1753568
Learning multispectral texture features for cervical cancer detection
We present a bottom-up approach for automatic cancer cell detection in multispectral microscopic thin Pap smear images. Around 4,000 multispectral texture features are explored for cancer cell detection. Using two feature screening measures, the initial feature set is effectively reduced to a computationally manageable size. Based on pixel-level screening results, cancerous regions can thus be detected through a relatively simple procedure. Our experiments have demonstrated the potential of both multispectral and texture information to serve as valuable complementary cues to traditional detection methods.
5285595
Periodic Pattern Analysis under Affine Distortions Using Wallpaper Groups
In this paper, the mathematical theory of wallpaper groups is used to construct a computational tool for symmetry analysis of periodic patterns. Starting with a novel peak detection algorithm based on “regions of dominance”, an input periodic pattern can be automatically classified into one of the 17 wallpaper groups. The orbits of stabilizer subgroups within the group lead to a small set of candidate motifs that exhibit local symmetry consistent with the global symmetry of the entire pattern. We further consider affine distorted periodic patterns and show that each such pattern can be classified into a small set of symmetry groups that describe the patterns’ potential symmetries under affine transformation.
14419032
Content-based 3D neuroradiologic image retrieval: preliminary results
A content-based 3D neuroradiologic image retrieval system is being developed at the Robotics Institute of CMU. The special characteristics of this system include: directly dealing with multimodal 3D images (MR/CT); image similarity based on anatomical structures of the human brain; and combining both visual and collateral information for indexing and retrieval. A testbed has been implemented for using detected salient visual features for indexing and retrieving 3D images.
1966938
Robust midsagittal plane extraction from normal and pathological 3-D neuroradiology images
This paper focuses on extracting the ideal midsagittal plane (iMSP) from three-dimensional (3-D) normal and pathological neuroimages. The main challenges in this work are the structural asymmetry that may exist in pathological brains, and the anisotropic, unevenly sampled image data that is common in clinical practice. We present an edge-based, cross-correlation approach that decomposes the plane fitting problem into discovery of two-dimensional symmetry axes on each slice, followed by a robust estimation of plane parameters. The algorithm's tolerance to brain asymmetries, input image offsets and image noise is quantitatively evaluated. We find that the algorithm can extract the iMSP from input 3-D images with 1) large asymmetrical lesions; 2) arbitrary initial rotation offsets; 3) low signal-to-noise ratio or high bias field. The iMSP algorithm is compared with an approach based on maximization of mutual information registration, and is found to exhibit superior performance under adverse conditions. Finally, no statistically significant difference is found between the midsagittal plane computed by the iMSP algorithm and that estimated by two trained neuroradiologists.
9599152
A classification based similarity metric for 3D image retrieval
We present a principled method of obtaining a weighted similarity metric for 3D image retrieval, firmly rooted in Bayes decision theory. The basic idea is to determine a set of most discriminative features by evaluating how well they perform on the task of classifying images according to predefined semantic categories. We propose this indirect method as a rigorous way to solve the difficult feature selection problem that comes up in most content based image retrieval tasks. The method is applied to normal and pathological neuroradiological CT images, where we take advantage of the fact that normal human brains present an approximate bilateral symmetry which is often absent in pathological brains. The quantitative evaluation of the retrieval system shows promising results.
1236601
Skewed symmetry groups
We introduce the term skewed symmetry groups and provide a complete theoretical treatment for 2D wallpaper groups under affine transformations. For the first time, a given periodic pattern can be classified not simply by its Euclidean symmetry group but,by its highest "potential" symmetry group under affine deformation. A concise wallpaper group migration map is constructed that separates the 17 affinely deformed wallpaper groups into small, distinct orbits. The practical value of this result includes a novel indexing and retrieval scheme for regular patterns, and a maximal-symmetry-based method for estimating shape and orientation from texture under unknown views.
14532857
Projecting registration error for accurate registration of overlapping range images
Abstract In this paper, we propose a novel algorithm for the automatic registration of two overlapping range images. Since it is relatively difficult to compare the registration errors of different point matches, we project them onto a virtual image plane for more accurate comparison using the classical pin-hole perspective projection camera model. While the traditional ICP algorithm is more interested in the points in the second image close to the sphere centred at the transformed point, the novel algorithm is more interested in the points in the second image as collinear as possible to the transformed point. The novel algorithm then extracts useful information from both the registration error and projected error histograms for the elimination of false matches without any feature extraction, image segmentation or the requirement of motion estimation from outliers corrupted data and, thus, has an advantage of easy implementation. A comparative study based on real images captured under typical imaging conditions has shown that the novel algorithm produces good registration results.
18374506
Design-level performance prediction of component-based applications
Server-side component technologies such as Enterprise JavaBeans (EJBs), .NET, and CORBA are commonly used in enterprise applications that have requirements for high performance and scalability. When designing such applications, architects must select suitable component technology platform and application architecture to provide the required performance. This is challenging as no methods or tools exist to predict application performance without building a significant prototype version for subsequent benchmarking. In this paper, we present an approach to predict the performance of component-based server-side applications during the design phase of software development. The approach constructs a quantitative performance model for a proposed application. The model requires inputs from an application-independent performance profile of the underlying component technology platform, and a design description of the application. The results from the model allow the architect to make early decisions between alternative application architectures in terms of their performance and scalability. We demonstrate the method using an EJB application and validate predictions from the model by implementing two different application architectures and measuring their performance on two different implementations of the EJB platform.
39617215
Counting rooted near-triangulations on the sphere
This paper provides the results on the enumerations of rooted simple outerplanar maps, rooted outerplanar near-triangulations, rooted 2-connected near-triangulations, rooted strict 2-connected near-triangulations and rooted simple 2-connected near-triangulations. The answer to an open problem proposed by one of the authors is also provided.
9822374
Initial Study on Automatic Identification of Speaker Role in Broadcast News Speech
Identifying a speaker's role (anchor, reporter, or guest speaker) is important for finding the structural information in broadcast news speech. We present an HMM-based approach and a maximum entropy model for speaker role labeling using Mandarin broadcast news speech. The algorithms achieve classification accuracy of about 80% (compared to the baseline of around 50%) using the human transcriptions and manually labeled speaker turns. We found that the maximum entropy model performs slightly better than the HMM, and that the combination of them outperforms any model alone. The impact of the contextual role information is also examined in this study.
17725399
Regulating gene expression using optimal control theory
We described development of a novel genome-based model-driven strategy useful for regulating eukaryotic gene expression. In order to extract biologically meaningful information from a large volume of mRNA expression data, we built previously a PROmoter-Based Estimation (PROBE) model. The PROBE model allowed us to establish a quantitative relationship between transcription-factor binding motifs in regulatory DNA sequences and mRNA expression levels. Here, we extended PROBE formulation to derive an optimal control law for gene regulation. The responses to shear stress in human synovial cells were chosen as a model biological system, and the system dynamics was identified from the expression pattern of the genes involved in degradation and maintenance of extracellular matrix. In order to suppress the responses to mechanical stimuli, a Ricatti equation was solved and an admissible control law was derived. The approach presented here can be implemented in any biological process, and it would be useful to develop a transcription-mediated strategy for gene therapies and tissue engineering.