scholarly journals A Novel Histogram Region Merging Based Multithreshold Segmentation Algorithm for MR Brain Images

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Siyan Liu ◽  
Xuanjing Shen ◽  
Yuncong Feng ◽  
Haipeng Chen
Keyword(s):  
Time Complexity ◽  
High Accuracy ◽  
Segmentation Algorithm ◽  
Gray Level ◽  
Original Image ◽  
Region Merging ◽  
Brain Images ◽  
Running Time ◽  
Mr Brain Images ◽  
Mr Brain

Multithreshold segmentation algorithm is time-consuming, and the time complexity will increase exponentially with the increase of thresholds. In order to reduce the time complexity, a novel multithreshold segmentation algorithm is proposed in this paper. First, all gray levels are used as thresholds, so the histogram of the original image is divided into 256 small regions, and each region corresponds to one gray level. Then, two adjacent regions are merged in each iteration by a new designed scheme, and a threshold is removed each time. To improve the accuracy of the merger operation, variance and probability are used as energy. No matter how many the thresholds are, the time complexity of the algorithm is stable at O(L). Finally, the experiment is conducted on many MR brain images to verify the performance of the proposed algorithm. Experiment results show that our method can reduce the running time effectively and obtain segmentation results with high accuracy.

scholarly journals Parameterization of BFO Algorithm for the Improved Functionality of MFKM Technique for Better Pathological Identification in Brain MR Image

2019 ◽  
Vol 9 (2S2) ◽  
pp. 956-961
Keyword(s):  
Volume Effect ◽  
High Accuracy ◽  
Medical Image Segmentation ◽  
Brain Images ◽  
Normal Tissues ◽  
Mr Brain Images ◽  
To Come ◽  
High Level ◽  
Mr Brain

Intensity inhomogeneity, high level of noise, partial volume effect and poor image contrast are the major artefacts in medical image segmentation. Any of these artefacts might lead to unclear boundaries of tissues, hence the segmentation of tissues in the MR brain image cannot be determined with high accuracy, and this would be a problem to the radiologists to diagnose or to start the treatment because of the lack of facility to operate over the brain in in-vivo condition. This makes the radiologist and surgeons/experts to take time to come for the conclusion on pathology of a particular patient. So, the radiologists and experts need to give more exertion when this condition is applied for many patients at a day, to diagnose and to start treatment. To make this effortless to them, also for accurate diagnosis, this research paper provides an robust algorithm using the Modified Fuzzy K-Means (MFKM) and Bacteria Foraging Optimization (BFO) algorithm, which segments the abnormal tissues among the normal tissues from MR brain images with high accuracy. The accuracy of the Improved MFKM (IMFKM) algorithm is obtained in terms of Sensitivity and Specificity, and the proposed algorithm proves better segmentation results than the other conventional algorithms.

Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

2017 ◽  
Vol 24 (6) ◽  
pp. 653-659
Author(s):  
Qiang Zheng ◽  
Honglun Li ◽  
Baode Fan ◽  
Shuanhu Wu ◽  
Jindong Xu
Keyword(s):  
Gaussian Distribution ◽  
Active Contour ◽  
Active Contour Model ◽  
Brain Images ◽  
Model Based ◽  
Contour Model ◽  
Mr Brain Images ◽  
Mr Brain

Automatic segmentation of MR brain images of preterm infants using supervised classification

NeuroImage ◽  
2015 ◽  
Vol 118 ◽  
pp. 628-641 ◽  
Author(s):  
Pim Moeskops ◽  
Manon J.N.L. Benders ◽  
Sabina M. Chiţǎ ◽  
Karina J. Kersbergen ◽  
Floris Groenendaal ◽  
...  
Keyword(s):  
Preterm Infants ◽  
Supervised Classification ◽  
Automatic Segmentation ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain

scholarly journals Registration-Based Approach for Reconstruction of High-Resolution In Utero Fetal MR Brain Images

2006 ◽  
Vol 13 (9) ◽  
pp. 1072-1081 ◽  
Author(s):  
Francois Rousseau ◽  
Orit A. Glenn ◽  
Bistra Iordanova ◽  
Claudia Rodriguez-Carranza ◽  
Daniel B. Vigneron ◽  
...  
Keyword(s):  
High Resolution ◽  
In Utero ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain ◽  
Fetal Mr

Classification of MR brain images by combination of multi-CNNs for AD diagnosis

2017 ◽  
Author(s):  
Danni Cheng ◽  
Manhua Liu ◽  
Jianliang Fu ◽  
Yaping Wang
Keyword(s):  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain
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