Browsing by Subject "Cluster analysis."

Now showing 1 - 4 of 4

Open Access
A circular layout algorithm for clustered graphs
(2009) Belviranlı, Mehmet Esat
Visualization of information is essential for comprehension and analysis of the acquired data in any field of study. Graph layout is an important problem in information visualization and plays a crucial role in the drawing of graph-based data. There are many styles and ways to draw a graph depending on the type of the data. Clustered graph visualization is one popular aspect of the graph layout problem and there have been many studies on it. However, only a few of them focus on using circular layout to represent clusters. We present a new, elegant algorithm for layout of clustered graphs using a circular style. The algorithm is based on traditional force-directed layout scheme and uses circles to draw each cluster in the graph. In addition it can handle non-uniform node dimensions. It is the first algorithm to properly address layout of the quotient graph while considering inter-cluster relations as well as intra-cluster edge crossings. Experimental results show that the execution time and quality of the produced drawings with respect to commonly accepted layout criteria are quite satisfactory. The algorithm has been successfully implemented as part of Chisio, version 1.1. Chisio is an open source general purpose graph editor developed by i-Vis (information visualization) Research Group of Bilkent University.
Open Access
Multilevel cluster ensembling for histopathological image segmentation
(2011) Şimşek, Ahmet Çağrı
In cancer diagnosis and grading, histopathological examination of tissues by pathologists is accepted as the gold standard. However, this procedure has observer variability and leads to subjectivity in diagnosis. In order to overcome such problems, computational methods which use quantitative measures are proposed. These methods extract mathematical features from tissue images assuming they are composed of homogeneous regions and classify images. This assumption is not always true and segmentation of images before classification is necessary. There are methods to segment images but most of them are proposed for generic images and work on the pixel-level. Recently few algorithms incorporated medical background knowledge into segmentation. Their high level feature definitions are very promising. However, in the segmentation step, they use region growing approaches which are not very stable and may lead to local optima. In this thesis, we present an efficient and stable method for the segmentation of histopathological images which produces high quality results. We use existing high level feature definitions to segment tissue images. Our segmentation method significantly improves the segmentation accuracy and stability, compared to existing methods which use the same feature definition. We tackle image segmentation problem as a clustering problem. To improve the quality and the stability of the clustering results, we combine different clustering solutions. This approach is also known as cluster ensembles. We formulate the clustering problem as a graph partitioning problem. In order to obtain diverse and high quality clustering results quickly, we made modifications and improvements on the well-known multilevel graph partitioning scheme. Our method clusters medically meaningful components in tissue images into regions and obtains the final segmentation. Experiments showed that our multilevel cluster ensembling approach performed significantly better than existing segmentation algorithms used for generic and tissue images. Although most of the images used in experiments, contain noise and artifacts, the proposed algorithm produced high quality results.
Open Access
Object-oriented testure analysis and unsupervised segmentation for histopathological images
(2012) Tosun, Akif Burak
The histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual interpretation of pathologists. To alleviate this problem, it is very important to develop computational quantitative tools, for which image segmentation constitutes the core step. The segmentation algorithms in literature commonly use pixel-level color/texture descriptors that they define on image pixels for quantizing a tissue. On the other hand, it is usually harder to express domain specific knowledge about tissues, such as the spatial organization of tissue components, using only the pixel-level descriptors. This may become even harder for tissue images, which typically consist of a considerable amount of variation and noise at their pixel-level, such as similar color distribution of different tissue components, distortion in cell alignments, and color contrast caused by too much stain in a particular region. The previous segmentation algorithms are more susceptible to these problems as they work on pixel-level descriptors. In order to successfully address these issues, in this thesis, we introduce three new texture descriptors, namely ObjSEG, GraphRLM, and ObjCooc textures, and implement algorithms that use these descriptors for segmenting histopathological tissue images. We extract these texture descriptors on tissue components that are approximately represented by circular objects. Since these objectoriented texture descriptors are defined on the tissue components, and hence domain specific knowledge, they represent the spatial organization of the components better than their previous counterparts. Thus, our algorithms based on these descriptors give more effective and robust segmentation results. Furthermore, since the descriptors are not directly defined on image pixels, they are effective to alleviate the pixel-level problems. In our experiments, we tested our algorithms that use the proposed objectoriented descriptors on a dataset of 200 colon tissue images. Our experiments demonstrated that our new object-oriented feature descriptors led to high segmentation accuracies, also providing a reasonable number of segmented regions. Compared with its previous counterparts, the experimental results also showed that our proposed algorithms are more effective in segmenting histopathological images.
Open Access
A part family formation algorithm in GT environment using a multi-objective cluster analysis
(1993) Balköse, Hasan Okan
In the existing literature, the part-family formation problem is nandled either by coding systems or the cluster analysis. In this study, we propose a new method that will consider both design attributes and operation sequences simultaneously in conjunction with the related performance measures such as the machine investment, within and between cell workload variabilities, and the number of skippings. Finally the proposed method is compared with the similarity coefficent method under different experimental settings and its robustness is chocked against the varying system parameters.