We present a novel method for the joint segmentation of anatomical and functional images. Our proposed methodology unifies the domains of anatomical and functional images, represents them in a product lattice, and performs simultaneous delineation of regions based on random walk image segmentation. Furthermore, we also propose a simple yet effective object/background seed localization method to make the proposed segmentation process fully automatic. Our study uses PET, PET-CT, MRI-PET, and fused MRI-PET-CT scans (77 studies in all) from 56 patients who had various lesions in different body regions. We validated the effectiveness of the proposed method on different PET phantoms as well as on clinical images with respect to the ground truth segmentation provided by clinicians. Experimental results indicate that the presented method is superior to threshold and Bayesian methods commonly used in PET image segmentation, is more accurate and robust compared to the other PET-CT segmentation methods recently published in the literature, and also it is general in the sense of simultaneously segmenting multiple scans in real-time with high accuracy needed in routine clinical use.

Purpose:To develop a cardiac computed tomographic (CT) method with which to determine extracellular volume (ECV) fraction, with cardiac magnetic resonance (MR) imaging as the reference standard. Materials and Methods:Study participants provided written informed consent to participate in this institutional review board-approved study. ECV was measured in healthy subjects and patients with heart failure by using cardiac CT and cardiac MR imaging. Paired Student t test, linear regression analysis, and Pearson correlation analysis were used to determine the relationship between cardiac CT and MR imaging ECV values and clinical parameters. Results:Twenty-four subjects were studied. There was good correlation between myocardial ECV measured at cardiac MR imaging and that measured at cardiac CT (r = 0.82, P , .001). As expected, ECV was higher in patients with heart failure than in healthy control subjects for both cardiac CT and cardiac MR imaging (P = .03, respectively). For both cardiac MR imaging and cardiac CT, ECV was positively associated with end diastolic and end systolic volume and inversely related to ejection fraction (P , .05 for all). Mean radiation dose was 1.98 mSv 6 0.16 (standard deviation) for each cardiac CT acquisition. Conclusion:ECV at cardiac CT and that at cardiac MR imaging showed good correlation, suggesting the potential for myocardial tissue characterization with cardiac CT.q RSNA, 2012

A textile-based wireless pressure sensor array (WiPSA) is proposed for flexible remote tactile sensing applications. The WiPSA device is composed of a fabric spacer sandwiched by two separate layers of passive antennas and ferrite film units. Under the external pressure, the mechanical compression of the flexible fabric spacer leads to an inductance change, which can further be transduced to a detectable shift of the resonant frequency. Importantly, WiPSA integrates the ferrite film featuring an ultrahigh permeability, which effectively improves the device sensitivity and avoids the interference of conductive materials simultaneously. The device performance with a high quality factor (>35) and sensitivity (−0.19 MHz kPa −1 ) within a pressure range of 0-20 kPa is demonstrated. In addition, WiPSA achieves excellent reproducibility under periodical pressures (>20 000 cycles), temperature fluctuations (15-103 °C), and humidity variations (40-99%). As a proof of concept for human-interactive sensing, WiPSA is successfully 1) integrated with a flexible wrist band for fingertip pressure-guided direction choices, 2) developed into a smart wireless insole to map the plantar stress distributions, and 3) embedded into a waist-supporting belt to resolve the contact pressure between the belt and human abdomen in a remote transmitting scheme.

Liver segmentation is still a challenging task in medical image processing area due to the complexity of the liver's anatomy, low contrast with adjacent organs, and presence of pathologies. This investigation was used to develop and validate an automated method to segment livers in CT images. The proposed framework consists of three steps: 1) preprocessing; 2) initialization; and 3) segmentation. In the first step, a statistical shape model is constructed based on the principal component analysis and the input image is smoothed using curvature anisotropic diffusion filtering. In the second step, the mean shape model is moved using thresholding and Euclidean distance transformation to obtain a coarse position in a test image, and then the initial mesh is locally and iteratively deformed to the coarse boundary, which is constrained to stay close to a subspace of shapes describing the anatomical variability. Finally, in order to accurately detect the liver surface, deformable graph cut was proposed, which effectively integrates the properties and inter-relationship of the input images and initialized surface. The proposed method was evaluated on 50 CT scan images, which are publicly available in two databases Sliver07 and 3Dircadb. The experimental results showed that the proposed method was effective and accurate for detection of the liver surface.

Abstract-Accurate lung tumor delineation plays an important role in radiotherapy treatment planning. Since the lung tumor has poor boundary in PET images and low contrast in CT images, segmentation of tumor in PET and CT images is a challenging task. In this study, we effectively integrate the two modalities by making fully use of the superior contrast of PET images and superior spatial resolution of CT images. Random walk and graph cut method are integrated to solve the segmentation problem, in which random walk is utilized as an initialization tool to provide object seeds for graph cut segmentation on PET and CT images. The co-segmentation problem is formulated as an energy minimization problem which is solved by max-flow/min-cut method. A graph includes two sub-graphs and a special link is constructed, in which one sub-graph is for PET and another is for CT, and the special link encodes a context term which penalizes the difference of the tumor segmentation on the two modalities. To fully utilize the characteristics of PET and CT images, a novel energy representation is devised. For PET, a downhill cost and a 3D derivative cost is proposed. For CT, a shape penalty cost is integrated into the region and boundary function which helps constrain the tumor location during the segmentation. We validate our algorithm on a dataset which consists of 18 PET-CT images. The experimental results indicate the proposed method is superior to the graph cut method solely using the PET or CT is more accurate compared with the random walk method, random walk co-segmentation method, and non-improved graph-cut method.Index Terms-Image segmentation, interactive segmentation, graph cut, random walks, prior information, lung tumor, positron emission tomography (PET), computed tomography (CT).