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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국조명·전기설비학회 조명·전기설비학회논문지 Journal of the Korean Institute of Illuminating and Electrical Installation Engineers Vol.24 No.11
- 발행연도
- 2010.11
- 수록면
- 30 - 41 (12page)
이용수
초록· 키워드
오류제보하기
In this study we propose a new texture feature extraction method based on an estimation of the brightness and structural uniformity of CT images representing the important characteristics for emphysema recognition.
The Center-Symmetric Local Binary Pattern (CS-LBP) is first used to combine gray level in order to describe the brightness uniformity characteristics of the CT image. Then the gradient orientation difference is proposed to generate another CS-LBP code combining with gray level to represent the structural uniformity characteristics of the CT image.
The usage of the gray level, CS-LBP and gradient orientation differences enables the proposed method to extract rich and distinctive information from the CT images in multiple directions. Experimental results showed that the performance of the proposed method is more stable with respect to sensitivity and specificity when compared with the SGLDM, GLRLM and GLDM The proposed method outperformed these three conventional methods (SGLDM, GLRLM, and GLDM) 7.85[%], 22.87[%], and 16.67[%] respectively, according to the diagnosis of average accuracy, demonstrated by the Receiver Operating Characteristic (ROC) curves.
The Center-Symmetric Local Binary Pattern (CS-LBP) is first used to combine gray level in order to describe the brightness uniformity characteristics of the CT image. Then the gradient orientation difference is proposed to generate another CS-LBP code combining with gray level to represent the structural uniformity characteristics of the CT image.
The usage of the gray level, CS-LBP and gradient orientation differences enables the proposed method to extract rich and distinctive information from the CT images in multiple directions. Experimental results showed that the performance of the proposed method is more stable with respect to sensitivity and specificity when compared with the SGLDM, GLRLM and GLDM The proposed method outperformed these three conventional methods (SGLDM, GLRLM, and GLDM) 7.85[%], 22.87[%], and 16.67[%] respectively, according to the diagnosis of average accuracy, demonstrated by the Receiver Operating Characteristic (ROC) curves.
목차
Abstract
1. Introduction
2. Related works
3. Lung region location
4. Proposed method
5. Experimental result
6. Conclusion
References
Biography
참고문헌 (20)
참고문헌 신청
1. [학술지(정기간행물)] - M. Park / 2009 / Computer Aided Diagnosis System of Medical Images using Incremental Learning Method / Expert Systems with Applications 36 : 7242 ~ 7251
2. [학술지(정기간행물)] - A. M. R. Schilham / 2006 / A Computer-Aided Diagnosis System for Detection of Lung Nodules in Chest Radiographs with an Evaluation on a Public Database / Medical Image Analysis 10 : 247 ~ 258
3. [학술대회논문] - K. Takei / 2007 / Computer Aided Diagnosis for Pulmonary Nodules by Shape Feature Extraction / SICE Annual Conference 2007 : 1964 ~ 1967
4. [학술지(정기간행물)] - H. Zhang / 2005 / A Fast Texture Feature Extraction Method for Region-based Image Segmentation / Image and video communications and processing 5685 (2) : 957 ~ 968
5. [학술대회논문] - T. K. Liang / 2007 / A Neural Network based Computer-Aided Diagnosis of Emphysema using CT Lung Images / SICE Annual Conference 2007 : 703 ~ 709
2. [학술지(정기간행물)] - A. M. R. Schilham / 2006 / A Computer-Aided Diagnosis System for Detection of Lung Nodules in Chest Radiographs with an Evaluation on a Public Database / Medical Image Analysis 10 : 247 ~ 258
3. [학술대회논문] - K. Takei / 2007 / Computer Aided Diagnosis for Pulmonary Nodules by Shape Feature Extraction / SICE Annual Conference 2007 : 1964 ~ 1967
4. [학술지(정기간행물)] - H. Zhang / 2005 / A Fast Texture Feature Extraction Method for Region-based Image Segmentation / Image and video communications and processing 5685 (2) : 957 ~ 968
5. [학술대회논문] - T. K. Liang / 2007 / A Neural Network based Computer-Aided Diagnosis of Emphysema using CT Lung Images / SICE Annual Conference 2007 : 703 ~ 709
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UCI(KEPA) : I410-ECN-0101-2012-565-003790428