Wyniki wyszukiwania - Biblioteka Nauki

1

GLCM and GLRLM based texture features for computer-aided breast cancer diagnosis

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Filipczuk P. , Fevens T. , Krzyżak A. , Obuchowicz A.

Journal of Medical Informatics & Technologies

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2012

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tom Vol. 19

109--115

EN

This paper presents 15 texture features based on GLCM (Gray-Level Co-occurrence Matrix) and GLRLM (Gray-Level Run-Length Matrix) to be used in an automatic computer system for breast cancer diagnosis. The task of the system is to distinguish benign from malignant tumors based on analysis of fine needle biopsy microscopic images. The features were tested whether they provide important diagnostic information. For this purpose the authors used a set of 550 real case medical images obtained from 50 patients of the Regional Hospital in Zielona Góra. The nuclei were isolated from other objects in the images using a hybrid segmentation method based on adaptive thresholding and kmeans clustering. Described texture features were then extracted and used in the classification procedure. Classification was performed using KNN classifier. Obtained results reaching 90% show that presented features are important and may significantly improve computer-aided breast cancer detection based on FNB images.

2

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.

3

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.

4

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.

5

Comparative study of shape, intensity and texture features and support vector machine for white blood cell classification

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Habibzadeh M. , Krzyżak A. , Fevens T.

Journal of Theoretical and Applied Computer Science

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2013

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tom Vol. 7, nr 1

20--35

EN

The complete blood count (CBC) is widely used test for counting and categorizing various peripheral particles in the blood. The main goal of the paper is to count and classify white blood cells (leukocytes) in microscopic images into five major categories using features such as shape, intensity and texture features. The first critical step of counting and classification procedure involves segmentation of individual cells in cytological images of thin blood smears. The quality of segmentation has significant impact on the cell type identification, but poor quality, noise, and/or low resolution images make segmentation less reliable. We analyze the performance of our system for three different sets of features and we determine that the best performance is achieved by wavelet features using the Dual-Tree Complex Wavelet Transform (DT-CWT) which is based on multi-resolution characteristics of the image. These features are combined with the Support Vector Machine (SVM) which classifies white blood cells into their five primary types. This approach was validated with experiments conducted on digital normal blood smear images with low resolution.

6

Application of pattern recognition techniques for the analysis of thin blood smear images

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Habibzadeh M. , Krzyżak A. , Fevens T.

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tom Vol. 18

29--40

EN

In this paper we discuss applications of pattern recognition and image processing to automatic processing and analysis of histopathological images. We focus on counting of Red and White blood cells using microscopic images of blood smear samples. We provide literature survey and point out new challenges. We present an improved cell counting algorithm.