Three-Dimensional Wavelet Transform in Multi-Dimensional Biomedical Volume Processing

Procházka, Aleš; Gráfová, Lucie; Vyšata, Oldřich

doi:10.2316/p.2011.741-010

Cited by 19 publications

(17 citation statements)

References 25 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where 1 ffiffi ffi A p is normalizing factor, P is the decomposition level, ψ p, a (x) are detailed or wavelet coefficients and τ P, a (x) are averaging or scaling coefficients are discrete functions in and where a ¼ f 0; 1; 2; …; A 2 p −1g [44]. The scaling and detailed coefficients are computed as:…”

Section: D-discrete Wavelet Transformmentioning

confidence: 99%

See 1 more Smart Citation

A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment

Bhasin

Agrawal

2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background: The detection of Alzheimer's Disease (AD) in its formative stages, especially in Mild Cognitive Impairments (MCI), has the potential of helping the clinicians in understanding the condition. The literature review shows that the classification of MCI-converts and MCI-non-converts has not been explored profusely and the maximum classification accuracy reported is rather low. Thus, this paper proposes a Machine Learning approach for classifying patients of MCI into two groups one who converted to AD and the others who are not diagnosed with any signs of AD. The proposed algorithm is also used to distinguish MCI patients from controls (CN). This work uses the Structural Magnetic Resonance Imaging data.Methods: This work proposes a 3-D variant of Local Binary Pattern (LBP), called LBP-20 for extracting features. The method has been compared with 3D-Discrete Wavelet Transform (3D-DWT). Subsequently, a combination of 3D-DWT and LBP-20 has been used for extracting features. The relevant features are selected using the Fisher Discriminant Ratio (FDR) and finally the classification has been carried out using the Support Vector Machine. Results: The combination of 3D-DWT with LBP-20 results in a maximum accuracy of 88.77. Similarly, the proposed combination of methods is also applied to distinguish MCI from CN. The proposed method results in the classification accuracy of 90.31 in this data. Conclusion: The proposed combination is able to extract relevant distribution of microstructures from each component, obtained with the use of DWT and thereby improving the classification accuracy. Moreover, the number of features used for classification is significantly less as compared to those obtained by 3D-DWT. The performance of the proposed method is measured in terms of accuracy, specificity and sensitivity and is found superior in comparison to the existing methods. Thus, the proposed method may contribute to effective diagnosis of MCI and may prove advantageous in clinical settings.

show abstract

Section: D-discrete Wavelet Transformmentioning

confidence: 99%

“…1D DWT can be extended to 3D DWT for 3D brain volumes. In 3D DWT [44], we have one 3D approximate coefficient (scaling function) τ(a, b, c) and seven 3D detailed coefficients ψ i (l, m, n), where i ∈ {1, 2, …, 7}. The function τ(a, b, c) in 3-D, is the product of τ(a), τ(b) and τ(c).…”

Section: D-discrete Wavelet Transformmentioning

confidence: 99%

A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment

Bhasin

Agrawal

2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…For instance, X LHL is obtained after applying a low-pass filter along the x-dimension, a high-pass filter along the y-dimension and a low-pass filter along the z-dimension. The remaining images are built in a similar way, applying their respective sequence of low or high-pass filters in x, y and z-direction 41 . Concerning the LoG, five filters with different sigma values were applied (sigma=1.0 mm, 2.0 mm, 3.0 mm, 4.0 mm, 5.0 mm), with the intention of improving texture analysis by detection of multi-scale edges and ridges 42 .…”

Section: /10mentioning

confidence: 99%

Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS

Pinheiro

Pereira

Dias

et al. 2019

Preprint

View full text Add to dashboard Cite

EGFR and KRAS are the most frequently mutated genes in lung cancer, being active research topics in targeted therapy. Biopsy is the traditional method to genetically characterise a tumour. However, it is a risky procedure, painful for the patient, and, occasionally, the tumour might be inaccessible. This work aims to study and debate the nature of the relationships between imaging phenotypes and lung cancer-related mutation status. Until now, the literature has failed to point to new research directions, mainly consisting of results-oriented works in a field where there is still not enough available data to train clinically viable models. We intend to open a discussion about critical points and to present new possibilities for future radiogenomics studies. We conducted high-dimensional data visualisation and developed classifiers, which allowed us to analyse the results for EGFR and KRAS biological markers according to different combinations of input features. We show that EGFR mutation status might be correlated to CT scans imaging phenotypes, however, the same does not seem to hold true for KRAS mutation status. Also, the experiments suggest that the best way to approach this problem is by combining nodule-related features with features from other lung structures. 2/103/10 7/10 is balanced individually for each fold using SMOTE-NC, avoiding data leakage. After parameter optimisation, probabilistic outputs of each model with optimal parameters were analysed using the AUC of Receiver Operating Characteristic (ROC). ROC is a probability curve and AUC represents degree or measure of separability, telling how much model is capable of distinguishing between classes. The ROC curve is plotted with True Positive Rate (TPR) against the False Positive Rate (FPR), usually with TPR on the y-axis and FPR on the x-axis. Experimental DesignWe designed four experiments in order to test and compare which type of input features allow to achieve better performance in gene mutation status prediction. We first trained a model that took nodule-related radiomic features as input. Then, for direct comparison purposes and to allow a modular evaluation, we split the semantic data into three parts: nodule, non-nodule and hybrid. The first one contains only nodular information, the second one contains only information external to the nodule and the third one is the combination of both. The split can be seen in detail in Table 2 of the supplementary material. 10/10

show abstract

“…In the same way, the approximation and detail subbands are further convolved in y dimension and z dimension, respectively, with both the lowpass and high-pass filters. As a result, eight subbands: LLL, LLH, LHL, HLL, LHH, HLH, HHL and HHH [21] are obtained, where L indicates low-pass-filtered subband and H indicates high-pass-filtered subband. Level 2 decomposition is achieved by considering the LLL subband as the main image and decomposing with the same process as above.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Performing scaling and shifting on initial wavelet and convolving it with the original image is a part of wavelet decomposition. It has the property to reconstruct the original image without loss of information [21]. Wavelet-based texture segmentation is compared with simple single resolution texture spectrum, co-occurrences and local linear transforms on Brodatz dataset, where wavelet-based texture segmentation performed better than other approaches [22].…”

Section: Feature Extractionmentioning

confidence: 99%

Brain tumor classification from multi-modality MRI using wavelets and machine learning

Khalid

Rajpoot

2017

Pattern Anal Applic

204

View full text Add to dashboard Cite

In this paper, we propose a brain tumor segmentation and classification method for multi-modality magnetic resonance imaging scans. The data from multimodal brain tumor segmentation challenge (MICCAI BraTS 2013) are utilized which are co-registered and skullstripped, and the histogram matching is performed with a reference volume of high contrast. From the preprocessed images, the following features are then extracted: intensity, intensity differences, local neighborhood and wavelet texture. The integrated features are subsequently provided to the random forest classifier to predict five classes: background, necrosis, edema, enhancing tumor and non-enhancing tumor, and then these class labels are used to hierarchically compute three different regions (complete tumor, active tumor and enhancing tumor). We performed a leave-one-out cross-validation and achieved 88% Dice overlap for the complete tumor region, 75% for the core tumor region and 95% for enhancing tumor region, which is higher than the Dice overlap reported from MICCAI BraTS challenge.

show abstract

Three-Dimensional Wavelet Transform in Multi-Dimensional Biomedical Volume Processing

Cited by 19 publications

References 25 publications

A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment

A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment

Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS

Brain tumor classification from multi-modality MRI using wavelets and machine learning

Contact Info

Product

Resources

About