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Fissures segmentation using surface features: content-based retrieval for mammographic mass using ensemble

Hong Liu1, Yihua Lan, Xiangyang Xu

  • 1School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P.R. China.

Academic Radiology
|November 8, 2011
PubMed
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This study developed an ensemble classifier (E-DGA-KNN) for mammography computer-aided diagnosis (CAD). The proposed method, utilizing domain knowledge and dual-stage genetic algorithms for feature selection, demonstrated superior performance in classifying mammograms compared to other algorithms.

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis (CAD)
  • Machine Learning

Background:

  • Accurate classification is crucial for mammography computer-aided diagnosis (CAD) employing content-based image retrieval (CBIR) approaches.
  • Existing methods require improvement for enhanced diagnostic accuracy in mammogram analysis.

Purpose of the Study:

  • To develop an accurate ensemble classifier for CBIR CAD, integrating domain knowledge and a robust feature selection method.
  • To introduce three novel features to enhance mammogram classification.
  • To evaluate the performance of the proposed method and new features on a substantial dataset.

Main Methods:

  • An ensemble classifier, E-DGA-KNN, was developed using a dual-stage genetic algorithm (DGA) for feature selection and weight determination.

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  • The method involved classifying 804 mass ROIs into five boundary types and creating sub-databases for analysis.
  • Five K-nearest neighbor (KNN) classifiers were trained and combined, with performance assessed using receiver operating characteristic (ROC) analysis.

    • Main Results:

    • The proposed E-DGA-KNN method achieved the highest area under the ROC curve (Az) of 0.8927 ± 0.0073.
    • This performance surpassed single KNN classifiers with various feature selection methods (e.g., DGA-KNN, SLDA-PSO-KNN) and standalone algorithms (SLDA, GA).
    • The Az value using all features with a single KNN classifier was 0.8478 ± 0.0088, highlighting the benefit of the ensemble approach.

    Conclusions:

    • The developed ensemble classifier, E-DGA-KNN, effectively utilizes domain knowledge and a dual-stage feature selection method.
    • The proposed method demonstrates superior performance compared to existing algorithms, achieving the highest ROC values.
    • E-DGA-KNN shows significant potential to enhance the accuracy of CBIR CAD systems in mammogram interpretation.