<title>Medical image application: differentiate the MRI image by the image comparison technique</title>

In the development of an image not only as a documentation of events. One area that requires image processing is in the field of medicine is radiology. In radiology there is a medical image required by doctors and researchers to be processed for patient analysis. One of the important problems in image processing and pattern recognition is image segmentation into homogeneous areas. Segmentation in medical images will result in a medical image with area boundaries that are important information for analysis. This research applies k-means algorithm to MRI (Magnetic Resonance Imaging) image segmentation. The input image used is the image of MRI (brain and breast) has gone through the compression stage. This compression process is done with the aim of reducing memory usage but the critical information content of MRI image is still maintained. The image of the segmentation result is evaluated through performance test using GCE, VOI, MSE, and PSNR parameters.

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Automatic Segmentation of the Skull in MRI Sequences Using Level Set Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.58-60.2370 ◽

2011 ◽

Vol 58-60 ◽

pp. 2370-2375

Author(s):

Wei Li Ding ◽

Feng Jiang ◽

Jia Qing Yan

Keyword(s):

Level Set ◽

Medical Image ◽

Automatic Segmentation ◽

Image Sequences ◽

Initial Contour ◽

Single Image ◽

Mri Sequences ◽

Magnetic Resonance Imaging Mri ◽

Mri Image

Magnetic Resonance Imaging (MRI) has been widely used in clinical diagnose. Segmentation of these images obtained by MRI is a necessary procedure in medical image processing. In this paper, an improved level set algorithm was proposed to optimize the segmentation of MRI image sequences based on article [1]. Firstly, we add an area term and the edge indicator function to the total energy function for single image segmentation. Secondly, we presented a new method which uses the circumscribed polygon of the previous segmentation result as the initial contour of the next image to achieve automatic segmentation of image sequences. The algorithm was tested on MRI image sequences provided by Chuiyanliu Hospital, Chaoyang District of Beijing; the results have indicated that the proposed algorithm can effectively enhance the segmentation speed and quality of MRI sequences.

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Level Set Segmentation Method in Medical Image Segmentation Research and Application

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.157-158.1012 ◽

2012 ◽

Vol 157-158 ◽

pp. 1012-1015 ◽

Cited By ~ 1

Author(s):

Yu Miao ◽

Wei Li Shi

Keyword(s):

Image Segmentation ◽

White Matter ◽

Level Set ◽

Medical Image ◽

Region Of Interest ◽

Medical Image Segmentation ◽

The Other ◽

Segmentation Method ◽

Level Set Segmentation ◽

Mri Image

Medical image segmentation can be divided into two categories: one is the region of interest (ROI) identification; the other is the description of the integrity and the extraction of interest region. The emergence of the level set method greatly promoted the development of medical image segmentation. This paper studies three different level set segmentation algorithm to achieve the effective segmentation for brain gray matter and white matter of MRI image.

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Review on 2D and 3D MRI Image Segmentation Techniques

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405613666171123160609 ◽

2019 ◽

Vol 15 (2) ◽

pp. 150-160 ◽

Cited By ~ 4

Author(s):

S. Shirly ◽

K. Ramesh

Keyword(s):

Image Processing ◽

Image Segmentation ◽

Medical Image ◽

Medical Diagnostics ◽

Processing Technique ◽

Image Features ◽

Medical Image Segmentation ◽

Image Processing Technique ◽

Edge Based ◽

Mri Image

Background: Magnetic Resonance Imaging is most widely used for early diagnosis of abnormalities in human organs. Due to the technical advancement in digital image processing, automatic computer aided medical image segmentation has been widely used in medical diagnostics. </P><P> Discussion: Image segmentation is an image processing technique which is used for extracting image features, searching and mining the medical image records for better and accurate medical diagnostics. Commonly used segmentation techniques are threshold based image segmentation, clustering based image segmentation, edge based image segmentation, region based image segmentation, atlas based image segmentation, and artificial neural network based image segmentation. Conclusion: This survey aims at providing an insight about different 2-Dimensional and 3- Dimensional MRI image segmentation techniques and to facilitate better understanding to the people who are new in this field. This comparative study summarizes the benefits and limitations of various segmentation techniques.

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A Study on Various Image Segmentation Algorithms

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset21841134 ◽

2018 ◽

pp. 272-276

Author(s):

S. DivyaMeena ◽

M. Mangaleswaran

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Medical Images ◽

Large Data ◽

Performance Measure ◽

Medical Image Segmentation ◽

3D Image ◽

Medical Field ◽

Segmentation Algorithms ◽

Mri Image

Medical images have made a great effect on medicine, diagnosis, and treatment. The most important part of image processing is image segmentation. Medical Image Segmentation is the development of programmed or semi-automatic detection of limitations within a 2D or 3D image. In medical field, image segmentation is one of the vital steps in Image identification and Object recognition. Image segmentation is a method in which large data is partitioned into small amount of data. If the input MRI image is segmented then identifying the lump attacked region will be easier for physicians. In recent days, many algorithms are proposed for the image segmentation. In this paper, an analysis is made on various segmentation algorithms for medical images. Furthermore, a comparison of existing segmentation algorithms is also discussed along with the performance measure of each.

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MRI Image Segmentation Using Bat Optimization Algorithm with Fuzzy C Means (BOA-FCM) Clustering

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3365 ◽

2021 ◽

Vol 11 (3) ◽

pp. 661-666

Author(s):

B. Jai Shankar ◽

K. Murugan ◽

A. Obulesu ◽

S. Finney Daniel Shadrach ◽

R. Anitha

Keyword(s):

Image Segmentation ◽

Information Extraction ◽

Optimization Algorithm ◽

Medical Image ◽

Magnetic Resonance Images ◽

Fuzzy C Means ◽

Segmentation Technique ◽

Fcm Clustering ◽

Anatomical Information ◽

Mri Image

Functional and anatomical information extraction from Magnetic Resonance Images (MRI) is important in medical image applications. The information extraction is highly influenced by the artifacts in the MRI images. The feature extraction involves the segmentation of MRI images. We present a MRI image segmentation using Bat Optimization Algorithm (BOA) with Fuzzy C Means (FCM) clustering. Echolocation of bats is utilized in Bat Optimization Algorithm. The proposed segmentation technique is evaluated with existing segmentation techniques. Results of experimentation shows that proposed segmentation technique outperforms existing methods and produces 98.5% better results.

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The Cyborg Astrobiologist: matching of prior textures by image compression for geological mapping and novelty detection

International Journal of Astrobiology ◽

10.1017/s1473550413000372 ◽

2014 ◽

Vol 13 (3) ◽

pp. 191-202 ◽

Cited By ~ 2

Author(s):

P.C. McGuire ◽

A. Bonnici ◽

K.R. Bruner ◽

C. Gross ◽

J. Ormö ◽

...

Keyword(s):

Image Compression ◽

Novelty Detection ◽

Current System ◽

Geological Mapping ◽

Coal Bed ◽

Field Site ◽

Image Comparison ◽

Similarity Detection ◽

Geological Units ◽

Comparison Technique

AbstractWe describe an image-comparison technique of Heidemann and Ritter (2008a, b), which uses image compression, and is capable of: (i) detecting novel textures in a series of images, as well as of: (ii) alerting the user to the similarity of a new image to a previously observed texture. This image-comparison technique has been implemented and tested using our Astrobiology Phone-cam system, which employs Bluetooth communication to send images to a local laptop server in the field for the image-compression analysis. We tested the system in a field site displaying a heterogeneous suite of sandstones, limestones, mudstones and coal beds. Some of the rocks are partly covered with lichen. The image-matching procedure of this system performed very well with data obtained through our field test, grouping all images of yellow lichens together and grouping all images of a coal bed together, and giving 91% accuracy for similarity detection. Such similarity detection could be employed to make maps of different geological units. The novelty-detection performance of our system was also rather good (64% accuracy). Such novelty detection may become valuable in searching for new geological units, which could be of astrobiological interest. The current system is not directly intended for mapping and novelty detection of a second field site based on image-compression analysis of an image database from a first field site, although our current system could be further developed towards this end. Furthermore, the image-comparison technique is an unsupervised technique that is not capable of directly classifying an image as containing a particular geological feature; labelling of such geological features is donepost factoby human geologists associated with this study, for the purpose of analysing the system's performance. By providing more advanced capabilities for similarity detection and novelty detection, this image-compression technique could be useful in giving more scientific autonomy to robotic planetary rovers, and in assisting human astronauts in their geological exploration and assessment.

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The Combination of Steganography and Cryptography for Medical Image Applications

International Journal of Advances in Applied Sciences ◽

10.11591/ijaas.v7.i2.pp102-104 ◽

2018 ◽

Vol 7 (2) ◽

pp. 102

Author(s):

B. Kiran Bala ◽

R. Sasi Kumar

Keyword(s):

Medical Image ◽

Third Parties ◽

Biomedical Images ◽

Mri Image

<p>To give more security for the biomedical images for the patient betterment as well privacy for the patient highly confidently patient image report can be placed in database. If unknown persons like hospital staffs, relatives and third parties like intruder trying to see the report it has in the form of hidden state in another image. The patient detail like MRI image has been converted into any form of steganography. Then, encrypt those image by using proposed cryptography algorithm and place in the database.</p>

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