Markov Random Field Segmentation of Brain MR Images

We describe a fully-automatic 3D-segmentation technique for brain MR images. Using Markov random fields the segmentation algorithm captures three important MR features, i.e. non-parametric distributions of tissue intensities, neighborhood correlations and signal inhomogeneities. Detailed simulations and real MR images demonstrate the performance of the segmentation algorithm. The impact of noise, inhomogeneity, smoothing and structure thickness is analyzed quantitatively. Even single echo MR images are well classified into gray matter, white matter, cerebrospinal fluid, scalp-bone and background. A simulated annealing and an iterated conditional modes implementation are presented. Keywords: Magnetic Resonance Imaging, Segmentation, Markov Random FieldsComment: 34 pages, 10 figures; the paper (published in 1997) has introduced the concept of Markov random field to the segmentation of medical MR images. For the published version see http://dx.doi.org/10.1109/42.65088

UFPR-Periocular: A Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarios

Recently, ocular biometrics in unconstrained environments using images obtained at visible wavelength have gained the researchers' attention, especially with images captured by mobile devices. Periocular recognition has been demonstrated to be an alternative when the iris trait is not available due to occlusions or low image resolution. However, the periocular trait does not have the high uniqueness presented in the iris trait. Thus, the use of datasets containing many subjects is essential to assess biometric systems' capacity to extract discriminating information from the periocular region. Also, to address the within-class variability caused by lighting and attributes in the periocular region, it is of paramount importance to use datasets with images of the same subject captured in distinct sessions. As the datasets available in the literature do not present all these factors, in this work, we present a new periocular dataset containing samples from 1,122 subjects, acquired in 3 sessions by 196 different mobile devices. The images were captured under unconstrained environments with just a single instruction to the participants: to place their eyes on a region of interest. We also performed an extensive benchmark with several Convolutional Neural Network (CNN) architectures and models that have been employed in state-of-the-art approaches based on Multi-class Classification, Multitask Learning, Pairwise Filters Network, and Siamese Network. The results achieved in the closed- and open-world protocol, considering the identification and verification tasks, show that this area still needs research and development

Image to physical space registration of supine breast MRI for image guided breast surgery

ABSTRACT Breast conservation therapy (BCT) is a desirable option for many women diagnosed with early stage breast cancer and involves a lumpectomy followed by radiotherapy. However, approximately 50% of eligible women will elect for mastectomy over BCT despite equal survival benefit (provided margins of excised tissue are cancer free) due to uncertainty in outcome with regards to complete excision of cancerous cells, risk of local recurrence, and cosmesis. Determining surgical margins intraoperatively is difficult and achieving negative margins is not as robust as it needs to be, resulting in high re-operation rates and often mastectomy. Magnetic resonance images (MRI) can provide detailed information about tumor margin extents, however diagnostic images are acquired in a fundamentally different patient presentation than that used in surgery. Therefore, the high quality diagnostic MRIs taken in the prone position with pendant breast are not optimal for use in surgical planning/guidance due to the drastic shape change between preoperative images and the common supine surgical position. This work proposes to investigate the value of supine MRI in an effort to localize tumors intraoperatively using image-guidance. Mock intraoperative setups (realistic patient positioning in non-sterile environment) and preoperative imaging data were collected from a patient scheduled for a lumpectomy. The mock intraoperative data included a tracked laser range scan of the patient's breast surface, tracked center points of MR visible fiducials on the patient's breast, and tracked B-mode ultrasound and strain images. The preoperative data included a supine MRI with visible fiducial markers. Fiducial markers localized in the MRI were rigidly registered to their mock intraoperative counterparts using an optically tracked stylus. The root mean square (RMS) fiducial registration error using the tracked markers was 3.4mm. Following registration, the average closest point distance between the MR generated surface nodes and the LRS point cloud was 1.76±0.502 mm