Multispectral Texture Analysis Using Interplane Sum- and Difference-Histograms

Münzenmayer, Christian; Volk, Heiko; Küblbeck, Christian; Spinnler, Klaus; Wittenberg, Thomas

doi:10.1007/3-540-45783-6_6

Cited by 12 publications

(10 citation statements)

References 9 publications

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“…The size of windows was also reduced a little -to 36 9 36 pixels. The second method, recently to be found effective for polyps [7] are the sum-and difference histogram (SDH) features [8] in an inter-plane color version [9]. For reference purposes we also use color histograms as a simple descriptor.…”

Section: Methodsmentioning

confidence: 99%

Poster Session: 11th International Workshop on Computer-Aided Diagnosis

Bagagli¹,

Camarlinghi²,

Fantacci³

et al. 2009

Int J CARS

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Universidad Autónoma de Madrid, SpainKeywords Mammogram Registration Á Temporal study Á Consistent registration Á Diffeomorphism Á Hormonal replacement therapy Purpose Mammographic density is a strong risk factor for breast cancer. However, whether changes in mammographic density due to HRT are associated with risk remains unclear. The aim of this study is to provide a framework for accurate interval change analysis in temporal pairs of mammograms of patients undergoing HRT treatment using consistent and fully automatic registration framework. MethodsThe combination of B-spline based registration and consistent image registration is proposed that simplifies the search for optimal deformation and does not require specification of landmarks. KullbackLieber distance is used as a similarity metric which makes this method more robust against different image resolutions and other intensity based artifacts during temporal scan of mammograms. ResultsRegistration was performed on the pairs of mammograms from longitudinal double blind placebo controlled HRT study that includes 39 placebo and 36 HRT treated volunteers for two years. The method's performance was evaluated by an experienced radiologist. Validation study showed that method has given an optimal consistency error of 36 pixels (mm) with average computation time 45 s (Figs. 1, 2). ConclusionsThe work describes a novel approach for consistent and robust registration framework for interval change analysis in pairs of mammograms. This would lead radiologist to asses the risk of individual patient by examining the changes in tissue structure and density due to hormonal replacement therapy.Reducing false positive marks in breast mass computer-aided detection via bilateral ranklet texture analysis Keywords Mammography Á Computer-aided detection Á Ranklets Á Support vector machine Á Bilateral analysis Purpose In order to reduce subtle false positive marks, our current breast mass computer-aided detection (CAD) system makes use of a ranklet texture false positive reduction (RT-FPR, hereafter) module, where regions of interest (ROIs) are encoded by means of a set of ranklet texture features and classified by means of a support vector machine (SVM) classifier.In an attempt to reduce even further the number of false positive marks, we herein propose an alternative, bilateral RT-FPR module (B-RT-FPR, hereafter): ROIs found in either one, left or right projection of the breast are encoded and fed into the SVM classifier as vectors containing their corresponding ranklet texture features, as well as those corresponding to the regions found symmetrically on the same projection of the contralateral mammogram.In this work, we present the B-RT-FPR module, then compare the CAD system's overall performance when combined with either the RT-FPR and B-RT-FPR modules. Methods In our CAD system, ROIs surviving the detection and first-level false positive reduction (FPR) modules appear quite subtle, so the associated classification task results particularly tough. Currently, such mass candi...

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Section: Methodsmentioning

confidence: 99%

Poster Session: 11th International Workshop on Computer-Aided Diagnosis

Bagagli¹,

Camarlinghi²,

Fantacci³

et al. 2009

Int J CARS

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“…Recently, a new benchmark colour texture image test suite, NewbarkTex from BarkTex dataset [28], [29], [30], [31] is proposed. Six tree bark classes with 68 images per class (128 × 128) are divided into 4 sub-images (size 64 × 64).…”

Section: A Datasetsmentioning

confidence: 99%

Texture Classification Using 2D LSTM Networks

Byeon

Liwicki

Breuel

2014

2014 22nd International Conference on Pattern Recognition

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In this paper, we investigate the ability of the Long short term memory (LSTM) recurrent neural network architecture to perform texture classification on images. Existing approaches to texture classification rely on manually designed preprocessing steps or selected feature extractors. Since LSTM networks are able to bridge over long time lags, we propose applying them directly on the image, circumventing any handcrafted prepocessing. We investigate different approaches with several input and output representations. In our experiments on a number of widely used texture benchmarking tasks (KTH-TIPS, OuTex, VisTexL, VisTexP, and NewbarkTex), we show that the performance is comparable to, or better than, existing state-ofthe-art methods for texture classification.Texture is a rich source of information about the contents of images and identity of objects. However, reliable texture recognition has been challenging because texture is a property of image pixels that is both stochastic and non-local. Most approaches to texture recognition manually design feature extractors to cope with the non-locality, choosing specific ways of integrating information about a region that is robust to changes in phase. Examples of such an approach are Haralick's texture features [1]. Numerous approaches using texture feature extractors had been investigated in 70-80's including Haralick [2], Gabor filters [3], wavelets [4] and grey level co-occurrence matrices (GLCM) [2], [1]. The main drawbacks of these approaches are the need to select the proper size of filter bank or neighborhoods and that they are computationally expensive. These methods were also applicable only on grayscale images.More recently, Drimbarean and Whelan [5], Mäenpää and Pietikäinen [6] and Iakovidis et al. [7] have incorporated color data into texture descriptors. Their works have been focused on the combination of color and texture either jointly or separately. Different texture descriptors under various color space were compared and different ways of combination were comparatively evaluated. For instance, Discrete Cosine Transform, Gabor filters, and co-occurrence matrices under separate or combined color channel. Their experiments have shown that joint color texture descriptors improve the performance. However, it is unclear what is the best way and method to describe a wide range of textures. It has so far been lacking a general and comprehensive framework to classify textures. It needs either static condition of texture or to be manually designed to obtain an optimal solution.Only a few methods, which unify the system between the feature extraction and classification step (i.e. machine learning based methods), were proposed to overcome this problem mentioned above. First, in [8], multichannel filtering scheme is combined with the neural network. More recently, Convolutional neural network [9] and Random neural network [10] were used for texture classification. Among all neural networks based approaches, Convolutional neural network has been successfully applied ...

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“…Nevertheless, texture analysis remains a difficult problem when applied to color [6], multi or hyperspectral images; where image pixel takes its values in a multidimensional space. For this purpose, a great deal of work has been done for modeling the multispectral and hyperspectral texture analysis [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Multispectral texture characterization: application to computer aided diagnosis on prostatic tissue images

Khelifi

Adel

Bourennane

2012

EURASIP J. Adv. Signal Process.

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Various approaches have been proposed in the literature for texture characterization of images. Some of them are based on statistical properties, others on fractal measures and some more on multi-resolution analysis. Basically, these approaches have been applied on mono-band images. However, most of them have been extended by including the additional information between spectral bands to deal with multi-band texture images. In this article, we investigate the problem of texture characterization for multi-band images. Therefore, we aim to add spectral information to classical texture analysis methods that only treat gray-level spatial variations. To achieve this goal, we propose a spatial and spectral gray level dependence method (SSGLDM) in order to extend the concept of gray level co-occurrence matrix (GLCM) by assuming the presence of texture joint information between spectral bands. Thus, we propose new multi-dimensional functions for estimating the second-order joint conditional probability density of spectral vectors. Theses functions can be represented in structure form which can help us to compute the occurrences while keeping the corresponding components of spectral vectors. In addition, new texture features measurements related to (SSGLDM) which define the multi-spectral image properties are proposed. Extensive experiments have been carried out on 624 textured multi-spectral images for use in prostate cancer diagnosis and quantitative results showed the efficiency of this method compared to the GLCM. The results indicate a significant improvement in terms of global accuracy rate. Thus, the proposed approach can provide clinically useful information for discriminating pathological tissue from healthy tissue.

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Multispectral Texture Analysis Using Interplane Sum- and Difference-Histograms

Cited by 12 publications

References 9 publications

Poster Session: 11th International Workshop on Computer-Aided Diagnosis

Poster Session: 11th International Workshop on Computer-Aided Diagnosis

Texture Classification Using 2D LSTM Networks

Multispectral texture characterization: application to computer aided diagnosis on prostatic tissue images

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