Wyniki wyszukiwania - BazTech

1

A novel approach for detection and delineation of cell nuclei using feature similarity index measure

John J., Nair M. S., Kumar P. R. A., Wilscy M.

Biocybernetics and Biomedical Engineering

|

2016

|

Vol. 36, no. 1

76--88

EN

Accurate image segmentation of cells and tissues is a challenging research area due to its vast applications in medical diagnosis. Seed detection is the basic and most essential step for the automated segmentation of microscopic images. This paper presents a robust, accurate and novel method for detecting cell nuclei which can be efficiently used for cell segmentation. We propose a template matching method using a feature similarity index measure (FSIM) for detecting nuclei positions in the image which can be further used as seeds for segmentation tasks. Initially, a Fuzzy C-Means clustering algorithm is applied on the image for separating the foreground region containing the individual and clustered nuclei regions. FSIM based template matching approach is then used for nuclei detection. FSIM makes use of low level texture features for comparisons and hence gives good results. The performance of the proposed method is evaluated on the gold standard dataset containing 36 images (_8000 nuclei) of tissue samples and also in vitro cultured cell images of Stromal Fibroblasts (5 images) and Human Macrophage cell line (4 images) using the statistical measures of Precision and Recall. The results are analyzed and compared with other state-of-the-art methods in the literature and software tools to prove its efficiency. Precision is found to be comparable and the Recall rate is found to exceed 92% for the gold standard dataset which shows considerable performance improvement over existing methods.

2

Selected aspects of corneal endothelial segmentation quality

Piorkowski A., Nurzynska K., Boldak C., Reska D., Gronkowska-Serafin J.

Journal of Medical Informatics & Technologies

|

2015

|

Vol. 24

155--163

EN

The number and shape of cells in endothelium layer is highly correlated with the quality of vision. Therefore, its precise and automatic description plays an important role in medicine. This work presents several aspects of image processing of endothelium layer acquired by specular microscope. The comparison of cell selection accuracy is discussed using two different approaches to solve this problem: convolution filtering methods, and snake-based method. Moreover, for verification results generated by dedicated software, supplied with the microscope, were utilized. Next, the precise segmentation method is applied to improve the segmentation. The results are inspected visually, but also CV, H, and CVSL parameters, used in medicine, are calculated. The research concludes that general visual outcomes achieved by all segmentation approaches give similar results, however deep insight into cell outline position reveals some differences, which were partially removed after precise segmentation application. The analysis of parameter values show high stability of CV and CVSL parameters.

3

Constructing software for analysis of neuron, glial and endothelial cell numbers and density in histological Nissl-stained rodent brain tissue

Kołodziejczyk A., Ładniak M., Piórkowski A.

Journal of Medical Informatics & Technologies

|

2014

|

Vol. 23

77--85

EN

Cell number, density and volume of white and gray matter in brain structures are not constant values. Cellular alterations in brain areas might coincide with neurological and psychiatric pathologies as well as with changes in brain functionality during selection experiments, pharmacological treatment or aging. Several softwares were created to facilitate quantitative analysis of brain tissues, however results obtained from these softwares require multiple manual settings making the computing process complex and time-consuming. This study attempts to establish half automated software for fast, ergonomic and an accurate analysis of cellular density, cell number and cellular surface in morphologically different brain areas: cerebral cortex, pond and cerebellum. Images of brain sections of bank voles stained with standard cresyl-violet technique (Nissl staining), were analyzed in designed software. Results were compared with other commercially available tools regarding number of steps to be done by user and number of parameters possible to measure.

4

Automatic segmentation of corneal endothelial cells using active contours

Charłampowicz K., Reska D., Bołdak C.

Advances in Computer Science Research

|

2014

|

Nr 11

47--60

EN

We present a new method for segmenting the corneal endothelial cells from microscopic images. It uses multiple active contours initialized by adaptive thresholding and limited with their growing to not overlap. Thanks to the inherent characteristics of the active contour both outcomes can be achieved: cell quantity and delimitation. The tool implementing this approach is built within the MESA framework - an environment for developing and evaluating segmentation techniques. The accuracy is estimated on the base of real microscopic cell images segmented manually.

PL

W artykule zaprezentowano autorską automatyczną metodę segmentacji komórek śródbłonka rogówki oka z obrazów mikroskopowych. Metoda używa wielu aktywnych konturów zainicjalizowanych wewnątrz komórek za pomocą adaptacyjnego progowania i ograniczonych w swoim rozroście tak, aby nie pokrywać się. Metoda został zaimplementowana w środowisku MESA przeznaczonym do rozwoju i ewaluacji technik segmentacji. Jakość segmentacji została oszacowana na rzeczywistych obrazach mikroskopowych w odniesieniu do ręcznie zaznaczonych konturów komórek.

5

Selected issues of corneal endothelial image segmentation

Piórkowski A., Gronkowska-Serafin J.

Journal of Medical Informatics & Technologies

|

2011

|

Vol. 17

239--245

EN

This article concerns the analysis of corneal endothelial image. The basic problems of binarization and segmentation of these images are discussed. Preprocessing methods are proposed, consisting of median and convolution filtration, to remove noise. An algorithm of normalization of the average brightness of the vertical and horizontal is presented. The problem of binarization is discussed. At the end the proposal of segmentation algorithm is reported.

6

Metoda automatycznego wyznaczania indeksu mitotycznego populacji komórek cebuli z wykorzystaniem drzewa decyzyjnego

Gocławski J., Sekulska-Nalewajko J., Anioł P.

Automatyka / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

|

2008

|

T. 12, z. 3

641-656

PL

Wyznaczanie indeksu mitotycznego jest metodą oceny zdolności podziału komórek w populacjach poddawanych oddziaływaniom różnorodnych czynników hamujących lub ułatwiających ich wzrost. Zaproponowano algorytmy segmentacji obrazów komórek cebuli i elementów jąder komórkowych wyodrębniających się w procesie podziału mitotycznego. Następnie wydobyto zestaw cech geometrycznych, teksturalnych i topologicznych elementów jąder komórkowych odróżniających interfazę od faz mitozy. Zbudowano drzewo decyzyjne oparte na algorytmie C4.5. W celu oszacowania błędu klasyfikacji przeprowadzono próby 10-krotnych walidacji skrośnych. Dokonano także redukcji przestrzeni cech za pomocą metody PCA. Wyliczono wartość indeksu mitotycznego badanej populacji komórek cebuli, błąd estymatora tego indeksu i przeprowadzono porównanie ze średnim błędem klasyfikacji.

EN

The evaluation of mitotic index is the method of estimation of cell division ability in cell populations treated by growth inhibitors or accelerators. The image processing algorithms for the segmentation of onion cells and their nuclei elements appearing in the process of mitosis is proposed. Then a set of geometrical, textural and topological features of nuclei elements was extracted, which can distinguish interphase from the stages of mitosis. A decision tree was built according to C4.5 method using the maximum of information gain ratio of the feature values. To evaluate classification error, a series of 10-fold crossvalidations were performed. The feature space was reduced by applying PCA method. The value of mitotic index for the tested onion cell population as well as the estimator index error was evaluated. The errors were compared with an average classification error.