Healthc Inform Res.  2016 Oct;22(4):293-298. 10.4258/hir.2016.22.4.293.

Automated Detection Algorithm of Breast Masses in Three-Dimensional Ultrasound Images

• Jeong JW
• Yu D
• Lee S
• Chang JM

• KMID: 2357383
• DOI: http://doi.org/10.4258/hir.2016.22.4.293

Abstract

OBJECTIVES
We propose an automatic breast mass detection algorithm in three-dimensional (3D) ultrasound (US) images using the Hough transform technique.
METHODS
One hundred twenty-five cropped images containing 68 benign and 60 malignant masses are acquired with clinical diagnosis by an experienced radiologist. The 3D US images are masked, subsampled, contrast-adjusted, and median-filtered as preprocessing steps before the Hough transform is used. Thereafter, we perform 3D Hough transform to detect spherical hyperplanes in 3D US breast image volumes, generate Hough spheres, and sort them in the order of votes. In order to reduce the number of the false positives in the breast mass detection algorithm, the Hough sphere with a mean or grey level value of the centroid higher than the mean of the 3D US image is excluded, and the remaining Hough sphere is converted into a circumscribing parallelepiped cube as breast mass lesion candidates. Finally, we examine whether or not the generated Hough cubes were overlapping each other geometrically, and the resulting Hough cubes are suggested as detected breast mass candidates.
RESULTS
An automatic breast mass detection algorithm is applied with mass detection sensitivity of 96.1% at 0.84 false positives per case, quite comparable to the results in previous research, and we note that in the case of malignant breast mass detection, every malignant mass is detected with false positives per case at a rate of 0.62.
CONCLUSIONS
The breast mass detection efficiency of our algorithm is assessed by performing a ROC analysis.

Keyword

Ultrasonic Tomography; Breast Cancer; Computer-Assisted Diagnosis; Computer Assisted Image Analysis; Cancer Early Detection

MeSH Terms

Breast Neoplasms
Breast*
Diagnosis
Diagnosis, Computer-Assisted
Early Detection of Cancer
Image Processing, Computer-Assisted
Masks
ROC Curve
Ultrasonography*

Figure

• 

  Figure 1 Illustration of pre-processing steps on 3D US images. (A) The original image. (B) RDCA is applied on (A). (C) Then, median filter is applied on (B). (D) Then, Canny edge is detected. (E) Finally, 3D Hough transform is performed on (D).

• 

  Figure 2 ROC curve of the breast mass detection algorithm using the 3D Hough transform. Solid line is for the ROC using ΔVc = 590 and dotted line for the ROC using ΔVc = 4720.

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