Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain

Latha, M. Madhavi; Kavitha, Ganesan

doi:10.1007/s10334-018-0674-z

Cited by 26 publications

(13 citation statements)

References 51 publications

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“…The texture can also be seen as a similarity grouping in an image [2] or as natural scenes containing semi-repetitive arrangements of pixels [3]. In spite of the difficulty to give a precise definition of this notion, the analysis of texture is used in many applications: biomedical field (see [4]- [7]), industrial automation (see [8]- [10]), document image analysis (see [11]- [13]), remote sensing (see [14]- [16]), face recognition (see [17]) to cite only a few. These applications use texture parameters for classification, segmentation, or synthesis of images.…”

Section: Introductionmentioning

confidence: 99%

Texture Feature Extraction Methods: A Survey

2019

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Texture analysis is used in a very broad range of fields and applications, from texture classification (e.g., for remote sensing) to segmentation (e.g., in biomedical imaging), passing through image synthesis or pattern recognition (e.g., for image inpainting). For each of these image processing procedures, first, it is necessary to extract-from raw images-meaningful features that describe the texture properties. Various feature extraction methods have been proposed in the last decades. Each of them has its advantages and limitations: performances of some of them are not modified by translation, rotation, affine, and perspective transform; others have a low computational complexity; others, again, are easy to implement; and so on. This paper provides a comprehensive survey of the texture feature extraction methods. The latter are categorized into seven classes: statistical approaches, structural approaches, transform-based approaches, model-based approaches, graph-based approaches, learning-based approaches, and entropybased approaches. For each method in these seven classes, we present the concept, the advantages, and the drawbacks and give examples of application. This survey allows us to identify two classes of methods that, particularly, deserve attention in the future, as their performances seem interesting, but their thorough study is not performed yet.

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Section: Introductionmentioning

confidence: 99%

Texture Feature Extraction Methods: A Survey

2019

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“…However, the specific mechanisms involved in producing these structural deficits remain incompletely understood. In recent years, it has been consistently reported that SZ patients have structural abnormalities in the brain, including the middle temporal gyrus, middle frontal gyrus, thalamus, and corpus callosum (CcSum) [6][7][8]. e brain structure location and neurobiological processes underlying these structural abnormalities are central to the pathophysiology of schizophrenia.…”

Section: Introductionmentioning

confidence: 99%

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

Chen

Yan

Wang

et al. 2020

Computational Intelligence and Neuroscience

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Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagnosis of schizophrenia. In this study, structural magnetic resonance images (sMRIs) from SZ patients and NCs were used for discriminative analysis. is study proposed an ML framework based on coarse-to-fine feature selection. e proposed framework used two-sample t-tests to extract the differences between groups first, then further eliminated the nonrelevant and redundant features with recursive feature elimination (RFE), and finally utilized the support vector machine (SVM) to learn the decision models with selected gray matter (GM) and white matter (WM) features. Previous studies have tended to report differences at the group level instead of at the individual level and cannot be widely applied. e method proposed in this study extends the diagnosis to the individual level and has a higher recognition rate than previous methods. e experimental results of this study demonstrate that the proposed framework distinguishes SZ patients from NCs, with the highest classification accuracy reaching over 85%. e identified biomarkers are also consistent with previous literature findings. As a universal method, the proposed framework can be extended to diagnose other diseases.

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“…In our previous work, optimized features are selected with binary particle swam optimization (BPSO) from whole‐brain region shows significant texture pattern change in SZ with Fuzzy SVM (FSVM) classifier . In addition, Laws texture feature identified a heterogeneous appearance in the brain stem, corpus callosum, and ventricular regions from SZ brain …”

Section: Introductionmentioning

confidence: 99%

“…23 In addition, Laws texture feature identified a heterogeneous appearance in the brain stem, corpus callosum, and ventricular regions from SZ brain. 24 The aim of the proposed work is to study the neuroanatomical and textural variations from cerebellum region in SZ and SA brain MR images based on radiomic features. Initially, level set based on shape constraint is used to segment cerebellum region from MR images.…”

Section: Introductionmentioning

confidence: 99%

Assessment of severity in neuropsychiatric disorders based on radiomic features with prior shape level set and metaheuristic algorithms

Latha

Kavitha

2019

Int J Imaging Syst Tech

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The diagnostic and clinical overlap between schizophrenia (SZ) and schizoaffective (SA) is vital oncological issue in psychiatry disorder. This study is designed to examine tissue variation in cerebellum region. Shape prior levelset method is used to segment cerebellum from MR image, and is subdivided by symmetry line detection. Radiomic features are extracted and subjected to Binary PSO (BPSO) fuzzy‐SVM classifier. This work is evaluated on COBRE database. The results show that, shape prior levelset could segment cerebellum region with better similarity values. The Laplacian of Gaussian and structure tensor features extracted from right cerebellum gives high significance between SA and SZ (P < 0.00001). The BPSO‐FSVM could classify the normal, SA, and SZ with an accuracy of 88.33%. The AUC is 0.913 for the combined features extracted from right cerebellum. It is concluded that optimized feature selection‐based classifier gives better results. This study is clinically helpful in the investigation of psychiatry disorder.

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Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain

Abstract: A framework of geometric and Laws texture features obtained from brain subregions can be used as a supplement for diagnosis of psychiatric disorders.

Cited by 26 publications

References 51 publications

Texture Feature Extraction Methods: A Survey

Texture Feature Extraction Methods: A Survey

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

Assessment of severity in neuropsychiatric disorders based on radiomic features with prior shape level set and metaheuristic algorithms

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