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RISS 인기검색어
- 검색키워드
- 저자 : Takumi Tokisa (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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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.
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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.
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A Temporal Subtraction Method for Thoracic CT Images Using Non Rigid Warping Technique
Takumi Tokisa,Hyoungseop Kim,Joo Kooi Tan,Seiji Ishikawa,Young Lae Moon,Sung Ho Yoon,Wontae Kim 제어로봇시스템학회 2012 제어로봇시스템학회 국제학술대회 논문집 Vol.2012 No.10
A temporal subtraction technique which is subtracted from previous image to current one is introduced as powerful tools in medical fields to diagnose abnormalities. It provided a computer aided diagnosis (CAD) tools on visual screening. Radiologist can detect lesions on image by compare the two images. It is because the subtraction image can enhance the temporal changes, such as shaped of new lesions and/or the temporal changes in existing abnormalities by removing most of the normal background structures by subtraction of a previous image from a current one. There are some technical reports to register the different images until now. But subtraction artifacts are still remained which are caused by mis-registration. In this paper, we propose a new method for temporal subtraction method on thoracic MDCT images using non-rigid image warping techniques based on free form deformation (FFD). We applied our method to two clinical cases of chest CT image sets and compare to conventional methods in terms of computational cost and accuracy.
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Takahiro MIYAJIMA,Takumi TOKISA,Shinya MAEDA,Hyoungseop KIM,Joo Kooi TAN,Seiji ISHIKAWA,Seiichi MURAKAMI,Takatoshi AOKI 제어로봇시스템학회 2012 제어로봇시스템학회 국제학술대회 논문집 Vol.2012 No.10
Recently, thorax MDCT images are used in visual screening for early detection of lung nodules. Radiologists can easily detect lung nodules on images, but it has enormous images and load of radiologist for visual screening. To reduce the load of radiologist and improve the detection accuracy, a CAD (Computer Aided Diagnosis) system is expected from medical fields. In the medical image processing fields, some related works are reported to develop the CAD system including temporal subtraction technique as helpful technical issues. In this paper, we propose a classification of lung nodules on temporal subtraction image based on image processing technique. At first, the candidate regions including nodules are detected by the multiple threshold technique in terms of the pixel value on the temporal subtraction images. Then, we remove vessel regions on nodules by the most suitable threshold technique and watershed method. Also we remove the false positives which are caused by mis-registration using selective enhancement filter, rule-base method and artificial neural networks. In this paper, we illustrate some experimental result which applied our algorithm to 31 chest MDCT cases including lung nodules.
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