Local wall motion classification of stress echocardiography using a Hidden Markov Model approach

Mansor, Sarina; Noble, J. Alison

doi:10.1109/isbi.2008.4541241

Cited by 19 publications

(8 citation statements)

References 5 publications

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“…Many applications have demonstrated the advantages of HMM in dealing with time-varying series, such as [3,4,14]. In [11], the authors used HMM to detect abnormal local wall motion in stress echocardiography. In [5], HMM is used in conjunction with a particle filter to track hand motion.…”

Section: Introductionmentioning

confidence: 99%

Minimum S-Excess Graph for Segmenting and Tracking Multiple Borders with HMM

Essa

Xie

Jones

2015

Lecture Notes in Computer Science

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We present a novel HMM based approach to simultaneous segmentation of vessel walls in Lymphatic confocal images. The vessel borders are parameterized using RBFs to minimize the number of tracking points. The proposed method tracks the hidden states that indicate border locations for both the inner and outer walls. The observation for both borders is obtained using edge-based features from steerable filters. Two separate Gaussian probability distributions for the vessel borders and background are used to infer the emission probability, and the transmission probability is learned using a Baum-Welch algorithm. We transform the segmentation problem into a minimization of an s-excess graph cost, with each node in the graph corresponding to a hidden state and the weight for each node being defined by its emission probability. We define the interrelations between neighboring nodes based on the transmission probability. We present both qualitative and quantitative analysis in comparison to the popular Viterbi algorithm.

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Section: Introductionmentioning

confidence: 99%

Minimum S-Excess Graph for Segmenting and Tracking Multiple Borders with HMM

Essa

Xie

Jones

2015

Lecture Notes in Computer Science

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“…In clinical practice, the regional myocardial function is commonly scored by following American Heart Association (AHA) standards [5], where the LV is divided into 17 segments. Existing regional heart function analysis methods are based on information theoretic measures and unscented Kalman filter approaches [6], differentiable manifolds [7], independent component analysis classifier [8], pattern recognition technique based on intra-segment correlation [9], and tensor-based classification [10]. Most of the existing methods require extensive user interaction or computationally expensive segmentation algorithms.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Regional Myocardial Function via Statistical Features in MR Images

Afshin

Ayed

Punithakumar

et al. 2011

Lecture Notes in Computer Science

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Abstract. Early and accurate detection of Left Ventricle (LV) regional wall motion abnormalities significantly helps in the diagnosis and followup of cardiovascular diseases. We present a regional myocardial abnormality detection framework based on image statistics. The proposed framework requires a minimal user interaction, only to specify initial delineation and anatomical landmarks on the first frame. Then, approximations of regional myocardial segments in subsequent frames were systematically obtained by superimposing the initial delineation on the rest of the frames. The proposed method exploits the Bhattacharyya coefficient to measure the similarity between the image distribution within each segment approximation and the distribution of the corresponding user-provided segment. Linear Discriminate Analysis (LDA) is applied to find the optimal direction along which the projected features are the most descriptive. Then a Linear Support Vector Machine (SVM) classifier is employed for each of the regional myocardial segments to automatically detect abnormally contracting regions of the myocardium. Based on a clinical dataset of 30 subjects, the evaluation demonstrates that the proposed method can be used as a promising diagnostic support tool to assist clinicians.

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“…Unfortunately, early detection by visual inspection is limited due to vast amount of information and uncertainty associated with heart motion. Computer-aided detection systems, which can analyze extensive amount of information associated with the heart motion, have attracted research attention in recent years [1,2,3]. Computer-aided abnormality detection primarily consists of two components: preprocessing and classification.…”

Section: Introductionmentioning

confidence: 99%

Heart Motion Abnormality Detection via an Information Measure and Bayesian Filtering

Punithakumar

Ayed

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. This study investigates heart wall motion abnormality detection with an information theoretic measure of heart motion based on the Shannon's differential entropy (SDE) and recursive Bayesian filtering. Heart wall motion is generally analyzed using functional images which are subject to noise and segmentation inaccuracies, and incorporation of prior knowledge is crucial in improving the accuracy. The Kalman filter, a well known recursive Bayesian filter, is used in this study to estimate the left ventricular (LV) cavity points given incomplete and noisy data, and given a dynamic model. However, due to similarities between the statistical information of normal and abnormal heart motions, detecting and classifying abnormality is a challenging problem which we proposed to investigate with a global measure based on the SDE. We further derive two other possible information theoretic abnormality detection criteria, one is based on Rényi entropy and the other on Fisher information. The proposed method analyzes wall motion quantitatively by constructing distributions of the normalized radial distance estimates of the LV cavity. Using 269×20 segmented LV cavities of short-axis magnetic resonance images obtained from 30 subjects, the experimental analysis demonstrates that the proposed SDE criterion can lead to significant improvement over other features that are prevalent in the literature related to the LV cavity, namely, mean radial displacement and mean radial velocity.

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Local wall motion classification of stress echocardiography using a Hidden Markov Model approach

Cited by 19 publications

References 5 publications

Minimum S-Excess Graph for Segmenting and Tracking Multiple Borders with HMM

Minimum S-Excess Graph for Segmenting and Tracking Multiple Borders with HMM

Assessment of Regional Myocardial Function via Statistical Features in MR Images

Heart Motion Abnormality Detection via an Information Measure and Bayesian Filtering

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