Optimized steerable wavelets for texture analysis of lung tissue in 3-D CT: Classification of usual interstitial pneumonia

Depeursinge, Adrien; Pad, Pedram; Chin, Anne S.; Leung, Ann N.; Rubin, Daniel L.; Müller, Henning; Unser, Michaël

doi:10.1109/isbi.2015.7163897

Cited by 19 publications

(10 citation statements)

References 17 publications

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“…Our preliminary investigations have revealed the practical benefits of such an optimization [22]- [24]. In this work, we extend the framework and present a unified treatment that relies on more sophisticated tools and improves on our previous findings.…”

mentioning

confidence: 62%

Maximally Localized Radial Profiles for Tight Steerable Wavelet Frames

Pad

Uhlmann

Unser

2016

IEEE Trans. on Image Process.

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Abstract-A crucial component of steerable wavelets is the radial profile of the generating function in the frequency domain. In this paper, we present an infinite-dimensional optimization scheme that helps us find the optimal profile for a given criterion over the space of tight frames. We consider two classes of criteria that measure the localization of the wavelet. The first class specifies the spatial localization of the wavelet profile, and the second that of the resulting wavelet coefficients. From these metrics and the proposed algorithm, we construct tight wavelet frames that are optimally localized and provide their analytical expression. In particular, one of the considered criterion helps us finding back the popular Simoncelli wavelet profile. Finally, the investigation of local orientation estimation, image reconstruction from detected contours in the wavelet domain, and denoising indicate that optimizing wavelet localization improves the performance of steerable wavelets, since our new wavelets outperform the traditional ones.

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confidence: 62%

Maximally Localized Radial Profiles for Tight Steerable Wavelet Frames

Pad

Uhlmann

Unser

2016

IEEE Trans. on Image Process.

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“…To maintain the property of translation-invariance, the undecimated wavelet transform was used. The wavelet framework used is based on isotropicwavelets with optimal bandwidth [33], [61]. The dimension M of the Riesz transform of each scale for the orders 1 to 4 are 3, 6, 10 and 15 respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Wavelets constitute an attractive solution to fully exploit texture information at multiple scales through multi-resolution image representations, where their Fourier transform is systematically split into a series of dyadic bands [27]. To that end, wavelets were successfully used for texture analysis in 2D [11], [14], [28]- [30] and 3D [31]- [33]. Fully leveraging the directional information of textures is a challenging problem, which is even more difficult in 3D.…”

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confidence: 99%

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3D Solid Texture Classification Using Locally-Oriented Wavelet Transforms

Cid

Müller

Platon

et al. 2017

IEEE Trans. on Image Process.

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Abstract-Many image acquisition techniques used in biomedical imaging, material analysis, and structural geology are capable of acquiring 3D solid images. Computational analysis of these images is complex but necessary, since it is difficult for humans to visualize and quantify their detailed 3D content. One of the most common methods to analyze 3D data is to characterize the volumetric texture patterns. Texture analysis generally consists of encoding the local organization of image scales and directions, which can be extremely diverse in 3D. Current state-of-the-art techniques face many challenges when working with 3D solid texture, where most approaches are not able to consistently characterize both scale and directional information. 3D Riesz-wavelets can deal with both properties. One key property of Riesz filterbanks is steerability, which can be used to locally align the filters and compare textures with arbitrary (local) orientations. This paper proposes and compares three novel local alignment criteria for higher-order 3D Rieszwavelet transforms. The estimations of local texture orientations are based on higher-order extensions of regularized structure tensors. An experimental evaluation of the proposed methods for the classification of synthetic 3D solid textures with alterations (such as rotations and noise) demonstrated the importance of local directional information for robust and accurate solid texture recognition. These alignment methods achieved an accuracy of 0.95 in the rotated data, three times more than the unaligned Riesz descriptor that achieved 0.32. The accuracy obtained is better than all other techniques that are published and tested on the same database.Index Terms-3D solid texture analysis, 3D texture classification, Riesz-wavelets steerability, aligned higher-order Riesz-wavelet transform, rotation-covariance.

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“…This causes the stiffness in lung tissues, which lead to reduce the capacity of the lungs to carry oxygen to the blood [2]. The major cause of ILD are genetic abnormalities, autoimmune disease and exposed to hazard environments and ILD cannot be diagnosed by the process of radiology [3]. The different ILD patter contains the similar clinical manifestation with each other, which are heterogeneous diseases.…”

Section: Introductionmentioning

confidence: 99%

Pattern classification of interstitial lung disease in high resolution clinical datasets: A systematic review

Atiya¹,

Ramesh²

2018

IJET

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Automated tissues characterization helps to diagnosis the various diseases including Interstitial lung diseases (ILD). The various features and the several classifiers are used in categorize the different layers depend on the pattern presented in the image. The different types of diseases may occur in the lungs and some of the diseases happen to leave the scars. These scars can be found in the High Resolution Computed Tomography (HRCT) and have different pattern. The different diseases cause the different pattern in the images and these is classified using the efficient classifier that helps to diagnosis the diseases. In this paper, review for the many researches regarding to the classification of the different pattern from the Computed Tomography (CT) images is presented. The evaluation of the efficiency of the methods in terms of classifier and database used for the research is made. The Deep Convolution Neural Network (CNN) provides the promising classifier efficiency compared to the other researches for different pattern. In general, there are five types of pattern is classified: Healthy, ground glass, honeycomb, Fibrosis, and emphysema.

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Optimized steerable wavelets for texture analysis of lung tissue in 3-D CT: Classification of usual interstitial pneumonia

Cited by 19 publications

References 17 publications

Maximally Localized Radial Profiles for Tight Steerable Wavelet Frames

Maximally Localized Radial Profiles for Tight Steerable Wavelet Frames

3D Solid Texture Classification Using Locally-Oriented Wavelet Transforms

Pattern classification of interstitial lung disease in high resolution clinical datasets: A systematic review

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