Eigenvalue-weighting and feature selection for computer-aided polyp detection in CT colonography

2010 ◽  
Author(s):  
Hongbin Zhu ◽  
Su Wang ◽  
Yi Fan ◽  
Hongbing Lu ◽  
Zhengrong Liang
Keyword(s):  
Feature Selection ◽  
Ct Colonography ◽  
Polyp Detection ◽  
Computer Aided ◽  
Selection For

scholarly journals Feature selection for computer-aided polyp detection using MRMR

2010 ◽  
Author(s):  
Xiaoyun Yang ◽  
Boray Tek ◽  
Gareth Beddoe ◽  
Greg Slabaugh
Keyword(s):  
Feature Selection ◽  
Polyp Detection ◽  
Computer Aided ◽  
Selection For

Feature selection for computer-aided polyp detection using genetic algorithms

2003 ◽  
Author(s):  
Meghan T. Miller ◽  
Anna K. Jerebko ◽  
James D. Malley ◽  
Ronald M. Summers
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Polyp Detection ◽  
Computer Aided ◽  
Selection For

Computer-Aided Detection of Polyps in CT Colonography by Means of Feature Selection and Massive-Training Support Vector Regression

2012 ◽  
pp. 178-201
Author(s):  
Jian-Wu Xu ◽  
Kenji Suzuki
Keyword(s):  
Feature Selection ◽  
Support Vector Regression ◽  
Ct Colonography ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Computer Aided Detection ◽  
Training Time ◽  
Computer Aided ◽  
Wilks Lambda

One of the major challenges in current Computer-Aided Detection (CADe) of polyps in CT Colonography (CTC) is to improve the specificity without sacrificing the sensitivity. If a large number of False Positive (FP) detections of polyps are produced by the scheme, radiologists might lose their confidence in the use of CADe. In this chapter, the authors used a nonlinear regression model operating on image voxels and a nonlinear classification model with extracted image features based on Support Vector Machines (SVMs). They investigated the feasibility of a Support Vector Regression (SVR) in the massive-training framework, and the authors developed a Massive-Training SVR (MTSVR) in order to reduce the long training time associated with the Massive-Training Artificial Neural Network (MTANN) for reduction of FPs in CADe of polyps in CTC. In addition, the authors proposed a feature selection method directly coupled with an SVM classifier to maximize the CADe system performance. They compared the proposed feature selection method with the conventional stepwise feature selection based on Wilks’ lambda with a linear discriminant analysis classifier. The FP reduction system based on the proposed feature selection method was able to achieve a 96.0% by-polyp sensitivity with an FP rate of 4.1 per patient. The performance is better than that of the stepwise feature selection based on Wilks’ lambda (which yielded the same sensitivity with 18.0 FPs/patient). To test the performance of the proposed MTSVR, the authors compared it with the original MTANN in the distinction between actual polyps and various types of FPs in terms of the training time reduction and FP reduction performance. The authors’ CTC database consisted of 240 CTC datasets obtained from 120 patients in the supine and prone positions. With MTSVR, they reduced the training time by a factor of 190, while achieving a performance (by-polyp sensitivity of 94.7% with 2.5 FPs/patient) comparable to that of the original MTANN (which has the same sensitivity with 2.6 FPs/patient).

AN EFFICIENT FEATURE SELECTION ALGORITHM FOR COMPUTER-AIDED POLYP DETECTION

2006 ◽  
Vol 15 (06) ◽  
pp. 893-915 ◽  
Author(s):  
JIANG LI ◽  
JIANHUA YAO ◽  
RONALD M. SUMMERS ◽  
NICHOLAS PETRICK ◽  
MICHAEL T. MANRY ◽  
...  
Keyword(s):  
Feature Selection ◽  
Cross Validation ◽  
Search Algorithm ◽  
Piecewise Linear ◽  
Least Square ◽  
Support Vector ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Polyp Detection ◽  
Computer Aided

We present an efficient feature selection algorithm for computer aided detection (CAD) computed tomographic (CT) colonography. The algorithm (1) determines an appropriate piecewise linear network (PLN) model by cross validation, (2) applies the orthonormal least square (OLS) procedure to the PLN model utilizing a Modified Schmidt procedure, and (3) uses a floating search algorithm to select features that minimize the output variance. The undesirable "nesting effect" is prevented by the floating search approach, and the piecewise linear OLS procedure makes this algorithm very computationally efficient because the Modified Schmidt procedure only requires one data pass during the whole searching process. The selected features are compared to those obtained by other methods, through cross validation with support vector machines (SVMs).

scholarly journals A Pipeline for Computer Aided Polyp Detection and Segmentation in Ct Colonography

2014 ◽  
Vol 9 (4) ◽  
pp. 68-75
Author(s):  
Janapriya A.S ◽  
◽  
Mythili C ◽  
Nishanthi P ◽  
Sunitha K
Keyword(s):  
Ct Colonography ◽  
Polyp Detection ◽  
Computer Aided

Massive-Training Support Vector Regression With Feature Selection in Application of Computer-Aided Detection of Polyps in CT Colonography

2018 ◽  
pp. 153-190
Author(s):  
Jianwu Xu ◽  
Amin Zarshenas ◽  
Yisong Chen ◽  
Kenji Suzuki
Keyword(s):  
Feature Selection ◽  
False Positive ◽  
Ct Colonography ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Training Time ◽  
Computer Aided ◽  
Wilks Lambda ◽  
Cade System

A major challenge in the latest computer-aided detection (CADe) of polyps in CT colonography (CTC) is to improve the false positive (FP) rate while maintaining detection sensitivity. Radiologists prefer CADe system produce small number of false positive detections, otherwise they might not consider CADe system improve their workflow. Towards this end, in this study, we applied a nonlinear regression model operating on CTC image voxels directly and a nonlinear classification model with extracted image features based on support vector machines (SVMs) in order to improve the specificity of CADe of polyps. We investigated the feasibility of a support vector regression (SVR) in the massive-training framework, and we developed a massive-training SVR (MTSVR) in order to reduce the long training time associated with the massive-training artificial neural network (MTANN) for reduction of FPs in CADe of polyps in CTC. In addition, we proposed a feature selection method directly coupled with an SVM classifier to maximize the CADe system performance. We compared the proposed feature selection method with the conventional stepwise feature selection based on Wilks' lambda with a linear discriminant analysis classifier. The FP reduction system based on the proposed feature selection method was able to achieve a 96.0% by-polyp sensitivity with an FP rate of 4.1 per patient. The performance is better than that of the stepwise feature selection based on Wilks' lambda (which yielded the same sensitivity with 18.0 FPs/patient). To test the performance of the proposed MTSVR, we compared it with the original MTANN in the distinction between actual polyps and various types of FPs in terms of the training time reduction and FP reduction performance. The CTC database used in this study consisted of 240 CTC datasets obtained from 120 patients in the supine and prone positions. With MTSVR, we reduced the training time by a factor of 190, while achieving a performance (by-polyp sensitivity of 94.7% with 2.5 FPs/patient) comparable to that of the original MTANN (which has the same sensitivity with 2.6 FPs/patient).

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