Wyniki wyszukiwania - Biblioteka Nauki

1

Influence of sample preparation methods on transmission electron micrographs of sol-gel materials

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Jeleń Ł. , Cegielski M. , Ulatowska-Jarża A. , Podbielska H.

Optica Applicata

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2002

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tom Vol. 32, nr 4

759-766

EN

The main goal of this research was to examine the influence of a sample preparation method on microscopic images obtained in transmission electron microscopy (TEM). The microscopic images of porous silica-based sol-gel materials were further processed with computer-aided methods, so thus to obtain information about distribution of pores. As a stain dopant osmium tetraoxide was used. Sol-gels were prepared from TEOS (tetraethyl orthosilicate) as a precursor and water as a solvent. The material was placed on microscopic grids, pure and with cellulose, or it was embedded in resin and then cut on a microtome. It has been demonstrated that the preparation procedure influences the pores distribution in a sol-gel silica network examined by TEM.

2

Role of image processing in the cancer diagnosis

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Detyna J. , Jeleń Ł. , Jeleń M.

Bio-Algorithms and Med-Systems

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2011

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tom Vol. 7, no. 4

5--9

EN

Cancer is still one of the most deadly diseases. It is a well known fact that the early diagnosis is crucial and allows for the successful treatment while cancers diagnosed in their late stage are almost impossible to treat. For precise and objective diagnosis there is a need for a computerized method for cytological image processing, which is an integral part of a diagnosis process. In this work we present a classification system for grading cancer malignancy. In particular, issues of image processing in the aspect of medical diagnosis presented by prof. R. Tadeusiewicz and Dr. J. Śmietański in [1].

3

Grading breast cancer malignancy with neural networks

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Jeleń Ł. , Lipiński A. , Detyna J. , Jeleń M.

Bio-Algorithms and Med-Systems

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2011

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tom Vol. 7, no. 2

47--53

EN

Breast cancer is one of the most often diagnosed cancers among middle-aged women. It is a well known fact that the early diagnosis is crucial and allows for the successful treatment while cancers diagnosed in their late stage are almost impossible to treat. For precise and objective diagnosis there is a need for a computerized method for malignancy grading, which is an integral part of a diagnosis process. In this work we present a classification system for grading cancer malignancy based on the Bloom – Richardson grading scheme. This is a well known grading scheme among the pathologist used during the diagnosis process. To achieve such a classification we extracted 16 features that were then used to classify the malignancy into two classes. Each class represents the malignancy of the cancer according to Bloom – Richardson grading scheme. According to that scheme two types of features are considered, where each type is extracted from images recorded at two different magnifications. Three structural features were calculated from low magnification images and thirteen polymorphic features were derived from high magnification images. To classify the malignancy grades, the multilayer perceptron was used. The described system was able to classify the malignancy with the error rate of 13.5%. In this paper we also present first clinical trials that allow for the verification of the obtained classification rate. The clinical trial showed that the depicted system has a high performance achieving an accuracy of 93.08% .

4

Web–based framework for breast cancer classification

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Bruździński T. , Krzyżak A. , Fevens T. , Jeleń Ł.

Journal of Artificial Intelligence and Soft Computing Research

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2014

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tom Vol. 4, No. 2

149--162

EN

The aim of this work is to create a web-based system that will assist its users in the cancer diagnosis process by means of automatic classification of cytological images obtained during fine needle aspiration biopsy. This paper contains a description of the study on the quality of the various algorithms used for the segmentation and classification of breast cancer malignancy. The object of the study is to classify the degree of malignancy of breast cancer cases from fine needle aspiration biopsy images into one of the two classes of malignancy, high or intermediate. For that purpose we have compared 3 segmentation methods: k-means, fuzzy c-means and watershed, and based on these segmentations we have constructed a 25–element feature vector. The feature vector was introduced as an input to 8 classifiers and their accuracy was checked. The results show that the highest classification accuracy of 89.02 % was recorded for the multilayer perceptron. Fuzzy c–means proved to be the most accurate segmentation algorithm, but at the same time it is the most computationally intensive among the three studied segmentation methods.

5

Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies

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Jeleń Ł. , Fevens T. , Krzyżak A.

International Journal of Applied Mathematics and Computer Science

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2008

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tom Vol. 18, no 1

75-83

EN

According to the World Health Organization (WHO), breast cancer (BC) is one of the most deadly cancers diagnosed among middle-aged women. Precise diagnosis and prognosis are crucial to reduce the high death rate. In this paper we present a framework for automatic malignancy grading of fine needle aspiration biopsy tissue. The malignancy grade is one of the most important factors taken into consideration during the prediction of cancer behavior after the treatment. Our framework is based on a classification using Support Vector Machines (SVM). The SVMs presented here are able to assign a malignancy grade based on preextracted features with the accuracy up to 94.24%. We also show that SVMs performed best out of four tested classifiers.

6

Wpływ technik rozpoznawania wzorców na ocene złośliwości nowotworów piersi

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Jeleń Ł. , Krzyżak A. , Fevens T. , Jeleń M.

Biuletyn Naukowy Wrocławskiej Wyższej Szkoły Informatyki Stosowanej. Informatyka

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2012

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tom vol. 2

16--23

PL

W ninieszym artykule prezentujemy zastosowania technik rozpoznawania wzorców oraz analizy obrazu do automatycznej obróbki i analizy obrazów cytologicznych. W celu wskazania nowych wyzwań w tej dziedzinie przegląd literatury zwiazanej z tym zagadnieniem został zaprezentowany. Ocena złośliwości nowotworów piersi jest skomplikowanym problemem gdzie doświadczenie jest bardzo istotne i może mieć wpływ na końcową diagnozę. Zastosowanie komputerowego systemu oceny pozwoli na zobiektywizowanie tego procesu. Artykuł prezentuje liczne zastosowania technik rozpozanwania wzorców w odniesieniu do zdjęć cytologicznych nowowtworów piersi w celu lepszej separowalności nietylko między komórkami nowtworowymi i zdrowymi, ale także między stpniami złośliwości. Wyznaczenie stopnia złośliwości jest bardzo istotne w diagnostyc, ponieważ ma wpływ na wybór sposobu leczenia. W niniejszym artykule prezentujemy także porównanie trzech sieci neuronowych wykorzystanych do oceny zdjęć cytologicznych piersi oraz porównujemy ich działanie z perceptronem wielowarstwowym opisanym w literaturze.

EN

In this paper we discuss applications of pattern recognition and image processing to automatic processing and analysis of cytological images. The literature survey of the problem is presented to point out new chalenges. The brest cancer malignancy grading is a difficult procedure that involves a lot of experiance which can have an impact on the diagnosis. A role of the computerized system is to help to make the diagnosis process more objective. The paper presents numerous applications of the patteren recognition techniques to breast cancer cytology to produce better discriminations not only between cancerous and helthy cells but also malignancy grades. Determination of the maligancy grade is crutial during the diagnosis because it will have an impact on the patient treatment. In the paper we also present a comparison of three neural networks applied to the breast cytology and compare them to the multilayer approach from the literature.