Wyniki wyszukiwania - BazTech

1

Skull stripping using traditional and soft-computing approaches for magnetic resonance images : a semi-systematic meta-analysis

Humera Azam, Humera Tariq

Machine Graphics and Vision

|

2020

|

Vol. 29, No. 1/4

33--53

EN

MRI scanner captures the skull along with the brain and the skull needs to be removed for enhanced reliability and validity of medical diagnostic practices. Skull Stripping from Brain MR Images is significantly a core area in medical applications. It is a complicated task to segment an image for skull stripping manually. It is not only time consuming but expensive as well. An automated skull stripping method with good efficiency and effectiveness is required. Currently, a number of skull stripping methods are used in practice. In this review paper, many soft-computing segmentation techniques have been discussed. The purpose of this research study is to review the existing literature to compare the existing traditional and modern methods used for skull stripping from Brain MR images along with their merits and demerits. The semi-systematic review of existing literature has been carried out using the meta-synthesis approach. Broadly, analyses are bifurcated into traditional and modern, i.e. soft-computing methods proposed, experimented with, or applied in practice for effective skull stripping. Popular databases with desired data of Brain MR Images have also been identified, categorized and discussed. Moreover, CPU and GPU based computer systems and their specifications used by different researchers for skull stripping have also been discussed. In the end, the research gap has been identified along with the proposed lead for future research work.

2

A study on spinal cord segmentation techniques

Jasim M., Brindha T.

Control and Cybernetics

|

2018

|

Vol. 47, no. 4

497--521

EN

One of the vital organs, which manage the communication between the brain and different body parts, is the spinal cord. It is highly prone to the traumatic injuries and to several diseases. The vital criteria for the clinical management are the appropriate localization and segmentation of the spinal cord. The segmentation experiences the risks, associated with the diversity in the human anatomy and contrast variation inMagnetic Resonance Imaging (MRI). Hence, an efficacious segmentation method must be devised for the effective segmentation and disc localization of the spinal cord. Correspondingly, the here contained survey provides the review of the distinct segmentation schemes for the spinal cord segmentation. At present, there is an urgent requirement for the development of an effective segmentation approach so as to outperform the existing segmentation methods. In this research article, a detailed survey on several research works presenting the recommended segmentation schemes, based on the active contour model, semi-automated segmentation, deformable model, probabilistic model, graph-based segmentation, and so on, is presented. Additionally, an in depth analysis and discussion are provided, in accordance with the publication year, evaluation metrics, segmentation scheme, Magnetic Resonance (MR) image datasets, Dice Similarity Coefficient (DSC) and accuracy. Subsequently, the research gaps and risks, related to distinct segmentation schemes are considered for directing the researchers towards a better future investigation field.

3

Bat optimization based neuron model of stochastic resonance for the enhancement of MR images

Singh M., Verma A., Sharma N.

Biocybernetics and Biomedical Engineering

|

2017

|

Vol. 37, no. 1

124--134

EN

Stochastic resonance (SR) performs the enhancement of the low in contrast image with the help of noise. The present paper proposes a modified neuron model based stochastic resonance approach applied for the enhancement of T1 weighted, T2 weighted, fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging (DWI) sequences of magnetic resonance imaging. Multi objective bat algorithm has been applied to tune the parameters of the modified neuron model for the maximization of two competitive image performance indices contrast enhancement factor (F) and mean opinion score (MOS). The quality of processed image depends on the choice of these image performance indices rather the selection of SR parameters. The proposed approach performs well on enhancement of magnetic resonance (MR) images, as a result there is improvement in the gray-white matter differentiation and has been found helpful in the better diagnosis of MR images.

4

Semi-Automatic Segmentation of Ct/Mri Images Based on Active Contour Method for 3D Reconstruction of Abdominal Aortic Aneurysms

Drapikowski P., Domagała Z.

Image Processing & Communications

|

2014

|

Vol. 19, no. 1

13--19

EN

The paper presents a CT/MRI image based semi-automatic AAA (abdominal aortic aneurysm) segmentation method. Segmentation process can run automatically with the active contour method but results are controlled by the operator. If incorrect segmentation is noticed, the operator may introduce corrections. The proposed method makes possible the segmentation of dissected aneurysms, with which no automatic analysis works. Controlling the segmentation process by the operator serves to ensure correct geometric shape reproduction, which is crucial in deploying aneurysm models to help assess rupture risk.

5

Fast FCM with spatial neighborhood information for brain MR image segmentation

Biniaz A., Abbasi A.

Journal of Artificial Intelligence and Soft Computing Research

|

2013

|

Vol. 3, No. 1

15--25

EN

Among different segmentation approaches Fuzzy c-Means clustering (FCM) is a welldeveloped algorithm for medical image segmentation. In emergency medical applications quick convergence of FCM is necessary. On the other hand spatial information is seldom exploited in standard FCM; therefore nuisance factors can simply affect it and cause misclassification. This paper aims to introduce a Fast FCM (FFCM) technique by incorporation of spatial neighborhood information which is exploited by a linear function on fuzzy membership. Applying proposed spatial Fast FCM (sFFCM), elapsed time is decreased and neighborhood spatial information is exploited in FFCM. Moreover, iteration numbers by proposed FFCM/sFFCM techniques are decreased efficiently. The FCM/FFCM techniques are examined on both simulated and real MR images. Furthermore, to considerably decrease of convergence time and iterations number, cluster centroids are initialized by an algorithm. Accuracy of the new approach is same as standard FCM. The quantitative assessments of presented FCM/FFCM techniques are evaluated by conventional validity functions. Experimental results demonstrate that sFFCM techniques efficiently handle noise interference and significantly decrease elapsed time.