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页次：1/171731 |
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| Angel Cruz-Roa[1];Jun Xu[2];Anant Madabhushi[3] |
| 10th International Symposium on Medical Information Processing and Analysis, October 14,2014, Pages |
| Abstract： |
Nuclear architecture or the spatial arrangement of individual cancer nuclei on histopathology images has been shown to be associated with different grades and differential risk for a number of solid tumors such as breast,prostate,and oropharyngeal. Graph-based representations of individual nuclei (nuclei representing the graph nodes) allows for mining of quantitative metrics to describe tumor morphology. These graph features can be broadly categorized into global and local depending on the type of graph construction method. While a number of local graph (e.g. Cell Cluster Graphs) and global graph (e.g. Voronoi,Delaunay Triangulation,Minimum Spanning Tree) features have been shown to associated with cancer grade,risk,and outcome for different cancer types,the sensitivity of the preceding segmentation algorithms in identifying individual nuclei can have a significant bearing on the discriminability of the resultant features. This therefore begs the question as to which features while being discriminative of cancer grade and aggressiveness are also the most resilient to the segmentation errors. These properties are particularly desirable in the context of digital pathology images,where the method of slide preparation,staining,and type of nuclear segmentation algorithm employed can all dramatically affect the quality of the nuclear graphs and corresponding features. In this paper we evaluated the trade off between discriminability and stability of both global and local graph-based features in conjunction with a few different segmentation algorithms and in the context of two different histopathology image datasets of breast cancer from whole-slide images (WSI) and tissue microarrays (TMA). Specifically in this paper we investigate a few different performance measures including stability,discriminability and stability vs discriminability trade off,all of which are based on p-values from the Kruskal-Wallis one-way analysis of variance for local and global graph features. Apart from identifying the set of local and global features that satisfied the trade off between stability and discriminability,our most interesting finding was that a simple segmentation method was sufficient to identify the most discriminant features for invasive tumour detection in TMAs,whereas for tumour grading in WSI,the graph based features were more sensitive to the accuracy of the segmentation algorithm employed. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. |
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| Juan Leon,Andrea Pulido,Eduardo Romero[1] |
| 10th International Symposium on Medical Information Processing and Analysis, October 14,2014, Pages |
| Abstract： |
Computational anatomy is a subdiscipline of the anatomy that studies macroscopic details of the human body structure using a set of automatic techniques. Different reference systems have been developed for brain mapping and morphometry in functional and structural studies. Several models integrate particular anatomical regions to highlight pathological patterns in structural brain MRI,a really challenging task due to the complexity,variability,and nonlinearity of the human brain anatomy. In this paper,we present a strategy that aims to find anatomical regions with pathological meaning by using a probabilistic analysis. Our method starts by extracting visual primitives from brain MRI that are partitioned into small patches and which are then softly clustered,forming different regions not necessarily connected. Each of these regions is described by a co- occurrence histogram of visual features,upon which a probabilistic semantic analysis is used to find the underlying structure of the information,i.e.,separated regions by their low level similarity. The proposed approach was tested with the OASIS data set which includes 69 Alzheimer’s disease (AD) patients and 65 healthy subjects (NC). © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. |
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| Diana L. Giraldo,Juan D. Garcia-Arteaga,Eduardo Romero[1] |
| 10th International Symposium on Medical Information Processing and Analysis, October 14,2014, Pages |
| Abstract： |
Morphometry based methods allow the detection of subtle anatomical differences in the Magnetic Resonance Images (MRI) between healthy subjects and Alzheimer's Disease (AD) patients. However,anatomical volumes are rarely used for clinical diagnosis as the changes induced by AD are hard to differentiate from normal brain aging. We present a morphometry method which uses brain models generated using Nonnegative Matrix Factorization (NMF) characterized by signatures calculated from perceptual features such as intensities,edges or orientations,of salient regions. The Earth Mover's Distance (EMD),a robust measure of the cost of transforming signature A into signature B,is used to calculate volume-models distances. The discerning power of these distances is tested by using them as features for a Support Vector Machine classifier. This work shows the usefulness of the EMD as a metric in medical image applications as it has proven to be robust to bin selection,takes into account cross bin relations,and allows high sensitivity with lower dimensionality. This method is able to find discerning regions which,besides aiding in classification,may provide new insights of the disease's development. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. |
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| Vidya Rajagopalan,Natasha Lepore[1];Armin Schwartzman[2];Xue Hua,Paul Thompson[3];Alex Leow[4] |
| 10th International Symposium on Medical Information Processing and Analysis, October 14,2014, Pages |
| Abstract： |
We develop a new algorithm to compute voxel-wise shape differences in tensor-based morphometry (TBM). As in standard TBM,we non-linearly register brain T1-weighed MRI data from a patient and control group to a template,and compute the Jacobian of the deformation fields. In standard TBM,the determinants of the Jacobian matrix at each voxel are statistically compared between the two groups. More recently,a multivariate extension of the statistical analysis involving the deformation tensors derived from the Jacobian matrices has been shown to improve statistical detection power.7 However,multivariate methods comprising large numbers of variables are computationally intensive and may be subject to noise. In addition,the anatomical interpretation of results is sometimes difficult. Here instead,we analyze the eigenvalues and the eigenvectors of the Jacobian matrices. Our method is validated on brain MRI data from Alzheimer’s patients and healthy elderly controls from the Alzheimer’s Disease Neuro Imaging Database. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. |
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| Germán D. Sosa,Angel Cruz-Roa,Fabio A. González[1] |
| 10th International Symposium on Medical Information Processing and Analysis, October 14,2014, Pages |
| Abstract： |
This work addresses the problem of lung sound classification,in particular,the problem of distinguishing between wheeze and normal sounds. Wheezing sound detection is an important step to associate lung sounds with an abnormal state of the respiratory system,usually associated with tuberculosis or another chronic obstructive pulmonary diseases (COPD). The paper presents an approach for automatic lung sound classification,which uses different state-of-the-art sound features in combination with a C-weighted support vector machine (SVM) classifier that works better for unbalanced data. Feature extraction methods used here are commonly applied in speech recognition and related problems thanks to the fact that they capture the most informative spectral content from the original signals. The evaluated methods were: Fourier transform (FT),wavelet decomposition using Wavelet Packet Transform bank of filters (WPT) and Mel Frequency Cepstral Coefficients (MFCC). For comparison,we evaluated and contrasted the proposed approach against previous works using different combination of features and/or classifiers. The different methods were evaluated on a set of lung sounds including normal and wheezing sounds. A leave-two-out per-case cross-validation approach was used,which,in each fold,chooses as validation set a couple of cases,one including normal sounds and the other including wheezing sounds. Experimental results were reported in terms of traditional classification performance measures: sensitivity,specificity and balanced accuracy. Our best results using the suggested approach,C-weighted SVM and MFCC,achieve a 82.1% of balanced accuracy obtaining the best result for this problem until now. These results suggest that supervised classifiers based on kernel methods are able to learn better models for this challenging classification problem even using the same feature extraction methods. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. |
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| Cristian Castro-Hoyos,Germán Castellanos-Domínguez[1];Diego Hernán Peluffo-Ordóñez[2];Jose Luis Rodríguez-Sotelo[3] |
| 10th International Symposium on Medical Information Processing and Analysis, October 14,2014, Pages |
| Abstract： |
Heartbeat characterization is an important issue in cardiac assistance diagnosis systems. In particular,wide sets of features are commonly used in long term electrocardiographic signals. Then,if such a feature space does not represent properly the arrhythmias to be grouped,classification or clustering process may fail. In this work a suitable feature set for different heartbeat types is studied,involving morphology,representation and time-frequency features. To determine what kind of features generate better clusters,feature selection procedure is used and assessed by means clustering validity measures. Then the feature subset is shown to produce fine clustering that yields into high sensitivity and specificity values for a broad range of heartbeat types. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. |
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| María G. Rodríguez,Carlos A. Ledezma,Gilberto Perpiñán,Sara Wong,Miguel Altuve[1] |
| 10th International Symposium on Medical Information Processing and Analysis, October 14,2014, Pages |
| Abstract： |
Physiological signals are commonly the result of complex interactions between systems and organs,these interactions lead to signals that exhibit a non-stationary behaviour. For cardiac signals,non-stationary heart rate variability (HRV) may produce misinterpretations. A previous work proposed to divide a non-stationary signal into stationary segments by looking for changes in the signal’s properties related to changes in the mean of the signal. In this paper,we extract stationary segments from non-stationary synthetic and cardiac signals. For synthetic signals with different signal-to-noise ratio levels,we detect the beginning and end of the stationary segments and the result is compared to the known values of the occurrence of these events. For cardiac signals,RR interval (cardiac cycle length) time series,obtained from electrocardiographic records during stress tests for two populations (diabetic patients with cardiovascular autonomic neuropathy and control subjects),were divided into stationary segments. Results on synthetic signals reveal that the non-stationary sequence is divided into more stationary segments than needed. Additionally,due to HRV reduction and exercise intolerance reported on diabetic cardiovascular autonomic neuropathy patients,non-stationary RR interval sequences from these subjects can be divided into longer stationary segments compared to the control group. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. |
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| R. E. Gutiérrez-Carvajal,A. Fargeas,O. Acosta,R. de Crevoisier[1];K. Gnep,Y. Rolland[2] |
| 10th International Symposium on Medical Information Processing and Analysis, October 14,2014, Pages |
| Abstract： |
Using multiparametric MRI (mpMRI) protocols to monitor prostate cancer could provide new insights into the biological mechanisms of developing tumours. Automatically discriminating tumour regions active area of research due to the complexity and plurality of cancer behaviour. This work evaluates four different Magnetic Resonance Imaging (MRI) image modalities,namely: Diffusion-Weighted Imaging evaluated at b = {0,100,1000},Apparent Diffusion Coefficient and Dynamic Contrast Enhanced MRI,by extracting texture and functional features and then selecting the optimal ones to discriminate anatomical prostate regions in each modality. The images used were taken prior to radiotherapy from eight patients previously diagnosed with moderate risk of recurrent cancer. Finally,we compared the relevance of each modality to discriminate between healthy tissue and tumour cells. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. |
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