scholarly journals Adaptive Image Segmentation for Traumatic Brain Haemorrhage

TEM Journal ◽  
2021 ◽  
pp. 1476-1487
Author(s):  
Ahmad Yahya Dawod ◽  
Aniwat Phaphuangwittayakul

It is challenging to establish a significant solution with computer techniques to improve the speed and efficiency of Traumatic Brain Injury (TBI) diagnosis. Several segmentation methods involving diverse precision and a degree of effort have been proposed and detailed within the related literature. Segmentation of Brain image is one of the significant clinical diagnostics implements. This paper proposes a modified (MDRLSE) calculation for haemorrhage segmentation on Computed Tomography (CT) images. The image noise that abdicates the obscured edges is utilized to portray the precise boundary of the haemorrhage region. The proposed segmentation technique achieved an accuracy rate of 97.16%. The technique is implemented using an edge-based involved contour model for image segmentation, providing a simple narrowband to significantly reduce computational costs. The performance results show that it is effective for TBI image segmentation in brain images with various characteristics.

TEM Journal ◽  
2021 ◽  
pp. 1476-1487
Author(s):  
Ahmad Yahya Dawod ◽  
Aniwat Phaphuangwittayakul

It is challenging to establish a significant solution with computer techniques to improve the speed and efficiency of Traumatic Brain Injury (TBI) diagnosis. Several segmentation methods involving diverse precision and a degree of effort have been proposed and detailed within the related literature. Segmentation of Brain image is one of the significant clinical diagnostics implements. This paper proposes a modified (MDRLSE) calculation for haemorrhage segmentation on Computed Tomography (CT) images. The image noise that abdicates the obscured edges is utilized to portray the precise boundary of the haemorrhage region. The proposed segmentation technique achieved an accuracy rate of 97.16%. The technique is implemented using an edge-based involved contour model for image segmentation, providing a simple narrowband to significantly reduce computational costs. The performance results show that it is effective for TBI image segmentation in brain images with various characteristics.


Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1051
Author(s):  
Wenyin Zhang ◽  
Yong Wu ◽  
Bo Yang ◽  
Shunbo Hu ◽  
Liang Wu ◽  
...  

The precise segmentation of brain tumor images is a vital step towards accurate diagnosis and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate brain images without tissue damage or skull artifacts, providing important discriminant information for clinicians in the study of brain tumors and other brain diseases. In this paper, we survey the field of brain tumor MRI images segmentation. Firstly, we present the commonly used databases. Then, we summarize multi-modal brain tumor MRI image segmentation methods, which are divided into three categories: conventional segmentation methods, segmentation methods based on classical machine learning methods, and segmentation methods based on deep learning methods. The principles, structures, advantages and disadvantages of typical algorithms in each method are summarized. Finally, we analyze the challenges, and suggest a prospect for future development trends.


2015 ◽  
Vol 32 (6) ◽  
pp. 413-427 ◽  
Author(s):  
Sepideh Yazdani ◽  
Rubiyah Yusof ◽  
Alireza Karimian ◽  
Mohsen Pashna ◽  
Amirshahram Hematian

2018 ◽  
Vol 4 (1) ◽  
pp. 345-349
Author(s):  
Tamara Wirth ◽  
Ady Naber ◽  
Werner Nahm

AbstractImage segmentation plays an increasingly important role in image processing. It allows for various applications including the analysis of an image for automatic image understanding and the integration of complementary data. During vascular surgeries, the blood flow in the vessels has to be checked constantly, which could be facilitated by a segmentation of the affected vessels. The segmentation of medical images is still done manually, which depends on the surgeon’s experience and is time-consuming. As a result, there is a growing need for automatic image segmentation methods. We propose an unsupervised method to detect the regions of no interest (RONI) in intraoperative images with low depth-of-field (DOF). The proposed method is divided into three steps. First, a color segmentation using a clustering algorithm is performed. In a second step, we assume that the regions of interest (ROI) are in focus whereas the RONI are unfocused. This allows us to segment the image using an edge-based focus measure. Finally, we combine the focused edges with the color RONI to determine the final segmentation result. When tested on different intraoperative images of aneurysm clipping surgeries, the algorithm is able to segment most of the RONI not belonging to the pulsating vessel of interest. Surgical instruments like the metallic clips can also be excluded. Although the image data for the validation of the proposed method is limited to one intraoperative video, a proof of concept is demonstrated.


1990 ◽  
Author(s):  
Γεώργιος Μάνος

"Bone age" age assessment is an important clinical tool in the area of paediatrics. The technique is based upon the appearance and growth of specific bones in a developing child. In particular most methods for "bone age" assessment are based on the examination of the growth of bones of the left hand and wrist on X-ray films. This assessment is useful in the treatment of growth disorders and also is used to predict adult height. One of the most reliable methods for "bone age" assessment is the TW2 method. The drawback of this method is that it is time consuming and therefore its automation is highly desirable. One of the most important aspects of the automation process is image segmentation i.e. the extraction of bones from soft-tissue and background. Over the past 10 years various attempts have been made at the segmentation of handwrist radiographs but with limited success. This can mainly be attributed to the characteristics of the scenes e.g. biological objects, penetrating nature of radiation, faint bone boundaries, uncertainty of scene content, and conjugation of bones. Experience in the field of radiographic image analysis has shown thatsegmentation of radiographic scenes is a difficult task requiring solutions which depend on the nature of the particular problem.There are two main approaches to image segmentation: edge based and region based. Most of the previous attempts at the segmentation of hand-wrist radiographs were edge based. Edge based methods usually require a w-ell defined model of the object boundaries in order to produce successful results. However, for this particular application it is difficult to derive such a model. Region based segmentation methods have produced promising results for scenes which exhibit uncertainty regarding their content and boundaries of objects in the image, as in the case, for example, of natural senes. This thesis presents a segmentation method based on the concept of regions. This method consists of region growing and region merging stages. A technique was developed for region merging which combines edge and region boundai^ information. A bone extraction stage follows which labels regions as either boneor background using heuristic rules based on the grey-level properties of the scene. Finally, a technique is proposed for the segmentation of bone outlines which helps in identifying conjugated bones. Experimental results have demonstrated that this method represents a significant improvement over existing segmentation methods for hand-wrist radiographs, particularly with regard to the segmentation of radiographs with varying degrees of bone maturity.


MRI is known as one of the best imaging modality used for neuro image analysis. Detection of abnormality regions in Brain image is critical due to its complex structure, which can be accurately analyzed with MRI. Several methods and segmentation algorithms have been proposed in the past to extract the abnormal region however there is further scope of increasing the segmentation efficiency. In this work abnormality region in brain is extracted with region based and edge based hybrid segmentation methods and thus obtained region is rendered for volumetric analysis. This analysis is used for depth measurement and localization of abnormal region accurately. Apart from this analysis mainly provides the information about the abnormal region distribution and its connectivity with other regions.


Author(s):  
Vaishnavi R ◽  
Manjula S ◽  
Lakshmi K

Segmentation is an important procedure in image processing. It splits a digital image into numerous sections to examine them. It is also used to make a distinction in an image. Several image segmentation techniques are available to make a smoothen image and to examine easily. The main purpose of this work is to categorize the results and compares the threshold-based, edge-based, and watershed-based image segmentation methods. This work is executed by MatlabR2016a.


2017 ◽  
Vol 13 (4-1) ◽  
pp. 408-411
Author(s):  
Maizatul Nadirah Mustaffa ◽  
Norma Alias ◽  
Faridah Mustapha

In this paper, we present an edge-based image segmentation technique using modified geodesic active contour model to detect the desired objects from an image. The stopping function of the proposed model has been modified from the usual geodesic active contour model. The modified geodesic active contour model is discretized using finite difference method based on the central difference formula. Then, some numerical methods such as RBGS and Jacobi methods are used for solving the linear system of equation. The accuracy and effectiveness of the proposed algorithm have been illustrated by applied to different images and some numerical methods.


2020 ◽  
Vol 1505 ◽  
pp. 012049
Author(s):  
Arif Rahmandinof ◽  
Fadil Nazir ◽  
Yanurita Dwihapsari

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