http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Detection of Lung Nodule on Temporal Subtraction Images Based on Artificial Neural Network
Takumi Tokisa,Noriaki Miyake,Shinya Maeda,Hyoungseop Kim,Joo Kooi Tan,Seiji Ishikawa,Seiichi Murakami,Takatoshi Aoki 한국지능시스템학회 2012 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.12 No.2
The temporal subtraction technique as one of computer aided diagnosis has been introduced in medical fields to enhance the interval changes such as formation of new lesions and changes in existing abnormalities on deference image. With the temporal subtraction technique radiologists can easily detect lung nodules on visual screening. Until now, two-dimensional temporal subtraction imaging technique has been introduced for the clinical test. We have developed new temporal subtraction method to remove the subtraction artifacts which is caused by mis-registration on temporal subtraction images of lungs on MDCT images. In this paper, we propose a new computer aided diagnosis scheme for automatic enhancing the lung nodules from the temporal subtraction of thoracic MDCT images. At first, the candidates regions included nodules are detected by the multiple threshold technique in terms of the pixel value on the temporal subtraction images. Then, a rule-base method and artificial neural networks is utilized to remove the false positives of nodule candidates which is obtained temporal subtraction images. We have applied our detection of lung nodules to 30 thoracic MDCT image sets including lung nodules. With the detection method, satisfactory experimental results are obtained. Some experimental results are shown with discussion.
Detection of Lung Nodule on Temporal Subtraction Images Based on Artificial Neural Network
Tokisa, Takumi,Miyake, Noriaki,Maeda, Shinya,Kim, Hyoung-Seop,Tan, Joo Kooi,Ishikawa, Seiji,Murakami, Seiichi,Aoki, Takatoshi Korean Institute of Intelligent Systems 2012 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.12 No.2
The temporal subtraction technique as one of computer aided diagnosis has been introduced in medical fields to enhance the interval changes such as formation of new lesions and changes in existing abnormalities on deference image. With the temporal subtraction technique radiologists can easily detect lung nodules on visual screening. Until now, two-dimensional temporal subtraction imaging technique has been introduced for the clinical test. We have developed new temporal subtraction method to remove the subtraction artifacts which is caused by mis-registration on temporal subtraction images of lungs on MDCT images. In this paper, we propose a new computer aided diagnosis scheme for automatic enhancing the lung nodules from the temporal subtraction of thoracic MDCT images. At first, the candidates regions included nodules are detected by the multiple threshold technique in terms of the pixel value on the temporal subtraction images. Then, a rule-base method and artificial neural networks is utilized to remove the false positives of nodule candidates which is obtained temporal subtraction images. We have applied our detection of lung nodules to 30 thoracic MDCT image sets including lung nodules. With the detection method, satisfactory experimental results are obtained. Some experimental results are shown with discussion.
Overview of JCGGDB including New Released GlycoProtDB
Toshihide Shikanai,Hiroyuki Kaji,Yoshinori Suzuki,Noriaki Fujita,Masako Maeda,HonglingWen,Madoka Ishizaki,Hiromichi Sawaki,Hisashi Narimatsu. 한국당과학회 2012 한국당과학회 학술대회 Vol.2012 No.1
The JST/NBDC integrated database project has kicked off last year. JCGGDB was selected as a promotion program of DB integration, aiming to integrate all the glycan-related databases in Japan and build user- friendly search systems. As part of the project, the construction of ACGG-DB (an integrated database for the ACGG: Asian Communications for Glycobiology and Glycotechnology) is also planned in cooperation with Asian countries. As of now we have consolidated data from various Japanese institutes into JCGGDB and developed a cross-search function by keyword entry and integrated search functions by glycan stcurctures. These functions enabled users to easily access various glycan-related databases with a single search. Cheminformatics technologies using chemical structural formula for glycan has been also adopted to provide a search for glycan structures, glycan synthetic products by organic chemistry and recombinant enzymes, glycogene inhibitors, glycosides, and commercial glycans. This Summer, we have released AIST GlycoProtDB, which stores the data of experimentally-proven glycosylation sites on each mouse tissue. We are continuously accumulating experimental results of glycosylation sites, while collecting more information from scientific journals, toward the release of ACGG Glycoprotein Database in autumn. For the future, we will keep developing base technologies for DB integration and linking with databases related to glycoscience as well as other study areas. Some more bioinformatics tools are also being developed to support experimental study. Our aim is to create contents which could be easily and intuitively understood by every user.