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Mammogram Computer Aided Diagnosis
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- 발행기관
- 보안공학연구지원센터(IJSIP) 바로가기
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- 간행물
- International Journal of Signal Processing, Image Processing and Pattern Recognition 바로가기
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- 통권
- Vol.5 No.4 (2012.12)바로가기
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- 페이지
- pp.1-30
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A208856
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- Computer-aided diagnosis (CADx) is used to help radiologists in interpretation mammograms and is usually used as a second opinion by the radiologists. Improving CADx increases the treatment options and a cure is more likely. The main objective of this research is to enhance and introduce a new method for feature extraction and selection in order to build a CADx model to discriminate between cancers, benign, and healthy parenchyma. For feature extraction, we use both human features, which are obtained by Digital Database for Screening Mammography (DDSM), and computational features. For computational feature extraction, we enhance and use two pre-existed feature extraction methods, which are the Run Difference Method (RDM) and the Spatial Gray Level Dependence Method (SGLDM). Then, we evaluate and introduce a new method for feature selection by running both of forward sequential and genetic algorithm search methods individually. Later we evaluate the results. Experimental results are obtained from a data set of 410 images taken from DDSM for different types. Our method select 14 features from 65 extracted features. We used both Receiver Operating Characteristics (ROC) and confusing matrix to measure the performance. In training stage, our proposed method achieved an overall classification accuracy of 94.6%, with 95.2% sensitivity and 84.8% specificity. In testing stage, our proposed method achieved an overall classification accuracy of 87%, with 88.6% sensitivity and 78.6% specificity.
목차
Abstract
1. Introduction
2. Related Work
3. Methodology
3.1 Feature Extraction
3.2 Feature Selection
4. Experiments
4.1 Implementation Environment
4.2 Manual Segmentation
4.3 Enhancement
4.4 Segmentation
4.5 Feature Extraction and Selection
4.6 Classification
4.7 Results Discussion
5. Conclusions and Future Work
References
키워드
Breast Cancer Mammogram Feature Extraction Feature Selection Computer Aided Diagnosis Genetic Algorithm Forward Sequential저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJSIP) [Science & Engineering Research Support Center, Republic of Korea(IJSIP)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of Signal Processing, Image Processing and Pattern Recognition
- 간기
- 격월간
- pISSN
- 2005-4254
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.5 No.4
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