Mutual Information Metric Evaluation for PET/MRI Image Fusion

Gupta, Smriti; Ramesh, K.P.; Blasch, Erik

doi:10.1109/naecon.2008.4806563

Cited by 28 publications

(10 citation statements)

References 16 publications

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“…This discussion is important because MIS and NMI have been commonly recommended for inter-modality neuroimaging registrations [41][42][43]. In comparative studies [44,45], as many as 16 retrospective registration methods from 12 research groups worldwide were evaluated with respect to the MIS and NMI performance in rigid multimodal brain images registration.…”

Section: Discussionmentioning

confidence: 99%

The Superiority of Tsallis Entropy over Traditional Cost Functions for Brain MRI and SPECT Registration

Amaral-Silva

Wichert‐Ana

Murta

et al. 2014

Entropy

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Abstract:Neuroimage registration has an important role in clinical (for both diagnostic and therapeutic purposes) and research applications. In this article we describe the applicability of Tsallis Entropy as a new cost function for neuroimage registration through a comparative analysis based on the performance of the traditional approaches (correlation based: Entropy Correlation Coefficient (ECC) and Normalized Cross Correlation (NCC); and Mutual Information (MI) based: Mutual Information using Shannon Entropy (MIS) and Normalized Mutual Information (NMI)) and the proposed one based on MI using Tsallis entropy (MIT). We created phantoms with known geometric transformations using Single Photon Emission Computed Tomography (SPECT) and Magnetic Resonance 1633Imaging from 3 morphologically normal subjects. The simulated volumes were registered to the original ones using both the proposed and traditional approaches. The comparative analysis of the Relative Error (RE) showed that MIT was more accurate in the intramodality registration, whereas for inter-modality registration, MIT presented the lowest RE for rotational transformations, and the ECC the lowest RE for translational transformations. In conclusion, we have shown that, with certain limitations, Tsallis Entropy has application as a better cost function for reliable neuroimage registration.

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Section: Discussionmentioning

confidence: 99%

The Superiority of Tsallis Entropy over Traditional Cost Functions for Brain MRI and SPECT Registration

Amaral-Silva

Wichert‐Ana

Murta

et al. 2014

Entropy

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show abstract

“…In astronomy and in remote sensing, multisensory fusion is used to achieve high spatial resolutions by combining images from two sensors, one of which has high spatial resolution and the other one high spectral resolution. Also in medical imaging like simultaneous evaluation of CT, MRI and PET images [4]. Use of mutlisensor fusion of visible and infrared images have appeared in military, security and surveillance areas.…”

Section: Introductionmentioning

confidence: 99%

Image Fusion using Biorthogonal Wavelet Transform

Kaur¹,

Dadhwal²

2014

IOSRJECE

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“…The technique has many applications in a variety of domains including geospatial information systems (GIS), wireless sensor networks, bioinformatics, and others. There have been many proposed metrics by which one can evaluate the quality of a fused signal [19], especially fused images [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]. However, it is not well known which metric provides the best performance evaluation for a given fusion algorithm since each algorithm has specific applications, advantages, and shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Seismic data fusion anomaly detection

et al. 2014

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Detecting anomalies in non-stationary signals has valuable applications in many fields including medicine and meteorology. These include uses such as identifying possible heart conditions from an Electrocardiography (ECG) signals or predicting earthquakes via seismographic data. Over the many choices of anomaly detection algorithms, it is important to compare possible methods. In this paper, we examine and compare two approaches to anomaly detection and see how data fusion methods may improve performance. The first approach involves using an artificial neural network (ANN) to detect anomalies in a wavelet de-noised signal. The other method uses a perspective neural network (PNN) to analyze an arbitrary number of "perspectives" or transformations of the observed signal for anomalies. Possible perspectives may include wavelet de-noising, Fourier transform, peak-filtering, etc.. In order to evaluate these techniques via signal fusion metrics, we must apply signal preprocessing techniques such as de-noising methods to the original signal and then use a neural network to find anomalies in the generated signal. From this secondary result it is possible to use data fusion techniques that can be evaluated via existing data fusion metrics for single and multiple perspectives. The result will show which anomaly detection method, according to the metrics, is better suited overall for anomaly detection applications. The method used in this study could be applied to compare other signal processing algorithms.

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Mutual Information Metric Evaluation for PET/MRI Image Fusion

Cited by 28 publications

References 16 publications

The Superiority of Tsallis Entropy over Traditional Cost Functions for Brain MRI and SPECT Registration

The Superiority of Tsallis Entropy over Traditional Cost Functions for Brain MRI and SPECT Registration

Image Fusion using Biorthogonal Wavelet Transform

Seismic data fusion anomaly detection

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