MRI Classification of Parkinson’s Disease Using SVM and Texture Features

Pazhanirajan, S.; Dhanalakshmi, P.

doi:10.1007/978-81-322-2523-2_34

Cited by 1 publication

(2 citation statements)

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“…A review of existing feature extraction methods can be found in [69,70]. Although methods based on the first order statistics (histogram features) are normally used in combination with other methods, as they may improve the texture-based classification or segmentation [10,[71][72][73], they are not presented here as they do not really describe the actual texture of the image or ROI being analyzed [70].…”

Section: Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Texture Analysis in Magnetic Resonance Imaging: Review and Considerations for Future Applications

Larroza¹,

Bodı́²,

Moratal³

2016

Assessment of Cellular and Organ Function and Dysfunction Using Direct and Derived MRI Methodologies

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Texture analysis is a technique used for the quantification of image texture. It has been successfully used in many fields, and in the past years it has been applied in magnetic resonance imaging (MRI) as a computer-aided diagnostic tool. Quantification of the intrinsic heterogeneity of different tissues and lesions is necessary as they are usually imperceptible to the human eye. In the present chapter, we describe texture analysis as a process consisting of six steps: MRI acquisition, region of interest (ROI) definition, ROI preprocessing, feature extraction, feature selection, and classification. There is a great variety of methods and techniques to be chosen at each step and all of them can somehow affect the outcome of the texture analysis application. We reviewed the literature regarding texture analysis in clinical MRI focusing on the important considerations to be taken at each step of the process in order to obtain maximum benefits and to avoid misleading results.

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Section: Feature Extractionmentioning

confidence: 99%

“…SVMs were also applied for brain tumor classification in [1,116]. Other applications of SVMs include the staging of liver fibrosis [33], detection of prostate [26], assessment of osteoarthritis [117], classification of cervical cancer [118], mammogram lesions [98], and Parkinson disease [73].…”

Section: Support Vector Machinesmentioning

confidence: 99%