Robust Volumetric Texture Classification of Magnetic Resonance Images of the Brain Using Local Frequency Descriptor

Maani, Rouzbeh; Kalra, Sanjay; Yang, Yee‐Hong

doi:10.1109/tip.2014.2351620

Cited by 22 publications

(13 citation statements)

References 46 publications

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“…Applied to MR images, the methods have been successfully used to study several neurological diseases including brain tumor [2–3], epilepsy [4–6], Alzheimer’s disease [7–8], and multiple sclerosis [9–11]. Robustness to MRI acquisition parameters [12] and noise [13–15] makes texture analysis a reliable and attractive tool for investigation of neuropsychiatric conditions. However, current texture analysis methods are limited to region of interest (ROI) based analysis and require a priori hypotheses directing the analysis to specific brain regions.…”

Section: Introductionmentioning

confidence: 99%

Voxel-Based Texture Analysis of the Brain

2015

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This paper presents a novel voxel-based method for texture analysis of brain images. Texture analysis is a powerful quantitative approach for analyzing voxel intensities and their interrelationships, but has been thus far limited to analyzing regions of interest. The proposed method provides a 3D statistical map comparing texture features on a voxel-by-voxel basis. The validity of the method was examined on artificially generated effects as well as on real MRI data in Alzheimer's Disease (AD). The artificially generated effects included hyperintense and hypointense signals added to T1-weighted brain MRIs from 30 healthy subjects. The AD dataset included 30 patients with AD and 30 age/sex matched healthy control subjects. The proposed method detected artificial effects with high accuracy and revealed statistically significant differences between the AD and control groups. This paper extends the usage of texture analysis beyond the current region of interest analysis to voxel-by-voxel 3D statistical mapping and provides a hypothesis-free analysis tool to study cerebral pathology in neurological diseases.

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Section: Introductionmentioning

confidence: 99%

Voxel-Based Texture Analysis of the Brain

2015

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“…Suzuki et al used the database to evaluate a texture retrieval technique developed previously [52], [53], as well as a novel key-point detector [54]. For the classification task, Maani et al used this database to compare existing 3DST descriptors in [55]. These were: 3D GLCM [25], 3D LBP [56], second orientation pyramid (SOP) filtering [57], [58] and a novel approach based on the local frequency descriptor (LFD) [59].…”

Section: D Solid Texture (3dst)mentioning

confidence: 99%

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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“…Even without specific knowledge to pathology, Pseudo-Fischer linear Discriminant Classifier (PFLDC) shows improved performance (Stoeckel et al, 2004). Maani et al (2014) proposed volumetric texture classification by computing local gradients in a 2D plane and 3D volume. Developing 2D and 3D kernels improve the speed of local gradient computation and also to generate the textural features.…”

Section: Classificationmentioning

confidence: 99%

Efficient Morphometric Techniques in Alzheimer’s Disease Detection: Survey and Tools

Vinutha

Shenoy

Venugopal

2016

Neuroscience International

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The development of advance techniques in the multiple fields such as image processing, data mining and machine learning are required for the early detection of Alzheimer's Disease (AD) and to prevent the progression of the disease to the later stages. The longitudinal and cross sectional images of elderly subjects were obtained from the standard datasets like ADNI, OASIS, MIRIAD and ICBM. The subject image obtained from the dataset, can be geometrically aligned to the template image through the process of registration. The registration techniques like Mutual Information Registration, Fluid registration, Rigid registration, Spatial Transformation algorithm for registration, Elastic Registration are selected based on type of transformation and similarity measures to suit the required application. The registered images are then subjected to the process of segmentation in order to segment relevant tissues or desired region of interest that are significant in AD detection. The different types of segmentation techniques such as Tissue Segmentation, Atlas based Segmentation, Hippocampus Segmentation and other segmentation techniques have been discussed. The segmented images are then subjected to morphometry techniques to identify the morphological changes developed in an abnormal image. The different types of morphometry techniques used are Voxel Based Morphometry (VBM), Deformation Based Morphometry (DBM), Shape Based Morphometry (SBM) and Feature Based Morphometry (FBM). But in recent years, the main focus of researchers is towards the FBM and SBM to overcome the disadvantage of group analysis that existed in VBM and DBM. Further the data is classified into healthy normal and AD by supervised, unsupervised or probabilistic methods.

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Robust Volumetric Texture Classification of Magnetic Resonance Images of the Brain Using Local Frequency Descriptor

Cited by 22 publications

References 46 publications

Voxel-Based Texture Analysis of the Brain

Voxel-Based Texture Analysis of the Brain

3D Solid Texture Classification Using Locally-Oriented Wavelet Transforms

Efficient Morphometric Techniques in Alzheimer’s Disease Detection: Survey and Tools

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Robust Volumetric Texture Classification of Magnetic Resonance Images of the Brain Using Local Frequency Descriptor

Cited by 22 publications

References 46 publications

Voxel-Based Texture Analysis of the Brain

Voxel-Based Texture Analysis of the Brain

3D Solid Texture Classification Using Locally-Oriented Wavelet Transforms

Efficient Morphometric Techniques in Alzheimer&rsquo;s Disease Detection: Survey and Tools

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Efficient Morphometric Techniques in Alzheimer’s Disease Detection: Survey and Tools