Integrated Chest Image Analysis System "BU-MIA"

We introduce "BU-MIA," a Medical Image Analysis system that integrates various advanced chest image analysis methods for detection, estimation, segmentation, and registration. BU-MIA evaluates repeated computed tomography (CT) scans of the same patient to facilitate identification and evaluation of pulmonary nodules for interval growth. It provides a user-friendly graphical user interface with a number of interaction tools for development, evaluation, and validation of chest image analysis methods. The structures that BU-MIA processes include the thorax, lungs, and trachea, pulmonary structures, such as lobes, fissures, nodules, and vessels, and bones, such as sternum, vertebrae, and ribs

Object Detection at the Optimal Scale with Hidden State Shape Models

Hidden State Shape Models (HSSMs) [2], a variant of Hidden Markov Models (HMMs) [9], were proposed to detect shape classes of variable structure in cluttered images. In this paper, we formulate a probabilistic framework for HSSMs which provides two major improvements in comparison to the previous method [2]. First, while the method in [2] required the scale of the object to be passed as an input, the method proposed here estimates the scale of the object automatically. This is achieved by introducing a new term for the observation probability that is based on a object-clutter feature model. Second, a segmental HMM [6, 8] is applied to model the "duration probability" of each HMM state, which is learned from the shape statistics in a training set and helps obtain meaningful registration results. Using a segmental HMM provides a principled way to model dependencies between the scales of different parts of the object. In object localization experiments on a dataset of real hand images, the proposed method significantly outperforms the method of [2], reducing the incorrect localization rate from 40% to 15%. The improvement in accuracy becomes more significant if we consider that the method proposed here is scale-independent, whereas the method of [2] takes as input the scale of the object we want to localize

Supervised cross-modal factor analysis for multiple modal data classification

In this paper we study the problem of learning from multiple modal data for purpose of document classification. In this problem, each document is composed two different modals of data, i.e., an image and a text. Cross-modal factor analysis (CFA) has been proposed to project the two different modals of data to a shared data space, so that the classification of a image or a text can be performed directly in this space. A disadvantage of CFA is that it has ignored the supervision information. In this paper, we improve CFA by incorporating the supervision information to represent and classify both image and text modals of documents. We project both image and text data to a shared data space by factor analysis, and then train a class label predictor in the shared space to use the class label information. The factor analysis parameter and the predictor parameter are learned jointly by solving one single objective function. With this objective function, we minimize the distance between the projections of image and text of the same document, and the classification error of the projection measured by hinge loss function. The objective function is optimized by an alternate optimization strategy in an iterative algorithm. Experiments in two different multiple modal document data sets show the advantage of the proposed algorithm over other CFA methods

Generating Sequence of Eye Fixations Using Decision-theoretic Attention Model

Human eyes scan images with serial eye fixations. We proposed a novel attention selectivity model for the automatic generation of eye fixations on 2D static scenes. An activation map was first computed by extracting primary visual features and detecting meaningful objects from the scene. An adaptable retinal filter was applied on this map to generate Regions of Interest (ROIs), whose locations corresponded to those of activation peaks and whose sizes were estimated by an iterative adjustment algorithm. The focus of attention was moved serially over the detected ROIs by a decision-theoretic mechanism. The generated sequence of eye fixations was determined from the perceptual benefit function based on perceptual costs and rewards, while the time distribution of different ROIs was estimated by a memory learning and decaying model. Finally, to demonstrate the effectiveness of the proposed attention model, the gaze tracking results of different human subjects and the simulated eye fixation shifting were compared

Feature extraction and identification of leak acoustic signal in water supply pipelines using correlation analysis and Lyapunov exponent

The leakage of water supply pipeline is a common problem in the world. Timely discovery and treatment of leakage can avoid drinking water pollution, save water resources or avoid road collapse accidents. Therefore, it is of great practical significance to study pipeline leak detection methods. In this experiment, piezoelectric acceleration sensors were placed in different locations of a leak pipe to acquire the leakage signals. According to the generation mechanism of leak acoustic signals, the unpredictability characteristics of leak signal are investigated. The autocorrelation function is used to describe the unpredictability of the leak signal because it has the ability to analyze the coherent structure of time series, and the Lyapunov exponent can describe its complexity. The autocorrelation function sequence is used as featured extraction object. The Lyapunov exponent of this sequence is used to quantify the signal characteristics. By this method, the leakage can be effectively identified

Research on pipeline leakage identification method based on Lyapunov exponent spectrum

In terms of resource transportation, pipelines play a very important role, however, it’s leakage is almost inevitable, so leakage monitoring has always been a research hotspot. The leakage identification of water supply pipeline is studied in this paper. The vibration caused by turbulence and cavitation at the leak point has chaotic characteristics, and the spectral distribution of which has its own special area. Chaos can be judged by the Lyapunov exponent, this means that the occurrence of the leak is judged. By this method, the leakage can be effectively identified. The above theory is verified by experiments in this paper