Breast Imaging with the SoftVue Imaging system: First results.

For women with dense breast tissue, who are at much higher risk for developing breast cancer, the performance of mammography is at its worst. Consequently, many early cancers go undetected when they are the most treatable. Improved cancer detection for women with dense breasts would decrease the proportion of breast cancers diagnosed at later stages, which would significantly lower the mortality rate. The emergence of whole breast ultrasound provides good performance for women with dense breast tissue, and may eliminate the current trade-off between the cost effectiveness of mammography and the imaging performance of more expensive systems such as magnetic resonance imaging. We report on the performance of SoftVue, a whole breast ultrasound imaging system, based on the principles of ultrasound tomography. SoftVue was developed by Delphinus Medical Technologies and builds on an early prototype developed at the Karmanos Cancer Institute. We present results from preliminary testing of the SoftVue system, performed both in the lab and in the clinic. These tests aimed to validate the expected improvements in image performance. Initial qualitative analyses showed major improvements in image quality, thereby validating the new imaging system design. Specifically, SoftVue's imaging performance was consistent across all breast density categories and had much better resolution and contrast. The implications of these results for clinical breast imaging are discussed and future work is described.

Fischer's Fused Full Field Digital Mammography and Ultrasound System (FFDMUS).

It has been well established that X-ray modality when combined with ultrasound modality increases sensitivity and specificity of breast lesion detections. Under the NIH grant, Fischer has developed a fused full-field digital mammography and ultrasound system (FFDMUS), which has ability to acquire 2-D X-ray mammogram and 3-D ultrasound images simultaneously. This novel technology generates co-registered breast images of X-ray and ultrasound images. The co-registration error between X-ray and ultrasound images acquired is within 2.00 mm in scan direction, and is 0.5 mm in anterior-posterior direction. We did the performance evaluation of the system, and concluded that the ultrasound image qualities from FFDMUS and Hand-held ultrasound (HHUS) are comparable, and the X-ray image qualities from FFDMUS and SenoScan(R) are also comparable. We also developed a preliminary CAD registration and segmentation system for FFDMUS datasets.

Image quality assessment via segmentation of breast lesion in X-ray and ultrasound phantom images from Fischer's full field digital mammography and ultrasound (FFDMUS) system.

Fischer has been developing a fused full-field digital mammography and ultrasound (FFDMUS) system funded by the National Institute of Health (NIH). In FFDMUS, two sets of acquisitions are performed: 2-D X-ray and 3-D ultrasound. The segmentation of acquired lesions in phantom images is important: (i) to assess the image quality of X-ray and ultrasound images; (ii) to register multi-modality images; and (iii) to establish an automatic lesion detection methodology to assist the radiologist. In this paper we developed lesion segmentation strategies for ultrasound and X-ray images acquired using FFDMUS. For ultrasound lesion segmentation, a signal-to-noise (SNR)-based method was adapted. For X-ray segmentation, we used gradient vector flow (GVF)-based deformable model. The performance of these segmentation algorithms was evaluated. We also performed partial volume correction (PVC) analysis on the segmentation of ultrasound images. For X-ray lesion segmentation, we also studied the effect of PDE smoothing on GVF's ability to segment the lesion. We conclude that ultrasound image qualities from FFDMUS and Hand-Held ultrasound (HHUS) are comparable. The mean percentage error with PVC was 4.56% (4.31%) and 6.63% (5.89%) for 5 mm lesion and 3 mm lesion respectively. The mean average error from the segmented X-ray images with PDE yielded an average error of 9.61%. We also tested our program on synthetic datasets. The system was developed for Linux workstation using C/C++.

Effect of adaptive-neighborhood contrast enhancement on the extraction of the breast skin-line in mammograms.

Extraction of the breast skin-line is crucial in computeraided analysis of mammograms. This paper presents an analysis of the effect of adaptive-neighborhood contrast enhancement (ANCE) [1] on skin-line extraction. ANCE is used to enhance the parenchyma of the breast and suppress the background noise. Suppression of the background noise can improve skin-line extraction. Our skin-line extraction method is based on the work by Ojala et al. [2]. We use the Hausdorff distance [3, 4] for quantitative comparison of the skin-lines. Our work shows that ANCE improves the skin-line extraction due to its ability of suppressing noise while improving the contrast. We have defined an improvement factor based on the Hausdorff distance. The metric allows us to spot automatically the mammograms with significant improvement in the detection of the skin-line because of ANCE. We tested 83 images from the MIAS database [5], with the ground-truth skin-lines hand-drawn by a radiologist [6]. The average Hausdorff distance improvement with ANCE was 11 pixels (2.2 mm).