Detection of colon glands using subgraph modeling [Altçi̇zge modellemesi̇ kullanarak kolon bez tespi̇ti̇]
2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/28386
The colon adenocarcinoma causes changes in glandular structures of colon tissues. Pathologists assess these changes to diagnose and grade the colon adenocarcinoma. However, this assessment may consist of a considerable amount of subjectivity. It is possible to reduce this subjectivity by characterizing the glands with mathematical features. For that, the first step is to detect gland locations. In literature, most of the gland detection methods are pixel-based. However, tissue images may show pixel-level variances due to their nature and differences in biopsy preparation and image acquisition procedures. On the other hand, in spite of these variances, the distribution of tissue components forming glands show similar properties. The methods that consider this distribution has the potential of improving the performance. The method proposed in this study first models the distribution of the components by constructing a graph on them. Then, it breaks the constructed graph down into subgraphs and detects the glands using the features of these subgraphs. The experiments conducted on colon tissue images show that the proposed method leads to promising results for detecting the glands. © 2011 IEEE.
- Conference Paper 
Showing items related by title, author, creator and subject.
Gunduz-Demir, C. (2011)In the current practice of medicine, histopathological examination is the gold standard for routine clinical diagnosis and grading of cancer. However, as this examination involves the visual analysis of biopsies, it is ...
Sur, S.; Pashuck, E.T.; Guler, M.O.; Ito, M.; Stupp, S.I.; Launey, T. (2012)Scaffold design plays a crucial role in developing graft-based regenerative strategies, especially when intended to be used in a highly ordered nerve tissue. Here we describe a hybrid matrix approach, which combines the ...
Ertan, A.B.; Yilgor P.; Bayyurt, B.; Çalikoǧlu, A.C.; Kaspar Ç.; Kök F.N.; Kose G.T.; Hasirci V. (2013)The effects of double release of insulin-like growth factor I (IGF-I) and growth factor β1 (TGF-β1) from nanoparticles on the growth of bone marrow mesenchymal stem cells and their differentiation into cartilage cells were ...