ZASTOSOWANIE METODY CHAN-VESE W SEGMENTACJI OBRAZÓW MEDYCZNYCH

The article presents the problem of determining the edges of objects enclosed in a medical CT images, which will be subject to further analysis, for the purpose of medical diagnosis. The use of a transformation which introduces two-point thresholding, eliminates presenting pixels of objects for tissues that are not a subject to further analysis. This approach allowed us to sharpen the edges of objects presenting soft tissue. A way to detect the edge of the soft tissue was compared for the original image and processed one using the transformation using the method of Chan-Vese. Sharpening of edges of the image have improved the accuracy of detection of objects presenting the soft tissue.W artykule przedstawiono problem wyznaczania krawędzi obiektów zamkniętych w obrazach medycznych CT, które będą podlegały dalszej analizie, na potrzeby diagnostyki medycznej. Zastosowanie przekształcenia, które wprowadza progowanie, pozwala na wyeliminowanie pikseli prezentujących obiekty dla tkanek, które nie podlegają dalszej analizie. Podejście to pozwoliło na wyostrzenie krawędzi obiektów prezentujących tkanki miękkie. Porównano sposób wykrycia krawędzi tkanek miękkich, dla obrazu pierwotnego i przetworzonego za pomocą przekształcenia, z zastosowaniem metody Chan-Vese. Wyostrzenie krawędzi obrazu poprawiło dokładność wykrywania obiektów prezentujących tkanki miękkie

Image Segmentation for Quantification of Air-Water Interface in Micro-CT Soil Images

Soils are complex environments comprising various biological (roots, water, air etc) and physical constituents (minerals, aggregates, etc). Synchrotron radiation based X-ray microtomography (XMT) is widely used in extracting qualitative and quantitative information regarding spatial distribution of biological and physical soil constituents. Segmentation of these micro-CT soil images is of interest to geologists, hydrologists, civil and petroleum engineers and soil scientists. In this present work, we study and implement segmentation algorithms for microhydrology studies, specifically for soil water conductivity. Three well-known image segmentation algorithms are studied for evaluating their performance for the task. We demonstrate the problems and ways to segment XMT images and extract data for evaluating the air pressure in the soil pores to promote soil hydrology studies. To this end we take the recommended in the literature approach to differentiate textures and segment images using Fuzzy C-means Clustering (FCM). Secondly, we demonstrate the performance of two state-of-the-art level-set based active contours methods followed by curve fitting for radii detection and air pressure calculation

Improvements on segment based contours method for DNA microarray image segmentation

DNA microarray is an efficient biotechnology tool for scientists to measure the expression levels of large numbers of genes, simultaneously. To obtain the gene expression, microarray image analysis needs to be conducted. Microarray image segmentation is a fundamental step in the microarray analysis process. Segmentation gives the intensities of each probe spot in the array image, and those intensities are used to calculate the gene expression in subsequent analysis procedures. Therefore, more accurate and efficient microarray image segmentation methods are being pursued all the time. In this dissertation, we are making efforts to obtain more accurate image segmentation results. We improve the Segment Based Contours (SBC) method by implementing a higher order of finite difference schemes in the partial differential equation used in our mathematical model. Therefore, we achieved two improved methods: the 4th order method and the 8th order method. The 4th order method could be applied to segment both the cDNA microarray images and the Affymetrix GeneChips, while the 8 th order method could be applied to segment only the cDNA microarray images, due to the limitation of the current image resolution. The mathematical derivation shows that both our 4th order method and 8th order method are better approximating the C-V model [Chan & Vese, 2001] than the SBC method, which means they will offer more accurate segmentation results than the SBC method. Besides mathematical proof, we do the practical experiments to double check the conclusion drawn from the mathematical derivation. Both the 4th order method and the 8th order method are used to segment microarray images, and the output segmentation results—the intensities of each probe cell in the microarray image—are being compared to the results from the SBC method and two other mainstream microarray image segmentation methods, the Globaly Optimal Geodesic Active Contours (GOGAC) method and the GeneChip Operating System (GCOS) software, for more valid evaluation. To give the ground true values of intensities as the standard for different segmentation methods comparison, a microarray image simulator is introduced to generate the simulated images used in our experiments. The simulated microarray images have all the characteristics that real microarray images have, and the true intensity values of each probe spot in the image are provided by this simulator. Intensity values segmented by those segmentation methods are compared to the true intensity values. Therefore, we could evaluate that one segmentation method is more accurate than the other methods if its intensity values are closer to the true values. We conduct several analysis procedures in the segmentation results comparison part to convince our analysis results. Intensity analysis, paired t-test and Unweighted Pair Group Method with Arithmetic Mean (UPGMA) hierarchy cluster experiments are applied to analyze intensity values of those methods. The segmentation output analysis results show that our 4th order method and the 8th order method could offer more accurate segmentation than the SBC method, the GCOS method and the GOGAC method on some kinds of the microarray images. There are accuracy improvements achieved with the 8 th order method over the 4th order method on the cDNA microarray image. On the Bovine type Affymetrix GeneChip image, there is no significant difference between the 4th order method and the 8th order method