Mri Image Segmentation Based on FCM Clustering Using an Adaptive Threshold Algorithm

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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Robust Adaptive Threshold Algorithm based on Kernel Fuzzy Clustering on Image segmentation

Computer Science Conference Proceedings ◽

10.5121/csit.2012.2109 ◽

2012 ◽

Cited By ~ 3

Author(s):

Tara Saikumar

Keyword(s):

Image Segmentation ◽

Fuzzy Clustering ◽

Adaptive Threshold ◽

Threshold Algorithm ◽

Robust Adaptive

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Automatic lumbar spinal MRI image segmentation with a multi-scale attention network

Neural Computing and Applications ◽

10.1007/s00521-021-05856-4 ◽

2021 ◽

Author(s):

Haixing Li ◽

Haibo Luo ◽

Wang Huan ◽

Zelin Shi ◽

Chongnan Yan ◽

...

Keyword(s):

Image Segmentation ◽

Attention Network ◽

Multi Scale ◽

Spinal Mri ◽

Lumbar Spinal ◽

Mri Image

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A New Adaptive Threshold Algorithm for Narrow-band Interference Suppression in Partial Discharge Based on Improved Empirical Mode Decomposition and MDL Criterion

2010 International Conference on Intelligent Computation Technology and Automation ◽

10.1109/icicta.2010.324 ◽

2010 ◽

Author(s):

Zha Yan

Keyword(s):

Empirical Mode Decomposition ◽

Narrow Band ◽

Interference Suppression ◽

Partial Discharge ◽

Adaptive Threshold ◽

Threshold Algorithm ◽

Mode Decomposition

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Application of Adaptive Threshold Algorithm with Selected Modified Parameters for the Detection of Flooding Based Denial-of-Service (DoS) Attack in Mobile AdHoc Network

SSRN Electronic Journal ◽

10.2139/ssrn.3781339 ◽

2020 ◽

Author(s):

Nishanth N ◽

Mujeeb A

Keyword(s):

Denial Of Service ◽

Adaptive Threshold ◽

Dos Attack ◽

Mobile Adhoc Network ◽

Adhoc Network ◽

Threshold Algorithm

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Development of Ground Truth Data for Automatic Lumbar Spine MRI Image Segmentation

2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) ◽

10.1109/hpcc/smartcity/dss.2018.00239 ◽

2018 ◽

Cited By ~ 1

Author(s):

Friska Natalia ◽

Hira Meidia ◽

Nunik Afriliana ◽

Ala S. Al-Kafri ◽

Sud Sudirman ◽

...

Keyword(s):

Image Segmentation ◽

Lumbar Spine ◽

Ground Truth ◽

Ground Truth Data ◽

Spine Mri ◽

Mri Image

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PENERAPAN CITRA TERKOMPRESI PADA SEGMENTASI CITRA MENGGUNAKAN ALGORITMA K-MEANS

Jurnal Terapan Teknologi Informasi ◽

10.21460/jutei.2018.21.65 ◽

2018 ◽

Vol 2 (1) ◽

pp. 65-74

Author(s):

Angga Wijaya Kusuma ◽

Rossy Lydia Ellyana

Keyword(s):

Image Processing ◽

Image Segmentation ◽

Medical Image ◽

Performance Test ◽

Input Image ◽

Magnetic Resonance Imaging Image ◽

Resonance Imaging ◽

Compression Process ◽

Mri Brain ◽

Mri Image

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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