Temat: cytological image segmentation - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Segmentation of breast cancer fine needle biopsy cytological images
Autorzy:: Hrebień, M.
Steć, P.
Nieczkowski, T.
Obuchowicz, A.
Powiązania:: https://bibliotekanauki.pl/articles/907946.pdf
Data publikacji:: 2008
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: segmentacja obrazów
algorytm działów wodnych
aktywne kontury
obraz cytologiczny
transformata Hough'a
automat komórkowy
metoda GrowCut
cytological image segmentation
Hough transform
watershed algorithm
active contours
cellular automata GrowCut method
Opis:: This paper describes three cytological image segmentation methods. The analysis includes the watershed algorithm, active contouring and a cellular automata GrowCut method. One can also find here a description of image pre-processing, Hough transform based pre-segmentation and an automatic nuclei localization mechanism used in our approach. Preliminary experimental results collected on a benchmark database present the quality of the methods in the analyzed issue. The discussion of common errors and possible future problems summarizes the work and points out regions that need further research.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2008, 18, 2; 159-170
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Web–based framework for breast cancer classification
Autorzy:: Bruździński, T.
Krzyżak, A.
Fevens, T.
Jeleń, Ł.
Powiązania:: https://bibliotekanauki.pl/articles/91866.pdf
Data publikacji:: 2014
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: breast cancer
classification
cytological image
aspiration biopsy
feature vector
classifier
multilayer perceptron
segmentation algorithm
Opis:: 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.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 2; 149-162
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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