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Computerized Breast Mass Detection Using Multi-Scale Hessian-Based Analysis for Dynamic Contrast-Enhanced MRI

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Abstract

This study aimed to investigate a computer-aided system for detecting breast masses using dynamic contrast-enhanced magnetic resonance imaging for clinical use. Detection performance of the system was analyzed on 61 biopsy-confirmed lesions (21 benign and 40 malignant lesions) in 34 women. The breast region was determined using the demons deformable algorithm. After the suspicious tissues were identified by kinetic feature (area under the curve) and the fuzzy c-means clustering method, all breast masses were detected based on the rotation-invariant and multi-scale blob characteristics. Subsequently, the masses were further distinguished from other detected non-tumor regions (false positives). Free-response operating characteristics (FROC) curve and detection rate were used to evaluate the detection performance. Using the combined features, including blob, enhancement, morphologic, and texture features with 10-fold cross validation, the mass detection rate was 100 % (61/61) with 15.15 false positives per case and 91.80 % (56/61) with 4.56 false positives per case. In conclusion, the proposed computer-aided detection system can help radiologists reduce inter-observer variability and the cost associated with detection of suspicious lesions from a large number of images. Our results illustrated that breast masses can be efficiently detected and that enhancement and morphologic characteristics were useful for reducing non-tumor regions.

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Acknowledgments

The authors would like to thank the National Science Council of the Republic of China (NSC 101-2221-E-002-068-MY3), Ministry of Economic Affairs (102-EC-17-A-19-S1-164), and Ministry of Education (AE-00-00-06) of the Republic of China for the financial support

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, 10617, Republic of China

    Yan-Hao Huang & Ruey-Feng Chang

  2. Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan, Republic of China

    Yeun-Chung Chang

  3. Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan

    Chiun-Sheng Huang

  4. Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA, USA

    Jeon-Hor Chen

  5. Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan

    Jeon-Hor Chen

  6. Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan

    Ruey-Feng Chang

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  1. Yan-Hao Huang
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  2. Yeun-Chung Chang
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  3. Chiun-Sheng Huang
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Correspondence to Yeun-Chung Chang or Ruey-Feng Chang.

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Huang, YH., Chang, YC., Huang, CS. et al. Computerized Breast Mass Detection Using Multi-Scale Hessian-Based Analysis for Dynamic Contrast-Enhanced MRI. J Digit Imaging 27, 649–660 (2014). https://doi.org/10.1007/s10278-014-9681-4

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  • DOI: https://doi.org/10.1007/s10278-014-9681-4

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