Biomedical subjects

Yujun Guo

Publications and source records attributed to Yujun Guo.

3 recordsLinked to original sources

PubMed2006

Breast image registration techniques: a survey.

Breast cancer is the most common type of cancer in women worldwide. Image registration plays an important role in breast cancer detection. This paper gives an overview of the current state-of-the-art in the breast image registration techniques. For the intramodality registration techniques, X-ray, MRI, and ultrasound are the primary focuses of interest. Intermodality techniques will cover the combination of different modalities. Validation of breast registration methods is also discussed.

Breast↗

PubMed2005

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++.

Breast Neoplasms↗

PubMed2005

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.

Breast↗