Kevin W. Bowyer - Medical Image Analysis

  • Synthetic Minority Image Over-sampling Technique: How to Improve AUC for Glioblastoma Patient Survival Prediction,
    Renhao Liu, Lawrence Hall, Kevin Bowyer, Dmitry Goldgof, Robert Gatenby, Kaoutar Ben Ahmed,
    IEEE International Conference on Systems, Man, and Cybernetics, October 2017, Banf.
    DOI link to this paper in IEEE Xplore.
    Real-world datasets are often imbalanced, with an important class having many fewer examples than other classes. In medical data, normal examples typically greatly outnumber disease examples. A classifier learned from imbalanced data, will tend to be very good at the predicting examples in the larger (normal) class, yet the smaller (disease) class is typically of more interest. Imbalance is dealt with at the feature vector level (create synthetic feature vectors or discard some examples from the larger class) or by assigning differential costs to errors. Here, we introduce a novel method for over-sampling minority class examples at the image level, rather than the feature vector level. Our method was applied to the problem of Glioblastoma patient survival group prediction. Synthetic minority class examples were created by adding Gaussian noise to original medical images from the minority class. Uniform local binary patterns (LBP) histogram features were then extracted from the original and synthetic image examples with a random forests classifier. Experimental results show the new method (Image SMOTE) increased minority class predictive accuracy and also the AUC (area under the receiver operating characteristic curve), compared to using the imbalanced dataset directly or to creating synthetic feature vectors.

  • The Digital Database for Screening Mammography,
    Michael Heath, Kevin Bowyer, Daniel Kopans, Richard Moore and W. Philip Kegelmeyer.
    in Proceedings of the Fifth International Workshop on Digital Mammography, M.J. Yaffe, ed., 212-218, Medical Physics Publishing, 2001.
    pdf of this paper.
    The Digital Database for Screening Mammography (DDSM) is a database of digitized film-screen mammograms with associated ground truth information. The purpose of this resource is to provide a large set of mammograms in a digital format that may be used by researchers to evaluate and compare the performance of computer-aided detection (CAD) algorithms. ...

  • Registration and Difference Analysis of Corresponding Nammogram Images,
    Maha Y. Sallam and Kevin W. Bowyer,
    Medical Image Analysis 3 (2), 103-118, 1999.
    DOI link.
    An automated technique is proposed for identifying differences between corresponding mammogram images. The technique recovers an approximate deformation between a pair of mammograms based on identifying corresponding features across the two images. The registration process is completed using an unwarping technique for transforming one image into the coordinate system of the other. A difference image is generated using intensity-weighted subtraction in order to identify regions of large difference. Evaluation of the technique is performed using 124 bilateral image pairs which contain a total of 77 abnormalities of different types. The purpose of this paper is to measure the extent to which the mammogram registration technique is able to provide useful information for identifying abnormalities in mammograms.

  • Current Status of the Digital Database for Screening Mammography,
    Michael Heath, Kevin Bowyer, Daniel Kopans, W. Philip Kegelmeyer, Richard Moore, Kyong Chang and S. Munishkumaran,
    in Digital Mammography, 457-460, Kluwer Academic Publishers, 1998 (proceedings of the Fourth International Workshop on Digital Mammography)
    pdf of this paper.
    The Digital Database for Screening Mammography is a resource for use by researchers investigating mammogram image analysis. In particular, the resource is focused on the context of image analysis to aid is screening for breast cancer. The database now contains substantial numbers of "normal" and "cancer" cases. ...

  • Generating ROC curves for artificial neural networks,
    Kevin S. Woods and Kevin W. Bowyer,
    IEEE Transactions on Medical Imaging 16 (3), 329-337, June 1997.
    DOI link.
    Receiver operating characteristic (ROC) analysis is an established method of measuring diagnostic performance in medical imaging studies. Traditionally, artificial neural networks (ANN's) have been applied as a classifier to find one "best" detection rate. Recently researchers have begun to report ROC curve results for ANN classifiers. The current standard method of generating ROC curves for an ANN is to vary the output node threshold for classification. Here, the authors propose a different technique for generating ROC curves for a two class ANN classifier. They show that this new technique generates better ROC curves in the sense of having greater area under the ROC curve (AUC), and in the sense of being composed of a better distribution of operating points.

  • Comparative evaluation of pattern recognition techniques for detection of microcalcifications in mammography,
    Kevin S. Woods, Christopher C. Doss, Kevin W. Bowyer, Jeffrey L. Solka, Carey E. Priebe, and W. Philip Kegelmeyer,
    International Journal of Pattern Recognition and Artificial Intelligence 7 (6), 1417-1436, December 1993.
    Computer-assisted detection of microcalcifications in mammographic images will likely require a multistage algorithm that includes segmentation of possible microcalcifications, pattern recognition techniques to classify the segmented objects, a method to determine if a cluster of calcifications exists, and possibly a method to determine the probability of malignancy. This paper focuses on the first three of these stages ...

  • The Effects of Age on Radionuclide Angiographic Detection and Quantitation of Left-to-right Shunts,
    Page A.W. Anderson, Kevin W. Bowyer, and Robert H. Jones,
    American Journal of Cardiology 53, 879-883, March 1984.

  • Radionuclide Analysis of Right and Left Ventricular Response to Exercise in Patients with Atrial and Ventricular Septal Defects,
    Claude A. Peter, Kevin W. Bowyer, and Robert H. Jones,
    American Heart Journal 105, 428-435, March 1983.

  • Error Sensitivity of Computed Tomography Guided Stereotaxis,
    Kevin W. Bowyer, C. Frank Starmer and P. J. DuBois,
    Computers and Biomedical Research 15, 272-280, 1982.

  • A Simulation-based Sensitivity Study of Angiocardiographic Approaches to Shunt Assessment,
    Kevin W. Bowyer and C. Frank Starmer,
    Computers and Biomedical Research 15, 111-128, 1982.

  • CT-guided Stereotaxis Using a Modified Conventional Stereotaxic Frame,
    P. J. DuBois, B. S. Nashold, J. Perry, P. Burger, Kevin W. Bowyer, E. R. Heinz, B. P. Drayer, S. Bigner, and A. C. Higgins,
    American Journal of Neuroradiology 3, 345-351, 1982.