oriented exploration of the feature space derived from the image variables. With the aid of visual data mining, the obtained data can be explored simultaneously in the image as well as in the feature domain. Especially when little is known of the underlying data, for example in the case of exploring the effects of a drug treatment, visual data mining can greatly aid the process of data evaluation. We demonstrate how our system is used for image evaluation to obtain information relevant to diabetes study and screening of new anti-diabetes treatments. Cells of the Islet of Langerhans and whole pancreas in pancreas tissue samples are annotated and object specific molecular features are extracted from aligned multichannel fluorescence images. These are interactively evaluated for cell type classification in order to determine the cell number and mass. Only few parameters need to be specified which makes it usable also for non computer experts and allows for high-throughput analysis.

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.

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.

The New Realm of 3-D Vision

NASA Technical Reports Server (NTRS)

2002-01-01

Dimension Technologies Inc., developed a line of 2-D/3-D Liquid Crystal Display (LCD) screens, including a 15-inch model priced at consumer levels. DTI's family of flat panel LCD displays, called the Virtual Window(TM), provide real-time 3-D images without the use of glasses, head trackers, helmets, or other viewing aids. Most of the company initial 3-D display research was funded through NASA's Small Business Innovation Research (SBIR) program. The images on DTI's displays appear to leap off the screen and hang in space. The display accepts input from computers or stereo video sources, and can be switched from 3-D to full-resolution 2-D viewing with the push of a button. The Virtual Window displays have applications in data visualization, medicine, architecture, business, real estate, entertainment, and other research, design, military, and consumer applications. Displays are currently used for computer games, protein analysis, and surgical imaging. The technology greatly benefits the medical field, as surgical simulators are helping to increase the skills of surgical residents. Virtual Window(TM) is a trademark of Dimension Technologies Inc.

Automatic rectum limit detection by anatomical markers correlation.

PubMed

Namías, R; D'Amato, J P; del Fresno, M; Vénere, M

2014-06-01

Several diseases take place at the end of the digestive system. Many of them can be diagnosed by means of different medical imaging modalities together with computer aided detection (CAD) systems. These CAD systems mainly focus on the complete segmentation of the digestive tube. However, the detection of limits between different sections could provide important information to these systems. In this paper we present an automatic method for detecting the rectum and sigmoid colon limit using a novel global curvature analysis over the centerline of the segmented digestive tube in different imaging modalities. The results are compared with the gold standard rectum upper limit through a validation scheme comprising two different anatomical markers: the third sacral vertebra and the average rectum length. Experimental results in both magnetic resonance imaging (MRI) and computed tomography colonography (CTC) acquisitions show the efficacy of the proposed strategy in automatic detection of rectum limits. The method is intended for application to the rectum segmentation in MRI for geometrical modeling and as contextual information source in virtual colonoscopies and CAD systems. Copyright © 2014 Elsevier Ltd. All rights reserved.

Retinal status analysis method based on feature extraction and quantitative grading in OCT images.

PubMed

Fu, Dongmei; Tong, Hejun; Zheng, Shuang; Luo, Ling; Gao, Fulin; Minar, Jiri

2016-07-22

Optical coherence tomography (OCT) is widely used in ophthalmology for viewing the morphology of the retina, which is important for disease detection and assessing therapeutic effect. The diagnosis of retinal diseases is based primarily on the subjective analysis of OCT images by trained ophthalmologists. This paper describes an OCT images automatic analysis method for computer-aided disease diagnosis and it is a critical part of the eye fundus diagnosis. This study analyzed 300 OCT images acquired by Optovue Avanti RTVue XR (Optovue Corp., Fremont, CA). Firstly, the normal retinal reference model based on retinal boundaries was presented. Subsequently, two kinds of quantitative methods based on geometric features and morphological features were proposed. This paper put forward a retinal abnormal grading decision-making method which was used in actual analysis and evaluation of multiple OCT images. This paper showed detailed analysis process by four retinal OCT images with different abnormal degrees. The final grading results verified that the analysis method can distinguish abnormal severity and lesion regions. This paper presented the simulation of the 150 test images, where the results of analysis of retinal status showed that the sensitivity was 0.94 and specificity was 0.92.The proposed method can speed up diagnostic process and objectively evaluate the retinal status. This paper aims on studies of retinal status automatic analysis method based on feature extraction and quantitative grading in OCT images. The proposed method can obtain the parameters and the features that are associated with retinal morphology. Quantitative analysis and evaluation of these features are combined with reference model which can realize the target image abnormal judgment and provide a reference for disease diagnosis.

Medical Imaging System

NASA Technical Reports Server (NTRS)

1991-01-01

The MD Image System, a true-color image processing system that serves as a diagnostic aid and tool for storage and distribution of images, was developed by Medical Image Management Systems, Huntsville, AL, as a "spinoff from a spinoff." The original spinoff, Geostar 8800, developed by Crystal Image Technologies, Huntsville, incorporates advanced UNIX versions of ELAS (developed by NASA's Earth Resources Laboratory for analysis of Landsat images) for general purpose image processing. The MD Image System is an application of this technology to a medical system that aids in the diagnosis of cancer, and can accept, store and analyze images from other sources such as Magnetic Resonance Imaging.

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

29 CFR 541.401 - Computer manufacture and repair.

Code of Federal Regulations, 2014 CFR

2014-07-01

..., the use of computers and computer software programs (e.g., engineers, drafters and others skilled in computer-aided design software), but who are not primarily engaged in computer systems analysis and...

29 CFR 541.401 - Computer manufacture and repair.

Code of Federal Regulations, 2012 CFR

2012-07-01

..., the use of computers and computer software programs (e.g., engineers, drafters and others skilled in computer-aided design software), but who are not primarily engaged in computer systems analysis and...

29 CFR 541.401 - Computer manufacture and repair.

Code of Federal Regulations, 2013 CFR

2013-07-01

..., the use of computers and computer software programs (e.g., engineers, drafters and others skilled in computer-aided design software), but who are not primarily engaged in computer systems analysis and...

29 CFR 541.401 - Computer manufacture and repair.

Code of Federal Regulations, 2010 CFR

2010-07-01

..., the use of computers and computer software programs (e.g., engineers, drafters and others skilled in computer-aided design software), but who are not primarily engaged in computer systems analysis and...

Computer-aided target tracking in motion analysis studies

NASA Astrophysics Data System (ADS)

Burdick, Dominic C.; Marcuse, M. L.; Mislan, J. D.

1990-08-01

Motion analysis studies require the precise tracking of reference objects in sequential scenes. In a typical situation, events of interest are captured at high frame rates using special cameras, and selected objects or targets are tracked on a frame by frame basis to provide necessary data for motion reconstruction. Tracking is usually done using manual methods which are slow and prone to error. A computer based image analysis system has been developed that performs tracking automatically. The objective of this work was to eliminate the bottleneck due to manual methods in high volume tracking applications such as the analysis of crash test films for the automotive industry. The system has proven to be successful in tracking standard fiducial targets and other objects in crash test scenes. Over 95 percent of target positions which could be located using manual methods can be tracked by the system, with a significant improvement in throughput over manual methods. Future work will focus on the tracking of clusters of targets and on tracking deformable objects such as airbags.

Ideal AFROC and FROC observers.

PubMed

Khurd, Parmeshwar; Liu, Bin; Gindi, Gene

2010-02-01

Detection of multiple lesions in images is a medically important task and free-response receiver operating characteristic (FROC) analyses and its variants, such as alternative FROC (AFROC) analyses, are commonly used to quantify performance in such tasks. However, ideal observers that optimize FROC or AFROC performance metrics have not yet been formulated in the general case. If available, such ideal observers may turn out to be valuable for imaging system optimization and in the design of computer aided diagnosis techniques for lesion detection in medical images. In this paper, we derive ideal AFROC and FROC observers. They are ideal in that they maximize, amongst all decision strategies, the area, or any partial area, under the associated AFROC or FROC curve. Calculation of observer performance for these ideal observers is computationally quite complex. We can reduce this complexity by considering forms of these observers that use false positive reports derived from signal-absent images only. We also consider a Bayes risk analysis for the multiple-signal detection task with an appropriate definition of costs. A general decision strategy that minimizes Bayes risk is derived. With particular cost constraints, this general decision strategy reduces to the decision strategy associated with the ideal AFROC or FROC observer.

Holodeck: Telepresence Dome Visualization System Simulations

NASA Technical Reports Server (NTRS)

Hite, Nicolas

2012-01-01

This paper explores the simulation and consideration of different image-projection strategies for the Holodeck, a dome that will be used for highly immersive telepresence operations in future endeavors of the National Aeronautics and Space Administration (NASA). Its visualization system will include a full 360 degree projection onto the dome's interior walls in order to display video streams from both simulations and recorded video. Because humans innately trust their vision to precisely report their surroundings, the Holodeck's visualization system is crucial to its realism. This system will be rigged with an integrated hardware and software infrastructure-namely, a system of projectors that will relay with a Graphics Processing Unit (GPU) and computer to both project images onto the dome and correct warping in those projections in real-time. Using both Computer-Aided Design (CAD) and ray-tracing software, virtual models of various dome/projector geometries were created and simulated via tracking and analysis of virtual light sources, leading to the selection of two possible configurations for installation. Research into image warping and the generation of dome-ready video content was also conducted, including generation of fisheye images, distortion correction, and the generation of a reliable content-generation pipeline.

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

NASA Technical Reports Server (NTRS)

Hoffer, R. M. (Principal Investigator); Latty, R. S.; Dean, E.; Knowlton, D. J.

1980-01-01

Separate holograms of horizontally (HH) and vertically (HV) polarized responses obtained by the APQ-102 side-looking radar were processed through an optical correlator and the resulting image was recorded on positive film from which black and white negative and positive prints were made. Visual comparison of the HH and HV images reveals a distinct dark band in the imagery which covers about 30% of the radar strip. Preliminary evaluaton of the flight line 1 date indicates that various features on the HH and HV images seem to have different response levels. The amount of sidelap due to the look angle between flight lines 1 and 2 is negligible. NASA mission #425 to obtain flightlines of NS-001 MSS data and supporting aerial photography was successfully flown. Flight line 3 data are of very good quality and virtually cloud-free. Results of data analysis for selection of test fields and for evaluation of waveband combination and spatial resolution are presented.

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.

The Use of Computers to Aid Instruction in Beginning Chemistry

ERIC Educational Resources Information Center

Grandey, Robert C.

1971-01-01

Describes computer-aided lessons for determining chemical formulas from composition by weight, quantities from chemical equations, and balancing equations for oxidation-reduction reactions. Lessons were developed and used on the PLATO system at the University of Illinois. A brief analysis of student attitudes and of effectiveness of the programs…

Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms.

PubMed

Coppini, Giuseppe; Diciotti, Stefano; Falchini, Massimo; Villari, Natale; Valli, Guido

2003-12-01

The paper describes a neural-network-based system for the computer aided detection of lung nodules in chest radiograms. Our approach is based on multiscale processing and artificial neural networks (ANNs). The problem of nodule detection is faced by using a two-stage architecture including: 1) an attention focusing subsystem that processes whole radiographs to locate possible nodular regions ensuring high sensitivity; 2) a validation subsystem that processes regions of interest to evaluate the likelihood of the presence of a nodule, so as to reduce false alarms and increase detection specificity. Biologically inspired filters (both LoG and Gabor kernels) are used to enhance salient image features. ANNs of the feedforward type are employed, which allow an efficient use of a priori knowledge about the shape of nodules, and the background structure. The images from the public JSRT database, including 247 radiograms, were used to build and test the system. We performed a further test by using a second private database with 65 radiograms collected and annotated at the Radiology Department of the University of Florence. Both data sets include nodule and nonnodule radiographs. The use of a public data set along with independent testing with a different image set makes the comparison with other systems easier and allows a deeper understanding of system behavior. Experimental results are described by ROC/FROC analysis. For the JSRT database, we observed that by varying sensitivity from 60 to 75% the number of false alarms per image lies in the range 4-10, while accuracy is in the range 95.7-98.0%. When the second data set was used comparable results were obtained. The observed system performances support the undertaking of system validation in clinical settings.

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.

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

Morphology filter bank for extracting nodular and linear patterns in medical images.

PubMed

Hashimoto, Ryutaro; Uchiyama, Yoshikazu; Uchimura, Keiichi; Koutaki, Gou; Inoue, Tomoki

2017-04-01

Using image processing to extract nodular or linear shadows is a key technique of computer-aided diagnosis schemes. This study proposes a new method for extracting nodular and linear patterns of various sizes in medical images. We have developed a morphology filter bank that creates multiresolution representations of an image. Analysis bank of this filter bank produces nodular and linear patterns at each resolution level. Synthesis bank can then be used to perfectly reconstruct the original image from these decomposed patterns. Our proposed method shows better performance based on a quantitative evaluation using a synthesized image compared with a conventional method based on a Hessian matrix, often used to enhance nodular and linear patterns. In addition, experiments show that our method can be applied to the followings: (1) microcalcifications of various sizes in mammograms can be extracted, (2) blood vessels of various sizes in retinal fundus images can be extracted, and (3) thoracic CT images can be reconstructed while removing normal vessels. Our proposed method is useful for extracting nodular and linear shadows or removing normal structures in medical images.

Fundamental Concepts of Digital Image Processing

DOE R&D Accomplishments Database

Twogood, R. E.

1983-03-01

The field of a digital-image processing has experienced dramatic growth and increasingly widespread applicability in recent years. Fortunately, advances in computer technology have kept pace with the rapid growth in volume of image data in these and other applications. Digital image processing has become economical in many fields of research and in industrial and military applications. While each application has requirements unique from the others, all are concerned with faster, cheaper, more accurate, and more extensive computation. The trend is toward real-time and interactive operations, where the user of the system obtains preliminary results within a short enough time that the next decision can be made by the human processor without loss of concentration on the task at hand. An example of this is the obtaining of two-dimensional (2-D) computer-aided tomography (CAT) images. A medical decision might be made while the patient is still under observation rather than days later.

Program Aids Design Of Fluid-Circulating Systems

NASA Technical Reports Server (NTRS)

Bacskay, Allen; Dalee, Robert

1992-01-01

Computer Aided Systems Engineering and Analysis (CASE/A) program is interactive software tool for trade study and analysis, designed to increase productivity during all phases of systems engineering. Graphics-based command-driven software package provides user-friendly computing environment in which engineer analyzes performance and interface characteristics of ECLS/ATC system. Useful during all phases of spacecraft-design program, from initial conceptual design trade studies to actual flight, including pre-flight prediction and in-flight analysis of anomalies. Written in FORTRAN 77.

Brain CT image similarity retrieval method based on uncertain location graph.

PubMed

Pan, Haiwei; Li, Pengyuan; Li, Qing; Han, Qilong; Feng, Xiaoning; Gao, Linlin

2014-03-01

A number of brain computed tomography (CT) images stored in hospitals that contain valuable information should be shared to support computer-aided diagnosis systems. Finding the similar brain CT images from the brain CT image database can effectively help doctors diagnose based on the earlier cases. However, the similarity retrieval for brain CT images requires much higher accuracy than the general images. In this paper, a new model of uncertain location graph (ULG) is presented for brain CT image modeling and similarity retrieval. According to the characteristics of brain CT image, we propose a novel method to model brain CT image to ULG based on brain CT image texture. Then, a scheme for ULG similarity retrieval is introduced. Furthermore, an effective index structure is applied to reduce the searching time. Experimental results reveal that our method functions well on brain CT images similarity retrieval with higher accuracy and efficiency.

Program design by a multidisciplinary team. [for structural finite element analysis on STAR-100 computer

NASA Technical Reports Server (NTRS)

Voigt, S.

1975-01-01

The use of software engineering aids in the design of a structural finite-element analysis computer program for the STAR-100 computer is described. Nested functional diagrams to aid in communication among design team members were used, and a standardized specification format to describe modules designed by various members was adopted. This is a report of current work in which use of the functional diagrams provided continuity and helped resolve some of the problems arising in this long-running part-time project.

Augmented reality to the rescue of the minimally invasive surgeon. The usefulness of the interposition of stereoscopic images in the Da Vinci™ robotic console.

PubMed

Volonté, Francesco; Buchs, Nicolas C; Pugin, François; Spaltenstein, Joël; Schiltz, Boris; Jung, Minoa; Hagen, Monika; Ratib, Osman; Morel, Philippe

2013-09-01

Computerized management of medical information and 3D imaging has become the norm in everyday medical practice. Surgeons exploit these emerging technologies and bring information previously confined to the radiology rooms into the operating theatre. The paper reports the authors' experience with integrated stereoscopic 3D-rendered images in the da Vinci surgeon console. Volume-rendered images were obtained from a standard computed tomography dataset using the OsiriX DICOM workstation. A custom OsiriX plugin was created that permitted the 3D-rendered images to be displayed in the da Vinci surgeon console and to appear stereoscopic. These rendered images were displayed in the robotic console using the TilePro multi-input display. The upper part of the screen shows the real endoscopic surgical field and the bottom shows the stereoscopic 3D-rendered images. These are controlled by a 3D joystick installed on the console, and are updated in real time. Five patients underwent a robotic augmented reality-enhanced procedure. The surgeon was able to switch between the classical endoscopic view and a combined virtual view during the procedure. Subjectively, the addition of the rendered images was considered to be an undeniable help during the dissection phase. With the rapid evolution of robotics, computer-aided surgery is receiving increasing interest. This paper details the authors' experience with 3D-rendered images projected inside the surgical console. The use of this intra-operative mixed reality technology is considered very useful by the surgeon. It has been shown that the usefulness of this technique is a step toward computer-aided surgery that will progress very quickly over the next few years. Copyright © 2012 John Wiley & Sons, Ltd.

Removal of Supernumerary Teeth Utilizing a Computer-Aided Design/Computer-Aided Manufacturing Surgical Guide.

PubMed

Jo, Chanwoo; Bae, Doohwan; Choi, Byungho; Kim, Jihun

2017-05-01

Supernumerary teeth need to be removed because they can cause various complications. Caution is needed because their removal can cause damage to permanent teeth or tooth germs in the local vicinity. Surgical guides have recently been used in maxillofacial surgery. Because surgical guides are designed through preoperative analysis by computer-aided design software and fabricated using a 3-dimensional printer applying computer-aided manufacturing technology, they increase the accuracy and predictability of surgery. This report describes 2 cases of removal of a mesiodens-1 from a child and 1 from an adolescent-using a surgical guide; these would have been difficult to remove with conventional surgical methods. Copyright © 2016 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition.

PubMed

Janousova, Eva; Schwarz, Daniel; Kasparek, Tomas

2015-06-30

We investigated a combination of three classification algorithms, namely the modified maximum uncertainty linear discriminant analysis (mMLDA), the centroid method, and the average linkage, with three types of features extracted from three-dimensional T1-weighted magnetic resonance (MR) brain images, specifically MR intensities, grey matter densities, and local deformations for distinguishing 49 first episode schizophrenia male patients from 49 healthy male subjects. The feature sets were reduced using intersubject principal component analysis before classification. By combining the classifiers, we were able to obtain slightly improved results when compared with single classifiers. The best classification performance (81.6% accuracy, 75.5% sensitivity, and 87.8% specificity) was significantly better than classification by chance. We also showed that classifiers based on features calculated using more computation-intensive image preprocessing perform better; mMLDA with classification boundary calculated as weighted mean discriminative scores of the groups had improved sensitivity but similar accuracy compared to the original MLDA; reducing a number of eigenvectors during data reduction did not always lead to higher classification accuracy, since noise as well as the signal important for classification were removed. Our findings provide important information for schizophrenia research and may improve accuracy of computer-aided diagnostics of neuropsychiatric diseases. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Patient-specific polyetheretherketone facial implants in a computer-aided planning workflow.

PubMed

Guevara-Rojas, Godoberto; Figl, Michael; Schicho, Kurt; Seemann, Rudolf; Traxler, Hannes; Vacariu, Apostolos; Carbon, Claus-Christian; Ewers, Rolf; Watzinger, Franz

2014-09-01

In the present study, we report an innovative workflow using polyetheretherketone (PEEK) patient-specific implants for esthetic corrections in the facial region through onlay grafting. The planning includes implant design according to virtual osteotomy and generation of a subtraction volume. The implant design was refined by stepwise changing the implant geometry according to soft tissue simulations. One patient was scanned using computed tomography. PEEK implants were interactively designed and manufactured using rapid prototyping techniques. Positioning intraoperatively was assisted by computer-aided navigation. Two months after surgery, a 3-dimensional surface model of the patient's face was generated using photogrammetry. Finally, the Hausdorff distance calculation was used to quantify the overall error, encompassing the failures in soft tissue simulation and implantation. The implant positioning process during surgery was satisfactory. The simulated soft tissue surface and the photogrammetry scan of the patient showed a high correspondence, especially where the skin covered the implants. The mean total error (Hausdorff distance) was 0.81 ± 1.00 mm (median 0.48, interquartile range 1.11). The spatial deviation remained less than 0.7 mm for the vast majority of points. The proposed workflow provides a complete computer-aided design, computer-aided manufacturing, and computer-aided surgery chain for implant design, allowing for soft tissue simulation, fabrication of patient-specific implants, and image-guided surgery to position the implants. Much of the surgical complexity resulting from osteotomies of the zygoma, chin, or mandibular angle might be transferred into the planning phase of patient-specific implants. Copyright © 2014 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

Pulmonary cryptococcosis in rheumatoid arthritis (RA) patients: comparison of imaging characteristics among RA, acquired immunodeficiency syndrome, and immunocompetent patients.

PubMed

Yanagawa, Noriyo; Sakai, Fumikazu; Takemura, Tamiko; Ishikawa, Satoru; Takaki, Yasunobu; Hishima, Tsunekazu; Kamata, Noriko

2013-11-01

The imaging characteristics of cryptococcosis in rheumatoid arthritis (RA) patients were analyzed by comparing them with those of acquired immunodeficiency syndrome (AIDS) and immunocompetent patients, and the imaging findings were correlated with pathological findings. Two radiologists retrospectively compared the computed tomographic (CT) findings of 35 episodes of pulmonary cryptococcosis in 31 patients with 3 kinds of underlying states (10 RA, 12 AIDS, 13 immunocompetent), focusing on the nature, number, and distribution of lesions. The pathological findings of 18 patients (8 RA, 2 AIDS, 8 immunocompetent) were analyzed by two pathologists, and then correlated with imaging findings. The frequencies of consolidation and ground glass attenuation (GGA) were significantly higher, and the frequency of peripheral distribution was significantly lower in the RA group than in the immunocompetent group. Peripheral distribution was less common and generalized distribution was more frequent in the RA group than in the AIDS group. The pathological findings of the AIDS and immunocompetent groups reflected their immune status: There was lack of a granuloma reaction in the AIDS group, and a complete granuloma reaction in the immunocompetent group, while the findings of the RA group varied, including a complete granuloma reaction, a loose granuloma reaction and a hyper-immune reaction. Cases with the last two pathologic findings were symptomatic and showed generalized or central distribution on CT. Cryptococcosis in the RA group showed characteristic radiological and pathological findings compared with the other 2 groups. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Benefit from NASA

NASA Image and Video Library

2001-09-01

The high-tech art of digital signal processing (DSP) was pioneered at NASA's Jet Propulsion Laboratory (JPL) in the mid-1960s for use in the Apollo Lunar Landing Program. Designed to computer enhance pictures of the Moon, this technology became the basis for the Landsat Earth resources satellites and subsequently has been incorporated into a broad range of Earthbound medical and diagnostic tools. DSP is employed in advanced body imaging techniques including Computer-Aided Tomography, also known as CT and CATScan, and Magnetic Resonance Imaging (MRI). CT images are collected by irradiating a thin slice of the body with a fan-shaped x-ray beam from a number of directions around the body's perimeter. A tomographic (slice-like) picture is reconstructed from these multiple views by a computer. MRI employs a magnetic field and radio waves, rather than x-rays, to create images.

Radiomics-based features for pattern recognition of lung cancer histopathology and metastases.

PubMed

Ferreira Junior, José Raniery; Koenigkam-Santos, Marcel; Cipriano, Federico Enrique Garcia; Fabro, Alexandre Todorovic; Azevedo-Marques, Paulo Mazzoncini de

2018-06-01

lung cancer is the leading cause of cancer-related deaths in the world, and its poor prognosis varies markedly according to tumor staging. Computed tomography (CT) is the imaging modality of choice for lung cancer evaluation, being used for diagnosis and clinical staging. Besides tumor stage, other features, like histopathological subtype, can also add prognostic information. In this work, radiomics-based CT features were used to predict lung cancer histopathology and metastases using machine learning models. local image datasets of confirmed primary malignant pulmonary tumors were retrospectively evaluated for testing and validation. CT images acquired with same protocol were semiautomatically segmented. Tumors were characterized by clinical features and computer attributes of intensity, histogram, texture, shape, and volume. Three machine learning classifiers used up to 100 selected features to perform the analysis. radiomics-based features yielded areas under the receiver operating characteristic curve of 0.89, 0.97, and 0.92 at testing and 0.75, 0.71, and 0.81 at validation for lymph nodal metastasis, distant metastasis, and histopathology pattern recognition, respectively. the radiomics characterization approach presented great potential to be used in a computational model to aid lung cancer histopathological subtype diagnosis as a "virtual biopsy" and metastatic prediction for therapy decision support without the necessity of a whole-body imaging scanning. Copyright © 2018 Elsevier B.V. All rights reserved.

COMPUTATIONAL ANALYSIS BASED ON ARTIFICIAL NEURAL NETWORKS FOR AIDING IN DIAGNOSING OSTEOARTHRITIS OF THE LUMBAR SPINE

PubMed Central

Veronezi, Carlos Cassiano Denipotti; de Azevedo Simões, Priscyla Waleska Targino; dos Santos, Robson Luiz; da Rocha, Edroaldo Lummertz; Meláo, Suelen; de Mattos, Merisandra Côrtes; Cechinel, Cristian

2015-01-01

Objective: To ascertain the advantages of applying artificial neural networks to recognize patterns on lumbar spine radiographies in order to aid in the process of diagnosing primary osteoarthritis. Methods: This was a cross-sectional descriptive analytical study with a quantitative approach and an emphasis on diagnosis. The training set was composed of images collected between January and July 2009 from patients who had undergone lateral-view digital radiographies of the lumbar spine, which were provided by a radiology clinic located in the municipality of Criciúma (SC). Out of the total of 260 images gathered, those with distortions, those presenting pathological conditions that altered the architecture of the lumbar spine and those with patterns that were difficult to characterize were discarded, resulting in 206 images. The image data base (n = 206) was then subdivided, resulting in 68 radiographies for the training stage, 68 images for tests and 70 for validation. A hybrid neural network based on Kohonen self-organizing maps and on Multilayer Perceptron networks was used. Results: After 90 cycles, the validation was carried out on the best results, achieving accuracy of 62.85%, sensitivity of 65.71% and specificity of 60%. Conclusions: Even though the effectiveness shown was moderate, this study is still innovative. The values show that the technique used has a promising future, pointing towards further studies on image and cycle processing methodology with a larger quantity of radiographies. PMID:27027010

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.

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.

Advances in computer-aided well-test interpretation

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

Horne, R.N.

1994-07-01

Despite the feeling expressed several times over the past 40 years that well-test analysis had reached it peak development, an examination of recent advances shows continuous expansion in capability, with future improvement likely. The expansion in interpretation capability over the past decade arose mainly from the development of computer-aided techniques, which, although introduced 20 years ago, have come into use only recently. The broad application of computer-aided interpretation originated with the improvement of the methodologies and continued with the expansion in computer access and capability that accompanied the explosive development of the microcomputer industry. This paper focuses on the differentmore » pieces of the methodology that combine to constitute a computer-aided interpretation and attempts to compare some of the approaches currently used. Future directions of the approach are also discussed. The separate areas discussed are deconvolution, pressure derivatives, model recognition, nonlinear regression, and confidence intervals.« less

Image-based diagnostic aid for interstitial lung disease with secondary data integration

NASA Astrophysics Data System (ADS)

Depeursinge, Adrien; Müller, Henning; Hidki, Asmâa; Poletti, Pierre-Alexandre; Platon, Alexandra; Geissbuhler, Antoine

2007-03-01

Interstitial lung diseases (ILDs) are a relatively heterogeneous group of around 150 illnesses with often very unspecific symptoms. The most complete imaging method for the characterisation of ILDs is the high-resolution computed tomography (HRCT) of the chest but a correct interpretation of these images is difficult even for specialists as many diseases are rare and thus little experience exists. Moreover, interpreting HRCT images requires knowledge of the context defined by clinical data of the studied case. A computerised diagnostic aid tool based on HRCT images with associated medical data to retrieve similar cases of ILDs from a dedicated database can bring quick and precious information for example for emergency radiologists. The experience from a pilot project highlighted the need for detailed database containing high-quality annotations in addition to clinical data. The state of the art is studied to identify requirements for image-based diagnostic aid for interstitial lung disease with secondary data integration. The data acquisition steps are detailed. The selection of the most relevant clinical parameters is done in collaboration with lung specialists from current literature, along with knowledge bases of computer-based diagnostic decision support systems. In order to perform high-quality annotations of the interstitial lung tissue in the HRCT images an annotation software and its own file format is implemented for DICOM images. A multimedia database is implemented to store ILD cases with clinical data and annotated image series. Cases from the University & University Hospitals of Geneva (HUG) are retrospectively and prospectively collected to populate the database. Currently, 59 cases with certified diagnosis and their clinical parameters are stored in the database as well as 254 image series of which 26 have their regions of interest annotated. The available data was used to test primary visual features for the classification of lung tissue patterns. These features show good discriminative properties for the separation of five classes of visual observations.

Computer-aided detection of initial polyp candidates with level set-based adaptive convolution

NASA Astrophysics Data System (ADS)

Zhu, Hongbin; Duan, Chaijie; Liang, Zhengrong

2009-02-01

In order to eliminate or weaken the interference between different topological structures on the colon wall, adaptive and normalized convolution methods were used to compute the first and second order spatial derivatives of computed tomographic colonography images, which is the beginning of various geometric analyses. However, the performance of such methods greatly depends on the single-layer representation of the colon wall, which is called the starting layer (SL) in the following text. In this paper, we introduce a level set-based adaptive convolution (LSAC) method to compute the spatial derivatives, in which the level set method is employed to determine a more reasonable SL. The LSAC was applied to a computer-aided detection (CAD) scheme to detect the initial polyp candidates, and experiments showed that it benefits the CAD scheme in both the detection sensitivity and specificity as compared to our previous work.

The utility of ERTS-1 data for applications in land use classification. [Texas Gulf Coast

NASA Technical Reports Server (NTRS)

Dornbach, J. E.; Mckain, G. E.

1974-01-01

A comprehensive study has been undertaken to determine the extent to which conventional image interpretation and computer-aided (spectral pattern recognition) analysis techniques using ERTS-1 data could be used to detect, identify (classify), locate, and measure current land use over large geographic areas. It can be concluded that most of the level 1 and 2 categories in the USGS Circular no. 671 can be detected in the Houston-Gulf Coast area using a combination of both techniques for analysis. These capabilities could be exercised over larger geographic areas, however, certain factors such as different vegetative cover, topography, etc. may have to be considered in other geographic regions. The best results in identification (classification), location, and measurement of level 1 and 2 type categories appear to be obtainable through automatic data processing of multispectral scanner computer compatible tapes.

Noise studies of communication systems using the SYSTID computer aided analysis program

NASA Technical Reports Server (NTRS)

Tranter, W. H.; Dawson, C. T.

1973-01-01

SYSTID computer aided design is a simple program for simulating data systems and communication links. A trial of the efficiency of the method was carried out by simulating a linear analog communication system to determine its noise performance and by comparing the SYSTID result with the result arrived at by theoretical calculation. It is shown that the SYSTID program is readily applicable to the analysis of these types of systems.

Electro-Optic Identification Research Program

DTIC Science & Technology

2002-04-01

Electro - optic identification (EOID) sensors provide photographic quality images that can be used to identify mine-like contacts provided by long...tasks such as validating existing electro - optic models, development of performance metrics, and development of computer aided identification and

[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.

Computer-aided resection and endoprosthesis design for the management of malignant bone tumors around the knee: outcomes of 12 cases.

PubMed

Ding, Huan-wen; Yu, Guang-wen; Tu, Qiang; Liu, Bao; Shen, Jian-jian; Wang, Hong; Wang, Ying-jun

2013-11-22

To report the outcomes of computer-aided resection and endoprosthesis design for the management of malignant bone tumors around the knee. Computed tomography (CT) and magnetic resonance imaging (MRI) data were input into computer software to produce three-dimensional (3D) models of the tumor extent. Imaging data was then used to create a template for surgical resection, and development of an individualized combined allogeneic bone/endoprosthesis. Surgical simulations were performed prior to the actual surgery. This study included 9 males and 3 females with a mean age of 25.3 years (range, 13 to 40 years). There were 9 tumors in the distal femur and 3 in the proximal tibia. There were no surgical complications. In all cases pathologically confirmed clear surgical margins were obtained. Postoperative radiographs showed the range of tumor resection was in accordance with the preoperative design, and the morphological reconstruction of the bone defect was satisfactory with complete bilateral symmetry. The mean follow-up time was 26.5 months. Two patients died of their disease and the remaining are alive and well without evidence of recurrence. All patients are able to ambulate freely without restrictions. At the last follow-up, the average International Society of Limb Salvage score was 25.8 (range, 18 to 27), and was excellent in 8 cases and good in 4 cases. Computer-aided design and modeling for the surgical management of bone tumors and subsequent limb reconstruction provides accurate tumor removal with the salvage of a maximal amount of unaffected bone and precise endoprosthesis reconstruction.

Hepatic MR imaging for in vivo differentiation of steatosis, iron deposition and combined storage disorder: single-ratio in/opposed phase analysis vs. dual-ratio Dixon discrimination.

PubMed

Bashir, Mustafa R; Merkle, Elmar M; Smith, Alastair D; Boll, Daniel T

2012-02-01

To assess whether in vivo dual-ratio Dixon discrimination can improve detection of diffuse liver disease, specifically steatosis, iron deposition and combined disease over traditional single-ratio in/opposed phase analysis. Seventy-one patients with biopsy-proven (17.7 ± 17.0 days) hepatic steatosis (n = 16), iron deposition (n = 11), combined deposition (n = 3) and neither disease (n = 41) underwent MR examinations. Dual-echo in/opposed-phase MR with Dixon water/fat reconstructions were acquired. Analysis consisted of: (a) single-ratio hepatic region-of-interest (ROI)-based assessment of in/opposed ratios; (b) dual-ratio hepatic ROI assessment of in/opposed and fat/water ratios; (c) computer-aided dual-ratio assessment evaluating all hepatic voxels. Disease-specific thresholds were determined; statistical analyses assessed disease-dependent voxel ratios, based on single-ratio (a) and dual-ratio (b and c) techniques. Single-ratio discrimination succeeded in identifying iron deposition (I/O(Ironthreshold)<0.88) and steatosis (I/O(Fatthreshold>1.15)) from normal parenchyma, sensitivity 70.0%; it failed to detect combined disease. Dual-ratio discrimination succeeded in identifying abnormal hepatic parenchyma (F/W(Normalthreshold)>0.05), sensitivity 96.7%; logarithmic functions for iron deposition (I/O(Irondiscriminator)e((F/W(Fat)-0.01)/0.48)) differentiated combined from isolated diseases, sensitivity 100.0%; computer-aided dual-ratio analysis was comparably sensitive but less specific, 90.2% vs. 97.6%. MR two-point-Dixon imaging using dual-ratio post-processing based on in/opposed and fat/water ratios improved in vivo detection of hepatic steatosis, iron deposition, and combined storage disease beyond traditional in/opposed analysis. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

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.

Preoperative (3-dimensional) computed tomography lung reconstruction before anatomic segmentectomy or lobectomy for stage I non-small cell lung cancer.

PubMed

Chan, Ernest G; Landreneau, James R; Schuchert, Matthew J; Odell, David D; Gu, Suicheng; Pu, Jiantao; Luketich, James D; Landreneau, Rodney J

2015-09-01

Accurate cancer localization and negative resection margins are necessary for successful segmentectomy. In this study, we evaluate a newly developed software package that permits automated segmentation of the pulmonary parenchyma, allowing 3-dimensional assessment of tumor size, location, and estimates of surgical margins. A pilot study using a newly developed 3-dimensional computed tomography analytic software package was performed to retrospectively evaluate preoperative computed tomography images of patients who underwent segmentectomy (n = 36) or lobectomy (n = 15) for stage 1 non-small cell lung cancer. The software accomplishes an automated reconstruction of anatomic pulmonary segments of the lung based on bronchial arborization. Estimates of anticipated surgical margins and pulmonary segmental volume were made on the basis of 3-dimensional reconstruction. Autosegmentation was achieved in 72.7% (32/44) of preoperative computed tomography images with slice thicknesses of 3 mm or less. Reasons for segmentation failure included local severe emphysema or pneumonitis, and lower computed tomography resolution. Tumor segmental localization was achieved in all autosegmented studies. The 3-dimensional computed tomography analysis provided a positive predictive value of 87% in predicting a marginal clearance greater than 1 cm and a 75% positive predictive value in predicting a margin to tumor diameter ratio greater than 1 in relation to the surgical pathology assessment. This preoperative 3-dimensional computed tomography analysis of segmental anatomy can confirm the tumor location within an anatomic segment and aid in predicting surgical margins. This 3-dimensional computed tomography information may assist in the preoperative assessment regarding the suitability of segmentectomy for peripheral lung cancers. Published by Elsevier Inc.

Rapid prototyping raw models on the basis of high resolution computed tomography lung data for respiratory flow dynamics.

PubMed

Giesel, Frederik L; Mehndiratta, Amit; von Tengg-Kobligk, Hendrik; Schaeffer, A; Teh, Kevin; Hoffman, E A; Kauczor, Hans-Ulrich; van Beek, E J R; Wild, Jim M

2009-04-01

Three-dimensional image reconstruction by volume rendering and rapid prototyping has made it possible to visualize anatomic structures in three dimensions for interventional planning and academic research. Volumetric chest computed tomography was performed on a healthy volunteer. Computed tomographic images of the larger bronchial branches were segmented by an extended three-dimensional region-growing algorithm, converted into a stereolithography file, and used for computer-aided design on a laser sintering machine. The injection of gases for respiratory flow modeling and measurements using magnetic resonance imaging were done on a hollow cast. Manufacturing the rapid prototype took about 40 minutes and included the airway tree from trackea to segmental bronchi (fifth generation). The branching of the airways are clearly visible in the (3)He images, and the radial imaging has the potential to elucidate the airway dimensions. The results for flow patterns in the human bronchial tree using the rapid-prototype model with hyperpolarized helium-3 magnetic resonance imaging show the value of this model for flow phantom studies.

Medical image computing for computer-supported diagnostics and therapy. Advances and perspectives.

PubMed

Handels, H; Ehrhardt, J

2009-01-01

Medical image computing has become one of the most challenging fields in medical informatics. In image-based diagnostics of the future software assistance will become more and more important, and image analysis systems integrating advanced image computing methods are needed to extract quantitative image parameters to characterize the state and changes of image structures of interest (e.g. tumors, organs, vessels, bones etc.) in a reproducible and objective way. Furthermore, in the field of software-assisted and navigated surgery medical image computing methods play a key role and have opened up new perspectives for patient treatment. However, further developments are needed to increase the grade of automation, accuracy, reproducibility and robustness. Moreover, the systems developed have to be integrated into the clinical workflow. For the development of advanced image computing systems methods of different scientific fields have to be adapted and used in combination. The principal methodologies in medical image computing are the following: image segmentation, image registration, image analysis for quantification and computer assisted image interpretation, modeling and simulation as well as visualization and virtual reality. Especially, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients and will gain importance in diagnostic and therapy of the future. From a methodical point of view the authors identify the following future trends and perspectives in medical image computing: development of optimized application-specific systems and integration into the clinical workflow, enhanced computational models for image analysis and virtual reality training systems, integration of different image computing methods, further integration of multimodal image data and biosignals and advanced methods for 4D medical image computing. The development of image analysis systems for diagnostic support or operation planning is a complex interdisciplinary process. Image computing methods enable new insights into the patient's image data and have the future potential to improve medical diagnostics and patient treatment.

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...

[Automated procedures for microscopic analyses of blood smears: medical testing a MECOS-Ts2 complex].

PubMed

Pliasunova, S A; Balugian, R Sh; Khmel'nitskiĭ, K E; Medovyĭ, V S; Parpara, A A; Piatnitskiĭ, A M; Sokolinskiĭ, B Z; Dem'ianov, V L; Nikolaenko, D S

2006-10-01

The paper presents the results of medical tests of a group of computer-aided procedures for microscopic analysis by means of a MECOS-Ts2 complex (ZAO "MECOS", Russia), which have been conducted at the Republican Children's Clinical Hospital, the Research Institute of Emergency Pediatric Surgery and Traumatology, and Moscow City Clinical Hospital No. 23. Computer-aided procedures for calculating the differential count and for analyzing the morphology of red blood cells were tested on blood smears from a total of 443 patients and donors, computer-aided calculation of the count of reticulocytes was tested on 318 smears. The tests were carried out under the US standard NCCLS-H20A. Manual microscopy (443 smears) and flow blood analysis on a Coulter GEN*S (125 smears) were used as reference methods. The quality of collection of samples and laboriousness were additionally assessed. The certified MECOS-Ts2 subsystems were additionally used as reference tools. The tests indicated the advantage of computer-aided MECOS-Tsl2 complex microscopy over manual microscopy.