Skull stripping and automatic segmentation of brain MRI using seed growth and threshold techniques

Shanthi, K.; Kumar, Mohit

doi:10.1109/icias.2007.4658421

Cited by 48 publications

(14 citation statements)

References 6 publications

(3 reference statements)

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“…A method based on seed growth and threshold techniques for automatic segmentation of brain MRI is employed by Shanthi and Sasikumar [37]. A method described by Mikheev et al [38] is an automatic segmentation of brain from T1-weighted MR brain images.…”

Section: Morphology-based Methodsmentioning

confidence: 99%

Methods on Skull Stripping of MRI Head Scan Images—a Review

2015

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The high resolution magnetic resonance (MR) brain images contain some non-brain tissues such as skin, fat, muscle, neck, and eye balls compared to the functional images namely positron emission tomography (PET), single photon emission computed tomography (SPECT), and functional magnetic resonance imaging (fMRI) which usually contain relatively less non-brain tissues. The presence of these non-brain tissues is considered as a major obstacle for automatic brain image segmentation and analysis techniques. Therefore, quantitative morphometric studies of MR brain images often require a preliminary processing to isolate the brain from extra-cranial or non-brain tissues, commonly referred to as skull stripping. This paper describes the available methods on skull stripping and an exploratory review of recent literature on the existing skull stripping methods.

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Section: Morphology-based Methodsmentioning

confidence: 99%

Methods on Skull Stripping of MRI Head Scan Images—a Review

2015

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“…The skull, scalp, fat, skin, eyeball, etc...Is the parts that are not required to be segmented, ie they come under the category of non brain tissues. For removing these parts we use the mathematical morphology technique [23]. In this proposed study, the skull stripping process based on the use of mathematical morphology.…”

Section: Pre-processingmentioning

confidence: 99%

Segmentation of MR images of degenerative diseases by automated method using ANN technique

Paul¹

2013

IET Chennai Fourth International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2013)

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Brain tissue segmentation of MRI helps in the possibility of improved clinical decision making and diagnosis, and it also gives a new insight into the mechanism of the disease. Manual interaction is time consuming and it may be bias and variable. We have developed an automatic segmentation algorithm for brain MRI using Artificial Neural Network (ANN) technique. The contribution of this work is an approach for automatically segmenting the brain tissues into White Matter (WM), Gray Matter (GM), Cerebro-Spinal Fluid (CSF). The algorithm developed achieves better performance comparable to the expert segmentation. However, in the clinical analysis accurate segmentation of MR image is very important and crucial for the early diagnosis. Segmentation procedure was done with real time MR images

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“…Haihong Zhuang et al [8] have proposed a level set method for skull stripping MR brain images. Shanthi, KJ et al [14] have developed an automatic skull removal technique using seed growth and thresholding. Somasundaram, K et al proposed a fully automatic brain extraction algorithm for axial Tl-weighted [16,18] and T2- [19].…”

Section: Introductionmentioning

confidence: 99%

Skull removal of noisy magnetic resonance brain images using Contourlet transform and morphological operations

Satheesh

Kumar

Prasad³

et al. 2011

Proceedings of 2011 International Conference on Computer Science and Network Technology

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Efficient segmentation of noisy Magnetic Resonance( MR) brain images is a challenging task, as pre and post surgery decisions are required to make accurately in achieving better medical practices while treating brain disorders. This paper presents an automatic segmentation technique to remove non brain tissue (skull, fat, skin, muscle) of noisy MR brain images and to extract brain tissue (cortex and cerebellum). Here, Contourlet transform is applied to denoise a noisy MR brain image and threshold based morphological operations are applied to extract brain region on denoised images. Hence a comparative study is developed on skull removed MR brain images with and without denoising based on similarity index and segmentation error. The experimental results prove that the proposed method yields consistent results irrespective of noise levels.

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Skull stripping and automatic segmentation of brain MRI using seed growth and threshold techniques

Cited by 48 publications

References 6 publications

Methods on Skull Stripping of MRI Head Scan Images—a Review

Methods on Skull Stripping of MRI Head Scan Images—a Review

Segmentation of MR images of degenerative diseases by automated method using ANN technique

Skull removal of noisy magnetic resonance brain images using Contourlet transform and morphological operations

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