Recently, breast MR images have been used in wider clinical area including diagnosis, treatment planning, and treatment response evaluation, which requests quantitative analysis and breast tissue segmentation. Although several methods have been proposed for segmenting MR images, segmenting out breast tissues robustly from surrounding structures in a wide range of anatomical diversity still remains challenging. Therefore, in this paper, we propose a practical and general-purpose approach for segmenting the pectoral muscle boundary based on the structure tensor and deformable model. The segmentation work flow comprises four key steps: preprocessing, detection of the region of interest (ROI) within the breast region, segmenting the pectoral muscle and finally extracting and refining the pectoral muscle boundary. From experimental results we show that the proposed method can segment the pectoral muscle robustly in diverse patient cases. In addition, the proposed method will allow the application of the quantification research for various breast images.
To prospectively evaluate the effects of z-axis spatial resolution and tube current on the sensitivity of a commercially available computed tomographic (CT) colonography computer-aided diagnosis (CAD) system for polyp detection by using pig colon phantoms.Ninety-six polyps were created and analyzed in 14 pig colon phantoms. CT colonography was performed by using a 16-detector CT scanner at 0.75-mm collimation; 10, 50, 100, and 160 mAs; and a pitch of 1.5. At each milliampere-second setting, the CT images were reconstructed with a section thickness (ST) of 1.5 mm and a reconstruction increment (RI) of 1.3 mm. To evaluate the effect of z-axis spatial resolution, CT images were also reconstructed at 100 mAs with various SI and RI combinations (respectively: 1.0 and 0.7 mm, 3.0 and 2.0 mm, 3.0 and 3.0 mm, 5.0 and 5.0 mm). The phantom data were then analyzed by using a CAD program. CAD performance with different CT parameters was calculated and compared in terms of per-polyp sensitivity and number of false-positive (FP) findings per data set.At a constant tube current of 100 mAs, the polyp detection rate was significantly higher in data sets obtained with SI and RI combinations of 1.0 and 0.7 mm, respectively (81% [78/96]), and 1.5 and 1.3 mm, respectively (75% [72/96]), than in those obtained with the three thicker ST-RI settings (27% [26/96] to 64% [61/96]) (P < .01). A similar trend was observed, regardless of polyp size or morphology. However, the number of FP findings at the 1.0 mm and 0.7 mm setting (8.9 per phantom) was also significantly greater than that at the thicker ST-RI settings (4.0-6.1 per phantom) (P < .05). At a constant z-axis spatial resolution (1.5-mm ST, 1.3-mm RI), CAD polyp detection rate and number of FP findings per phantom remained nearly constant-close to 78% (75/96) and 6.1, respectively-at various tube current settings.CAD performance in polyp detection at CT colonography is highly dependent on z-axis spatial resolution. However, tube current is not an influencing factor in CAD performance at a given z-axis spatial resolution.http://radiology.rsnajnls.org/cgi/content/full/2482071025/DC1.
In this study, we propose a computer-aided classification scheme of liver tumor in 3D ultrasound by using a combination of deformable model segmentation and support vector machine. For segmentation of tumors in 3D ultrasound images, a novel segmentation model was used which combined edge, region, and contour smoothness energies. Then four features were extracted from the segmented tumor including tumor edge, roundness, contrast, and internal texture. We used a support vector machine for the classification of features. The performance of the developed method was evaluated with a dataset of 79 cases including 20 cysts, 20 hemangiomas, and 39 hepatocellular carcinomas, as determined by the radiologist's visual scoring. Evaluation of the results showed that our proposed method produced tumor boundaries that were equal to or better than acceptable in 89.8% of cases, and achieved 93.7% accuracy in classification of cyst and hemangioma.
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