Segmentation of Lung Lobes in High-Resolution Isotropic CT Images

Modern multislice computed tomography (CT) scanners produce isotropic CT images with a thickness of 0.6 mm. These CT images offer detailed information of lung cavities, which could be used for better surgical planning of treating lung cancer. The major challenge for developing a surgical planning system is the automatic segmentation of lung lobes by identifying the lobar fissures. This paper presents a lobe segmentation algorithm that uses a two-stage approach: 1) adaptive fissure sweeping to find fissure regions and 2) wavelet transform to identify the fissure locations and curvatures within these regions. Tested on isotropic CT image stacks from nine anonymous patients with pathological lungs, the algorithm yielded an accuracy of 76.7%–94.8% with strict evaluation criteria. In comparison, surgeons obtain an accuracy of 80% for localizing the fissure regions in clinical CT images with a thickness of 2.5–7.0 mm. As well, this paper describes a procedure for visualizing lung lobes in three dimensions using software—amira—and the segmentation algorithm. The procedure, including the segmentation, needed about 5 min for each patient. These results provide promising potential for developing an automatic algorithm to segment lung lobes for surgical planning of treating lung cancer.      

A Computational Geometry Approach to Automated Pulmonary Fissure Segmentation in CT Examinations

Identification of pulmonary fissures, which form the boundaries between the lobes in the lungs, may be useful during clinical interpretation of computed tomography (CT) examinations to assess the early presence and characterization of manifestation of several lung diseases. Motivated by the unique nature of the surface shape of pulmonary fissures in 3-D space, we developed a new automated scheme using computational geometry methods to detect and segment fissures depicted on CT images. After a geometric modeling of the lung volume using the marching cubes algorithm, Laplacian smoothing is applied iteratively to enhance pulmonary fissures by depressing nonfissure structures while smoothing the surfaces of lung fissures. Next, an extended Gaussian image based procedure is used to locate the fissures in a statistical manner that approximates the fissures using a set of plane ldquopatchesrdquo. This approach has several advantages such as independence of anatomic knowledge of the lung structure except the surface shape of fissures, limited sensitivity to other lung structures, and ease of implementation. The scheme performance was evaluated by two experienced thoracic radiologists using a set of 100 images (slices) randomly selected from 10 screening CT examinations. In this preliminary evaluation 98.7% and 94.9% of scheme segmented fissure voxels are within 2 mm of the fissures marked independently by two radiologists in the testing image dataset. Using the scheme detected fissures as reference, 89.4% and 90.1% of manually marked fissure points have distance les2 mm to the reference suggesting a possible under-segmentation of the scheme. The case-based root mean square (rms) distances (ldquoerrorsrdquo) between our scheme and the radiologist ranged from 1.48plusmn0.92 to 2.04plusmn3.88 mm. The discrepancy of fissure detection results between the automated scheme and either radiologist is smaller in this dataset than the interreader variability.     

一种全自动的肺裂分割方法

CT（Computer Tomography）图像中自动分割肺裂是很困难的,肺裂往往存在不完整、形变、断裂和附裂等现象.本文提出一种融合肺部解剖结构特征来实现自动分割肺裂的方法.首先结合肺部气管和动脉血管信息定位肺裂感兴趣区域.然后利用肺裂方向信息增强肺裂,并利用多剖面滤波器滤除噪声从而对肺裂进行预分割.最后融合已定位的肺裂感兴趣区域和肺裂预分割结果来自动分割肺裂.与人工参考对比,提出的算法在人体左肺和右肺中分割的肺裂的F₁-score中值分别为0.881和0.878.     

Atlas-driven lung lobe segmentation in volumetric X-ray CT images

High-resolution X-ray computed tomography (CT) imaging is routinely used for clinical pulmonary applications. Since lung function varies regionally and because pulmonary disease is usually not uniformly distributed in the lungs, it is useful to study the lungs on a lobe-by-lobe basis. Thus, it is important to segment not only the lungs, but the lobar fissures as well. In this paper, we demonstrate the use of an anatomic pulmonary atlas, encoded with a priori information on the pulmonary anatomy, to automatically segment the oblique lobar fissures. Sixteen volumetric CT scans from 16 subjects are used to construct the pulmonary atlas. A ridgeness measure is applied to the original CT images to enhance the fissure contrast. Fissure detection is accomplished in two stages: an initial fissure search and a final fissure search. A fuzzy reasoning system is used in the fissure search to analyze information from three sources: the image intensity, an anatomic smoothness constraint, and the atlas-based search initialization. Our method has been tested on 22 volumetric thin-slice CT scans from 12 subjects, and the results are compared to manual tracings. Averaged across all 22 data sets, the RMS error between the automatically segmented and manually segmented fissures is 1.96 +/- 0.71 mm and the mean of the similarity indices between the manually defined and computer-defined lobe regions is 0.988. The results indicate a strong agreement between the automatic and manual lobe segmentations.     

Optical Phase Add–Drop for Format Conversion Between DQPSK and DPSK and its Application in Optical Label Switching Systems

We propose and experimentally demonstrate an optical phase “add–drop” scheme to enable the format conversion between a 20-Gb/s differential quadrature phase-shift keying (DQPSK) and 10-Gb/s differential phase-shift keying. Meanwhile, the incoming DQPSK could be directly forwarded to another wavelength through the proposed optical phase “drop” scheme, which is implemented by four-wave-mixing effect in highly nonlinear fiber. By orthogonally encoding the label and payload of the optical packet on the in-phase ( $I$) and quadrature ( $ Q $) components of DQPSK, the proposed scheme could be applied in the optical label switching networks. The label erasing and rewriting of optical packets can be achieved through the proposed optical phase “drop” and “add” schemes, respectively.      

Active Mask Segmentation of Fluorescence Microscope Images

We propose a new active mask algorithm for the segmentation of fluorescence microscope images of punctate patterns. It combines the (a) flexibility offered by active-contour methods, (b) speed offered by multiresolution methods, (c) smoothing offered by multiscale methods, and (d) statistical modeling offered by region-growing methods into a fast and accurate segmentation tool. The framework moves from the idea of the “contour” to that of “inside and outside,” or masks, allowing for easy multidimensional segmentation. It adapts to the topology of the image through the use of multiple masks. The algorithm is almost invariant under initialization, allowing for random initialization, and uses a few easily tunable parameters. Experiments show that the active mask algorithm matches the ground truth well and outperforms the algorithm widely used in fluorescence microscopy, seeded watershed, both qualitatively, as well as quantitatively.      

On the Loss of Single-Letter Characterization: The Dirty Multiple Access Channel

For general memoryless systems, the existing information-theoretic solutions have a “single-letter” form. This reflects the fact that optimum performance can be approached by a random code (or a random binning scheme), generated using independent and identically distributed copies of some scalar distribution. Is that the form of the solution of any (information-theoretic) problem? In fact, some counter examples are known. The most famous one is the “two help one” problem: KÖrner and Marton showed that if we want to decode the modulo-two sum of two correlated binary sources from their independent encodings, then linear coding is better than random coding. In this paper we provide another counter example, the “doubly-dirty” multiple-access channel (MAC). Like the KÖrner–Marton problem, this is a multiterminal scenario where side information is distributed among several terminals; each transmitter knows part of the channel interference while the receiver only observes the channel output. We give an explicit solution for the capacity region of the binary doubly-dirty MAC, demonstrate how this region can be approached using a linear coding scheme, and prove that the “best known single-letter region” is strictly contained in it. We also state a conjecture regarding the capacity loss of single-letter characterization in the Gaussian case.      

An Active Contour Model for Segmenting and Measuring Retinal Vessels

This paper presents an algorithm for segmenting and measuring retinal vessels, by growing a “Ribbon of Twins” active contour model, which uses two pairs of contours to capture each vessel edge, while maintaining width consistency. The algorithm is initialized using a generalized morphological order filter to identify approximate vessels centerlines. Once the vessel segments are identified the network topology is determined using an implicit neural cost function to resolve junction configurations. The algorithm is robust, and can accurately locate vessel edges under difficult conditions, including noisy blurred edges, closely parallel vessels, light reflex phenomena, and very fine vessels. It yields precise vessel width measurements, with subpixel average width errors. We compare the algorithm with several benchmarks from the literature, demonstrating higher segmentation sensitivity and more accurate width measurement.      

Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation

In this paper, we present a novel multiscale texture model and a related algorithm for the unsupervised segmentation of color images. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled, in turn, by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmentation problem based on the H-MMC model. The “fragmentation” step allows one to find the elementary textures of the model, while the “reconstruction” step defines the hierarchical image segmentation based on a probabilistic measure (texture score) which takes into account both region scale and inter-region interactions. The performance of the proposed method was assessed through the Prague segmentation benchmark, based on mosaics of real natural textures, and also tested on real-world natural and remote sensing images.      

Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the “MICCAI 2007 Grand Challenge” workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.      

Topological and Modulation Design of Three-Level Z-Source Inverters

This paper presents the development of two three- level cascaded Z-source inverters, whose output voltage can be stepped down or up unlike a traditional buck three-level inverter. The proposed inverters are designed using two three-phase voltage-source inverter bridges, supplied by two uniquely designed Z-source impedance networks. These three-phase bridges can either be cascaded at their dc sides to form a dc-link-cascaded Z-source inverter or at their ac outputs using single-phase transformers to form a dual Z-source inverter. The dc-link-cascaded inverter has the advantages of not using any clamping diodes and transformers, but does not have redundant switching states within a phase leg for equalizing switching losses among the power devices. This constraint limits the modulation options for the dc-link-cascaded inverter, and indeed, it can only be controlled using the modified carrier disposition technique with appropriate “Z-source shoot-through” states inserted for achieving balanced voltage boosting and optimal “nearest-three-vectors” switching. On the other hand, the dual Z-source inverter with transformer isolation can be controlled using different modulation approaches due to the presence of redundant switching states within a phase leg. Particularly, using a modified phase-shifted-carrier (PSC) scheme with shoot-through states inserted, it is shown that the dual inverter can be implemented using only a single Z-source network, while still achieving the correct volt-sec average and switching loss equalization. This represents a significant reduction in cost, and can more than compensate for the slightly degraded spectral characteristics of the PSC scheme. To verify the theoretical concepts discussed, experimental testing has been performed with the captured results presented in a later section of the paper.      

Topological Design and Modulation Strategy for Buck–Boost Three-Level Inverters

To date, designed topologies for dc–ac inversion with both voltage buck and boost capabilities are mainly focused on two-level circuitries with extensions to three-level possibilities left nearly unexplored. Contributing to this area of research, this paper presents the design of a number of viable buck–boost three-level inverters that can also support bidirectional power conversion. The proposed front-end circuitry is developed from the Ćuk-derived buck–boost two-level inverter, and by using the “alternative phase opposition disposition” modulation scheme, the buck–boost three-level inverters can perform distinct five-level line voltage and three-level phase voltage switching by simply controlling the active switches located in the designed voltage boost section of the circuits. As a cost saving option, one active switch can further be removed from the voltage boost section of the circuits by simply rerouting the gating commands of the remaining switches without influencing the ac output voltage amplitude. To verify the validity of the proposed inverters, MATLAB/PLECS simulations were performed before a laboratory prototype was implemented for experimental testing.      

Single and Multiwavelength All-Optical Clock Recovery Using Fabry–PÉrot Semiconductor Optical Amplifier

We experimentally demonstrate an all-optical clock recovery scheme for the return-to-zero data with single- and multiwavelength. This scheme is mainly based on clock-like pulse generation from an active filter with a multiquantum-well Fabry–PÉrot semiconductor optical amplifier and amplitude-equalization with self-nonlinear polarization switching. We obtain stable and low jitter optical clock signals in single and dual channels using this scheme, which has some distinct advantages including easy integration, free preamplification, convenient tuning, good tolerance to long “0s” data, and greater tolerance to the stability of the input wavelength.      

Reproducibility of Laplacian Wall Thickness Measurements of the Gallbladder with Varying CT Slice Thickness

In measuring changes of gallbladder wall thickness using CT, robustness to differences in acquisition protocols including slice thickness can be important. We have developed an automated technique based on Laplace’s equation to measure the gallbladder wall thickness using computer tomography (CT). The purpose of this work is to investigate the usefulness of the Laplacian technique in obtaining gallbladder wall thickness measurements that are reproducible with variations in CT slice thickness. This study included 2D (2D) and 3D (3D) wall thickness measurements using Laplace’s equation. Ten subjects who had 5 mm (thick) and 2.5 mm (thin) reconstruction (from a single set of raw data) through the abdomen were randomly selected from a research database. Their volumetric CT images were acquired using a multidetector GE MEDICAL SYSTEMS–LightSpeed 16 scanner at 120 KVP, ~250 mAs, with standard filter reconstruction algorithm and manually segmented on all CT cross sections by a radiologist. The inner and outer boundaries of the gallbladder wall were obtained from the segmentation. The thickness of the wall was quantified by computing the distance between the boundaries for each scan and over the entire volume using Laplace’s equation from mathematical physics. The distance between the surfaces is found by computing normalized gradients that form a vector field. The vector fields represent tangent vectors along field lines connecting both boundaries. The Laplacian technique was compared with the conventional Euclidean distance transformation (EDT) technique using coefficient of variation. EDT results in an Euclidean distance mapping between the two extracted surfaces. Both techniques were compared in 2D and 3D. For the 2D and 3D wall thickness measurements, a mean difference of 0.35 and 0.25 mm between thin and thick reconstruction was found respectively using Laplace’s equation. EDT resulted in a higher mean difference for both 2D and 3D. In addition, a significant difference in thickness between the Laplacian technique and EDT techniques (p?<?0.001) were obtained. The Laplacian measurement of gallbladder wall showed significantly lower variation compared to EDT on different CT slice thickness for both 2D and 3D techniques. Hence, proving to be an important technique for obtaining reproducible wall thickness measurements of the gallbladder using CT.     

Segmentation of Tracking Sequences Using Dynamically Updated Adaptive Learning

The problem of segmentation of tracking sequences is of central importance in a multitude of applications. In the current paper, a different approach to the problem is discussed. Specifically, the proposed segmentation algorithm is implemented in conjunction with estimation of the dynamic parameters of moving objects represented by the tracking sequence. While the information on objects' motion allows one to transfer some valuable segmentation priors along the tracking sequence, the segmentation allows substantially reducing the complexity of motion estimation, thereby facilitating the computation. Thus, in the proposed methodology, the processes of segmentation and motion estimation work simultaneously, in a sort of “collaborative” manner. The Bayesian estimation framework is used here to perform the segmentation, while Kalman filtering is used to estimate the motion and to convey useful segmentation information along the image sequence. The proposed method is demonstrated on a number of both computed-simulated and real-life examples, and the obtained results indicate its advantages over some alternative approaches.      

Anatomy-Guided Lung Lobe Segmentation in X-Ray CT Images

The human lungs are divided into five distinct anatomic compartments called the lobes, which are separated by the pulmonary fissures. The accurate identification of the fissures is of increasing importance in the early detection of pathologies, and in the regional functional analysis of the lungs. We have developed an automatic method for the segmentation and analysis of the fissures, based on the information provided by the segmentation and analysis of the airway and vascular trees. This information is used to provide a close initial approximation to the fissures, using a watershed transform on a distance map of the vasculature. In a further refinement step, this estimate is used to construct a region of interest (ROI) encompassing the fissures. The ROI is enhanced using a ridgeness measure, which is followed by a 3-D graph search to find the optimal surface within the ROI. We have also developed an automatic method to detect incomplete fissures, using a fast-marching based segmentation of a projection of the optimal surface. The detected incomplete fissure is used to extrapolate and smoothly complete the fissure. We evaluate the method by testing on data sets from normal subjects and subjects with mild to moderate emphysema.      

Automatic Detection of Anatomical Landmarks in Uterine Cervix Images

The work focuses on a unique medical repository of digital cervicographic images (“Cervigrams”) collected by the National Cancer Institute (NCI) in longitudinal multiyear studies. NCI, together with the National Library of Medicine (NLM), is developing a unique web-accessible database of the digitized cervix images to study the evolution of lesions related to cervical cancer. Tools are needed for automated analysis of the cervigram content to support cancer research. We present a multistage scheme for segmenting and labeling regions of anatomical interest within the cervigrams. In particular, we focus on the extraction of the cervix region and fine detection of the cervix boundary; specular reflection is eliminated as an important preprocessing step; in addition, the entrance to the endocervical canal (the “os”), is detected. Segmentation results are evaluated on three image sets of cervigrams that were manually labeled by NCI experts.      

Wafer-Level Defect Screening for “Big-D/Small-A” Mixed-Signal SoCs

Product cost is a key driver in the consumer electronics market, which is characterized by low profit margins and the use of a variety of “big-D/small-A” mixed-signal system-on-chip (SoC) designs. Packaging cost has recently emerged as a major contributor to the product cost for such SoCs. Wafer-level testing can be used to screen defective dies, thereby reducing packaging cost. We propose a new correlation-based signature analysis technique that is especially suitable for mixed-signal test at the wafer-level using low-cost digital testers. The proposed method overcomes the limitations of measurement inaccuracies at the wafer-level. A generic cost model is used to evaluate the effectiveness of wafer-level testing of analog and digital cores in a mixed-signal SoC, and to study its impact on test escapes, yield loss, and packaging costs. Experimental results are presented for a typical mixed-signal “big-D/small-A” SoC, which contains a large section of flattened digital logic and several large mixed-signal cores.      

SSMCB: Low-Power Variation-Tolerant Source-Synchronous Multicycle Bus

In this paper, a variation-tolerant low-power source-synchronous multicycle bus (SSMCB) interconnect scheme is proposed. This scheme is scalable and suitable for transferring data across different clock domains such as those in “many-core” SoCs and in 3-D ICs. SSMCB replaces intermediate flip-flops by a source-synchronous synchronization scheme. Removing the intermediate flip-flops in the SSMCB scheme enables better averaging of delay variations across the whole interconnect, which reduces bit-rate degradation due to within-die process variations. Monte Carlo circuit simulations show that SSMCB eliminates 90% of the variation-induced performance degradation in a six-cycle 9-mm-long 16-bit conventional bus.      

Properties of the Stimulus Router System, a Novel Neural Prosthesis

Various types of neural prostheses (NPs) have been developed to restore motor function after neural injury. Surface NPs are noninvasive and inexpensive, but are often poorly selective, activating nontargeted muscles and cutaneous sensory nerves that can cause discomfort or pain. Implantable NPs are highly selective, but invasive and costly. The stimulus router system (SRS) is a novel NP consisting of fully implanted leads that “capture” and route some of the current flowing between a pair of surface electrodes to the vicinity of a target nerve. An SRS lead consists of a “pick-up” terminal that is implanted subcutaneously under one of the surface electrodes and a “delivery” terminal that is secured on or near the target nerve. We have published a preliminary report on the basic properties of the SRS [L. S. Gan , “A new means of transcutaneous coupling for neural prostheses,” IEEE Trans. Biomed. Eng., vol. 54, no. 3, pp. 509–517, Mar. 2007]. Here, we further characterize the SRS and identify aspects that maximize its performance as a motor NP. The surface current needed to activate nerves with an SRS, was found to depend on the proximity of the delivery terminal(s) to the nerve, electrode configurations, contact areas of the surface electrodes and implanted terminals, and the distance between the surface anode and the delivery terminal.