Combining extreme learning machines using support vector machines for breast tissue classification.

Science.gov (United States)

Daliri, Mohammad Reza

2015-01-01

In this paper, we present a new approach for breast tissue classification using the features derived from electrical impedance spectroscopy. This method is composed of a feature extraction method, feature selection phase and a classification step. The feature extraction phase derives the features from the electrical impedance spectra. The extracted features consist of the impedivity at zero frequency (I0), the phase angle at 500 KHz, the high-frequency slope of phase angle, the impedance distance between spectral ends, the area under spectrum, the normalised area, the maximum of the spectrum, the distance between impedivity at I0 and the real part of the maximum frequency point and the length of the spectral curve. The system uses the information theoretic criterion as a strategy for feature selection and the combining extreme learning machines (ELMs) for the classification phase. The results of several ELMs are combined using the support vector machines classifier, and the result of classification is reported as a measure of the performance of the system. The results indicate that the proposed system achieves high accuracy in classification of breast tissues using the electrical impedance spectroscopy.

Classification between normal and tumor tissues based on the pair-wise gene expression ratio

International Nuclear Information System (INIS)

Yap, YeeLeng; Zhang, XueWu; Ling, MT; Wang, XiangHong; Wong, YC; Danchin, Antoine

2004-01-01

Precise classification of cancer types is critically important for early cancer diagnosis and treatment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. However, reliable cancer-related signals are generally lacking. Using recent datasets on colon and prostate cancer, a data transformation procedure from single gene expression to pair-wise gene expression ratio is proposed. Making use of the internal consistency of each expression profiling dataset this transformation improves the signal to noise ratio of the dataset and uncovers new relevant cancer-related signals (features). The efficiency in using the transformed dataset to perform normal/tumor classification was investigated using feature partitioning with informative features (gene annotation) as discriminating axes (single gene expression or pair-wise gene expression ratio). Classification results were compared to the original datasets for up to 10-feature model classifiers. 82 and 262 genes that have high correlation to tissue phenotype were selected from the colon and prostate datasets respectively. Remarkably, data transformation of the highly noisy expression data successfully led to lower the coefficient of variation (CV) for the within-class samples as well as improved the correlation with tissue phenotypes. The transformed dataset exhibited lower CV when compared to that of single gene expression. In the colon cancer set, the minimum CV decreased from 45.3% to 16.5%. In prostate cancer, comparable CV was achieved with and without transformation. This improvement in CV, coupled with the improved correlation between the pair-wise gene expression ratio and tissue phenotypes, yielded higher classification efficiency, especially with the colon dataset – from 87.1% to 93.5%. Over 90% of the top ten discriminating axes in both datasets showed significant improvement after data transformation. The high classification efficiency achieved suggested

Quantum Cascade Laser-Based Infrared Microscopy for Label-Free and Automated Cancer Classification in Tissue Sections.

Science.gov (United States)

Kuepper, Claus; Kallenbach-Thieltges, Angela; Juette, Hendrik; Tannapfel, Andrea; Großerueschkamp, Frederik; Gerwert, Klaus

2018-05-16

A feasibility study using a quantum cascade laser-based infrared microscope for the rapid and label-free classification of colorectal cancer tissues is presented. Infrared imaging is a reliable, robust, automated, and operator-independent tissue classification method that has been used for differential classification of tissue thin sections identifying tumorous regions. However, long acquisition time by the so far used FT-IR-based microscopes hampered the clinical translation of this technique. Here, the used quantum cascade laser-based microscope provides now infrared images for precise tissue classification within few minutes. We analyzed 110 patients with UICC-Stage II and III colorectal cancer, showing 96% sensitivity and 100% specificity of this label-free method as compared to histopathology, the gold standard in routine clinical diagnostics. The main hurdle for the clinical translation of IR-Imaging is overcome now by the short acquisition time for high quality diagnostic images, which is in the same time range as frozen sections by pathologists.

Classification of breast tissue using a laboratory system for small-angle x-ray scattering (SAXS)

International Nuclear Information System (INIS)

Sidhu, S; Siu, K K W; Falzon, G; Hart, S A; Fox, J G; Lewis, R A

2011-01-01

Structural changes in breast tissue at the nanometre scale have been shown to differentiate between tissue types using synchrotron SAXS techniques. Classification of breast tissues using information acquired from a laboratory SAXS camera source could possibly provide a means of adopting SAXS as a viable diagnostic procedure. Tissue samples were obtained from surgical waste from 66 patients and structural components of the tissues were examined between q = 0.25 and 2.3 nm -1 . Principal component analysis showed that the amplitude of the fifth-order axial Bragg peak, the magnitude of the integrated intensity and the full-width at half-maximum of the fat peak were significantly different between tissue types. A discriminant analysis showed that excellent classification can be achieved; however, only 30% of the tissue samples provided the 16 variables required for classification. This suggests that the presence of disease is represented by a combination of factors, rather than one specific trait. A closer examination of the amorphous scattering intensity showed not only a trend of increased scattering intensity with disease severity, but also a corresponding decrease in the size of the scatterers contributing to this intensity.

The method of diagnosis and classification of the gingival line defects of the teeth hard tissues

Directory of Open Access Journals (Sweden)

Olena Bulbuk

2017-06-01

Full Text Available For solving the problem of diagnosis and treatment of hard tissue defects the significant role belongs to the choice of tactics for dental treatment of hard tissue defects located in the gingival line of any tooth. This work aims to study the problems of diagnosis and classification of gingival line defects of the teeth hard tissues. That will contribute to the objectification of differentiated diagnostic and therapeutic approaches in the dental treatment of various clinical variants of these defects localization. The objective of the study – is to develop the anatomical-functional classification for differentiated estimation of hard tissue defects in the gingival part, as the basis for the application of differential diagnostic-therapeutic approaches to the dental treatment of hard tissue defects disposed in the gingival part of any tooth. Materials and methods of investigation: There was conducted the examination of 48 patients with hard tissue defects located in the gingival part of any tooth. To assess the magnitude of gingival line destruction the periodontal probe and X-ray examination were used. Results. The result of the performed research the classification of the gingival line defects of the hard tissues was offered using exponent power. The value of this indicator is equal to an integer number expressed in millimeters of distance from the epithelial attachment to the cavity’s bottom of defect. Conclusions. The proposed classification fills an obvious gap in academic representations about hard tissue defects located in the gingival part of any tooth. Also it offers the prospects of consensus on differentiated diagnostic-therapeutic approaches in different clinical variants of location.  This classification builds methodological “bridge of continuity” between therapeutic and prosthetic dentistry in the field of treatment of the gingival line defects of dental hard tissues.

Automating the expert consensus paradigm for robust lung tissue classification

Science.gov (United States)

Rajagopalan, Srinivasan; Karwoski, Ronald A.; Raghunath, Sushravya; Bartholmai, Brian J.; Robb, Richard A.

2012-03-01

Clinicians confirm the efficacy of dynamic multidisciplinary interactions in diagnosing Lung disease/wellness from CT scans. However, routine clinical practice cannot readily accomodate such interactions. Current schemes for automating lung tissue classification are based on a single elusive disease differentiating metric; this undermines their reliability in routine diagnosis. We propose a computational workflow that uses a collection (#: 15) of probability density functions (pdf)-based similarity metrics to automatically cluster pattern-specific (#patterns: 5) volumes of interest (#VOI: 976) extracted from the lung CT scans of 14 patients. The resultant clusters are refined for intra-partition compactness and subsequently aggregated into a super cluster using a cluster ensemble technique. The super clusters were validated against the consensus agreement of four clinical experts. The aggregations correlated strongly with expert consensus. By effectively mimicking the expertise of physicians, the proposed workflow could make automation of lung tissue classification a clinical reality.

A classification of the mechanisms producing pathological tissue changes.

Science.gov (United States)

Grippo, John O; Oh, Daniel S

2013-05-01

The objectives are to present a classification of mechanisms which can produce pathological changes in body tissues and fluids, as well as to clarify and define the term biocorrosion, which has had a singular use in engineering. Considering the emerging field of biomedical engineering, it is essential to use precise definitions in the lexicons of engineering, bioengineering and related sciences such as medicine, dentistry and veterinary medicine. The mechanisms of stress, friction and biocorrosion and their pathological effects on tissues are described. Biocorrosion refers to the chemical, biochemical and electrochemical changes by degradation or induced growth of living body tissues and fluids. Various agents which can affect living tissues causing biocorrosion are enumerated which support the necessity and justify the use of this encompassing and more precise definition of biocorrosion. A distinction is made between the mechanisms of corrosion and biocorrosion.

A Dirichlet process mixture model for brain MRI tissue classification.

Science.gov (United States)

Ferreira da Silva, Adelino R

2007-04-01

Accurate classification of magnetic resonance images according to tissue type or region of interest has become a critical requirement in diagnosis, treatment planning, and cognitive neuroscience. Several authors have shown that finite mixture models give excellent results in the automated segmentation of MR images of the human normal brain. However, performance and robustness of finite mixture models deteriorate when the models have to deal with a variety of anatomical structures. In this paper, we propose a nonparametric Bayesian model for tissue classification of MR images of the brain. The model, known as Dirichlet process mixture model, uses Dirichlet process priors to overcome the limitations of current parametric finite mixture models. To validate the accuracy and robustness of our method we present the results of experiments carried out on simulated MR brain scans, as well as on real MR image data. The results are compared with similar results from other well-known MRI segmentation methods.

75 FR 68972 - Medical Devices; General and Plastic Surgery Devices; Classification of Tissue Adhesive With...

Science.gov (United States)

2010-11-10

.... FDA-2010-N-0512] Medical Devices; General and Plastic Surgery Devices; Classification of Tissue... running to unintended areas, etc. B. Wound dehiscence C. Adverse tissue reaction and chemical burns D..., Clinical Studies, Labeling. Adverse tissue reaction and chemical Biocompatibility Animal burns. Testing...

Visualization and tissue classification of human breast cancer images using ultrahigh-resolution OCT (Conference Presentation)

Science.gov (United States)

Yao, Xinwen; Gan, Yu; Chang, Ernest W.; Hibshoosh, Hanina; Feldman, Sheldon; Hendon, Christine P.

2017-02-01

We employed a home-built ultrahigh resolution (UHR) OCT system at 800nm to image human breast cancer sample ex vivo. The system has an axial resolution of 2.72µm and a lateral resolution of 5.52µm with an extended imaging range of 1.78mm. Over 900 UHR OCT volumes were generated on specimens from 23 breast cancer cases. With better spatial resolution, detailed structures in the breast tissue were better defined. Different types of breast cancer as well as healthy breast tissue can be well delineated from the UHR OCT images. To quantitatively evaluate the advantages of UHR OCT imaging of breast cancer, features derived from OCT intensity images were used as inputs to a machine learning model, the relevance vector machine. A trained machine learning model was employed to evaluate the performance of tissue classification based on UHR OCT images for differentiating tissue types in the breast samples, including adipose tissue, healthy stroma and cancerous region. For adipose tissue, grid-based local features were extracted from OCT intensity data, including standard deviation, entropy, and homogeneity. We showed that it was possible to enhance the classification performance on distinguishing fat tissue from non-fat tissue by using the UHR images when compared with the results based on OCT images from a commercial 1300 nm OCT system. For invasive ductal carcinoma (IDC) and normal stroma differentiation, the classification was based on frame-based features that portray signal penetration depth and tissue reflectivity. The confusing matrix indicated a sensitivity of 97.5% and a sensitivity of 77.8%.

Support vector machine classification and validation of cancer tissue samples using microarray expression data.

Science.gov (United States)

Furey, T S; Cristianini, N; Duffy, N; Bednarski, D W; Schummer, M; Haussler, D

2000-10-01

DNA microarray experiments generating thousands of gene expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease. We have developed a new method to analyse this kind of data using support vector machines (SVMs). This analysis consists of both classification of the tissue samples, and an exploration of the data for mis-labeled or questionable tissue results. We demonstrate the method in detail on samples consisting of ovarian cancer tissues, normal ovarian tissues, and other normal tissues. The dataset consists of expression experiment results for 97,802 cDNAs for each tissue. As a result of computational analysis, a tissue sample is discovered and confirmed to be wrongly labeled. Upon correction of this mistake and the removal of an outlier, perfect classification of tissues is achieved, but not with high confidence. We identify and analyse a subset of genes from the ovarian dataset whose expression is highly differentiated between the types of tissues. To show robustness of the SVM method, two previously published datasets from other types of tissues or cells are analysed. The results are comparable to those previously obtained. We show that other machine learning methods also perform comparably to the SVM on many of those datasets. The SVM software is available at http://www.cs. columbia.edu/ approximately bgrundy/svm.

Classification of fibroglandular tissue distribution in the breast based on radiotherapy planning CT

International Nuclear Information System (INIS)

Juneja, Prabhjot; Evans, Philip; Windridge, David; Harris, Emma

2016-01-01

Accurate segmentation of breast tissues is required for a number of applications such as model based deformable registration in breast radiotherapy. The accuracy of breast tissue segmentation is affected by the spatial distribution (or pattern) of fibroglandular tissue (FT). The goal of this study was to develop and evaluate texture features, determined from planning computed tomography (CT) data, to classify the spatial distribution of FT in the breast. Planning CT data of 23 patients were evaluated in this study. Texture features were derived from the radial glandular fraction (RGF), which described the distribution of FT within three breast regions (posterior, middle, and anterior). Using visual assessment, experts grouped patients according to FT spatial distribution: sparse or non-sparse. Differences in the features between the two groups were investigated using the Wilcoxon rank test. Classification performance of the features was evaluated for a range of support vector machine (SVM) classifiers. Experts found eight patients and 15 patients had sparse and non-sparse spatial distribution of FT, respectively. A large proportion of features (>9 of 13) from the individual breast regions had significant differences (p <0.05) between the sparse and non-sparse group. The features from middle region had most significant differences and gave the highest classification accuracy for all the SVM kernels investigated. Overall, the features from middle breast region achieved highest accuracy (91 %) with the linear SVM kernel. This study found that features based on radial glandular fraction provide a means for discriminating between fibroglandular tissue distributions and could achieve a classification accuracy of 91 %

Robust multi-site MR data processing: iterative optimization of bias correction, tissue classification, and registration.

Science.gov (United States)

Young Kim, Eun; Johnson, Hans J

2013-01-01

A robust multi-modal tool, for automated registration, bias correction, and tissue classification, has been implemented for large-scale heterogeneous multi-site longitudinal MR data analysis. This work focused on improving the an iterative optimization framework between bias-correction, registration, and tissue classification inspired from previous work. The primary contributions are robustness improvements from incorporation of following four elements: (1) utilize multi-modal and repeated scans, (2) incorporate high-deformable registration, (3) use extended set of tissue definitions, and (4) use of multi-modal aware intensity-context priors. The benefits of these enhancements were investigated by a series of experiments with both simulated brain data set (BrainWeb) and by applying to highly-heterogeneous data from a 32 site imaging study with quality assessments through the expert visual inspection. The implementation of this tool is tailored for, but not limited to, large-scale data processing with great data variation with a flexible interface. In this paper, we describe enhancements to a joint registration, bias correction, and the tissue classification, that improve the generalizability and robustness for processing multi-modal longitudinal MR scans collected at multi-sites. The tool was evaluated by using both simulated and simulated and human subject MRI images. With these enhancements, the results showed improved robustness for large-scale heterogeneous MRI processing.

Efficacy of hidden markov model over support vector machine on multiclass classification of healthy and cancerous cervical tissues

Science.gov (United States)

Mukhopadhyay, Sabyasachi; Kurmi, Indrajit; Pratiher, Sawon; Mukherjee, Sukanya; Barman, Ritwik; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2018-02-01

In this paper, a comparative study between SVM and HMM has been carried out for multiclass classification of cervical healthy and cancerous tissues. In our study, the HMM methodology is more promising to produce higher accuracy in classification.

The differentiation of malignant and benign human breast tissue at surgical margins and biopsy using x-ray interaction data and Bayesian classification

International Nuclear Information System (INIS)

Mersov, A.; Mersov, G.; Al-Ebraheem, A.; Cornacchi, S.; Gohla, G.; Lovrics, P.; Farquharson, M.J.

2014-01-01

Worldwide, about 1.3 million women are diagnosed with breast cancer annually with an estimated 465,000 deaths. Accordingly, there is a need for high accuracy and speed in diagnosis of lesions suspected of being cancerous. This study assesses the interaction data collected from low energy x-rays within breast tissue samples. Trace element concentrations are assessed using x-ray fluorescence, as well as electron density, and molecular structure which are examined using incoherent and coherent scatter, respectively. Our work to date has shown that such data can provide a quantitative measure of certain tissue characterising parameters and hence, through appropriate modelling, could be used to classify samples for uses such as surgical margin detection and biopsy examination. The parameters used in this study for comparing the normal and tumour tissue sample populations are: levels of elements Ca, Cu, Fe, Br, Zn, Rb, K; the area, FWHM and amplitude from peaks fitted to the coherent scatter profile that are associated with fat, fibre and water content; the ratio of the Compton and coherent scatter peak area, FWHM and amplitude from the incoherent scatter profile. The novelty of the approach to this work lies in the fact that the classification process does not rely on one source of data but combines several measurements, the data from which in this application are modelled using a method based on Bayesian classification. The reliability of the classifications was assessed by its application to diagnostically known data that was not itself included in the thresholds determination. The results of the classification of over 70 breast tissue samples will be presented in this study. Bayesian modelling was carried out using selected significant parameters for classification resulting in 71% of normal tissue samples (n=35) and 66% of tumour tissue samples (n=35) being correctly classified when using all the samples. Bayesian classification using the same variables on all

Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation

Directory of Open Access Journals (Sweden)

Richard eBeare

2016-03-01

Full Text Available Measuring the distribution of brain tissue types (tissue classification in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation, which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF, hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T2-weighted images of preterm infants (born ≤30 weeks’ gestation acquired at 30 weeks’ corrected gestational age (n= 5, coronal T2-weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n= 5 and axial T2-weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n= 5. The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR group, consisted of T2-weighted images of preterm infants (born <30 weeks’ gestation acquired shortly after birth (n= 12, preterm infants acquired at term-equivalent age (n= 12, and healthy term-born infants (born ≥38 weeks’ gestation acquired within the first nine days of life (n= 12. For the NeoBrainS12 dataset, mean Dice scores comparing MANTiS with manual segmentations were all above 0.7, except for

Joint learning and weighting of visual vocabulary for bag-of-feature based tissue classification

KAUST Repository

Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

2013-01-01

their power in this field. Two important issues of bag-of-feature strategy for tissue classification are investigated in this paper: the visual vocabulary learning and weighting, which are always considered independently in traditional methods by neglecting

Breast tissue classification in digital tomosynthesis images based on global gradient minimization and texture features

Science.gov (United States)

Qin, Xulei; Lu, Guolan; Sechopoulos, Ioannis; Fei, Baowei

2014-03-01

Digital breast tomosynthesis (DBT) is a pseudo-three-dimensional x-ray imaging modality proposed to decrease the effect of tissue superposition present in mammography, potentially resulting in an increase in clinical performance for the detection and diagnosis of breast cancer. Tissue classification in DBT images can be useful in risk assessment, computer-aided detection and radiation dosimetry, among other aspects. However, classifying breast tissue in DBT is a challenging problem because DBT images include complicated structures, image noise, and out-of-plane artifacts due to limited angular tomographic sampling. In this project, we propose an automatic method to classify fatty and glandular tissue in DBT images. First, the DBT images are pre-processed to enhance the tissue structures and to decrease image noise and artifacts. Second, a global smooth filter based on L0 gradient minimization is applied to eliminate detailed structures and enhance large-scale ones. Third, the similar structure regions are extracted and labeled by fuzzy C-means (FCM) classification. At the same time, the texture features are also calculated. Finally, each region is classified into different tissue types based on both intensity and texture features. The proposed method is validated using five patient DBT images using manual segmentation as the gold standard. The Dice scores and the confusion matrix are utilized to evaluate the classified results. The evaluation results demonstrated the feasibility of the proposed method for classifying breast glandular and fat tissue on DBT images.

Automated classification and visualization of healthy and pathological dental tissues based on near-infrared hyper-spectral imaging

Science.gov (United States)

Usenik, Peter; Bürmen, Miran; Vrtovec, Tomaž; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

2011-03-01

Despite major improvements in dental healthcare and technology, dental caries remains one of the most prevalent chronic diseases of modern society. The initial stages of dental caries are characterized by demineralization of enamel crystals, commonly known as white spots which are difficult to diagnose. If detected early enough, such demineralization can be arrested and reversed by non-surgical means through well established dental treatments (fluoride therapy, anti-bacterial therapy, low intensity laser irradiation). Near-infrared (NIR) hyper-spectral imaging is a new promising technique for early detection of demineralization based on distinct spectral features of healthy and pathological dental tissues. In this study, we apply NIR hyper-spectral imaging to classify and visualize healthy and pathological dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized areas. For this purpose, a standardized teeth database was constructed consisting of 12 extracted human teeth with different degrees of natural dental lesions imaged by NIR hyper-spectral system, X-ray and digital color camera. The color and X-ray images of teeth were presented to a clinical expert for localization and classification of the dental tissues, thereby obtaining the gold standard. Principal component analysis was used for multivariate local modeling of healthy and pathological dental tissues. Finally, the dental tissues were classified by employing multiple discriminant analysis. High agreement was observed between the resulting classification and the gold standard with the classification sensitivity and specificity exceeding 85 % and 97 %, respectively. This study demonstrates that NIR hyper-spectral imaging has considerable diagnostic potential for imaging hard dental tissues.

Biased visualization of hypoperfused tissue by computed tomography due to short imaging duration: improved classification by image down-sampling and vascular models

Energy Technology Data Exchange (ETDEWEB)

Mikkelsen, Irene Klaerke; Ribe, Lars Riisgaard; Bekke, Susanne Lise; Tietze, Anna; Oestergaard, Leif; Mouridsen, Kim [Aarhus University Hospital, Center of Functionally Integrative Neuroscience, Aarhus C (Denmark); Jones, P.S.; Alawneh, Josef [University of Cambridge, Department of Clinical Neurosciences, Cambridge (United Kingdom); Puig, Josep; Pedraza, Salva [Dr. Josep Trueta Girona University Hospitals, Department of Radiology, Girona Biomedical Research Institute, Girona (Spain); Gillard, Jonathan H. [University of Cambridge, Department of Radiology, Cambridge (United Kingdom); Warburton, Elisabeth A. [Cambrigde University Hospitals, Addenbrooke, Stroke Unit, Cambridge (United Kingdom); Baron, Jean-Claude [University of Cambridge, Department of Clinical Neurosciences, Cambridge (United Kingdom); Centre Hospitalier Sainte Anne, INSERM U894, Paris (France)

2015-07-15

Lesion detection in acute stroke by computed-tomography perfusion (CTP) can be affected by incomplete bolus coverage in veins and hypoperfused tissue, so-called bolus truncation (BT), and low contrast-to-noise ratio (CNR). We examined the BT-frequency and hypothesized that image down-sampling and a vascular model (VM) for perfusion calculation would improve normo- and hypoperfused tissue classification. CTP datasets from 40 acute stroke patients were retrospectively analysed for BT. In 16 patients with hypoperfused tissue but no BT, repeated 2-by-2 image down-sampling and uniform filtering was performed, comparing CNR to perfusion-MRI levels and tissue classification to that of unprocessed data. By simulating reduced scan duration, the minimum scan-duration at which estimated lesion volumes came within 10 % of their true volume was compared for VM and state-of-the-art algorithms. BT in veins and hypoperfused tissue was observed in 9/40 (22.5 %) and 17/40 patients (42.5 %), respectively. Down-sampling to 128 x 128 resolution yielded CNR comparable to MR data and improved tissue classification (p = 0.0069). VM reduced minimum scan duration, providing reliable maps of cerebral blood flow and mean transit time: 5 s (p = 0.03) and 7 s (p < 0.0001), respectively. BT is not uncommon in stroke CTP with 40-s scan duration. Applying image down-sampling and VM improve tissue classification. (orig.)

Biased visualization of hypoperfused tissue by computed tomography due to short imaging duration: improved classification by image down-sampling and vascular models

International Nuclear Information System (INIS)

Mikkelsen, Irene Klaerke; Ribe, Lars Riisgaard; Bekke, Susanne Lise; Tietze, Anna; Oestergaard, Leif; Mouridsen, Kim; Jones, P.S.; Alawneh, Josef; Puig, Josep; Pedraza, Salva; Gillard, Jonathan H.; Warburton, Elisabeth A.; Baron, Jean-Claude

2015-01-01

Lesion detection in acute stroke by computed-tomography perfusion (CTP) can be affected by incomplete bolus coverage in veins and hypoperfused tissue, so-called bolus truncation (BT), and low contrast-to-noise ratio (CNR). We examined the BT-frequency and hypothesized that image down-sampling and a vascular model (VM) for perfusion calculation would improve normo- and hypoperfused tissue classification. CTP datasets from 40 acute stroke patients were retrospectively analysed for BT. In 16 patients with hypoperfused tissue but no BT, repeated 2-by-2 image down-sampling and uniform filtering was performed, comparing CNR to perfusion-MRI levels and tissue classification to that of unprocessed data. By simulating reduced scan duration, the minimum scan-duration at which estimated lesion volumes came within 10 % of their true volume was compared for VM and state-of-the-art algorithms. BT in veins and hypoperfused tissue was observed in 9/40 (22.5 %) and 17/40 patients (42.5 %), respectively. Down-sampling to 128 x 128 resolution yielded CNR comparable to MR data and improved tissue classification (p = 0.0069). VM reduced minimum scan duration, providing reliable maps of cerebral blood flow and mean transit time: 5 s (p = 0.03) and 7 s (p < 0.0001), respectively. BT is not uncommon in stroke CTP with 40-s scan duration. Applying image down-sampling and VM improve tissue classification. (orig.)

Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin

DEFF Research Database (Denmark)

Hoadley, Katherine A; Yau, Christina; Wolf, Denise M

2014-01-01

Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform...... on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset...

Automated Analysis and Classification of Histological Tissue Features by Multi-Dimensional Microscopic Molecular Profiling.

Directory of Open Access Journals (Sweden)

Daniel P Riordan

Full Text Available Characterization of the molecular attributes and spatial arrangements of cells and features within complex human tissues provides a critical basis for understanding processes involved in development and disease. Moreover, the ability to automate steps in the analysis and interpretation of histological images that currently require manual inspection by pathologists could revolutionize medical diagnostics. Toward this end, we developed a new imaging approach called multidimensional microscopic molecular profiling (MMMP that can measure several independent molecular properties in situ at subcellular resolution for the same tissue specimen. MMMP involves repeated cycles of antibody or histochemical staining, imaging, and signal removal, which ultimately can generate information analogous to a multidimensional flow cytometry analysis on intact tissue sections. We performed a MMMP analysis on a tissue microarray containing a diverse set of 102 human tissues using a panel of 15 informative antibody and 5 histochemical stains plus DAPI. Large-scale unsupervised analysis of MMMP data, and visualization of the resulting classifications, identified molecular profiles that were associated with functional tissue features. We then directly annotated H&E images from this MMMP series such that canonical histological features of interest (e.g. blood vessels, epithelium, red blood cells were individually labeled. By integrating image annotation data, we identified molecular signatures that were associated with specific histological annotations and we developed statistical models for automatically classifying these features. The classification accuracy for automated histology labeling was objectively evaluated using a cross-validation strategy, and significant accuracy (with a median per-pixel rate of 77% per feature from 15 annotated samples for de novo feature prediction was obtained. These results suggest that high-dimensional profiling may advance the

Systematic bias in genomic classification due to contaminating non-neoplastic tissue in breast tumor samples.

Science.gov (United States)

Elloumi, Fathi; Hu, Zhiyuan; Li, Yan; Parker, Joel S; Gulley, Margaret L; Amos, Keith D; Troester, Melissa A

2011-06-30

Genomic tests are available to predict breast cancer recurrence and to guide clinical decision making. These predictors provide recurrence risk scores along with a measure of uncertainty, usually a confidence interval. The confidence interval conveys random error and not systematic bias. Standard tumor sampling methods make this problematic, as it is common to have a substantial proportion (typically 30-50%) of a tumor sample comprised of histologically benign tissue. This "normal" tissue could represent a source of non-random error or systematic bias in genomic classification. To assess the performance characteristics of genomic classification to systematic error from normal contamination, we collected 55 tumor samples and paired tumor-adjacent normal tissue. Using genomic signatures from the tumor and paired normal, we evaluated how increasing normal contamination altered recurrence risk scores for various genomic predictors. Simulations of normal tissue contamination caused misclassification of tumors in all predictors evaluated, but different breast cancer predictors showed different types of vulnerability to normal tissue bias. While two predictors had unpredictable direction of bias (either higher or lower risk of relapse resulted from normal contamination), one signature showed predictable direction of normal tissue effects. Due to this predictable direction of effect, this signature (the PAM50) was adjusted for normal tissue contamination and these corrections improved sensitivity and negative predictive value. For all three assays quality control standards and/or appropriate bias adjustment strategies can be used to improve assay reliability. Normal tissue sampled concurrently with tumor is an important source of bias in breast genomic predictors. All genomic predictors show some sensitivity to normal tissue contamination and ideal strategies for mitigating this bias vary depending upon the particular genes and computational methods used in the predictor.

Classification of cardiovascular tissues using LBP based descriptors and a cascade SVM.

Science.gov (United States)

Mazo, Claudia; Alegre, Enrique; Trujillo, Maria

2017-08-01

Histological images have characteristics, such as texture, shape, colour and spatial structure, that permit the differentiation of each fundamental tissue and organ. Texture is one of the most discriminative features. The automatic classification of tissues and organs based on histology images is an open problem, due to the lack of automatic solutions when treating tissues without pathologies. In this paper, we demonstrate that it is possible to automatically classify cardiovascular tissues using texture information and Support Vector Machines (SVM). Additionally, we realised that it is feasible to recognise several cardiovascular organs following the same process. The texture of histological images was described using Local Binary Patterns (LBP), LBP Rotation Invariant (LBPri), Haralick features and different concatenations between them, representing in this way its content. Using a SVM with linear kernel, we selected the more appropriate descriptor that, for this problem, was a concatenation of LBP and LBPri. Due to the small number of the images available, we could not follow an approach based on deep learning, but we selected the classifier who yielded the higher performance by comparing SVM with Random Forest and Linear Discriminant Analysis. Once SVM was selected as the classifier with a higher area under the curve that represents both higher recall and precision, we tuned it evaluating different kernels, finding that a linear SVM allowed us to accurately separate four classes of tissues: (i) cardiac muscle of the heart, (ii) smooth muscle of the muscular artery, (iii) loose connective tissue, and (iv) smooth muscle of the large vein and the elastic artery. The experimental validation was conducted using 3000 blocks of 100 × 100 sized pixels, with 600 blocks per class and the classification was assessed using a 10-fold cross-validation. using LBP as the descriptor, concatenated with LBPri and a SVM with linear kernel, the main four classes of tissues were

Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images

International Nuclear Information System (INIS)

Sahiner, B.; Chan, H.P.; Petrick, N.; Helvie, M.A.; Adler, D.D.; Goodsitt, M.M.; Wei, D.

1996-01-01

The authors investigated the classification of regions of interest (ROI's) on mammograms as either mass or normal tissue using a convolution neural network (CNN). A CNN is a back-propagation neural network with two-dimensional (2-D) weight kernels that operate on images. A generalized, fast and stable implementation of the CNN was developed. The input images to the CNN were obtained form the ROI's using two techniques. The first technique employed averaging and subsampling. The second technique employed texture feature extraction methods applied to small subregions inside the ROI. Features computed over different subregions were arranged as texture images, which were subsequently used as CNN inputs. The effects of CNN architecture and texture feature parameters on classification accuracy were studied. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. A data set consisting of 168 ROI's containing biopsy-proven masses and 504 ROI's containing normal breast tissue was extracted from 168 mammograms by radiologists experienced in mammography. This data set was used for training and testing the CNN. With the best combination of CNN architecture and texture feature parameters, the area under the test ROC curve reached 0.87, which corresponded to a true-positive fraction of 90% at a false positive fraction of 31%. The results demonstrate the feasibility of using a CNN for classification of masses and normal tissue on mammograms

A new classification method for MALDI imaging mass spectrometry data acquired on formalin-fixed paraffin-embedded tissue samples.

Science.gov (United States)

Boskamp, Tobias; Lachmund, Delf; Oetjen, Janina; Cordero Hernandez, Yovany; Trede, Dennis; Maass, Peter; Casadonte, Rita; Kriegsmann, Jörg; Warth, Arne; Dienemann, Hendrik; Weichert, Wilko; Kriegsmann, Mark

2017-07-01

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) shows a high potential for applications in histopathological diagnosis, and in particular for supporting tumor typing and subtyping. The development of such applications requires the extraction of spectral fingerprints that are relevant for the given tissue and the identification of biomarkers associated with these spectral patterns. We propose a novel data analysis method based on the extraction of characteristic spectral patterns (CSPs) that allow automated generation of classification models for spectral data. Formalin-fixed paraffin embedded (FFPE) tissue samples from N=445 patients assembled on 12 tissue microarrays were analyzed. The method was applied to discriminate primary lung and pancreatic cancer, as well as adenocarcinoma and squamous cell carcinoma of the lung. A classification accuracy of 100% and 82.8%, resp., could be achieved on core level, assessed by cross-validation. The method outperformed the more conventional classification method based on the extraction of individual m/z values in the first application, while achieving a comparable accuracy in the second. LC-MS/MS peptide identification demonstrated that the spectral features present in selected CSPs correspond to peptides relevant for the respective classification. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann. Copyright © 2016 Elsevier B.V. All rights reserved.

Polarimetry based partial least square classification of ex vivo healthy and basal cell carcinoma human skin tissues.

Science.gov (United States)

Ahmad, Iftikhar; Ahmad, Manzoor; Khan, Karim; Ikram, Masroor

2016-06-01

Optical polarimetry was employed for assessment of ex vivo healthy and basal cell carcinoma (BCC) tissue samples from human skin. Polarimetric analyses revealed that depolarization and retardance for healthy tissue group were significantly higher (ppolarimetry together with PLS statistics hold promise for automated pathology classification. Copyright © 2016 Elsevier B.V. All rights reserved.

Effects on MR images compression in tissue classification quality

International Nuclear Information System (INIS)

Santalla, H; Meschino, G; Ballarin, V

2007-01-01

It is known that image compression is required to optimize the storage in memory. Moreover, transmission speed can be significantly improved. Lossless compression is used without controversy in medicine, though benefits are limited. If we compress images lossy, where image can not be totally recovered; we can only recover an approximation. In this point definition of 'quality' is essential. What we understand for 'quality'? How can we evaluate a compressed image? Quality in images is an attribute whit several definitions and interpretations, which actually depend on the posterior use we want to give them. This work proposes a quantitative analysis of quality for lossy compressed Magnetic Resonance (MR) images, and their influence in automatic tissue classification, accomplished with these images

Chronic radiation effects on dental hard tissue (''radiation carries''). Classification and therapeutic strategies

International Nuclear Information System (INIS)

Groetz, K.A.; Brahm, R.; Al-Nawas, B.; Wagner, W.; Riesenbeck, D.; Willich, N.; Seegenschmiedt, M.H.

2001-01-01

Objectives: Since the first description of rapid destruction of dental hard tissues following head and neck radiotherapy 80 years ago, 'radiation caries' is an established clinical finding. The internationally accepted clinical evaluation score RTOG/EORTC however is lacking a classification of this frequent radiogenic alteration. Material and Methods: Medical records, data and images of radiation effects on the teeth of more than 1,500 patients, who underwent periradiotherapeutic care, were analyzed. Macroscopic alterations regarding the grade of late lesions of tooth crowns were used for a classification into 4 grades according to the RTOG/EORTC guidelines. Results: No early radiation effects were found by macroscopic inspection. In the first 90 days following radiotherapy 1/3 of the patients complained of reversible hypersensitivity, which may be related to a temporary hyperemia of the pulp. It was possible to classify radiation caries as a late radiation effect on a graded scale as known from RTOG/EORTC for other organ systems. This is a prerequisite for the integration of radiation caries into the international nomenclature of the RTOG/EORTC classification. Conclusions: The documentation of early radiation effects on dental hard tissues seems to be neglectable. On the other hand the documentation of late radiation effects has a high clinical impact. The identification of an initial lesion at the high-risk areas of the neck and incisal part of the tooth can lead to a successful therapy as a major prerequisite for orofacial rehabilitation. An internationally standardized documentation is a basis for the evaluation of the side effects of radiooncotic therapy as well as the effectiveness of protective and supportive procedures. (orig.) [de

Classification of coronary artery tissues using optical coherence tomography imaging in Kawasaki disease

Science.gov (United States)

Abdolmanafi, Atefeh; Prasad, Arpan Suravi; Duong, Luc; Dahdah, Nagib

2016-03-01

Intravascular imaging modalities, such as Optical Coherence Tomography (OCT) allow nowadays improving diagnosis, treatment, follow-up, and even prevention of coronary artery disease in the adult. OCT has been recently used in children following Kawasaki disease (KD), the most prevalent acquired coronary artery disease during childhood with devastating complications. The assessment of coronary artery layers with OCT and early detection of coronary sequelae secondary to KD is a promising tool for preventing myocardial infarction in this population. More importantly, OCT is promising for tissue quantification of the inner vessel wall, including neo intima luminal myofibroblast proliferation, calcification, and fibrous scar deposits. The goal of this study is to classify the coronary artery layers of OCT imaging obtained from a series of KD patients. Our approach is focused on developing a robust Random Forest classifier built on the idea of randomly selecting a subset of features at each node and based on second- and higher-order statistical texture analysis which estimates the gray-level spatial distribution of images by specifying the local features of each pixel and extracting the statistics from their distribution. The average classification accuracy for intima and media are 76.36% and 73.72% respectively. Random forest classifier with texture analysis promises for classification of coronary artery tissue.

Improved classification and visualization of healthy and pathological hard dental tissues by modeling specular reflections in NIR hyperspectral images

Science.gov (United States)

Usenik, Peter; Bürmen, Miran; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

2012-03-01

Despite major improvements in dental healthcare and technology, dental caries remains one of the most prevalent chronic diseases of modern society. The initial stages of dental caries are characterized by demineralization of enamel crystals, commonly known as white spots, which are difficult to diagnose. Near-infrared (NIR) hyperspectral imaging is a new promising technique for early detection of demineralization which can classify healthy and pathological dental tissues. However, due to non-ideal illumination of the tooth surface the hyperspectral images can exhibit specular reflections, in particular around the edges and the ridges of the teeth. These reflections significantly affect the performance of automated classification and visualization methods. Cross polarized imaging setup can effectively remove the specular reflections, however is due to the complexity and other imaging setup limitations not always possible. In this paper, we propose an alternative approach based on modeling the specular reflections of hard dental tissues, which significantly improves the classification accuracy in the presence of specular reflections. The method was evaluated on five extracted human teeth with corresponding gold standard for 6 different healthy and pathological hard dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized regions. Principal component analysis (PCA) was used for multivariate local modeling of healthy and pathological dental tissues. The classification was performed by employing multiple discriminant analysis. Based on the obtained results we believe the proposed method can be considered as an effective alternative to the complex cross polarized imaging setups.

Multispectral imaging burn wound tissue classification system: a comparison of test accuracies between several common machine learning algorithms

Science.gov (United States)

Squiers, John J.; Li, Weizhi; King, Darlene R.; Mo, Weirong; Zhang, Xu; Lu, Yang; Sellke, Eric W.; Fan, Wensheng; DiMaio, J. Michael; Thatcher, Jeffrey E.

2016-03-01

The clinical judgment of expert burn surgeons is currently the standard on which diagnostic and therapeutic decisionmaking regarding burn injuries is based. Multispectral imaging (MSI) has the potential to increase the accuracy of burn depth assessment and the intraoperative identification of viable wound bed during surgical debridement of burn injuries. A highly accurate classification model must be developed using machine-learning techniques in order to translate MSI data into clinically-relevant information. An animal burn model was developed to build an MSI training database and to study the burn tissue classification ability of several models trained via common machine-learning algorithms. The algorithms tested, from least to most complex, were: K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), weighted linear discriminant analysis (W-LDA), quadratic discriminant analysis (QDA), ensemble linear discriminant analysis (EN-LDA), ensemble K-nearest neighbors (EN-KNN), and ensemble decision tree (EN-DT). After the ground-truth database of six tissue types (healthy skin, wound bed, blood, hyperemia, partial injury, full injury) was generated by histopathological analysis, we used 10-fold cross validation to compare the algorithms' performances based on their accuracies in classifying data against the ground truth, and each algorithm was tested 100 times. The mean test accuracy of the algorithms were KNN 68.3%, DT 61.5%, LDA 70.5%, W-LDA 68.1%, QDA 68.9%, EN-LDA 56.8%, EN-KNN 49.7%, and EN-DT 36.5%. LDA had the highest test accuracy, reflecting the bias-variance tradeoff over the range of complexities inherent to the algorithms tested. Several algorithms were able to match the current standard in burn tissue classification, the clinical judgment of expert burn surgeons. These results will guide further development of an MSI burn tissue classification system. Given that there are few surgeons and facilities specializing in burn care

The use of Compton scattering to differentiate between classifications of normal and diseased breast tissue

Energy Technology Data Exchange (ETDEWEB)

Ryan, Elaine A; Farquharson, Michael J; Flinton, David M [School of Allied Health Sciences, City University, Charterhouse Square, London EC1M 6PA (United Kingdom)

2005-07-21

This study describes a technique for measuring the electron density of breast tissue utilizing Compton scattered photons. The K{sub {alpha}}{sub 2} line from a tungsten target industrial x-ray tube (57.97 keV) was used and the scattered x-rays collected at an angle of 30{sup 0}. At this angle the Compton and coherent photon peaks can be resolved using an energy dispersive detector and a peak fitting algorithm. The system was calibrated using solutions of known electron density. The results obtained from a pilot study of 22 tissues are presented. The tissue samples investigated comprise four different tissue classifications: adipose, malignancy, fibroadenoma and fibrocystic change (FCC). It is shown that there is a difference between adipose and malignant tissue, to a value of 9.0%, and between adipose and FCC, to a value of 12.7%. These figures are found to be significant by statistical analysis. The differences between adipose and fibroadenoma tissues (2.2%) and between malignancy and FCC (3.4%) are not significant. It is hypothesized that the alteration in glucose uptake within malignant cells may cause these tissues to have an elevated electron density. The fibrotic nature of tissue that has undergone FCC gives the highest measure of all tissue types.

The use of Compton scattering to differentiate between classifications of normal and diseased breast tissue

Science.gov (United States)

Ryan, Elaine A.; Farquharson, Michael J.; Flinton, David M.

2005-07-01

This study describes a technique for measuring the electron density of breast tissue utilizing Compton scattered photons. The Kα2 line from a tungsten target industrial x-ray tube (57.97 keV) was used and the scattered x-rays collected at an angle of 30°. At this angle the Compton and coherent photon peaks can be resolved using an energy dispersive detector and a peak fitting algorithm. The system was calibrated using solutions of known electron density. The results obtained from a pilot study of 22 tissues are presented. The tissue samples investigated comprise four different tissue classifications: adipose, malignancy, fibroadenoma and fibrocystic change (FCC). It is shown that there is a difference between adipose and malignant tissue, to a value of 9.0%, and between adipose and FCC, to a value of 12.7%. These figures are found to be significant by statistical analysis. The differences between adipose and fibroadenoma tissues (2.2%) and between malignancy and FCC (3.4%) are not significant. It is hypothesized that the alteration in glucose uptake within malignant cells may cause these tissues to have an elevated electron density. The fibrotic nature of tissue that has undergone FCC gives the highest measure of all tissue types.

The use of Compton scattering to differentiate between classifications of normal and diseased breast tissue

International Nuclear Information System (INIS)

Ryan, Elaine A; Farquharson, Michael J; Flinton, David M

2005-01-01

This study describes a technique for measuring the electron density of breast tissue utilizing Compton scattered photons. The K α2 line from a tungsten target industrial x-ray tube (57.97 keV) was used and the scattered x-rays collected at an angle of 30 0 . At this angle the Compton and coherent photon peaks can be resolved using an energy dispersive detector and a peak fitting algorithm. The system was calibrated using solutions of known electron density. The results obtained from a pilot study of 22 tissues are presented. The tissue samples investigated comprise four different tissue classifications: adipose, malignancy, fibroadenoma and fibrocystic change (FCC). It is shown that there is a difference between adipose and malignant tissue, to a value of 9.0%, and between adipose and FCC, to a value of 12.7%. These figures are found to be significant by statistical analysis. The differences between adipose and fibroadenoma tissues (2.2%) and between malignancy and FCC (3.4%) are not significant. It is hypothesized that the alteration in glucose uptake within malignant cells may cause these tissues to have an elevated electron density. The fibrotic nature of tissue that has undergone FCC gives the highest measure of all tissue types

Spiral wave classification using normalized compression distance: Towards atrial tissue spatiotemporal electrophysiological behavior characterization.

Science.gov (United States)

Alagoz, Celal; Guez, Allon; Cohen, Andrew; Bullinga, John R

2015-08-01

Analysis of electrical activation patterns such as re-entries during atrial fibrillation (Afib) is crucial in understanding arrhythmic mechanisms and assessment of diagnostic measures. Spiral waves are a phenomena that provide intuitive basis for re-entries occurring in cardiac tissue. Distinct spiral wave behaviors such as stable spiral waves, meandering spiral waves, and spiral wave break-up may have distinct electrogram manifestations on a mapping catheter. Hence, it is desirable to have an automated classification of spiral wave behavior based on catheter recordings for a qualitative characterization of spatiotemporal electrophysiological activity on atrial tissue. In this study, we propose a method for classification of spatiotemporal characteristics of simulated atrial activation patterns in terms of distinct spiral wave behaviors during Afib using two different techniques: normalized compressed distance (NCD) and normalized FFT (NFFTD). We use a phenomenological model for cardiac electrical propagation to produce various simulated spiral wave behaviors on a 2D grid and labeled them as stable, meandering, or breakup. By mimicking commonly used catheter types, a star shaped and a circular shaped both of which do the local readings from atrial wall, monopolar and bipolar intracardiac electrograms are simulated. Virtual catheters are positioned at different locations on the grid. The classification performance for different catheter locations, types and for monopolar or bipolar readings were also compared. We observed that the performance for each case differed slightly. However, we found that NCD performance is superior to NFFTD. Through the simulation study, we showed the theoretical validation of the proposed method. Our findings suggest that a qualitative wavefront activation pattern can be assessed during Afib without the need for highly invasive mapping techniques such as multisite simultaneous electrogram recordings.

The Cerefy registered clinical brain atlas on CD-ROM. Based on the classic Talairach-Tournoux and Schaltenbrand-Wahren brain atlases. 2. ed.

International Nuclear Information System (INIS)

Nowinski, W.L.; Thirunavuukarasuu, A.

2001-01-01

This remarkable CD-ROM provides enhanced and extended versions of three world-famous Thieme atlases, (Schaltenbrand and Wahren's Atlas for Stereotaxy of the Human Brain, Talairach and Tournoux's Co-Planar Stereotaxis Atlas of the Human Brain and Referentially Oriented Cerebral MRI Anatomy). It contains the electronic atlases as well as an easy navigation system to facilitate searching for and displaying more than 525 anatomical structures. Revolutionizing the field of brain anatomy, the authors have segmented, labeled, and cross referenced all the information contained in the books, and created contours for all three atlases. The Cerefy registered Clinical Brain Atlas now allows you to electronically navigate these atlases simultaneously on axial, coronal, and sagittal planes, and enjoy the ability to: 1. Access 210 high-quality, fully segmented, and labeled atlas images with corresponding contours, 2. Display and manipulate spatially co-registered atlases, 3. Dynamically label images with structure names and descriptions, and then highlight selected structures in the atlas image, 4. Image zoom in five different levels, mensurate, search, set triplanar, get coordinates, save, and print, 5. Access on-line help, glossary, and supportive atlas materials. (orig.)

Tissue classification and diagnostics using a fiber probe for combined Raman and fluorescence spectroscopy

Science.gov (United States)

Cicchi, Riccardo; Anand, Suresh; Crisci, Alfonso; Giordano, Flavio; Rossari, Susanna; De Giorgi, Vincenzo; Maio, Vincenza; Massi, Daniela; Nesi, Gabriella; Buccoliero, Anna Maria; Guerrini, Renzo; Pimpinelli, Nicola; Pavone, Francesco S.

2015-07-01

Two different optical fiber probes for combined Raman and fluorescence spectroscopic measurements were designed, developed and used for tissue diagnostics. Two visible laser diodes were used for fluorescence spectroscopy, whereas a laser diode emitting in the NIR was used for Raman spectroscopy. The two probes were based on fiber bundles with a central multimode optical fiber, used for delivering light to the tissue, and 24 surrounding optical fibers for signal collection. Both fluorescence and Raman spectra were acquired using the same detection unit, based on a cooled CCD camera, connected to a spectrograph. The two probes were successfully employed for diagnostic purposes on various tissues in a good agreement with common routine histology. This study included skin, brain and bladder tissues and in particular the classification of: malignant melanoma against melanocytic lesions and healthy skin; urothelial carcinoma against healthy bladder mucosa; brain tumor against dysplastic brain tissue. The diagnostic capabilities were determined using a cross-validation method with a leave-one-out approach, finding very high sensitivity and specificity for all the examined tissues. The obtained results demonstrated that the multimodal approach is crucial for improving diagnostic capabilities. The system presented here can improve diagnostic capabilities on a broad range of tissues and has the potential of being used for endoscopic inspections in the near future.

Spatial cluster analysis of nanoscopically mapped serotonin receptors for classification of fixed brain tissue

Science.gov (United States)

Sams, Michael; Silye, Rene; Göhring, Janett; Muresan, Leila; Schilcher, Kurt; Jacak, Jaroslaw

2014-01-01

We present a cluster spatial analysis method using nanoscopic dSTORM images to determine changes in protein cluster distributions within brain tissue. Such methods are suitable to investigate human brain tissue and will help to achieve a deeper understanding of brain disease along with aiding drug development. Human brain tissue samples are usually treated postmortem via standard fixation protocols, which are established in clinical laboratories. Therefore, our localization microscopy-based method was adapted to characterize protein density and protein cluster localization in samples fixed using different protocols followed by common fluorescent immunohistochemistry techniques. The localization microscopy allows nanoscopic mapping of serotonin 5-HT1A receptor groups within a two-dimensional image of a brain tissue slice. These nanoscopically mapped proteins can be confined to clusters by applying the proposed statistical spatial analysis. Selected features of such clusters were subsequently used to characterize and classify the tissue. Samples were obtained from different types of patients, fixed with different preparation methods, and finally stored in a human tissue bank. To verify the proposed method, samples of a cryopreserved healthy brain have been compared with epitope-retrieved and paraffin-fixed tissues. Furthermore, samples of healthy brain tissues were compared with data obtained from patients suffering from mental illnesses (e.g., major depressive disorder). Our work demonstrates the applicability of localization microscopy and image analysis methods for comparison and classification of human brain tissues at a nanoscopic level. Furthermore, the presented workflow marks a unique technological advance in the characterization of protein distributions in brain tissue sections.

Data-driven classification of ventilated lung tissues using electrical impedance tomography

International Nuclear Information System (INIS)

Gómez-Laberge, Camille; Hogan, Matthew J; Elke, Gunnar; Weiler, Norbert; Frerichs, Inéz; Adler, Andy

2011-01-01

Current methods for identifying ventilated lung regions utilizing electrical impedance tomography images rely on dividing the image into arbitrary regions of interest (ROI), manually delineating ROI, or forming ROI with pixels whose signal properties surpass an arbitrary threshold. In this paper, we propose a novel application of a data-driven classification method to identify ventilated lung ROI based on forming k clusters from pixels with correlated signals. A standard first-order model for lung mechanics is then applied to determine which ROI correspond to ventilated lung tissue. We applied the method in an experimental study of 16 mechanically ventilated swine in the supine position, which underwent changes in positive end-expiratory pressure (PEEP) and fraction of inspired oxygen (F I O 2 ). In each stage of the experimental protocol, the method performed best with k = 4 and consistently identified 3 lung tissue ROI and 1 boundary tissue ROI in 15 of the 16 subjects. When testing for changes from baseline in lung position, tidal volume, and respiratory system compliance, we found that PEEP displaced the ventilated lung region dorsally by 2 cm, decreased tidal volume by 1.3%, and increased the respiratory system compliance time constant by 0.3 s. F I O 2 decreased tidal volume by 0.7%. All effects were tested at p < 0.05 with n = 16. These findings suggest that the proposed ROI detection method is robust and sensitive to ventilation dynamics in the experimental setting

Automatic classification of prostate stromal tissue in histological images using Haralick descriptors and Local Binary Patterns

International Nuclear Information System (INIS)

Oliveira, D L L; Batista, V R; Duarte, Y A S; Nascimento, M Z; Neves, L A; Godoy, M F; Jacomini, R S; Arruda, P F F; Neto, D S

2014-01-01

In this paper we presente a classification system that uses a combination of texture features from stromal regions: Haralick features and Local Binary Patterns (LBP) in wavelet domain. The system has five steps for classification of the tissues. First, the stromal regions were detected and extracted using segmentation techniques based on thresholding and RGB colour space. Second, the Wavelet decomposition was applied in the extracted regions to obtain the Wavelet coefficients. Third, the Haralick and LBP features were extracted from the coefficients. Fourth, relevant features were selected using the ANOVA statistical method. The classication (fifth step) was performed with Radial Basis Function (RBF) networks. The system was tested in 105 prostate images, which were divided into three groups of 35 images: normal, hyperplastic and cancerous. The system performance was evaluated using the area under the ROC curve and resulted in 0.98 for normal versus cancer, 0.95 for hyperplasia versus cancer and 0.96 for normal versus hyperplasia. Our results suggest that texture features can be used as discriminators for stromal tissues prostate images. Furthermore, the system was effective to classify prostate images, specially the hyperplastic class which is the most difficult type in diagnosis and prognosis

Breast tissue classification in digital breast tomosynthesis images using texture features: a feasibility study

Science.gov (United States)

Kontos, Despina; Berger, Rachelle; Bakic, Predrag R.; Maidment, Andrew D. A.

2009-02-01

Mammographic breast density is a known breast cancer risk factor. Studies have shown the potential to automate breast density estimation by using computerized texture-based segmentation of the dense tissue in mammograms. Digital breast tomosynthesis (DBT) is a tomographic x-ray breast imaging modality that could allow volumetric breast density estimation. We evaluated the feasibility of distinguishing between dense and fatty breast regions in DBT using computer-extracted texture features. Our long-term hypothesis is that DBT texture analysis can be used to develop 3D dense tissue segmentation algorithms for estimating volumetric breast density. DBT images from 40 women were analyzed. The dense tissue area was delineated within each central source projection (CSP) image using a thresholding technique (Cumulus, Univ. Toronto). Two (2.5cm)2 ROIs were manually selected: one within the dense tissue region and another within the fatty region. Corresponding (2.5cm)3 ROIs were placed within the reconstructed DBT images. Texture features, previously used for mammographic dense tissue segmentation, were computed. Receiver operating characteristic (ROC) curve analysis was performed to evaluate feature classification performance. Different texture features appeared to perform best in the 3D reconstructed DBT compared to the 2D CSP images. Fractal dimension was superior in DBT (AUC=0.90), while contrast was best in CSP images (AUC=0.92). We attribute these differences to the effects of tissue superimposition in CSP and the volumetric visualization of the breast tissue in DBT. Our results suggest that novel approaches, different than those conventionally used in projection mammography, need to be investigated in order to develop DBT dense tissue segmentation algorithms for estimating volumetric breast density.

Tissue classifications in Monte Carlo simulations of patient dose for photon beam tumor treatments

Science.gov (United States)

Lin, Mu-Han; Chao, Tsi-Chian; Lee, Chung-Chi; Tung-Chieh Chang, Joseph; Tung, Chuan-Jong

2010-07-01

The purpose of this work was to study the calculated dose uncertainties induced by the material classification that determined the interaction cross-sections and the water-to-material stopping-power ratios. Calculations were made for a head- and neck-cancer patient treated with five intensity-modulated radiotherapy fields using 6 MV photon beams. The patient's CT images were reconstructed into two voxelized patient phantoms based on different CT-to-material classification schemes. Comparisons of the depth-dose curve of the anterior-to-posterior field and the dose-volume-histogram of the treatment plan were used to evaluate the dose uncertainties from such schemes. The results indicated that any misassignment of tissue materials could lead to a substantial dose difference, which would affect the treatment outcome. To assure an appropriate material assignment, it is desirable to have different conversion tables for various parts of the body. The assignment of stopping-power ratio should be based on the chemical composition and the density of the material.

Tissue classifications in Monte Carlo simulations of patient dose for photon beam tumor treatments

International Nuclear Information System (INIS)

Lin, Mu-Han; Chao, Tsi-Chian; Lee, Chung-Chi; Tung-Chieh Chang, Joseph; Tung, Chuan-Jong

2010-01-01

The purpose of this work was to study the calculated dose uncertainties induced by the material classification that determined the interaction cross-sections and the water-to-material stopping-power ratios. Calculations were made for a head- and neck-cancer patient treated with five intensity-modulated radiotherapy fields using 6 MV photon beams. The patient's CT images were reconstructed into two voxelized patient phantoms based on different CT-to-material classification schemes. Comparisons of the depth-dose curve of the anterior-to-posterior field and the dose-volume-histogram of the treatment plan were used to evaluate the dose uncertainties from such schemes. The results indicated that any misassignment of tissue materials could lead to a substantial dose difference, which would affect the treatment outcome. To assure an appropriate material assignment, it is desirable to have different conversion tables for various parts of the body. The assignment of stopping-power ratio should be based on the chemical composition and the density of the material.

Multiplex coherent anti-Stokes Raman scattering microspectroscopy of brain tissue with higher ranking data classification for biomedical imaging

Science.gov (United States)

Pohling, Christoph; Bocklitz, Thomas; Duarte, Alex S.; Emmanuello, Cinzia; Ishikawa, Mariana S.; Dietzeck, Benjamin; Buckup, Tiago; Uckermann, Ortrud; Schackert, Gabriele; Kirsch, Matthias; Schmitt, Michael; Popp, Jürgen; Motzkus, Marcus

2017-06-01

Multiplex coherent anti-Stokes Raman scattering (MCARS) microscopy was carried out to map a solid tumor in mouse brain tissue. The border between normal and tumor tissue was visualized using support vector machines (SVM) as a higher ranking type of data classification. Training data were collected separately in both tissue types, and the image contrast is based on class affiliation of the single spectra. Color coding in the image generated by SVM is then related to pathological information instead of single spectral intensities or spectral differences within the data set. The results show good agreement with the H&E stained reference and spontaneous Raman microscopy, proving the validity of the MCARS approach in combination with SVM.

Gynecomastia Classification for Surgical Management: A Systematic Review and Novel Classification System.

Science.gov (United States)

Waltho, Daniel; Hatchell, Alexandra; Thoma, Achilleas

2017-03-01

Gynecomastia is a common deformity of the male breast, where certain cases warrant surgical management. There are several surgical options, which vary depending on the breast characteristics. To guide surgical management, several classification systems for gynecomastia have been proposed. A systematic review was performed to (1) identify all classification systems for the surgical management of gynecomastia, and (2) determine the adequacy of these classification systems to appropriately categorize the condition for surgical decision-making. The search yielded 1012 articles, and 11 articles were included in the review. Eleven classification systems in total were ascertained, and a total of 10 unique features were identified: (1) breast size, (2) skin redundancy, (3) breast ptosis, (4) tissue predominance, (5) upper abdominal laxity, (6) breast tuberosity, (7) nipple malposition, (8) chest shape, (9) absence of sternal notch, and (10) breast skin elasticity. On average, classification systems included two or three of these features. Breast size and ptosis were the most commonly included features. Based on their review of the current classification systems, the authors believe the ideal classification system should be universal and cater to all causes of gynecomastia; be surgically useful and easy to use; and should include a comprehensive set of clinically appropriate patient-related features, such as breast size, breast ptosis, tissue predominance, and skin redundancy. None of the current classification systems appears to fulfill these criteria.

Automated classification of immunostaining patterns in breast tissue from the human protein atlas.

Science.gov (United States)

Swamidoss, Issac Niwas; Kårsnäs, Andreas; Uhlmann, Virginie; Ponnusamy, Palanisamy; Kampf, Caroline; Simonsson, Martin; Wählby, Carolina; Strand, Robin

2013-01-01

The Human Protein Atlas (HPA) is an effort to map the location of all human proteins (http://www.proteinatlas.org/). It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA) are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples. The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM) features, complex wavelet co-occurrence matrix (CWCM) features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM)-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM) and linear discriminant analysis (LDA) classifier). Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue. We present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert. Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for quantification of staining patterns in histopathology have many

Automated classification of immunostaining patterns in breast tissue from the human protein Atlas

Directory of Open Access Journals (Sweden)

Issac Niwas Swamidoss

2013-01-01

Full Text Available Background: The Human Protein Atlas (HPA is an effort to map the location of all human proteins (http://www.proteinatlas.org/. It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples. Materials and Methods: The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM features, complex wavelet co-occurrence matrix (CWCM features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM and linear discriminant analysis (LDA classifier. Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue. Results: We present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert. Conclusions: Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for

Subcutaneous adipose tissue classification

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A. Sbarbati

2010-11-01

Full Text Available The developments in the technologies based on the use of autologous adipose tissue attracted attention to minor depots as possible sampling areas. Some of those depots have never been studied in detail. The present study was performed on subcutaneous adipose depots sampled in different areas with the aim of explaining their morphology, particularly as far as regards stem niches. The results demonstrated that three different types of white adipose tissue (WAT can be differentiated on the basis of structural and ultrastructural features: deposit WAT (dWAT, structural WAT (sWAT and fibrous WAT (fWAT. dWAT can be found essentially in large fatty depots in the abdominal area (periumbilical. In the dWAT, cells are tightly packed and linked by a weak net of isolated collagen fibers. Collagenic components are very poor, cells are large and few blood vessels are present. The deep portion appears more fibrous then the superficial one. The microcirculation is formed by thin walled capillaries with rare stem niches. Reinforcement pericyte elements are rarely evident. The sWAT is more stromal; it is located in some areas in the limbs and in the hips. The stroma is fairly well represented, with a good vascularity and adequate staminality. Cells are wrapped by a basket of collagen fibers. The fatty depots of the knees and of the trochanteric areas have quite loose meshes. The fWAT has a noteworthy fibrous component and can be found in areas where a severe mechanic stress occurs. Adipocytes have an individual thick fibrous shell. In conclusion, the present study demonstrates evident differences among subcutaneous WAT deposits, thus suggesting that in regenerative procedures based on autologous adipose tissues the sampling area should not be randomly chosen, but it should be oriented by evidence based evaluations. The structural peculiarities of the sWAT, and particularly of its microcirculation, suggest that it could represent a privileged source for

Automatic classification of tissue malignancy for breast carcinoma diagnosis.

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Fondón, Irene; Sarmiento, Auxiliadora; García, Ana Isabel; Silvestre, María; Eloy, Catarina; Polónia, António; Aguiar, Paulo

2018-05-01

Breast cancer is the second leading cause of cancer death among women. Its early diagnosis is extremely important to prevent avoidable deaths. However, malignancy assessment of tissue biopsies is complex and dependent on observer subjectivity. Moreover, hematoxylin and eosin (H&E)-stained histological images exhibit a highly variable appearance, even within the same malignancy level. In this paper, we propose a computer-aided diagnosis (CAD) tool for automated malignancy assessment of breast tissue samples based on the processing of histological images. We provide four malignancy levels as the output of the system: normal, benign, in situ and invasive. The method is based on the calculation of three sets of features related to nuclei, colour regions and textures considering local characteristics and global image properties. By taking advantage of well-established image processing techniques, we build a feature vector for each image that serves as an input to an SVM (Support Vector Machine) classifier with a quadratic kernel. The method has been rigorously evaluated, first with a 5-fold cross-validation within an initial set of 120 images, second with an external set of 30 different images and third with images with artefacts included. Accuracy levels range from 75.8% when the 5-fold cross-validation was performed to 75% with the external set of new images and 61.11% when the extremely difficult images were added to the classification experiment. The experimental results indicate that the proposed method is capable of distinguishing between four malignancy levels with high accuracy. Our results are close to those obtained with recent deep learning-based methods. Moreover, it performs better than other state-of-the-art methods based on feature extraction, and it can help improve the CAD of breast cancer. Copyright © 2018 Elsevier Ltd. All rights reserved.

Wagner classification and culture analysis of diabetic foot infection

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Fatma Bozkurt

2011-03-01

Full Text Available The aim of this study was to determine the concordance ratio between microorganisms isolated from deep tissue culture and those from superficial culture in patients with diabetic foot according to Wagner’s wound classification method.Materials and methods: A total of 63 patients with Diabetic foot infection, who were admitted to Dicle University Hospital between October 2006 and November 2007, were included into the study. Wagner’s classification method was used for wound classification. For microbiologic studies superficial and deep tissue specimens were obtained from each patient, and were rapidly sent to laboratory for aerob and anaerob cultures. Microbiologic data were analyzed and interpreted in line with sensitivity and specifity formula.Results: Thirty-eight (60% of the patients were in Wagner’s classification ≤2, while 25 (40% patients were Wagner’s classification ≥3. According to our culture results, 66 (69% Gr (+ and 30 (31% Gr (- microorganisms grew in Wagner classification ≤2 patients. While in Wagner classification ≥3; 25 (35% Gr (+ and 46 (65% Gr (- microorganisms grew. Microorganisms grew in 89% of superficial cultures and 64% of the deep tissue cultures in patients with Wagner classification ≤2, while microorganism grew in 64% of Wagner classification ≥3.Conclusion: In ulcers of diabetic food infections, initial treatment should be started according to result of sterile superficial culture, but deep tissue culture should be taken, if unresponsive to initial treatment.

Classification of Laser Induced Fluorescence Spectra from Normal and Malignant bladder tissues using Learning Vector Quantization Neural Network in Bladder Cancer Diagnosis

DEFF Research Database (Denmark)

Karemore, Gopal Raghunath; Mascarenhas, Kim Komal; Patil, Choudhary

2008-01-01

In the present work we discuss the potential of recently developed classification algorithm, Learning Vector Quantization (LVQ), for the analysis of Laser Induced Fluorescence (LIF) Spectra, recorded from normal and malignant bladder tissue samples. The algorithm is prototype based and inherently...

Three-dimensional analysis and classification of arteries in the skin and subcutaneous adipofascial tissue by computer graphics imaging.

Science.gov (United States)

Nakajima, H; Minabe, T; Imanishi, N

1998-09-01

To develop new types of surgical flaps that utilize portions of the skin and subcutaneous tissue (e.g., a thin flap or an adipofascial flap), three-dimensional investigation of the vasculature in the skin and subcutaneous tissue has been anticipated. In the present study, total-body arterial injection and three-dimensional imaging of the arteries by computer graphics were performed. The full-thickness skin and subcutaneous adipofascial tissue samples, which were obtained from fresh human cadavers injected with radio-opaque medium, were divided into three distinct layers. Angiograms of each layer were introduced into a personal computer to construct three-dimensional images. On a computer monitor, each artery was shown color-coded according to the three portions: the deep adipofascial layer, superficial adipofascial layer, and dermis. Three-dimensional computerized images of each artery in the skin and subcutaneous tissue revealed the components of each vascular plexus and permitted their classification into six types. The distribution of types in the body correlated with the tissue mobility of each area. Clinically, appreciation of the three-dimensional structure of the arteries allowed the development of several new kinds of flaps.

Automatic multi-modal MR tissue classification for the assessment of response to bevacizumab in patients with glioblastoma

International Nuclear Information System (INIS)

Liberman, Gilad; Louzoun, Yoram; Aizenstein, Orna; Blumenthal, Deborah T.; Bokstein, Felix; Palmon, Mika; Corn, Benjamin W.; Ben Bashat, Dafna

2013-01-01

Background: Current methods for evaluation of treatment response in glioblastoma are inaccurate, limited and time-consuming. This study aimed to develop a multi-modal MRI automatic classification method to improve accuracy and efficiency of treatment response assessment in patients with recurrent glioblastoma (GB). Materials and methods: A modification of the k-Nearest-Neighbors (kNN) classification method was developed and applied to 59 longitudinal MR data sets of 13 patients with recurrent GB undergoing bevacizumab (anti-angiogenic) therapy. Changes in the enhancing tumor volume were assessed using the proposed method and compared with Macdonald's criteria and with manual volumetric measurements. The edema-like area was further subclassified into peri- and non-peri-tumoral edema, using both the kNN method and an unsupervised method, to monitor longitudinal changes. Results: Automatic classification using the modified kNN method was applicable in all scans, even when the tumors were infiltrative with unclear borders. The enhancing tumor volume obtained using the automatic method was highly correlated with manual measurements (N = 33, r = 0.96, p < 0.0001), while standard radiographic assessment based on Macdonald's criteria matched manual delineation and automatic results in only 68% of cases. A graded pattern of tumor infiltration within the edema-like area was revealed by both automatic methods, showing high agreement. All classification results were confirmed by a senior neuro-radiologist and validated using MR spectroscopy. Conclusion: This study emphasizes the important role of automatic tools based on a multi-modal view of the tissue in monitoring therapy response in patients with high grade gliomas specifically under anti-angiogenic therapy

Machine Learning of Human Pluripotent Stem Cell-Derived Engineered Cardiac Tissue Contractility for Automated Drug Classification

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Eugene K. Lee

2017-11-01

Full Text Available Accurately predicting cardioactive effects of new molecular entities for therapeutics remains a daunting challenge. Immense research effort has been focused toward creating new screening platforms that utilize human pluripotent stem cell (hPSC-derived cardiomyocytes and three-dimensional engineered cardiac tissue constructs to better recapitulate human heart function and drug responses. As these new platforms become increasingly sophisticated and high throughput, the drug screens result in larger multidimensional datasets. Improved automated analysis methods must therefore be developed in parallel to fully comprehend the cellular response across a multidimensional parameter space. Here, we describe the use of machine learning to comprehensively analyze 17 functional parameters derived from force readouts of hPSC-derived ventricular cardiac tissue strips (hvCTS electrically paced at a range of frequencies and exposed to a library of compounds. A generated metric is effective for then determining the cardioactivity of a given drug. Furthermore, we demonstrate a classification model that can automatically predict the mechanistic action of an unknown cardioactive drug.

Visualization and classification in biomedical terahertz pulsed imaging

International Nuclear Information System (INIS)

Loeffler, Torsten; Siebert, Karsten; Czasch, Stephanie; Bauer, Tobias; Roskos, Hartmut G

2002-01-01

'Visualization' in imaging is the process of extracting useful information from raw data in such a way that meaningful physical contrasts are developed. 'Classification' is the subsequent process of defining parameter ranges which allow us to identify elements of images such as different tissues or different objects. In this paper, we explore techniques for visualization and classification in terahertz pulsed imaging (TPI) for biomedical applications. For archived (formalin-fixed, alcohol-dehydrated and paraffin-mounted) test samples, we investigate both time- and frequency-domain methods based on bright- and dark-field TPI. Successful tissue classification is demonstrated

Classification of breast tissue for protocols development in mammography exam;Classificacao dos tecidos mamarios para elaboracao de protocolos em exames de mamografia

Energy Technology Data Exchange (ETDEWEB)

Teixeira, M. Ines; Caldas, Linda V.E. [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil); Chohfi, Adriana C.S.; Figueiredo, Zenaide A. [Universidade Nove de Julho (UNINOVE), Sao Paulo, SP (Brazil). Dept. da Saude. Radiologia Medica

2009-07-01

For accomplishment of a mammogram should be considered several factors such as equipment used and the classification of breast cancer patients. The density of the breast is different of patient to patient, thus making it difficult many precocious diagnostic of breast cancer. There is a significant variation in the breast to be radiographed, depending on several factors presented in the breast tissue of breast prosthesis with or without breasts or with implants. This work had as objective main to make a survey of the factors that influence in the classification of mammary fabrics. With this survey and classification it can be better adjusted the protocols which will be more efficient in the mammography exam, preventing excess of radiation dose, stress for the patient, economy of raw material, control of the quality and rapidity in the process of image attainment. (author)

Segmentation methodology for automated classification and differentiation of soft tissues in multiband images of high-resolution ultrasonic transmission tomography.

Science.gov (United States)

Jeong, Jeong-Won; Shin, Dae C; Do, Synho; Marmarelis, Vasilis Z

2006-08-01

This paper presents a novel segmentation methodology for automated classification and differentiation of soft tissues using multiband data obtained with the newly developed system of high-resolution ultrasonic transmission tomography (HUTT) for imaging biological organs. This methodology extends and combines two existing approaches: the L-level set active contour (AC) segmentation approach and the agglomerative hierarchical kappa-means approach for unsupervised clustering (UC). To prevent the trapping of the current iterative minimization AC algorithm in a local minimum, we introduce a multiresolution approach that applies the level set functions at successively increasing resolutions of the image data. The resulting AC clusters are subsequently rearranged by the UC algorithm that seeks the optimal set of clusters yielding the minimum within-cluster distances in the feature space. The presented results from Monte Carlo simulations and experimental animal-tissue data demonstrate that the proposed methodology outperforms other existing methods without depending on heuristic parameters and provides a reliable means for soft tissue differentiation in HUTT images.

A minimum spanning forest based classification method for dedicated breast CT images

International Nuclear Information System (INIS)

Pike, Robert; Sechopoulos, Ioannis; Fei, Baowei

2015-01-01

Purpose: To develop and test an automated algorithm to classify different types of tissue in dedicated breast CT images. Methods: Images of a single breast of five different patients were acquired with a dedicated breast CT clinical prototype. The breast CT images were processed by a multiscale bilateral filter to reduce noise while keeping edge information and were corrected to overcome cupping artifacts. As skin and glandular tissue have similar CT values on breast CT images, morphologic processing is used to identify the skin based on its position information. A support vector machine (SVM) is trained and the resulting model used to create a pixelwise classification map of fat and glandular tissue. By combining the results of the skin mask with the SVM results, the breast tissue is classified as skin, fat, and glandular tissue. This map is then used to identify markers for a minimum spanning forest that is grown to segment the image using spatial and intensity information. To evaluate the authors’ classification method, they use DICE overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on five patient images. Results: Comparison between the automatic and the manual segmentation shows that the minimum spanning forest based classification method was able to successfully classify dedicated breast CT image with average DICE ratios of 96.9%, 89.8%, and 89.5% for fat, glandular, and skin tissue, respectively. Conclusions: A 2D minimum spanning forest based classification method was proposed and evaluated for classifying the fat, skin, and glandular tissue in dedicated breast CT images. The classification method can be used for dense breast tissue quantification, radiation dose assessment, and other applications in breast imaging

The classification of benign and malignant human prostate tissue by multivariate analysis of {sup 1}H magnetic resonance spectra

Energy Technology Data Exchange (ETDEWEB)

Hahn, P.; Smith, I.; Leboldus, L.; Littman, C.; Somorjai, L.; Bezabeh, T. [Institute for Biodiagnostic, National Research Council, Manitoba (Canada)

1998-04-01

{sup 1}H magnetic resonance spectroscopy studies (360 MHz) were performed on specimens of benign (n = 66) and malignant (n = 21) human prostate tissue from 50 patients and the spectral data were subjected to multivariate analysis, specifically linear-discriminant analysis. On the basis of histopathological assessments, an overall classification accuracy of 96.6 % was achieved, with a sensitivity of 100 % and a specificity of 95.5 % in classifying benign prostatic hyperplasia from prostatic cancer. Resonances due to citrate, glutamate, and taurine were among the six spectral subregions identified by our algorithm as having diagnostic potential. Significantly higher levels of citrate were observed in glandular than in stromal benign prostatic hyperplasia (P < 0.05). This method shows excellent promise for the possibility of in vivo assessment of prostate tissue by magnetic resonance. (author)

Proposal of a new classification scheme for periocular injuries

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Devi Prasad Mohapatra

2017-01-01

Full Text Available Background: Eyelids are important structures and play a role in protecting the globe from trauma, brightness, in maintaining the integrity of tear films and moving the tears towards the lacrimal drainage system and contribute to aesthetic appearance of the face. Ophthalmic trauma is an important cause of morbidity among individuals and has also been responsible for additional cost of healthcare. Periocular trauma involving eyelids and adjacent structures has been found to have increased recently probably due to increased pace of life and increased dependence on machinery. A comprehensive classification of periocular trauma would help in stratifying these injuries as well as study outcomes. Material and Methods: This study was carried out at our institute from June 2015 to Dec 2015. We searched multiple English language databases for existing classification systems for periocular trauma. We designed a system of classification of periocular soft tissue injuries based on clinico-anatomical presentations. This classification was applied prospectively to patients presenting with periocular soft tissue injuries to our department. Results: A comprehensive classification scheme was designed consisting of five types of periocular injuries. A total of 38 eyelid injuries in 34 patients were evaluated in this study. According to the System for Peri-Ocular Trauma (SPOT classification, Type V injuries were most common. SPOT Type II injuries were more common isolated injuries among all zones. Discussion: Classification systems are necessary in order to provide a framework in which to scientifically study the etiology, pathogenesis, and treatment of diseases in an orderly fashion. The SPOT classification has taken into account the periocular soft tissue injuries i.e., upper eyelid, lower eyelid, medial and lateral canthus injuries., based on observed clinico-anatomical patterns of eyelid injuries. Conclusion: The SPOT classification seems to be a reliable

Classification of first branchial cleft anomalies: is it clinically relevant ...

African Journals Online (AJOL)

Background: There are three classification systems for first branchial cleft anomalies currently in use. The Arnot, Work and Olsen classifications describe these lesions on the basis of morphology, tissue of origin and clinical appearance. However, the clinical relevance of these classifications is debated, as they may not be ...

A new classification system for congenital laryngeal cysts.

Science.gov (United States)

Forte, Vito; Fuoco, Gabriel; James, Adrian

2004-06-01

A new classification system for congenital laryngeal cysts based on the extent of the cyst and on the embryologic tissue of origin is proposed. Retrospective chart review. The charts of 20 patients with either congenital or acquired laryngeal cysts that were treated surgically between 1987 and 2002 at the Hospital for Sick Children, Toronto were retrospectively reviewed. Clinical presentation, radiologic findings, surgical management, histopathology, and outcome were recorded. A new classification system is proposed to better appreciate the origin of these cysts and to guide in their successful surgical management. Fourteen of the supraglottic and subglottic simple mucous retention cysts posed no diagnostic or therapeutic challenge and were treated successfully by a single endoscopic excision or marsupialization. The remaining six patients with congenital cysts in the study were deemed more complex, and all required open surgical procedures for cure. On the basis of the analysis of the data of these patients, a new classification of congenital laryngeal cysts is proposed. Type I cysts are confined to the larynx, the cyst wall composed of endodermal elements only, and can be managed endoscopically. Type II cysts extend beyond the confines of the larynx and require an external approach. The Type II cysts are further subclassified histologically on the basis of the embryologic tissue of origin: IIa, composed of endoderm only and IIb, containing endodermal and mesodermal elements (epithelium and cartilage) in the wall of the cyst. A new classification system for congenital laryngeal cysts is proposed on the basis of the extent of the cyst and the embryologic tissue of origin. This classification can help guide the surgeon with initial management and help us better understand the origin of these cysts.

Learning semantic histopathological representation for basal cell carcinoma classification

Science.gov (United States)

Gutiérrez, Ricardo; Rueda, Andrea; Romero, Eduardo

2013-03-01

Diagnosis of a histopathology glass slide is a complex process that involves accurate recognition of several structures, their function in the tissue and their relation with other structures. The way in which the pathologist represents the image content and the relations between those objects yields a better and accurate diagnoses. Therefore, an appropriate semantic representation of the image content will be useful in several analysis tasks such as cancer classification, tissue retrieval and histopahological image analysis, among others. Nevertheless, to automatically recognize those structures and extract their inner semantic meaning are still very challenging tasks. In this paper we introduce a new semantic representation that allows to describe histopathological concepts suitable for classification. The approach herein identify local concepts using a dictionary learning approach, i.e., the algorithm learns the most representative atoms from a set of random sampled patches, and then models the spatial relations among them by counting the co-occurrence between atoms, while penalizing the spatial distance. The proposed approach was compared with a bag-of-features representation in a tissue classification task. For this purpose, 240 histological microscopical fields of view, 24 per tissue class, were collected. Those images fed a Support Vector Machine classifier per class, using 120 images as train set and the remaining ones for testing, maintaining the same proportion of each concept in the train and test sets. The obtained classification results, averaged from 100 random partitions of training and test sets, shows that our approach is more sensitive in average than the bag-of-features representation in almost 6%.

Classification of parotidectomy: a proposed modification to the European Salivary Gland Society classification system.

Science.gov (United States)

Wong, Wai Keat; Shetty, Subhaschandra

2017-08-01

Parotidectomy remains the mainstay of treatment for both benign and malignant lesions of the parotid gland. There exists a wide range of possible surgical options in parotidectomy in terms of extent of parotid tissue removed. There is increasing need for uniformity of terminology resulting from growing interest in modifications of the conventional parotidectomy. It is, therefore, of paramount importance for a standardized classification system in describing extent of parotidectomy. Recently, the European Salivary Gland Society (ESGS) proposed a novel classification system for parotidectomy. The aim of this study is to evaluate this system. A classification system proposed by the ESGS was critically re-evaluated and modified to increase its accuracy and its acceptability. Modifications mainly focused on subdividing Levels I and II into IA, IB, IIA, and IIB. From June 2006 to June 2016, 126 patients underwent 130 parotidectomies at our hospital. The classification system was tested in that cohort of patient. While the ESGS classification system is comprehensive, it does not cover all possibilities. The addition of Sublevels IA, IB, IIA, and IIB may help to address some of the clinical situations seen and is clinically relevant. We aim to test the modified classification system for partial parotidectomy to address some of the challenges mentioned.

Evaluation of thyroid tissue by Raman spectroscopy

Science.gov (United States)

Teixeira, C. S. B.; Bitar, R. A.; Santos, A. B. O.; Kulcsar, M. A. V.; Friguglietti, C. U. M.; Martinho, H. S.; da Costa, R. B.; Martin, A. A.

2010-02-01

Thyroid gland is a small gland in the neck consisting of two lobes connected by an isthmus. Thyroid's main function is to produce the hormones thyroxine (T4), triiodothyronine (T3) and calcitonin. Thyroid disorders can disturb the production of these hormones, which will affect numerous processes within the body such as: regulating metabolism and increasing utilization of cholesterol, fats, proteins, and carbohydrates. The gland itself can also be injured; for example, neoplasias, which have been considered the most important, causing damage of to the gland and are difficult to diagnose. There are several types of thyroid cancer: Papillary, Follicular, Medullary, and Anaplastic. The occurrence rate, in general is between 4 and 7%; which is on the increase (30%), probably due to new technology that is able to find small thyroid cancers that may not have been found previously. The most common method used for thyroid diagnoses are: anamnesis, ultrasonography, and laboratory exams (Fine Needle Aspiration Biopsy- FNAB). However, the sensitivity of those test are rather poor, with a high rate of false-negative results, therefore there is an urgent need to develop new diagnostic techniques. Raman spectroscopy has been presented as a valuable tool for cancer diagnosis in many different tissues. In this work, 27 fragments of the thyroid were collected from 18 patients, comprising the following histologic groups: goitre adjacent tissue, goitre nodular tissue, follicular adenoma, follicular carcinoma, and papillary carcinoma. Spectral collection was done with a commercial FTRaman Spectrometer (Bruker RFS100/S) using a 1064 nm laser excitation and Ge detector. Principal Component Analysis, Cluster Analysis, and Linear Discriminant Analysis with cross-validation were applied as spectral classification algorithm. Comparing the goitre adjacent tissue with the goitre nodular region, an index of 58.3% of correct classification was obtained. Between goitre (nodular region and

Correlated regions of cerebral blood flow with clinical parameters in Parkinson's disease. Comparison using 'Anatomy' and 'Talairach Daemon' software

International Nuclear Information System (INIS)

Yoon, Hyun Jin; Cheon, Sang Myung; Jeong, Young Jin; Kang, Do Young

2012-01-01

We assign the anatomical names of functional activation regions in the brain, based on the probabilistic cyto-architectonic atlas by Anatomy 1.7 from an analysis of correlations between regional cerebral blood flow (rCBF) and clinical parameters of the non-demented Parkinson's disease (PD) patients by statistical parametric mapping (SPM) 8. We evaluated Anatomy 1.7 of SPM toolbox compared to 'Talairach Daemon' (TD) Client 2.4.2 software. One hundred and thirty-six patients (mean age 60.0±9.09 years; 73 women and 63 men) with non-demented PD were selected. Tc-99m-HMPAO brain single-photon emission computed tomography (SPECT) scans were performed on the patients using a two-head gamma-camera. We analyzed the brain image of PD patients by SPM8 and found the anatomical names of correlated regions of rCBF perfusion with the clinical parameters using TD Client 2.4.2 and Anatomy 1.7. The SPM8 provided a correlation coefficient between clinical parameters and cerebral hypoperfusion by a simple regression method. To the clinical parameters were added age, duration of disease, education period, Hoehn and Yahr (H and Y) stage and Korean mini-mental state examination (K-MMSE) score. Age was correlated with cerebral perfusion in the Brodmann area (BA) 6 and BA 3b assigned by Anatomy 1.7 and BA 6 and pyramis in gray matter by TD Client 2.4.2 with p<0.001 uncorrected. Also, assigned significant correlated regions were found in the left and right lobules VI (Hem) with duration of disease, in left and right lobules VIIa crus I (Hem) with education, in left insula (Ig2), left and right lobules VI (Hem) with H and Y, and in BA 4a and 6 with K-MMSE score with p<0.05 uncorrected by Anatomy 1.7, respectively. Most areas of correlation were overlapped by two different anatomical labeling methods, but some correlation areas were found with different names. Age was the most significantly correlated clinical parameter with rCBF. TD Client found the exact anatomical name by the peak

Three-class classification in computer-aided diagnosis of breast cancer by support vector machine

Science.gov (United States)

Sun, Xuejun; Qian, Wei; Song, Dansheng

2004-05-01

Design of classifier in computer-aided diagnosis (CAD) scheme of breast cancer plays important role to its overall performance in sensitivity and specificity. Classification of a detected object as malignant lesion, benign lesion, or normal tissue on mammogram is a typical three-class pattern recognition problem. This paper presents a three-class classification approach by using two-stage classifier combined with support vector machine (SVM) learning algorithm for classification of breast cancer on mammograms. The first classification stage is used to detect abnormal areas and normal breast tissues, and the second stage is for classification of malignant or benign in detected abnormal objects. A series of spatial, morphology and texture features have been extracted on detected objects areas. By using genetic algorithm (GA), different feature groups for different stage classification have been investigated. Computerized free-response receiver operating characteristic (FROC) and receiver operating characteristic (ROC) analyses have been employed in different classification stages. Results have shown that obvious performance improvement in both sensitivity and specificity was observed through proposed classification approach compared with conventional two-class classification approaches, indicating its effectiveness in classification of breast cancer on mammograms.

Identification of immune cell infiltration in hematoxylin-eosin stained breast cancer samples: texture-based classification of tissue morphologies

Science.gov (United States)

Turkki, Riku; Linder, Nina; Kovanen, Panu E.; Pellinen, Teijo; Lundin, Johan

2016-03-01

The characteristics of immune cells in the tumor microenvironment of breast cancer capture clinically important information. Despite the heterogeneity of tumor-infiltrating immune cells, it has been shown that the degree of infiltration assessed by visual evaluation of hematoxylin-eosin (H and E) stained samples has prognostic and possibly predictive value. However, quantification of the infiltration in H and E-stained tissue samples is currently dependent on visual scoring by an expert. Computer vision enables automated characterization of the components of the tumor microenvironment, and texture-based methods have successfully been used to discriminate between different tissue morphologies and cell phenotypes. In this study, we evaluate whether local binary pattern texture features with superpixel segmentation and classification with support vector machine can be utilized to identify immune cell infiltration in H and E-stained breast cancer samples. Guided with the pan-leukocyte CD45 marker, we annotated training and test sets from 20 primary breast cancer samples. In the training set of arbitrary sized image regions (n=1,116) a 3-fold cross-validation resulted in 98% accuracy and an area under the receiver-operating characteristic curve (AUC) of 0.98 to discriminate between immune cell -rich and - poor areas. In the test set (n=204), we achieved an accuracy of 96% and AUC of 0.99 to label cropped tissue regions correctly into immune cell -rich and -poor categories. The obtained results demonstrate strong discrimination between immune cell -rich and -poor tissue morphologies. The proposed method can provide a quantitative measurement of the degree of immune cell infiltration and applied to digitally scanned H and E-stained breast cancer samples for diagnostic purposes.

Added soft tissue contrast using signal attenuation and the fractal dimension for optical coherence tomography images of porcine arterial tissue

International Nuclear Information System (INIS)

Flueraru, C; Mao, Y; Chang, S; Popescu, D P; Sowa, M G

2010-01-01

Optical coherence tomography (OCT) images of left-descending coronary tissues harvested from three porcine specimens were acquired with a home-build swept-source OCT setup. Despite the fact that OCT is capable of acquiring high resolution circumferential images of vessels, many distinct histological features of a vessel have comparable optical properties leading to poor contrast in OCT images. Two classification methods were tested in this report for the purpose of enhancing contrast between soft-tissue components of porcine coronary vessels. One method involved analyzing the attenuation of the OCT signal as a function of light penetration into the tissue. We demonstrated that by analyzing the signal attenuation in this manner we were able to differentiate two media sub-layers with different orientations of the smooth muscle cells. The other classification method used in our study was fractal analysis. Fractal analysis was implemented in a box-counting (fractal dimension) image-processing code and was used as a tool to differentiate and quantify variations in tissue texture at various locations in the OCT images. The calculated average fractal dimensions had different values in distinct regions of interest (ROI) within the imaged coronary samples. When compared to the results obtained by using the attenuation of the OCT signal, the method of fractal analysis demonstrated better classification potential for distinguishing amongst the tissue ROI.

Advances in the classification and treatment of mastocytosis

DEFF Research Database (Denmark)

Valent, Peter; Akin, Cem; Hartmann, Karin

2017-01-01

Mastocytosis is a term used to denote a heterogeneous group of conditions defined by the expansion and accumulation of clonal (neoplastic) tissue mast cells in various organs. The classification of the World Health Organization (WHO) divides the disease into cutaneous mastocytosis, systemic...... leukemia. The clinical impact and prognostic value of this classification has been confirmed in numerous studies, and its basic concept remains valid. However, refinements have recently been proposed by the consensus group, the WHO, and the European Competence Network on Mastocytosis. In addition, new...... of mastocytosis, with emphasis on classification, prognostication, and emerging new treatment options in advanced systemic mastocytosis....

A Raman spectroscopy bio-sensor for tissue discrimination in surgical robotics.

Science.gov (United States)

Ashok, Praveen C; Giardini, Mario E; Dholakia, Kishan; Sibbett, Wilson

2014-01-01

We report the development of a fiber-based Raman sensor to be used in tumour margin identification during endoluminal robotic surgery. Although this is a generic platform, the sensor we describe was adapted for the ARAKNES (Array of Robots Augmenting the KiNematics of Endoluminal Surgery) robotic platform. On such a platform, the Raman sensor is intended to identify ambiguous tissue margins during robot-assisted surgeries. To maintain sterility of the probe during surgical intervention, a disposable sleeve was specially designed. A straightforward user-compatible interface was implemented where a supervised multivariate classification algorithm was used to classify different tissue types based on specific Raman fingerprints so that it could be used without prior knowledge of spectroscopic data analysis. The protocol avoids inter-patient variability in data and the sensor system is not restricted for use in the classification of a particular tissue type. Representative tissue classification assessments were performed using this system on excised tissue. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Three-dimensional CT imaging of soft-tissue anatomy

International Nuclear Information System (INIS)

Fishman, E.K.; Ney, D.R.; Magid, D.; Kuhlman, J.E.

1988-01-01

Three-dimensional display of computed tomographic data has been limited to skeletal structures. This was in part related to the reconstruction algorithm used, which relied on a binary classification scheme. A new algorithm, volumetric rendering with percentage classification, provides the ability to display three-dimensional images of muscle and soft tissue. A review was conducted of images in 35 cases in which muscle and/or soft tissue were part of the clinical problem. In all cases, individual muscle groups could be clearly identified and discriminated. Branching vessels in the range of 2.3 mm could be identified. Similarly, lymph nodes could be clearly defined. High-resolution three-dimensional images were found to be useful both in providing an increased understanding of complex muscle and soft tissue anatomy and in surgical planning

Pancreatic neuroendocrine tumours: correlation between MSCT features and pathological classification

Energy Technology Data Exchange (ETDEWEB)

Luo, Yanji; Dong, Zhi; Li, Zi-Ping; Feng, Shi-Ting [The First Affiliated Hospital, Sun Yat-Sen University, Department of Radiology, Guangzhou, Guangdong (China); Chen, Jie [The First Affiliated Hospital, Sun Yat-Sen University, Department of Gastroenterology, Guangzhou, Guangdong (China); Chan, Tao; Chen, Minhu [Union Hospital, Hong Kong, Medical Imaging Department, Shatin, N.T. (China); Lin, Yuan [The First Affiliated Hospital, Sun Yat-Sen University, Department of Pathology, Guangzhou, Guangdong (China)

2014-11-15

We aimed to evaluate the multi-slice computed tomography (MSCT) features of pancreatic neuroendocrine neoplasms (P-NENs) and analyse the correlation between the MSCT features and pathological classification of P-NENs. Forty-one patients, preoperatively investigated by MSCT and subsequently operated on with a histological diagnosis of P-NENs, were included. Various MSCT features of the primary tumour, lymph node, and distant metastasis were analysed. The relationship between MSCT features and pathologic classification of P-NENs was analysed with univariate and multivariate models. Contrast-enhanced images showed significant differences among the three grades of tumours in the absolute enhancement (P = 0.013) and relative enhancement (P = 0.025) at the arterial phase. Univariate analysis revealed statistically significant differences among the tumours of different grades (based on World Health Organization [WHO] 2010 classification) in tumour size (P = 0.001), tumour contour (P < 0.001), cystic necrosis (P = 0.001), tumour boundary (P = 0.003), dilatation of the main pancreatic duct (P = 0.001), peripancreatic tissue or vascular invasion (P < 0.001), lymphadenopathy (P = 0.011), and distant metastasis (P = 0.012). Multivariate analysis suggested that only peripancreatic tissue or vascular invasion (HR 3.934, 95 % CI, 0.426-7.442, P = 0.028) was significantly associated with WHO 2010 pathological classification. MSCT is helpful in evaluating the pathological classification of P-NENs. (orig.)

Behavioral state classification in epileptic brain using intracranial electrophysiology

Science.gov (United States)

Kremen, Vaclav; Duque, Juliano J.; Brinkmann, Benjamin H.; Berry, Brent M.; Kucewicz, Michal T.; Khadjevand, Fatemeh; Van Gompel, Jamie; Stead, Matt; St. Louis, Erik K.; Worrell, Gregory A.

2017-04-01

Objective. Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. Approach. Data from seven patients (age 34+/- 12 , 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1-600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier. Main results. Classification accuracy of 97.8  ±  0.3% (normal tissue) and 89.4  ±  0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8  ±  0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1  ±  1.6%). Spectral power in high frequency band features (Ripple (80-250 Hz), Fast Ripple (250-600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy  ⩾90% using a single electrode contact and single spectral feature. Significance. Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.

Contributions for classification of platelet rich plasma - proposal of a new classification: MARSPILL.

Science.gov (United States)

Lana, Jose Fabio Santos Duarte; Purita, Joseph; Paulus, Christian; Huber, Stephany Cares; Rodrigues, Bruno Lima; Rodrigues, Ana Amélia; Santana, Maria Helena; Madureira, João Lopo; Malheiros Luzo, Ângela Cristina; Belangero, William Dias; Annichino-Bizzacchi, Joyce Maria

2017-07-01

Platelet-rich plasma (PRP) has emerged as a significant therapy used in medical conditions with heterogeneous results. There are some important classifications to try to standardize the PRP procedure. The aim of this report is to describe PRP contents studying celular and molecular components, and also propose a new classification for PRP. The main focus is on mononuclear cells, which comprise progenitor cells and monocytes. In addition, there are important variables related to PRP application incorporated in this study, which are the harvest method, activation, red blood cells, number of spins, image guidance, leukocytes number and light activation. The other focus is the discussion about progenitor cells presence on peripherial blood which are interesting due to neovasculogenesis and proliferation. The function of monocytes (in tissue-macrophages) are discussed here and also its plasticity, a potential property for regenerative medicine treatments.

The impact of laser ablation on optical soft tissue differentiation for tissue specific laser surgery-an experimental ex vivo study

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Stelzle Florian

2012-06-01

Full Text Available Abstract Background Optical diffuse reflectance can remotely differentiate various bio tissues. To implement this technique in an optical feedback system to guide laser surgery in a tissue-specific way, the alteration of optical tissue properties by laser ablation has to be taken into account. It was the aim of this study to evaluate the general feasibility of optical soft tissue differentiation by diffuse reflectance spectroscopy under the influence of laser ablation, comparing the tissue differentiation results before and after laser intervention. Methods A total of 70 ex vivo tissue samples (5 tissue types were taken from 14 bisected pig heads. Diffuse reflectance spectra were recorded before and after Er:YAG-laser ablation. The spectra were analyzed and differentiated using principal component analysis (PCA, followed by linear discriminant analysis (LDA. To assess the potential of tissue differentiation, area under the curve (AUC, sensitivity and specificity was computed for each pair of tissue types before and after laser ablation, and compared to each other. Results Optical tissue differentiation showed good results before laser exposure (total classification error 13.51%. However, the tissue pair nerve and fat yielded lower AUC results of only 0.75. After laser ablation slightly reduced differentiation results were found with a total classification error of 16.83%. The tissue pair nerve and fat showed enhanced differentiation (AUC: 0.85. Laser ablation reduced the sensitivity in 50% and specificity in 80% of the cases of tissue pair comparison. The sensitivity of nerve–fat differentiation was enhanced by 35%. Conclusions The observed results show the general feasibility of tissue differentiation by diffuse reflectance spectroscopy even under conditions of tissue alteration by laser ablation. The contrast enhancement for the differentiation between nerve and fat tissue after ablation is assumed to be due to laser removal of the

Current concepts in non-gastrointestinal stromal tumor soft tissue sarcomas: A primer for radiologists

Energy Technology Data Exchange (ETDEWEB)

Baheti, Akahay D. [Dept. of Radiology, Tata Memorial Centre, Mumbai (India); Tirumani, Harika [Dept. of Radiology, University of Arkansas for Medical Sciences, Little Rock (United States); O' Neill, Alibhe; Jagannathan, Jyothi P. [Dept. of Imaging, Dana-Farber Cancer Institute, Boston (United States)

2017-01-15

Non-gastrointestinal stromal tumor (GIST) soft tissue sarcomas (STSs) are a heterogeneous group of neoplasms whose classification and management continues to evolve with better understanding of their biologic behavior. The 2013 World Health Organization (WHO) has revised their classification based on new immunohistochemical and cytogenetic data. In this article, we will provide a brief overview of the revised WHO classification of soft tissue tumors, discuss in detail the radiology and management of the two most common adult non-GIST STS, namely liposarcoma and leiomyosarcoma, and review some of the emerging histology-driven targeted therapies in non-GIST STS, focusing on the role of the radiologist.

Current concepts in non-gastrointestinal stromal tumor soft tissue sarcomas: A primer for radiologists

International Nuclear Information System (INIS)

Baheti, Akahay D.; Tirumani, Harika; O'Neill, Alibhe; Jagannathan, Jyothi P.

2017-01-01

Non-gastrointestinal stromal tumor (GIST) soft tissue sarcomas (STSs) are a heterogeneous group of neoplasms whose classification and management continues to evolve with better understanding of their biologic behavior. The 2013 World Health Organization (WHO) has revised their classification based on new immunohistochemical and cytogenetic data. In this article, we will provide a brief overview of the revised WHO classification of soft tissue tumors, discuss in detail the radiology and management of the two most common adult non-GIST STS, namely liposarcoma and leiomyosarcoma, and review some of the emerging histology-driven targeted therapies in non-GIST STS, focusing on the role of the radiologist

Mass Spectrometry Imaging for the Classification of Tumor Tissue

NARCIS (Netherlands)

Mascini, N.E.

2016-01-01

Mass spectrometry imaging (MSI) can detect and identify many different molecules without the need for labeling. In addition, it can provide their spatial distributions as ‘molecular maps’. These features make MSI well suited for studying the molecular makeup of tumor tissue. Currently, there is an

The 2017 international classification of the Ehlers-Danlos syndromes

DEFF Research Database (Denmark)

Malfait, Fransiska; Francomano, Clair; Byers, Peter H

2017-01-01

The Ehlers-Danlos syndromes (EDS) are a clinically and genetically heterogeneous group of heritable connective tissue disorders (HCTDs) characterized by joint hypermobility, skin hyperextensibility, and tissue fragility. Over the past two decades, the Villefranche Nosology, which delineated six s...... that the revised International EDS Classification will serve as a new standard for the diagnosis of EDS and will provide a framework for future research purposes. © 2017 Wiley Periodicals, Inc......., and mutations have been identified in an array of novel genes. The International EDS Consortium proposes a revised EDS classification, which recognizes 13 subtypes. For each of the subtypes, we propose a set of clinical criteria that are suggestive for the diagnosis. However, in view of the vast genetic...... revised the clinical criteria for hypermobile EDS in order to allow for a better distinction from other joint hypermobility disorders. To satisfy research needs, we also propose a pathogenetic scheme, that regroups EDS subtypes for which the causative proteins function within the same pathway. We hope...

Exploiting unsupervised and supervised classification for segmentation of the pathological lung in CT

International Nuclear Information System (INIS)

Korfiatis, P; Costaridou, L; Kalogeropoulou, C; Petsas, T; Daoussis, D; Adonopoulos, A

2009-01-01

Delineation of lung fields in presence of diffuse lung diseases (DLPDs), such as interstitial pneumonias (IP), challenges segmentation algorithms. To deal with IP patterns affecting the lung border an automated image texture classification scheme is proposed. The proposed segmentation scheme is based on supervised texture classification between lung tissue (normal and abnormal) and surrounding tissue (pleura and thoracic wall) in the lung border region. This region is coarsely defined around an initial estimate of lung border, provided by means of Markov Radom Field modeling and morphological operations. Subsequently, a support vector machine classifier was trained to distinguish between the above two classes of tissue, using textural feature of gray scale and wavelet domains. 17 patients diagnosed with IP, secondary to connective tissue diseases were examined. Segmentation performance in terms of overlap was 0.924±0.021, and for shape differentiation mean, rms and maximum distance were 1.663±0.816, 2.334±1.574 and 8.0515±6.549 mm, respectively. An accurate, automated scheme is proposed for segmenting abnormal lung fields in HRC affected by IP

Exploiting unsupervised and supervised classification for segmentation of the pathological lung in CT

Science.gov (United States)

Korfiatis, P.; Kalogeropoulou, C.; Daoussis, D.; Petsas, T.; Adonopoulos, A.; Costaridou, L.

2009-07-01

Delineation of lung fields in presence of diffuse lung diseases (DLPDs), such as interstitial pneumonias (IP), challenges segmentation algorithms. To deal with IP patterns affecting the lung border an automated image texture classification scheme is proposed. The proposed segmentation scheme is based on supervised texture classification between lung tissue (normal and abnormal) and surrounding tissue (pleura and thoracic wall) in the lung border region. This region is coarsely defined around an initial estimate of lung border, provided by means of Markov Radom Field modeling and morphological operations. Subsequently, a support vector machine classifier was trained to distinguish between the above two classes of tissue, using textural feature of gray scale and wavelet domains. 17 patients diagnosed with IP, secondary to connective tissue diseases were examined. Segmentation performance in terms of overlap was 0.924±0.021, and for shape differentiation mean, rms and maximum distance were 1.663±0.816, 2.334±1.574 and 8.0515±6.549 mm, respectively. An accurate, automated scheme is proposed for segmenting abnormal lung fields in HRC affected by IP

Morphological classification of plant cell deaths.

Science.gov (United States)

van Doorn, W G; Beers, E P; Dangl, J L; Franklin-Tong, V E; Gallois, P; Hara-Nishimura, I; Jones, A M; Kawai-Yamada, M; Lam, E; Mundy, J; Mur, L A J; Petersen, M; Smertenko, A; Taliansky, M; Van Breusegem, F; Wolpert, T; Woltering, E; Zhivotovsky, B; Bozhkov, P V

2011-08-01

Programmed cell death (PCD) is an integral part of plant development and of responses to abiotic stress or pathogens. Although the morphology of plant PCD is, in some cases, well characterised and molecular mechanisms controlling plant PCD are beginning to emerge, there is still confusion about the classification of PCD in plants. Here we suggest a classification based on morphological criteria. According to this classification, the use of the term 'apoptosis' is not justified in plants, but at least two classes of PCD can be distinguished: vacuolar cell death and necrosis. During vacuolar cell death, the cell contents are removed by a combination of autophagy-like process and release of hydrolases from collapsed lytic vacuoles. Necrosis is characterised by early rupture of the plasma membrane, shrinkage of the protoplast and absence of vacuolar cell death features. Vacuolar cell death is common during tissue and organ formation and elimination, whereas necrosis is typically found under abiotic stress. Some examples of plant PCD cannot be ascribed to either major class and are therefore classified as separate modalities. These are PCD associated with the hypersensitive response to biotrophic pathogens, which can express features of both necrosis and vacuolar cell death, PCD in starchy cereal endosperm and during self-incompatibility. The present classification is not static, but will be subject to further revision, especially when specific biochemical pathways are better defined.

Spiral waves characterization: Implications for an automated cardiodynamic tissue characterization.

Science.gov (United States)

Alagoz, Celal; Cohen, Andrew R; Frisch, Daniel R; Tunç, Birkan; Phatharodom, Saran; Guez, Allon

2018-07-01

Spiral waves are phenomena observed in cardiac tissue especially during fibrillatory activities. Spiral waves are revealed through in-vivo and in-vitro studies using high density mapping that requires special experimental setup. Also, in-silico spiral wave analysis and classification is performed using membrane potentials from entire tissue. In this study, we report a characterization approach that identifies spiral wave behaviors using intracardiac electrogram (EGM) readings obtained with commonly used multipolar diagnostic catheters that perform localized but high-resolution readings. Specifically, the algorithm is designed to distinguish between stationary, meandering, and break-up rotors. The clustering and classification algorithms are tested on simulated data produced using a phenomenological 2D model of cardiac propagation. For EGM measurements, unipolar-bipolar EGM readings from various locations on tissue using two catheter types are modeled. The distance measure between spiral behaviors are assessed using normalized compression distance (NCD), an information theoretical distance. NCD is a universal metric in the sense it is solely based on compressibility of dataset and not requiring feature extraction. We also introduce normalized FFT distance (NFFTD) where compressibility is replaced with a FFT parameter. Overall, outstanding clustering performance was achieved across varying EGM reading configurations. We found that effectiveness in distinguishing was superior in case of NCD than NFFTD. We demonstrated that distinct spiral activity identification on a behaviorally heterogeneous tissue is also possible. This report demonstrates a theoretical validation of clustering and classification approaches that provide an automated mapping from EGM signals to assessment of spiral wave behaviors and hence offers a potential mapping and analysis framework for cardiac tissue wavefront propagation patterns. Copyright © 2018 Elsevier B.V. All rights reserved.

Classifying Classifications

DEFF Research Database (Denmark)

Debus, Michael S.

2017-01-01

This paper critically analyzes seventeen game classifications. The classifications were chosen on the basis of diversity, ranging from pre-digital classification (e.g. Murray 1952), over game studies classifications (e.g. Elverdam & Aarseth 2007) to classifications of drinking games (e.g. LaBrie et...... al. 2013). The analysis aims at three goals: The classifications’ internal consistency, the abstraction of classification criteria and the identification of differences in classification across fields and/or time. Especially the abstraction of classification criteria can be used in future endeavors...... into the topic of game classifications....

Inguinal hernia recurrence: Classification and approach

Directory of Open Access Journals (Sweden)

Campanelli Giampiero

2006-01-01

Full Text Available The authors reviewed the records of 2,468 operations of groin hernia in 2,350 patients, including 277 recurrent hernias updated to January 2005. The data obtained - evaluating technique, results and complications - were used to propose a simple anatomo-clinical classification into three types which could be used to plan the surgical strategy:Type R1: first recurrence ′high,′ oblique external, reducible hernia with small (< 2 cm defect in non-obese patients, after pure tissue or mesh repairType R2: first recurrence ′low,′ direct, reducible hernia with small (< 2 cm defect in non-obese patients, after pure tissue or mesh repairType R3: all the other recurrences - including femoral recurrences; recurrent groin hernia with big defect (inguinal eventration; multirecurrent hernias; nonreducible, linked with a controlateral primitive or recurrent hernia; and situations compromised from aggravating factors (for example obesity or anyway not easily included in R1 or R2, after pure tissue or mesh repair.

Classification of multiple sclerosis lesions using adaptive dictionary learning.

Science.gov (United States)

Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

2015-12-01

This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

Cell-based product classification procedure: What can be done differently to improve decisions on borderline products?

Science.gov (United States)

Izeta, Ander; Herrera, Concha; Mata, Rosario; Astori, Giuseppe; Giordano, Rosaria; Hernández, Carmen; Leyva, Laura; Arias, Salvador; Oyonarte, Salvador; Carmona, Gloria; Cuende, Natividad

2016-07-01

In June 2015, European Medicines Agency/Committee for Advanced Therapies (CAT) released the new version of the reflection paper on classification of advanced therapy medicinal products (ATMPs) established to address questions of borderline cases in which classification of a product based on genes, cells or tissues is unclear. The paper shows CAT's understanding of substantial manipulation and essential function(s) criteria that define the legal scope of cell-based medicinal products. This article aims to define the authors' viewpoint on the reflection paper. ATMP classification has intrinsic weaknesses derived from the lack of clarity of the evolving concepts of substantial manipulation and essential function(s) as stated in the EU Regulation, leading to the risk of differing interpretations and misclassification. This might result in the broadening of ATMP scope at the expense of other products such as cell/tissue transplants and blood products, or even putting some present and future clinical practice at risk of being classified as ATMP. Because of the major organizational, economic and regulatory implications of product classification, we advocate for increased interaction between CAT and competent authorities (CAs) for medicines, blood and blood components and tissues and cells or for the creation of working groups including representatives of all parties as recently suggested by several CAs. Copyright © 2016 International Society for Cellular Therapy. Published by Elsevier Inc. All rights reserved.

Automatic breast tissue density estimation scheme in digital mammography images

Science.gov (United States)

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.

Sensitivity and Specificity of Cardiac Tissue Discrimination Using Fiber-Optics Confocal Microscopy.

Science.gov (United States)

Huang, Chao; Sachse, Frank B; Hitchcock, Robert W; Kaza, Aditya K

2016-01-01

Disturbances of the cardiac conduction system constitute a major risk after surgical repair of complex cases of congenital heart disease. Intraoperative identification of the conduction system may reduce the incidence of these disturbances. We previously developed an approach to identify cardiac tissue types using fiber-optics confocal microscopy and extracellular fluorophores. Here, we applied this approach to investigate sensitivity and specificity of human and automated classification in discriminating images of atrial working myocardium and specialized tissue of the conduction system. Two-dimensional image sequences from atrial working myocardium and nodal tissue of isolated perfused rodent hearts were acquired using a fiber-optics confocal microscope (Leica FCM1000). We compared two methods for local application of extracellular fluorophores: topical via pipette and with a dye carrier. Eight blinded examiners evaluated 162 randomly selected images of atrial working myocardium (n = 81) and nodal tissue (n = 81). In addition, we evaluated the images using automated classification. Blinded examiners achieved a sensitivity and specificity of 99.2 ± 0.3% and 98.0 ± 0.7%, respectively, with the dye carrier method of dye application. Sensitivity and specificity was similar for dye application via a pipette (99.2 ± 0.3% and 94.0 ± 2.4%, respectively). Sensitivity and specificity for automated methods of tissue discrimination were similarly high. Human and automated classification achieved high sensitivity and specificity in discriminating atrial working myocardium and nodal tissue. We suggest that our findings facilitate clinical translation of fiber-optics confocal microscopy as an intraoperative imaging modality to reduce the incidence of conduction disturbances during surgical correction of congenital heart disease.

Automated classification of cell morphology by coherence-controlled holographic microscopy

Science.gov (United States)

Strbkova, Lenka; Zicha, Daniel; Vesely, Pavel; Chmelik, Radim

2017-08-01

In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity.

Classification of breast cancer histology images using Convolutional Neural Networks.

Directory of Open Access Journals (Sweden)

Teresa Araújo

Full Text Available Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%.

A simplified classification system for partially edentulous spaces

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Bhandari Aruna J, Bhandari Akshay J

2014-04-01

Full Text Available Background: There is no single universally employed classification system that will specify the exact edentulous situation. Several classification systems exist to group the situation and avoid confusion. Classifications based on edentulous areas, finished restored prostheses, type of direct retainers or fulcrum lines are there. Some are based depending on the placement of the implants. Widely accepted Kennedy Applegate classification does not give any idea about length, span or number of teeth missing. Rule 6 governing the application of Kennedy method states that additional edentulous areas are referred as modification number 1,2 etc. Rule 7 states that extent of the modification is not considered; only the number of edentulous areas is considered. Hence there is a need to modify the Kennedy –Applegate System. Aims: This new classification system is an attempt to modify Kennedy –Applegate System so as to give the exact idea about missing teeth, space, span, side and areas of partially edentulous arches. Methods and Material: This system will provide the information regarding Maxillary or Mandibular partially edentulous arches, Left or Right side, length of the edentulous space, number of teeth missing and whether there will be tooth borne or tooth – tissue borne prosthesis. Conclusions: This classification is easy for application, communication and will also help to design the removable cast partial denture in a better logical and systematic way. Also, this system will give the idea of the edentulous status and the number of missing teeth in fixed, hybrid or implant prosthesis.

[Classification of cell-based medicinal products and legal implications: An overview and an update].

Science.gov (United States)

Scherer, Jürgen; Flory, Egbert

2015-11-01

In general, cell-based medicinal products do not represent a uniform class of medicinal products, but instead comprise medicinal products with diverse regulatory classification as advanced-therapy medicinal products (ATMP), medicinal products (MP), tissue preparations, or blood products. Due to the legal and scientific consequences of the development and approval of MPs, classification should be clarified as early as possible. This paper describes the legal situation in Germany and highlights specific criteria and concepts for classification, with a focus on, but not limited to, ATMPs and non-ATMPs. Depending on the stage of product development and the specific application submitted to a competent authority, legally binding classification is done by the German Länder Authorities, Paul-Ehrlich-Institut, or European Medicines Agency. On request by the applicants, the Committee for Advanced Therapies may issue scientific recommendations for classification.

Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization

Directory of Open Access Journals (Sweden)

Philipp Kainz

2017-10-01

Full Text Available Segmentation of histopathology sections is a necessary preprocessing step for digital pathology. Due to the large variability of biological tissue, machine learning techniques have shown superior performance over conventional image processing methods. Here we present our deep neural network-based approach for segmentation and classification of glands in tissue of benign and malignant colorectal cancer, which was developed to participate in the GlaS@MICCAI2015 colon gland segmentation challenge. We use two distinct deep convolutional neural networks (CNN for pixel-wise classification of Hematoxylin-Eosin stained images. While the first classifier separates glands from background, the second classifier identifies gland-separating structures. In a subsequent step, a figure-ground segmentation based on weighted total variation produces the final segmentation result by regularizing the CNN predictions. We present both quantitative and qualitative segmentation results on the recently released and publicly available Warwick-QU colon adenocarcinoma dataset associated with the GlaS@MICCAI2015 challenge and compare our approach to the simultaneously developed other approaches that participated in the same challenge. On two test sets, we demonstrate our segmentation performance and show that we achieve a tissue classification accuracy of 98% and 95%, making use of the inherent capability of our system to distinguish between benign and malignant tissue. Our results show that deep learning approaches can yield highly accurate and reproducible results for biomedical image analysis, with the potential to significantly improve the quality and speed of medical diagnoses.

Fourier Transform Infrared (FT-IR) and Laser Ablation Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) Imaging of Cerebral Ischemia: Combined Analysis of Rat Brain Thin Cuts Toward Improved Tissue Classification.

Science.gov (United States)

Balbekova, Anna; Lohninger, Hans; van Tilborg, Geralda A F; Dijkhuizen, Rick M; Bonta, Maximilian; Limbeck, Andreas; Lendl, Bernhard; Al-Saad, Khalid A; Ali, Mohamed; Celikic, Minja; Ofner, Johannes

2018-02-01

Microspectroscopic techniques are widely used to complement histological studies. Due to recent developments in the field of chemical imaging, combined chemical analysis has become attractive. This technique facilitates a deepened analysis compared to single techniques or side-by-side analysis. In this study, rat brains harvested one week after induction of photothrombotic stroke were investigated. Adjacent thin cuts from rats' brains were imaged using Fourier transform infrared (FT-IR) microspectroscopy and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The LA-ICP-MS data were normalized using an internal standard (a thin gold layer). The acquired hyperspectral data cubes were fused and subjected to multivariate analysis. Brain regions affected by stroke as well as unaffected gray and white matter were identified and classified using a model based on either partial least squares discriminant analysis (PLS-DA) or random decision forest (RDF) algorithms. The RDF algorithm demonstrated the best results for classification. Improved classification was observed in the case of fused data in comparison to individual data sets (either FT-IR or LA-ICP-MS). Variable importance analysis demonstrated that both molecular and elemental content contribute to the improved RDF classification. Univariate spectral analysis identified biochemical properties of the assigned tissue types. Classification of multisensor hyperspectral data sets using an RDF algorithm allows access to a novel and in-depth understanding of biochemical processes and solid chemical allocation of different brain regions.

3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading

Science.gov (United States)

Cho, Nam-Hoon; Choi, Heung-Kook

2014-01-01

One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system. PMID:25371701

3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading

Directory of Open Access Journals (Sweden)

Tae-Yun Kim

2014-01-01

Full Text Available One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system.

Automated classification of cell morphology by coherence-controlled holographic microscopy.

Science.gov (United States)

Strbkova, Lenka; Zicha, Daniel; Vesely, Pavel; Chmelik, Radim

2017-08-01

In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

MicroRNAs in the Tumor Biology of Soft Tissue Sarcomas

NARCIS (Netherlands)

C.M.M. Gits (Caroline)

2013-01-01

markdownabstract__Abstract__ Soft tissue sarcomas represent a rare, heterogeneous group of mesenchymal tumors. In sarcomas, histological classification, prediction of clinical behaviour and prognosis, and targeted treatment is often a challenge. A better understanding of the biology of soft

Classification, diagnosis, and approach to treatment for angioedema

DEFF Research Database (Denmark)

Cicardi, M; Aberer, W; Banerji, A

2014-01-01

Angioedema is defined as localized and self-limiting edema of the subcutaneous and submucosal tissue, due to a temporary increase in vascular permeability caused by the release of vasoactive mediator(s). When angioedema recurs without significant wheals, the patient should be diagnosed to have...... angioedema as a distinct disease. In the absence of accepted classification, different types of angioedema are not uniquely identified. For this reason, the European Academy of Allergy and Clinical Immunology gave its patronage to a consensus conference aimed at classifying angioedema. Four types of acquired...... and three types of hereditary angioedema were identified as separate forms from the analysis of the literature and were presented in detail at the meeting. Here, we summarize the analysis of the data and the resulting classification of angioedema....

Benign fatty tumors: classification, clinical course, imaging appearance, and treatment

International Nuclear Information System (INIS)

Bancroft, Laura W.; Kransdorf, Mark J.; Peterson, Jeffrey J.; O'Connor, Mary I.

2006-01-01

Lipoma is the most common soft-tissue tumor, with a wide spectrum of clinical presentations and imaging appearances. Several subtypes are described, ranging from lesions entirely composed of mature adipose tissue to tumors intimately associated with nonadipose tissue, to those composed of brown fat. The imaging appearance of these fatty masses is frequently sufficiently characteristic to allow a specific diagnosis. However, in other cases, although a specific diagnosis is not achievable, a meaningful limited differential diagnosis can be established. The purpose of this manuscript is to review the spectrum of benign fatty tumors highlighting the current classification system, clinical presentation and behavior, spectrum of imaging appearances, and treatment. The imaging review emphasizes computed tomography (CT) scanning and magnetic resonance (MR) imaging, differentiating radiologic features. (orig.)

A New Classification Approach Based on Multiple Classification Rules

OpenAIRE

Zhongmei Zhou

2014-01-01

A good classifier can correctly predict new data for which the class label is unknown, so it is important to construct a high accuracy classifier. Hence, classification techniques are much useful in ubiquitous computing. Associative classification achieves higher classification accuracy than some traditional rule-based classification approaches. However, the approach also has two major deficiencies. First, it generates a very large number of association classification rules, especially when t...

A texton-based approach for the classification of lung parenchyma in CT images

DEFF Research Database (Denmark)

Gangeh, Mehrdad J.; Sørensen, Lauge; Shaker, Saher B.

2010-01-01

In this paper, a texton-based classification system based on raw pixel representation along with a support vector machine with radial basis function kernel is proposed for the classification of emphysema in computed tomography images of the lung. The proposed approach is tested on 168 annotated...... regions of interest consisting of normal tissue, centrilobular emphysema, and paraseptal emphysema. The results show the superiority of the proposed approach to common techniques in the literature including moments of the histogram of filter responses based on Gaussian derivatives. The performance...

On Predicting lung cancer subtypes using ‘omic’ data from tumor and tumor-adjacent histologically-normal tissue

International Nuclear Information System (INIS)

Pineda, Arturo López; Ogoe, Henry Ato; Balasubramanian, Jeya Balaji; Rangel Escareño, Claudia; Visweswaran, Shyam; Herman, James Gordon; Gopalakrishnan, Vanathi

2016-01-01

Adenocarcinoma (ADC) and squamous cell carcinoma (SCC) are the most prevalent histological types among lung cancers. Distinguishing between these subtypes is critically important because they have different implications for prognosis and treatment. Normally, histopathological analyses are used to distinguish between the two, where the tissue samples are collected based on small endoscopic samples or needle aspirations. However, the lack of cell architecture in these small tissue samples hampers the process of distinguishing between the two subtypes. Molecular profiling can also be used to discriminate between the two lung cancer subtypes, on condition that the biopsy is composed of at least 50 % of tumor cells. However, for some cases, the tissue composition of a biopsy might be a mix of tumor and tumor-adjacent histologically normal tissue (TAHN). When this happens, a new biopsy is required, with associated cost, risks and discomfort to the patient. To avoid this problem, we hypothesize that a computational method can distinguish between lung cancer subtypes given tumor and TAHN tissue. Using publicly available datasets for gene expression and DNA methylation, we applied four classification tasks, depending on the possible combinations of tumor and TAHN tissue. First, we used a feature selector (ReliefF/Limma) to select relevant variables, which were then used to build a simple naïve Bayes classification model. Then, we evaluated the classification performance of our models by measuring the area under the receiver operating characteristic curve (AUC). Finally, we analyzed the relevance of the selected genes using hierarchical clustering and IPA® software for gene functional analysis. All Bayesian models achieved high classification performance (AUC > 0.94), which were confirmed by hierarchical cluster analysis. From the genes selected, 25 (93 %) were found to be related to cancer (19 were associated with ADC or SCC), confirming the biological relevance of our

78 FR 68983 - Cotton Futures Classification: Optional Classification Procedure

Science.gov (United States)

2013-11-18

...-AD33 Cotton Futures Classification: Optional Classification Procedure AGENCY: Agricultural Marketing... regulations to allow for the addition of an optional cotton futures classification procedure--identified and... response to requests from the U.S. cotton industry and ICE, AMS will offer a futures classification option...

Adaptive partial volume classification of MRI data

International Nuclear Information System (INIS)

Chiverton, John P; Wells, Kevin

2008-01-01

Tomographic biomedical images are commonly affected by an imaging artefact known as the partial volume (PV) effect. The PV effect produces voxels composed of a mixture of tissues in anatomical magnetic resonance imaging (MRI) data resulting in a continuity of these tissue classes. Anatomical MRI data typically consist of a number of contiguous regions of tissues or even contiguous regions of PV voxels. Furthermore discontinuities exist between the boundaries of these contiguous image regions. The work presented here probabilistically models the PV effect using spatial regularization in the form of continuous Markov random fields (MRFs) to classify anatomical MRI brain data, simulated and real. A unique approach is used to adaptively control the amount of spatial regularization imposed by the MRF. Spatially derived image gradient magnitude is used to identify the discontinuities between image regions of contiguous tissue voxels and PV voxels, imposing variable amounts of regularization determined by simulation. Markov chain Monte Carlo (MCMC) is used to simulate the posterior distribution of the probabilistic image model. Promising quantitative results are presented for PV classification of simulated and real MRI data of the human brain.

Proteomic patterns analysis with multivariate calculations as a promising tool for prompt differentiation of early stage lung tissue with cancer and unchanged tissue material

Directory of Open Access Journals (Sweden)

Grodzki Tomasz

2011-03-01

Full Text Available Abstract Background Lung cancer diagnosis in tissue material with commonly used histological techniques is sometimes inconvenient and in a number of cases leads to ambiguous conclusions. Frequently advanced immunostaining techniques have to be employed, yet they are both time consuming and limited. In this study a proteomic approach is presented which may help provide unambiguous pathologic diagnosis of tissue material. Methods Lung tissue material found to be pathologically changed was prepared to isolate proteome with fast and non selective procedure. Isolated peptides and proteins in ranging from 3.5 to 20 kDa were analysed directly using high resolution mass spectrometer (MALDI-TOF/TOF with sinapic acid as a matrix. Recorded complex spectra of a single run were then analyzed with multivariate statistical analysis algorithms (principle component analysis, classification methods. In the applied protocol we focused on obtaining the spectra richest in protein signals constituting a pattern of change within the sample containing detailed information about its protein composition. Advanced statistical methods were to indicate differences between examined groups. Results Obtained results indicate changes in proteome profiles of changed tissues in comparison to physiologically unchanged material (control group which were reflected in the result of principle component analysis (PCA. Points representing spectra of control group were located in different areas of multidimensional space and were less diffused in comparison to cancer tissues. Three different classification algorithms showed recognition capability of 100% regarding classification of examined material into an appropriate group. Conclusion The application of the presented protocol and method enabled finding pathological changes in tissue material regardless of localization and size of abnormalities in the sample volume. Proteomic profile as a complex, rich in signals spectrum of proteins

Wavelet-based feature extraction applied to small-angle x-ray scattering patterns from breast tissue: a tool for differentiating between tissue types

International Nuclear Information System (INIS)

Falzon, G; Pearson, S; Murison, R; Hall, C; Siu, K; Evans, A; Rogers, K; Lewis, R

2006-01-01

This paper reports on the application of wavelet decomposition to small-angle x-ray scattering (SAXS) patterns from human breast tissue produced by a synchrotron source. The pixel intensities of SAXS patterns of normal, benign and malignant tissue types were transformed into wavelet coefficients. Statistical analysis found significant differences between the wavelet coefficients describing the patterns produced by different tissue types. These differences were then correlated with position in the image and have been linked to the supra-molecular structural changes that occur in breast tissue in the presence of disease. Specifically, results indicate that there are significant differences between healthy and diseased tissues in the wavelet coefficients that describe the peaks produced by the axial d-spacing of collagen. These differences suggest that a useful classification tool could be based upon the spectral information within the axial peaks

An approach for leukemia classification based on cooperative game theory.

Science.gov (United States)

Torkaman, Atefeh; Charkari, Nasrollah Moghaddam; Aghaeipour, Mahnaz

2011-01-01

Hematological malignancies are the types of cancer that affect blood, bone marrow and lymph nodes. As these tissues are naturally connected through the immune system, a disease affecting one of them will often affect the others as well. The hematological malignancies include; Leukemia, Lymphoma, Multiple myeloma. Among them, leukemia is a serious malignancy that starts in blood tissues especially the bone marrow, where the blood is made. Researches show, leukemia is one of the common cancers in the world. So, the emphasis on diagnostic techniques and best treatments would be able to provide better prognosis and survival for patients. In this paper, an automatic diagnosis recommender system for classifying leukemia based on cooperative game is presented. Through out this research, we analyze the flow cytometry data toward the classification of leukemia into eight classes. We work on real data set from different types of leukemia that have been collected at Iran Blood Transfusion Organization (IBTO). Generally, the data set contains 400 samples taken from human leukemic bone marrow. This study deals with cooperative game used for classification according to different weights assigned to the markers. The proposed method is versatile as there are no constraints to what the input or output represent. This means that it can be used to classify a population according to their contributions. In other words, it applies equally to other groups of data. The experimental results show the accuracy rate of 93.12%, for classification and compared to decision tree (C4.5) with (90.16%) in accuracy. The result demonstrates that cooperative game is very promising to be used directly for classification of leukemia as a part of Active Medical decision support system for interpretation of flow cytometry readout. This system could assist clinical hematologists to properly recognize different kinds of leukemia by preparing suggestions and this could improve the treatment of leukemic

Synthetic aperture tissue and flow ultrasound imaging

DEFF Research Database (Denmark)

Nikolov, Svetoslav

imaging applied to medical ultrasound. It is divided into two major parts: tissue and blood flow imaging. Tissue imaging using synthetic aperture algorithms has been investigated for about two decades, but has not been implemented in medical scanners yet. Among the other reasons, the conventional scanning...... and beamformation methods are adequate for the imaging modalities in clinical use - the B-mode imaging of tissue structures, and the color mapping of blood flow. The acquisition time, however, is too long, and these methods fail to perform real-time three-dimensional scans. The synthetic transmit aperture......, on the other hand, can create a Bmode image with as little as 2 emissions, thus significantly speeding-up the scan procedure. The first part of the dissertation describes the synthetic aperture tissue imaging. It starts with an overview of the efforts previously made by other research groups. A classification...

Training echo state networks for rotation-invariant bone marrow cell classification.

Science.gov (United States)

Kainz, Philipp; Burgsteiner, Harald; Asslaber, Martin; Ahammer, Helmut

2017-01-01

The main principle of diagnostic pathology is the reliable interpretation of individual cells in context of the tissue architecture. Especially a confident examination of bone marrow specimen is dependent on a valid classification of myeloid cells. In this work, we propose a novel rotation-invariant learning scheme for multi-class echo state networks (ESNs), which achieves very high performance in automated bone marrow cell classification. Based on representing static images as temporal sequence of rotations, we show how ESNs robustly recognize cells of arbitrary rotations by taking advantage of their short-term memory capacity. The performance of our approach is compared to a classification random forest that learns rotation-invariance in a conventional way by exhaustively training on multiple rotations of individual samples. The methods were evaluated on a human bone marrow image database consisting of granulopoietic and erythropoietic cells in different maturation stages. Our ESN approach to cell classification does not rely on segmentation of cells or manual feature extraction and can therefore directly be applied to image data.

Development and validation of a microRNA based diagnostic assay for primary tumor site classification of liver core biopsies

DEFF Research Database (Denmark)

Perell, Katharina; Vincent, Martin; Vainer, Ben

2015-01-01

for normal liver tissue contamination. Performance was estimated by cross-validation, followed by independent validation on 55 liver core biopsies with a tumor content as low as 10%. A microRNA classifier developed, using the statistical contamination model, showed an overall classification accuracy of 74...... on classification. MicroRNA profiling was performed using quantitative Real-Time PCR on formalin-fixed paraffin-embedded samples. 278 primary tumors and liver metastases, representing nine primary tumor classes, as well as normal liver samples were used as a training set. A statistical model was applied to adjust.......5% upon independent validation. Two-thirds of the samples were classified with high-confidence, with an accuracy of 92% on high-confidence predictions. A classifier trained without adjusting for liver tissue contamination, showed a classification accuracy of 38.2%. Our results indicate that surrounding...

Is overall similarity classification less effortful than single-dimension classification?

Science.gov (United States)

Wills, Andy J; Milton, Fraser; Longmore, Christopher A; Hester, Sarah; Robinson, Jo

2013-01-01

It is sometimes argued that the implementation of an overall similarity classification is less effortful than the implementation of a single-dimension classification. In the current article, we argue that the evidence securely in support of this view is limited, and report additional evidence in support of the opposite proposition--overall similarity classification is more effortful than single-dimension classification. Using a match-to-standards procedure, Experiments 1A, 1B and 2 demonstrate that concurrent load reduces the prevalence of overall similarity classification, and that this effect is robust to changes in the concurrent load task employed, the level of time pressure experienced, and the short-term memory requirements of the classification task. Experiment 3 demonstrates that participants who produced overall similarity classifications from the outset have larger working memory capacities than those who produced single-dimension classifications initially, and Experiment 4 demonstrates that instructions to respond meticulously increase the prevalence of overall similarity classification.

A simple working classification proposed for the latrogenic lesions of teeth and associated structures in the oral cavity.

Science.gov (United States)

Shamim, Thorakkal

2013-09-01

Iatrogenic lesions can affect both hard and soft tissues in the oral cavity, induced by the dentist's activity, manner or therapy. There is no approved simple working classification for the iatrogenic lesions of teeth and associated structures in the oral cavity in the literature. A simple working classification is proposed here for iatrogenic lesions of teeth and associated structures in the oral cavity based on its relation with dental specialities. The dental specialities considered in this classification are conservative dentistry and endodontics, orthodontics, oral and maxillofacial surgery and prosthodontics. This classification will be useful for the dental clinician who is dealing with diseases of oral cavity.

Classification of bone scintigrams in hemodialysis patients

International Nuclear Information System (INIS)

Ishibashi, Kazunari; Miyamae, Tatsuya

1985-01-01

Bone scintigrams from a total of 75 hemodialysis patients using sup(99m)Tc-methylene-diphosphonate (MDP) were classified into two groups; Group I (56 patients) in which the uptake of the radioactivity appeared to be relatively high in the soft tissue and low in the bone, and Group II (19 patients) in which high uptake in the bone and low uptake in the soft tissue were observed. Patients in Group I were further classified into two subgroups; Group I sub(A) (articular type, 21 patients) which was characterized by relatively high uptake into the joint, and Group I sub(B) (reduction type, 35 patients) where uptake was faint in the whole region of the bone. The classification of patients in Group II was also performed; Group II sub(A) (spinal type, 14 patients) where high spinal uptake was observed, and Group II sub(B) (cranio-facial type, 5 patients) where high uptake into the cranio-facial region was observed. The results were compared with 146 subjects with normal bone scintigram in terms of the ratio of bone to soft tissue uptake (B/S ratio) for the cranial bone, jaw bone, lumbar vertebra and femoral bone, and the ratio of epiphysis to diaphysis uptake (E/D ratio) for the femoral bone. The B/S ratio was low in Group I and high in Group II for the bone studied, and the E/D ratio was markedly high in Group I sub(A). Histobiochemical examination indicated that patients in Group I sub(A) and Group II may have osteomalacia and secondary hyperparathyroidism, respectively. It was considered that the visual classification and semiquantitative study as described here were useful for evaluating the pathological condition of renal osteodystrophy. (author)

Classification of bone scintigrams in hemodialysis patients

Energy Technology Data Exchange (ETDEWEB)

Ishibashi, Kazunari; Miyamae, Tatsuya

1985-03-01

Bone scintigrams from a total of 75 hemodialysis patients using /sup 99m/Tc-methylene-diphosphonate (MDP) were classified into two groups; Group I (56 patients) in which the uptake of the radioactivity appeared to be relatively high in the soft tissue and low in the bone, and Group II (19 patients) in which high uptake in the bone and low uptake in the soft tissue were observed. Patients in Group I were further classified into two subgroups; Group I sub(A) (articular type, 21 patients) which was characterized by relatively high uptake into the joint, and Group I sub(B) (reduction type, 35 patients) where uptake was faint in the whole region of the bone. The classification of patients in Group II was also performed; Group II sub(A) (spinal type, 14 patients) where high spinal uptake was observed, and Group II sub(B) (cranio-facial type, 5 patients) where high uptake into the cranio-facial region was observed. The results were compared with 146 subjects with normal bone scintigram in terms of the ratio of bone to soft tissue uptake (B/S ratio) for the cranial bone, jaw bone, lumbar vertebra and femoral bone, and the ratio of epiphysis to diaphysis uptake (E/D ratio) for the femoral bone. The B/S ratio was low in Group I and high in Group II for the bone studied, and the E/D ratio was markedly high in Group I sub(A). Histobiochemical examination indicated that patients in Group I sub(A) and Group II may have osteomalacia and secondary hyperparathyroidism, respectively. It was considered that the visual classification and semiquantitative study as described here were useful for evaluating the pathological condition of renal osteodystrophy.

[Definition, etiology, classification and presentation forms].

Science.gov (United States)

Mas Garriga, Xavier

2014-01-01

Osteoarthritis is defined as a degenerative process affecting the joints as a result of mechanical and biological disorders that destabilize the balance between the synthesis and degradation of joint cartilage, stimulating the growth of subchondral bone; chronic synovitis is also present. Currently, the joint is considered as a functional unit that includes distinct tissues, mainly cartilage, the synovial membrane, and subchondral bone, all of which are involved in the pathogenesis of the disease. Distinct risk factors for the development of osteoarthritis have been described: general, unmodifiable risk factors (age, sex, and genetic makeup), general, modifiable risk factors (obesity and hormonal factors) and local risk factors (prior joint anomalies and joint overload). Notable among the main factors related to disease progression are joint alignment defects and generalized osteoarthritis. Several classifications of osteoarthritis have been proposed but none is particularly important for the primary care management of the disease. These classifications include etiological (primary or idiopathic forms and secondary forms) and topographical (typical and atypical localizations) classifications, the Kellgren and Lawrence classification (radiological repercussions) and that of the American College of Rheumatology for osteoarthritis of the hand, hip and knee. The prevalence of knee osteoarthritis is 10.2% in Spain and shows a marked discrepancy between clinical and radiological findings. Hand osteoarthritis, with a prevalence of symptomatic involvement of around 6.2%, has several forms of presentation (nodal osteoarthritis, generalized osteoarthritis, rhizarthrosis, and erosive osteoarthritis). Symptomatic osteoarthritis of the hip affects between 3.5% and 5.6% of persons older than 50 years and has different radiological patterns depending on femoral head migration. Copyright © 2014 Elsevier España, S.L. All rights reserved.

Tissues Use Resident Dendritic Cells and Macrophages to Maintain Homeostasis and to Regain Homeostasis upon Tissue Injury: The Immunoregulatory Role of Changing Tissue Environments

Science.gov (United States)

Lech, Maciej; Gröbmayr, Regina; Weidenbusch, Marc; Anders, Hans-Joachim

2012-01-01

Most tissues harbor resident mononuclear phagocytes, that is, dendritic cells and macrophages. A classification that sufficiently covers their phenotypic heterogeneity and plasticity during homeostasis and disease does not yet exist because cell culture-based phenotypes often do not match those found in vivo. The plasticity of mononuclear phagocytes becomes obvious during dynamic or complex disease processes. Different data interpretation also originates from different conceptual perspectives. An immune-centric view assumes that a particular priming of phagocytes then causes a particular type of pathology in target tissues, conceptually similar to antigen-specific T-cell priming. A tissue-centric view assumes that changing tissue microenvironments shape the phenotypes of their resident and infiltrating mononuclear phagocytes to fulfill the tissue's need to maintain or regain homeostasis. Here we discuss the latter concept, for example, why different organs host different types of mononuclear phagocytes during homeostasis. We further discuss how injuries alter tissue environments and how this primes mononuclear phagocytes to enforce this particular environment, for example, to support host defense and pathogen clearance, to support the resolution of inflammation, to support epithelial and mesenchymal healing, and to support the resolution of fibrosis to the smallest possible scar. Thus, organ- and disease phase-specific microenvironments determine macrophage and dendritic cell heterogeneity in a temporal and spatial manner, which assures their support to maintain and regain homeostasis in whatever condition. Mononuclear phagocytes contributions to tissue pathologies relate to their central roles in orchestrating all stages of host defense and wound healing, which often become maladaptive processes, especially in sterile and/or diffuse tissue injuries. PMID:23251037

Discriminant analysis of normal and malignant breast tissue based upon INAA investigation of elemental concentration

International Nuclear Information System (INIS)

Kwanhoong Ng; Senghuat Ong; Bradley, D.A.; Laimeng Looi

1997-01-01

Discriminant analysis of six trace element concentrations measured by instrumental neutron activation analysis (INAA) in 26 paired-samples of malignant and histologically normal human breast tissues shows the technique to be a potentially valuable clinical tool for making malignant-normal classification. Nonparametric discriminant analysis is performed for the data obtained. Linear and quadratic discriminant analyses are also carried out for comparison. For this data set a formal analysis shows that the elements which may be useful in distinguishing between malignant and normal tissues are Ca, Rb and Br, providing correct classification for 24 out of 26 normal samples and 22 out of 26 malignant samples. (Author)

Classification system for oral submucous fibrosis

Directory of Open Access Journals (Sweden)

Chandramani Bhagvan More

2012-01-01

Full Text Available Oral submucous fibrosis (OSMF is a potentially malignant disorder (PMD and crippling condition of oral mucosa. It is a chronic insidious scarring disease of oral cavity, pharynx and upper digestive tract, characterized by progressive inability to open the mouth due to loss of elasticity and development of vertical fibrous bands in labial and buccal tissues. OSMF is a debilitating but preventable oral disease. It predominantly affects people of Southeast Asia and Indian subcontinent, where chewing of arecanut and its commercial preparation is high. Presence of fibrous bands is the main characteristic feature of OSMF. The present literature review provides the compilation of various classification system based on clinical and/or histopathological features of OSMF from several databases. The advantages and drawbacks of these classifications supersede each other, leading to perplexity. An attempt is made to provide and update the knowledge about this potentially malignant disorder to health care providers in order to help in early detection and treatment, thus reducing the mortality of oral cancer.

Tissue discrimination in magnetic resonance imaging of the rotator cuff

International Nuclear Information System (INIS)

Meschino, G J; Comas, D S; González, M A; Ballarin, V L; Capiel, C

2016-01-01

Evaluation and diagnosis of diseases of the muscles within the rotator cuff can be done using different modalities, being the Magnetic Resonance the method more widely used. There are criteria to evaluate the degree of fat infiltration and muscle atrophy, but these have low accuracy and show great variability inter and intra observer. In this paper, an analysis of the texture features of the rotator cuff muscles is performed to classify them and other tissues. A general supervised classification approach was used, combining forward-search as feature selection method with kNN as classification rule. Sections of Magnetic Resonance Images of the tissues of interest were selected by specialist doctors and they were considered as Gold Standard. Accuracies obtained were of 93% for T1-weighted images and 92% for T2-weighted images. As an immediate future work, the combination of both sequences of images will be considered, expecting to improve the results, as well as the use of other sequences of Magnetic Resonance Images. This work represents an initial point for the classification and quantification of fat infiltration and muscle atrophy degree. From this initial point, it is expected to make an accurate and objective system which will result in benefits for future research and for patients’ health. (paper)

Tissue discrimination in magnetic resonance imaging of the rotator cuff

Science.gov (United States)

Meschino, G. J.; Comas, D. S.; González, M. A.; Capiel, C.; Ballarin, V. L.

2016-04-01

Evaluation and diagnosis of diseases of the muscles within the rotator cuff can be done using different modalities, being the Magnetic Resonance the method more widely used. There are criteria to evaluate the degree of fat infiltration and muscle atrophy, but these have low accuracy and show great variability inter and intra observer. In this paper, an analysis of the texture features of the rotator cuff muscles is performed to classify them and other tissues. A general supervised classification approach was used, combining forward-search as feature selection method with kNN as classification rule. Sections of Magnetic Resonance Images of the tissues of interest were selected by specialist doctors and they were considered as Gold Standard. Accuracies obtained were of 93% for T1-weighted images and 92% for T2-weighted images. As an immediate future work, the combination of both sequences of images will be considered, expecting to improve the results, as well as the use of other sequences of Magnetic Resonance Images. This work represents an initial point for the classification and quantification of fat infiltration and muscle atrophy degree. From this initial point, it is expected to make an accurate and objective system which will result in benefits for future research and for patients’ health.

Diffuse reflectance spectroscopy for optical soft tissue differentiation as remote feedback control for tissue-specific laser surgery.

Science.gov (United States)

Stelzle, Florian; Tangermann-Gerk, Katja; Adler, Werner; Zam, Azhar; Schmidt, Michael; Douplik, Alexandre; Nkenke, Emeka

2010-04-01

Laser surgery does not provide haptic feedback for operating layer-by-layer and thereby preserving vulnerable anatomical structures like nerve tissue or blood vessels. Diffuse reflectance spectra can facilitate remote optical tissue differentiation. It is the aim of the study to use this technique on soft tissue samples, to set a technological basis for a remote optical feedback system for tissue-specific laser surgery. Diffuse reflectance spectra (wavelength range: 350-650 nm) of ex vivo types of soft tissue (a total of 10,800 spectra) of the midfacial region of domestic pigs were remotely measured under reduced environmental light conditions and analyzed in order to differentiate between skin, mucosa, muscle, subcutaneous fat, and nerve tissue. We performed a principal components (PC) analysis (PCA) to reduce the number of variables. Linear discriminant analysis (LDA) was utilized for classification. For the tissue differentiation, we calculated the specificity and sensitivity by receiver operating characteristic (ROC) analysis and the area under curve (AUC). Six PCs were found to be adequate for tissue differentiation with diffuse reflectance spectra using LDA. All of the types of soft tissue could be differentiated with high specificity and sensitivity. Only the tissue pairs nervous tissue/fatty tissue and nervous tissue/mucosa showed a decline of differentiation due to bio-structural similarity. However, both of these tissue pairs could still be differentiated with a specificity and sensitivity of more than 90%. Analyzing diffuse reflectance spectroscopy with PCA and LDA allows for remote differentiation of biological tissue. Considering the limitations of the ex vivo conditions, the obtained results are promising and set a basis for the further development of a feedback system for tissue-specific laser surgery. (c) 2010 Wiley-Liss, Inc.

SAW Classification Algorithm for Chinese Text Classification

OpenAIRE

Xiaoli Guo; Huiyu Sun; Tiehua Zhou; Ling Wang; Zhaoyang Qu; Jiannan Zang

2015-01-01

Considering the explosive growth of data, the increased amount of text data’s effect on the performance of text categorization forward the need for higher requirements, such that the existing classification method cannot be satisfied. Based on the study of existing text classification technology and semantics, this paper puts forward a kind of Chinese text classification oriented SAW (Structural Auxiliary Word) algorithm. The algorithm uses the special space effect of Chinese text where words...

Soft Tissue Tumor Immunohistochemistry Update: Illustrative Examples of Diagnostic Pearls to Avoid Pitfalls.

Science.gov (United States)

Wei, Shi; Henderson-Jackson, Evita; Qian, Xiaohua; Bui, Marilyn M

2017-08-01

- Current 2013 World Health Organization classification of tumors of soft tissue arranges these tumors into 12 groups according to their histogenesis. Tumor behavior is classified as benign, intermediate (locally aggressive), intermediate (rarely metastasizing), and malignant. In our practice, a general approach to reaching a definitive diagnosis of soft tissue tumors is to first evaluate clinicoradiologic, histomorphologic, and cytomorphologic features of the tumor to generate some pertinent differential diagnoses. These include the potential line of histogenesis and whether the tumor is benign or malignant, and low or high grade. Although molecular/genetic testing is increasingly finding its applications in characterizing soft tissue tumors, currently immunohistochemistry still not only plays an indispensable role in defining tumor histogenesis, but also serves as a surrogate for underlining molecular/genetic alterations. Objective- To provide an overview focusing on the current concepts in the classification and diagnosis of soft tissue tumors, incorporating immunohistochemistry. This article uses examples to discuss how to use the traditional and new immunohistochemical markers for the diagnosis of soft tissue tumors. Practical diagnostic pearls, summary tables, and figures are used to show how to avoid diagnostic pitfalls. - Data were obtained from pertinent peer-reviewed English-language literature and the authors' first-hand experience as bone and soft tissue pathologists. - -The ultimate goal for a pathologist is to render a specific diagnosis that provides diagnostic, prognostic, and therapeutic information to guide patient care. Immunohistochemistry is integral to the diagnosis and management of soft tissue tumors.

Classification in context

DEFF Research Database (Denmark)

Mai, Jens Erik

2004-01-01

This paper surveys classification research literature, discusses various classification theories, and shows that the focus has traditionally been on establishing a scientific foundation for classification research. This paper argues that a shift has taken place, and suggests that contemporary...... classification research focus on contextual information as the guide for the design and construction of classification schemes....

Feasibility of a novel deformable image registration technique to facilitate classification, targeting, and monitoring of tumor and normal tissue

International Nuclear Information System (INIS)

Brock, Kristy K.; Dawson, Laura A.; Sharpe, Michael B.; Moseley, Douglas J.; Jaffray, David A.

2006-01-01

Purpose: To investigate the feasibility of a biomechanical-based deformable image registration technique for the integration of multimodality imaging, image guided treatment, and response monitoring. Methods and Materials: A multiorgan deformable image registration technique based on finite element modeling (FEM) and surface projection alignment of selected regions of interest with biomechanical material and interface models has been developed. FEM also provides an inherent method for direct tracking specified regions through treatment and follow-up. Results: The technique was demonstrated on 5 liver cancer patients. Differences of up to 1 cm of motion were seen between the diaphragm and the tumor center of mass after deformable image registration of exhale and inhale CT scans. Spatial differences of 5 mm or more were observed for up to 86% of the surface of the defined tumor after deformable image registration of the computed tomography (CT) and magnetic resonance images. Up to 6.8 mm of motion was observed for the tumor after deformable image registration of the CT and cone-beam CT scan after rigid registration of the liver. Deformable registration of the CT to the follow-up CT allowed a more accurate assessment of tumor response. Conclusions: This biomechanical-based deformable image registration technique incorporates classification, targeting, and monitoring of tumor and normal tissue using one methodology

Classification

DEFF Research Database (Denmark)

Hjørland, Birger

2017-01-01

This article presents and discusses definitions of the term “classification” and the related concepts “Concept/conceptualization,”“categorization,” “ordering,” “taxonomy” and “typology.” It further presents and discusses theories of classification including the influences of Aristotle...... and Wittgenstein. It presents different views on forming classes, including logical division, numerical taxonomy, historical classification, hermeneutical and pragmatic/critical views. Finally, issues related to artificial versus natural classification and taxonomic monism versus taxonomic pluralism are briefly...

Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm.

Science.gov (United States)

Al Ajmi, Eiman; Forghani, Behzad; Reinhold, Caroline; Bayat, Maryam; Forghani, Reza

2018-06-01

There is a rich amount of quantitative information in spectral datasets generated from dual-energy CT (DECT). In this study, we compare the performance of texture analysis performed on multi-energy datasets to that of virtual monochromatic images (VMIs) at 65 keV only, using classification of the two most common benign parotid neoplasms as a testing paradigm. Forty-two patients with pathologically proven Warthin tumour (n = 25) or pleomorphic adenoma (n = 17) were evaluated. Texture analysis was performed on VMIs ranging from 40 to 140 keV in 5-keV increments (multi-energy analysis) or 65-keV VMIs only, which is typically considered equivalent to single-energy CT. Random forest (RF) models were constructed for outcome prediction using separate randomly selected training and testing sets or the entire patient set. Using multi-energy texture analysis, tumour classification in the independent testing set had accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 92%, 86%, 100%, 100%, and 83%, compared to 75%, 57%, 100%, 100%, and 63%, respectively, for single-energy analysis. Multi-energy texture analysis demonstrates superior performance compared to single-energy texture analysis of VMIs at 65 keV for classification of benign parotid tumours. • We present and validate a paradigm for texture analysis of DECT scans. • Multi-energy dataset texture analysis is superior to single-energy dataset texture analysis. • DECT texture analysis has high accura\\cy for diagnosis of benign parotid tumours. • DECT texture analysis with machine learning can enhance non-invasive diagnostic tumour evaluation.

Determination of quantitative tissue composition by iterative reconstruction on 3D DECT volumes

Energy Technology Data Exchange (ETDEWEB)

Magnusson, Maria [Linkoeping Univ. (Sweden). Dept. of Electrical Engineering; Linkoeping Univ. (Sweden). Dept. of Medical and Health Sciences, Radiation Physics; Linkoeping Univ. (Sweden). Center for Medical Image Science and Visualization (CMIV); Malusek, Alexandr [Linkoeping Univ. (Sweden). Dept. of Medical and Health Sciences, Radiation Physics; Linkoeping Univ. (Sweden). Center for Medical Image Science and Visualization (CMIV); Nuclear Physics Institute AS CR, Prague (Czech Republic). Dept. of Radiation Dosimetry; Muhammad, Arif [Linkoeping Univ. (Sweden). Dept. of Medical and Health Sciences, Radiation Physics; Carlsson, Gudrun Alm [Linkoeping Univ. (Sweden). Dept. of Medical and Health Sciences, Radiation Physics; Linkoeping Univ. (Sweden). Center for Medical Image Science and Visualization (CMIV)

2011-07-01

Quantitative tissue classification using dual-energy CT has the potential to improve accuracy in radiation therapy dose planning as it provides more information about material composition of scanned objects than the currently used methods based on single-energy CT. One problem that hinders successful application of both single- and dual-energy CT is the presence of beam hardening and scatter artifacts in reconstructed data. Current pre- and post-correction methods used for image reconstruction often bias CT attenuation values and thus limit their applicability for quantitative tissue classification. Here we demonstrate simulation studies with a novel iterative algorithm that decomposes every soft tissue voxel into three base materials: water, protein, and adipose. The results demonstrate that beam hardening artifacts can effectively be removed and accurate estimation of mass fractions of each base material can be achieved. Our iterative algorithm starts with calculating parallel projections on two previously reconstructed DECT volumes reconstructed from fan-beam or helical projections with small conebeam angle. The parallel projections are then used in an iterative loop. Future developments include segmentation of soft and bone tissue and subsequent determination of bone composition. (orig.)

The cranial cartilages of teleosts and their classification.

OpenAIRE

Benjamin, M

1990-01-01

The structure and distribution of cartilages has been studied in 45 species from 24 families. The resulting data have been used as a basis for establishing a new classification. A cartilage is regarded as 'cell-rich' if its cells or their lacunae occupy more than half of the tissue volume. Five classes of cell-rich cartilage are recognised (a) hyaline-cell cartilage (common in the lips of bottom-dwelling cyprinids) and its subtypes fibro/hyaline-cell cartilage, elastic/hyaline-cell cartilage ...

78 FR 54970 - Cotton Futures Classification: Optional Classification Procedure

Science.gov (United States)

2013-09-09

... Service 7 CFR Part 27 [AMS-CN-13-0043] RIN 0581-AD33 Cotton Futures Classification: Optional Classification Procedure AGENCY: Agricultural Marketing Service, USDA. ACTION: Proposed rule. SUMMARY: The... optional cotton futures classification procedure--identified and known as ``registration'' by the U.S...

75 FR 70112 - Medical Devices; General and Plastic Surgery Devices; Classification of Non-Powered Suction...

Science.gov (United States)

2010-11-17

.... FDA-2010-N-0513] Medical Devices; General and Plastic Surgery Devices; Classification of Non-Powered... risks. Adverse tissue reaction Material degradation Improper function of suction apparatus (e.g., reflux.... Material degradation Section 8. Stability and Shelf Life. [[Page 70113

Computer-aided classification of lesions by means of their kinetic signatures in dynamic contrast-enhanced MR images

Science.gov (United States)

Twellmann, Thorsten; ter Haar Romeny, Bart

2008-03-01

The kinetic characteristics of tissue in dynamic contrast-enhanced magnetic resonance imaging data are an important source of information for the differentiation of benign and malignant lesions. Kinetic curves measured for each lesion voxel allow to infer information about the state of the local tissue. As a whole, they reflect the heterogeneity of the vascular structure within a lesion, an important criterion for the preoperative classification of lesions. Current clinical practice in analysis of tissue kinetics however is mainly based on the evaluation of the "most-suspect curve", which is only related to a small, manually or semi-automatically selected region-of-interest within a lesion and does not reflect any information about tissue heterogeneity. We propose a new method which exploits the full range of kinetic information for the automatic classification of lesions. Instead of breaking down the large amount of kinetic information to a single curve, each lesion is considered as a probability distribution in a space of kinetic features, efficiently represented by its kinetic signature obtained by adaptive vector quantization of the corresponding kinetic curves. Dissimilarity of two signatures can be objectively measured using the Mallows distance, which is a metric defined on probability distributions. The embedding of this metric in a suitable kernel function enables us to employ modern kernel-based machine learning techniques for the classification of signatures. In a study considering 81 breast lesions, the proposed method yielded an A z value of 0.89+/-0.01 for the discrimination of benign and malignant lesions in a nested leave-one-lesion-out evaluation setting.

Measurement of the hyperelastic properties of 44 pathological ex vivo breast tissue samples

International Nuclear Information System (INIS)

O'Hagan, Joseph J; Samani, Abbas

2009-01-01

The elastic and hyperelastic properties of biological soft tissues have been of interest to the medical community. There are several biomedical applications where parameters characterizing such properties are critical for a reliable clinical outcome. These applications include surgery planning, needle biopsy and brachtherapy where tissue biomechanical modeling is involved. Another important application is interpreting nonlinear elastography images. While there has been considerable research on the measurement of the linear elastic modulus of small tissue samples, little research has been conducted for measuring parameters that characterize the nonlinear elasticity of tissues included in tissue slice specimens. This work presents hyperelastic measurement results of 44 pathological ex vivo breast tissue samples. For each sample, five hyperelastic models have been used, including the Yeoh, N = 2 polynomial, N = 1 Ogden, Arruda-Boyce, and Veronda-Westmann models. Results show that the Yeoh, polynomial and Ogden models are the most accurate in terms of fitting experimental data. The results indicate that almost all of the parameters corresponding to the pathological tissues are between two times to over two orders of magnitude larger than those of normal tissues, with C 11 showing the most significant difference. Furthermore, statistical analysis indicates that C 02 of the Yeoh model, and C 11 and C 20 of the polynomial model have very good potential for cancer classification as they show statistically significant differences for various cancer types, especially for invasive lobular carcinoma. In addition to the potential for use in cancer classification, the presented data are very important for applications such as surgery planning and virtual reality based clinician training systems where accurate nonlinear tissue response modeling is required.

Measurement of the hyperelastic properties of 44 pathological ex vivo breast tissue samples

Energy Technology Data Exchange (ETDEWEB)

O' Hagan, Joseph J; Samani, Abbas [Department of Electrical and Computer Engineering, University of Western Ontario, London, ON (Canada)], E-mail: asamani@uwo.ca

2009-04-21

The elastic and hyperelastic properties of biological soft tissues have been of interest to the medical community. There are several biomedical applications where parameters characterizing such properties are critical for a reliable clinical outcome. These applications include surgery planning, needle biopsy and brachtherapy where tissue biomechanical modeling is involved. Another important application is interpreting nonlinear elastography images. While there has been considerable research on the measurement of the linear elastic modulus of small tissue samples, little research has been conducted for measuring parameters that characterize the nonlinear elasticity of tissues included in tissue slice specimens. This work presents hyperelastic measurement results of 44 pathological ex vivo breast tissue samples. For each sample, five hyperelastic models have been used, including the Yeoh, N = 2 polynomial, N = 1 Ogden, Arruda-Boyce, and Veronda-Westmann models. Results show that the Yeoh, polynomial and Ogden models are the most accurate in terms of fitting experimental data. The results indicate that almost all of the parameters corresponding to the pathological tissues are between two times to over two orders of magnitude larger than those of normal tissues, with C{sub 11} showing the most significant difference. Furthermore, statistical analysis indicates that C{sub 02} of the Yeoh model, and C{sub 11} and C{sub 20} of the polynomial model have very good potential for cancer classification as they show statistically significant differences for various cancer types, especially for invasive lobular carcinoma. In addition to the potential for use in cancer classification, the presented data are very important for applications such as surgery planning and virtual reality based clinician training systems where accurate nonlinear tissue response modeling is required.

Measurement of the hyperelastic properties of 44 pathological ex vivo breast tissue samples

Science.gov (United States)

O'Hagan, Joseph J.; Samani, Abbas

2009-04-01

The elastic and hyperelastic properties of biological soft tissues have been of interest to the medical community. There are several biomedical applications where parameters characterizing such properties are critical for a reliable clinical outcome. These applications include surgery planning, needle biopsy and brachtherapy where tissue biomechanical modeling is involved. Another important application is interpreting nonlinear elastography images. While there has been considerable research on the measurement of the linear elastic modulus of small tissue samples, little research has been conducted for measuring parameters that characterize the nonlinear elasticity of tissues included in tissue slice specimens. This work presents hyperelastic measurement results of 44 pathological ex vivo breast tissue samples. For each sample, five hyperelastic models have been used, including the Yeoh, N = 2 polynomial, N = 1 Ogden, Arruda-Boyce, and Veronda-Westmann models. Results show that the Yeoh, polynomial and Ogden models are the most accurate in terms of fitting experimental data. The results indicate that almost all of the parameters corresponding to the pathological tissues are between two times to over two orders of magnitude larger than those of normal tissues, with C11 showing the most significant difference. Furthermore, statistical analysis indicates that C02 of the Yeoh model, and C11 and C20 of the polynomial model have very good potential for cancer classification as they show statistically significant differences for various cancer types, especially for invasive lobular carcinoma. In addition to the potential for use in cancer classification, the presented data are very important for applications such as surgery planning and virtual reality based clinician training systems where accurate nonlinear tissue response modeling is required.

Using cell nuclei features to detect colon cancer tissue in hematoxylin and eosin stained slides.

Science.gov (United States)

Jørgensen, Alex Skovsbo; Rasmussen, Anders Munk; Andersen, Niels Kristian Mäkinen; Andersen, Simon Kragh; Emborg, Jonas; Røge, Rasmus; Østergaard, Lasse Riis

2017-08-01

Currently, diagnosis of colon cancer is based on manual examination of histopathological images by a pathologist. This can be time consuming and interpretation of the images is subject to inter- and intra-observer variability. This may be improved by introducing a computer-aided diagnosis (CAD) system for automatic detection of cancer tissue within whole slide hematoxylin and eosin (H&E) stains. Cancer disrupts the normal control mechanisms of cell proliferation and differentiation, affecting the structure and appearance of the cells. Therefore, extracting features from segmented cell nuclei structures may provide useful information to detect cancer tissue. A framework for automatic classification of regions of interest (ROI) containing either benign or cancerous colon tissue extracted from whole slide H&E stained images using cell nuclei features was proposed. A total of 1,596 ROI's were extracted from 87 whole slide H&E stains (44 benign and 43 cancer). A cell nuclei segmentation algorithm consisting of color deconvolution, k-means clustering, local adaptive thresholding, and cell separation was performed within the ROI's to extract cell nuclei features. From the segmented cell nuclei structures a total of 750 texture and intensity-based features were extracted for classification of the ROI's. The nine most discriminative cell nuclei features were used in a random forest classifier to determine if the ROI's contained benign or cancer tissue. The ROI classification obtained an area under the curve (AUC) of 0.96, sensitivity of 0.88, specificity of 0.92, and accuracy of 0.91 using an optimized threshold. The developed framework showed promising results in using cell nuclei features to classify ROIs into containing benign or cancer tissue in H&E stained tissue samples. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

Laser Raman detection for oral cancer based on an adaptive Gaussian process classification method with posterior probabilities

International Nuclear Information System (INIS)

Du, Zhanwei; Yang, Yongjian; Bai, Yuan; Wang, Lijun; Su, Le; Chen, Yong; Li, Xianchang; Zhou, Xiaodong; Shen, Aiguo; Hu, Jiming; Jia, Jun

2013-01-01

The existing methods for early and differential diagnosis of oral cancer are limited due to the unapparent early symptoms and the imperfect imaging examination methods. In this paper, the classification models of oral adenocarcinoma, carcinoma tissues and a control group with just four features are established by utilizing the hybrid Gaussian process (HGP) classification algorithm, with the introduction of the mechanisms of noise reduction and posterior probability. HGP shows much better performance in the experimental results. During the experimental process, oral tissues were divided into three groups, adenocarcinoma (n = 87), carcinoma (n = 100) and the control group (n = 134). The spectral data for these groups were collected. The prospective application of the proposed HGP classification method improved the diagnostic sensitivity to 56.35% and the specificity to about 70.00%, and resulted in a Matthews correlation coefficient (MCC) of 0.36. It is proved that the utilization of HGP in LRS detection analysis for the diagnosis of oral cancer gives accurate results. The prospect of application is also satisfactory. (paper)

Laser Raman detection for oral cancer based on an adaptive Gaussian process classification method with posterior probabilities

Science.gov (United States)

Du, Zhanwei; Yang, Yongjian; Bai, Yuan; Wang, Lijun; Su, Le; Chen, Yong; Li, Xianchang; Zhou, Xiaodong; Jia, Jun; Shen, Aiguo; Hu, Jiming

2013-03-01

The existing methods for early and differential diagnosis of oral cancer are limited due to the unapparent early symptoms and the imperfect imaging examination methods. In this paper, the classification models of oral adenocarcinoma, carcinoma tissues and a control group with just four features are established by utilizing the hybrid Gaussian process (HGP) classification algorithm, with the introduction of the mechanisms of noise reduction and posterior probability. HGP shows much better performance in the experimental results. During the experimental process, oral tissues were divided into three groups, adenocarcinoma (n = 87), carcinoma (n = 100) and the control group (n = 134). The spectral data for these groups were collected. The prospective application of the proposed HGP classification method improved the diagnostic sensitivity to 56.35% and the specificity to about 70.00%, and resulted in a Matthews correlation coefficient (MCC) of 0.36. It is proved that the utilization of HGP in LRS detection analysis for the diagnosis of oral cancer gives accurate results. The prospect of application is also satisfactory.

Texture analysis of speckle in optical coherence tomography images of tissue phantoms

International Nuclear Information System (INIS)

Gossage, Kirk W; Smith, Cynthia M; Kanter, Elizabeth M; Hariri, Lida P; Stone, Alice L; Rodriguez, Jeffrey J; Williams, Stuart K; Barton, Jennifer K

2006-01-01

Optical coherence tomography (OCT) is an imaging modality capable of acquiring cross-sectional images of tissue using back-reflected light. Conventional OCT images have a resolution of 10-15 μm, and are thus best suited for visualizing tissue layers and structures. OCT images of collagen (with and without endothelial cells) have no resolvable features and may appear to simply show an exponential decrease in intensity with depth. However, examination of these images reveals that they display a characteristic repetitive structure due to speckle.The purpose of this study is to evaluate the application of statistical and spectral texture analysis techniques for differentiating living and non-living tissue phantoms containing various sizes and distributions of scatterers based on speckle content in OCT images. Statistically significant differences between texture parameters and excellent classification rates were obtained when comparing various endothelial cell concentrations ranging from 0 cells/ml to 25 million cells/ml. Statistically significant results and excellent classification rates were also obtained using various sizes of microspheres with concentrations ranging from 0 microspheres/ml to 500 million microspheres/ml. This study has shown that texture analysis of OCT images may be capable of differentiating tissue phantoms containing various sizes and distributions of scatterers

Classification of refrigerants; Classification des fluides frigorigenes

Energy Technology Data Exchange (ETDEWEB)

NONE

2001-07-01

This document was made from the US standard ANSI/ASHRAE 34 published in 2001 and entitled 'designation and safety classification of refrigerants'. This classification allows to clearly organize in an international way the overall refrigerants used in the world thanks to a codification of the refrigerants in correspondence with their chemical composition. This note explains this codification: prefix, suffixes (hydrocarbons and derived fluids, azeotropic and non-azeotropic mixtures, various organic compounds, non-organic compounds), safety classification (toxicity, flammability, case of mixtures). (J.S.)

Classification of hydration status using electrocardiogram and machine learning

Science.gov (United States)

Kaveh, Anthony; Chung, Wayne

2013-10-01

The electrocardiogram (ECG) has been used extensively in clinical practice for decades to non-invasively characterize the health of heart tissue; however, these techniques are limited to time domain features. We propose a machine classification system using support vector machines (SVM) that uses temporal and spectral information to classify health state beyond cardiac arrhythmias. Our method uses single lead ECG to classify volume depletion (or dehydration) without the lengthy and costly blood analysis tests traditionally used for detecting dehydration status. Our method builds on established clinical ECG criteria for identifying electrolyte imbalances and lends to automated, computationally efficient implementation. The method was tested on the MIT-BIH PhysioNet database to validate this purely computational method for expedient disease-state classification. The results show high sensitivity, supporting use as a cost- and time-effective screening tool.

Non-Hodgkin lymphoma response evaluation with MRI texture classification

Directory of Open Access Journals (Sweden)

Heinonen Tomi T

2009-06-01

Full Text Available Abstract Background To show magnetic resonance imaging (MRI texture appearance change in non-Hodgkin lymphoma (NHL during treatment with response controlled by quantitative volume analysis. Methods A total of 19 patients having NHL with an evaluable lymphoma lesion were scanned at three imaging timepoints with 1.5T device during clinical treatment evaluation. Texture characteristics of images were analyzed and classified with MaZda application and statistical tests. Results NHL tissue MRI texture imaged before treatment and under chemotherapy was classified within several subgroups, showing best discrimination with 96% correct classification in non-linear discriminant analysis of T2-weighted images. Texture parameters of MRI data were successfully tested with statistical tests to assess the impact of the separability of the parameters in evaluating chemotherapy response in lymphoma tissue. Conclusion Texture characteristics of MRI data were classified successfully; this proved texture analysis to be potential quantitative means of representing lymphoma tissue changes during chemotherapy response monitoring.

An Approach for Leukemia Classification Based on Cooperative Game Theory

Directory of Open Access Journals (Sweden)

Atefeh Torkaman

2011-01-01

Full Text Available Hematological malignancies are the types of cancer that affect blood, bone marrow and lymph nodes. As these tissues are naturally connected through the immune system, a disease affecting one of them will often affect the others as well. The hematological malignancies include; Leukemia, Lymphoma, Multiple myeloma. Among them, leukemia is a serious malignancy that starts in blood tissues especially the bone marrow, where the blood is made. Researches show, leukemia is one of the common cancers in the world. So, the emphasis on diagnostic techniques and best treatments would be able to provide better prognosis and survival for patients. In this paper, an automatic diagnosis recommender system for classifying leukemia based on cooperative game is presented. Through out this research, we analyze the flow cytometry data toward the classification of leukemia into eight classes. We work on real data set from different types of leukemia that have been collected at Iran Blood Transfusion Organization (IBTO. Generally, the data set contains 400 samples taken from human leukemic bone marrow. This study deals with cooperative game used for classification according to different weights assigned to the markers. The proposed method is versatile as there are no constraints to what the input or output represent. This means that it can be used to classify a population according to their contributions. In other words, it applies equally to other groups of data. The experimental results show the accuracy rate of 93.12%, for classification and compared to decision tree (C4.5 with (90.16% in accuracy. The result demonstrates that cooperative game is very promising to be used directly for classification of leukemia as a part of Active Medical decision support system for interpretation of flow cytometry readout. This system could assist clinical hematologists to properly recognize different kinds of leukemia by preparing suggestions and this could improve the treatment

Unclassified sarcomas : a study to improve classification in a cohort of Golden Retriever dogs

NARCIS (Netherlands)

Boerkamp, Kim M; Hellmén, Eva; Willén, Helena; Grinwis, Guy C M; Teske, Erik; Rutteman, Gerard R

2016-01-01

Morphologically, canine soft-tissue sarcomas (STSs) resemble human STSs. In humans, proper classification of STSs is considered essential to improve insight in the biology of these tumors, and to optimize diagnosis and therapy. To date, there is a paucity of data published on the significance of

Differentiation of osteophyte types in osteoarthritis - proposal of a histological classification.

Science.gov (United States)

Junker, Susann; Krumbholz, Grit; Frommer, Klaus W; Rehart, Stefan; Steinmeyer, Jürgen; Rickert, Markus; Schett, Georg; Müller-Ladner, Ulf; Neumann, Elena

2016-01-01

Osteoarthritis is not only characterized by cartilage degradation but also involves subchondral bone remodeling and osteophyte formation. Osteophytes are fibrocartilage-capped bony outgrowths originating from the periosteum. The pathophysiology of osteophyte formation is not completely understood. Yet, different research approaches are under way. Therefore, a histological osteophyte classification to achieve comparable results in osteophyte research was established for application to basic science research questions. The osteophytes were collected from knee joints of osteoarthritis patients (n=10, 94 osteophytes in total) after joint replacement surgery. Their size and origin in the respective joint were photo-documented. To develop an osteophyte classification, serial tissue sections were evaluated using histological (hematoxylin and eosin, Masson's trichrome, toluidine blue) and immunohistochemical staining (collagen type II). Based on the histological and immunohistochemical evaluation, osteophytes were categorized into four different types depending on the degree of ossification and the percentage of mesenchymal connective tissue. Size and localization of osteophytes were independent from the histological stages. This histological classification system of osteoarthritis osteophytes provides a helpful tool for analyzing and monitoring osteophyte development and for characterizing osteophyte types within a single human joint and may therefore contribute to achieve comparable results when analyzing histological findings in osteophytes. Copyright © 2015 Société française de rhumatologie. Published by Elsevier SAS. All rights reserved.

[Biocybernetic approach to the thermometric methods of blood supply measurements of periodontal tissues].

Science.gov (United States)

Pastusiak, J; Zakrzewski, J

1988-11-01

Specific biocybernetic approach to the problem of the blood supply determination of paradontium tissues by means of thermometric methods has been presented in the paper. The compartment models of the measuring procedure have been given. Dilutodynamic methology and classification has been applied. Such an approach enables to select appropriate biophysical parameters describing the state of blood supply of paradontium tissues and optimal design of transducers and measuring methods.

An application to pulmonary emphysema classification based on model of texton learning by sparse representation

Science.gov (United States)

Zhang, Min; Zhou, Xiangrong; Goshima, Satoshi; Chen, Huayue; Muramatsu, Chisako; Hara, Takeshi; Yokoyama, Ryojiro; Kanematsu, Masayuki; Fujita, Hiroshi

2012-03-01

We aim at using a new texton based texture classification method in the classification of pulmonary emphysema in computed tomography (CT) images of the lungs. Different from conventional computer-aided diagnosis (CAD) pulmonary emphysema classification methods, in this paper, firstly, the dictionary of texton is learned via applying sparse representation(SR) to image patches in the training dataset. Then the SR coefficients of the test images over the dictionary are used to construct the histograms for texture presentations. Finally, classification is performed by using a nearest neighbor classifier with a histogram dissimilarity measure as distance. The proposed approach is tested on 3840 annotated regions of interest consisting of normal tissue and mild, moderate and severe pulmonary emphysema of three subtypes. The performance of the proposed system, with an accuracy of about 88%, is comparably higher than state of the art method based on the basic rotation invariant local binary pattern histograms and the texture classification method based on texton learning by k-means, which performs almost the best among other approaches in the literature.

State-of-the-Art Methods for Brain Tissue Segmentation: A Review.

Science.gov (United States)

Dora, Lingraj; Agrawal, Sanjay; Panda, Rutuparna; Abraham, Ajith

2017-01-01

Brain tissue segmentation is one of the most sought after research areas in medical image processing. It provides detailed quantitative brain analysis for accurate disease diagnosis, detection, and classification of abnormalities. It plays an essential role in discriminating healthy tissues from lesion tissues. Therefore, accurate disease diagnosis and treatment planning depend merely on the performance of the segmentation method used. In this review, we have studied the recent advances in brain tissue segmentation methods and their state-of-the-art in neuroscience research. The review also highlights the major challenges faced during tissue segmentation of the brain. An effective comparison is made among state-of-the-art brain tissue segmentation methods. Moreover, a study of some of the validation measures to evaluate different segmentation methods is also discussed. The brain tissue segmentation, content in terms of methodologies, and experiments presented in this review are encouraging enough to attract researchers working in this field.

Classification of interstitial lung disease patterns with topological texture features

Science.gov (United States)

Huber, Markus B.; Nagarajan, Mahesh; Leinsinger, Gerda; Ray, Lawrence A.; Wismüller, Axel

2010-03-01

Topological texture features were compared in their ability to classify morphological patterns known as 'honeycombing' that are considered indicative for the presence of fibrotic interstitial lung diseases in high-resolution computed tomography (HRCT) images. For 14 patients with known occurrence of honey-combing, a stack of 70 axial, lung kernel reconstructed images were acquired from HRCT chest exams. A set of 241 regions of interest of both healthy and pathological (89) lung tissue were identified by an experienced radiologist. Texture features were extracted using six properties calculated from gray-level co-occurrence matrices (GLCM), Minkowski Dimensions (MDs), and three Minkowski Functionals (MFs, e.g. MF.euler). A k-nearest-neighbor (k-NN) classifier and a Multilayer Radial Basis Functions Network (RBFN) were optimized in a 10-fold cross-validation for each texture vector, and the classification accuracy was calculated on independent test sets as a quantitative measure of automated tissue characterization. A Wilcoxon signed-rank test was used to compare two accuracy distributions and the significance thresholds were adjusted for multiple comparisons by the Bonferroni correction. The best classification results were obtained by the MF features, which performed significantly better than all the standard GLCM and MD features (p < 0.005) for both classifiers. The highest accuracy was found for MF.euler (97.5%, 96.6%; for the k-NN and RBFN classifier, respectively). The best standard texture features were the GLCM features 'homogeneity' (91.8%, 87.2%) and 'absolute value' (90.2%, 88.5%). The results indicate that advanced topological texture features can provide superior classification performance in computer-assisted diagnosis of interstitial lung diseases when compared to standard texture analysis methods.

Classification of hydrocephalus: critical analysis of classification categories and advantages of "Multi-categorical Hydrocephalus Classification" (Mc HC).

Science.gov (United States)

Oi, Shizuo

2011-10-01

Hydrocephalus is a complex pathophysiology with disturbed cerebrospinal fluid (CSF) circulation. There are numerous numbers of classification trials published focusing on various criteria, such as associated anomalies/underlying lesions, CSF circulation/intracranial pressure patterns, clinical features, and other categories. However, no definitive classification exists comprehensively to cover the variety of these aspects. The new classification of hydrocephalus, "Multi-categorical Hydrocephalus Classification" (Mc HC), was invented and developed to cover the entire aspects of hydrocephalus with all considerable classification items and categories. Ten categories include "Mc HC" category I: onset (age, phase), II: cause, III: underlying lesion, IV: symptomatology, V: pathophysiology 1-CSF circulation, VI: pathophysiology 2-ICP dynamics, VII: chronology, VII: post-shunt, VIII: post-endoscopic third ventriculostomy, and X: others. From a 100-year search of publication related to the classification of hydrocephalus, 14 representative publications were reviewed and divided into the 10 categories. The Baumkuchen classification graph made from the round o'clock classification demonstrated the historical tendency of deviation to the categories in pathophysiology, either CSF or ICP dynamics. In the preliminary clinical application, it was concluded that "Mc HC" is extremely effective in expressing the individual state with various categories in the past and present condition or among the compatible cases of hydrocephalus along with the possible chronological change in the future.

Classification

Science.gov (United States)

Clary, Renee; Wandersee, James

2013-01-01

In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

Automatic tissue characterization from ultrasound imagery

Science.gov (United States)

Kadah, Yasser M.; Farag, Aly A.; Youssef, Abou-Bakr M.; Badawi, Ahmed M.

1993-08-01

In this work, feature extraction algorithms are proposed to extract the tissue characterization parameters from liver images. Then the resulting parameter set is further processed to obtain the minimum number of parameters representing the most discriminating pattern space for classification. This preprocessing step was applied to over 120 pathology-investigated cases to obtain the learning data for designing the classifier. The extracted features are divided into independent training and test sets and are used to construct both statistical and neural classifiers. The optimal criteria for these classifiers are set to have minimum error, ease of implementation and learning, and the flexibility for future modifications. Various algorithms for implementing various classification techniques are presented and tested on the data. The best performance was obtained using a single layer tensor model functional link network. Also, the voting k-nearest neighbor classifier provided comparably good diagnostic rates.

SECONDARY PULMONARY ARTERIAL HYPERTENSION IN SYSTEMIC DISEASES OF CONNECTIVE TISSUE

Directory of Open Access Journals (Sweden)

N. A. Shostak

2016-01-01

Full Text Available Modern definition of pulmonary arterial hypertension (PAH as well as data on prevalence and incidence of secondary PAH in systemic disease of connective tissue is presented,  including data of USA, France and Scotland registers. The main chains of pathogenesis, classification approaches, clinical features and diagnostics are described. 

SECONDARY PULMONARY ARTERIAL HYPERTENSION IN SYSTEMIC DISEASES OF CONNECTIVE TISSUE

Directory of Open Access Journals (Sweden)

N. A. Shostak

2009-01-01

Full Text Available Modern definition of pulmonary arterial hypertension (PAH as well as data on prevalence and incidence of secondary PAH in systemic disease of connective tissue is presented,  including data of USA, France and Scotland registers. The main chains of pathogenesis, classification approaches, clinical features and diagnostics are described. 

Clinically-inspired automatic classification of ovarian carcinoma subtypes

Directory of Open Access Journals (Sweden)

Aicha BenTaieb

2016-01-01

Full Text Available Context: It has been shown that ovarian carcinoma subtypes are distinct pathologic entities with differing prognostic and therapeutic implications. Histotyping by pathologists has good reproducibility, but occasional cases are challenging and require immunohistochemistry and subspecialty consultation. Motivated by the need for more accurate and reproducible diagnoses and to facilitate pathologists′ workflow, we propose an automatic framework for ovarian carcinoma classification. Materials and Methods: Our method is inspired by pathologists′ workflow. We analyse imaged tissues at two magnification levels and extract clinically-inspired color, texture, and segmentation-based shape descriptors using image-processing methods. We propose a carefully designed machine learning technique composed of four modules: A dissimilarity matrix, dimensionality reduction, feature selection and a support vector machine classifier to separate the five ovarian carcinoma subtypes using the extracted features. Results: This paper presents the details of our implementation and its validation on a clinically derived dataset of eighty high-resolution histopathology images. The proposed system achieved a multiclass classification accuracy of 95.0% when classifying unseen tissues. Assessment of the classifier′s confusion (confusion matrix between the five different ovarian carcinoma subtypes agrees with clinician′s confusion and reflects the difficulty in diagnosing endometrioid and serous carcinomas. Conclusions: Our results from this first study highlight the difficulty of ovarian carcinoma diagnosis which originate from the intrinsic class-imbalance observed among subtypes and suggest that the automatic analysis of ovarian carcinoma subtypes could be valuable to clinician′s diagnostic procedure by providing a second opinion.

A comparative study of PCA, SIMCA and Cole model for classification of bioimpedance spectroscopy measurements.

Science.gov (United States)

Nejadgholi, Isar; Bolic, Miodrag

2015-08-01

Due to safety and low cost of bioimpedance spectroscopy (BIS), classification of BIS can be potentially a preferred way of detecting changes in living tissues. However, for longitudinal datasets linear classifiers fail to classify conventional Cole parameters extracted from BIS measurements because of their high variability. In some applications, linear classification based on Principal Component Analysis (PCA) has shown more accurate results. Yet, these methods have not been established for BIS classification, since PCA features have neither been investigated in combination with other classifiers nor have been compared to conventional Cole features in benchmark classification tasks. In this work, PCA and Cole features are compared in three synthesized benchmark classification tasks which are expected to be detected by BIS. These three tasks are classification of before and after geometry change, relative composition change and blood perfusion in a cylindrical organ. Our results show that in all tasks the features extracted by PCA are more discriminant than Cole parameters. Moreover, a pilot study was done on a longitudinal arm BIS dataset including eight subjects and three arm positions. The goal of the study was to compare different methods in arm position classification which includes all three synthesized changes mentioned above. Our comparative study on various classification methods shows that the best classification accuracy is obtained when PCA features are classified by a K-Nearest Neighbors (KNN) classifier. The results of this work suggest that PCA+KNN is a promising method to be considered for classification of BIS datasets that deal with subject and time variability. Copyright © 2015 Elsevier Ltd. All rights reserved.

Discrimination of soft tissues using laser-induced breakdown spectroscopy in combination with k nearest neighbors (kNN) and support vector machine (SVM) classifiers

Science.gov (United States)

Li, Xiaohui; Yang, Sibo; Fan, Rongwei; Yu, Xin; Chen, Deying

2018-06-01

In this paper, discrimination of soft tissues using laser-induced breakdown spectroscopy (LIBS) in combination with multivariate statistical methods is presented. Fresh pork fat, skin, ham, loin and tenderloin muscle tissues are manually cut into slices and ablated using a 1064 nm pulsed Nd:YAG laser. Discrimination analyses between fat, skin and muscle tissues, and further between highly similar ham, loin and tenderloin muscle tissues, are performed based on the LIBS spectra in combination with multivariate statistical methods, including principal component analysis (PCA), k nearest neighbors (kNN) classification, and support vector machine (SVM) classification. Performances of the discrimination models, including accuracy, sensitivity and specificity, are evaluated using 10-fold cross validation. The classification models are optimized to achieve best discrimination performances. The fat, skin and muscle tissues can be definitely discriminated using both kNN and SVM classifiers, with accuracy of over 99.83%, sensitivity of over 0.995 and specificity of over 0.998. The highly similar ham, loin and tenderloin muscle tissues can also be discriminated with acceptable performances. The best performances are achieved with SVM classifier using Gaussian kernel function, with accuracy of 76.84%, sensitivity of over 0.742 and specificity of over 0.869. The results show that the LIBS technique assisted with multivariate statistical methods could be a powerful tool for online discrimination of soft tissues, even for tissues of high similarity, such as muscles from different parts of the animal body. This technique could be used for discrimination of tissues suffering minor clinical changes, thus may advance the diagnosis of early lesions and abnormalities.

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

Science.gov (United States)

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.

Hyperspectral Image Enhancement and Mixture Deep-Learning Classification of Corneal Epithelium Injuries.

Science.gov (United States)

Noor, Siti Salwa Md; Michael, Kaleena; Marshall, Stephen; Ren, Jinchang

2017-11-16

In our preliminary study, the reflectance signatures obtained from hyperspectral imaging (HSI) of normal and abnormal corneal epithelium tissues of porcine show similar morphology with subtle differences. Here we present image enhancement algorithms that can be used to improve the interpretability of data into clinically relevant information to facilitate diagnostics. A total of 25 corneal epithelium images without the application of eye staining were used. Three image feature extraction approaches were applied for image classification: (i) image feature classification from histogram using a support vector machine with a Gaussian radial basis function (SVM-GRBF); (ii) physical image feature classification using deep-learning Convolutional Neural Networks (CNNs) only; and (iii) the combined classification of CNNs and SVM-Linear. The performance results indicate that our chosen image features from the histogram and length-scale parameter were able to classify with up to 100% accuracy; particularly, at CNNs and CNNs-SVM, by employing 80% of the data sample for training and 20% for testing. Thus, in the assessment of corneal epithelium injuries, HSI has high potential as a method that could surpass current technologies regarding speed, objectivity, and reliability.

False-positive reduction in CAD mass detection using a competitive classification strategy

International Nuclear Information System (INIS)

Li Lihua; Zheng Yang; Zhang Lei; Clark, Robert A.

2001-01-01

High false-positive (FP) rate remains to be one of the major problems to be solved in CAD study because too many false-positively cued signals will potentially degrade the performance of detecting true-positive regions and increase the call-back rate in CAD environment. In this paper, we proposed a novel classification method for FP reduction, where the conventional 'hard' decision classifier is cascaded with a 'soft' decision classification with the objective to reduce false-positives in the cases with multiple FPs retained after the 'hard' decision classification. The 'soft' classification takes a competitive classification strategy in which only the 'best' ones are selected from the pre-classified suspicious regions as the true mass in each case. A neural network structure is designed to implement the proposed competitive classification. Comparative studies of FP reduction on a database of 79 images by a 'hard' decision classification and a combined 'hard'-'soft' classification method demonstrated the efficiency of the proposed classification strategy. For example, for the high FP sub-database which has only 31.7% of total images but accounts for 63.5% of whole FPs generated in single 'hard' classification, the FPs can be reduced for 56% (from 8.36 to 3.72 per image) by using the proposed method at the cost of 1% TP loss (from 69% to 68%) in whole database, while it can only be reduced for 27% (from 8.36 to 6.08 per image) by simply increasing the threshold of 'hard' classifier with a cost of TP loss as high as 14% (from 69% to 55%). On the average in whole database, the FP reduction by hybrid 'hard'-'soft' classification is 1.58 per image as compared to 1.11 by 'hard' classification at the TP costs described above. Because the cases with high dense tissue are of higher risk of cancer incidence and false-negative detection in mammogram screening, and usually generate more FPs in CAD detection, the method proposed in this paper will be very helpful in improving

Hand eczema classification

DEFF Research Database (Denmark)

Diepgen, T L; Andersen, Klaus Ejner; Brandao, F M

2008-01-01

of the disease is rarely evidence based, and a classification system for different subdiagnoses of hand eczema is not agreed upon. Randomized controlled trials investigating the treatment of hand eczema are called for. For this, as well as for clinical purposes, a generally accepted classification system...... A classification system for hand eczema is proposed. Conclusions It is suggested that this classification be used in clinical work and in clinical trials....

Classification of the web

DEFF Research Database (Denmark)

Mai, Jens Erik

2004-01-01

This paper discusses the challenges faced by investigations into the classification of the Web and outlines inquiries that are needed to use principles for bibliographic classification to construct classifications of the Web. This paper suggests that the classification of the Web meets challenges...... that call for inquiries into the theoretical foundation of bibliographic classification theory....

Security classification of information

Energy Technology Data Exchange (ETDEWEB)

Quist, A.S.

1993-04-01

This document is the second of a planned four-volume work that comprehensively discusses the security classification of information. The main focus of Volume 2 is on the principles for classification of information. Included herein are descriptions of the two major types of information that governments classify for national security reasons (subjective and objective information), guidance to use when determining whether information under consideration for classification is controlled by the government (a necessary requirement for classification to be effective), information disclosure risks and benefits (the benefits and costs of classification), standards to use when balancing information disclosure risks and benefits, guidance for assigning classification levels (Top Secret, Secret, or Confidential) to classified information, guidance for determining how long information should be classified (classification duration), classification of associations of information, classification of compilations of information, and principles for declassifying and downgrading information. Rules or principles of certain areas of our legal system (e.g., trade secret law) are sometimes mentioned to .provide added support to some of those classification principles.

Classification of clinical autofluorescence spectra of oral leukoplakia using an artificial neural network : a pilot study

NARCIS (Netherlands)

van Staveren, HJ; van Veen, RLP; Speelman, OC; Witjes, MJH; Roodenburg, JLN

The performance of an artificial neural network was evaluated as an alternative classification technique of autofluorescence spectra of oral leukoplakia, which may reflect the grade of tissue dysplasia. Twenty-two visible lesions of 21 patients suffering from oral leukoplakia and six locations on

Malignant fatty tumors: classification, clinical course, imaging appearance and treatment

International Nuclear Information System (INIS)

Peterson, J.J.; Kransdorf, M.J.; Bancroft, L.W.; O'Connor, M.I.

2003-01-01

Liposarcoma is a relatively common soft tissue malignancy with a wide spectrum of clinical presentations and imaging appearances. Several subtypes are described, ranging from lesions nearly entirely composed of mature adipose tissue, to tumors with very sparse adipose elements. The imaging appearance of these fatty masses is frequently sufficiently characteristic to allow a specific diagnosis, while in other cases, although a specific diagnosis is not achievable, a meaningful limited differential diagnosis can be established. The purpose of this paper is to review the spectrum of malignant fatty tumors, highlighting the current classification system, clinical presentation and behavior, treatment and spectrum of imaging appearances. The imaging review will emphasize CT scanning and MR imaging, and will stress differentiating radiologic features. (orig.)

Hazard classification methodology

International Nuclear Information System (INIS)

Brereton, S.J.

1996-01-01

This document outlines the hazard classification methodology used to determine the hazard classification of the NIF LTAB, OAB, and the support facilities on the basis of radionuclides and chemicals. The hazard classification determines the safety analysis requirements for a facility

The value of virtual touch tissue image (VTI) and virtual touch tissue quantification (VTQ) in the differential diagnosis of thyroid nodules

International Nuclear Information System (INIS)

Zhang, Feng-Juan; Han, Ruo-Ling; Zhao, Xin-Ming

2014-01-01

Highlights: • All nodules in the research were confirmed by histopathology. • The classification method of VTI was easy to learn. • VTQ could provide quantitative elasticity measurements for thyroid nodules. • VTI classification could provide semi-quantitative elasticity analysis. • The area ratio could show invasive extent of malignant tumor. - Abstract: Objectives: To explore the value of virtual touch tissue image (VTI) and virtual touch tissue quantification (VTQ) in the differential diagnosis of thyroid nodules. Methods: One-hundred and seven patients with 113 thyroid nodules were performed conventional ultrasound and acoustic radiation force impulse (ARFI) elastography. The stiffness of the nodules on virtual touch tissue image (VTI) was graded, and the area ratios (AR) of nodules on VTI images versus on B-mode images were calculated. Shear wave velocity (SWV) within the thyroid nodules were measured using virtual touch tissue quantification (VTQ) technique. The pathological diagnosis as the gold standard draws the receiver-operating characteristic curve (ROC) to find the cut-off point of VTI grades, AR and SWV to predict thyroid cancer. Results: The difference in VTI grades of malignant and benign nodules was statistically significant (P < 0.05), as well as in AR and SWV. There was no significant difference in the AR of nodules or the SWV of nodules in benign group or in malignant group. The sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of VTI grades, AR, and SWV in the differential diagnosis of thyroid nodules were calculated. There was no significant difference in diagnostic accuracy among the three methods. Conclusion: VTI grades, AR of nodules on VTI images versus on B-mode images and SWV within the nodules can help the differential diagnosis of thyroid nodules

Lichenoid tissue reaction/interface dermatitis: Recognition, classification, etiology, and clinicopathological overtones

Directory of Open Access Journals (Sweden)

Virendra N Sehgal

2011-01-01

Full Text Available Lichenoid tissue reaction or interface dermatitis embrace several clinical conditions, the prototype of which is lichen planus and its variants, drug induced lichenoid dermatitis, special forms of lichenoid dermatitis, lichenoid dermatitis in lupus erythematosus, and miscellaneous disorders showing lichenoid dermatitis, the salient clinical and histological features of which are described to facilitate their diagnosis. Background of lichenoid reaction pattern has been briefly outlined to enlighten those interested in this entity.

Cancer classification using the Immunoscore: a worldwide task force.

Science.gov (United States)

Galon, Jérôme; Pagès, Franck; Marincola, Francesco M; Angell, Helen K; Thurin, Magdalena; Lugli, Alessandro; Zlobec, Inti; Berger, Anne; Bifulco, Carlo; Botti, Gerardo; Tatangelo, Fabiana; Britten, Cedrik M; Kreiter, Sebastian; Chouchane, Lotfi; Delrio, Paolo; Arndt, Hartmann; Asslaber, Martin; Maio, Michele; Masucci, Giuseppe V; Mihm, Martin; Vidal-Vanaclocha, Fernando; Allison, James P; Gnjatic, Sacha; Hakansson, Leif; Huber, Christoph; Singh-Jasuja, Harpreet; Ottensmeier, Christian; Zwierzina, Heinz; Laghi, Luigi; Grizzi, Fabio; Ohashi, Pamela S; Shaw, Patricia A; Clarke, Blaise A; Wouters, Bradly G; Kawakami, Yutaka; Hazama, Shoichi; Okuno, Kiyotaka; Wang, Ena; O'Donnell-Tormey, Jill; Lagorce, Christine; Pawelec, Graham; Nishimura, Michael I; Hawkins, Robert; Lapointe, Réjean; Lundqvist, Andreas; Khleif, Samir N; Ogino, Shuji; Gibbs, Peter; Waring, Paul; Sato, Noriyuki; Torigoe, Toshihiko; Itoh, Kyogo; Patel, Prabhu S; Shukla, Shilin N; Palmqvist, Richard; Nagtegaal, Iris D; Wang, Yili; D'Arrigo, Corrado; Kopetz, Scott; Sinicrope, Frank A; Trinchieri, Giorgio; Gajewski, Thomas F; Ascierto, Paolo A; Fox, Bernard A

2012-10-03

Prediction of clinical outcome in cancer is usually achieved by histopathological evaluation of tissue samples obtained during surgical resection of the primary tumor. Traditional tumor staging (AJCC/UICC-TNM classification) summarizes data on tumor burden (T), presence of cancer cells in draining and regional lymph nodes (N) and evidence for metastases (M). However, it is now recognized that clinical outcome can significantly vary among patients within the same stage. The current classification provides limited prognostic information, and does not predict response to therapy. Recent literature has alluded to the importance of the host immune system in controlling tumor progression. Thus, evidence supports the notion to include immunological biomarkers, implemented as a tool for the prediction of prognosis and response to therapy. Accumulating data, collected from large cohorts of human cancers, has demonstrated the impact of immune-classification, which has a prognostic value that may add to the significance of the AJCC/UICC TNM-classification. It is therefore imperative to begin to incorporate the 'Immunoscore' into traditional classification, thus providing an essential prognostic and potentially predictive tool. Introduction of this parameter as a biomarker to classify cancers, as part of routine diagnostic and prognostic assessment of tumors, will facilitate clinical decision-making including rational stratification of patient treatment. Equally, the inherent complexity of quantitative immunohistochemistry, in conjunction with protocol variation across laboratories, analysis of different immune cell types, inconsistent region selection criteria, and variable ways to quantify immune infiltration, all underline the urgent requirement to reach assay harmonization. In an effort to promote the Immunoscore in routine clinical settings, an international task force was initiated. This review represents a follow-up of the announcement of this initiative, and of the J

Cancer classification using the Immunoscore: a worldwide task force

Directory of Open Access Journals (Sweden)

Galon Jérôme

2012-10-01

Full Text Available Abstract Prediction of clinical outcome in cancer is usually achieved by histopathological evaluation of tissue samples obtained during surgical resection of the primary tumor. Traditional tumor staging (AJCC/UICC-TNM classification summarizes data on tumor burden (T, presence of cancer cells in draining and regional lymph nodes (N and evidence for metastases (M. However, it is now recognized that clinical outcome can significantly vary among patients within the same stage. The current classification provides limited prognostic information, and does not predict response to therapy. Recent literature has alluded to the importance of the host immune system in controlling tumor progression. Thus, evidence supports the notion to include immunological biomarkers, implemented as a tool for the prediction of prognosis and response to therapy. Accumulating data, collected from large cohorts of human cancers, has demonstrated the impact of immune-classification, which has a prognostic value that may add to the significance of the AJCC/UICC TNM-classification. It is therefore imperative to begin to incorporate the ‘Immunoscore’ into traditional classification, thus providing an essential prognostic and potentially predictive tool. Introduction of this parameter as a biomarker to classify cancers, as part of routine diagnostic and prognostic assessment of tumors, will facilitate clinical decision-making including rational stratification of patient treatment. Equally, the inherent complexity of quantitative immunohistochemistry, in conjunction with protocol variation across laboratories, analysis of different immune cell types, inconsistent region selection criteria, and variable ways to quantify immune infiltration, all underline the urgent requirement to reach assay harmonization. In an effort to promote the Immunoscore in routine clinical settings, an international task force was initiated. This review represents a follow-up of the announcement of

A logic programming and statistical systems approach for tissue characterization in magnetic resonance imaging

International Nuclear Information System (INIS)

Levy, G.C.; Dudewicz, E.J.; Harner, T.J.

1989-01-01

The main research goal has been to evalute significant factors affecting the in vivo magnetic resonance imaging (MRI) parameters R 1 , T 2 , and 1 H density. This approach differs significantly from other such projects in that the experimental data analysis is being performed while concurrently developing automated, computer-aided analysis software for such MRI tissue parameters. In the experimental portion of the project, statistical analyses, and a heuristic minimum/maximum discriminant analysis algorithm have been explored. Both methods have been used to classify tissue types from 1.5 Tesla transaxial MR images of the human brain. The developing program, written in the logic programming language Prolog, similar in a number of ways to many existing expert systems now in use for other medical applications; inclusion of the underlying statistical data base and advanced statistical analyses is the main differentiating feature of the current approach. First results indicate promising classification accuracy of various brain tissues such as gray and white matter, as well as differentiation of different types of gray matter and white matter (e.g.: caudate-nucleus vs. thalamus, both representatives of gray matter; and, cortical white matter vs. internal capsule as representative of white matter). Taking all four tissue types together, the percentage of correct classifications ranges from 73 to 87%. (author)

Mechanical properties of human atherosclerotic intima tissue.

Science.gov (United States)

Akyildiz, Ali C; Speelman, Lambert; Gijsen, Frank J H

2014-03-03

Progression and rupture of atherosclerotic plaques in coronary and carotid arteries are the key processes underlying myocardial infarctions and strokes. Biomechanical stress analyses to compute mechanical stresses in a plaque can potentially be used to assess plaque vulnerability. The stress analyses strongly rely on accurate representation of the mechanical properties of the plaque components. In this review, the composition of intima tissue and how this changes during plaque development is discussed from a mechanical perspective. The plaque classification scheme of the American Heart Association is reviewed and plaques originating from different vascular territories are compared. Thereafter, an overview of the experimental studies on tensile and compressive plaque intima properties are presented and the results are linked to the pathology of atherosclerotic plaques. This overview revealed a considerable variation within studies, and an enormous dispersion between studies. Finally, the implications of the dispersion in experimental data on the clinical applications of biomechanical plaque modeling are presented. Suggestions are made on mechanical testing protocol for plaque tissue and on using a standardized plaque classification scheme. This review identifies the current status of knowledge on plaque mechanical properties and the future steps required for a better understanding of the plaque type specific material properties. With this understanding, biomechanical plaque modeling may eventually provide essential support for clinical plaque risk stratification. Copyright © 2014 Elsevier Ltd. All rights reserved.

Classification, disease, and diagnosis.

Science.gov (United States)

Jutel, Annemarie

2011-01-01

Classification shapes medicine and guides its practice. Understanding classification must be part of the quest to better understand the social context and implications of diagnosis. Classifications are part of the human work that provides a foundation for the recognition and study of illness: deciding how the vast expanse of nature can be partitioned into meaningful chunks, stabilizing and structuring what is otherwise disordered. This article explores the aims of classification, their embodiment in medical diagnosis, and the historical traditions of medical classification. It provides a brief overview of the aims and principles of classification and their relevance to contemporary medicine. It also demonstrates how classifications operate as social framing devices that enable and disable communication, assert and refute authority, and are important items for sociological study.

Automated tissue classification of pediatric brains from magnetic resonance images using age-specific atlases

Science.gov (United States)

Metzger, Andrew; Benavides, Amanda; Nopoulos, Peg; Magnotta, Vincent

2016-03-01

The goal of this project was to develop two age appropriate atlases (neonatal and one year old) that account for the rapid growth and maturational changes that occur during early development. Tissue maps from this age group were initially created by manually correcting the resulting tissue maps after applying an expectation maximization (EM) algorithm and an adult atlas to pediatric subjects. The EM algorithm classified each voxel into one of ten possible tissue types including several subcortical structures. This was followed by a novel level set segmentation designed to improve differentiation between distal cortical gray matter and white matter. To minimize the req uired manual corrections, the adult atlas was registered to the pediatric scans using high -dimensional, symmetric image normalization (SyN) registration. The subject images were then mapped to an age specific atlas space, again using SyN registration, and the resulting transformation applied to the manually corrected tissue maps. The individual maps were averaged in the age specific atlas space and blurred to generate the age appropriate anatomical priors. The resulting anatomical priors were then used by the EM algorithm to re-segment the initial training set as well as an independent testing set. The results from the adult and age-specific anatomical priors were compared to the manually corrected results. The age appropriate atlas provided superior results as compared to the adult atlas. The image analysis pipeline used in this work was built using the open source software package BRAINSTools.

Standard classification: Physics

International Nuclear Information System (INIS)

1977-01-01

This is a draft standard classification of physics. The conception is based on the physics part of the systematic catalogue of the Bayerische Staatsbibliothek and on the classification given in standard textbooks. The ICSU-AB classification now used worldwide by physics information services was not taken into account. (BJ) [de

Interval-value Based Particle Swarm Optimization algorithm for cancer-type specific gene selection and sample classification

Directory of Open Access Journals (Sweden)

D. Ramyachitra

2015-09-01

Full Text Available Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM, K-nearest neighbor (KNN, Interval Valued Classification (IVC and the improvised Interval Value based Particle Swarm Optimization (IVPSO algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions.

Interval-value Based Particle Swarm Optimization algorithm for cancer-type specific gene selection and sample classification.

Science.gov (United States)

Ramyachitra, D; Sofia, M; Manikandan, P

2015-09-01

Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM), K-nearest neighbor (KNN), Interval Valued Classification (IVC) and the improvised Interval Value based Particle Swarm Optimization (IVPSO) algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions.

The paradox of atheoretical classification

DEFF Research Database (Denmark)

Hjørland, Birger

2016-01-01

A distinction can be made between “artificial classifications” and “natural classifications,” where artificial classifications may adequately serve some limited purposes, but natural classifications are overall most fruitful by allowing inference and thus many different purposes. There is strong...... support for the view that a natural classification should be based on a theory (and, of course, that the most fruitful theory provides the most fruitful classification). Nevertheless, atheoretical (or “descriptive”) classifications are often produced. Paradoxically, atheoretical classifications may...... be very successful. The best example of a successful “atheoretical” classification is probably the prestigious Diagnostic and Statistical Manual of Mental Disorders (DSM) since its third edition from 1980. Based on such successes one may ask: Should the claim that classifications ideally are natural...

Transporter Classification Database (TCDB)

Data.gov (United States)

U.S. Department of Health & Human Services — The Transporter Classification Database details a comprehensive classification system for membrane transport proteins known as the Transporter Classification (TC)...

Diagnosis of breast cancer using diffuse optical spectroscopy from 500 to 1600 nm: comparison of classification methods

Science.gov (United States)

Nachabé, Rami; Evers, Daniel J.; Hendriks, Benno H. W.; Lucassen, Gerald W.; van der Voort, Marjolein; Rutgers, Emiel J.; Peeters, Marie-Jeanne Vrancken; van der Hage, Jos A.; Oldenburg, Hester S.; Wesseling, Jelle; Ruers, Theo J. M.

2011-08-01

We report on the use of diffuse optical spectroscopy analysis of breast spectra acquired in the wavelength range from 500 to 1600 nm with a fiber optic probe. A total of 102 ex vivo samples of five different breast tissue types, namely adipose, glandular, fibroadenoma, invasive carcinoma, and ductal carcinoma in situ from 52 patients were measured. A model deriving from the diffusion theory was applied to the measured spectra in order to extract clinically relevant parameters such as blood, water, lipid, and collagen volume fractions, β-carotene concentration, average vessels radius, reduced scattering amplitude, Mie slope, and Mie-to-total scattering fraction. Based on a classification and regression tree algorithm applied to the derived parameters, a sensitivity-specificity of 98%-99%, 84%-95%, 81%-98%, 91%-95%, and 83%-99% were obtained for discrimination of adipose, glandular, fibroadenoma, invasive carcinoma, and ductal carcinoma in situ, respectively; and a multiple classes overall diagnostic performance of 94%. Sensitivity-specificity values obtained for discriminating malignant from nonmalignant tissue were compared to existing reported studies by applying the different classification methods that were used in each of these studies. Furthermore, in these reported studies, either lipid or β-carotene was considered as adipose tissue precursors. We estimate both chromophore concentrations and demonstrate that lipid is a better discriminator for adipose tissue than β-carotene.

Monitoring nanotechnology using patent classifications: an overview and comparison of nanotechnology classification schemes

Energy Technology Data Exchange (ETDEWEB)

Jürgens, Björn, E-mail: bjurgens@agenciaidea.es [Agency of Innovation and Development of Andalusia, CITPIA PATLIB Centre (Spain); Herrero-Solana, Victor, E-mail: victorhs@ugr.es [University of Granada, SCImago-UGR (SEJ036) (Spain)

2017-04-15

Patents are an essential information source used to monitor, track, and analyze nanotechnology. When it comes to search nanotechnology-related patents, a keyword search is often incomplete and struggles to cover such an interdisciplinary discipline. Patent classification schemes can reveal far better results since they are assigned by experts who classify the patent documents according to their technology. In this paper, we present the most important classifications to search nanotechnology patents and analyze how nanotechnology is covered in the main patent classification systems used in search systems nowadays: the International Patent Classification (IPC), the United States Patent Classification (USPC), and the Cooperative Patent Classification (CPC). We conclude that nanotechnology has a significantly better patent coverage in the CPC since considerable more nanotechnology documents were retrieved than by using other classifications, and thus, recommend its use for all professionals involved in nanotechnology patent searches.

Monitoring nanotechnology using patent classifications: an overview and comparison of nanotechnology classification schemes

International Nuclear Information System (INIS)

Jürgens, Björn; Herrero-Solana, Victor

2017-01-01

Patents are an essential information source used to monitor, track, and analyze nanotechnology. When it comes to search nanotechnology-related patents, a keyword search is often incomplete and struggles to cover such an interdisciplinary discipline. Patent classification schemes can reveal far better results since they are assigned by experts who classify the patent documents according to their technology. In this paper, we present the most important classifications to search nanotechnology patents and analyze how nanotechnology is covered in the main patent classification systems used in search systems nowadays: the International Patent Classification (IPC), the United States Patent Classification (USPC), and the Cooperative Patent Classification (CPC). We conclude that nanotechnology has a significantly better patent coverage in the CPC since considerable more nanotechnology documents were retrieved than by using other classifications, and thus, recommend its use for all professionals involved in nanotechnology patent searches.

A discriminative model-constrained EM approach to 3D MRI brain tissue classification and intensity non-uniformity correction

International Nuclear Information System (INIS)

Wels, Michael; Hornegger, Joachim; Zheng Yefeng; Comaniciu, Dorin; Huber, Martin

2011-01-01

We describe a fully automated method for tissue classification, which is the segmentation into cerebral gray matter (GM), cerebral white matter (WM), and cerebral spinal fluid (CSF), and intensity non-uniformity (INU) correction in brain magnetic resonance imaging (MRI) volumes. It combines supervised MRI modality-specific discriminative modeling and unsupervised statistical expectation maximization (EM) segmentation into an integrated Bayesian framework. While both the parametric observation models and the non-parametrically modeled INUs are estimated via EM during segmentation itself, a Markov random field (MRF) prior model regularizes segmentation and parameter estimation. Firstly, the regularization takes into account knowledge about spatial and appearance-related homogeneity of segments in terms of pairwise clique potentials of adjacent voxels. Secondly and more importantly, patient-specific knowledge about the global spatial distribution of brain tissue is incorporated into the segmentation process via unary clique potentials. They are based on a strong discriminative model provided by a probabilistic boosting tree (PBT) for classifying image voxels. It relies on the surrounding context and alignment-based features derived from a probabilistic anatomical atlas. The context considered is encoded by 3D Haar-like features of reduced INU sensitivity. Alignment is carried out fully automatically by means of an affine registration algorithm minimizing cross-correlation. Both types of features do not immediately use the observed intensities provided by the MRI modality but instead rely on specifically transformed features, which are less sensitive to MRI artifacts. Detailed quantitative evaluations on standard phantom scans and standard real-world data show the accuracy and robustness of the proposed method. They also demonstrate relative superiority in comparison to other state-of-the-art approaches to this kind of computational task: our method achieves average

Classification of instability after reverse shoulder arthroplasty guides surgical management and outcomes.

Science.gov (United States)

Abdelfattah, Adham; Otto, Randall J; Simon, Peter; Christmas, Kaitlyn N; Tanner, Gregory; LaMartina, Joey; Levy, Jonathan C; Cuff, Derek J; Mighell, Mark A; Frankle, Mark A

2018-04-01

Revision of unstable reverse shoulder arthroplasty (RSA) remains a significant challenge. The purpose of this study was to determine the reliability of a new treatment-guiding classification for instability after RSA, to describe the clinical outcomes of patients stabilized operatively, and to identify those with higher risk of recurrence. All patients undergoing revision for instability after RSA were identified at our institution. Demographic, clinical, radiographic, and intraoperative data were collected. A classification was developed using all identified causes of instability after RSA and allocating them to 1 of 3 defined treatment-guiding categories. Eight surgeons reviewed all data and applied the classification scheme to each case. Interobserver and intraobserver reliability was used to evaluate the classification scheme. Preoperative clinical outcomes were compared with final follow-up in stabilized shoulders. Forty-three revision cases in 34 patients met the inclusion for study. Five patients remained unstable after revision. Persistent instability most commonly occurred in persistent deltoid dysfunction and postoperative acromial fractures but also in 1 case of soft tissue impingement. Twenty-one patients remained stable at minimum 2 years of follow-up and had significant improvement of clinical outcome scores and range of motion. Reliability of the classification scheme showed substantial and almost perfect interobserver and intraobserver agreement among all the participants (κ = 0.699 and κ = 0.851, respectively). Instability after RSA can be successfully treated with revision surgery using the reliable treatment-guiding classification scheme presented herein. However, more understanding is needed for patients with greater risk of recurrent instability after revision surgery. Copyright © 2017 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

Toward a Reasoned Classification of Diseases Using Physico-Chemical Based Phenotypes

Directory of Open Access Journals (Sweden)

Laurent Schwartz

2018-02-01

Full Text Available Background: Diseases and health conditions have been classified according to anatomical site, etiological, and clinical criteria. Physico-chemical mechanisms underlying the biology of diseases, such as the flow of energy through cells and tissues, have been often overlooked in classification systems.Objective: We propose a conceptual framework toward the development of an energy-oriented classification of diseases, based on the principles of physical chemistry.Methods: A review of literature on the physical chemistry of biological interactions in a number of diseases is traced from the point of view of the fluid and solid mechanics, electricity, and chemistry.Results: We found consistent evidence in literature of decreased and/or increased physical and chemical forces intertwined with biological processes of numerous diseases, which allowed the identification of mechanical, electric and chemical phenotypes of diseases.Discussion: Biological mechanisms of diseases need to be evaluated and integrated into more comprehensive theories that should account with principles of physics and chemistry. A hypothetical model is proposed relating the natural history of diseases to mechanical stress, electric field, and chemical equilibria (ATP changes. The present perspective toward an innovative disease classification may improve drug-repurposing strategies in the future.

Toward a Reasoned Classification of Diseases Using Physico-Chemical Based Phenotypes

Science.gov (United States)

Schwartz, Laurent; Lafitte, Olivier; da Veiga Moreira, Jorgelindo

2018-01-01

Background: Diseases and health conditions have been classified according to anatomical site, etiological, and clinical criteria. Physico-chemical mechanisms underlying the biology of diseases, such as the flow of energy through cells and tissues, have been often overlooked in classification systems. Objective: We propose a conceptual framework toward the development of an energy-oriented classification of diseases, based on the principles of physical chemistry. Methods: A review of literature on the physical chemistry of biological interactions in a number of diseases is traced from the point of view of the fluid and solid mechanics, electricity, and chemistry. Results: We found consistent evidence in literature of decreased and/or increased physical and chemical forces intertwined with biological processes of numerous diseases, which allowed the identification of mechanical, electric and chemical phenotypes of diseases. Discussion: Biological mechanisms of diseases need to be evaluated and integrated into more comprehensive theories that should account with principles of physics and chemistry. A hypothetical model is proposed relating the natural history of diseases to mechanical stress, electric field, and chemical equilibria (ATP) changes. The present perspective toward an innovative disease classification may improve drug-repurposing strategies in the future. PMID:29541031

Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

Science.gov (United States)

Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

1990-01-01

Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

Small-scale classification schemes

DEFF Research Database (Denmark)

Hertzum, Morten

2004-01-01

Small-scale classification schemes are used extensively in the coordination of cooperative work. This study investigates the creation and use of a classification scheme for handling the system requirements during the redevelopment of a nation-wide information system. This requirements...... classification inherited a lot of its structure from the existing system and rendered requirements that transcended the framework laid out by the existing system almost invisible. As a result, the requirements classification became a defining element of the requirements-engineering process, though its main...... effects remained largely implicit. The requirements classification contributed to constraining the requirements-engineering process by supporting the software engineers in maintaining some level of control over the process. This way, the requirements classification provided the software engineers...

Estimating local scaling properties for the classification of interstitial lung disease patterns

Science.gov (United States)

Huber, Markus B.; Nagarajan, Mahesh B.; Leinsinger, Gerda; Ray, Lawrence A.; Wismueller, Axel

2011-03-01

Local scaling properties of texture regions were compared in their ability to classify morphological patterns known as 'honeycombing' that are considered indicative for the presence of fibrotic interstitial lung diseases in high-resolution computed tomography (HRCT) images. For 14 patients with known occurrence of honeycombing, a stack of 70 axial, lung kernel reconstructed images were acquired from HRCT chest exams. 241 regions of interest of both healthy and pathological (89) lung tissue were identified by an experienced radiologist. Texture features were extracted using six properties calculated from gray-level co-occurrence matrices (GLCM), Minkowski Dimensions (MDs), and the estimation of local scaling properties with Scaling Index Method (SIM). A k-nearest-neighbor (k-NN) classifier and a Multilayer Radial Basis Functions Network (RBFN) were optimized in a 10-fold cross-validation for each texture vector, and the classification accuracy was calculated on independent test sets as a quantitative measure of automated tissue characterization. A Wilcoxon signed-rank test was used to compare two accuracy distributions including the Bonferroni correction. The best classification results were obtained by the set of SIM features, which performed significantly better than all the standard GLCM and MD features (p < 0.005) for both classifiers with the highest accuracy (94.1%, 93.7%; for the k-NN and RBFN classifier, respectively). The best standard texture features were the GLCM features 'homogeneity' (91.8%, 87.2%) and 'absolute value' (90.2%, 88.5%). The results indicate that advanced texture features using local scaling properties can provide superior classification performance in computer-assisted diagnosis of interstitial lung diseases when compared to standard texture analysis methods.

Application of texture analysis method for mammogram density classification

Science.gov (United States)

Nithya, R.; Santhi, B.

2017-07-01

Mammographic density is considered a major risk factor for developing breast cancer. This paper proposes an automated approach to classify breast tissue types in digital mammogram. The main objective of the proposed Computer-Aided Diagnosis (CAD) system is to investigate various feature extraction methods and classifiers to improve the diagnostic accuracy in mammogram density classification. Texture analysis methods are used to extract the features from the mammogram. Texture features are extracted by using histogram, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Difference Matrix (GLDM), Local Binary Pattern (LBP), Entropy, Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), Gabor transform and trace transform. These extracted features are selected using Analysis of Variance (ANOVA). The features selected by ANOVA are fed into the classifiers to characterize the mammogram into two-class (fatty/dense) and three-class (fatty/glandular/dense) breast density classification. This work has been carried out by using the mini-Mammographic Image Analysis Society (MIAS) database. Five classifiers are employed namely, Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Experimental results show that ANN provides better performance than LDA, NB, KNN and SVM classifiers. The proposed methodology has achieved 97.5% accuracy for three-class and 99.37% for two-class density classification.

Localized Tissue Surrogate Deformation due to Controlled Single Bubble Cavitation

Science.gov (United States)

2014-08-27

studies using ultrasound shock waves also support cavitation induced damage, e.g. hemorrhage and cellular membrane poration 26-28. In addition...SECURITY CLASSIFICATION OF: Cavitation -induced shock wave, as might occur in the head during exposure to blast waves, was investigated as a possible...damage mechanism for soft brain tissues. A novel experimental scheme was developed to visualize and control single bubble cavitation and its

Information gathering for CLP classification

Directory of Open Access Journals (Sweden)

Ida Marcello

2011-01-01

Full Text Available Regulation 1272/2008 includes provisions for two types of classification: harmonised classification and self-classification. The harmonised classification of substances is decided at Community level and a list of harmonised classifications is included in the Annex VI of the classification, labelling and packaging Regulation (CLP. If a chemical substance is not included in the harmonised classification list it must be self-classified, based on available information, according to the requirements of Annex I of the CLP Regulation. CLP appoints that the harmonised classification will be performed for carcinogenic, mutagenic or toxic to reproduction substances (CMR substances and for respiratory sensitisers category 1 and for other hazard classes on a case-by-case basis. The first step of classification is the gathering of available and relevant information. This paper presents the procedure for gathering information and to obtain data. The data quality is also discussed.

Automatic classification of DMSA scans using an artificial neural network

Science.gov (United States)

Wright, J. W.; Duguid, R.; Mckiddie, F.; Staff, R. T.

2014-04-01

DMSA imaging is carried out in nuclear medicine to assess the level of functional renal tissue in patients. This study investigated the use of an artificial neural network to perform diagnostic classification of these scans. Using the radiological report as the gold standard, the network was trained to classify DMSA scans as positive or negative for defects using a representative sample of 257 previously reported images. The trained network was then independently tested using a further 193 scans and achieved a binary classification accuracy of 95.9%. The performance of the network was compared with three qualified expert observers who were asked to grade each scan in the 193 image testing set on a six point defect scale, from ‘definitely normal’ to ‘definitely abnormal’. A receiver operating characteristic analysis comparison between a consensus operator, generated from the scores of the three expert observers, and the network revealed a statistically significant increase (α quality assurance assistant in clinical practice.

ASIST SIG/CR Classification Workshop 2000: Classification for User Support and Learning.

Science.gov (United States)

Soergel, Dagobert

2001-01-01

Reports on papers presented at the 62nd Annual Meeting of ASIST (American Society for Information Science and Technology) for the Special Interest Group in Classification Research (SIG/CR). Topics include types of knowledge; developing user-oriented classifications, including domain analysis; classification in the user interface; and automatic…

Couinaud's classification v.s. Cho's classification. Their feasibility in the right hepatic lobe

International Nuclear Information System (INIS)

Shioyama, Yasukazu; Ikeda, Hiroaki; Sato, Motohito; Yoshimi, Fuyo; Kishi, Kazushi; Sato, Morio; Kimura, Masashi

2008-01-01

The objective of this study was to investigate if the new classification system proposed by Cho is feasible to clinical usage comparing with the classical Couinaud's one. One hundred consecutive cases of abdominal CT were studied using a 64 or an 8 slice multislice CT and created three dimensional portal vein images for analysis by the Workstation. We applied both Cho's classification and the classical Couinaud's one for each cases according to their definitions. Three diagnostic radiologists assessed their feasibility as category one (unable to classify) to five (clear to classify with total suit with the original classification criteria). And in each cases, we tried to judge whether Cho's or the classical Couinaud' classification could more easily transmit anatomical information. Analyzers could classified portal veins clearly (category 5) in 77 to 80% of cases and clearly (category 5) or almost clearly (category 4) in 86-93% along with both classifications. In the feasibility of classification, there was no statistically significant difference between two classifications. In 15 cases we felt that using Couinaud's classification is more convenient for us to transmit anatomical information to physicians than using Cho's one, because in these cases we noticed two large portal veins ramify from right main portal vein cranialy and caudaly and then we could not classify P5 as a branch of antero-ventral segment (AVS). Conversely in 17 cases we felt Cho's classification is more convenient because we could not divide right posterior branch as P6 and P7 and in these cases the right posterior portal vein ramified to several small branches. The anterior fissure vein was clearly noticed in only 60 cases. Comparing the classical Couinaud's classification and Cho's one in feasility of classification, there was no statistically significant difference. We propose we routinely report hepatic anatomy with the classical Couinauds classification and in the preoperative cases we

Influence of keratinized tissue on spontaneous exposure of submerged implants: classification and clinical observations

Directory of Open Access Journals (Sweden)

G. Mendoza

2014-10-01

Full Text Available Aim: The reasons for spontaneous early exposure (SEE of dental implants during healing have not been established yet. The objective of this study was to assess whether the width of keratinized tissue (KT and other site-related conditions could be associated with implants’ SEE. Materials and methods: Data from 500 implants placed in 138 non-smoking patients, between September 2009 and June 2010, were evaluated. Implants were submerged and allowed to heal for 3 to 6 months. At baseline, the following conditions were documented: the presence of keratinized tissue width > 2 mm; the type of implant site (i.e. fresh extraction socket or edentulous alveolar ridge; concomitant use of guided tissue regeneration. During the healing period, the occurrence of partial or total implants SEE was recorded; thus, a mixed-effects logistic regression analysis was performed to investigate the association between implant site conditions and implant exposure. Results: One hundred and eighty-five implants (37.0% remained submerged after healing and were classified as Class I, whereas 215 (43.0% showed partial spontaneous early exposure (SEE at the first week after implant placement (Class II, and 100 implants (20.0% developed more extensive exposures (Class III. The variables, baseline width of KT (p = 0.18, fresh extraction socket (p = 0.88 and guided tissue regeneration (GTR plus bone substitutes (p = 0.42, were not found to be correlated with implants` SEE, with an odds ratio (OR of 1.29 (95% confidence interval: -0.12–0.63, 1.03 (95% confidence interval: -0.46–0.53 and 1.22 (95% confidence interval: -0.29–0.68, respectively. Conclusion: It was not possible to establish an association between SEE and some implant-related factors; therefore, further investigations focused on the reasons associated to implants’ SEE are needed.

Cell of origin associated classification of B-cell malignancies by gene signatures of the normal B-cell hierarchy.

Science.gov (United States)

Johnsen, Hans Erik; Bergkvist, Kim Steve; Schmitz, Alexander; Kjeldsen, Malene Krag; Hansen, Steen Møller; Gaihede, Michael; Nørgaard, Martin Agge; Bæch, John; Grønholdt, Marie-Louise; Jensen, Frank Svendsen; Johansen, Preben; Bødker, Julie Støve; Bøgsted, Martin; Dybkær, Karen

2014-06-01

Recent findings have suggested biological classification of B-cell malignancies as exemplified by the "activated B-cell-like" (ABC), the "germinal-center B-cell-like" (GCB) and primary mediastinal B-cell lymphoma (PMBL) subtypes of diffuse large B-cell lymphoma and "recurrent translocation and cyclin D" (TC) classification of multiple myeloma. Biological classification of B-cell derived cancers may be refined by a direct and systematic strategy where identification and characterization of normal B-cell differentiation subsets are used to define the cancer cell of origin phenotype. Here we propose a strategy combining multiparametric flow cytometry, global gene expression profiling and biostatistical modeling to generate B-cell subset specific gene signatures from sorted normal human immature, naive, germinal centrocytes and centroblasts, post-germinal memory B-cells, plasmablasts and plasma cells from available lymphoid tissues including lymph nodes, tonsils, thymus, peripheral blood and bone marrow. This strategy will provide an accurate image of the stage of differentiation, which prospectively can be used to classify any B-cell malignancy and eventually purify tumor cells. This report briefly describes the current models of the normal B-cell subset differentiation in multiple tissues and the pathogenesis of malignancies originating from the normal germinal B-cell hierarchy.

Classification with support hyperplanes

NARCIS (Netherlands)

G.I. Nalbantov (Georgi); J.C. Bioch (Cor); P.J.F. Groenen (Patrick)

2006-01-01

textabstractA new classification method is proposed, called Support Hy- perplanes (SHs). To solve the binary classification task, SHs consider the set of all hyperplanes that do not make classification mistakes, referred to as semi-consistent hyperplanes. A test object is classified using

Classification of Flotation Frothers

Directory of Open Access Journals (Sweden)

Jan Drzymala

2018-02-01

Full Text Available In this paper, a scheme of flotation frothers classification is presented. The scheme first indicates the physical system in which a frother is present and four of them i.e., pure state, aqueous solution, aqueous solution/gas system and aqueous solution/gas/solid system are distinguished. As a result, there are numerous classifications of flotation frothers. The classifications can be organized into a scheme described in detail in this paper. The frother can be present in one of four physical systems, that is pure state, aqueous solution, aqueous solution/gas and aqueous solution/gas/solid system. It results from the paper that a meaningful classification of frothers relies on choosing the physical system and next feature, trend, parameter or parameters according to which the classification is performed. The proposed classification can play a useful role in characterizing and evaluation of flotation frothers.

DOE LLW classification rationale

International Nuclear Information System (INIS)

Flores, A.Y.

1991-01-01

This report was about the rationale which the US Department of Energy had with low-level radioactive waste (LLW) classification. It is based on the Nuclear Regulatory Commission's classification system. DOE site operators met to review the qualifications and characteristics of the classification systems. They evaluated performance objectives, developed waste classification tables, and compiled dose limits on the waste. A goal of the LLW classification system was to allow each disposal site the freedom to develop limits to radionuclide inventories and concentrations according to its own site-specific characteristics. This goal was achieved with the adoption of a performance objectives system based on a performance assessment, with site-specific environmental conditions and engineered disposal systems

MO-DE-207B-03: Improved Cancer Classification Using Patient-Specific Biological Pathway Information Via Gene Expression Data

Energy Technology Data Exchange (ETDEWEB)

Young, M; Craft, D [Massachusetts General Hospital and Harvard Medical School, Boston, MA (United States)

2016-06-15

Purpose: To develop an efficient, pathway-based classification system using network biology statistics to assist in patient-specific response predictions to radiation and drug therapies across multiple cancer types. Methods: We developed PICS (Pathway Informed Classification System), a novel two-step cancer classification algorithm. In PICS, a matrix m of mRNA expression values for a patient cohort is collapsed into a matrix p of biological pathways. The entries of p, which we term pathway scores, are obtained from either principal component analysis (PCA), normal tissue centroid (NTC), or gene expression deviation (GED). The pathway score matrix is clustered using both k-means and hierarchical clustering, and a clustering is judged by how well it groups patients into distinct survival classes. The most effective pathway scoring/clustering combination, per clustering p-value, thus generates various ‘signatures’ for conventional and functional cancer classification. Results: PICS successfully regularized large dimension gene data, separated normal and cancerous tissues, and clustered a large patient cohort spanning six cancer types. Furthermore, PICS clustered patient cohorts into distinct, statistically-significant survival groups. For a suboptimally-debulked ovarian cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00127) showed significant improvement over that of a prior gene expression-classified study (p = .0179). For a pancreatic cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00141) showed significant improvement over that of a prior gene expression-classified study (p = .04). Pathway-based classification confirmed biomarkers for the pyrimidine, WNT-signaling, glycerophosphoglycerol, beta-alanine, and panthothenic acid pathways for ovarian cancer. Despite its robust nature, PICS requires significantly less run time than current pathway scoring methods. Conclusion: This work validates the PICS method to improve

Asteroid taxonomic classifications

International Nuclear Information System (INIS)

Tholen, D.J.

1989-01-01

This paper reports on three taxonomic classification schemes developed and applied to the body of available color and albedo data. Asteroid taxonomic classifications according to two of these schemes are reproduced

Update on the classification and treatment of localized scleroderma.

Science.gov (United States)

Bielsa Marsol, I

2013-10-01

Morphea or localized scleroderma is a distinctive inflammatory disease that leads to sclerosis of the skin and subcutaneous tissues. It comprises a number of subtypes differentiated according to their clinical presentation and the structure of the skin and underlying tissues involved in the fibrotic process. However, classification is difficult because the boundaries between the different types of morphea are blurred and different entities frequently overlap. The main subtypes are plaque morphea, linear scleroderma, generalized morphea, and pansclerotic morphea. With certain exceptions, the disorder does not have serious systemic repercussions, but it can cause considerable morbidity. In the case of lesions affecting the head, neurological and ocular complications may occur. There is no really effective and universal treatment so it is important to make a correct assessment of the extent and severity of the disease before deciding on a treatment approach. Copyright © 2011 Elsevier España, S.L. and AEDV. All rights reserved.

Circadian clocks are resounding in peripheral tissues.

Directory of Open Access Journals (Sweden)

Andrey A Ptitsyn

2006-03-01

Full Text Available Circadian rhythms are prevalent in most organisms. Even the smallest disturbances in the orchestration of circadian gene expression patterns among different tissues can result in functional asynchrony, at the organism level, and may to contribute to a wide range of physiologic disorders. It has been reported that as many as 5%-10% of transcribed genes in peripheral tissues follow a circadian expression pattern. We have conducted a comprehensive study of circadian gene expression on a large dataset representing three different peripheral tissues. The data have been produced in a large-scale microarray experiment covering replicate daily cycles in murine white and brown adipose tissues as well as in liver. We have applied three alternative algorithmic approaches to identify circadian oscillation in time series expression profiles. Analyses of our own data indicate that the expression of at least 7% to 21% of active genes in mouse liver, and in white and brown adipose tissues follow a daily oscillatory pattern. Indeed, analysis of data from other laboratories suggests that the percentage of genes with an oscillatory pattern may approach 50% in the liver. For the rest of the genes, oscillation appears to be obscured by stochastic noise. Our phase classification and computer simulation studies based on multiple datasets indicate no detectable boundary between oscillating and non-oscillating fractions of genes. We conclude that greater attention should be given to the potential influence of circadian mechanisms on any biological pathway related to metabolism and obesity.

BrainK for Structural Image Processing: Creating Electrical Models of the Human Head

Directory of Open Access Journals (Sweden)

Kai Li

2016-01-01

Full Text Available BrainK is a set of automated procedures for characterizing the tissues of the human head from MRI, CT, and photogrammetry images. The tissue segmentation and cortical surface extraction support the primary goal of modeling the propagation of electrical currents through head tissues with a finite difference model (FDM or finite element model (FEM created from the BrainK geometries. The electrical head model is necessary for accurate source localization of dense array electroencephalographic (dEEG measures from head surface electrodes. It is also necessary for accurate targeting of cerebral structures with transcranial current injection from those surface electrodes. BrainK must achieve five major tasks: image segmentation, registration of the MRI, CT, and sensor photogrammetry images, cortical surface reconstruction, dipole tessellation of the cortical surface, and Talairach transformation. We describe the approach to each task, and we compare the accuracies for the key tasks of tissue segmentation and cortical surface extraction in relation to existing research tools (FreeSurfer, FSL, SPM, and BrainVisa. BrainK achieves good accuracy with minimal or no user intervention, it deals well with poor quality MR images and tissue abnormalities, and it provides improved computational efficiency over existing research packages.

32 CFR 2001.15 - Classification guides.

Science.gov (United States)

2010-07-01

... 32 National Defense 6 2010-07-01 2010-07-01 false Classification guides. 2001.15 Section 2001.15..., NATIONAL ARCHIVES AND RECORDS ADMINISTRATION CLASSIFIED NATIONAL SECURITY INFORMATION Classification § 2001.15 Classification guides. (a) Preparation of classification guides. Originators of classification...

FACET CLASSIFICATIONS OF E-LEARNING TOOLS

Directory of Open Access Journals (Sweden)

Olena Yu. Balalaieva

2013-12-01

Full Text Available The article deals with the classification of e-learning tools based on the facet method, which suggests the separation of the parallel set of objects into independent classification groups; at the same time it is not assumed rigid classification structure and pre-built finite groups classification groups are formed by a combination of values taken from the relevant facets. An attempt to systematize the existing classification of e-learning tools from the standpoint of classification theory is made for the first time. Modern Ukrainian and foreign facet classifications of e-learning tools are described; their positive and negative features compared to classifications based on a hierarchical method are analyzed. The original author's facet classification of e-learning tools is proposed.

Maxillectomy defects: a suggested classification scheme.

Science.gov (United States)

Akinmoladun, V I; Dosumu, O O; Olusanya, A A; Ikusika, O F

2013-06-01

The term "maxillectomy" has been used to describe a variety of surgical procedures for a spectrum of diseases involving a diverse anatomical site. Hence, classifications of maxillectomy defects have often made communication difficult. This article highlights this problem, emphasises the need for a uniform system of classification and suggests a classification system which is simple and comprehensive. Articles related to this subject, especially those with specified classifications of maxillary surgical defects were sourced from the internet through Google, Scopus and PubMed using the search terms maxillectomy defects classification. A manual search through available literature was also done. The review of the materials revealed many classifications and modifications of classifications from the descriptive, reconstructive and prosthodontic perspectives. No globally acceptable classification exists among practitioners involved in the management of diseases in the mid-facial region. There were over 14 classifications of maxillary defects found in the English literature. Attempts made to address the inadequacies of previous classifications have tended to result in cumbersome and relatively complex classifications. A single classification that is based on both surgical and prosthetic considerations is most desirable and is hereby proposed.

Characterization of a sequential pipeline approach to automatic tissue segmentation from brain MR Images

International Nuclear Information System (INIS)

Hou, Zujun; Huang, Su

2008-01-01

Quantitative analysis of gray matter and white matter in brain magnetic resonance imaging (MRI) is valuable for neuroradiology and clinical practice. Submission of large collections of MRI scans to pipeline processing is increasingly important. We characterized this process and suggest several improvements. To investigate tissue segmentation from brain MR images through a sequential approach, a pipeline that consecutively executes denoising, skull/scalp removal, intensity inhomogeneity correction and intensity-based classification was developed. The denoising phase employs a 3D-extension of the Bayes-Shrink method. The inhomogeneity is corrected by an improvement of the Dawant et al.'s method with automatic generation of reference points. The N3 method has also been evaluated. Subsequently the brain tissue is segmented into cerebrospinal fluid, gray matter and white matter by a generalized Otsu thresholding technique. Intensive comparisons with other sequential or iterative methods have been carried out using simulated and real images. The sequential approach with judicious selection on the algorithm selection in each stage is not only advantageous in speed, but also can attain at least as accurate segmentation as iterative methods under a variety of noise or inhomogeneity levels. A sequential approach to tissue segmentation, which consecutively executes the wavelet shrinkage denoising, scalp/skull removal, inhomogeneity correction and intensity-based classification was developed to automatically segment the brain tissue into CSF, GM and WM from brain MR images. This approach is advantageous in several common applications, compared with other pipeline methods. (orig.)

Constructing criticality by classification

DEFF Research Database (Denmark)

Machacek, Erika

2017-01-01

" in the bureaucratic practice of classification: Experts construct material criticality in assessments as they allot information on the materials to the parameters of the assessment framework. In so doing, they ascribe a new set of connotations to the materials, namely supply risk, and their importance to clean energy......, legitimizing a criticality discourse.Specifically, the paper introduces a typology delineating the inferences made by the experts from their produced recommendations in the classification of rare earth element criticality. The paper argues that the classification is a specific process of constructing risk....... It proposes that the expert bureaucratic practice of classification legitimizes (i) the valorisation that was made in the drafting of the assessment framework for the classification, and (ii) political operationalization when enacted that might have (non-)distributive implications for the allocation of public...

3D atlas of brain connections and functional circuits

Science.gov (United States)

Pan, Jinghong; Nowinski, Wieslaw L.; Fock, Loe K.; Dow, Douglas E.; Chuan, Teh H.

1997-05-01

This work aims at the construction of an extendable brain atlas system which contains: (i) 3D models of cortical and subcortical structures along with their connections; (ii) visualization and exploration tools; and (iii) structures and connections editors. A 3D version of the Talairach- Tournoux brain atlas along with 3D Brodmann's areas are developed, co-registered, and placed in the Talairach stereotactic space. The initial built-in connections are thalamocortical ones. The structures and connections editors are provided to allow the user to add and modify cerebral structures and connections. Visualization and explorations tools are developed with four ways of exploring the brain connections model: composition, interrogation, navigation and diagnostic queries. The atlas is designed as an open system which can be extended independently in other centers according to their needs and discoveries.

12 CFR 403.4 - Derivative classification.

Science.gov (United States)

2010-01-01

... SAFEGUARDING OF NATIONAL SECURITY INFORMATION § 403.4 Derivative classification. (a) Use of derivative classification. (1) Unlike original classification which is an initial determination, derivative classification... 12 Banks and Banking 4 2010-01-01 2010-01-01 false Derivative classification. 403.4 Section 403.4...

Supernova Photometric Lightcurve Classification

Science.gov (United States)

Zaidi, Tayeb; Narayan, Gautham

2016-01-01

This is a preliminary report on photometric supernova classification. We first explore the properties of supernova light curves, and attempt to restructure the unevenly sampled and sparse data from assorted datasets to allow for processing and classification. The data was primarily drawn from the Dark Energy Survey (DES) simulated data, created for the Supernova Photometric Classification Challenge. This poster shows a method for producing a non-parametric representation of the light curve data, and applying a Random Forest classifier algorithm to distinguish between supernovae types. We examine the impact of Principal Component Analysis to reduce the dimensionality of the dataset, for future classification work. The classification code will be used in a stage of the ANTARES pipeline, created for use on the Large Synoptic Survey Telescope alert data and other wide-field surveys. The final figure-of-merit for the DES data in the r band was 60% for binary classification (Type I vs II).Zaidi was supported by the NOAO/KPNO Research Experiences for Undergraduates (REU) Program which is funded by the National Science Foundation Research Experiences for Undergraduates Program (AST-1262829).

Project implementation : classification of organic soils and classification of marls - training of INDOT personnel.

Science.gov (United States)

2012-09-01

This is an implementation project for the research completed as part of the following projects: SPR3005 Classification of Organic Soils : and SPR3227 Classification of Marl Soils. The methods developed for the classification of both soi...

45 CFR 601.5 - Derivative classification.

Science.gov (United States)

2010-10-01

... CLASSIFICATION AND DECLASSIFICATION OF NATIONAL SECURITY INFORMATION § 601.5 Derivative classification. Distinct... 45 Public Welfare 3 2010-10-01 2010-10-01 false Derivative classification. 601.5 Section 601.5... classification guide, need not possess original classification authority. (a) If a person who applies derivative...

Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes.

Science.gov (United States)

Cannistraci, Carlo Vittorio; Ravasi, Timothy; Montevecchi, Franco Maria; Ideker, Trey; Alessio, Massimo

2010-09-15

Nonlinear small datasets, which are characterized by low numbers of samples and very high numbers of measures, occur frequently in computational biology, and pose problems in their investigation. Unsupervised hybrid-two-phase (H2P) procedures-specifically dimension reduction (DR), coupled with clustering-provide valuable assistance, not only for unsupervised data classification, but also for visualization of the patterns hidden in high-dimensional feature space. 'Minimum Curvilinearity' (MC) is a principle that-for small datasets-suggests the approximation of curvilinear sample distances in the feature space by pair-wise distances over their minimum spanning tree (MST), and thus avoids the introduction of any tuning parameter. MC is used to design two novel forms of nonlinear machine learning (NML): Minimum Curvilinear embedding (MCE) for DR, and Minimum Curvilinear affinity propagation (MCAP) for clustering. Compared with several other unsupervised and supervised algorithms, MCE and MCAP, whether individually or combined in H2P, overcome the limits of classical approaches. High performance was attained in the visualization and classification of: (i) pain patients (proteomic measurements) in peripheral neuropathy; (ii) human organ tissues (genomic transcription factor measurements) on the basis of their embryological origin. MC provides a valuable framework to estimate nonlinear distances in small datasets. Its extension to large datasets is prefigured for novel NMLs. Classification of neuropathic pain by proteomic profiles offers new insights for future molecular and systems biology characterization of pain. Improvements in tissue embryological classification refine results obtained in an earlier study, and suggest a possible reinterpretation of skin attribution as mesodermal. https://sites.google.com/site/carlovittoriocannistraci/home.

Burn-injured tissue detection for debridement surgery through the combination of non-invasive optical imaging techniques.

Science.gov (United States)

Heredia-Juesas, Juan; Thatcher, Jeffrey E; Lu, Yang; Squiers, John J; King, Darlene; Fan, Wensheng; DiMaio, J Michael; Martinez-Lorenzo, Jose A

2018-04-01

The process of burn debridement is a challenging technique requiring significant skills to identify the regions that need excision and their appropriate excision depths. In order to assist surgeons, a machine learning tool is being developed to provide a quantitative assessment of burn-injured tissue. This paper presents three non-invasive optical imaging techniques capable of distinguishing four kinds of tissue-healthy skin, viable wound bed, shallow burn, and deep burn-during serial burn debridement in a porcine model. All combinations of these three techniques have been studied through a k-fold cross-validation method. In terms of global performance, the combination of all three techniques significantly improves the classification accuracy with respect to just one technique, from 0.42 up to more than 0.76. Furthermore, a non-linear spatial filtering based on the mode of a small neighborhood has been applied as a post-processing technique, in order to improve the performance of the classification. Using this technique, the global accuracy reaches a value close to 0.78 and, for some particular tissues and combination of techniques, the accuracy improves by 13%.

Classification of smooth Fano polytopes

DEFF Research Database (Denmark)

Øbro, Mikkel

A simplicial lattice polytope containing the origin in the interior is called a smooth Fano polytope, if the vertices of every facet is a basis of the lattice. The study of smooth Fano polytopes is motivated by their connection to toric varieties. The thesis concerns the classification of smooth...... Fano polytopes up to isomorphism. A smooth Fano -polytope can have at most vertices. In case of vertices an explicit classification is known. The thesis contains the classification in case of vertices. Classifications of smooth Fano -polytopes for fixed exist only for . In the thesis an algorithm...... for the classification of smooth Fano -polytopes for any given is presented. The algorithm has been implemented and used to obtain the complete classification for ....

Maximum mutual information regularized classification

KAUST Repository

Wang, Jim Jing-Yan

2014-09-07

In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reducing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradient descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization.

Maximum mutual information regularized classification

KAUST Repository

Wang, Jim Jing-Yan; Wang, Yi; Zhao, Shiguang; Gao, Xin

2014-01-01

In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reducing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradient descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization.

Segmentation and labeling of the ventricular system in normal pressure hydrocephalus using patch-based tissue classification and multi-atlas labeling

Science.gov (United States)

Ellingsen, Lotta M.; Roy, Snehashis; Carass, Aaron; Blitz, Ari M.; Pham, Dzung L.; Prince, Jerry L.

2016-03-01

Normal pressure hydrocephalus (NPH) affects older adults and is thought to be caused by obstruction of the normal flow of cerebrospinal fluid (CSF). NPH typically presents with cognitive impairment, gait dysfunction, and urinary incontinence, and may account for more than five percent of all cases of dementia. Unlike most other causes of dementia, NPH can potentially be treated and the neurological dysfunction reversed by shunt surgery or endoscopic third ventriculostomy (ETV), which drain excess CSF. However, a major diagnostic challenge remains to robustly identify shunt-responsive NPH patients from patients with enlarged ventricles due to other neurodegenerative diseases. Currently, radiologists grade the severity of NPH by detailed examination and measurement of the ventricles based on stacks of 2D magnetic resonance images (MRIs). Here we propose a new method to automatically segment and label different compartments of the ventricles in NPH patients from MRIs. While this task has been achieved in healthy subjects, the ventricles in NPH are both enlarged and deformed, causing current algorithms to fail. Here we combine a patch-based tissue classification method with a registration-based multi-atlas labeling method to generate a novel algorithm that labels the lateral, third, and fourth ventricles in subjects with ventriculomegaly. The method is also applicable to other neurodegenerative diseases such as Alzheimer's disease; a condition considered in the differential diagnosis of NPH. Comparison with state of the art segmentation techniques demonstrate substantial improvements in labeling the enlarged ventricles, indicating that this strategy may be a viable option for the diagnosis and characterization of NPH.

[Research progress on real-time deformable models of soft tissues for surgery simulation].

Science.gov (United States)

Xu, Shaoping; Liu, Xiaoping; Zhang, Hua; Luo, Jie

2010-04-01

Biological tissues generally exhibit nonlinearity, anisotropy, quasi-incompressibility and viscoelasticity about material properties. Simulating the behaviour of elastic objects in real time is one of the current objectives of virtual surgery simulation which is still a challenge for researchers to accurately depict the behaviour of human tissues. In this paper, we present a classification of the different deformable models that have been developed. We present the advantages and disadvantages of each one. Finally, we make a comparison of deformable models and perform an evaluation of the state of the art and the future of deformable models.

Study of Image Analysis Algorithms for Segmentation, Feature Extraction and Classification of Cells

Directory of Open Access Journals (Sweden)

Margarita Gamarra

2017-08-01

Full Text Available Recent advances in microcopy and improvements in image processing algorithms have allowed the development of computer-assisted analytical approaches in cell identification. Several applications could be mentioned in this field: Cellular phenotype identification, disease detection and treatment, identifying virus entry in cells and virus classification; these applications could help to complement the opinion of medical experts. Although many surveys have been presented in medical image analysis, they focus mainly in tissues and organs and none of the surveys about image cells consider an analysis following the stages in the typical image processing: Segmentation, feature extraction and classification. The goal of this study is to provide comprehensive and critical analyses about the trends in each stage of cell image processing. In this paper, we present a literature survey about cell identification using different image processing techniques.

A software tool for automatic classification and segmentation of 2D/3D medical images

International Nuclear Information System (INIS)

Strzelecki, Michal; Szczypinski, Piotr; Materka, Andrzej; Klepaczko, Artur

2013-01-01

Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human experts is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation compensation, motion detection, and correction of partial volume effect in PET images, acquired with PET/MR scanners. This article presents briefly a MaZda software package, which supports 2D and 3D medical image analysis aiming at quantification of image texture. MaZda implements procedures for evaluation, selection and extraction of highly discriminative texture attributes combined with various classification, visualization and segmentation tools. Examples of MaZda application in medical studies are also provided

A software tool for automatic classification and segmentation of 2D/3D medical images

Energy Technology Data Exchange (ETDEWEB)

Strzelecki, Michal, E-mail: michal.strzelecki@p.lodz.pl [Institute of Electronics, Technical University of Lodz, Wolczanska 211/215, 90-924 Lodz (Poland); Szczypinski, Piotr; Materka, Andrzej; Klepaczko, Artur [Institute of Electronics, Technical University of Lodz, Wolczanska 211/215, 90-924 Lodz (Poland)

2013-02-21

Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human experts is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation compensation, motion detection, and correction of partial volume effect in PET images, acquired with PET/MR scanners. This article presents briefly a MaZda software package, which supports 2D and 3D medical image analysis aiming at quantification of image texture. MaZda implements procedures for evaluation, selection and extraction of highly discriminative texture attributes combined with various classification, visualization and segmentation tools. Examples of MaZda application in medical studies are also provided.

Robust multi-tissue gene panel for cancer detection

Directory of Open Access Journals (Sweden)

Talantov Dmitri

2010-06-01

Full Text Available Abstract Background We have identified a set of genes whose relative mRNA expression levels in various solid tumors can be used to robustly distinguish cancer from matching normal tissue. Our current feature set consists of 113 gene probes for 104 unique genes, originally identified as differentially expressed in solid primary tumors in microarray data on Affymetrix HG-U133A platform in five tissue types: breast, colon, lung, prostate and ovary. For each dataset, we first identified a set of genes significantly differentially expressed in tumor vs. normal tissue at p-value = 0.05 using an experimentally derived error model. Our common cancer gene panel is the intersection of these sets of significantly dysregulated genes and can distinguish tumors from normal tissue on all these five tissue types. Methods Frozen tumor specimens were obtained from two commercial vendors Clinomics (Pittsfield, MA and Asterand (Detroit, MI. Biotinylated targets were prepared using published methods (Affymetrix, CA and hybridized to Affymetrix U133A GeneChips (Affymetrix, CA. Expression values for each gene were calculated using Affymetrix GeneChip analysis software MAS 5.0. We then used a software package called Genes@Work for differential expression discovery, and SVM light linear kernel for building classification models. Results We validate the predictability of this gene list on several publicly available data sets generated on the same platform. Of note, when analysing the lung cancer data set of Spira et al, using an SVM linear kernel classifier, our gene panel had 94.7% leave-one-out accuracy compared to 87.8% using the gene panel in the original paper. In addition, we performed high-throughput validation on the Dana Farber Cancer Institute GCOD database and several GEO datasets. Conclusions Our result showed the potential for this panel as a robust classification tool for multiple tumor types on the Affymetrix platform, as well as other whole genome arrays

MRI-based treatment plan simulation and adaptation for ion radiotherapy using a classification-based approach

International Nuclear Information System (INIS)

Rank, Christopher M; Tremmel, Christoph; Hünemohr, Nora; Nagel, Armin M; Jäkel, Oliver; Greilich, Steffen

2013-01-01

In order to benefit from the highly conformal irradiation of tumors in ion radiotherapy, sophisticated treatment planning and simulation are required. The purpose of this study was to investigate the potential of MRI for ion radiotherapy treatment plan simulation and adaptation using a classification-based approach. Firstly, a voxelwise tissue classification was applied to derive pseudo CT numbers from MR images using up to 8 contrasts. Appropriate MR sequences and parameters were evaluated in cross-validation studies of three phantoms. Secondly, ion radiotherapy treatment plans were optimized using both MRI-based pseudo CT and reference CT and recalculated on reference CT. Finally, a target shift was simulated and a treatment plan adapted to the shift was optimized on a pseudo CT and compared to reference CT optimizations without plan adaptation. The derivation of pseudo CT values led to mean absolute errors in the range of 81 - 95 HU. Most significant deviations appeared at borders between air and different tissue classes and originated from partial volume effects. Simulations of ion radiotherapy treatment plans using pseudo CT for optimization revealed only small underdosages in distal regions of a target volume with deviations of the mean dose of PTV between 1.4 - 3.1% compared to reference CT optimizations. A plan adapted to the target volume shift and optimized on the pseudo CT exhibited a comparable target dose coverage as a non-adapted plan optimized on a reference CT. We were able to show that a MRI-based derivation of pseudo CT values using a purely statistical classification approach is feasible although no physical relationship exists. Large errors appeared at compact bone classes and came from an imperfect distinction of bones and other tissue types in MRI. In simulations of treatment plans, it was demonstrated that these deviations are comparable to uncertainties of a target volume shift of 2 mm in two directions indicating that especially

Cluster Based Text Classification Model

DEFF Research Database (Denmark)

Nizamani, Sarwat; Memon, Nasrullah; Wiil, Uffe Kock

2011-01-01

We propose a cluster based classification model for suspicious email detection and other text classification tasks. The text classification tasks comprise many training examples that require a complex classification model. Using clusters for classification makes the model simpler and increases...... the accuracy at the same time. The test example is classified using simpler and smaller model. The training examples in a particular cluster share the common vocabulary. At the time of clustering, we do not take into account the labels of the training examples. After the clusters have been created......, the classifier is trained on each cluster having reduced dimensionality and less number of examples. The experimental results show that the proposed model outperforms the existing classification models for the task of suspicious email detection and topic categorization on the Reuters-21578 and 20 Newsgroups...

The 2016 WHO classification and diagnostic criteria for myeloproliferative neoplasms: document summary and in-depth discussion

OpenAIRE

Barbui, Tiziano; Thiele, Jürgen; Gisslinger, Heinz; Kvasnicka, Hans Michael; Vannucchi, Alessandro M.; Guglielmelli, Paola; Orazi, Attilio; Tefferi, Ayalew

2018-01-01

The new edition of the 2016 World Health Organization (WHO) classification system for tumors of the hematopoietic and lymphoid tissues was published in September 2017. Under the category of myeloproliferative neoplasms (MPNs), the revised document includes seven subcategories: chronic myeloid leukemia, chronic neutrophilic leukemia, polycythemia vera (PV), primary myelofibrosis (PMF), essential thrombocythemia (ET), chronic eosinophilic leukemia-not otherwise specified and MPN, unclassifiable...

Ontologies vs. Classification Systems

DEFF Research Database (Denmark)

Madsen, Bodil Nistrup; Erdman Thomsen, Hanne

2009-01-01

What is an ontology compared to a classification system? Is a taxonomy a kind of classification system or a kind of ontology? These are questions that we meet when working with people from industry and public authorities, who need methods and tools for concept clarification, for developing meta...... data sets or for obtaining advanced search facilities. In this paper we will present an attempt at answering these questions. We will give a presentation of various types of ontologies and briefly introduce terminological ontologies. Furthermore we will argue that classification systems, e.g. product...... classification systems and meta data taxonomies, should be based on ontologies....

Optical signature of nerve tissue-Exploratory ex vivo study comparing optical, histological, and molecular characteristics of different adipose and nerve tissues.

Science.gov (United States)

Balthasar, Andrea J R; Bydlon, Torre M; Ippel, Hans; van der Voort, Marjolein; Hendriks, Benno H W; Lucassen, Gerald W; van Geffen, Geert-Jan; van Kleef, Maarten; van Dijk, Paul; Lataster, Arno

2018-05-14

During several anesthesiological procedures, needles are inserted through the skin of a patient to target nerves. In most cases, the needle traverses several tissues-skin, subcutaneous adipose tissue, muscles, nerves, and blood vessels-to reach the target nerve. A clear identification of the target nerve can improve the success of the nerve block and reduce the rate of complications. This may be accomplished with diffuse reflectance spectroscopy (DRS) which can provide a quantitative measure of the tissue composition. The goal of the current study was to further explore the morphological, biological, chemical, and optical characteristics of the tissues encountered during needle insertion to improve future DRS classification algorithms. To compare characteristics of nerve tissue (sciatic nerve) and adipose tissues, the following techniques were used: histology, DRS, absorption spectrophotometry, high-resolution magic-angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy, and solution 2D 13 C- 1 H heteronuclear single-quantum coherence spectroscopy. Tissues from five human freshly frozen cadavers were examined. Histology clearly highlights a higher density of cellular nuclei, collagen, and cytoplasm in fascicular nerve tissue (IFAS). IFAS showed lower absorption of light around 1200 nm and 1750 nm, higher absorption around 1500 nm and 2000 nm, and a shift in the peak observed around 1000 nm. DRS measurements showed a higher water percentage and collagen concentration in IFAS and a lower fat percentage compared to all other tissues. The scattering parameter (b) was highest in IFAS. The HR-MAS NMR data showed three extra chemical peak shifts in IFAS tissue. Collagen, water, and cellular nuclei concentration are clearly different between nerve fascicular tissue and other adipose tissue and explain some of the differences observed in the optical absorption, DRS, and HR-NMR spectra of these tissues. Some differences observed between fascicular

Current classification, treatment options, and new perspectives in the management of adipocytic sarcomas

Directory of Open Access Journals (Sweden)

De Vita A

2016-10-01

Full Text Available Alessandro De Vita,1 Laura Mercatali,1 Federica Recine,1 Federica Pieri,2 Nada Riva,1 Alberto Bongiovanni,1 Chiara Liverani,1 Chiara Spadazzi,1 Giacomo Miserocchi,1 Dino Amadori,1 Toni Ibrahim1 1Osteoncology and Rare Tumors Center, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST IRCCS, Meldola, FC, 2Pathology Unit, Morgagni-Pierantoni Hospital, Forlì, Italy Abstract: Sarcomas are a heterogeneous group of mesenchymal tumors arising from soft tissue or bone, with an uncertain etiology and difficult classification. Soft tissue sarcomas (STSs account for around 1% of all adult cancers. Till date, more than 50 histologic subtypes have been identified. Adipocyte sarcoma or liposarcoma (LPS is one of the most common STS subtypes, accounting for 15% of all sarcomas, with an incidence of 24% of all extremity STSs and 45% of all retroperitoneal STSs. The new World Health Organization classification system has divided LPS into four different subgroups: atypical lipomatous tumor/well-differentiated LPS, dedifferentiated LPS, myxoid LPS, and pleomorphic LPS. These lesions can develop at any location and exhibit different aggressive potentials reflecting their morphologic diversity and clinical behavior. Patients affected by LPS should be managed in specialized multidisciplinary cancer centers. Whereas surgical resection is the mainstay of treatment for localized disease, the benefits of adjuvant and neoadjuvant chemotherapy are still unclear. Systemic treatment, particularly chemotherapy, is still limited in metastatic disease. Despite the efforts toward a better understanding of the biology of LPS, the outcome of advanced and metastatic patients remains poor. The advent of targeted therapies may lead to an improvement of treatment options and clinical outcomes. A larger patient enrollment into translational and clinical studies will help increase the knowledge of the biological behavior of LPSs, test new drugs, and introduce new

The book classification of William Torrey Harris: influences of Bacon and Hegel in library classification

Directory of Open Access Journals (Sweden)

Rodrigo de Sales

2017-09-01

Full Text Available The studies of library classification generally interact with the historical contextualization approach and with the classification ideas typical of Philosophy. In the 19th century, the North-American philosopher and educator William Torrey Harris developed a book classification at the St. Louis Public School, based on Francis Bacon and Georg Wilhelm Friedrich Hegel. The objective of this essay is to analyze Harris’s classification, reflecting upon his theoretical and philosophical backgrounds. To achieve such objective, this essay adopts a critical-descriptive approach for analysis. Results show some influences of Bacon and Hegel in Harris’s classification.

Linear-fitting-based similarity coefficient map for tissue dissimilarity analysis in -w magnetic resonance imaging

International Nuclear Information System (INIS)

Yu Shao-De; Wu Shi-Bin; Xie Yao-Qin; Wang Hao-Yu; Wei Xin-Hua; Chen Xin; Pan Wan-Long; Hu Jiani

2015-01-01

Similarity coefficient mapping (SCM) aims to improve the morphological evaluation of weighted magnetic resonance imaging However, how to interpret the generated SCM map is still pending. Moreover, is it probable to extract tissue dissimilarity messages based on the theory behind SCM? The primary purpose of this paper is to address these two questions. First, the theory of SCM was interpreted from the perspective of linear fitting. Then, a term was embedded for tissue dissimilarity information. Finally, our method was validated with sixteen human brain image series from multi-echo . Generated maps were investigated from signal-to-noise ratio (SNR) and perceived visual quality, and then interpreted from intra- and inter-tissue intensity. Experimental results show that both perceptibility of anatomical structures and tissue contrast are improved. More importantly, tissue similarity or dissimilarity can be quantified and cross-validated from pixel intensity analysis. This method benefits image enhancement, tissue classification, malformation detection and morphological evaluation. (paper)

Reliability of Oronasal Fistula Classification.

Science.gov (United States)

Sitzman, Thomas J; Allori, Alexander C; Matic, Damir B; Beals, Stephen P; Fisher, David M; Samson, Thomas D; Marcus, Jeffrey R; Tse, Raymond W

2018-01-01

Objective Oronasal fistula is an important complication of cleft palate repair that is frequently used to evaluate surgical quality, yet reliability of fistula classification has never been examined. The objective of this study was to determine the reliability of oronasal fistula classification both within individual surgeons and between multiple surgeons. Design Using intraoral photographs of children with repaired cleft palate, surgeons rated the location of palatal fistulae using the Pittsburgh Fistula Classification System. Intrarater and interrater reliability scores were calculated for each region of the palate. Participants Eight cleft surgeons rated photographs obtained from 29 children. Results Within individual surgeons reliability for each region of the Pittsburgh classification ranged from moderate to almost perfect (κ = .60-.96). By contrast, reliability between surgeons was lower, ranging from fair to substantial (κ = .23-.70). Between-surgeon reliability was lowest for the junction of the soft and hard palates (κ = .23). Within-surgeon and between-surgeon reliability were almost perfect for the more general classification of fistula in the secondary palate (κ = .95 and κ = .83, respectively). Conclusions This is the first reliability study of fistula classification. We show that the Pittsburgh Fistula Classification System is reliable when used by an individual surgeon, but less reliable when used among multiple surgeons. Comparisons of fistula occurrence among surgeons may be subject to less bias if they use the more general classification of "presence or absence of fistula of the secondary palate" rather than the Pittsburgh Fistula Classification System.

Cutaneous Leiomyoma: Novel Histologic Findings for Classification and Diagnosis

Directory of Open Access Journals (Sweden)

Kambiz Kamyab Hesari

2013-01-01

Full Text Available Smooth muscle tumors rather benign or malignant can arise wherever the muscular tissue presents but cutaneous leiomyoma is one of the rare benign tumors of the which even the diagnostic criteria from the malignant type of the tumor is still in doubt. This study was aimed to compare the subtypes of cutaneous leiomyoma from different histologic aspects in order to find unique criteria for better classification and diagnosis. The six year data base of our center was reviewed and 25 patients with cutaneous leiomyoma were included in this study. Of 25 patients, 5 were female and 20 were male. 5 patients had angioleiomyoma (ALM and 20 had pilar leiomyoma (PLM. ALM had following characteristics: dilated vascular canals intermingled with compact smooth muscle bundles; well circumscribe counter and myxoid and hyaline changes through the tumor. In contrast, PLMs had following histologic features: poor defined outline, entrapped hair follicles and eccrine glands, acanthosis and elongated rete ridges with hyperpigmentation and smooth muscle bundles which are interdigitated with elongated rete ridges. Here we introduced some distinct histological features for each subtype of the cutaneous leiomyoma which can lead to create novel criteria for classification and diagnosis of the lesion.

5 CFR 1312.7 - Derivative classification.

Science.gov (United States)

2010-01-01

..., DOWNGRADING, DECLASSIFICATION AND SAFEGUARDING OF NATIONAL SECURITY INFORMATION Classification and Declassification of National Security Information § 1312.7 Derivative classification. A derivative classification... 5 Administrative Personnel 3 2010-01-01 2010-01-01 false Derivative classification. 1312.7 Section...

Munitions Classification Library

Science.gov (United States)

2016-04-04

members of the community to make their own additions to any, or all, of the classification libraries . The next phase entailed data collection over less......Include area code) 04/04/2016 Final Report August 2014 - August 2015 MUNITIONS CLASSIFICATION LIBRARY Mr. Craig Murray, Parsons Dr. Thomas H. Bell, Leidos

IAEA Classification of Uranium Deposits

International Nuclear Information System (INIS)

Bruneton, Patrice

2014-01-01

Classifications of uranium deposits follow two general approaches, focusing on: • descriptive features such as the geotectonic position, the host rock type, the orebody morphology, …… : « geologic classification »; • or on genetic aspects: « genetic classification »

[Update on soft tissue sarcomas].

Science.gov (United States)

Bui, Binh Nguyen; Tabrizi, Reza; Dagada, Corinne; Trufflandier, Nathalie; St ckle, Eberhard; Coindre, Jean-Michel

2002-01-01

Important refinements have taken place in the diagnosis of soft tissue sarcoma with extensive use of immuno-histochemistry. New entities have been described, while malignant histiocytofibroma, the most diagnosed sarcoma type during the last two decades, has been dismembered. As for prognosis, the new UICC classification is effectively more discriminating in the definition of prognostic groups; but the usefullness of new biological or genetic markers remains to be assessed. Several breakthrough have taken place in the last years in the treatment of soft tissue sarcoma. Isolated limb perfusion with TNF, hyperthermia and melphalan have proven its efficacy, and is now an alternative to preoperative chemotherapy and/or radiotherapy for limb sparing treatment of the primary tumor site or to amputation. For systemic treatments, novel cytostatic drugs have been shown to be active in sarcomas, including ecteinascidine (ET743) and Glivec (STI571). This last drug has been shown to be remarkably active in c-kit+ stromal sarcoma of the gastro-intestinal tract. It can hopefully regarded as an example for targeted therapies, which may come with a better understanding of the molecular mechanisms triggered by the fundamental, specific genetic alterations shown in sarcoma.

32 CFR 2400.15 - Classification guides.

Science.gov (United States)

2010-07-01

... REGULATIONS TO IMPLEMENT E.O. 12356; OFFICE OF SCIENCE AND TECHNOLOGY POLICY INFORMATION SECURITY PROGRAM Derivative Classification § 2400.15 Classification guides. (a) OSTP shall issue and maintain classification guides to facilitate the proper and uniform derivative classification of information. These guides shall...

Classification of prostate cancer grade using temporal ultrasound: in vivo feasibility study

Science.gov (United States)

Ghavidel, Sahar; Imani, Farhad; Khallaghi, Siavash; Gibson, Eli; Khojaste, Amir; Gaed, Mena; Moussa, Madeleine; Gomez, Jose A.; Siemens, D. Robert; Leveridge, Michael; Chang, Silvia; Fenster, Aaron; Ward, Aaron D.; Abolmaesumi, Purang; Mousavi, Parvin

2016-03-01

Temporal ultrasound has been shown to have high classification accuracy in differentiating cancer from benign tissue. In this paper, we extend the temporal ultrasound method to classify lower grade Prostate Cancer (PCa) from all other grades. We use a group of nine patients with mostly lower grade PCa, where cancerous regions are also limited. A critical challenge is to train a classifier with limited aggressive cancerous tissue compared to low grade cancerous tissue. To resolve the problem of imbalanced data, we use Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic samples for the minority class. We calculate spectral features of temporal ultrasound data and perform feature selection using Random Forests. In leave-one-patient-out cross-validation strategy, an area under receiver operating characteristic curve (AUC) of 0.74 is achieved with overall sensitivity and specificity of 70%. Using an unsupervised learning approach prior to proposed method improves sensitivity and AUC to 80% and 0.79. This work represents promising results to classify lower and higher grade PCa with limited cancerous training samples, using temporal ultrasound.

New guidelines for dam safety classification

International Nuclear Information System (INIS)

Dascal, O.

1999-01-01

Elements are outlined of recommended new guidelines for safety classification of dams. Arguments are provided for the view that dam classification systems should require more than one system as follows: (a) classification for selection of design criteria, operation procedures and emergency measures plans, based on potential consequences of a dam failure - the hazard classification of water retaining structures; (b) classification for establishment of surveillance activities and for safety evaluation of dams, based on the probability and consequences of failure - the risk classification of water retaining structures; and (c) classification for establishment of water management plans, for safety evaluation of the entire project, for preparation of emergency measures plans, for definition of the frequency and extent of maintenance operations, and for evaluation of changes and modifications required - the hazard classification of the project. The hazard classification of the dam considers, as consequence, mainly the loss of lives or persons in jeopardy and the property damages to third parties. Difficulties in determining the risk classification of the dam lie in the fact that no tool exists to evaluate the probability of the dam's failure. To overcome this, the probability of failure can be substituted for by a set of dam characteristics that express the failure potential of the dam and its foundation. The hazard classification of the entire project is based on the probable consequences of dam failure influencing: loss of life, persons in jeopardy, property and environmental damage. The classification scheme is illustrated for dam threatening events such as earthquakes and floods. 17 refs., 5 tabs

Oxidative stress and antioxidant activity in orbital fibroadipose tissue in Graves' ophthalmopathy.

Science.gov (United States)

Hondur, Ahmet; Konuk, Onur; Dincel, Aylin Sepici; Bilgihan, Ayse; Unal, Mehmet; Hasanreisoglu, Berati

2008-05-01

To investigate the oxidative stress and antioxidant activity in the orbit in Graves' ophthalmopathy (GO). Orbital fibroadipose tissue samples were obtained from 13 cases during orbital fat decompression surgery. All cases demonstrated features of moderate or severe GO according to the European Group on Graves' Orbitopathy classification. The disease activity was evaluated with the Clinical Activity Score, and the clinical features of GO were evaluated with the Ophthalmopathy Index. Orbital fibroadipose tissue samples of 8 patients without any thyroid or autoimmune disease were studied as controls. In the tissue samples, lipid hydroperoxide level was examined to determine the level of oxidative stress; glutathione level to determine antioxidant level; superoxide dismutase, glutathione reductase, and glutathione peroxidase activities to determine antioxidant activity. Lipid hydroperoxide level and all three antioxidant enzyme activities were found to be significantly elevated, while glutathione level significantly diminished in tissue samples from GO cases compared to controls (p < 0.05). Glutathione levels in tissue samples of GO cases showed negative correlation with Ophthalmopathy Index (r = -0.59, p < 0.05). The antioxidant activity in the orbit is enhanced in GO. However, the oxidative stress appears to be severe enough to deplete the tissue antioxidants and leads to oxidative tissue damage. This study may support the possible value of antioxidant treatment in GO.

Esophageal involvement and interstitial lung disease in mixed connective tissue disease.

Science.gov (United States)

Fagundes, M N; Caleiro, M T C; Navarro-Rodriguez, T; Baldi, B G; Kavakama, J; Salge, J M; Kairalla, R; Carvalho, C R R

2009-06-01

Mixed connective tissue disease is a systemic inflammatory disorder that results in both pulmonary and esophageal manifestations. We sought to evaluate the relationship between esophageal dysfunction and interstitial lung disease in patients with mixed connective tissue disease. We correlated the pulmonary function data and the high-resolution computed tomography findings of interstitial lung disease with the results of esophageal evaluation in manometry, 24-hour intraesophageal pH measurements, and the presence of esophageal dilatation on computed tomography scan. Fifty consecutive patients with mixed connective tissue disease, according to Kasukawa's classification criteria, were included in this prospective study. High-resolution computed tomography parenchymal abnormalities were present in 39 of 50 patients. Esophageal dilatation, gastroesophageal reflux, and esophageal motor impairment were also very prevalent (28 of 50, 18 of 36, and 30 of 36, respectively). The presence of interstitial lung disease on computed tomography was significantly higher among patients with esophageal dilatation (92% vs. 45%; pmotor dysfunction (90% vs. 35%; pesophageal and pulmonary involvement, our series revealed a strong association between esophageal motor dysfunction and interstitial lung disease in patients with mixed connective tissue disease.

7 CFR 28.911 - Review classification.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Review classification. 28.911 Section 28.911... REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Cotton Classification and Market News Service for Producers Classification § 28.911 Review classification. (a) A producer may request one review...

Packet Classification by Multilevel Cutting of the Classification Space: An Algorithmic-Architectural Solution for IP Packet Classification in Next Generation Networks

Directory of Open Access Journals (Sweden)

Motasem Aldiab

2008-01-01

Full Text Available Traditionally, the Internet provides only a “best-effort” service, treating all packets going to the same destination equally. However, providing differentiated services for different users based on their quality requirements is increasingly becoming a demanding issue. For this, routers need to have the capability to distinguish and isolate traffic belonging to different flows. This ability to determine the flow each packet belongs to is called packet classification. Technology vendors are reluctant to support algorithmic solutions for classification due to their nondeterministic performance. Although content addressable memories (CAMs are favoured by technology vendors due to their deterministic high-lookup rates, they suffer from the problems of high-power consumption and high-silicon cost. This paper provides a new algorithmic-architectural solution for packet classification that mixes CAMs with algorithms based on multilevel cutting of the classification space into smaller spaces. The provided solution utilizes the geometrical distribution of rules in the classification space. It provides the deterministic performance of CAMs, support for dynamic updates, and added flexibility for system designers.

Raman spectroscopy of normal oral buccal mucosa tissues: study on intact and incised biopsies

Science.gov (United States)

Deshmukh, Atul; Singh, S. P.; Chaturvedi, Pankaj; Krishna, C. Murali

2011-12-01

Oral squamous cell carcinoma is one of among the top 10 malignancies. Optical spectroscopy, including Raman, is being actively pursued as alternative/adjunct for cancer diagnosis. Earlier studies have demonstrated the feasibility of classifying normal, premalignant, and malignant oral ex vivo tissues. Spectral features showed predominance of lipids and proteins in normal and cancer conditions, respectively, which were attributed to membrane lipids and surface proteins. In view of recent developments in deep tissue Raman spectroscopy, we have recorded Raman spectra from superior and inferior surfaces of 10 normal oral tissues on intact, as well as incised, biopsies after separation of epithelium from connective tissue. Spectral variations and similarities among different groups were explored by unsupervised (principal component analysis) and supervised (linear discriminant analysis, factorial discriminant analysis) methodologies. Clusters of spectra from superior and inferior surfaces of intact tissues show a high overlap; whereas spectra from separated epithelium and connective tissue sections yielded clear clusters, though they also overlap on clusters of intact tissues. Spectra of all four groups of normal tissues gave exclusive clusters when tested against malignant spectra. Thus, this study demonstrates that spectra recorded from the superior surface of an intact tissue may have contributions from deeper layers but has no bearing from the classification of a malignant tissues point of view.

7 CFR 1794.31 - Classification.

Science.gov (United States)

2010-01-01

... 7 Agriculture 12 2010-01-01 2010-01-01 false Classification. 1794.31 Section 1794.31 Agriculture... Classification. (a) Electric and telecommunications programs. RUS will normally determine the proper environmental classification of projects based on its evaluation of the project description set forth in the...

32 CFR 2400.34 - Classification.

Science.gov (United States)

2010-07-01

... 32 National Defense 6 2010-07-01 2010-07-01 false Classification. 2400.34 Section 2400.34 National... Government Information § 2400.34 Classification. (a) Foreign government information classified by a foreign government or international organization of governments shall retain its original classification designation...

Seismic texture classification. Final report

Energy Technology Data Exchange (ETDEWEB)

Vinther, R.

1997-12-31

The seismic texture classification method, is a seismic attribute that can both recognize the general reflectivity styles and locate variations from these. The seismic texture classification performs a statistic analysis for the seismic section (or volume) aiming at describing the reflectivity. Based on a set of reference reflectivities the seismic textures are classified. The result of the seismic texture classification is a display of seismic texture categories showing both the styles of reflectivity from the reference set and interpolations and extrapolations from these. The display is interpreted as statistical variations in the seismic data. The seismic texture classification is applied to seismic sections and volumes from the Danish North Sea representing both horizontal stratifications and salt diapers. The attribute succeeded in recognizing both general structure of successions and variations from these. Also, the seismic texture classification is not only able to display variations in prospective areas (1-7 sec. TWT) but can also be applied to deep seismic sections. The seismic texture classification is tested on a deep reflection seismic section (13-18 sec. TWT) from the Baltic Sea. Applied to this section the seismic texture classification succeeded in locating the Moho, which could not be located using conventional interpretation tools. The seismic texture classification is a seismic attribute which can display general reflectivity styles and deviations from these and enhance variations not found by conventional interpretation tools. (LN)

Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes

KAUST Repository

Cannistraci, Carlo

2010-09-01

Motivation: Nonlinear small datasets, which are characterized by low numbers of samples and very high numbers of measures, occur frequently in computational biology, and pose problems in their investigation. Unsupervised hybrid-two-phase (H2P) procedures-specifically dimension reduction (DR), coupled with clustering-provide valuable assistance, not only for unsupervised data classification, but also for visualization of the patterns hidden in high-dimensional feature space. Methods: \\'Minimum Curvilinearity\\' (MC) is a principle that-for small datasets-suggests the approximation of curvilinear sample distances in the feature space by pair-wise distances over their minimum spanning tree (MST), and thus avoids the introduction of any tuning parameter. MC is used to design two novel forms of nonlinear machine learning (NML): Minimum Curvilinear embedding (MCE) for DR, and Minimum Curvilinear affinity propagation (MCAP) for clustering. Results: Compared with several other unsupervised and supervised algorithms, MCE and MCAP, whether individually or combined in H2P, overcome the limits of classical approaches. High performance was attained in the visualization and classification of: (i) pain patients (proteomic measurements) in peripheral neuropathy; (ii) human organ tissues (genomic transcription factor measurements) on the basis of their embryological origin. Conclusion: MC provides a valuable framework to estimate nonlinear distances in small datasets. Its extension to large datasets is prefigured for novel NMLs. Classification of neuropathic pain by proteomic profiles offers new insights for future molecular and systems biology characterization of pain. Improvements in tissue embryological classification refine results obtained in an earlier study, and suggest a possible reinterpretation of skin attribution as mesodermal. © The Author(s) 2010. Published by Oxford University Press.

28 CFR 345.20 - Position classification.

Science.gov (United States)

2010-07-01

... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Position classification. 345.20 Section... INDUSTRIES (FPI) INMATE WORK PROGRAMS Position Classification § 345.20 Position classification. (a) Inmate... the objectives and principles of pay classification as a part of the routine orientation of new FPI...

7 CFR 51.2284 - Size classification.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Size classification. 51.2284 Section 51.2284... Size classification. The following classifications are provided to describe the size of any lot... shall conform to the requirements of the specified classification as defined below: (a) Halves. Lot...

22 CFR 9.8 - Classification challenges.

Science.gov (United States)

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Classification challenges. 9.8 Section 9.8 Foreign Relations DEPARTMENT OF STATE GENERAL SECURITY INFORMATION REGULATIONS § 9.8 Classification... classification status is improper are expected and encouraged to challenge the classification status of the...

32 CFR 2001.21 - Original classification.

Science.gov (United States)

2010-07-01

... 32 National Defense 6 2010-07-01 2010-07-01 false Original classification. 2001.21 Section 2001.21... Markings § 2001.21 Original classification. (a) Primary markings. At the time of original classification... authority. The name and position, or personal identifier, of the original classification authority shall...

Classification of genes and putative biomarker identification using distribution metrics on expression profiles.

Directory of Open Access Journals (Sweden)

Hung-Chung Huang

Full Text Available BACKGROUND: Identification of genes with switch-like properties will facilitate discovery of regulatory mechanisms that underlie these properties, and will provide knowledge for the appropriate application of Boolean networks in gene regulatory models. As switch-like behavior is likely associated with tissue-specific expression, these gene products are expected to be plausible candidates as tissue-specific biomarkers. METHODOLOGY/PRINCIPAL FINDINGS: In a systematic classification of genes and search for biomarkers, gene expression profiles (GEPs of more than 16,000 genes from 2,145 mouse array samples were analyzed. Four distribution metrics (mean, standard deviation, kurtosis and skewness were used to classify GEPs into four categories: predominantly-off, predominantly-on, graded (rheostatic, and switch-like genes. The arrays under study were also grouped and examined by tissue type. For example, arrays were categorized as 'brain group' and 'non-brain group'; the Kolmogorov-Smirnov distance and Pearson correlation coefficient were then used to compare GEPs between brain and non-brain for each gene. We were thus able to identify tissue-specific biomarker candidate genes. CONCLUSIONS/SIGNIFICANCE: The methodology employed here may be used to facilitate disease-specific biomarker discovery.

Imaging the spectral reflectance properties of bipolar radiofrequency-fused bowel tissue

Science.gov (United States)

Clancy, Neil T.; Arya, Shobhit; Stoyanov, Danail; Du, Xiaofei; Hanna, George B.; Elson, Daniel S.

2015-07-01

Delivery of radiofrequency (RF) electrical energy is used during surgery to heat and seal tissue, such as vessels, allowing resection without blood loss. Recent work has suggested that this approach may be extended to allow surgical attachment of larger tissue segments for applications such as bowel anastomosis. In a large series of porcine surgical procedures bipolar RF energy was used to resect and re-seal the small bowel in vivo with a commercial tissue fusion device (Ligasure; Covidien PLC, USA). The tissue was then imaged with a multispectral imaging laparoscope to obtain a spectral datacube comprising both fused and healthy tissue. Maps of blood volume, oxygen saturation and scattering power were derived from the measured reflectance spectra using an optimised light-tissue interaction model. A 60% increase in reflectance of visible light (460-700 nm) was observed after fusion, with the tissue taking on a white appearance. Despite this the distinctive shape of the haemoglobin absorption spectrum was still noticeable in the 460-600 nm wavelength range. Scattering power increased in the fused region in comparison to normal serosa, while blood volume and oxygen saturation decreased. Observed fusion-induced changes in the reflectance spectrum are consistent with the biophysical changes induced through tissue denaturation and increased collagen cross-linking. The multispectral imager allows mapping of the spatial extent of these changes and classification of the zone of damaged tissue. Further analysis of the spectral data in parallel with histopathological examination of excised specimens will allow correlation of the optical property changes with microscopic alterations in tissue structure.

When machine vision meets histology: A comparative evaluation of model architecture for classification of histology sections.

Science.gov (United States)

Zhong, Cheng; Han, Ju; Borowsky, Alexander; Parvin, Bahram; Wang, Yunfu; Chang, Hang

2017-01-01

Classification of histology sections in large cohorts, in terms of distinct regions of microanatomy (e.g., stromal) and histopathology (e.g., tumor, necrosis), enables the quantification of tumor composition, and the construction of predictive models of genomics and clinical outcome. To tackle the large technical variations and biological heterogeneities, which are intrinsic in large cohorts, emerging systems utilize either prior knowledge from pathologists or unsupervised feature learning for invariant representation of the underlying properties in the data. However, to a large degree, the architecture for tissue histology classification remains unexplored and requires urgent systematical investigation. This paper is the first attempt to provide insights into three fundamental questions in tissue histology classification: I. Is unsupervised feature learning preferable to human engineered features? II. Does cellular saliency help? III. Does the sparse feature encoder contribute to recognition? We show that (a) in I, both Cellular Morphometric Feature and features from unsupervised feature learning lead to superior performance when compared to SIFT and [Color, Texture]; (b) in II, cellular saliency incorporation impairs the performance for systems built upon pixel-/patch-level features; and (c) in III, the effect of the sparse feature encoder is correlated with the robustness of features, and the performance can be consistently improved by the multi-stage extension of systems built upon both Cellular Morphmetric Feature and features from unsupervised feature learning. These insights are validated with two cohorts of Glioblastoma Multiforme (GBM) and Kidney Clear Cell Carcinoma (KIRC). Copyright © 2016 Elsevier B.V. All rights reserved.

A texture based pattern recognition approach to distinguish melanoma from non-melanoma cells in histopathological tissue microarray sections.

Directory of Open Access Journals (Sweden)

Elton Rexhepaj

Full Text Available AIMS: Immunohistochemistry is a routine practice in clinical cancer diagnostics and also an established technology for tissue-based research regarding biomarker discovery efforts. Tedious manual assessment of immunohistochemically stained tissue needs to be fully automated to take full advantage of the potential for high throughput analyses enabled by tissue microarrays and digital pathology. Such automated tools also need to be reproducible for different experimental conditions and biomarker targets. In this study we present a novel supervised melanoma specific pattern recognition approach that is fully automated and quantitative. METHODS AND RESULTS: Melanoma samples were immunostained for the melanocyte specific target, Melan-A. Images representing immunostained melanoma tissue were then digitally processed to segment regions of interest, highlighting Melan-A positive and negative areas. Color deconvolution was applied to each region of interest to separate the channel containing the immunohistochemistry signal from the hematoxylin counterstaining channel. A support vector machine melanoma classification model was learned from a discovery melanoma patient cohort (n = 264 and subsequently validated on an independent cohort of melanoma patient tissue sample images (n = 157. CONCLUSION: Here we propose a novel method that takes advantage of utilizing an immuhistochemical marker highlighting melanocytes to fully automate the learning of a general melanoma cell classification model. The presented method can be applied on any protein of interest and thus provides a tool for quantification of immunohistochemistry-based protein expression in melanoma.

7 CFR 51.1860 - Color classification.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Color classification. 51.1860 Section 51.1860... STANDARDS) United States Standards for Fresh Tomatoes 1 Color Classification § 51.1860 Color classification... illustrating the color classification requirements, as set forth in this section. This visual aid may be...

22 CFR 42.11 - Classification symbols.

Science.gov (United States)

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Classification symbols. 42.11 Section 42.11... NATIONALITY ACT, AS AMENDED Classification and Foreign State Chargeability § 42.11 Classification symbols. A... visa symbol to show the classification of the alien. Immigrants Symbol Class Section of law Immediate...

46 CFR 503.54 - Original classification.

Science.gov (United States)

2010-10-01

... 46 Shipping 9 2010-10-01 2010-10-01 false Original classification. 503.54 Section 503.54 Shipping... Program § 503.54 Original classification. (a) No Commission Member or employee has the authority to... classification, it shall be sent to the appropriate agency with original classification authority over the...

Pitch Based Sound Classification

DEFF Research Database (Denmark)

Nielsen, Andreas Brinch; Hansen, Lars Kai; Kjems, U

2006-01-01

A sound classification model is presented that can classify signals into music, noise and speech. The model extracts the pitch of the signal using the harmonic product spectrum. Based on the pitch estimate and a pitch error measure, features are created and used in a probabilistic model with soft......-max output function. Both linear and quadratic inputs are used. The model is trained on 2 hours of sound and tested on publicly available data. A test classification error below 0.05 with 1 s classification windows is achieved. Further more it is shown that linear input performs as well as a quadratic......, and that even though classification gets marginally better, not much is achieved by increasing the window size beyond 1 s....

Diffuse reflectance spectroscopy as a tool for real-time tissue assessment during colorectal cancer surgery

Science.gov (United States)

Baltussen, Elisabeth J. M.; Snaebjornsson, Petur; de Koning, Susan G. Brouwer; Sterenborg, Henricus J. C. M.; Aalbers, Arend G. J.; Kok, Niels; Beets, Geerard L.; Hendriks, Benno H. W.; Kuhlmann, Koert F. D.; Ruers, Theo J. M.

2017-10-01

Colorectal surgery is the standard treatment for patients with colorectal cancer. To overcome two of the main challenges, the circumferential resection margin and postoperative complications, real-time tissue assessment could be of great benefit during surgery. In this ex vivo study, diffuse reflectance spectroscopy (DRS) was used to differentiate tumor tissue from healthy surrounding tissues in patients with colorectal neoplasia. DRS spectra were obtained from tumor tissue, healthy colon, or rectal wall and fat tissue, for every patient. Data were randomly divided into training (80%) and test (20%) sets. After spectral band selection, the spectra were classified using a quadratic classifier and a linear support vector machine. Of the 38 included patients, 36 had colorectal cancer and 2 had an adenoma. When the classifiers were applied to the test set, colorectal cancer could be discriminated from healthy tissue with an overall accuracy of 0.95 (±0.03). This study demonstrates the possibility to separate colorectal cancer from healthy surrounding tissue by applying DRS. High classification accuracies were obtained both in homogeneous and inhomogeneous tissues. This is a fundamental step toward the development of a tool for real-time in vivo tissue assessment during colorectal surgery.

Robust volume assessment of brain tissues for 3-dimensional fourier transformation MRI via a novel multispectral technique.

Directory of Open Access Journals (Sweden)

Jyh-Wen Chai

Full Text Available A new TRIO algorithm method integrating three different algorithms is proposed to perform brain MRI segmentation in the native coordinate space, with no need of transformation to a standard coordinate space or the probability maps for segmentation. The method is a simple voxel-based algorithm, derived from multispectral remote sensing techniques, and only requires minimal operator input to depict GM, WM, and CSF tissue clusters to complete classification of a 3D high-resolution multislice-multispectral MRI data. Results showed very high accuracy and reproducibility in classification of GM, WM, and CSF in multislice-multispectral synthetic MRI data. The similarity indexes, expressing overlap between classification results and the ground truth, were 0.951, 0.962, and 0.956 for GM, WM, and CSF classifications in the image data with 3% noise level and 0% non-uniformity intensity. The method particularly allows for classification of CSF with 0.994, 0.961 and 0.996 of accuracy, sensitivity and specificity in images data with 3% noise level and 0% non-uniformity intensity, which had seldom performed well in previous studies. As for clinical MRI data, the quantitative data of brain tissue volumes aligned closely with the brain morphometrics in three different study groups of young adults, elderly volunteers, and dementia patients. The results also showed very low rates of the intra- and extra-operator variability in measurements of the absolute volumes and volume fractions of cerebral GM, WM, and CSF in three different study groups. The mean coefficients of variation of GM, WM, and CSF volume measurements were in the range of 0.03% to 0.30% of intra-operator measurements and 0.06% to 0.45% of inter-operator measurements. In conclusion, the TRIO algorithm exhibits a remarkable ability in robust classification of multislice-multispectral brain MR images, which would be potentially applicable for clinical brain volumetric analysis and explicitly promising

Classification of Osteogenesis Imperfecta revisited

NARCIS (Netherlands)

van Dijk, F. S.; Pals, G.; van Rijn, R. R.; Nikkels, P. G. J.; Cobben, J. M.

2010-01-01

In 1979 Sillence proposed a classification of Osteogenesis Imperfecta (OI) in OI types I, II, III and IV. In 2004 and 2007 this classification was expanded with OI types V-VIII because of distinct clinical features and/or different causative gene mutations. We propose a revised classification of OI

14 CFR 1203.701 - Classification.

Science.gov (United States)

2010-01-01

... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Classification. 1203.701 Section 1203.701... Government Information § 1203.701 Classification. (a) Foreign government information that is classified by a foreign entity shall either retain its original classification designation or be marked with a United...

32 CFR 1602.7 - Classification.

Science.gov (United States)

2010-07-01

... 32 National Defense 6 2010-07-01 2010-07-01 false Classification. 1602.7 Section 1602.7 National Defense Other Regulations Relating to National Defense SELECTIVE SERVICE SYSTEM DEFINITIONS § 1602.7 Classification. Classification is the exercise of the power to determine claims or questions with respect to...

32 CFR 644.426 - Classification.

Science.gov (United States)

2010-07-01

... 32 National Defense 4 2010-07-01 2010-07-01 true Classification. 644.426 Section 644.426 National... HANDBOOK Disposal Disposal of Fee-Owned Real Property and Easement Interests § 644.426 Classification... required by the special acts, classification will be coordinated with the interested Federal agency. The...

46 CFR 132.210 - Classification.

Science.gov (United States)

2010-10-01

... 46 Shipping 4 2010-10-01 2010-10-01 false Classification. 132.210 Section 132.210 Shipping COAST... Portable and Semiportable Fire Extinguishers § 132.210 Classification. (a) Each portable fire extinguisher... Classification Type Size Halon 1211, 1301, and 1211-1301 mixtures kgs. (lbs.) Foam, liters (gallons) Carbon...

Soft tissue recurrence of giant cell tumor of the bone: Prevalence and radiographic features

Directory of Open Access Journals (Sweden)

Leilei Xu

2017-11-01

Full Text Available Aim: Recurrence of giant cell tumor of bone (GCTB in the soft tissue is rarely seen in the clinical practice. This study aims to determine the prevalence of soft tissue recurrence of GCTB, and to characterize its radiographic features. Methods: A total of 291 patients treated by intralesional curettage for histologically diagnosed GCTB were reviewed. 6 patients were identified to have the recurrence of GCTB in the soft tissue, all of whom had undergone marginal resection of the lesion. Based on the x-ray, CT and MRI imaging, the radiographic features of soft tissue recurrence were classified into 3 types. Type I was defined as soft tissue recurrence with peripheral ossification, type II was defined as soft tissue recurrence with central ossification, and type III was defined as pure soft tissue recurrence without ossification. Demographic data including period of recurrence and follow-up duration after the second surgery were recorded for these 6 patients. Musculoskeletal Tumor Society (MSTS scoring system was used to evaluate functional outcomes. Results: The overall recurrence rate was 2.1% (6/291. The mean interval between initial surgery and recurrence was 11.3 ± 4.1 months (range, 5–17. The recurrence lesions were located in the thigh of 2 patients, in the forearm of 2 patients and in the leg of the other 2 patients. According to the classification system mentioned above, 2 patients were classified with type I, 1 as type II and 3 as type III. After the marginal excision surgery, all patients were consistently followed up for a mean period of 13.4 ± 5.3 months (range, 6–19, with no recurrence observed at the final visit. All the patients were satisfied with the surgical outcome. According to the MSTS scale, the mean postoperative functional score was 28.0 ± 1.2 (range, 26–29. Conclusions: The classification of soft tissue recurrence of GCTB may be helpful for the surgeon to select the appropriate imaging procedure to

Hyperspectral Imaging and SPA-LDA Quantitative Analysis for Detection of Colon Cancer Tissue

Science.gov (United States)

Yuan, X.; Zhang, D.; Wang, Ch.; Dai, B.; Zhao, M.; Li, B.

2018-05-01

Hyperspectral imaging (HSI) has been demonstrated to provide a rapid, precise, and noninvasive method for cancer detection. However, because HSI contains many data, quantitative analysis is often necessary to distill information useful for distinguishing cancerous from normal tissue. To demonstrate that HSI with our proposed algorithm can make this distinction, we built a Vis-NIR HSI setup and made many spectral images of colon tissues, and then used a successive projection algorithm (SPA) to analyze the hyperspectral image data of the tissues. This was used to build an identification model based on linear discrimination analysis (LDA) using the relative reflectance values of the effective wavelengths. Other tissues were used as a prediction set to verify the reliability of the identification model. The results suggest that Vis-NIR hyperspectral images, together with the spectroscopic classification method, provide a new approach for reliable and safe diagnosis of colon cancer and could lead to advances in cancer diagnosis generally.

Library Classification 2020

Science.gov (United States)

Harris, Christopher

2013-01-01

In this article the author explores how a new library classification system might be designed using some aspects of the Dewey Decimal Classification (DDC) and ideas from other systems to create something that works for school libraries in the year 2020. By examining what works well with the Dewey Decimal System, what features should be carried…

14 CFR 298.3 - Classification.

Science.gov (United States)

2010-01-01

... 14 Aeronautics and Space 4 2010-01-01 2010-01-01 false Classification. 298.3 Section 298.3... REGULATIONS EXEMPTIONS FOR AIR TAXI AND COMMUTER AIR CARRIER OPERATIONS General § 298.3 Classification. (a) There is hereby established a classification of air carriers, designated as “air taxi operators,” which...

Mass spectrometry protein expression profiles in colorectal cancer tissue associated with clinico-pathological features of disease

Directory of Open Access Journals (Sweden)

Liao Christopher CL

2010-08-01

Full Text Available Abstract Background Studies of several tumour types have shown that expression profiling of cellular protein extracted from surgical tissue specimens by direct mass spectrometry analysis can accurately discriminate tumour from normal tissue and in some cases can sub-classify disease. We have evaluated the potential value of this approach to classify various clinico-pathological features in colorectal cancer by employing matrix-assisted laser desorption ionisation time of-flight-mass spectrometry (MALDI-TOF MS. Methods Protein extracts from 31 tumour and 33 normal mucosa specimens were purified, subjected to MALDI-Tof MS and then analysed using the 'GenePattern' suite of computational tools (Broad Institute, MIT, USA. Comparative Gene Marker Selection with either a t-test or a signal-to-noise ratio (SNR test statistic was used to identify and rank differentially expressed marker peaks. The k-nearest neighbours algorithm was used to build classification models either using separate training and test datasets or else by using an iterative, 'leave-one-out' cross-validation method. Results 73 protein peaks in the mass range 1800-16000Da were differentially expressed in tumour verses adjacent normal mucosa tissue (P ≤ 0.01, false discovery rate ≤ 0.05. Unsupervised hierarchical cluster analysis classified most tumour and normal mucosa into distinct cluster groups. Supervised prediction correctly classified the tumour/normal mucosa status of specimens in an independent test spectra dataset with 100% sensitivity and specificity (95% confidence interval: 67.9-99.2%. Supervised prediction using 'leave-one-out' cross validation algorithms for tumour spectra correctly classified 10/13 poorly differentiated and 16/18 well/moderately differentiated tumours (P = P = P = 0.001; ROC error, 0.212. Conclusions Protein expression profiling of surgically resected CRC tissue extracts by MALDI-TOF MS has potential value in studies aimed at improved molecular

32 CFR 2700.22 - Classification guides.

Science.gov (United States)

2010-07-01

... SECURITY INFORMATION REGULATIONS Derivative Classification § 2700.22 Classification guides. OMSN shall... direct derivative classification, shall identify the information to be protected in specific and uniform...

Update on diabetes classification.

Science.gov (United States)

Thomas, Celeste C; Philipson, Louis H

2015-01-01

This article highlights the difficulties in creating a definitive classification of diabetes mellitus in the absence of a complete understanding of the pathogenesis of the major forms. This brief review shows the evolving nature of the classification of diabetes mellitus. No classification scheme is ideal, and all have some overlap and inconsistencies. The only diabetes in which it is possible to accurately diagnose by DNA sequencing, monogenic diabetes, remains undiagnosed in more than 90% of the individuals who have diabetes caused by one of the known gene mutations. The point of classification, or taxonomy, of disease, should be to give insight into both pathogenesis and treatment. It remains a source of frustration that all schemes of diabetes mellitus continue to fall short of this goal. Copyright © 2015 Elsevier Inc. All rights reserved.

The classification of osteonecrosis in patients with cancer: validation of a new radiological classification system

International Nuclear Information System (INIS)

Niinimäki, T.; Niinimäki, J.; Halonen, J.; Hänninen, P.; Harila-Saari, A.; Niinimäki, R.

2015-01-01

Aim: To validate a new, non-joint-specific radiological classification system that is suitable regardless of the site of the osteonecrosis (ON) in patients with cancer. Material and methods: Critical deficiencies in the existing ON classification systems were identified and a new, non-joint-specific radiological classification system was developed. Seventy-two magnetic resonance imaging (MRI) images of patients with cancer and ON lesions were graded, and the validation of the new system was performed by assessing inter- and intra-observer reliability. Results: Intra-observer reliability of ON grading was good or very good, with kappa values of 0.79–0.86. Interobserver agreement was lower but still good, with kappa values of 0.62–0.77. Ninety-eight percent of all intra- or interobserver differences were within one grade. Interobserver reliability of assessing the location of ON was very good, with kappa values of 0.93–0.98. Conclusion: All the available radiological ON classification systems are joint specific. This limitation has spurred the development of multiple systems, which has led to the insufficient use of classifications in ON studies among patients with cancer. The introduced radiological classification system overcomes the problem of joint-specificity, was found to be reliable, and can be used to classify all ON lesions regardless of the affected site. - Highlights: • Patients with cancer may have osteonecrosis lesions at multiple sites. • There is no non-joint-specific osteonecrosis classification available. • We introduced a new non-joint-specific osteonecrosis classification. • The validation was performed by assessing inter- and intra-observer reliability. • The classification was reliable and could be used regardless of the affected site.

Finding related functional neuroimaging volumes

DEFF Research Database (Denmark)

Nielsen, Finn Årup; Hansen, Lars Kai

2004-01-01

We describe a content-based image retrieval technique for finding related functional neuroimaging experiments by voxelization of sets of stereotactic coordinates in Talairach space, comparing the volumes and reporting related volumes in a sorted list. Voxelization is accomplished by convolving ea...

Tissue spray ionization mass spectrometry for rapid recognition of human lung squamous cell carcinoma

Science.gov (United States)

Wei, Yiping; Chen, Liru; Zhou, Wei; Chingin, Konstantin; Ouyang, Yongzhong; Zhu, Tenggao; Wen, Hua; Ding, Jianhua; Xu, Jianjun; Chen, Huanwen

2015-05-01

Tissue spray ionization mass spectrometry (TSI-MS) directly on small tissue samples has been shown to provide highly specific molecular information. In this study, we apply this method to the analysis of 38 pairs of human lung squamous cell carcinoma tissue (cancer) and adjacent normal lung tissue (normal). The main components of pulmonary surfactants, dipalmitoyl phosphatidylcholine (DPPC, m/z 757.47), phosphatidylcholine (POPC, m/z 782.52), oleoyl phosphatidylcholine (DOPC, m/z 808.49), and arachidonic acid stearoyl phosphatidylcholine (SAPC, m/z 832.43), were identified using high-resolution tandem mass spectrometry. Monte Carlo sampling partial least squares linear discriminant analysis (PLS-LDA) was used to distinguish full-mass-range mass spectra of cancer samples from the mass spectra of normal tissues. With 5 principal components and 30 - 40 Monte Carlo samplings, the accuracy of cancer identification in matched tissue samples reached 94.42%. Classification of a tissue sample required less than 1 min, which is much faster than the analysis of frozen sections. The rapid, in situ diagnosis with minimal sample consumption provided by TSI-MS is advantageous for surgeons. TSI-MS allows them to make more informed decisions during surgery.

MR/PET quantification tools: Registration, segmentation, classification, and MR-based attenuation correction

Science.gov (United States)

Fei, Baowei; Yang, Xiaofeng; Nye, Jonathon A.; Aarsvold, John N.; Raghunath, Nivedita; Cervo, Morgan; Stark, Rebecca; Meltzer, Carolyn C.; Votaw, John R.

2012-01-01

Purpose: Combined MR/PET is a relatively new, hybrid imaging modality. A human MR/PET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MR/PET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MR/PET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with [11C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MR

MR/PET quantification tools: Registration, segmentation, classification, and MR-based attenuation correction

Energy Technology Data Exchange (ETDEWEB)

Fei, Baowei, E-mail: bfei@emory.edu [Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1841 Clifton Road Northeast, Atlanta, Georgia 30329 (United States); Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia 30322 (United States); Department of Mathematics and Computer Sciences, Emory University, Atlanta, Georgia 30322 (United States); Yang, Xiaofeng; Nye, Jonathon A.; Raghunath, Nivedita; Votaw, John R. [Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329 (United States); Aarsvold, John N. [Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329 (United States); Nuclear Medicine Service, Atlanta Veterans Affairs Medical Center, Atlanta, Georgia 30033 (United States); Cervo, Morgan; Stark, Rebecca [The Medical Physics Graduate Program in the George W. Woodruff School, Georgia Institute of Technology, Atlanta, Georgia 30332 (United States); Meltzer, Carolyn C. [Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329 (United States); Department of Neurology and Department of Psychiatry and Behavior Sciences, Emory University School of Medicine, Atlanta, Georgia 30322 (United States)

2012-10-15

Purpose: Combined MR/PET is a relatively new, hybrid imaging modality. A human MR/PET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MR/PET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MR/PET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with [{sup 11}C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MR/PET.

MR/PET quantification tools: Registration, segmentation, classification, and MR-based attenuation correction

International Nuclear Information System (INIS)

Fei, Baowei; Yang, Xiaofeng; Nye, Jonathon A.; Raghunath, Nivedita; Votaw, John R.; Aarsvold, John N.; Cervo, Morgan; Stark, Rebecca; Meltzer, Carolyn C.

2012-01-01

Purpose: Combined MR/PET is a relatively new, hybrid imaging modality. A human MR/PET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MR/PET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MR/PET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with ["1"1C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MR/PET.

Border Lakes land-cover classification

Science.gov (United States)

Marvin Bauer; Brian Loeffelholz; Doug. Shinneman

2009-01-01

This document contains metadata and description of land-cover classification of approximately 5.1 million acres of land bordering Minnesota, U.S.A. and Ontario, Canada. The classification focused on the separation and identification of specific forest-cover types. Some separation of the nonforest classes also was performed. The classification was derived from multi-...

The reliability and reproducibility of the Hertel classification for comminuted proximal humeral fractures compared with the Neer classification

NARCIS (Netherlands)

Iordens, Gijs I. T.; Mahabier, Kiran C.; Buisman, Florian E.; Schep, Niels W. L.; Muradin, Galied S. R.; Beenen, Ludo F. M.; Patka, Peter; van Lieshout, Esther M. M.; den Hartog, Dennis

2016-01-01

The Neer classification is the most commonly used fracture classification system for proximal humeral fractures. Inter- and intra-observer agreement is limited, especially for comminuted fractures. A possibly more straightforward and reliable classification system is the Hertel classification. The

Definition of pertinent parameters for the evaluation of articular cartilage repair tissue with high-resolution magnetic resonance imaging

International Nuclear Information System (INIS)

Marlovits, Stefan; Striessnig, Gabriele; Resinger, Christoph T.; Aldrian, Silke M.; Vecsei, Vilmos; Imhof, Herwig; Trattnig, Siegfried

2004-01-01

To evaluate articular cartilage repair tissue after biological cartilage repair, we propose a new technique of non-invasive, high-resolution magnetic resonance imaging (MRI) and define a new classification system. For the definition of pertinent variables the repair tissue of 45 patients treated with three different techniques for cartilage repair (microfracture, autologous osteochondral transplantation, and autologous chondrocyte transplantation) was analyzed 6 and 12 months after the procedure. High-resolution imaging was obtained with a surface phased array coil placed over the knee compartment of interest and adapted sequences were used on a 1 T MRI scanner. The analysis of the repair tissue included the definition and rating of nine pertinent variables: the degree of filling of the defect, the integration to the border zone, the description of the surface and structure, the signal intensity, the status of the subchondral lamina and subchondral bone, the appearance of adhesions and the presence of synovitis. High-resolution MRI, using a surface phased array coil and specific sequences, can be used on every standard 1 or 1.5 T MRI scanner according to the in-house standard protocols for knee imaging in patients who have had cartilage repair procedures without substantially prolonging the total imaging time. The new classification and grading system allows a subtle description and suitable assessment of the articular cartilage repair tissue

Classification of movement disorders.

Science.gov (United States)

Fahn, Stanley

2011-05-01

The classification of movement disorders has evolved. Even the terminology has shifted, from an anatomical one of extrapyramidal disorders to a phenomenological one of movement disorders. The history of how this shift came about is described. The history of both the definitions and the classifications of the various neurologic conditions is then reviewed. First is a review of movement disorders as a group; then, the evolving classifications for 3 of them--parkinsonism, dystonia, and tremor--are covered in detail. Copyright © 2011 Movement Disorder Society.

Information Classification on University Websites

DEFF Research Database (Denmark)

Nawaz, Ather; Clemmensen, Torkil; Hertzum, Morten

2011-01-01

Websites are increasingly used as a medium for providing information to university students. The quality of a university website depends on how well the students’ information classification fits with the structure of the information on the website. This paper investigates the information classifi......Websites are increasingly used as a medium for providing information to university students. The quality of a university website depends on how well the students’ information classification fits with the structure of the information on the website. This paper investigates the information...... classification of 14 Danish and 14 Pakistani students and compares it with the information classification of their university website. Brainstorming, card sorting, and task exploration activities were used to discover similarities and differences in the participating students’ classification of website...... information and their ability to navigate the websites. The results of the study indicate group differences in user classification and related taskperformance differences. The main implications of the study are that (a) the edit distance appears a useful measure in cross-country HCI research and practice...

Information Classification on University Websites

DEFF Research Database (Denmark)

Nawaz, Ather; Clemmensen, Torkil; Hertzum, Morten

2011-01-01

Websites are increasingly used as a medium for providing information to university students. The quality of a university website depends on how well the students’ information classification fits with the structure of the information on the website. This paper investigates the information classifi......Websites are increasingly used as a medium for providing information to university students. The quality of a university website depends on how well the students’ information classification fits with the structure of the information on the website. This paper investigates the information...... classification of 14 Danish and 14 Pakistani students and compares it with the information classification of their university website. Brainstorming, card sorting, and task exploration activities were used to discover similarities and differences in the participating students’ classification of website...... information and their ability to navigate the websites. The results of the study indicate group differences in user classification and related task-performance differences. The main implications of the study are that (a) the edit distance appears a useful measure in cross-country HCI research and practice...

Colombia: Territorial classification

International Nuclear Information System (INIS)

Mendoza Morales, Alberto

1998-01-01

The article is about the approaches of territorial classification, thematic axes, handling principles and territorial occupation, politician and administrative units and administration regions among other topics. Understanding as Territorial Classification the space distribution on the territory of the country, of the geographical configurations, the human communities, the political-administrative units and the uses of the soil, urban and rural, existent and proposed

Latent classification models

DEFF Research Database (Denmark)

Langseth, Helge; Nielsen, Thomas Dyhre

2005-01-01

parametric family ofdistributions.  In this paper we propose a new set of models forclassification in continuous domains, termed latent classificationmodels. The latent classification model can roughly be seen ascombining the \\NB model with a mixture of factor analyzers,thereby relaxing the assumptions...... classification model, and wedemonstrate empirically that the accuracy of the proposed model issignificantly higher than the accuracy of other probabilisticclassifiers....

Arabic text classification using Polynomial Networks

Directory of Open Access Journals (Sweden)

Mayy M. Al-Tahrawi

2015-10-01

Full Text Available In this paper, an Arabic statistical learning-based text classification system has been developed using Polynomial Neural Networks. Polynomial Networks have been recently applied to English text classification, but they were never used for Arabic text classification. In this research, we investigate the performance of Polynomial Networks in classifying Arabic texts. Experiments are conducted on a widely used Arabic dataset in text classification: Al-Jazeera News dataset. We chose this dataset to enable direct comparisons of the performance of Polynomial Networks classifier versus other well-known classifiers on this dataset in the literature of Arabic text classification. Results of experiments show that Polynomial Networks classifier is a competitive algorithm to the state-of-the-art ones in the field of Arabic text classification.

Angle′s Molar Classification Revisited

Directory of Open Access Journals (Sweden)

Devanshi Yadav

2014-01-01

Results: Of the 500 pretreatment study casts assessed 52.4% were definitive Class I, 23.6% were Class II, 2.6% were Class III and the ambiguous cases were 21%. These could be easily classified with our method of classification. Conclusion: This improvised classification technique will help orthodontists in making classification of malocclusion accurate and simple.

The Oxford classification of IgA nephropathy: rationale, clinicopathological correlations, and classification

NARCIS (Netherlands)

Cattran, Daniel C.; Coppo, Rosanna; Cook, H. Terence; Feehally, John; Roberts, Ian S. D.; Troyanov, Stéphan; Alpers, Charles E.; Amore, Alessandro; Barratt, Jonathan; Berthoux, Francois; Bonsib, Stephen; Bruijn, Jan A.; D'Agati, Vivette; D'Amico, Giuseppe; Emancipator, Steven; Emma, Francesco; Ferrario, Franco; Fervenza, Fernando C.; Florquin, Sandrine; Fogo, Agnes; Geddes, Colin C.; Groene, Hermann-Josef; Haas, Mark; Herzenberg, Andrew M.; Hill, Prue A.; Hogg, Ronald J.; Hsu, Stephen I.; Jennette, J. Charles; Joh, Kensuke; Julian, Bruce A.; Kawamura, Tetsuya; Lai, Fernand M.; Leung, Chi Bon; Li, Lei-Shi; Li, Philip K. T.; Liu, Zhi-Hong; Mackinnon, Bruce; Mezzano, Sergio; Schena, F. Paolo; Tomino, Yasuhiko; Walker, Patrick D.; Wang, Haiyan; Weening, Jan J.; Yoshikawa, Nori; Zhang, Hong

2009-01-01

IgA nephropathy is the most common glomerular disease worldwide, yet there is no international consensus for its pathological or clinical classification. Here a new classification for IgA nephropathy is presented by an international consensus working group. The goal of this new system was to

An edit script for taxonomic classifications

Directory of Open Access Journals (Sweden)

Valiente Gabriel

2005-08-01

Full Text Available Abstract Background The NCBI taxonomy provides one of the most powerful ways to navigate sequence data bases but currently users are forced to formulate queries according to a single taxonomic classification. Given that there is not universal agreement on the classification of organisms, providing a single classification places constraints on the questions biologists can ask. However, maintaining multiple classifications is burdensome in the face of a constantly growing NCBI classification. Results In this paper, we present a solution to the problem of generating modifications of the NCBI taxonomy, based on the computation of an edit script that summarises the differences between two classification trees. Our algorithms find the shortest possible edit script based on the identification of all shared subtrees, and only take time quasi linear in the size of the trees because classification trees have unique node labels. Conclusion These algorithms have been recently implemented, and the software is freely available for download from http://darwin.zoology.gla.ac.uk/~rpage/forest/.

Classification of huminite-ICCP System 1994

Energy Technology Data Exchange (ETDEWEB)

Sykorova, I. [Institute of Rock Structure and Mechanics, Academy of Science of the Czech Republic, V Holesovicka 41, 182 09 Prague 8 (Czech Republic); Pickel, W. [Coal and Organic Petrology Services Pty Ltd, 23/80 Box Road, Taren Point, NSW 2229 (Australia); Christanis, K. [Department of Geology, University of Patras, 26500 Rio-Patras (Greece); Wolf, M. [Mergelskull 29, 47802 Krefeld (Germany); Taylor, G.H. [15 Hawkesbury Cres, Farrer Act 2607 (Australia); Flores, D. [Departamento de Geologia, Faculdade de Ciencias do Porto, Praca de Gomes Teixeira, 4099-002 Porto (Portugal)

2005-04-12

In the new classification (ICCP System 1994), the maceral group huminite has been revised from the previous classification (ICCP, 1971. Int. Handbook Coal Petr., suppl. to 2nd ed.) to accommodate the nomenclature to changes in the other maceral groups, especially the changes in the vitrinite classification (ICCP, 1998. The new vitrinite classification (ICCP System 1994). Fuel 77, 349-358.). The vitrinite and huminite systems have been correlated so that down to the level of sub-maceral groups, the two systems can be used in parallel. At the level of macerals and for finer classifications, the analyst now has, according to the nature of the coal and the purpose of the analysis, a choice of using either of the two classification systems for huminite and vitrinite. This is in accordance with the new ISO Coal Classification that covers low rank coals as well and allows for the simultaneous use of the huminite and vitrinite nomenclature for low rank coals.

Efficient Fingercode Classification

Science.gov (United States)

Sun, Hong-Wei; Law, Kwok-Yan; Gollmann, Dieter; Chung, Siu-Leung; Li, Jian-Bin; Sun, Jia-Guang

In this paper, we present an efficient fingerprint classification algorithm which is an essential component in many critical security application systems e. g. systems in the e-government and e-finance domains. Fingerprint identification is one of the most important security requirements in homeland security systems such as personnel screening and anti-money laundering. The problem of fingerprint identification involves searching (matching) the fingerprint of a person against each of the fingerprints of all registered persons. To enhance performance and reliability, a common approach is to reduce the search space by firstly classifying the fingerprints and then performing the search in the respective class. Jain et al. proposed a fingerprint classification algorithm based on a two-stage classifier, which uses a K-nearest neighbor classifier in its first stage. The fingerprint classification algorithm is based on the fingercode representation which is an encoding of fingerprints that has been demonstrated to be an effective fingerprint biometric scheme because of its ability to capture both local and global details in a fingerprint image. We enhance this approach by improving the efficiency of the K-nearest neighbor classifier for fingercode-based fingerprint classification. Our research firstly investigates the various fast search algorithms in vector quantization (VQ) and the potential application in fingerprint classification, and then proposes two efficient algorithms based on the pyramid-based search algorithms in VQ. Experimental results on DB1 of FVC 2004 demonstrate that our algorithms can outperform the full search algorithm and the original pyramid-based search algorithms in terms of computational efficiency without sacrificing accuracy.

The future of general classification

DEFF Research Database (Denmark)

Mai, Jens Erik

2013-01-01

Discusses problems related to accessing multiple collections using a single retrieval language. Surveys the concepts of interoperability and switching language. Finds that mapping between more indexing languages always will be an approximation. Surveys the issues related to general classification...... and contrasts that to special classifications. Argues for the use of general classifications to provide access to collections nationally and internationally....

Magnetic resonance imaging-based cerebral tissue classification reveals distinct spatiotemporal patterns of changes after stroke in non-human primates.

Science.gov (United States)

Bouts, Mark J R J; Westmoreland, Susan V; de Crespigny, Alex J; Liu, Yutong; Vangel, Mark; Dijkhuizen, Rick M; Wu, Ona; D'Arceuil, Helen E

2015-12-15

Spatial and temporal changes in brain tissue after acute ischemic stroke are still poorly understood. Aims of this study were three-fold: (1) to determine unique temporal magnetic resonance imaging (MRI) patterns at the acute, subacute and chronic stages after stroke in macaques by combining quantitative T2 and diffusion MRI indices into MRI 'tissue signatures', (2) to evaluate temporal differences in these signatures between transient (n = 2) and permanent (n = 2) middle cerebral artery occlusion, and (3) to correlate histopathology findings in the chronic stroke period to the acute and subacute MRI derived tissue signatures. An improved iterative self-organizing data analysis algorithm was used to combine T2, apparent diffusion coefficient (ADC), and fractional anisotropy (FA) maps across seven successive timepoints (1, 2, 3, 24, 72, 144, 240 h) which revealed five temporal MRI signatures, that were different from the normal tissue pattern (P MRI signatures associated with specific tissue fates may further aid in assessing and monitoring the efficacy of novel pharmaceutical treatments for stroke in a pre-clinical and clinical setting.

The diagnostic value of plasma N-terminal connective tissue growth factor levels in children with heart failure.

Science.gov (United States)

Li, Gang; Song, Xueqing; Xia, Jiyi; Li, Jing; Jia, Peng; Chen, Pengyuan; Zhao, Jian; Liu, Bin

2017-01-01

The aim of this study was to assess the diagnostic value of plasma N-terminal connective tissue growth factor in children with heart failure. Methods and results Plasma N-terminal connective tissue growth factor was determined in 61 children, including 41 children with heart failure, 20 children without heart failure, and 30 healthy volunteers. The correlations between plasma N-terminal connective tissue growth factor levels and clinical parameters were investigated. Moreover, the diagnostic value of N-terminal connective tissue growth factor levels was evaluated. Compared with healthy volunteers and children without heart failure, plasma N-terminal connective tissue growth factor levels were significantly elevated in those with heart failure (p0.05), but it obviously improved the ability of diagnosing heart failure in children, as demonstrated by the integrated discrimination improvement (6.2%, p=0.013) and net re-classification improvement (13.2%, p=0.017) indices. Plasma N-terminal connective tissue growth factor is a promising diagnostic biomarker for heart failure in children.

Graves’ Ophthalmopathy: VISA versus EUGOGO Classification, Assessment, and Management

Directory of Open Access Journals (Sweden)

Jesús Barrio-Barrio

2015-01-01

Full Text Available Graves’ ophthalmopathy (GO is an autoimmune inflammatory disorder associated with thyroid disease which affects ocular and orbital tissues. GO follows a biphasic course in which an initial active phase of progression is followed by a subsequent partial regression and a static inactive phase. Although the majority of GO patients have a mild, self-limiting, and nonprogressive ocular involvement, about 3–7% of GO patients exhibit a severe sight-threatening form of the disease due to corneal exposure or compressive optic neuropathy. An appropriate assessment of both severity and activity of the disease warrants an adequate treatment. The VISA (vision, inflammation, strabismus, and appearance, and the European Group of Graves’ Orbitopathy (EUGOGO classifications are the two widely used grading systems conceived to assess the activity and severity of GO and guide the therapeutic decision making. A critical analysis of classification, assessment, and management systems is reported. A simplified “GO activity assessment checklist” for routine clinical practice is proposed. Current treatments are reviewed and management guidelines according to the severity and activity of the disease are provided. New treatment modalities such as specific monoclonal antibodies, TSH-R antagonists, and other immunomodulatory agents show a promising outcome for GO patients.

Improved pulmonary nodule classification utilizing quantitative lung parenchyma features.

Science.gov (United States)

Dilger, Samantha K N; Uthoff, Johanna; Judisch, Alexandra; Hammond, Emily; Mott, Sarah L; Smith, Brian J; Newell, John D; Hoffman, Eric A; Sieren, Jessica C

2015-10-01

Current computer-aided diagnosis (CAD) models for determining pulmonary nodule malignancy characterize nodule shape, density, and border in computed tomography (CT) data. Analyzing the lung parenchyma surrounding the nodule has been minimally explored. We hypothesize that improved nodule classification is achievable by including features quantified from the surrounding lung tissue. To explore this hypothesis, we have developed expanded quantitative CT feature extraction techniques, including volumetric Laws texture energy measures for the parenchyma and nodule, border descriptors using ray-casting and rubber-band straightening, histogram features characterizing densities, and global lung measurements. Using stepwise forward selection and leave-one-case-out cross-validation, a neural network was used for classification. When applied to 50 nodules (22 malignant and 28 benign) from high-resolution CT scans, 52 features (8 nodule, 39 parenchymal, and 5 global) were statistically significant. Nodule-only features yielded an area under the ROC curve of 0.918 (including nodule size) and 0.872 (excluding nodule size). Performance was improved through inclusion of parenchymal (0.938) and global features (0.932). These results show a trend toward increased performance when the parenchyma is included, coupled with the large number of significant parenchymal features that support our hypothesis: the pulmonary parenchyma is influenced differentially by malignant versus benign nodules, assisting CAD-based nodule characterizations.

Accessory cardiac bronchus: Proposed imaging classification on multidetector CT

Energy Technology Data Exchange (ETDEWEB)

Kim, Kang Min; Kim, Young Tong; Han, Jong Kyu; Jou, Sung Shick [Dept. of Radiology, Soonchunhyang University College of Medicine, Cheonan Hospital, Cheonan (Korea, Republic of)

2016-02-15

To propose the classification of accessory cardiac bronchus (ACB) based on imaging using multidetector computed tomography (MDCT), and evaluate follow-up changes of ACB. This study included 58 patients diagnosed as ACB since 9 years, using MDCT. We analyzed the types, division locations and division directions of ACB, and also evaluated changes on follow-up. We identified two main types of ACB: blind-end (51.7%) and lobule (48.3%). The blind-end ACB was further classified into three subtypes: blunt (70%), pointy (23.3%) and saccular (6.7%). The lobule ACB was also further classified into three subtypes: complete (46.4%), incomplete (28.6%) and rudimentary (25%). Division location to the upper half bronchus intermedius (79.3%) and medial direction (60.3%) were the most common in all patients. The difference in division direction was statistically significant between the blind-end and lobule types (p = 0.019). Peribronchial soft tissue was found in five cases. One calcification case was identified in the lobule type. During follow-up, ACB had disappeared in two cases of the blind-end type and in one case of the rudimentary subtype. The proposed classification of ACB based on imaging, and the follow-up CT, helped us to understand the various imaging features of ACB.

Bosniak Classification system

DEFF Research Database (Denmark)

Graumann, Ole; Osther, Susanne Sloth; Karstoft, Jens

2014-01-01

Background: The Bosniak classification is a diagnostic tool for the differentiation of cystic changes in the kidney. The process of categorizing renal cysts may be challenging, involving a series of decisions that may affect the final diagnosis and clinical outcome such as surgical management....... Purpose: To investigate the inter- and intra-observer agreement among experienced uroradiologists when categorizing complex renal cysts according to the Bosniak classification. Material and Methods: The original categories of 100 cystic renal masses were chosen as “Gold Standard” (GS), established...... to the calculated weighted κ all readers performed “very good” for both inter-observer and intra-observer variation. Most variation was seen in cysts catagorized as Bosniak II, IIF, and III. These results show that radiologists who evaluate complex renal cysts routinely may apply the Bosniak classification...

Mining for associations between text and brain activation in a functional neuroimaging database

DEFF Research Database (Denmark)

Nielsen, Finn Årup; Hansen, Lars Kai; Balslev, D.

2004-01-01

We describe a method for mining a neuroimaging database for associations between text and brain locations. The objective is to discover association rules between words indicative of cognitive function as described in abstracts of neuroscience papers and sets of reported stereotactic Talairach...

Definition and classification of epilepsy. Classification of epileptic seizures 2016

Directory of Open Access Journals (Sweden)

K. Yu. Mukhin

2017-01-01

Full Text Available Epilepsy is one of the most common neurological diseases, especially in childhood and adolescence. The incidence varies from 15 to 113 cases per 100 000 population with the maximum among children under 1 year old. The prevalence of epilepsy is high, ranging from 5 to 8 cases (in some regions – 10 cases per 1000 children under 15 years old. Classification of the disease has great importance for diagnosis, treatment and prognosis. The article presents a novel strategy for classification of epileptic seizures, developed in 2016. It contains a number of brand new concepts, including a very important one, saying that some seizures, previously considered as generalized or focal only, can be, in fact, both focal and generalized. They include tonic, atonic, myoclonic seizures and epileptic spasms. The term “secondarily generalized seizure” is replace by the term “bilateral tonic-clonic seizure” (as soon as it is not a separate type of epileptic seizures, and the term reflects the spread of discharge from any area of cerebral cortex and evolution of any types of focal seizures. International League Against Epilepsy recommends to abandon the term “pseudo-epileptic seizures” and replace it by the term “psychogenic non-epileptic seizures”. If a doctor is not sure that seizures have epileptic nature, the term “paroxysmal event” should be used without specifying the disease. The conception of childhood epileptic encephalopathies, developed within this novel classification project, is one of the most significant achievements, since in this case not only the seizures, but even epileptiform activity can induce severe disorders of higher mental functions. In addition to detailed description of the new strategy for classification of epileptic seizures, the article contains a comprehensive review of the existing principles of epilepsy and epileptic seizures classification.

Cell-surface glycoproteins of human sarcomas: differential expression in normal and malignant tissues and cultured cells

International Nuclear Information System (INIS)

Rettig, W.F.; Garin-Chesa, P.; Beresford, H.R.; Oettgen, H.F.; Melamed, M.R.; Old, L.J.

1988-01-01

Normal differentiation and malignant transformation of human cells are characterized by specific changes in surface antigen phenotype. In the present study, the authors have defined six cell-surface antigens of human sarcomas and normal mesenchymal cells, by using mixed hemadsorption assays and immunochemical methods for the analysis of cultured cells and immunohistochemical staining for the analysis of normal tissues and > 200 tumor specimens. Differential patterns of F19, F24, G171, G253, S5, and Thy-1 antigen expression were found to characterize (i) subsets of cultured sarcoma cell lines, (ii) cultured fibroblasts derived from various organs, (iii) normal resting and activated mesenchymal tissues, and (iv) sarcoma and nonmesenchymal tumor tissues. These results provide a basic surface antigenic map for cultured mesenchymal cells and mesenchymal tissues and permit the classification of human sarcomas according to their antigenic phenotypes

Of mice and women: a comparative tissue biology perspective of breast stem cells and differentiation.

Science.gov (United States)

Dontu, Gabriela; Ince, Tan A

2015-06-01

Tissue based research requires a background in human and veterinary pathology, developmental biology, anatomy, as well as molecular and cellular biology. This type of comparative tissue biology (CTB) expertise is necessary to tackle some of the conceptual challenges in human breast stem cell research. It is our opinion that the scarcity of CTB expertise contributed to some erroneous interpretations in tissue based research, some of which are reviewed here in the context of breast stem cells. In this article we examine the dissimilarities between mouse and human mammary tissue and suggest how these may impact stem cell studies. In addition, we consider the differences between breast ducts vs. lobules and clarify how these affect the interpretation of results in stem cell research. Lastly, we introduce a new elaboration of normal epithelial cell types in human breast and discuss how this provides a clinically useful basis for breast cancer classification.

Detection of Connective Tissue Disorders from 3D Aortic MR Images Using Independent Component Analysis

DEFF Research Database (Denmark)

Hansen, Michael Sass; Zhao, Fei; Zhang, Honghai

2006-01-01

A computer-aided diagnosis (CAD) method is reported that allows the objective identification of subjects with connective tissue disorders from 3D aortic MR images using segmentation and independent component analysis (ICA). The first step to extend the model to 4D (3D + time) has also been taken....... ICA is an effective tool for connective tissue disease detection in the presence of sparse data using prior knowledge to order the components, and the components can be inspected visually. 3D+time MR image data sets acquired from 31 normal and connective tissue disorder subjects at end-diastole (R......-wave peak) and at 45\\$\\backslash\\$% of the R-R interval were used to evaluate the performance of our method. The automated 3D segmentation result produced accurate aortic surfaces covering the aorta. The CAD method distinguished between normal and connective tissue disorder subjects with a classification...

5 CFR 2500.3 - Original classification.

Science.gov (United States)

2010-01-01

... 5 Administrative Personnel 3 2010-01-01 2010-01-01 false Original classification. 2500.3 Section... SECURITY REGULATION § 2500.3 Original classification. No one in the Office of Administration has been granted authority for original classification of information. ...

10 CFR 61.55 - Waste classification.

Science.gov (United States)

2010-01-01

... REGULATORY COMMISSION (CONTINUED) LICENSING REQUIREMENTS FOR LAND DISPOSAL OF RADIOACTIVE WASTE Technical Requirements for Land Disposal Facilities § 61.55 Waste classification. (a) Classification of waste for near surface disposal—(1) Considerations. Determination of the classification of radioactive waste involves two...

World Health Organisation Classification of Lymphoid Tumours in Veterinary and Human Medicine: a Comparative Evaluation of Gastrointestinal Lymphomas in 61 Cats.

Science.gov (United States)

Wolfesberger, B; Fuchs-Baumgartinger, A; Greß, V; Hammer, S E; Gradner, G; Knödl, K; Tichy, A; Rütgen, B C; Beham-Schmid, C

2018-02-01

To diagnose and classify the various entities of lymphomas, the World Health Organisation (WHO) classification is applied in human as well as in veterinary medicine. We validated the concordance of these classification systems by having a veterinary and human pathologist evaluate gastrointestinal lymphoma tissue from 61 cats. In 59% of all cases, there was a match between their respective diagnoses of the lymphoma subtype. A complete consensus between the two evaluators was obtained for all samples with a diagnosis of diffuse large B-cell lymphoma, T-cell anaplastic large cell lymphoma and extranodal marginal zone lymphoma of mucosa-associated lymphoid tissue. A corresponding diagnosis was also made in the majority of samples with enteropathy associated T-cell lymphoma (EATL) type II, although this subtype in cats has similarities to the 'indolent T-cell lymphoproliferative disorder of the gastrointestinal tract', a provisional entity newly added to the revised human WHO classification in 2016. Very little consensus has been found with cases of EATL type I due to the fact that most did not meet all of the criteria of human EATL I. Hence, the human pathologist assigned them to the heterogeneous group of peripheral T-cell lymphomas (not otherwise specified). Consequently, concrete guidelines and advanced immunophenotyping based on the model of human medicine are essential to differentiate these challenging entities in veterinary medicine. Copyright © 2017 Elsevier Ltd. All rights reserved.

Surgical Masculinization of the Breast: Clinical Classification and Surgical Procedures.

Science.gov (United States)

Cardenas-Camarena, Lazaro; Dorado, Carlos; Guerrero, Maria Teresa; Nava, Rosa

2017-06-01

Aesthetic breast area improvements for gynecomastia and gender dysphoria patients who seek a more masculine appearance have increased recently. We present our clinical experience in breast masculinization and a classification for these patients. From July 2003 to May 2014, 68 patients seeking a more masculine thorax underwent surgery. They were divided into five groups depending on three factors: excess fatty tissue, breast tissue, and skin. A specific surgical treatment was assigned according to each group. The surgical treatments included thoracic liposuction, subcutaneous mastectomy, periareolar skin resection in one or two stages, and mastectomy with a nipple areola complex graft. The evaluation was performed 6 months after surgery to determine the degree of satisfaction and presence of complications. Surgery was performed on a total of 68 patients, 45 male and 22 female, with ages ranging from 18 to 49 years, and an average age of 33 years. Liposuction alone was performed on five patients; subcutaneous mastectomy was performed on eight patients; subcutaneous mastectomy combined with liposuction was performed on 27 patients; periareolar skin resection was performed on 11 patients; and mastectomy with NAC free grafts was performed on 16 patients. The surgical procedure satisfied 94% of the patients, with very few complications. All patients who wish to obtain a masculine breast shape should be treated with only one objective regardless patient's gender: to obtain a masculine thorax. We recommend a simple mammary gland classification for determining the best surgical treatment for these patients LEVEL OF EVIDENCE V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Vietnamese Document Representation and Classification

Science.gov (United States)

Nguyen, Giang-Son; Gao, Xiaoying; Andreae, Peter

Vietnamese is very different from English and little research has been done on Vietnamese document classification, or indeed, on any kind of Vietnamese language processing, and only a few small corpora are available for research. We created a large Vietnamese text corpus with about 18000 documents, and manually classified them based on different criteria such as topics and styles, giving several classification tasks of different difficulty levels. This paper introduces a new syllable-based document representation at the morphological level of the language for efficient classification. We tested the representation on our corpus with different classification tasks using six classification algorithms and two feature selection techniques. Our experiments show that the new representation is effective for Vietnamese categorization, and suggest that best performance can be achieved using syllable-pair document representation, an SVM with a polynomial kernel as the learning algorithm, and using Information gain and an external dictionary for feature selection.

Bosniak classification system

DEFF Research Database (Denmark)

Graumann, Ole; Osther, Susanne Sloth; Karstoft, Jens

2016-01-01

BACKGROUND: The Bosniak classification was originally based on computed tomographic (CT) findings. Magnetic resonance (MR) and contrast-enhanced ultrasonography (CEUS) imaging may demonstrate findings that are not depicted at CT, and there may not always be a clear correlation between the findings...... at MR and CEUS imaging and those at CT. PURPOSE: To compare diagnostic accuracy of MR, CEUS, and CT when categorizing complex renal cystic masses according to the Bosniak classification. MATERIAL AND METHODS: From February 2011 to June 2012, 46 complex renal cysts were prospectively evaluated by three...... readers. Each mass was categorized according to the Bosniak classification and CT was chosen as gold standard. Kappa was calculated for diagnostic accuracy and data was compared with pathological results. RESULTS: CT images found 27 BII, six BIIF, seven BIII, and six BIV. Forty-three cysts could...

46 CFR Sec. 18 - Group classification.

Science.gov (United States)

2010-10-01

... 46 Shipping 8 2010-10-01 2010-10-01 false Group classification. Sec. 18 Section 18 Shipping... Sec. 18 Group classification. In the preparation of specifications, Job Orders, Supplemental Job... inserted thereon: Number Classification 41 Maintenance Repairs (deck, engine and stewards department...

22 CFR 9.6 - Derivative classification.

Science.gov (United States)

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Derivative classification. 9.6 Section 9.6 Foreign Relations DEPARTMENT OF STATE GENERAL SECURITY INFORMATION REGULATIONS § 9.6 Derivative classification. (a) Definition. Derivative classification is the incorporating, paraphrasing, restating or...

22 CFR 9.4 - Original classification.

Science.gov (United States)

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Original classification. 9.4 Section 9.4 Foreign Relations DEPARTMENT OF STATE GENERAL SECURITY INFORMATION REGULATIONS § 9.4 Original classification. (a) Definition. Original classification is the initial determination that certain information...

28 CFR 524.73 - Classification procedures.

Science.gov (United States)

2010-07-01

... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Classification procedures. 524.73 Section 524.73 Judicial Administration BUREAU OF PRISONS, DEPARTMENT OF JUSTICE INMATE ADMISSION, CLASSIFICATION, AND TRANSFER CLASSIFICATION OF INMATES Central Inmate Monitoring (CIM) System § 524.73...

Gender classification under extended operating conditions

Science.gov (United States)

Rude, Howard N.; Rizki, Mateen

2014-06-01

Gender classification is a critical component of a robust image security system. Many techniques exist to perform gender classification using facial features. In contrast, this paper explores gender classification using body features extracted from clothed subjects. Several of the most effective types of features for gender classification identified in literature were implemented and applied to the newly developed Seasonal Weather And Gender (SWAG) dataset. SWAG contains video clips of approximately 2000 samples of human subjects captured over a period of several months. The subjects are wearing casual business attire and outer garments appropriate for the specific weather conditions observed in the Midwest. The results from a series of experiments are presented that compare the classification accuracy of systems that incorporate various types and combinations of features applied to multiple looks at subjects at different image resolutions to determine a baseline performance for gender classification.

Protein profiling in potato (Solanum tuberosum L.) leaf tissues by differential centrifugation.

Science.gov (United States)

Lim, Sanghyun; Chisholm, Kenneth; Coffin, Robert H; Peters, Rick D; Al-Mughrabi, Khalil I; Wang-Pruski, Gefu; Pinto, Devanand M

2012-04-06

Foliar diseases, such as late blight, result in serious threats to potato production. As such, potato leaf tissue becomes an important substrate to study biological processes, such as plant defense responses to infection. Nonetheless, the potato leaf proteome remains poorly characterized. Here, we report protein profiling of potato leaf tissues using a modified differential centrifugation approach to separate the leaf tissues into cell wall and cytoplasmic fractions. This method helps to increase the number of identified proteins, including targeted putative cell wall proteins. The method allowed for the identification of 1484 nonredundant potato leaf proteins, of which 364 and 447 were reproducibly identified proteins in the cell wall and cytoplasmic fractions, respectively. Reproducibly identified proteins corresponded to over 70% of proteins identified in each replicate. A diverse range of proteins was identified based on their theoretical pI values, molecular masses, functional classification, and biological processes. Such a protein extraction method is effective for the establishment of a highly qualified proteome profile.

Mammogram classification scheme using 2D-discrete wavelet and local binary pattern for detection of breast cancer

Science.gov (United States)

Adi Putra, Januar

2018-04-01

In this paper, we propose a new mammogram classification scheme to classify the breast tissues as normal or abnormal. Feature matrix is generated using Local Binary Pattern to all the detailed coefficients from 2D-DWT of the region of interest (ROI) of a mammogram. Feature selection is done by selecting the relevant features that affect the classification. Feature selection is used to reduce the dimensionality of data and features that are not relevant, in this paper the F-test and Ttest will be performed to the results of the feature extraction dataset to reduce and select the relevant feature. The best features are used in a Neural Network classifier for classification. In this research we use MIAS and DDSM database. In addition to the suggested scheme, the competent schemes are also simulated for comparative analysis. It is observed that the proposed scheme has a better say with respect to accuracy, specificity and sensitivity. Based on experiments, the performance of the proposed scheme can produce high accuracy that is 92.71%, while the lowest accuracy obtained is 77.08%.

Radon classification of building ground

International Nuclear Information System (INIS)

Slunga, E.

1988-01-01

The Laboratories of Building Technology and Soil Mechanics and Foundation Engineering at the Helsinki University of Technology in cooperation with The Ministry of the Environment have proposed a radon classification for building ground. The proposed classification is based on the radon concentration in soil pores and on the permeability of the foundation soil. The classification includes four radon classes: negligible, normal, high and very high. Depending on the radon class the radon-technical solution for structures is chosen. It is proposed that the classification be done in general terms in connection with the site investigations for the planning of land use and in more detail in connection with the site investigations for an individual house. (author)

Formalization of the classification pattern: survey of classification modeling in information systems engineering.

Science.gov (United States)

Partridge, Chris; de Cesare, Sergio; Mitchell, Andrew; Odell, James

2018-01-01

Formalization is becoming more common in all stages of the development of information systems, as a better understanding of its benefits emerges. Classification systems are ubiquitous, no more so than in domain modeling. The classification pattern that underlies these systems provides a good case study of the move toward formalization in part because it illustrates some of the barriers to formalization, including the formal complexity of the pattern and the ontological issues surrounding the "one and the many." Powersets are a way of characterizing the (complex) formal structure of the classification pattern, and their formalization has been extensively studied in mathematics since Cantor's work in the late nineteenth century. One can use this formalization to develop a useful benchmark. There are various communities within information systems engineering (ISE) that are gradually working toward a formalization of the classification pattern. However, for most of these communities, this work is incomplete, in that they have not yet arrived at a solution with the expressiveness of the powerset benchmark. This contrasts with the early smooth adoption of powerset by other information systems communities to, for example, formalize relations. One way of understanding the varying rates of adoption is recognizing that the different communities have different historical baggage. Many conceptual modeling communities emerged from work done on database design, and this creates hurdles to the adoption of the high level of expressiveness of powersets. Another relevant factor is that these communities also often feel, particularly in the case of domain modeling, a responsibility to explain the semantics of whatever formal structures they adopt. This paper aims to make sense of the formalization of the classification pattern in ISE and surveys its history through the literature, starting from the relevant theoretical works of the mathematical literature and gradually shifting focus

Ototoxicity (cochleotoxicity) classifications: A review.

Science.gov (United States)

Crundwell, Gemma; Gomersall, Phil; Baguley, David M

2016-01-01

Drug-mediated ototoxicity, specifically cochleotoxicity, is a concern for patients receiving medications for the treatment of serious illness. A number of classification schemes exist, most of which are based on pure-tone audiometry, in order to assist non-audiological/non-otological specialists in the identification and monitoring of iatrogenic hearing loss. This review identifies the primary classification systems used in cochleototoxicity monitoring. By bringing together classifications published in discipline-specific literature, the paper aims to increase awareness of their relative strengths and limitations in the assessment and monitoring of ototoxic hearing loss and to indicate how future classification systems may improve upon the status-quo. Literature review. PubMed identified 4878 articles containing the search term ototox*. A systematic search identified 13 key classification systems. Cochleotoxicity classification systems can be divided into those which focus on hearing change from a baseline audiogram and those that focus on the functional impact of the hearing loss. Common weaknesses of these grading scales included a lack of sensitivity to small adverse changes in hearing thresholds, a lack of high-frequency audiometry (>8 kHz), and lack of indication of which changes are likely to be clinically significant for communication and quality of life.

10 CFR 1045.37 - Classification guides.

Science.gov (United States)

2010-01-01

... 10 Energy 4 2010-01-01 2010-01-01 false Classification guides. 1045.37 Section 1045.37 Energy DEPARTMENT OF ENERGY (GENERAL PROVISIONS) NUCLEAR CLASSIFICATION AND DECLASSIFICATION Generation and Review of Documents Containing Restricted Data and Formerly Restricted Data § 1045.37 Classification guides...

75 FR 10529 - Mail Classification Change

Science.gov (United States)

2010-03-08

... POSTAL REGULATORY COMMISSION [Docket Nos. MC2010-19; Order No. 415] Mail Classification Change...-filed Postal Service request to make a minor modification to the Mail Classification Schedule. The.... concerning a change in classification which reflects a change in terminology from Bulk Mailing Center (BMC...

32 CFR 1602.13 - Judgmental Classification.

Science.gov (United States)

2010-07-01

... 32 National Defense 6 2010-07-01 2010-07-01 false Judgmental Classification. 1602.13 Section 1602.13 National Defense Other Regulations Relating to National Defense SELECTIVE SERVICE SYSTEM DEFINITIONS § 1602.13 Judgmental Classification. A classification action relating to a registrant's claim for...

6 CFR 7.26 - Derivative classification.

Science.gov (United States)

2010-01-01

... 6 Domestic Security 1 2010-01-01 2010-01-01 false Derivative classification. 7.26 Section 7.26 Domestic Security DEPARTMENT OF HOMELAND SECURITY, OFFICE OF THE SECRETARY CLASSIFIED NATIONAL SECURITY INFORMATION Classified Information § 7.26 Derivative classification. (a) Derivative classification is defined...

Hydropedological insights when considering catchment classification

NARCIS (Netherlands)

Bouma, J.; Droogers, P.; Sonneveld, M.P.W.; Ritsema, C.J.; Hunink, J.E.; Immerzeel, W.W.; Kauffman, S.

2011-01-01

Soil classification systems are analysed to explore the potential of developing classification systems for catchments. Soil classifications are useful to create systematic order in the overwhelming quantity of different soils in the world and to extrapolate data available for a given soil type to

Plexiform neurofibroma tissue classification

Science.gov (United States)

Weizman, L.; Hoch, L.; Ben Sira, L.; Joskowicz, L.; Pratt, L.; Constantini, S.; Ben Bashat, D.

2011-03-01

Plexiform Neurofibroma (PN) is a major complication of NeuroFibromatosis-1 (NF1), a common genetic disease that involving the nervous system. PNs are peripheral nerve sheath tumors extending along the length of the nerve in various parts of the body. Treatment decision is based on tumor volume assessment using MRI, which is currently time consuming and error prone, with limited semi-automatic segmentation support. We present in this paper a new method for the segmentation and tumor mass quantification of PN from STIR MRI scans. The method starts with a user-based delineation of the tumor area in a single slice and automatically detects the PN lesions in the entire image based on the tumor connectivity. Experimental results on seven datasets yield a mean volume overlap difference of 25% as compared to manual segmentation by expert radiologist with a mean computation and interaction time of 12 minutes vs. over an hour for manual annotation. Since the user interaction in the segmentation process is minimal, our method has the potential to successfully become part of the clinical workflow.

14 CFR 1203.407 - Duration of classification.

Science.gov (United States)

2010-01-01

... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Duration of classification. 1203.407... PROGRAM Guides for Original Classification § 1203.407 Duration of classification. (a) Information shall be... date or event for declassification shall be set by the original classification authority at the time...

HIV classification using coalescent theory

Energy Technology Data Exchange (ETDEWEB)

Zhang, Ming [Los Alamos National Laboratory; Letiner, Thomas K [Los Alamos National Laboratory; Korber, Bette T [Los Alamos National Laboratory

2008-01-01

Algorithms for subtype classification and breakpoint detection of HIV-I sequences are based on a classification system of HIV-l. Hence, their quality highly depend on this system. Due to the history of creation of the current HIV-I nomenclature, the current one contains inconsistencies like: The phylogenetic distance between the subtype B and D is remarkably small compared with other pairs of subtypes. In fact, it is more like the distance of a pair of subsubtypes Robertson et al. (2000); Subtypes E and I do not exist any more since they were discovered to be composed of recombinants Robertson et al. (2000); It is currently discussed whether -- instead of CRF02 being a recombinant of subtype A and G -- subtype G should be designated as a circulating recombination form (CRF) nd CRF02 as a subtype Abecasis et al. (2007); There are 8 complete and over 400 partial HIV genomes in the LANL-database which belong neither to a subtype nor to a CRF (denoted by U). Moreover, the current classification system is somehow arbitrary like all complex classification systems that were created manually. To this end, it is desirable to deduce the classification system of HIV systematically by an algorithm. Of course, this problem is not restricted to HIV, but applies to all fast mutating and recombining viruses. Our work addresses the simpler subproblem to score classifications of given input sequences of some virus species (classification denotes a partition of the input sequences in several subtypes and CRFs). To this end, we reconstruct ancestral recombination graphs (ARG) of the input sequences under restrictions determined by the given classification. These restritions are imposed in order to ensure that the reconstructed ARGs do not contradict the classification under consideration. Then, we find the ARG with maximal probability by means of Markov Chain Monte Carlo methods. The probability of the most probable ARG is interpreted as a score for the classification. To our

A practicable approach for periodontal classification

Science.gov (United States)

Mittal, Vishnu; Bhullar, Raman Preet K.; Bansal, Rachita; Singh, Karanprakash; Bhalodi, Anand; Khinda, Paramjit K.

2013-01-01

The Diagnosis and classification of periodontal diseases has remained a dilemma since long. Two distinct concepts have been used to define diseases: Essentialism and Nominalism. Essentialistic concept implies the real existence of disease whereas; nominalistic concept states that the names of diseases are the convenient way of stating concisely the endpoint of a diagnostic process. It generally advances from assessment of symptoms and signs toward knowledge of causation and gives a feasible option to name the disease for which etiology is either unknown or it is too complex to access in routine clinical practice. Various classifications have been proposed by the American Academy of Periodontology (AAP) in 1986, 1989 and 1999. The AAP 1999 classification is among the most widely used classification. But this classification also has demerits which provide impediment for its use in day to day practice. Hence a classification and diagnostic system is required which can help the clinician to access the patient's need and provide a suitable treatment which is in harmony with the diagnosis for that particular case. Here is an attempt to propose a practicable classification and diagnostic system of periodontal diseases for better treatment outcome. PMID:24379855

Lossless Compression of Classification-Map Data

Science.gov (United States)

Hua, Xie; Klimesh, Matthew

2009-01-01

A lossless image-data-compression algorithm intended specifically for application to classification-map data is based on prediction, context modeling, and entropy coding. The algorithm was formulated, in consideration of the differences between classification maps and ordinary images of natural scenes, so as to be capable of compressing classification- map data more effectively than do general-purpose image-data-compression algorithms. Classification maps are typically generated from remote-sensing images acquired by instruments aboard aircraft (see figure) and spacecraft. A classification map is a synthetic image that summarizes information derived from one or more original remote-sensing image(s) of a scene. The value assigned to each pixel in such a map is the index of a class that represents some type of content deduced from the original image data for example, a type of vegetation, a mineral, or a body of water at the corresponding location in the scene. When classification maps are generated onboard the aircraft or spacecraft, it is desirable to compress the classification-map data in order to reduce the volume of data that must be transmitted to a ground station.

Idiopathic Inflammatory Myopathies; Association with Overlap Myositis and Syndromes: Classification, Clinical Characteristics, and Associated Autoantibodies

Directory of Open Access Journals (Sweden)

Pari Basharat

2016-07-01

Full Text Available Idiopathic inflammatory myopathies (IIM are traditionally identified as a group of disorders that target skeletal muscle due to autoimmune dysfunction. The IIM can be divided into subtypes based on certain clinical characteristics, and several classification schemes have been proposed. The predominant diagnostic criteria for IIM is the Bohan and Peter criteria, which subdivides IIM into primary polymyositis (PM, primary dermatomyositis (DM, myositis with another connective tissue disease, and myositis associated with cancer. However, this measure has been criticised for several reasons including lack of specific criteria to help distinguish between muscle biopsy findings of PM, DM, and immune-mediated necrotising myopathy, as well as the lack of identification of cases of overlap myositis (OM. Because of this issue, other classification criteria for IIM have been proposed, which include utilising myositis-associated antibodies and myositis-specific antibodies, as well as overlap features such as Raynaud’s phenomenon, polyarthritis, oesophageal abnormalities, interstitial lung disease, small bowel abnormalities such as hypomotility and malabsorption, and renal crises, amongst others. Indeed, the identification of autoantibodies associated with certain clinical phenotypes of myositis, in particular connective tissue disease-myositis overlap, has further helped divide IIM into distinct clinical subsets, which include OM and overlap syndromes (OS. This paper reviews the concepts of OM and OS as they pertain to IIM, including definitions in the literature, clinical characteristics, and overlap autoantibodies.

Automatic segmentation of MR brain images of preterm infants using supervised classification.

Science.gov (United States)

Moeskops, Pim; Benders, Manon J N L; Chiţ, Sabina M; Kersbergen, Karina J; Groenendaal, Floris; de Vries, Linda S; Viergever, Max A; Išgum, Ivana

2015-09-01

Preterm birth is often associated with impaired brain development. The state and expected progression of preterm brain development can be evaluated using quantitative assessment of MR images. Such measurements require accurate segmentation of different tissue types in those images. This paper presents an algorithm for the automatic segmentation of unmyelinated white matter (WM), cortical grey matter (GM), and cerebrospinal fluid in the extracerebral space (CSF). The algorithm uses supervised voxel classification in three subsequent stages. In the first stage, voxels that can easily be assigned to one of the three tissue types are labelled. In the second stage, dedicated analysis of the remaining voxels is performed. The first and the second stages both use two-class classification for each tissue type separately. Possible inconsistencies that could result from these tissue-specific segmentation stages are resolved in the third stage, which performs multi-class classification. A set of T1- and T2-weighted images was analysed, but the optimised system performs automatic segmentation using a T2-weighted image only. We have investigated the performance of the algorithm when using training data randomly selected from completely annotated images as well as when using training data from only partially annotated images. The method was evaluated on images of preterm infants acquired at 30 and 40weeks postmenstrual age (PMA). When the method was trained using random selection from the completely annotated images, the average Dice coefficients were 0.95 for WM, 0.81 for GM, and 0.89 for CSF on an independent set of images acquired at 30weeks PMA. When the method was trained using only the partially annotated images, the average Dice coefficients were 0.95 for WM, 0.78 for GM and 0.87 for CSF for the images acquired at 30weeks PMA, and 0.92 for WM, 0.80 for GM and 0.85 for CSF for the images acquired at 40weeks PMA. Even though the segmentations obtained using training data

Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach

Directory of Open Access Journals (Sweden)

Parida Shreemanta K

2010-01-01

Full Text Available Abstract Background For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues. Results Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach. Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available. Conclusions The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in

6 CFR 7.30 - Classification challenges.

Science.gov (United States)

2010-01-01

... 6 Domestic Security 1 2010-01-01 2010-01-01 false Classification challenges. 7.30 Section 7.30... INFORMATION Classified Information § 7.30 Classification challenges. (a) Authorized holders of information... classified are encouraged and expected to challenge the classification status of that information pursuant to...

7 CFR 51.1904 - Maturity classification.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Maturity classification. 51.1904 Section 51.1904... STANDARDS) United States Consumer Standards for Fresh Tomatoes Size and Maturity Classification § 51.1904 Maturity classification. Tomatoes which are characteristically red when ripe, but are not overripe or soft...

Classification of radiation effects for dose limitation purposes: history, current situation and future prospects

Science.gov (United States)

Hamada, Nobuyuki; Fujimichi, Yuki

2014-01-01

Radiation exposure causes cancer and non-cancer health effects, each of which differs greatly in the shape of the dose–response curve, latency, persistency, recurrence, curability, fatality and impact on quality of life. In recent decades, for dose limitation purposes, the International Commission on Radiological Protection has divided such diverse effects into tissue reactions (formerly termed non-stochastic and deterministic effects) and stochastic effects. On the one hand, effective dose limits aim to reduce the risks of stochastic effects (cancer/heritable effects) and are based on the detriment-adjusted nominal risk coefficients, assuming a linear-non-threshold dose response and a dose and dose rate effectiveness factor of 2. On the other hand, equivalent dose limits aim to avoid tissue reactions (vision-impairing cataracts and cosmetically unacceptable non-cancer skin changes) and are based on a threshold dose. However, the boundary between these two categories is becoming vague. Thus, we review the changes in radiation effect classification, dose limitation concepts, and the definition of detriment and threshold. Then, the current situation is overviewed focusing on (i) stochastic effects with a threshold, (ii) tissue reactions without a threshold, (iii) target organs/tissues for circulatory disease, (iv) dose levels for limitation of cancer risks vs prevention of non-life-threatening tissue reactions vs prevention of life-threatening tissue reactions, (v) mortality or incidence of thyroid cancer, and (vi) the detriment for tissue reactions. For future discussion, one approach is suggested that classifies radiation effects according to whether effects are life threatening, and radiobiological research needs are also briefly discussed. PMID:24794798

Classification of radiological procedures

International Nuclear Information System (INIS)

1989-01-01

A classification for departments in Danish hospitals which use radiological procedures. The classification codes consist of 4 digits, where the first 2 are the codes for the main groups. The first digit represents the procedure's topographical object and the second the techniques. The last 2 digits describe individual procedures. (CLS)

Actionable gene-based classification toward precision medicine in gastric cancer

Directory of Open Access Journals (Sweden)

Hiroshi Ichikawa

2017-10-01

Full Text Available Abstract Background Intertumoral heterogeneity represents a significant hurdle to identifying optimized targeted therapies in gastric cancer (GC. To realize precision medicine for GC patients, an actionable gene alteration-based molecular classification that directly associates GCs with targeted therapies is needed. Methods A total of 207 Japanese patients with GC were included in this study. Formalin-fixed, paraffin-embedded (FFPE tumor tissues were obtained from surgical or biopsy specimens and were subjected to DNA extraction. We generated comprehensive genomic profiling data using a 435-gene panel including 69 actionable genes paired with US Food and Drug Administration-approved targeted therapies, and the evaluation of Epstein-Barr virus (EBV infection and microsatellite instability (MSI status. Results Comprehensive genomic sequencing detected at least one alteration of 435 cancer-related genes in 194 GCs (93.7% and of 69 actionable genes in 141 GCs (68.1%. We classified the 207 GCs into four The Cancer Genome Atlas (TCGA subtypes using the genomic profiling data; EBV (N = 9, MSI (N = 17, chromosomal instability (N = 119, and genomically stable subtype (N = 62. Actionable gene alterations were not specific and were widely observed throughout all TCGA subtypes. To discover a novel classification which more precisely selects candidates for targeted therapies, 207 GCs were classified using hypermutated phenotype and the mutation profile of 69 actionable genes. We identified a hypermutated group (N = 32, while the others (N = 175 were sub-divided into six clusters including five with actionable gene alterations: ERBB2 (N = 25, CDKN2A, and CDKN2B (N = 10, KRAS (N = 10, BRCA2 (N = 9, and ATM cluster (N = 12. The clinical utility of this classification was demonstrated by a case of unresectable GC with a remarkable response to anti-HER2 therapy in the ERBB2 cluster. Conclusions This actionable gene

Expected Classification Accuracy

Directory of Open Access Journals (Sweden)

Lawrence M. Rudner

2005-08-01

Full Text Available Every time we make a classification based on a test score, we should expect some number..of misclassifications. Some examinees whose true ability is within a score range will have..observed scores outside of that range. A procedure for providing a classification table of..true and expected scores is developed for polytomously scored items under item response..theory and applied to state assessment data. A simplified procedure for estimating the..table entries is also presented.

12 CFR 560.160 - Asset classification.

Science.gov (United States)

2010-01-01

... 12 Banks and Banking 5 2010-01-01 2010-01-01 false Asset classification. 560.160 Section 560.160... Lending and Investment Provisions Applicable to all Savings Associations § 560.160 Asset classification... consistent with, or reconcilable to, the asset classification system used by OTS in its Thrift Activities...

33 CFR 154.1216 - Facility classification.

Science.gov (United States)

2010-07-01

... 33 Navigation and Navigable Waters 2 2010-07-01 2010-07-01 false Facility classification. 154.1216... Vegetable Oils Facilities § 154.1216 Facility classification. (a) The Coast Guard classifies facilities that... classification of a facility that handles, stores, or transports animal fats or vegetable oils. The COTP may...

76 FR 47614 - Mail Classification Change

Science.gov (United States)

2011-08-05

... POSTAL REGULATORY COMMISSION [Docket No. MC2011-27; Order No. 785] Mail Classification Change...-filed Postal Service request for a change in classification to the ``Reply Rides Free'' program. The... Service filed a notice of classification change pursuant to 39 CFR 3020.90 and 3020.91 concerning the...

A pentatonic classification of extreme events

International Nuclear Information System (INIS)

Eliazar, Iddo; Cohen, Morrel H.

2015-01-01

In this paper we present a classification of the extreme events – very small and very large outcomes – of positive-valued random variables. The classification distinguishes five different categories of randomness, ranging from the very ‘mild’ to the very ‘wild’. In analogy with the common five-tone musical scale we term the classification ‘pentatonic’. The classification is based on the analysis of the inherent Gibbsian ‘forces’ and ‘temperatures’ existing on the logarithmic scale of the random variables under consideration, and provides a statistical-physics insight regarding the nature of these random variables. The practical application of the pentatonic classification is remarkably straightforward, it can be performed by non-experts, and it is demonstrated via an array of examples

Sow-activity classification from acceleration patterns

DEFF Research Database (Denmark)

Escalante, Hugo Jair; Rodriguez, Sara V.; Cordero, Jorge

2013-01-01

sow-activity classification can be approached with standard machine learning methods for pattern classification. Individual predictions for elements of times series of arbitrary length are combined to classify it as a whole. An extensive comparison of representative learning algorithms, including......This paper describes a supervised learning approach to sow-activity classification from accelerometer measurements. In the proposed methodology, pairs of accelerometer measurements and activity types are considered as labeled instances of a usual supervised classification task. Under this scenario...... neural networks, support vector machines, and ensemble methods, is presented. Experimental results are reported using a data set for sow-activity classification collected in a real production herd. The data set, which has been widely used in related works, includes measurements from active (Feeding...

Active Learning for Text Classification

OpenAIRE

Hu, Rong

2011-01-01

Text classification approaches are used extensively to solve real-world challenges. The success or failure of text classification systems hangs on the datasets used to train them, without a good dataset it is impossible to build a quality system. This thesis examines the applicability of active learning in text classification for the rapid and economical creation of labelled training data. Four main contributions are made in this thesis. First, we present two novel selection strategies to cho...

Unsupervised Classification Using Immune Algorithm

OpenAIRE

Al-Muallim, M. T.; El-Kouatly, R.

2012-01-01

Unsupervised classification algorithm based on clonal selection principle named Unsupervised Clonal Selection Classification (UCSC) is proposed in this paper. The new proposed algorithm is data driven and self-adaptive, it adjusts its parameters to the data to make the classification operation as fast as possible. The performance of UCSC is evaluated by comparing it with the well known K-means algorithm using several artificial and real-life data sets. The experiments show that the proposed U...

Examining applying high performance genetic data feature selection and classification algorithms for colon cancer diagnosis.

Science.gov (United States)

Al-Rajab, Murad; Lu, Joan; Xu, Qiang

2017-07-01

This paper examines the accuracy and efficiency (time complexity) of high performance genetic data feature selection and classification algorithms for colon cancer diagnosis. The need for this research derives from the urgent and increasing need for accurate and efficient algorithms. Colon cancer is a leading cause of death worldwide, hence it is vitally important for the cancer tissues to be expertly identified and classified in a rapid and timely manner, to assure both a fast detection of the disease and to expedite the drug discovery process. In this research, a three-phase approach was proposed and implemented: Phases One and Two examined the feature selection algorithms and classification algorithms employed separately, and Phase Three examined the performance of the combination of these. It was found from Phase One that the Particle Swarm Optimization (PSO) algorithm performed best with the colon dataset as a feature selection (29 genes selected) and from Phase Two that the Support Vector Machine (SVM) algorithm outperformed other classifications, with an accuracy of almost 86%. It was also found from Phase Three that the combined use of PSO and SVM surpassed other algorithms in accuracy and performance, and was faster in terms of time analysis (94%). It is concluded that applying feature selection algorithms prior to classification algorithms results in better accuracy than when the latter are applied alone. This conclusion is important and significant to industry and society. Copyright © 2017 Elsevier B.V. All rights reserved.

MRI findings of inflammatory myofibroblastic tumor of the soft tissue

International Nuclear Information System (INIS)

Deng Demao; Meng Quanfei; Chen Yinming; Zhou Chunxiang; Gao Zhenhua; Yang Zheng; Wang Liantang

2008-01-01

Objective: To describe MR findings in inflammatory myofibroblastic tumor (IMT) of the soft tissue. Methods: MR manifestations of 11 cases of IMT of the soft tissue were retrospectively analyzed, and the MR findings were correlated with surgical and histological results. Results: The pathological classification of the tumors was type I in 1 case, type II in 4 cases, mainly type II admixed with type I in 3 cases, and mainly type II admixed with type III in 3 eases. In 4 cases with primary tumor, the tumors were spheroid in shape, with well-defined margin and pseudocapsule. In 2 eases with primary axillary tumor and 5 cases with recurrent tumor, the tumors were irregular in shape, with ill-defined margin and invasion of adjacent structures. The tumors were mainly isointensive in T 1 -weighted images. Tumors of different pathological classifications had different signal intensities in T 2 -weighted images: 1 case of type I tumor was bright; 4 cases of type II tumor and 3 cases of type II tumor admixed with type I tumor were slightly bright; 3 cases of type II tumor admixed with type III were isointense or slightly hypointense in signal. All of the 11 cases in the study exhibited 'pitaya cross-section sign' in T 2 -weighted sequence, which referred to discrete punctuate foci of relatively hypointensity in the background of hyperintensity, slightly hypointensity or isointensity. All of the 11 cases exhibited inhomogeneously significant enhancement after gadolinium administration. In the follow-up of the 6 eases of primary tumor, 4 cases had recurrence, 1 case had no recurrence, and 1 case was lost in the follow-up process. In the follow-up of the 5 cases of recurrent tumor, 4 cases showed recurrence again, and 3 cases were lost in the follow-up process. Conclusions: The IMT of the soft tissue has characteristic MR features. The signal intensity of the tumor on T2-weighted sequence could reflect the pathological type of the tumor' to some extent. 'pitaya cross

7 CFR 51.1903 - Size classification.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Size classification. 51.1903 Section 51.1903... STANDARDS) United States Consumer Standards for Fresh Tomatoes Size and Maturity Classification § 51.1903 Size classification. The following terms may be used for describing the size of the tomatoes in any lot...

Boosted classification trees result in minor to modest improvement in the accuracy in classifying cardiovascular outcomes compared to conventional classification trees

Science.gov (United States)

Austin, Peter C; Lee, Douglas S

2011-01-01

Purpose: Classification trees are increasingly being used to classifying patients according to the presence or absence of a disease or health outcome. A limitation of classification trees is their limited predictive accuracy. In the data-mining and machine learning literature, boosting has been developed to improve classification. Boosting with classification trees iteratively grows classification trees in a sequence of reweighted datasets. In a given iteration, subjects that were misclassified in the previous iteration are weighted more highly than subjects that were correctly classified. Classifications from each of the classification trees in the sequence are combined through a weighted majority vote to produce a final classification. The authors' objective was to examine whether boosting improved the accuracy of classification trees for predicting outcomes in cardiovascular patients. Methods: We examined the utility of boosting classification trees for classifying 30-day mortality outcomes in patients hospitalized with either acute myocardial infarction or congestive heart failure. Results: Improvements in the misclassification rate using boosted classification trees were at best minor compared to when conventional classification trees were used. Minor to modest improvements to sensitivity were observed, with only a negligible reduction in specificity. For predicting cardiovascular mortality, boosted classification trees had high specificity, but low sensitivity. Conclusions: Gains in predictive accuracy for predicting cardiovascular outcomes were less impressive than gains in performance observed in the data mining literature. PMID:22254181

High throughput assessment of cells and tissues: Bayesian classification of spectral metrics from infrared vibrational spectroscopic imaging data.

Science.gov (United States)

Bhargava, Rohit; Fernandez, Daniel C; Hewitt, Stephen M; Levin, Ira W

2006-07-01

Vibrational spectroscopy allows a visualization of tissue constituents based on intrinsic chemical composition and provides a potential route to obtaining diagnostic markers of diseases. Characterizations utilizing infrared vibrational spectroscopy, in particular, are conventionally low throughput in data acquisition, generally lacking in spatial resolution with the resulting data requiring intensive numerical computations to extract information. These factors impair the ability of infrared spectroscopic measurements to represent accurately the spatial heterogeneity in tissue, to incorporate robustly the diversity introduced by patient cohorts or preparative artifacts and to validate developed protocols in large population studies. In this manuscript, we demonstrate a combination of Fourier transform infrared (FTIR) spectroscopic imaging, tissue microarrays (TMAs) and fast numerical analysis as a paradigm for the rapid analysis, development and validation of high throughput spectroscopic characterization protocols. We provide an extended description of the data treatment algorithm and a discussion of various factors that may influence decision-making using this approach. Finally, a number of prostate tissue biopsies, arranged in an array modality, are employed to examine the efficacy of this approach in histologic recognition of epithelial cell polarization in patients displaying a variety of normal, malignant and hyperplastic conditions. An index of epithelial cell polarization, derived from a combined spectral and morphological analysis, is determined to be a potentially useful diagnostic marker.

Morphological classification of plant cell deaths

DEFF Research Database (Denmark)

van Doorn, W.G.; Beers, E.P.; Dangl, J.L.

2011-01-01

, which can express features of both necrosis and vacuolar cell death, PCD in starchy cereal endosperm and during self-incompatibility. The present classification is not static, but will be subject to further revision, especially when specific biochemical pathways are better defined....... the classification of PCD in plants. Here we suggest a classification based on morphological criteria. According to this classification, the use of the term 'apoptosis' is not justified in plants, but at least two classes of PCD can be distinguished: vacuolar cell death and necrosis. During vacuolar cell death...

22 CFR 9a.4 - Classification.

Science.gov (United States)

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Classification. 9a.4 Section 9a.4 Foreign... ENERGY PROGRAMS; RELATED MATERIAL § 9a.4 Classification. (a) Section 1 of E.O. 11932, August 4, 1976.... If the officer determines that the information or material warrants classification, he shall assign...

7 CFR 51.1402 - Size classification.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Size classification. 51.1402 Section 51.1402... STANDARDS) United States Standards for Grades of Pecans in the Shell 1 Size Classification § 51.1402 Size classification. Size of pecans may be specified in connection with the grade in accordance with one of the...

Classification of high resolution satellite images

OpenAIRE

Karlsson, Anders

2003-01-01

In this thesis the Support Vector Machine (SVM)is applied on classification of high resolution satellite images. Sveral different measures for classification, including texture mesasures, 1st order statistics, and simple contextual information were evaluated. Additionnally, the image was segmented, using an enhanced watershed method, in order to improve the classification accuracy.

Phylogenetic classification of bony fishes.

Science.gov (United States)

Betancur-R, Ricardo; Wiley, Edward O; Arratia, Gloria; Acero, Arturo; Bailly, Nicolas; Miya, Masaki; Lecointre, Guillaume; Ortí, Guillermo

2017-07-06

Fish classifications, as those of most other taxonomic groups, are being transformed drastically as new molecular phylogenies provide support for natural groups that were unanticipated by previous studies. A brief review of the main criteria used by ichthyologists to define their classifications during the last 50 years, however, reveals slow progress towards using an explicit phylogenetic framework. Instead, the trend has been to rely, in varying degrees, on deep-rooted anatomical concepts and authority, often mixing taxa with explicit phylogenetic support with arbitrary groupings. Two leading sources in ichthyology frequently used for fish classifications (JS Nelson's volumes of Fishes of the World and W. Eschmeyer's Catalog of Fishes) fail to adopt a global phylogenetic framework despite much recent progress made towards the resolution of the fish Tree of Life. The first explicit phylogenetic classification of bony fishes was published in 2013, based on a comprehensive molecular phylogeny ( www.deepfin.org ). We here update the first version of that classification by incorporating the most recent phylogenetic results. The updated classification presented here is based on phylogenies inferred using molecular and genomic data for nearly 2000 fishes. A total of 72 orders (and 79 suborders) are recognized in this version, compared with 66 orders in version 1. The phylogeny resolves placement of 410 families, or ~80% of the total of 514 families of bony fishes currently recognized. The ordinal status of 30 percomorph families included in this study, however, remains uncertain (incertae sedis in the series Carangaria, Ovalentaria, or Eupercaria). Comments to support taxonomic decisions and comparisons with conflicting taxonomic groups proposed by others are presented. We also highlight cases were morphological support exist for the groups being classified. This version of the phylogenetic classification of bony fishes is substantially improved, providing resolution

Functional classifications for cerebral palsy: correlations between the gross motor function classification system (GMFCS), the manual ability classification system (MACS) and the communication function classification system (CFCS).

Science.gov (United States)

Compagnone, Eliana; Maniglio, Jlenia; Camposeo, Serena; Vespino, Teresa; Losito, Luciana; De Rinaldis, Marta; Gennaro, Leonarda; Trabacca, Antonio

2014-11-01

This study aimed to investigate a possible correlation between the gross motor function classification system-expanded and revised (GMFCS-E&R), the manual abilities classification system (MACS) and the communication function classification system (CFCS) functional levels in children with cerebral palsy (CP) by CP subtype. It was also geared to verify whether there is a correlation between these classification systems and intellectual functioning (IF) and parental socio-economic status (SES). A total of 87 children (47 males and 40 females, age range 4-18 years, mean age 8.9±4.2) were included in the study. A strong correlation was found between the three classifications: Level V of the GMFCS-E&R corresponds to Level V of the MACS (rs=0.67, p=0.001); the same relationship was found for the CFCS and the MACS (rs=0.73, p<0.001) and for the GMFCS-E&R and the CFCS (rs=0.61, p=0.001). The correlations between the IQ and the global functional disability profile were strong or moderate (GMFCS and IQ: rs=0.66, p=0.001; MACS and IQ: rs=0.58, p=0.001; CFCS and MACS: rs=0.65, p=0.001). The Kruskal-Wallis test was used to determine if there were differences between the GMFCS-E&R, the CFCS and the MACS by CP type. CP types showed different scores for the IQ level (Chi-square=8.59, df=2, p=0.014), the GMFCS-E&R (Chi-square=36.46, df=2, p<0.001), the CFCS (Chi-square=12.87, df=2, p=0.002), and the MACS Level (Chi-square=13.96, df=2, p<0.001) but no significant differences emerged for the SES (Chi-square=1.19, df=2, p=0.554). This study shows how the three functional classifications (GMFCS-E&R, CFCS and MACS) complement each other to provide a better description of the functional profile of CP. The systematic evaluation of the IQ can provide useful information about a possible future outcome for every functional level. The SES does not appear to affect functional profiles. Copyright © 2014 Elsevier Ltd. All rights reserved.

Clustering and classification of email contents

Directory of Open Access Journals (Sweden)

Izzat Alsmadi

2015-01-01

Full Text Available Information users depend heavily on emails’ system as one of the major sources of communication. Its importance and usage are continuously growing despite the evolution of mobile applications, social networks, etc. Emails are used on both the personal and professional levels. They can be considered as official documents in communication among users. Emails’ data mining and analysis can be conducted for several purposes such as: Spam detection and classification, subject classification, etc. In this paper, a large set of personal emails is used for the purpose of folder and subject classifications. Algorithms are developed to perform clustering and classification for this large text collection. Classification based on NGram is shown to be the best for such large text collection especially as text is Bi-language (i.e. with English and Arabic content.

Statistical analysis of textural features for improved classification of oral histopathological images.

Science.gov (United States)

Muthu Rama Krishnan, M; Shah, Pratik; Chakraborty, Chandan; Ray, Ajoy K

2012-04-01

The objective of this paper is to provide an improved technique, which can assist oncopathologists in correct screening of oral precancerous conditions specially oral submucous fibrosis (OSF) with significant accuracy on the basis of collagen fibres in the sub-epithelial connective tissue. The proposed scheme is composed of collagen fibres segmentation, its textural feature extraction and selection, screening perfomance enhancement under Gaussian transformation and finally classification. In this study, collagen fibres are segmented on R,G,B color channels using back-probagation neural network from 60 normal and 59 OSF histological images followed by histogram specification for reducing the stain intensity variation. Henceforth, textural features of collgen area are extracted using fractal approaches viz., differential box counting and brownian motion curve . Feature selection is done using Kullback-Leibler (KL) divergence criterion and the screening performance is evaluated based on various statistical tests to conform Gaussian nature. Here, the screening performance is enhanced under Gaussian transformation of the non-Gaussian features using hybrid distribution. Moreover, the routine screening is designed based on two statistical classifiers viz., Bayesian classification and support vector machines (SVM) to classify normal and OSF. It is observed that SVM with linear kernel function provides better classification accuracy (91.64%) as compared to Bayesian classifier. The addition of fractal features of collagen under Gaussian transformation improves Bayesian classifier's performance from 80.69% to 90.75%. Results are here studied and discussed.

Oral soft tissue disorders are associated with gastroesophageal reflux disease: retrospective study.

Science.gov (United States)

Watanabe, Masaaki; Nakatani, Eiji; Yoshikawa, Hiroo; Kanno, Takahiro; Nariai, Yoshiki; Yoshino, Aya; Vieth, Michael; Kinoshita, Yoshikazu; Sekine, Joji

2017-08-07

Dental erosion (DE), one of oral hard tissue diseases, is one of the extraoesophageal symptoms defined as the Montreal Definition and Classification of gastroesophageal reflux disease (GERD). However, no study evaluated the relationship between GERD and oral soft tissues. We hypothesized that oral soft tissue disorders (OSTDs) would be related to GERD. The study aimed to investigate the association OSTDs and GERD. GERD patients (105 cases), older and younger controls (25 cases each) were retrospectively examined for oral symptoms, salivary flow volume (Saxon test), swallowing function (repetitive saliva swallowing test [RSST]), teeth (decayed, missing, and filled [DMF] indices), and soft tissues (as evaluation of OSTDs, gingivitis; papillary, marginal, and attached [PMA] gingival indexes, simplified oral hygiene indices [OHI-S], and inflammatory oral mucosal regions). Clinical histories, which included body mass index [BMI], the existence of alcohol and tobacco use, and bruxism, were also investigated. A P value of bruxism, as an exacerbation factor of periodontal disease, in the GERD patients was significantly more frequent than in either control group (P = 0.041). OSTDs were associated with GERD, which was similar to the association between DE and GERD.

Classification of Building Object Types

DEFF Research Database (Denmark)

Jørgensen, Kaj Asbjørn

2011-01-01

made. This is certainly the case in the Danish development. Based on the theories about these abstraction mechanisms, the basic principles for classification systems are presented and the observed misconceptions are analyses and explained. Furthermore, it is argued that the purpose of classification...... systems has changed and that new opportunities should be explored. Some proposals for new applications are presented and carefully aligned with IT opportunities. Especially, the use of building modelling will give new benefits and many of the traditional uses of classification systems will instead...... be managed by software applications and on the basis of building models. Classification systems with taxonomies of building object types have many application opportunities but can still be beneficial in data exchange between building construction partners. However, this will be performed by new methods...

Using a multidisciplinary classification in nursing : The international classification of functioning disability and health

NARCIS (Netherlands)

Van Achterberg, T; Holleman, G; Heijnen-Kaales, Y; Van der Brug, Y; Roodbol, G; Stallinga, HA; Hellema, F; Frederiks, CMA

This paper reports a study to explore systematically the usefulness of the International Classification of Functioning, Disability and Health to nurses giving patient care. The International Classification of Functioning, Disability and Health has a history of more than 20 years. Although this World

Using a multidisciplinary classification in nursing: the International Classification of Functioning Disability and Health

NARCIS (Netherlands)

van Achterberg, Theo; Holleman, Gerda; Heijnen-Kaales, Yvonne; van der Brug, Ype; Roodbol, Gabriël; Stallinga, Hillegonda A.; Hellema, Fokje; Frederiks, Carla M. A.

2005-01-01

This paper reports a study to explore systematically the usefulness of the International Classification of Functioning, Disability and Health to nurses giving patient care. The International Classification of Functioning, Disability and Health has a history of more than 20 years. Although this World

46 CFR 76.50-5 - Classification.

Science.gov (United States)

2010-10-01

... 46 Shipping 3 2010-10-01 2010-10-01 false Classification. 76.50-5 Section 76.50-5 Shipping COAST... Classification. (a) Hand portable fire extinguishers and semiportable fire extinguishing systems shall be... extinguishing systems are set forth in table 76.50-5(c). Table 76.50-5(c) Classification Type Size Soda acid and...

46 CFR 193.50-5 - Classification.

Science.gov (United States)

2010-10-01

... 46 Shipping 7 2010-10-01 2010-10-01 false Classification. 193.50-5 Section 193.50-5 Shipping COAST... Details § 193.50-5 Classification. (a) Hand portable fire extinguishers and semiportable fire...) Classification Type Size Soda-acid and water, gals. Foam, gals. Carbon dioxide, lbs. Dry chemical, lbs. A II 21/2...

The Classification of Romanian High-Schools

Science.gov (United States)

Ivan, Ion; Milodin, Daniel; Naie, Lucian

2006-01-01

The article tries to tackle the issue of high-schools classification from one city, district or from Romania. The classification criteria are presented. The National Database of Education is also presented and the application of criteria is illustrated. An algorithm for high-school multi-rang classification is proposed in order to build classes of…

Recursive automatic classification algorithms

Energy Technology Data Exchange (ETDEWEB)

Bauman, E V; Dorofeyuk, A A

1982-03-01

A variational statement of the automatic classification problem is given. The dependence of the form of the optimal partition surface on the form of the classification objective functional is investigated. A recursive algorithm is proposed for maximising a functional of reasonably general form. The convergence problem is analysed in connection with the proposed algorithm. 8 references.

Spectroscopic classification of transients

DEFF Research Database (Denmark)

Stritzinger, M. D.; Fraser, M.; Hummelmose, N. N.

2017-01-01

We report the spectroscopic classification of several transients based on observations taken with the Nordic Optical Telescope (NOT) equipped with ALFOSC, over the nights 23-25 August 2017.......We report the spectroscopic classification of several transients based on observations taken with the Nordic Optical Telescope (NOT) equipped with ALFOSC, over the nights 23-25 August 2017....

Cellular image classification

CERN Document Server

Xu, Xiang; Lin, Feng

2017-01-01

This book introduces new techniques for cellular image feature extraction, pattern recognition and classification. The authors use the antinuclear antibodies (ANAs) in patient serum as the subjects and the Indirect Immunofluorescence (IIF) technique as the imaging protocol to illustrate the applications of the described methods. Throughout the book, the authors provide evaluations for the proposed methods on two publicly available human epithelial (HEp-2) cell datasets: ICPR2012 dataset from the ICPR'12 HEp-2 cell classification contest and ICIP2013 training dataset from the ICIP'13 Competition on cells classification by fluorescent image analysis. First, the reading of imaging results is significantly influenced by one’s qualification and reading systems, causing high intra- and inter-laboratory variance. The authors present a low-order LP21 fiber mode for optical single cell manipulation and imaging staining patterns of HEp-2 cells. A focused four-lobed mode distribution is stable and effective in optical...

[Landscape classification: research progress and development trend].

Science.gov (United States)

Liang, Fa-Chao; Liu, Li-Ming

2011-06-01

Landscape classification is the basis of the researches on landscape structure, process, and function, and also, the prerequisite for landscape evaluation, planning, protection, and management, directly affecting the precision and practicability of landscape research. This paper reviewed the research progress on the landscape classification system, theory, and methodology, and summarized the key problems and deficiencies of current researches. Some major landscape classification systems, e. g. , LANMAP and MUFIC, were introduced and discussed. It was suggested that a qualitative and quantitative comprehensive classification based on the ideology of functional structure shape and on the integral consideration of landscape classification utility, landscape function, landscape structure, physiogeographical factors, and human disturbance intensity should be the major research directions in the future. The integration of mapping, 3S technology, quantitative mathematics modeling, computer artificial intelligence, and professional knowledge to enhance the precision of landscape classification would be the key issues and the development trend in the researches of landscape classification.

Classifications of Patterned Hair Loss: A Review.

Science.gov (United States)

Gupta, Mrinal; Mysore, Venkataram

2016-01-01

Patterned hair loss is the most common cause of hair loss seen in both the sexes after puberty. Numerous classification systems have been proposed by various researchers for grading purposes. These systems vary from the simpler systems based on recession of the hairline to the more advanced multifactorial systems based on the morphological and dynamic parameters that affect the scalp and the hair itself. Most of these preexisting systems have certain limitations. Currently, the Hamilton-Norwood classification system for males and the Ludwig system for females are most commonly used to describe patterns of hair loss. In this article, we review the various classification systems for patterned hair loss in both the sexes. Relevant articles were identified through searches of MEDLINE and EMBASE. Search terms included but were not limited to androgenic alopecia classification, patterned hair loss classification, male pattern baldness classification, and female pattern hair loss classification. Further publications were identified from the reference lists of the reviewed articles.

Classifications of patterned hair loss: a review

Directory of Open Access Journals (Sweden)

Mrinal Gupta

2016-01-01

Full Text Available Patterned hair loss is the most common cause of hair loss seen in both the sexes after puberty. Numerous classification systems have been proposed by various researchers for grading purposes. These systems vary from the simpler systems based on recession of the hairline to the more advanced multifactorial systems based on the morphological and dynamic parameters that affect the scalp and the hair itself. Most of these preexisting systems have certain limitations. Currently, the Hamilton-Norwood classification system for males and the Ludwig system for females are most commonly used to describe patterns of hair loss. In this article, we review the various classification systems for patterned hair loss in both the sexes. Relevant articles were identified through searches of MEDLINE and EMBASE. Search terms included but were not limited to androgenic alopecia classification, patterned hair loss classification, male pattern baldness classification, and female pattern hair loss classification. Further publications were identified from the reference lists of the reviewed articles.

Protist classification and the kingdoms of organisms.

Science.gov (United States)

Whittaker, R H; Margulis, L

1978-04-01

Traditional classification imposed a division into plant-like and animal-like forms on the unicellular eukaryotes, or protists; in a current view the protists are a diverse assemblage of plant-, animal- and fungus-like groups. Classification of these into phyla is difficult because of their relatively simple structure and limited geological record, but study of ultrastructure and other characteristics is providing new insight on protist classification. Possible classifications are discussed, and a summary classification of the living world into kingdoms (Monera, Protista, Fungi, Animalia, Plantae) and phyla is suggested. This classification also suggests groupings of phyla into superphyla and form-superphyla, and a broadened kingdom Protista (including green algae, oomycotes and slime molds but excluding red and brown algae). The classification thus seeks to offer a compromise between the protist and protoctist kingdoms of Whittaker and Margulis and to combine a full listing of phyla with grouping of these for synoptic treatment.

Brain atlas for functional imaging. Clinical and research applications

International Nuclear Information System (INIS)

Nowinski, W.L.; Thirunavuukarasuu, A.; Kennedy, D.N

2001-01-01

This CD-ROM: Allows anatomical and functional images to be loaded and registered. Enables interactive placement of the Talairach landmarks in 3D Space. Provides automatic data-to-atlas warping based on the Talairaich proportional gridsystem transformation. Real-time interactive warping for fine tuning is also available. Allows the user to place marks on the activation loci in the warped functional images, display these marks with the atlas, and edit them in three planes. Mark placement is assisted by image thresholding. Provides simultaneous display of the atlas, anatomical image and functional image within one interactively blended image. Atlas-data blending and anatomical-functional image blending are controlled independently. Labels the data by means of the atlas. The atlas can be flipped left/right so that Brodmann's areas and gyri can be labeled on both hemispheres. Provides additional functions such as friendly navigation, cross-referenced display, readout of the Talairach coordinates and intensities, load coordinates, save, on-line help. (orig.)

Brain atlas for functional imaging. Clinical and research applications

Energy Technology Data Exchange (ETDEWEB)

Nowinski, W.L.; Thirunavuukarasuu, A.; Kennedy, D.N

2001-07-01

This CD-ROM: Allows anatomical and functional images to be loaded and registered. Enables interactive placement of the Talairach landmarks in 3D Space. Provides automatic data-to-atlas warping based on the Talairaich proportional gridsystem transformation. Real-time interactive warping for fine tuning is also available. Allows the user to place marks on the activation loci in the warped functional images, display these marks with the atlas, and edit them in three planes. Mark placement is assisted by image thresholding. Provides simultaneous display of the atlas, anatomical image and functional image within one interactively blended image. Atlas-data blending and anatomical-functional image blending are controlled independently. Labels the data by means of the atlas. The atlas can be flipped left/right so that Brodmann's areas and gyri can be labeled on both hemispheres. Provides additional functions such as friendly navigation, cross-referenced display, readout of the Talairach coordinates and intensities, load coordinates, save, on-line help. (orig.)

46 CFR 95.50-5 - Classification.

Science.gov (United States)

2010-10-01

... 46 Shipping 4 2010-10-01 2010-10-01 false Classification. 95.50-5 Section 95.50-5 Shipping COAST... Details § 95.50-5 Classification. (a) Hand portable fire extinguishers and semiportable fire extinguishing... extinguishing systems are set forth in Table 95.50-5(c). Table 95.50-5(c) Classification Type Size Soda-acid and...

Pattern Classification with Memristive Crossbar Circuits

Science.gov (United States)

2016-03-31

Pattern Classification with Memristive Crossbar Circuits Dmitri B. Strukov Department of Electrical and Computer Engineering Department UC Santa...pattern classification ; deep learning; convolutional neural network networks. Introduction Deep-learning convolutional neural networks (DLCNN), which...the best classification performances on a variety of benchmark tasks [1]. The major challenge in building fast and energy- efficient networks of this

Histological image classification using biologically interpretable shape-based features

International Nuclear Information System (INIS)

Kothari, Sonal; Phan, John H; Young, Andrew N; Wang, May D

2013-01-01

Automatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis. We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features. The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors. Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions

Casemix classification systems.

Science.gov (United States)

Fetter, R B

1999-01-01

The idea of using casemix classification to manage hospital services is not new, but has been limited by available technology. It was not until after the introduction of Medicare in the United States in 1965 that serious attempts were made to measure hospital production in order to contain spiralling costs. This resulted in a system of casemix classification known as diagnosis related groups (DRGs). This paper traces the development of DRGs and their evolution from the initial version to the All Patient Refined DRGs developed in 1991.

Video genre classification using multimodal features

Science.gov (United States)

Jin, Sung Ho; Bae, Tae Meon; Choo, Jin Ho; Ro, Yong Man

2003-12-01

We propose a video genre classification method using multimodal features. The proposed method is applied for the preprocessing of automatic video summarization or the retrieval and classification of broadcasting video contents. Through a statistical analysis of low-level and middle-level audio-visual features in video, the proposed method can achieve good performance in classifying several broadcasting genres such as cartoon, drama, music video, news, and sports. In this paper, we adopt MPEG-7 audio-visual descriptors as multimodal features of video contents and evaluate the performance of the classification by feeding the features into a decision tree-based classifier which is trained by CART. The experimental results show that the proposed method can recognize several broadcasting video genres with a high accuracy and the classification performance with multimodal features is superior to the one with unimodal features in the genre classification.

Modified Angle's Classification for Primary Dentition.

Science.gov (United States)

Chandranee, Kaushik Narendra; Chandranee, Narendra Jayantilal; Nagpal, Devendra; Lamba, Gagandeep; Choudhari, Purva; Hotwani, Kavita

2017-01-01

This study aims to propose a modification of Angle's classification for primary dentition and to assess its applicability in children from Central India, Nagpur. Modification in Angle's classification has been proposed for application in primary dentition. Small roman numbers i/ii/iii are used for primary dentition notation to represent Angle's Class I/II/III molar relationships as in permanent dentition, respectively. To assess applicability of modified Angle's classification a cross-sectional preschool 2000 children population from central India; 3-6 years of age residing in Nagpur metropolitan city of Maharashtra state were selected randomly as per the inclusion and exclusion criteria. Majority 93.35% children were found to have bilateral Class i followed by 2.5% bilateral Class ii and 0.2% bilateral half cusp Class iii molar relationships as per the modified Angle's classification for primary dentition. About 3.75% children had various combinations of Class ii relationships and 0.2% children were having Class iii subdivision relationship. Modification of Angle's classification for application in primary dentition has been proposed. A cross-sectional investigation using new classification revealed various 6.25% Class ii and 0.4% Class iii molar relationships cases in preschool children population in a metropolitan city of Nagpur. Application of the modified Angle's classification to other population groups is warranted to validate its routine application in clinical pediatric dentistry.

A Hybrid DE-RGSO-ELM for Brain Tumor Tissue Categorization in 3D Magnetic Resonance Images

Directory of Open Access Journals (Sweden)

K. Kothavari

2014-01-01

Full Text Available Medical diagnostics, a technique used for visualizing the internal structures and functions of human body, serves as a scientific tool to assist physicians and involves direct use of digital imaging system analysis. In this scenario, identification of brain tumors is complex in the diagnostic process. Magnetic resonance imaging (MRI technique is noted to best assist tissue contrast for anatomical details and also carries out mechanisms for investigating the brain by functional imaging in tumor predictions. Considering 3D MRI model, analyzing the anatomy features and tissue characteristics of brain tumor is complex in nature. Henceforth, in this work, feature extraction is carried out by computing 3D gray-level cooccurence matrix (3D GLCM and run-length matrix (RLM and feature subselection for dimensionality reduction is performed with basic differential evolution (DE algorithm. Classification is performed using proposed extreme learning machine (ELM, with refined group search optimizer (RGSO technique, to select the best parameters for better simplification and training of the classifier for brain tissue and tumor characterization as white matter (WM, gray matter (GM, cerebrospinal fluid (CSF, and tumor. Extreme learning machine outperforms the standard binary linear SVM and BPN for medical image classifier and proves better in classifying healthy and tumor tissues. The comparison between the algorithms proves that the mean and standard deviation produced by volumetric feature extraction analysis are higher than the other approaches. The proposed work is designed for pathological brain tumor classification and for 3D MRI tumor image segmentation. The proposed approaches are applied for real time datasets and benchmark datasets taken from dataset repositories.

14 CFR 1203.501 - Applying derivative classification markings.

Science.gov (United States)

2010-01-01

... INFORMATION SECURITY PROGRAM Derivative Classification § 1203.501 Applying derivative classification markings. Persons who apply derivative classification markings shall: (a) Observe and respect original... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Applying derivative classification markings...

Improvement of Classification of Enterprise Circulating Funds

OpenAIRE

Rohanova Hanna O.

2014-01-01

The goal of the article lies in revelation of possibilities of increase of efficiency of managing enterprise circulating funds by means of improvement of their classification features. Having analysed approaches of many economists to classification of enterprise circulating funds, systemised and supplementing them, the article offers grouping classification features of enterprise circulating funds. In the result of the study the article offers an expanded classification of circulating funds, ...

Land-cover classification with an expert classification algorithm using digital aerial photographs

Directory of Open Access Journals (Sweden)

José L. de la Cruz

2010-05-01

Full Text Available The purpose of this study was to evaluate the usefulness of the spectral information of digital aerial sensors in determining land-cover classification using new digital techniques. The land covers that have been evaluated are the following, (1 bare soil, (2 cereals, including maize (Zea mays L., oats (Avena sativa L., rye (Secale cereale L., wheat (Triticum aestivum L. and barley (Hordeun vulgare L., (3 high protein crops, such as peas (Pisum sativum L. and beans (Vicia faba L., (4 alfalfa (Medicago sativa L., (5 woodlands and scrublands, including holly oak (Quercus ilex L. and common retama (Retama sphaerocarpa L., (6 urban soil, (7 olive groves (Olea europaea L. and (8 burnt crop stubble. The best result was obtained using an expert classification algorithm, achieving a reliability rate of 95%. This result showed that the images of digital airborne sensors hold considerable promise for the future in the field of digital classifications because these images contain valuable information that takes advantage of the geometric viewpoint. Moreover, new classification techniques reduce problems encountered using high-resolution images; while reliabilities are achieved that are better than those achieved with traditional methods.

Trends and concepts in fern classification

Science.gov (United States)

Christenhusz, Maarten J. M.; Chase, Mark W.

2014-01-01

Background and Aims Throughout the history of fern classification, familial and generic concepts have been highly labile. Many classifications and evolutionary schemes have been proposed during the last two centuries, reflecting different interpretations of the available evidence. Knowledge of fern structure and life histories has increased through time, providing more evidence on which to base ideas of possible relationships, and classification has changed accordingly. This paper reviews previous classifications of ferns and presents ideas on how to achieve a more stable consensus. Scope An historical overview is provided from the first to the most recent fern classifications, from which conclusions are drawn on past changes and future trends. The problematic concept of family in ferns is discussed, with a particular focus on how this has changed over time. The history of molecular studies and the most recent findings are also presented. Key Results Fern classification generally shows a trend from highly artificial, based on an interpretation of a few extrinsic characters, via natural classifications derived from a multitude of intrinsic characters, towards more evolutionary circumscriptions of groups that do not in general align well with the distribution of these previously used characters. It also shows a progression from a few broad family concepts to systems that recognized many more narrowly and highly controversially circumscribed families; currently, the number of families recognized is stabilizing somewhere between these extremes. Placement of many genera was uncertain until the arrival of molecular phylogenetics, which has rapidly been improving our understanding of fern relationships. As a collective category, the so-called ‘fern allies’ (e.g. Lycopodiales, Psilotaceae, Equisetaceae) were unsurprisingly found to be polyphyletic, and the term should be abandoned. Lycopodiaceae, Selaginellaceae and Isoëtaceae form a clade (the lycopods) that is

Trends and concepts in fern classification.

Science.gov (United States)

Christenhusz, Maarten J M; Chase, Mark W

2014-03-01

Throughout the history of fern classification, familial and generic concepts have been highly labile. Many classifications and evolutionary schemes have been proposed during the last two centuries, reflecting different interpretations of the available evidence. Knowledge of fern structure and life histories has increased through time, providing more evidence on which to base ideas of possible relationships, and classification has changed accordingly. This paper reviews previous classifications of ferns and presents ideas on how to achieve a more stable consensus. An historical overview is provided from the first to the most recent fern classifications, from which conclusions are drawn on past changes and future trends. The problematic concept of family in ferns is discussed, with a particular focus on how this has changed over time. The history of molecular studies and the most recent findings are also presented. Fern classification generally shows a trend from highly artificial, based on an interpretation of a few extrinsic characters, via natural classifications derived from a multitude of intrinsic characters, towards more evolutionary circumscriptions of groups that do not in general align well with the distribution of these previously used characters. It also shows a progression from a few broad family concepts to systems that recognized many more narrowly and highly controversially circumscribed families; currently, the number of families recognized is stabilizing somewhere between these extremes. Placement of many genera was uncertain until the arrival of molecular phylogenetics, which has rapidly been improving our understanding of fern relationships. As a collective category, the so-called 'fern allies' (e.g. Lycopodiales, Psilotaceae, Equisetaceae) were unsurprisingly found to be polyphyletic, and the term should be abandoned. Lycopodiaceae, Selaginellaceae and Isoëtaceae form a clade (the lycopods) that is sister to all other vascular plants, whereas

Classification across gene expression microarray studies

Directory of Open Access Journals (Sweden)

Kuner Ruprecht

2009-12-01

Full Text Available Abstract Background The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive and histological grade (low/high of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM, predictive analysis of microarrays (PAM, random forest (RF and k-top scoring pairs (kTSP. Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing. Results For each individual study the generalization error was benchmarked via complete cross-validation and was found to be similar for all classification methods. The misclassification rates were substantially higher in classification across studies, when each single study was used as an independent test set while all remaining studies were combined for the training of the classifier. However, with increasing number of independent microarray studies used in the training, the overall classification performance improved. DV performed better than the average and showed slightly less variance. In

32 CFR 2400.14 - Use of derivative classification.

Science.gov (United States)

2010-07-01

... SECURITY PROGRAM Derivative Classification § 2400.14 Use of derivative classification. (a) Derivative... 32 National Defense 6 2010-07-01 2010-07-01 false Use of derivative classification. 2400.14... person who applies derivative classification markings believes that the paraphrasing, restating, or...

The Importance of Classification to Business Model Research

OpenAIRE

Susan Lambert

2015-01-01

Purpose: To bring to the fore the scientific significance of classification and its role in business model theory building. To propose a method by which existing classifications of business models can be analyzed and new ones developed. Design/Methodology/Approach: A review of the scholarly literature relevant to classifications of business models is presented along with a brief overview of classification theory applicable to business model research. Existing business model classification...

Diffusion Tensor Magnetic Resonance Imaging Finding of Discrepant Fractional Anisotropy Between the Frontal and Parietal Lobes After Whole-Brain Irradiation in Childhood Medulloblastoma Survivors: Reflection of Regional White Matter Radiosensitivity?

International Nuclear Information System (INIS)

Qiu Deqiang; Kwong, Dora; Chan, Godfrey; Leung, Lucullus; Khong, P.-L.

2007-01-01

Purpose: To test the hypothesis that fractional anisotropy (FA) is more severely reduced in white matter of the frontal lobe compared with the parietal lobe after receiving the same whole-brain irradiation dose in a cohort of childhood medulloblastoma survivors. Methods and Materials: Twenty-two medulloblastoma survivors (15 male, mean [± SD] age = 12.1 ± 4.6 years) and the same number of control subjects (15 male, aged 12.0 ± 4.2 years) were recruited for diffusion tensor magnetic resonance imaging scans. Using an automated tissue classification method and the Talairach Daemon atlas, FA values of frontal and parietal lobes receiving the same radiation dose, and the ratio between them were quantified and denoted as FFA, PFA, and FA f/p , respectively. The Mann-Whitney U test was used to test for significant differences of FFA, PFA, and FA f/p between medulloblastoma survivors and control subjects. Results: Frontal lobe and parietal lobe white matter FA were found to be significantly less in medulloblastoma survivors compared with control subjects (frontal p = 0.001, parietal p = 0.026). Moreover, these differences were found to be discrepant, with the frontal lobe having a significantly larger difference in FA compared with the parietal lobe. The FA f/p of control and medulloblastoma survivors was 1.110 and 1.082, respectively (p = 0.029). Conclusion: Discrepant FA changes after the same irradiation dose suggest radiosensitivity of the frontal lobe white matter compared with the parietal lobe. Special efforts to address the potentially vulnerable frontal lobe after treatment with whole-brain radiation may be needed so as to balance disease control and treatment-related morbidity

Asynchronous data-driven classification of weapon systems

International Nuclear Information System (INIS)

Jin, Xin; Mukherjee, Kushal; Gupta, Shalabh; Ray, Asok; Phoha, Shashi; Damarla, Thyagaraju

2009-01-01

This communication addresses real-time weapon classification by analysis of asynchronous acoustic data, collected from microphones on a sensor network. The weapon classification algorithm consists of two parts: (i) feature extraction from time-series data using symbolic dynamic filtering (SDF), and (ii) pattern classification based on the extracted features using the language measure (LM) and support vector machine (SVM). The proposed algorithm has been tested on field data, generated by firing of two types of rifles. The results of analysis demonstrate high accuracy and fast execution of the pattern classification algorithm with low memory requirements. Potential applications include simultaneous shooter localization and weapon classification with soldier-wearable networked sensors. (rapid communication)

Discriminant forest classification method and system

Science.gov (United States)

Chen, Barry Y.; Hanley, William G.; Lemmond, Tracy D.; Hiller, Lawrence J.; Knapp, David A.; Mugge, Marshall J.

2012-11-06

A hybrid machine learning methodology and system for classification that combines classical random forest (RF) methodology with discriminant analysis (DA) techniques to provide enhanced classification capability. A DA technique which uses feature measurements of an object to predict its class membership, such as linear discriminant analysis (LDA) or Andersen-Bahadur linear discriminant technique (AB), is used to split the data at each node in each of its classification trees to train and grow the trees and the forest. When training is finished, a set of n DA-based decision trees of a discriminant forest is produced for use in predicting the classification of new samples of unknown class.

Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies

Science.gov (United States)

Zhang, Shujun

2018-01-01

Genome-wide association studies (GWASs) have identified many disease associated loci, the majority of which have unknown biological functions. Understanding the mechanism underlying trait associations requires identifying trait-relevant tissues and investigating associations in a trait-specific fashion. Here, we extend the widely used linear mixed model to incorporate multiple SNP functional annotations from omics studies with GWAS summary statistics to facilitate the identification of trait-relevant tissues, with which to further construct powerful association tests. Specifically, we rely on a generalized estimating equation based algorithm for parameter inference, a mixture modeling framework for trait-tissue relevance classification, and a weighted sequence kernel association test constructed based on the identified trait-relevant tissues for powerful association analysis. We refer to our analytic procedure as the Scalable Multiple Annotation integration for trait-Relevant Tissue identification and usage (SMART). With extensive simulations, we show how our method can make use of multiple complementary annotations to improve the accuracy for identifying trait-relevant tissues. In addition, our procedure allows us to make use of the inferred trait-relevant tissues, for the first time, to construct more powerful SNP set tests. We apply our method for an in-depth analysis of 43 traits from 28 GWASs using tissue-specific annotations in 105 tissues derived from ENCODE and Roadmap. Our results reveal new trait-tissue relevance, pinpoint important annotations that are informative of trait-tissue relationship, and illustrate how we can use the inferred trait-relevant tissues to construct more powerful association tests in the Wellcome trust case control consortium study. PMID:29377896

Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies.

Directory of Open Access Journals (Sweden)

Xingjie Hao

2018-01-01

Full Text Available Genome-wide association studies (GWASs have identified many disease associated loci, the majority of which have unknown biological functions. Understanding the mechanism underlying trait associations requires identifying trait-relevant tissues and investigating associations in a trait-specific fashion. Here, we extend the widely used linear mixed model to incorporate multiple SNP functional annotations from omics studies with GWAS summary statistics to facilitate the identification of trait-relevant tissues, with which to further construct powerful association tests. Specifically, we rely on a generalized estimating equation based algorithm for parameter inference, a mixture modeling framework for trait-tissue relevance classification, and a weighted sequence kernel association test constructed based on the identified trait-relevant tissues for powerful association analysis. We refer to our analytic procedure as the Scalable Multiple Annotation integration for trait-Relevant Tissue identification and usage (SMART. With extensive simulations, we show how our method can make use of multiple complementary annotations to improve the accuracy for identifying trait-relevant tissues. In addition, our procedure allows us to make use of the inferred trait-relevant tissues, for the first time, to construct more powerful SNP set tests. We apply our method for an in-depth analysis of 43 traits from 28 GWASs using tissue-specific annotations in 105 tissues derived from ENCODE and Roadmap. Our results reveal new trait-tissue relevance, pinpoint important annotations that are informative of trait-tissue relationship, and illustrate how we can use the inferred trait-relevant tissues to construct more powerful association tests in the Wellcome trust case control consortium study.

Land Cover and Land Use Classification with TWOPAC: towards Automated Processing for Pixel- and Object-Based Image Classification

Directory of Open Access Journals (Sweden)

Stefan Dech

2012-09-01

Full Text Available We present a novel and innovative automated processing environment for the derivation of land cover (LC and land use (LU information. This processing framework named TWOPAC (TWinned Object and Pixel based Automated classification Chain enables the standardized, independent, user-friendly, and comparable derivation of LC and LU information, with minimized manual classification labor. TWOPAC allows classification of multi-spectral and multi-temporal remote sensing imagery from different sensor types. TWOPAC enables not only pixel-based classification, but also allows classification based on object-based characteristics. Classification is based on a Decision Tree approach (DT for which the well-known C5.0 code has been implemented, which builds decision trees based on the concept of information entropy. TWOPAC enables automatic generation of the decision tree classifier based on a C5.0-retrieved ascii-file, as well as fully automatic validation of the classification output via sample based accuracy assessment.Envisaging the automated generation of standardized land cover products, as well as area-wide classification of large amounts of data in preferably a short processing time, standardized interfaces for process control, Web Processing Services (WPS, as introduced by the Open Geospatial Consortium (OGC, are utilized. TWOPAC’s functionality to process geospatial raster or vector data via web resources (server, network enables TWOPAC’s usability independent of any commercial client or desktop software and allows for large scale data processing on servers. Furthermore, the components of TWOPAC were built-up using open source code components and are implemented as a plug-in for Quantum GIS software for easy handling of the classification process from the user’s perspective.

Transcriptome classification reveals molecular subtypes in psoriasis

Directory of Open Access Journals (Sweden)

Ainali Chrysanthi

2012-09-01

Full Text Available Abstract Background Psoriasis is an immune-mediated disease characterised by chronically elevated pro-inflammatory cytokine levels, leading to aberrant keratinocyte proliferation and differentiation. Although certain clinical phenotypes, such as plaque psoriasis, are well defined, it is currently unclear whether there are molecular subtypes that might impact on prognosis or treatment outcomes. Results We present a pipeline for patient stratification through a comprehensive analysis of gene expression in paired lesional and non-lesional psoriatic tissue samples, compared with controls, to establish differences in RNA expression patterns across all tissue types. Ensembles of decision tree predictors were employed to cluster psoriatic samples on the basis of gene expression patterns and reveal gene expression signatures that best discriminate molecular disease subtypes. This multi-stage procedure was applied to several published psoriasis studies and a comparison of gene expression patterns across datasets was performed. Conclusion Overall, classification of psoriasis gene expression patterns revealed distinct molecular sub-groups within the clinical phenotype of plaque psoriasis. Enrichment for TGFb and ErbB signaling pathways, noted in one of the two psoriasis subgroups, suggested that this group may be more amenable to therapies targeting these pathways. Our study highlights the potential biological relevance of using ensemble decision tree predictors to determine molecular disease subtypes, in what may initially appear to be a homogenous clinical group. The R code used in this paper is available upon request.

47 CFR 64.2345 - Primary advertising classification.

Science.gov (United States)

2010-10-01

... 47 Telecommunication 3 2010-10-01 2010-10-01 false Primary advertising classification. 64.2345 Section 64.2345 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) COMMON CARRIER SERVICES... advertising classification. A primary advertising classification is assigned at the time of the establishment...

S.E. Mitchell Vascular Anomalies Flow Chart (SEMVAFC): A visual pathway combining clinical and imaging findings for classification of soft-tissue vascular anomalies

International Nuclear Information System (INIS)

Tekes, A.; Koshy, J.; Kalayci, T.O.; Puttgen, K.; Cohen, B.; Redett, R.; Mitchell, S.E.

2014-01-01

Classification of vascular anomalies (VAs) is challenging due to overlapping clinical symptoms, confusing terminology in the literature and unfamiliarity with this complex entity. It is important to recognize that VAs include two distinct entities, vascular tumours (VTs) and vascular malformations (VaMs). In this article, we describe SE Mitchell Vascular Anomalies Flow Chart (SEMVAFC), which arises from a multidisciplinary approach that incorporates clinical symptoms, physical examination and magnetic resonance imaging (MRI) findings to establish International Society for the Study of Vascular Anomalies (ISSVA)-based classification of the VAs. SEMVAFC provides a clear visual pathway for physicians to accurately diagnose Vas, which is important as treatment, management, and prognosis differ between VTs and VaMs

47 CFR 10.400 - Classification.

Science.gov (United States)

2010-10-01

... 47 Telecommunication 1 2010-10-01 2010-10-01 false Classification. 10.400 Section 10.400 Telecommunication FEDERAL COMMUNICATIONS COMMISSION GENERAL COMMERCIAL MOBILE ALERT SYSTEM Alert Message Requirements § 10.400 Classification. A Participating CMS Provider is required to receive and transmit three...

Deep Learning for ECG Classification

Science.gov (United States)

Pyakillya, B.; Kazachenko, N.; Mikhailovsky, N.

2017-10-01

The importance of ECG classification is very high now due to many current medical applications where this problem can be stated. Currently, there are many machine learning (ML) solutions which can be used for analyzing and classifying ECG data. However, the main disadvantages of these ML results is use of heuristic hand-crafted or engineered features with shallow feature learning architectures. The problem relies in the possibility not to find most appropriate features which will give high classification accuracy in this ECG problem. One of the proposing solution is to use deep learning architectures where first layers of convolutional neurons behave as feature extractors and in the end some fully-connected (FCN) layers are used for making final decision about ECG classes. In this work the deep learning architecture with 1D convolutional layers and FCN layers for ECG classification is presented and some classification results are showed.

Classification of line features from remote sensing data

OpenAIRE

Kolankiewiczová, Soňa

2009-01-01

This work deals with object-based classification of high resolution data. The aim of the thesis (paper, work) is to develope an acceptable classification process of linear features (roads and railways) from high-resolution satellite images. The first part shows different approaches of the linear feature classification and compares theoretic differences between an object-oriented and a pixel-based classification. Linear feature classification was created in the second part. The high-resolution...

Modified angle's classification for primary dentition

Directory of Open Access Journals (Sweden)

Kaushik Narendra Chandranee

2017-01-01

Full Text Available Aim: This study aims to propose a modification of Angle's classification for primary dentition and to assess its applicability in children from Central India, Nagpur. Methods: Modification in Angle's classification has been proposed for application in primary dentition. Small roman numbers i/ii/iii are used for primary dentition notation to represent Angle's Class I/II/III molar relationships as in permanent dentition, respectively. To assess applicability of modified Angle's classification a cross-sectional preschool 2000 children population from central India; 3–6 years of age residing in Nagpur metropolitan city of Maharashtra state were selected randomly as per the inclusion and exclusion criteria. Results: Majority 93.35% children were found to have bilateral Class i followed by 2.5% bilateral Class ii and 0.2% bilateral half cusp Class iii molar relationships as per the modified Angle's classification for primary dentition. About 3.75% children had various combinations of Class ii relationships and 0.2% children were having Class iii subdivision relationship. Conclusions: Modification of Angle's classification for application in primary dentition has been proposed. A cross-sectional investigation using new classification revealed various 6.25% Class ii and 0.4% Class iii molar relationships cases in preschool children population in a metropolitan city of Nagpur. Application of the modified Angle's classification to other population groups is warranted to validate its routine application in clinical pediatric dentistry.

Proposal plan of classification faceted for federal universities

Directory of Open Access Journals (Sweden)

Renata Santos Brandão

2017-09-01

Full Text Available This study aims to present a faceted classification plan for the archival management of documents in the federal universities of Brazil. For this, was done a literature review on the archival management in Brazil, the types of classification plans and the theory of the Ranganathan faceted classification, through searches in databases in the areas of Librarianship and Archivology. It was identified the classification plan used in the Federal Institutions of Higher Education to represent the functional facet and created the structural classification plan to represent the structural facet. The two classification plans were inserted into a digital repository management system to give rise to the faceted classification plan. The system used was Tainacan, free software wordpress-based used in digital document management. The developed faceted classification plan allows the user to choose and even combine the way to look for the information that guarantees agreater efficiency in the information retrieval.

28 CFR 524.76 - Appeals of CIM classification.

Science.gov (United States)

2010-07-01

... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Appeals of CIM classification. 524.76..., CLASSIFICATION, AND TRANSFER CLASSIFICATION OF INMATES Central Inmate Monitoring (CIM) System § 524.76 Appeals of CIM classification. An inmate may at any time appeal (through the Administrative Remedy Program) the...

Classification and localization of acetabular labral tears

International Nuclear Information System (INIS)

Blankenbaker, D.G.; De Smet, A.A.; Keene, J.S.; Fine, J.P.

2007-01-01

The purpose of this study was to compare the findings on hip MR arthrography (MRA) with the published MRA and arthroscopic classifications of hip labral tears and to evaluate a clock-face method for localizing hip labral tears. We retrospectively reviewed 65 hip MRA studies with correlative hip arthroscopies. Each labrum was evaluated on MRA using the classification system of Czerny and an MRA modification of the Lage arthroscopic classification. In addition, each tear was localized on MRA by using a clock-face description where 6 o'clock was the transverse ligament and 3 o'clock was anterior. These MRA findings were then correlated with the arthroscopic findings using the clock-face method of localization and the Lage arthroscopic classification of labral tears. At MRA, there were 42 Czerny grade 2 and 23 grade 3 labral tears and 22 MRA Lage type 1, 11 type 2, 22 type 3 and 10 type 4 tears. At arthroscopy, there were 10 Lage type 1 flap tears, 20 Lage type 2 fibrillated tears, 18 Lage type 3 longitudinal peripheral tears and 17 Lage type 4 unstable tears. The Czerny MRA classification and the modified MRA Lage classification had borderline correlation with the arthroscopic Lage classification. Localization of the tears using a clock-face description was within 1 o'clock of the arthroscopic localization of the tears in 85% of the patients. The Lage classification, which is the only published arthroscopic classification system for hip labral tears, does not correlate well with the Czerny MRA or an MRA modification of the Lage classification. Using a clock-face description to localize tears provides a way to accurately localize a labral tear and define its extent. (orig.)

Automated Decision Tree Classification of Corneal Shape

Science.gov (United States)

Twa, Michael D.; Parthasarathy, Srinivasan; Roberts, Cynthia; Mahmoud, Ashraf M.; Raasch, Thomas W.; Bullimore, Mark A.

2011-01-01

Purpose The volume and complexity of data produced during videokeratography examinations present a challenge of interpretation. As a consequence, results are often analyzed qualitatively by subjective pattern recognition or reduced to comparisons of summary indices. We describe the application of decision tree induction, an automated machine learning classification method, to discriminate between normal and keratoconic corneal shapes in an objective and quantitative way. We then compared this method with other known classification methods. Methods The corneal surface was modeled with a seventh-order Zernike polynomial for 132 normal eyes of 92 subjects and 112 eyes of 71 subjects diagnosed with keratoconus. A decision tree classifier was induced using the C4.5 algorithm, and its classification performance was compared with the modified Rabinowitz–McDonnell index, Schwiegerling’s Z3 index (Z3), Keratoconus Prediction Index (KPI), KISA%, and Cone Location and Magnitude Index using recommended classification thresholds for each method. We also evaluated the area under the receiver operator characteristic (ROC) curve for each classification method. Results Our decision tree classifier performed equal to or better than the other classifiers tested: accuracy was 92% and the area under the ROC curve was 0.97. Our decision tree classifier reduced the information needed to distinguish between normal and keratoconus eyes using four of 36 Zernike polynomial coefficients. The four surface features selected as classification attributes by the decision tree method were inferior elevation, greater sagittal depth, oblique toricity, and trefoil. Conclusions Automated decision tree classification of corneal shape through Zernike polynomials is an accurate quantitative method of classification that is interpretable and can be generated from any instrument platform capable of raw elevation data output. This method of pattern classification is extendable to other classification

Efficient AUC optimization for classification

NARCIS (Netherlands)

Calders, T.; Jaroszewicz, S.; Kok, J.N.; Koronacki, J.; Lopez de Mantaras, R.; Matwin, S.; Mladenic, D.; Skowron, A.

2007-01-01

In this paper we show an efficient method for inducing classifiers that directly optimize the area under the ROC curve. Recently, AUC gained importance in the classification community as a mean to compare the performance of classifiers. Because most classification methods do not optimize this

Latent class models for classification

NARCIS (Netherlands)

Vermunt, J.K.; Magidson, J.

2003-01-01

An overview is provided of recent developments in the use of latent class (LC) and other types of finite mixture models for classification purposes. Several extensions of existing models are presented. Two basic types of LC models for classification are defined: supervised and unsupervised

Specific classification of financial analysis of enterprise activity

Directory of Open Access Journals (Sweden)

Synkevych Nadiia I.

2014-01-01

Full Text Available Despite the fact that one can find a big variety of classifications of types of financial analysis of enterprise activity, which differ with their approach to classification and a number of classification features and their content, in modern scientific literature, their complex comparison and analysis of existing classification have not been done. This explains urgency of this study. The article studies classification of types of financial analysis of scientists and presents own approach to this problem. By the results of analysis the article improves and builds up a specific classification of financial analysis of enterprise activity and offers classification by the following features: objects, subjects, goals of study, automation level, time period of the analytical base, scope of study, organisation system, classification features of the subject, spatial belonging, sufficiency, information sources, periodicity, criterial base, method of data selection for analysis and time direction. All types of financial analysis significantly differ with their inherent properties and parameters depending on the goals of financial analysis. The developed specific classification provides subjects of financial analysis of enterprise activity with a possibility to identify a specific type of financial analysis, which would correctly meet the set goals.

Blind Signal Classification via Spare Coding

Science.gov (United States)

2016-04-10

Blind Signal Classification via Sparse Coding Youngjune Gwon MIT Lincoln Laboratory gyj@ll.mit.edu Siamak Dastangoo MIT Lincoln Laboratory sia...achieve blind signal classification with no prior knowledge about signals (e.g., MCS, pulse shaping) in an arbitrary RF channel. Since modulated RF...classification method. Our results indicate that we can separate different classes of digitally modulated signals from blind sampling with 70.3% recall and 24.6

The rationale behind Pierre Duhem's natural classification.

Science.gov (United States)

Bhakthavatsalam, Sindhuja

2015-06-01

The central concern of this paper is the interpretation of Duhem's attitude towards physical theory. Based on his view that the classification of experimental laws yielded by theory progressively approaches a natural classification-a classification reflecting that of underlying realities-Duhem has been construed as a realist of sorts in recent literature. Here I argue that his positive attitude towards the theoretic classification of laws had rather to do with the pragmatic rationality of the physicist. Duhem's idea of natural classification was an intuitive idea in the mind of the physicist that had to be affirmed in order to justify the physicist's pursuit of theory. Copyright © 2015 Elsevier Ltd. All rights reserved.

Biocompatibility of a novel cyanoacrylate based tissue adhesive: cytotoxicity and biochemical property evaluation.

Directory of Open Access Journals (Sweden)

Young Ju Lee

Full Text Available Cyanoacrylate (CA is most widely used as a medical and commercial tissue adhesive because of easier wound closure, good cosmetic results and little discomfort. But, CA-based tissue adhesives have some limitations including the release of cytotoxic chemicals during biodegradation. In previous study, we made prepolymerized allyl 2-CA (PACA based tissue adhesive, resulting in longer chain structure. In this study, we investigated a biocompatibility of PACA as alternative tissue adhesive for medical application, comparing with that of Dermabond® as commercial tissue adhesive. The biocompatibility of PACA was evaluated for short-term (24 hr and long-term (3 and 7 days using conventional cytotoxicity (WST, neutral red, LIVE/DEAD and TUNEL assays, hematoxylin-eosin (H&E and Masson trichrome (MT staining. Besides we examined the biochemical changes in cells and DNA induced by PACA and Dermabond® utilizing Raman spectroscopy which could observe the denaturation and conformational changes in protein, as well as disintegration of the DNA/RNA by cell death. In particular, we analyzed Raman spectrum using the multivariate statistical methods including principal component analysis (PCA and support vector machine (SVM. As a result, PACA and Dermabond® tissue adhesive treated cells and tissues showed no difference of the cell viability values, histological analysis and Raman spectral intensity. Also, the classification analysis by means of PCA-SVM classifier could not discriminate the difference between the PACA and Dermabond® treated cells and DNA. Therefore we suggest that novel PACA might be useful as potential tissue adhesive with effective biocompatibility.

32 CFR 2400.7 - Original classification authority.

Science.gov (United States)

2010-07-01

... original classification of information as Top Secret shall be exercised within OSTP only by the Director... Top Secret classification authority, and any other officials delegated in writing to have original... shall be exercised within OSTP only by officials with original Top Secret or Secret classification...

Stellar Spectral Classification with Locality Preserving Projections ...

Indian Academy of Sciences (India)

With the help of computer tools and algorithms, automatic stellar spectral classification has become an area of current interest. The process of stellar spectral classification mainly includes two steps: dimension reduction and classification. As a popular dimensionality reduction technique, Principal Component Analysis (PCA) ...

7 CFR 58.132 - Basis for classification.

Science.gov (United States)

2010-01-01

... 7 Agriculture 3 2010-01-01 2010-01-01 false Basis for classification. 58.132 Section 58.132 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Standards... Milk § 58.132 Basis for classification. The quality classification of raw milk for manufacturing...

7 CFR 30.31 - Classification of leaf tobacco.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Classification of leaf tobacco. 30.31 Section 30.31... REGULATIONS TOBACCO STOCKS AND STANDARDS Classification of Leaf Tobacco Covering Classes, Types and Groups of Grades § 30.31 Classification of leaf tobacco. For the purpose of this classification leaf tobacco shall...

KINEMATIC CLASSIFICATIONS OF LOCAL INTERACTING GALAXIES: IMPLICATIONS FOR THE MERGER/DISK CLASSIFICATIONS AT HIGH-z

International Nuclear Information System (INIS)

Hung, Chao-Ling; Larson, Kirsten L.; Sanders, D. B.; Rich, Jeffrey A.; Yuan, Tiantian; Kewley, Lisa J.; Casey, Caitlin M.; Smith, Howard A.; Hayward, Christopher C.

2015-01-01

The classification of galaxy mergers and isolated disks is key for understanding the relative importance of galaxy interactions and secular evolution during the assembly of galaxies. Galaxy kinematics as traced by emission lines have been used to suggest the existence of a significant population of high-z star-forming galaxies consistent with isolated rotating disks. However, recent studies have cautioned that post-coalescence mergers may also display disk-like kinematics. To further investigate the robustness of merger/disk classifications based on kinematic properties, we carry out a systematic classification of 24 local (U)LIRGs spanning a range of morphologies: from isolated spiral galaxies, ongoing interacting systems, to fully merged remnants. We artificially redshift the Wide Field Spectrograph observations of these local (U)LIRGs to z = 1.5 to make a realistic comparison with observations at high-z, and also to ensure that all galaxies have the same spatial sampling of ∼900 pc. Using both kinemetry-based and visual classifications, we find that the reliability of kinematic classification shows a strong trend with the interaction stage of galaxies. Mergers with two nuclei and tidal tails have the most distinct kinematics compared to isolated disks, whereas a significant population of the interacting disks and merger remnants are indistinguishable from isolated disks. The high fraction of mergers displaying disk-like kinematics reflects the complexity of the dynamics during galaxy interactions. Additional merger indicators such as morphological properties traced by stars or molecular gas are required to further constrain the merger/disk classifications at high-z

Understanding about the classification of pulp inflammation

Directory of Open Access Journals (Sweden)

Trijoedani Widodo

2007-03-01

Full Text Available Since most authors use the reversible pulpitis and irreversible pulpitis classification, however, many dentists still do not implement these new classifications. Research was made using a descriptive method by proposing questionnaire to dentists from various dental clinics. The numbers of the dentists participating in this research are 22 dentists. All respondents use the diagnosis sheet during their examinations on patients. Nonetheless, it can't be known what diagnosis card used and most of the dentists are still using the old classification. Concerning responses given towards the new classification: a the new classification had been heard, however, it was not clear (36.3%; b the new classification has never been heard at all (63.6%. Then, responses concerning whether a new development is important to be followed-up or not: a there are those who think that information concerning new development is very important (27.2%; b those who feel that it is important to have new information (68.3%; c those who think that new information is not important (8%. It concluded that information concerning the development of classification of pulp inflammation did not reach the dentists.

Critical Evaluation of Headache Classifications.

Science.gov (United States)

Özge, Aynur

2013-08-01

Transforming a subjective sense like headache into an objective state and establishing a common language for this complaint which can be both a symptom and a disease all by itself have kept the investigators busy for years. Each recommendation proposed has brought along a set of patients who do not meet the criteria. While almost the most ideal and most comprehensive classification studies continued at this point, this time criticisims about withdrawing from daily practice came to the fore. In this article, the classification adventure of scientists who work in the area of headache will be summarized. More specifically, 2 classifications made by the International Headache Society (IHS) and the point reached in relation with the 3rd classification which is still being worked on will be discussed together with headache subtypes. It has been presented with the wish and belief that it will contribute to the readers and young investigators who are interested in this subject.

Rapid intra-operative diagnosis of kidney cancer by attenuated total reflection infrared spectroscopy of tissue smears.

Science.gov (United States)

Pucetaite, Milda; Velicka, Martynas; Urboniene, Vidita; Ceponkus, Justinas; Bandzeviciute, Rimante; Jankevicius, Feliksas; Zelvys, Arunas; Sablinskas, Valdas; Steiner, Gerald

2018-01-09

Herein, a technique to analyze air-dried kidney tissue impression smears by means of attenuated total reflection infrared (ATR-IR) spectroscopy is presented. Spectral tumor markers-absorption bands of glycogen-are identified in the ATR-IR spectra of the kidney tissue smear samples. Thin kidney tissue cryo-sections currently used for IR spectroscopic analysis lack such spectral markers as the sample preparation causes irreversible molecular changes in the tissue. In particular, freeze-thaw cycle results in degradation of the glycogen and reduction or complete dissolution of its content. Supervised spectral classification was applied to the recorded spectra of the smears and the test spectra were classified with a high accuracy of 92% for normal tissue and 94% for tumor tissue, respectively. For further development, we propose that combination of the method with optical fiber ATR probes could potentially be used for rapid real-time intra-operative tissue analysis without interfering with either the established protocols of pathological examination or the ordinary workflow of operating surgeon. Such approach could ensure easier transition of the method to clinical applications where it may complement the results of gold standard histopathology examination and aid in more precise resection of kidney tumors. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

ON DEPARTURES FROM INDEPENDENCE IN CROSS-CLASSIFICATIONS.

Science.gov (United States)

CASE, C. MARSTON

THIS NOTE IS CONCERNED WITH IDEAS AND PROBLEMS INVOLVED IN CROSS-CLASSIFICATION OF OBSERVATIONS ON A GIVEN POPULATION, ESPECIALLY TWO-DIMENSIONAL CROSS-CLASSIFICATIONS. MAIN OBJECTIVES OF THE NOTE INCLUDE--(1) ESTABLISHMENT OF A CONCEPTUAL FRAMEWORK FOR CHARACTERIZATION AND COMPARISON OF CROSS-CLASSIFICATIONS, (2) DISCUSSION OF EXISTING METHODS…

22 CFR 9.5 - Original classification authority.

Science.gov (United States)

2010-04-01