Automatic segmentation of head mri images by knowledge guided thresholding

Suzuki, Hirotoshi; Toriwaki, Jun‐ichiro

doi:10.1016/0895-6111(91)90081-6

Cited by 131 publications

(45 citation statements)

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“…One such technique is presented by Suzuki and Toriwaki in [3] which proposes a knowledge guided thresholding technique for brain tumor segmentation. The problem with thresholding techniques is that it is normally difficult to determine any threshold value for tumor segmentation because intensities in MRI images are normally scattered; wrong threshold selection can either neglect tumor portion or label many healthy parts as tumors.…”

Section: Related Workmentioning

confidence: 99%

Application of Wavelet based K-means Algorithm in Mammogram Segmentation

Dalmiya¹,

Dasgupta²,

Datta³

2012

IJCA

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Research in image processing has gained lots of momentum during past two decades. Now-a-days image processing techniques have found their way into computer vision, image compression, image security, medical imaging and more. This paper presents a research on mammography images using wavelet transformation and K -means clustering for cancer tumor mass segmentation. The first step is to perform image segmentation. It allows distinguishing masses and micro calcifications from background tissue. In this paper wavelet transformation and K-means clustering algorithm have been used for intensity based segmentation. The proposed algorithm is robust against noise. In this case, discrete wavelet transform (DWT) is used to extract high level details from MRI images. The processed image is added to the original image to get the sharpened image. Then K-means algorithm is applied to the sharpened image in which the tumor region can be located using the thresholding method. This paper validates the algorithm by detecting tumor region from an MRI image of mammogram. The combination of noise-robust nature of applied processes and the simple K-means algorithm gives better results. General TermsImage Processing

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Section: Related Workmentioning

confidence: 99%

Application of Wavelet based K-means Algorithm in Mammogram Segmentation

Dalmiya¹,

Dasgupta²,

Datta³

2012

IJCA

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“…Mostly, thresholding method is combined with other methods [28]. The region growing method extends thresholding by combining it with connectivity.…”

Section: Manymentioning

confidence: 99%

“…This form of FI measure constitute the main ingredient of a powerful variational principal devised by Frieden [28], that gives rise to a substantial portion of the physics. In the consideration that follow we shall restrict ourselves to the form (3) of Fisher information measure.…”

Section: A Fisher Information Measurementioning

confidence: 99%

A Novel Approach for MRI Brain Images Segmentation

A¹,

Abdel-Azim²

2013

IJIGSP

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Abstract-Segmentation of brain from magnetic resonance (MR) images has important applications in neuroimaging, in particular it facilitates in extracting different brain tissues such as cerebrospinal fluids, white matter and gray matter. That helps in determining the volume of the tissues in three-dimensional brain MR images, which yields in analyzing many neural disorders such as epilepsy and Alzheimer disease. The Fisher information is a measure of the fluctuations in the observations. In a sense, the Fisher information of an image specifies the quality of the image. In this paper, we developed a new thresholding method using the Fisher information measure and intensity contrast to segment medical images. It is the weighted sum of the Fisher information measure and intensity contrast between the object and background. This technique is a powerful method for noisy image segmentation. The method applied on a normal MR brain images and a glioma MR brain images. Experimental results show that the use of the Fisher information effectively segmented MR brain images.

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“…However, the distribution of intensities in brain MR images is usually very complex, and determining a threshold is difficult. In most cases, thresholding is combined with other methods (Brummer et al, 1993;Suzuki & Toriwaki, 1991).…”

Section: Thresholdingmentioning

confidence: 99%