Wyniki wyszukiwania - BazTech

1

Klasyfikator jądrowej wersji maszyny wektorów podpierających wspomagający diagnostykę obszarów nowotworowych w wielospektralnym obrazowaniu endoskopowym

Bieda R., Świtoński A., Kwiatek S., Latos W., Cieślar G., Sieroń A., Wojciechowski K.

Przegląd Elektrotechniczny

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2010

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R. 86, nr 12

13-16

PL

W pracy przedstawiono podstawy teoretyczne klasyfikatora maszyny wektorów podpierających oraz uzyskane przy jego zastosowaniu wyniki klasyfikacji obrazów wielospektralnych pozyskanych z 21 kanałowego systemu wielospektralnego obrazowania endoskopowego. Uzyskana rozróżnialność obszarów zmienionych chorobowo jest w ocenie lekarzy diagnostów wyższa niż w przypadku systemu Xillix Onco LIFE.

EN

The paper presents the theoretical basis and results obtained with use of the support vector machine classifier on multispectral image classification gained with 21-channel system for endoscopic multispectral imaging. Obtained distinguishability of pathological changes areas is higher than in the case of Xillix Onco LIFE system in the medical diagnosticians opinion.

2

Performance of the Support Vector Machines for medical classification problems

Ćwiklińska-Jurkowska M.

Biocybernetics and Biomedical Engineering

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2009

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Vol. 29, no. 4

63-81

EN

In the Support Vector Machines classification technique the best possible discriminating hyperplane between two populations is looked for by maximizing of margin between the populations' closest points. This idea is also applied for obtaining nonlinear discriminant boundaries by using different kernels for transformations, thus obtaining a nonlinear Support Vector Machines method. The nonlinear Support Vector Machines method is based on pre-processing of data to represent patterns in high dimension- usually much higher than the original variable feature space. In the presented work the dependency of Support Vector Machines performance on the kind of kernel and Support Vector Machines parameters is presented. The performance was assessed by resubstitution, 10- fold cross-validation, leave-one-out error, learning curves and Receiver Operating Characteristic curves. The kind and shape of the kernel is more important than regularization constant allowing different levels of overlapping classes. Combining boosting and Support Vector Machines did not improved performance in comparison to Support Vector Machines method alone, because both Support Vector Machines procedure and boosting are focused on observations difficult to classify.

3

Classification of microcalcifications into BI-RADS™ morphologic categories - preliminary results

Podsiadły-Marczykowska T., Wróblewska A., Przelaskowski A.

Biocybernetics and Biomedical Engineering

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2009

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Vol. 29, no. 4

83-93

EN

In the paper, preliminary results for the classification of microcalcifications (MCs) into the three BIRADSTM morphologic categories (punctate, pleomorphic and linear) are presented. To classify the microcalcifications into morphologic types the set of 27 shape descriptors was constructed. The morphology of the cluster was determined as the mean values of shape descriptors for single microcalcifications. SVM classifier was used to differentiate MCs clusters into BI-RADS morphologic types. Classification of the clustered MCs into linear or pleomorphic morphologic types obtained accuracy ranging from 84 to 88% depending on the MCs features and the SVM parameters. The most discriminate features for the classification of clustered linear and pleomorphic MCs are: inner compactness, major axis and first invariant shape moment calculated from binary image of segmented MCs.

4

Ocena rzeczywistej wydajności wybranych regresorów

Michalak M., Stąpor K.

Studia Informatica

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2008

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Vol. 29, nr 1

23-33

PL

Poniższa praca porusza temat nieparametrycznej estymacji funkcji regresji oraz jej efektywności czasowej. Autorzy porównują dokładność regresji, ale i czas potrzebny na wyznaczenie wartości dla obiektu testowego. Czas ten uwzględnia nie tylko samo wyznaczanie wartości, ale i etap tworzenia regresora. Eksperymenty zostały przeprowadzone na wielowymiarowych danych rzeczywistych.

EN

This paper raises a problem of nonparametric estimation of the regression function and its time efficiency. Authors compare the regression accuracy but considers also the time that is needed to evaluate the value for the test object. That time takes into consideration the evaluation time, but also the time of regressor creating. Experiments were conducted with the usage of multidimensional real data.

5

Signature pre - procesing based on walsh coefficients

Porwik P., Wróbel K.

Journal of Medical Informatics & Technologies

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2008

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Vol. 12

57--61

EN

Recognition and verification of persons are difficult and important tasks today. In many fields of human activities (driver's licenses, passports, electronic cards, etc.), signature recognition of person is needed. Hence, it inspires the development of a wide range of automatic identification systems. Signatures have been used for many centuries as a method of people's identification. Signatures recognition was performed manually by experts in the past. Nowadays, these procedures are very often automatically applied. In this paper the system that automatically authenticates documents based on the owner's handwritten signature is presented.