Estimation of interlayer textural relationships to discriminate the benignancy/malignancy of brain tumors

A computer-aided diagnosis system is a popular tool to predict the risk factors of brain tumors. However, the existing techniques are unable to provide high detection accuracy due to the inability of capturing the hidden features of brain tumors. In this view, the present work decomposes the MR moda...

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Vydáno v:Pattern recognition Ročník 144; s. 109879
Hlavní autoři: Das, Poulomi, Das, Arpita
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.12.2023
Témata:
Advanced PCNN module
Classification
FCM clustering algorithm
Interlayer feature quantifiers
NSST based decomposition
Interlayer feature quantifiers
NSST based decomposition
FCM clustering algorithm
Advanced PCNN module
Classification
ISSN:0031-3203
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Shrnutí:A computer-aided diagnosis system is a popular tool to predict the risk factors of brain tumors. However, the existing techniques are unable to provide high detection accuracy due to the inability of capturing the hidden features of brain tumors. In this view, the present work decomposes the MR modality images into different layers using non-sub-sampled shearlet transformation (NSST). Following this, the proposed detection pipeline employs adaptive pulse-coupled neural network (A-PCNN) module and a fuzzy c-means (FCM) clustering algorithm for enhancement and segmentation of suspicious regions respectively. Interlayer textural relationships of tumors are estimated in terms of the layer-wise difference of the gray-level cooccurrence matrix (GLCM), similarity indices, and entropy distributions. Thenceforth, those feature coefficients are coded with binary sequences to express the textural homogeneity/randomness of tumors. Finally, these handcrafted multi-scale feature quantifiers are fed to some standard classifiers such as the k-nearest neighbor (kNN) technique, the linear square support vector machine (LS-SVM), and the decision tree (DT) for discriminating the state of benignancy/malignancy of tumors. Experimental results ensure that the proposed detection model shows superior performance compared to existing methods.
ISSN:0031-3203
DOI:10.1016/j.patcog.2023.109879