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Malignant lesion segmentation in contrast-enhanced breast MR images based on the marker-controlled watershed
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10.1118/1.3213514
/content/aapm/journal/medphys/36/10/10.1118/1.3213514
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/36/10/10.1118/1.3213514

Figures

Image of FIG. 1.
FIG. 1.

Schematic diagram of the method.

Image of FIG. 2.
FIG. 2.

Illustration of the marker-controlled watershed segmentation in a single slice where the ROI was manually drawn. (a) Original sagittal slice of MR image with manually drawn ROI (white ellipse) on it. (b) Image intensity histogram of the region inside ROI from (a). axis is image intensity, and axis is normalized frequency of the intensity. (c) Truncated histogram of (b) and the three Gaussian curves representing three different Gaussian components from modeling. and are the mean value and standard deviation of the tumor distribution. and are the parameters of the Gaussian component next to the tumor class. (d) Extracted internal marker (white region) of image (a). (e) Extracted external marker (white region) of image (a). (f) Gradient image of (a). (g) Internal and external markers (white regions) from (d) and (e) are superimposed upon the gradient image from (f). (h) Tumor segmentation result of image (a). The white contour represents the segmented tumor boundary.

Image of FIG. 3.
FIG. 3.

Illustration of the difference between results derived from ROIs in different slices. (a) Consecutive sagittal slices of original postcontrast MRI (from image numbers 20 to 31). Two ROIs were given on slice 22 (ROI 1) and slice 30 (ROI 2), respectively, to generate two different segmentation results. (b) Initial segmentation result from ROI 1. (c) Initial segmentation result from ROI 2.

Image of FIG. 4.
FIG. 4.

Segmented tumor area in each slice (calculated as pixel number inside tumor contour) as a function of slice number. Solid line is from initial segmentation result in Fig. 3(b). Dotted line is from initial segmentation result in Fig. 3(c).

Image of FIG. 5.
FIG. 5.

Tumor segmentation result. (a) Original sequential images containing a malignant lesion. A manually selected ROI was shown in slice 37. (b) Computer-delineated tumor contours. (c) First radiologist’s manual segmentation result. VOR between (b) and (c) was 63.3%. (d) Second radiologist’s manual segmentation result. VOR between (b) and (d) was 58.5%. The VOR between (c) and (d) was 64.6%.

Image of FIG. 6.
FIG. 6.

Tumor segmentation result. (a) The original sequential images containing a malignant lesion. An initial ROI was given in slice 17. (b) Computer segmentation result. (c) First radiologist’s manual segmentation result. VOR between the computer and first manual results for this case was 51.7%. The VOR between first radiologist’s segmentation result and second radiologist’s result (not shown in here) was 55.6%.

Image of FIG. 7.
FIG. 7.

3D visualization of segmented tumor volumes from two cases (a) and (b). Computer segmentation result (upper row) viewed in three different angles and first radiologist’s manual result (lower row) viewed in the corresponding angles. The VORs were (a) 56.5% and (b) 69.9%.

Image of FIG. 8.
FIG. 8.

Four different patterns of breast lesions for the evaluation of algorithm’s robustness.

Image of FIG. 9.
FIG. 9.

Correlation between tumor volume changes computed by computer segmentation results and manual segmentation results. Scatter diagram, least-squares regression line, and regression equation are provided. (a) Correlation between computer and first radiologist’s results. Correlation coefficient is . The significance level is . (b) Correlation between computer and second radiologist’s results. Correlation coefficient is . The significance level is . (c) Correlation between first radiologist’s results and second radiologist’s results. Correlation coefficient is . The significance level is .

Tables

Generic image for table
TABLE I.

Summary of the performance of the proposed tumor segmentation algorithm. (Com-R1: Comparison between computer and first radiologist’s segmentation results. Com-R2: Comparison between computer and second radiologist’s results. R1-R2: Comparison between the two radiologists’ results.)

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/content/aapm/journal/medphys/36/10/10.1118/1.3213514
2009-09-04
2014-04-16
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Malignant lesion segmentation in contrast-enhanced breast MR images based on the marker-controlled watershed
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/36/10/10.1118/1.3213514
10.1118/1.3213514
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