1,721,116 research outputs found
A conceptual framework for concept definition in measurement: The case of 'sensitivity'
No full textThe concept 'sensitivity' has multiple and sometimes incompatible usages and definitions, as they can be found in the scientific and technical literature. A strategy is proposed toward a conceptual framework in which sensitivity is qualitatively intended as a feature of a black box behavior and quantitatively is defined according to specific evaluation types (interval/ratio, ordinal, nominal) for both deterministic and stochastic behaviors. The proposed formal definitions characterize stochastic sensitivity as constituted of "effective" and "confounding" components, that can be simultaneously present and contribute to a desirable and unwanted increment of global sensitivity respectively. Two examples taken from the context of imaging systems and image-based measuring systems, in which sensitivity is computed in presence of non-negligible uncertainty sources, provide some hints on the usefulness of the proposed framework
Extracting fuzzy classification rules from texture segmented hrct lung images
No full textAutomatic tools for detection and identification of lung and lesion from high-resolution CT (HRCT) are becoming increasingly important both for diagnosis and for delivering high-precision radiation therapy. However, development of robust and interpretable classifiers still presents a challenge especially in case of non-small cell lung carcinoma (NSCLC) patients. In this paper, we have attempted to devise such a classifier by extracting fuzzy rules from texture segmented regions from HRCT images of NSCLC patients. A fuzzy inference system (FIS) has been constructed starting from a feature extraction procedure applied on overlapping regions from the same organs and deriving simple if-then rules so that more linguistically interpretable decisions can be implemented. The proposed method has been tested on 138 regions extracted from CT scan images acquired from patients with lung cancer. Assuming two classes of tissues C1 (healthy tissues) and C2 (lesion) as negative and positive, respectively; preliminary results report an AUC 0.98 for lesions and AUC 0.93 for healthy tissue, with an optimal operating condition related to sensitivity 0.96, and specificity 0.98 for lesions and sensitivity 0.99, and specificity 0.94 for healthy tissue. Finally, the following results have been obtained: false-negative rate (FNR)06 % (C1), FNR 02 % (C2), false-positive rate (FPR) 04 % (C1), FPR 03 % (C2), true-positive rate (TPR) 0.94 %, (C1) and TPR 0.98 % (C2)
A structured strategy of concept definition in measurement: the case of sensitivity
Full text linkThe paper emphasizes the importance that fundamental concepts in measurement science are defined according to a structured strategy, which provides both a general, qualitative characterization and a specific, type-related, quantitative definition. As a significant case, the concept 'sensitivity' is discussed and a definition for it proposed
Breast masses detection using phase portrait analysis and fuzzy inference systems
No full textPURPOSE: Breast masses exhibit variability in margins, shapes, and dimensions, so their detection is a difficult task in mammographic computer-aided diagnosis. Mass detection is usually a two-step procedure: mass identification and false-positive reduction. A new method to automatically detect mass lesions in mammographic images with tuning according to the breast tissue density was developed and tested. METHODS: A modified phase portrait analysis method was introduced, based on the eigenvalue condition number and an eigenvalue intensity map. The method uses an iterative and tissue density-adaptive segmentation procedure with extraction of geometric features. False-positive reduction is accomplished using a fuzzy inference-based classifier. A leave-one-image-out cross-validation procedure was implemented, and stepwise regression analysis was used to automatically extract an optimal set of features. Testing and validation were performed on two different data sets containing at least one malignant mass D1 (388 images) and D2 (674 images), and a third data set N1 (50 images) was used consisting of normal controls. These three data sets were taken from the Digital Database for Screening Mammography. RESULTS: For sensitivities of 0.9, 0.85, 0.80, and 0.75, the best results on cancer images exhibit an False-Positive per Image (FPpI) equal to 0.6, 0.45, 0.35, and 0.3, respectively, using a Bayes Linear Discriminant Analysis (LDA) classifier and an FPpI of 0.85, 0.7, 0.55, and 0.45 using a fuzzy inference system (FIS) for false-positive reduction. When the algorithm is tested on normal images, an FPpI equal to 0.4, 0.3, 0.25, and 0.2 was observed using LDA and 0.3, 0.25, 0.2, and 0.15 using the FIS. CONCLUSION: A preclinical study of an automatic breast mass detection algorithm provided promising results in terms of sensitivity and low false-positive rate. Further development and clinical testing are justified based on the results
RAPPRESENTAZIONE DELL'INCERTEZZA DI MISURA, MEDIANTE RANDOM FUZZY VARIABLES, DI DATI ESTRATTI DA ALGORITMI DI STIMA DEL RUMORE APPLICATI AD IMMAGINI RADIOGRAFICHE
No full textValutazione dell’incertezza di misura in un sistema fuzzy per la classificazione di microcalcificazioni nella mammografia digitale
No full textReference folding subranging caliper ADC
No full textThe paper presents a reduced ADC architecture obtained by introducing the subranging technique into the scheme of a caliper AD converter. This last converter was already proposed as an application of a theory which describes the comparison between scales having the steps prime each other. This converter architecture drastically reduces the number of the required resistors for a full flash realization. The introduction of the subranging technique into the caliper ADC here presented reduces also the number of the required comparators. The result is a very compact architecture. The paper describes a first intention architecture based on ideal components. An example of SPICE simulation is given
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