Nonrigid Image Registration Using Conditional Mutual Information

Loeckx, Dirk; Slagmolen, Pieter; Maes, Frederik; Vandermeulen, Dirk; Suetens, Paul

doi:10.1109/tmi.2009.2021843

Cited by 214 publications

(64 citation statements)

References 14 publications

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“…A number of disadvantages of using the traditional global MI approach have been analysed by Loeckx et al (2010), Haber and Modersitzki (2006), and Studholme et al, 2006. These lie mainly in the sensitivity of MI (or NMI) to non-uniform bias fields in MRI.…”

Section: Conditional Mutual Informationmentioning

confidence: 99%

MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration

Heinrich

Jenkinson

Bhushan

et al. 2012

Medical Image Analysis

541

386

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Section: Conditional Mutual Informationmentioning

confidence: 99%

MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration

Heinrich

Jenkinson

Bhushan

et al. 2012

Medical Image Analysis

541

386

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“…Therefore, in imaging applications involving multiple modalities, opportunity exists to improve registration by adopting Multivariate Mutual Information (MMI). MMI has been widely applied in registering medical images [60][61][62], satellite images [63][64][65][66], and images in general [67]. In addition to apparent benefits, MMI has also been used in pattern recognition for feature selection [68] and classification [69].…”

Section: Mutual Information (Mi)mentioning

confidence: 99%

Entropy-Based Fusion of Water Indices and DSM Derivatives for Automatic Water Surfaces Extraction and Flood Monitoring

Horkaew

Puttinaovarat

2017

IJGI

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Abstract:Reliable water surface extraction is essential for river delineation and flood monitoring. Obtaining such information from fine resolution satellite imagery has attracted much interest for geographic and remote sensing applications. However, those images are often expensive and difficult to acquire. This study proposes a more cost-effective technique, employing freely available Landsat images. Despite its extensive spectrum and robust discrimination capability, Landsat data are normally of medium spatial resolution and, as such, fail to delineate smaller hydrological features. Based on Multivariate Mutual Information (MMI), the Landsat images were fused with Digital Surface Model (DSM) on the spatial domain. Each coinciding pixel would then contain not only rich indices but also intricate topographic attributes, derived from its respective sources. The proposed data fusion ensures robust, precise, and observer-invariable extraction of water surfaces and their branching, while eliminating spurious details. Its merit was demonstrated by effective discrimination of flooded regions from natural rivers for flood monitoring. The assessments we completed suggest improved extraction compared to traditional methods. Compared with manual digitizing, this method also exhibited promising consistency. Extraction using Dempster-Shafer fusion provided a 91.81% F-measure, 93.09% precision, 90.74% recall, and 98.25% accuracy, while using Majority Voting fusion resulted in an 84.91% F-measure, 75.44% precision, 97.37% recall, and 97.21% accuracy.

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“…Regional MI (RMI) has been proposed in (Studholme et al, 2006), while Conditional MI (CMI) has been proposed in (Loeckx et al, 2010). These methods depend on summing local MI for regions of the images, rather than finding the global MI.…”

Section: Information-theoretic Measuresmentioning

confidence: 99%

An Information-Theoretic Image Quality Measure: Comparison With Statistical Similarity

Hassan¹,

Dong²,

Hussain³

2014

Journal of Computer Science

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We present an information-theoretic approach for structural similarity for assessing gray scale image quality. The structural similarity measure SSIM, proposed in 2004, has been successflly used and verfied. SSIM is based on statistical similarity between the two images. However, SSIM can produce confusing results in some cases where it may give a non-trivial amount of similarity for two different images. Also, SSIM cannot perform well (in detecting similarity or dissimilarity) at low peak signal to noise ratio (PSNR). In this study, we present a novel image similarity measure, HSSIM, by using information -theoretic technique based on joint histogram. The proposed method has been tested under Gaussian noise. Simulation results show that the proposed measure HSSIM outperforms statistical similarity SSIM by ability to detect similarity under very low PSNR. The average difference is about 20dB.

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Nonrigid Image Registration Using Conditional Mutual Information

Cited by 214 publications

References 14 publications

MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration

MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration

Entropy-Based Fusion of Water Indices and DSM Derivatives for Automatic Water Surfaces Extraction and Flood Monitoring

An Information-Theoretic Image Quality Measure: Comparison With Statistical Similarity

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