Browsing by Keywords "Histopathological image analysis"
Now showing items 1-17 of 17
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Automatic segmentation of colon glands using object-graphs
(Elsevier BV, 2010)Gland segmentation is an important step to automate the analysis of biopsies that contain glandular structures. However, this remains a challenging problem as the variation in staining, fixation, and sectioning procedures ... -
Color graph representation for structural analysis of tissue images
(Bilkent University, 2010)Computer aided image analysis tools are becoming increasingly important in automated cancer diagnosis and grading. They have the potential of assisting pathologists in histopathological examination of tissues, which may ... -
Color graphs for automated cancer diagnosis and grading
(Institute of Electrical and Electronics Engineers, 2010-03)This paper reports a new structural method to mathematically represent and quantify a tissue for the purpose of automated and objective cancer diagnosis and grading. Unlike the previous structural methods, which quantify ... -
Constrained Delaunay triangulation for diagnosis and grading of colon cancer
(Bilkent University, 2009)In our century, the increasing rate of cancer incidents makes it inevitable to employ computerized tools that aim to help pathologists more accurately diagnose and grade cancerous tissues. These mathematical tools offer ... -
Deep learning for digital pathology
(Bilkent University, 2020-11)Histopathological examination is today’s gold standard for cancer diagnosis and grading. However, this task is time consuming and prone to errors as it requires detailed visual inspection and interpretation of a ... -
Graph run-length matrices for histopathological image segmentation
(Institute of Electrical and Electronics Engineers, 2011-03)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 ... -
Graph walks for classification of histopathological images
(IEEE, 2013)This paper reports a new structural approach for automated classification of histopathological tissue images. It has two main contributions: First, unlike previous structural approaches that use a single graph for representing ... -
Histopathological image classification using salient point patterns
(Bilkent University, 2011)Over the last decade, computer aided diagnosis (CAD) systems have gained great importance to help pathologists improve the interpretation of histopathological tissue images for cancer detection. These systems offer ... -
A hybrid classification model for digital pathology using structural and statistical pattern recognition
(Institute of Electrical and Electronics Engineers, 2013)Cancer causes deviations in the distribution of cells, leading to changes in biological structures that they form. Correct localization and characterization of these structures are crucial for accurate cancer diagnosis and ... -
Mathematical modeling of the malignancy of cancer using graph evolution
(Elsevier Inc., 2007)We report a novel computational method based on graph evolution process to model the malignancy of brain cancer called glioma. In this work, we analyze the phases that a graph passes through during its evolution and ... -
Multilevel segmentation of histopathological images using cooccurance of tissue objects
(Institute of Electrical and Electronics Engineers, 2012-06)This paper presents a new approach for unsupervised segmentation of histopathological tissue images. This approach has two main contributions. First, it introduces a new set of high-level texture features to represent the ... -
Object-oriented testure analysis and unsupervised segmentation for histopathological images
(Bilkent University, 2012)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 ... -
Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection
(Elsevier BV, 2009-06)Staining methods routinely used in pathology lead to similar color distributions in the biologically different regions of histopathological images. This causes problems in image segmentation for the quantitative analysis ... -
A resampling-based Markovian model for automated colon cancer diagnosis
(Institute of Electrical and Electronics Engineers, 2012-01)In recent years, there has been a great effort in the research of implementing automated diagnostic systems for tissue images. One major challenge in this implementation is to design systems that are robust to image ... -
Resampling-based Markovian modeling for automated cancer diagnosis
(Bilkent University, 2011)Correct diagnosis and grading of cancer is very crucial for planning an effective treatment. However, cancer diagnosis on biopsy images involves visual interpretation of a pathologist, which is highly subjective. This ... -
Segmentation of colon glands by object graphs
(Bilkent University, 2008)Histopathological examination is the most frequently used technique for clinical diagnosis of a large group of diseases including cancer. In order to reduce the observer variability and the manual effort involving in ... -
Self-supervised learning with graph neural networks for region of interest retrieval in histopathology
(IEEE, 2021-05-05)Deep learning has achieved successful performance in representation learning and content-based retrieval of histopathology images. The commonly used setting in deep learning-based approaches is supervised training of deep ...
