39036081
Tongue image matching using color content
This paper addresses the problem of color image matching in medical diagnosis. The color matching of tongue images in different color spaces with different metrics have been investigated and is reported in this article. Two new metrics namely, sorted metric and probabilistic combined metric, are proposed. Existing distance measurements in coordinate space do not satisfy the reflexivity axiom. That means, they are not the valid metrics. To overcome this limitation, the sorted metric in coordinate space is proposed in coordinate space. To improve the matching performance, a probabilistic combined metric is proposed based on the theory of combining classifier. These metrics are applied for the matching of tongue color images and the results are encouraging.
46337154
Minimum cross entropy thresholding
Abstract The threshold selection problem is solved by minimizing the cross entropy between the image and its segmented version. The cross entropy is formulated in a pixel-to-pixel basis between the two images and a computationally attractive algorithm employing the histogram is developed. Without making a priori assumptions about the population distribution, this method provides an unbiased estimate of a binarized version of the image in an information theoretic sense.
8216205
Dissimilarity learning for nominal data
Defining a good distance (dissimilarity) measure between patterns is of crucial importance in many classification and clustering algorithms. While a lot of work has been performed on continuous attributes, nominal attributes are more difficult to handle. A popular approach is to use the value difference metric (VDM) to define a real-valued distance measure on nominal values. However, VDM treats the attributes separately and ignores any possible interactions among attributes. In this paper, we propose the use of adaptive dissimilarity matrices for measuring the dissimilarities between nominal values. These matrices are learned via optimizing an error function on the training samples. Experimental results show that this approach leads to better classification performance. Moreover, it also allows easier interpretation of (dis)similarity between different nominal values.
29013572
Semi-supervised Learning in Medical Image Database
This paper presents a novel graph-based algorithm for solving the semi-supervised learning problem. The graph-based algorithm makes use of the recent advances in stochastic graph sampling technqiue and a modeling of the labeling consistency in semi-supervised learning. The quality of the algorithm is empirically evaluated on a synthetic clustering problem. The semi-supervised clustering is also applied to the problem of symptoms classification in medical image database and shows promising results.
19930573
Learning Pathological Characteristics from User's Relevance Feedback for Content-Based Mammogram Retrieval
Content-based image retrieval (CBIR) has been proposed to address the problem of image retrieval from medical image databases. Relevance feedback, explaining the user's query concept, can be used to bridge the semantic gap and improve the performance of CBIR systems. This paper proposes a learning method for relevance feedback, which utilizes probabilistic model to generalize the 2-class problem and provide an estimate of probability of class membership. To build the probabilistic model, support vector machine (SVM) is applied to classify the mammograms, and then scale them to the probability of class membership. Experimental results show that the proposed learning method can effectively improve the average precision rate from 40% to 62% through five iterations of relevance feedback rounds
3090145
XGuard: a system for publishing XML documents without information leakage in the presence of data inference
In data publishing, if the data is published carelessly, public users could use common knowledge to infer more information from the published data, causing leakage of sensitive information. To address related research challenges, we develop a system called XGuard, which can help data owners publish a partial XML document without leaking sensitive information, even if public users can do inference. Specifically, the system has the following functionalities. i) It allows the data owner to define sensitive information and specify common knowledge as XML constraints. ii) Given a partial document, the system can validate if the document can cause information leakage due to common knowledge and how much data can be leaked. iii) The system can help the data owner interactively analyze the data inference and produce a secure valid partial document using the algorithms.
14325657
An approach to reducing the labeling cost of Markov random fields within an infinite label space
This work proposes a novel idea, called SOIL, for reducing the computational complexity of the maximum a posteriori optimization problem using Markov random fields (MRFs). The local characteristics of MRFs are employed so that the searching in a virtually infinite label space is confined in a small finite space. Globally, the number of labels allowed is as many as the number of image sites while locally the optimal label is sampled from a space consisting of the labels assigned to the four-neighbor plus a random one. Neither the prior knowledge about the number of classes nor the estimation phase of the class number is required in this work. The proposed method is applied to the problem of texture segmentation and the result is compared with those obtained from conventional methods.
17566608
Porosity of mutually nearest and mutually furthest points in Banach spaces
Let X be a real strictly convex and Kadec Banach space and G a nonempty closed relatively boundedly weakly compact subset of X. Let B(X) (resp. K(X)) be the family of nonempty bounded closed (resp. compact) subsets of X endowed with the Hausdorff distance and let BG(X) denote the closure of the set {A ∈ B(X) : A ∩ G = φ} and KG(X) = BG(X)∩K(X). We introduce the admissible family A of B(X) and prove that EAo(G) (resp. EoA(G)), the set of all subsets F ∈ A ⊆ BG(X) (resp. F ∈ A ⊆ BG(X)) such that the minimization problem min(F, G) (resp. the maximization problem max(F, G)) is well-posed, is a dense Gδ-subset of A. Furthermore, when X is uniformly convex, we prove that A\EAo(G) and A\EoA(G) are σ-porous in A.
701496
Distributed Priority Scheduling and Medium Access in Ad Hoc Networks
Providing Quality-of-Service in random access multi-hop wireless networks requires support from both medium access and packet scheduling algorithms. However, due to the distributed nature of ad hoc networks, nodes may not be able to determine the next packet that would be transmitted in a (hypothetical) centralized and ideal dynamic priority scheduler. In this paper, we develop two mechanisms for QoS communication in multi-hop wireless networks. First, we devise distributed priority scheduling, a technique that piggybacks the priority tag of a node's head-of-line packet onto handshake and data packets; e.g., RTS/DATA packets in IEEE 802.11. By monitoring transmitted packets, each node maintains a scheduling table which is used to assess the node's priority level relative to other nodes. We then incorporate this scheduling table into existing IEEE 802.11 priority backoff schemes to approximate the idealized schedule. Second, we observe that congestion, link errors, and the random nature of medium access prohibit an exact realization of the ideal schedule. Consequently, we devise a scheduling scheme termed multi-hop coordination so that downstream nodes can increase a packet's relative priority to make up for excessive delays incurred upstream. We next develop a simple analytical model to quantitatively explore these two mechanisms. In the former case, we study the impact of the probability of overhearing another packet's priority index on the scheme's ability to achieve the ideal schedule. In the latter case, we explore the role of multi-hop coordination in increasing the probability that a packet satisfies its end-to-end QoS target. Finally, we perform a set of ns-2 simulations to study the scheme's performance under more realistic conditions.