A hybrid algorithm for segmentation of MRI images based on edge detection

Taghizadeh, Moslem; Hajipoor, Mahboobeh

doi:10.1109/socpar.2011.6089123

Cited by 10 publications

(7 citation statements)

References 12 publications

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“…This process detects boundaries between objects and the background in the image at which the image brightness changes sharply or more formally has discontinuities [4]. The image containing these boundaries is known as edge map.…”

Section: Edge Detection Methodsmentioning

confidence: 99%

Optimized Adaptive Thresholding based Edge Detection Method for MRI Brain Images

Jansi¹,

Subashini²

2012

IJCA

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Edge detection is one of the fundamental tool in image processing, machine vision and computer vision, which aim at identifying points in a digital image. It is an important tool for medical image segmentation and 3D reconstruction. Generally, edge has detected according to some early brought forward algorithms such as gradient-based algorithm and templatebased algorithm, but they are not so good for noisy medical image edge detection. In order to overcome this problem, adaptive threshold using ACO has proposed. Ant colony optimization technique is used for computing an optimal threshold value used by adaptive threshold for edge detection. The various edge detection algorithms are compared with the proposed algorithm and their performance are evaluated using the evaluation metrics. From the experimental results, the proposed algorithm was better than the adaptive threshold method.

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Section: Edge Detection Methodsmentioning

confidence: 99%

Optimized Adaptive Thresholding based Edge Detection Method for MRI Brain Images

Jansi¹,

Subashini²

2012

IJCA

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“…Different approaches, used to segment the MR images, are reported in the literature. 19,20,22,23,31,35 It is observed that the captured MR images suffer from different limitations like broken boundaries, presence of noise, imprecise or incomplete lines, curvatures etc. which demand some form of pre-processing operations and denoising before segmentation technique is applied.…”

Section: Literature Review On the Existing Work And Limitationsmentioning

confidence: 99%

“…In Ref. 35, a modified method of MR image segmentation is proposed where the combination of p-tile and edge detection is used. In Ref.…”

Section: Literature Review On the Existing Work And Limitationsmentioning

confidence: 99%

On curvelet CS reconstructed MR images and GA-based fuzzy conditional entropy maximization for segmentation

Roy¹,

Maity

2019

Int. J. Wavelets Multiresolut Inf. Process.

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In many practical situations, magnetic resonance imaging (MRI) needs reconstruction of images at low measurements, far below the Nyquist rate, as sensing process may be very costly and slow enough so that one can measure the coefficients only a few times. Segmentation of such subsampled reconstructed MR images for medical analysis and diagnosis becomes a challenging task due to the inherent complex characteristics of the MR images. This paper considers reconstruction of MR images at compressive sampling (or compressed sensing (CS)) paradigm followed by its segmentation in an integrated platform. Image reconstruction is done from incomplete measurement space with random noise injection iteratively. A weighted linear prediction is done for the unobserved space followed by spatial domain denoising through adaptive recursive filtering. The reconstructed images, however, suffer from imprecise and/or missing edges, boundaries, lines, curvatures etc. and residual noise. Curvelet transform (CT) is purposely used for removal of noise and for edge enhancement through hard thresholding and suppression of approximate subbands, respectively. Then a fuzzy entropy-based clustering, using genetic algorithms (GAs), is done for segmentation of sharpen MR Image. Extensive simulation results are shown to highlight performance improvement of both image reconstruction and segmentation of the reconstructed images along with relative gain over the existing works.

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“…Thresholding is one of the easiest and most frequently used techniques to segment MRI data by separating the foreground from background of image [12,13]. Thresholding approach can further be classified as global thresholding and local (adaptive) thresholding.…”

Section: Thresholdingmentioning

confidence: 99%

“…Due to simplicity and easy implementation of global thresholding, this method is more popular. P-tile method is one of the earliest thresholding methods based on the gray level histogram [12,14]. Here P refers to the word "percentile".…”

Section: Thresholdingmentioning

confidence: 99%

Analysis of Watermarking Techniques for Medical Images Preserving ROI

Rathi¹

2012

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Telemedicine is a well-known application, where enormous amount of medical data need to be securely transfer over the public network and manipulate effectively. Medical image watermarking is an appropriate method used for enhancing security and authentication of medical data, which is crucial and used for further diagnosis and reference. This paper discusses the available medical image watermarking methods for protecting and authenticating medical data. The paper focuses on algorithms for application of watermarking technique on Region of Non Interest (RONI) of the medical image preserving Region of Interest (ROI). The medical images can be transferred securely by embedding watermarks in RONI allowingverification of the legitimate changes at the receiving end without affecting ROI.

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A hybrid algorithm for segmentation of MRI images based on edge detection

Cited by 10 publications

References 12 publications

Optimized Adaptive Thresholding based Edge Detection Method for MRI Brain Images

Optimized Adaptive Thresholding based Edge Detection Method for MRI Brain Images

On curvelet CS reconstructed MR images and GA-based fuzzy conditional entropy maximization for segmentation

Analysis of Watermarking Techniques for Medical Images Preserving ROI

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