Implementation of 2D Discrete Wavelet Transform by Number Theoretic Transform and 2D Overlap‐Save Method

Yang, Lina; Tang, Yuan Yan; Sun, Qing

doi:10.1155/2014/532979

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Since a thorough description of the mathematical background is demanding and beyond the scope of this work, we briefly, present the one dimensional continuous wavelet transform below. Further in depth information regarding the two dimensional discrete wavelet transform applied to images can be found in literarure 22 , 23 .…”

Section: Methodsmentioning

confidence: 99%

A neural pathomics framework for classifying colorectal cancer histopathology images based on wavelet multi-scale texture analysis

Trivizakis

Ioannidis

Souglakos

et al. 2021

Sci Rep

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Colorectal cancer (CRC) constitutes the third most commonly diagnosed cancer in males and the second in females. Precise histopathological classification of CRC tissue pathology is the cornerstone not only for diagnosis but also for patients’ management decision making. An automated system able to accurately classify different CRC tissue regions may increase diagnostic precision and alleviate clinical workload. However, tissue classification is a challenging task due to the variability in morphological and textural characteristics present in histopathology images. In this study, an artificial neural network was trained to classify between eight classes of CRC tissue image patches derived from a public dataset with 5000 CRC histopathology image tiles. A total of 532 multi-level pathomics features examined at different scales were extracted by visual descriptors such as local binary patterns, wavelet transforms and Gabor filters. An exhaustive evaluation involving a variety of wavelet families and parameters was performed in order to shed light on the impact of scale on pathomics based CRC tissue differentiation. Our model achieved a performance accuracy of 95.3% with tenfold cross validation demonstrating superior performance compared to 87.4% reported in recent studies. Furthermore, we experimentally showed that the first and the second levels of the wavelet approximations can be used without compromising classification performance.

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Section: Methodsmentioning

confidence: 99%

A neural pathomics framework for classifying colorectal cancer histopathology images based on wavelet multi-scale texture analysis

Trivizakis

Ioannidis

Souglakos

et al. 2021

Sci Rep

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show abstract

“…Therefore, Wavelet transform is appropriate for signal processing and digital image processing (Feng et al, 2011). The signal is formed in a sequence of discrete signals that is called 2D discrete wavelet transform (DWT-2D) (Yang et al, 2014).…”

Section: Waveletmentioning

confidence: 99%

Combination of Morphology, Wavelet and Convex Hull Features in Classification of Patchouli Varieties with Imbalance Data using Artificial Neural Network

Mahmudy

Dewi

Arifando

et al. 2021

JART

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Patchouli plants are main raw materials for essential oils in Indonesia. Patchouli leaves have a very varied physical form based on the area planted, making it difficult to recognize the variety. This condition makes it difficult for farmers to recognize these varieties and they need experts’ advice. As there are few experts in this field, a technology for identifying the types of patchouli varieties is required. In this study, the identification model is constructed using a combination of leaf morphological features, texture features extracted with Wavelet and shape features extracted with convex hull. The results of feature extraction are used as input data for training of classification algorithms. The effectiveness of the input features is tested using three classification methods in class artificial neural network algorithms: (1) feedforward neural networks with backpropagation algorithm for training, (2) learning vector quantization (LVQ), (3) extreme learning machine (ELM). Synthetic minority over-sampling technique (SMOTE) is applied to solve the problem of class imbalance in the patchouli variety dataset. The results of the patchouli variety identification system by combining these three features indicate the level of recognition with an average accuracy of 72.61%, accuracy with the combination of these three features is higher when compared to using only morphological features (58.68%) or using only Wavelet features (59.03 %) or both (67.25%). In this study also showed that the use of SMOTE in imbalance data increases the accuracy with the highest average accuracy of 88.56%.

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On the Hölder Evaluation of the Prostate Adenocarcinoma Cellular Architecture

Popescu

Ivănescu

Nitulescu

et al. 2020

2020 24th International Conference on System Theory, Control and Computing (ICSTCC)

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Implementation of 2D Discrete Wavelet Transform by Number Theoretic Transform and 2D Overlap‐Save Method

Cited by 4 publications

References 15 publications

A neural pathomics framework for classifying colorectal cancer histopathology images based on wavelet multi-scale texture analysis

A neural pathomics framework for classifying colorectal cancer histopathology images based on wavelet multi-scale texture analysis

Combination of Morphology, Wavelet and Convex Hull Features in Classification of Patchouli Varieties with Imbalance Data using Artificial Neural Network

On the Hölder Evaluation of the Prostate Adenocarcinoma Cellular Architecture

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