title: Homology-based method for detecting regions of interest in colonic digital images.
abstract: BACKGROUND
  A region of interest (ROI) is a part of tissue that contains important information for diagnosis. To use many image analysis methods efficiently, a technique that would allow for ROI identification is required. For the colon, ROIs are characterized by areas of stronger color intensity of hematoxylin. Since malignant tumors grow in the innermost layer, most ROIs will be located in the colonic mucosa and will be an accumulation of tumor cells and/or integrated cells with distorted architecture.
  
  
  METHODS
  Using homology theory, our group proposed a method to estimate the contact degree of elements in a unit area of tissue. Homology is a concept that is used in many branches of algebra and topology, and it can quantify the contact degree. Due to the lack of contact inhibition of cancer cells, an area with unusual contact degree is expected to be a potential ROI.
  
  
  RESULTS
  The current work verifies the accuracy of this method against the results of pathological diagnosis, based on 1825 colonic images provided by the Osaka Medical Center for Cancer and Cardiovascular Diseases. Although we have many false positives and there is a possibility of missing undifferentiated types of cancer, this system is very effective for detecting ROIs.
  
  
  CONCLUSIONS
  The mathematical system proposed by our group successfully detects ROIs and is a potentially useful tool for differentiating tumor areas in microscopic examination very quickly. Because we use only the information from low-power field images, there is room for further improvement. This system could be used to screen for not only colon cancer but other cancers as well. More sophisticated and more efficient automated pathological diagnosis systems can be developed by integrating various techniques available today.
  The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/7129390011429407 .
authors.affiliation: Department of Molecular Pathology, Osaka University Graduate School of Medicine and Health Science, 1-7 Yamadaoka, Suita, Osaka, 565-0871, Japan. k-nakane@sahs.med.osaka-u.ac.jp.
authors.forename: Kazuaki
authors.initials: K
authors.lastname: Nakane
authors.affiliation: Department of Cancer Pathology, Hokkaido University Graduate School of Medicine, Nishi 7 kita 15 Kita ward, Sapporo, Hokkaido, 060-8638, Japan. takiyama@med.hokudai.ac.jp.
authors.forename: Akihiro
authors.initials: A
authors.lastname: Takiyama
authors.affiliation: Department of Molecular Pathology, Osaka University Graduate School of Medicine and Health Science, 1-7 Yamadaoka, Suita, Osaka, 565-0871, Japan. mori@sahs.med.osaka-u.ac.jp.
authors.forename: Seiji
authors.initials: S
authors.lastname: Mori
authors.affiliation: Department of Molecular Pathology, Osaka University Graduate School of Medicine and Health Science, 1-7 Yamadaoka, Suita, Osaka, 565-0871, Japan. matsuura@sahs.med.osaka-u.ac.jp.
authors.forename: Nariaki
authors.initials: N
authors.lastname: Matsuura
mesh_terms: D000465:Algorithms; D003106:Colon / Q000473:pathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D006416:Hematoxylin; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted; D008853:Microscopy / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods
chemical_list: D006416:Hematoxylin