34872336
Maintaining valid topology with active contours: theory and application
We develop and prove correct an algorithm that enables active contours to correctly represent regions that undergo topology changes as the contours evolve. Using the incremental motion typical of active contours, we introduce the concept of motion regions to determine the new topology. When the topology changes (e.g. by contours intersecting), the motion regions are used to delete and and reconnect the contours to accurately describe the new region. Contour intersections can also occur without topology changes. These are also appropriately handled. The algorithm to perform this task is proved correct in a general framework that makes few assumptions about the contour representation. We describe how this algorithm is applied to a polygonal representation of the contours, and argue that it does not significantly affect execution time. Finally, this polygonal implementation is used in surface extraction and phytoplankton classification.
13950166
Disease-Oriented Evaluation of Dual-Bootstrap Retinal Image Registration
This paper presents a disease-oriented evaluation of two recent retinal image registration algorithms, one for aligning pairs of retinal images and one for simultaneously aligning all images in a set. Medical conditions studied include diabetic retinopathy, vein occlusion, and both dry and wet age-related macular degeneration. The multi-image alignment worked virtually flawlessly, missing only 2 of 855 images. Pairwise registration, the Dual-Bootstrap ICP algorithm, worked nearly as well, successfully aligning 99.5% of the image pairs having a sufficient set of common features and 78.5% overall. Images of retinas having an edema and pairs of images taken before and after laser treatment proved the most difficult to register.
11904027
Automated fault localization with statistically suspicious program states
A number of fault localization techniques have been developed to reduce the time in manually debugging a faulty program. The technique of code coverage [8] has been recognized by its effectiveness in identifying suspicious statements that may contain the fault. However, a programmer still needs to manually examine each variable referenced in a suspicious statement and such a process can become extremely time-consuming if this suspicious statement is part of a loop. In this paper, we propose a novel technique called state coverage to significantly reduce the time in examining variables. We first insert a set of checkpoints to record the state of each variable referenced in a branching statement. We next execute the faulty program by a test suite consisting of both passed and failed cases. A state is statistically considered to be more suspicious if it appears more in failed cases and less in passed cases. We implemented both code coverage and state coverage in a debugging tool and used a commonly-used benchmark consisting of 58 faulty programs to evaluate their performance. For 34% of these programs, their faults are within 20 statement steps of the most suspicious statement identified by code coverage. By adding state coverage and breaking at the most suspicious state, we increase this ratio to 64%, an 88% performance improvement. Finally, we also explain a few cases in which both state coverage and code coverage cannot perform well.
5873569
Polygonal approximation using a competitive Hopfield neural network
Abstract Polygonal approximation plays an important role in pattern recognition and computer vision. In this paper, a parallel method using a Competitive Hopfield Neural Network (CHNN) is proposed for polygonal approximation. Based on the CHNN, the polygonal approximation is regarded as a minimization of a criterion function which is defined as the arc-to-chord deviation between the curve and the polygon. The CHNN differs from the original Hopfield network in that a competitive winner-take-all mechanism is imposed. The winner-take-all mechanism adeptly precludes the necessity of determining the values for the weighting factors in the energy function in maintaining a feasible result. The proposed method is compared to several existing methods by the approximation error norms L2 and L∞ with the result that promising approximation polygons are obtained.
2128704
Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes
BackgroundDue to the high cost and low reproducibility of many microarray experiments, it is not surprising to find a limited number of patient samples in each study, and very few common identified marker genes among different studies involving patients with the same disease. Therefore, it is of great interest and challenge to merge data sets from multiple studies to increase the sample size, which may in turn increase the power of statistical inferences. In this study, we combined two lung cancer studies using micorarray GeneChip®, employed two gene shaving methods and a two-step survival test to identify genes with expression patterns that can distinguish diseased from normal samples, and to indicate patient survival, respectively.ResultsIn addition to common data transformation and normalization procedures, we applied a distribution transformation method to integrate the two data sets. Gene shaving (GS) methods based on Random Forests (RF) and Fisher's Linear Discrimination (FLD) were then applied separately to the joint data set for cancer gene selection. The two methods discovered 13 and 10 marker genes (5 in common), respectively, with expression patterns differentiating diseased from normal samples. Among these marker genes, 8 and 7 were found to be cancer-related in other published reports. Furthermore, based on these marker genes, the classifiers we built from one data set predicted the other data set with more than 98% accuracy. Using the univariate Cox proportional hazard regression model, the expression patterns of 36 genes were found to be significantly correlated with patient survival (p < 0.05). Twenty-six of these 36 genes were reported as survival-related genes from the literature, including 7 known tumor-suppressor genes and 9 oncogenes. Additional principal component regression analysis further reduced the gene list from 36 to 16.ConclusionThis study provided a valuable method of integrating microarray data sets with different origins, and new methods of selecting a minimum number of marker genes to aid in cancer diagnosis. After careful data integration, the classification method developed from one data set can be applied to the other with high prediction accuracy.
37507961
A new model-based approach for industrial visual inspection
Abstract Two critical steps in industrial visual inspection are to design a flexible representation scheme and to match it with an effective inspection algorithm. Good representation and inspection algorithms can drastically speed up the subsequent matching process, and strengthen the feasibility of complicated on-line inspection. A novel pictorial representation called pattern attributed hypergraph (PAHG) and a new structural inspection algorithm are presented. The PAHG is used to describe the pictorial information of an input pattern, which includes attributed nodes, connectivity between nodes (intra-relations), regional primitives, and neighborhood relations between regions (inter-relations). The new structural inspection algorithm based on PAHG is proposed to match the inspected scene with the model efficiently. Consequently, the amount of matching computation is significantly reduced to roughly 1/ k 3 of the currently available method (Darwish and Jain, IEEE Trans. Pattern Analysis Mach. Intell. 10 , 56–68 (1988)), where k is the number of partitioned regions. The theoretical analysis and experimental results have shown the superiority of the proposed method.
34749384
Endocardial boundary detection using a neural network
Abstract Echocardiography has been widely used as a real-time non-invasive clinical tool to diagnose cardiac functions. Due to the poor quality and inherent ambiguity in echocardiograms, it is difficult to detect the myocardial boundaries of the left ventricle. Many existing methods are semi-automatic and detect cardial boundaries by serial computation which is too slow to be practical in real applications. In this paper, a new method for detecting the endocardial boundary by using a Hopfield neural network is proposed. Taking advantage of parallel computation and energy convergence capability in the Hopfield network, this method is faster and more stable for the detection of the endocardial border. Moreover, neither manual operations nor a priori assumptions are needed in this method. Experiments on several LV echocardiograms and clinical validation have shown the effectiveness of our method in these patient studies.