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Classification of breast computed tomography data
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10.1118/1.2839439
/content/aapm/journal/medphys/35/3/10.1118/1.2839439
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/35/3/10.1118/1.2839439
View: Figures

Figures

Image of FIG. 1.
FIG. 1.

Three component model of breast tissues (fat, skin, gland).

Image of FIG. 2.
FIG. 2.

Selected original mid-breast images from breast data CT scan for a typical breast.

Image of FIG. 3.
FIG. 3.

Distribution of biopsy confirmed breast diagnoses. Benign lesions (Adenosis; Columnar alteration with prominent apical snouts and secretions (CAPSS); fibrocystic changes (FC); fibroadenoma (FA); lobular carcinoma in situ (LCIS); benign) and Malignant Lesions (ductal carcinoma in situ (DCIS); invasive ductal carcinoma (IDC); invasive lobular carcinoma (ILC); lymphoma).

Image of FIG. 4.
FIG. 4.

Voxel histogram for image slice used for tissue classification showing the original histogram and the Gaussian fit curves for the fat and skin-gland components plus the automatically determined threshold value.

Image of FIG. 5.
FIG. 5.

Corresponding segmented images from slices shown in Fig. 2. Note the excellent agreement between the images and original breast CT slices.

Image of FIG. 6.
FIG. 6.

Histogram distribution of pixel intensity in original slice and test cases derived from segmented slice with Gaussian noise added. Gaussian noise with mean CT number values ranging from 10 to 50 were segmented with the algorithm. The segmented original image served as the reference image to compare algorithm performance. Image histograms demonstrate varying degrees of pixel distribution across the images.

Image of FIG. 7.
FIG. 7.

Radiologist breast CT slice evaluation image set. The radiologist was presented with three subimages: the first was the original breast CT slice, the second was the segmented slice and the third was a composite color coded slice showing the segmented regions superimposed on the original breast CT slice.

Image of FIG. 8.
FIG. 8.

Patient breast density ( deviation) as rated by a mammographer vs the fractional glandular tissue composition from breast CT. The large symbols correspond to the breast CT images presented later [Fig. 14, fatty replacement (triangle); Fig. 15 scattered fibroglandular (circle); and Fig. 16 heterogeneously dense (square)]. The large overlap between mammographic estimates of breast densities suggests the difficulty in assessing glandular tissue composition in mammograms.

Image of FIG. 9.
FIG. 9.

Selected slices from algorithm validation slices. Each column represents a different percentage of breast glandular tissue in that slice for the breast CT data presented later (Fig. 14, fatty replacement; Fig. 15 scattered fibroglandular; and Fig. 16 heterogeneously dense). The row sequence is as follows: breast CT slice; segmented breast CT slice; segmented slice with Gaussian noise of mean 25 CT units added; segmented slice with Gaussian noise of mean 40 CT units added; classification results for noise added images for 25 CT units; classification results for noise added image for 40 CT units; difference image between segmented noise image (25 CT units) and original segmentation; difference image between segmented noise image (40 CT units) and original segmentation.

Image of FIG. 10.
FIG. 10.

Histogram showing radiologist rating of algorithm classification performance. 97.7% of the classifications were judged to be very good or better (81.1% excellent; 16.6% very good; 1.3% good; 0.2% OK; 0.8% poor).

Image of FIG. 11.
FIG. 11.

Distribution of fractional breast composition as a function of age.

Image of FIG. 12.
FIG. 12.

Histogram of the patient distribution of the fractional breast composition showing the majority of breasts composition is fat with a relatively smaller distribution being predominantly glandular.

Image of FIG. 13.
FIG. 13.

Comparison of right and left composition by tissue classification.

Image of FIG. 14.
FIG. 14.

Classification of fatty replacement breast. (Upper) One mid-breast slice of the breast CT with the corresponding segmented image. (Lower) The composition analysis through all slices. The total breast volume was with a volume fractional composition of 6.5% skin, 80.4% fat and 12.9% gland.

Image of FIG. 15.
FIG. 15.

Classification of a scattered fibroglandular breast. (Upper) One mid-breast slice of the breast CT with the corresponding segmented image. (Lower) The composition analysis through all slices. The total breast volume was with a volume fractional composition of 9.7% skin, 53.9% fat and 36.4% gland.

Image of FIG. 16.
FIG. 16.

Classification of a heterogeneously dense breast. (Upper) One mid-breast slice of the breast CT with the corresponding segmented image. (Lower) The composition analysis through all slices. The total breast volume was with a volume fractional composition of 10.2% skin, 23.4% fat and 66.4% gland.

Image of FIG. 17.
FIG. 17.

Classification of a highly glandular breast. (Upper Panel) Three orthogonal slices from the original CT scan plus a volume rendered image of the breast CT with the corresponding segmented images (Lower Panel).

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/content/aapm/journal/medphys/35/3/10.1118/1.2839439
2008-02-26
2014-04-16
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Classification of breast computed tomography data
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/35/3/10.1118/1.2839439
10.1118/1.2839439
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