Analysis of dual tree M‐band wavelet transform based features for brain image classification

Ayalapogu, Ratna Raju; Pabboju, Suresh; Ramisetty, Rajeswara Rao

doi:10.1002/mrm.27210

Cited by 18 publications

(17 citation statements)

References 24 publications

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“…All approaches fall into two categories: supervised and unsupervised. The former one uses Artificial Neural Network (ANN) [1][2][3], Support Vector Machine (SVM) [4][5], Naive Bayes (NB), and k-Nearest Neighbour (k-NN) [1,6] and the later one uses Fuzzy C-Means (FCM) [7] and self-organizing map [8]. While comparing the performance of these two types of approaches, supervised classification is superior to unsupervised approaches in terms of classification accuracy, as the unsupervised approaches require experts with strong knowledge to select the meaningful features and also prone to error for the classification of large scale data.…”

Section: Introductionmentioning

confidence: 99%

“…The feature extraction techniques utilize spatial [6] and frequency domain [1][2][3][4][5] analysis methods and it is well known that frequency domain features give rich texture information than spatial domain features for classification [9]. Among the frequency domain analysis, Discrete Wavelet Transform (DWT) is a powerful tool for a signal as well as image processing [10].…”

Section: Introductionmentioning

confidence: 99%

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Modified Visual Geometric Group Architecture for MRI Brain Image Classification

Veni¹,

Manjula²

2022

CSSE

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The advancement of automated medical diagnosis in biomedical engineering has become an important area of research. Image classification is one of the diagnostic approaches that do not require segmentation which can draw quicker inferences. The proposed non-invasive diagnostic support system in this study is considered as an image classification system where the given brain image is classified as normal or abnormal. The ability of deep learning allows a single model for feature extraction as well as classification whereas the rational models require separate models. One of the best models for image localization and classification is the Visual Geometric Group (VGG) model. In this study, an efficient modified VGG architecture for brain image classification is developed using transfer learning. The pooling layer is modified to enhance the classification capability of VGG architecture. Results show that the modified VGG architecture outperforms the conventional VGG architecture with a 5% improvement in classification accuracy using 16 layers on MRI images of the REpository of Molecular BRAin Neoplasia DaTa (REMBRANDT) database.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modified Visual Geometric Group Architecture for MRI Brain Image Classification

Veni¹,

Manjula²

2022

CSSE

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“…The traditional machine learning techniques can be characterized based on the extracted features and the type of classifier. The feature extraction techniques include gray-level co-occurrence matrix (GLCM), gray-level run-length (GLRL), speededup robust feature extraction (SURF), and dual-tree m-band wavelet transform (DTMBWT) algorithms [171][172][173][174][175][176]. The classifiers include support vector machine (SVM), k-nearest neighbor (K-NN), Random Forest (ensemble classifiers on three ensemble algorithms: bagging, Adaboost, and random subspace), and fuzzy c-means clustering (FCM) [177].…”

Section: Image Analysis Of Inflammatory Diseasementioning

confidence: 99%

Molecular Imaging of Inflammatory Disease

et al. 2021

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Inflammatory diseases include a wide variety of highly prevalent conditions with high mortality rates in severe cases ranging from cardiovascular disease, to rheumatoid arthritis, to chronic obstructive pulmonary disease, to graft vs. host disease, to a number of gastrointestinal disorders. Many diseases that are not considered inflammatory per se are associated with varying levels of inflammation. Imaging of the immune system and inflammatory response is of interest as it can give insight into disease progression and severity. Clinical imaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) are traditionally limited to the visualization of anatomical information; then, the presence or absence of an inflammatory state must be inferred from the structural abnormalities. Improvement in available contrast agents has made it possible to obtain functional information as well as anatomical. In vivo imaging of inflammation ultimately facilitates an improved accuracy of diagnostics and monitoring of patients to allow for better patient care. Highly specific molecular imaging of inflammatory biomarkers allows for earlier diagnosis to prevent irreversible damage. Advancements in imaging instruments, targeted tracers, and contrast agents represent a rapidly growing area of preclinical research with the hopes of quick translation to the clinic.

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“…The important features used in many medial image analysis systems [19][20] which uses are energy and entropy features. Also, the spatial information's are described by the co-occurrence features.…”

Section: Fig 2 Frequency Domain Of Shearletsmentioning

confidence: 99%

Pulmonary Emphysema Analysis Using Shearlet Based Textures and Radial Basis Function Network

Semunigus¹

2020

International Journal of Advances in Signal and Image Sciences

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The emergence of High Resolution Computed Tomography (HRCT) images of the lungs clearly shows the parenchymal lung architecture and thus the quantification of obstructive lung disease becomes most accurate. In this study, an automated system to diagnose obstructive lung disease called emphysema is presented using HRCT images of the lungs. The kind of texture information that ideally can be extracted from HRCT images depends on the multi-resolution representation system. The proposed Pulmonary Emphysema Analysis (PEA) system employs Shearlets as it can extracts more texture information than wavelets in different directions and levels. Radial Basis Function Network (RBFN) is employed for the classification of HRCT images into three categories; Normal Tissue (NT), Paraseptal Emphysema (PSE) and Centrilobular Emphysema (CLE). Results prove that a confident diagnosis of pulmonary emphysema is established to help clinicians which will also increase the precision of diagnosis.

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Analysis of dual tree M‐band wavelet transform based features for brain image classification

Abstract: Viewing the experimental results, we conclude that the system gives a satisfactory performance for the brain image classification.

Cited by 18 publications

References 24 publications

Modified Visual Geometric Group Architecture for MRI Brain Image Classification

Modified Visual Geometric Group Architecture for MRI Brain Image Classification

Molecular Imaging of Inflammatory Disease

Pulmonary Emphysema Analysis Using Shearlet Based Textures and Radial Basis Function Network

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