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Quick texture generation for multiobject image analysis in brain pathology

Warowny M., Markiewicz T.

Przegląd Elektrotechniczny

2010

R. 86, nr 1

50-52

The paper presents two methods of texture features generation for recognition between neoplasm and non-neoplasm cells in cancer diagnosis. There are few problems which need to be solved to achieve the best results: differentiable images, extraction of the individual cell image, selection of the most important features. We propose two models solving all of these problems. We compare the consequences of implementation Unser’s selected texture features and Markov Random Field model. The results of numerical experiments have shown in both methods quite good accuracy in recognizing cells. The proposed methods have proved to be useful in practical application at the diagnosis of cancer.

Referat przedstawia zastosowanie generacji cech teksturalnych w rozpoznawaniu komórek nowotworowych. Proces rozróżniania komórek jest dość złożony ze względu naturalną złożoność obrazów, konieczność ekstrakcji pojedynczej komórki obrazu oraz trudności w wyborze odpowiednio różnicującej cechy. W pracy porównane zostały efekty zastosowania dwóch rodzajów modeli – opartego na cechach Unsera oraz modelu Markova. Główny nacisk pracy położony jest na praktyczne zastosowanie obu metod w diagnozie nowotworowej.

Joint analysis of histological and ultrasonic images to lean state of thyroid gland

Nedzved A., Zalesky B., Ablameyko S., Drozd V., Fridman M.

Machine Graphics and Vision

2007

Vol. 16, No. 3/4

293-304

Simultaneous analysis of histological and ultrasonic (US) images of human thyroid glands for thyroid cancer diagnostics is proposed in the paper. It allows to explain the characteristics of US pictures of the thyroid gland via the sizes of its follicles. To show the dependence of US image features on the state of follicles, statistical analysis of US-texture is performed. In addition, the size of follicles in histological images is calculated by analysis of a distance map for the nuclei of cells. It is shown that echogenicity of the thyroid gland in US images depends essentially on the size of its follicles. The organ regions that contain many follicles of a size smaller than the size of healthy follicles, or contain many destroyed follicles, have low echogenicity. The same effect is observed for regions with oversized follicles. This information can be used to avoid a surgical procedure, including histological analysis.

Analysis of cell structure in color histological image

Nedzved A., Ablameyko S., Oczeretko E., Półjanowicz W.

Image Processing & Communications

2005

Vol. 10, no 1

43-51

One of the basic subjects of studying in histology is the cell structure. The image of a histological cell is characterized by a geometrical complexity and the certain hierarchy of cell structure. In this paper, algorithm for cell structure extraction is proposed. The algorithm consists of two branches. The first is intended for extraction of cells with an unpainted nucleus, another for the painted nucleus. According to cell hierarchical structure, binary images of cells, nucleus, nucleolus and inclusions are created. For computing of topological characteristics, cell is presented as hierarchy of binary images. The first level contains a binary image of cell, the second level contains an image of a nucleus, the third level contains nucleolus and various cellular inclusions.