An Extended Method of Higher-order Local Autocorrelation Feature Extraction for Classification of Histopathological Images

• Item type: Trans(1)
• 公開日: 2011-12-28
• タイトル: An Extended Method of Higher-order Local Autocorrelation Feature Extraction for Classification of Histopathological Images
• タイトル / 言語: en
• タイトル: An Extended Method of Higher-order Local Autocorrelation Feature Extraction for Classification of Histopathological Images
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: Special Issue on MIRU2010 - Research Paper
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属: Department of Information Science, Toho University／Presently with NEC Soft Ltd.
• 著者所属: National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属: National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属: National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属: Department of Information Science, Toho University
• 著者所属: National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属: National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属: Department of Surgical Pathology, Toho University Sakura Medical Center
• 著者所属: Department of Surgical Pathology, Toho University Sakura Medical Center
• 著者所属(英): en
• 著者所属(英): National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属(英): en
• 著者所属(英): Department of Information Science, Toho University／Presently with NEC Soft Ltd.
• 著者所属(英): en
• 著者所属(英): National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属(英): en
• 著者所属(英): National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属(英): en
• 著者所属(英): National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属(英): en
• 著者所属(英): Department of Information Science, Toho University
• 著者所属(英): en
• 著者所属(英): National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属(英): en
• 著者所属(英): National Institute of Advanced Industrial Science and Technology (AIST)
• 著者所属(英): en
• 著者所属(英): Department of Surgical Pathology, Toho University Sakura Medical Center
• 著者所属(英): en
• 著者所属(英): Department of Surgical Pathology, Toho University Sakura Medical Center
• 著者名: Hirokazu, Nosato; Tsukasa, Kurihara; Hidenori, Sakanashi; Masahiro, Murakawa; Takumi, Kobayashi; Tatsumi, Furuya; Tetsuya, Higuchi; Nobuyuki, Otsu; Kensuke, Terai; Nobuyuki, Hiruta
• 著者名(英): Hirokazu, Nosato; Tsukasa, Kurihara; Hidenori, Sakanashi; Masahiro, Murakawa; Takumi, Kobayashi; Tatsumi, Furuya; Tetsuya, Higuchi; Nobuyuki, Otsu; Kensuke, Terai; Nobuyuki, Hiruta
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In histopathological diagnosis, a clinical pathologist discriminates between normal tissues and cancerous tissues. However, recently, the shortage of clinical pathologists is posing increasing burdens on meeting the demands for such diagnoses, and this is becoming a serious social problem. Currently, it is necessary to develop new medical technologies to help reduce their burdens. Therefore, as a diagnostic support technology, this paper describes an extended method of HLAC feature extraction for classification of histopathological images into normal and anomaly. The proposed method can automatically classify cancerous images as anomaly by using an extended geometric invariant HLAC features with rotation- and reflection-invariant properties from three-level histopathological images, which are segmented into nucleus, cytoplasm and background. In conducted experiments, we demonstrate a reduction in the rate of not only false-negative errors but also of false-positive errors, where a normal image is falsely classified as an image with an anomaly that is suspected as being cancerous.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In histopathological diagnosis, a clinical pathologist discriminates between normal tissues and cancerous tissues. However, recently, the shortage of clinical pathologists is posing increasing burdens on meeting the demands for such diagnoses, and this is becoming a serious social problem. Currently, it is necessary to develop new medical technologies to help reduce their burdens. Therefore, as a diagnostic support technology, this paper describes an extended method of HLAC feature extraction for classification of histopathological images into normal and anomaly. The proposed method can automatically classify cancerous images as anomaly by using an extended geometric invariant HLAC features with rotation- and reflection-invariant properties from three-level histopathological images, which are segmented into nucleus, cytoplasm and background. In conducted experiments, we demonstrate a reduction in the rate of not only false-negative errors but also of false-positive errors, where a normal image is falsely classified as an image with an anomaly that is suspected as being cancerous.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12394973
• 書誌情報: IPSJ Transactions on Computer Vision and Applications（CVA）; 巻 3, p. 211-221, 発行日 2011-12-28
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6695
• 出版者 / 言語: ja
• 出版者: 情報処理学会