Two-step verification of brain tumor segmentation using watershed-matching algorithm

被引：19

作者：

Hasan S.M.K. ^{[1
,2
]}

Ahmad M. ^{[1
]}

机构：

[1] Department of Electrical and Electronic Engineering, Khulna University of Engineering Technology (KUET), Khulna

[2] Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology (RIT), Rochester, 14623, NY

来源：

Brain Informatics | 2018年 / 5卷 / 02期

关键词：

Brain tumor segmentation; Magnetic resonance imaging; Median filter; SIFT algorithm; Status checking; Topology; Watershed-matching algorithm;

D O I：

10.1186/s40708-018-0086-x

中图分类号：

学科分类号：

摘要：

Though the modern medical imaging research is advancing at a booming rate, it is still a very challenging task to detect brain tumor perfectly. Medical imaging unlike other imaging system has highest penalty for a minimal error. So, the detection of tumor should be accurate to minimize the error. Past researchers used biopsy to detect the tumor tissue from the other soft tissues in the brain which is time-consuming and may have errors. We outlined a two-stage verification-based tumor segmentation that makes the detection more accurate. We segmented the tumor area from the MR image and then used another algorithm to match the segmented portion with the ground truth image. We named this new algorithm as watershed-matching algorithm. The most promising part of our model is the status checking of the tumor by finding the area of the tumor. Our proposed model works better than other state-of-the art works on BRATS 2017 dataset. © 2018, The Author(s).

引用

共 24 条

[1]

Bhima K., Jagan A., Novel techniques for detection of anomalies in brain MR images, Proceedings of International Conference Frontiers in Intelligent Computing: Theory and Applications, pp. 219-226, (2017)

[2]

Ayachi R., Amor N.B., Brain tumor segmentation using support vector machines, Eur Conf Symb Quant Approach Reason Uncertain, 5590, pp. 736-747, (2009)

[3]

Samuel J., Dong M., Hua J., Haacke E.M., Brain tumor detection using scale invariant feature transform, Proceedings of International Society for Magnetic Resonance, (2007)

[4]

Besbes A., Komodakis N., Langs G., Paragios N., Shape priors and discrete MRFs for knowledge-based segmentation, IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1295-1302, (2009)

[5]

Shen S., Sandham W., Granat M., Sterr A., MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization, IEEE Trans Inf Technol Biomed Pattern Anal Mach Intell, 5, 3, pp. 459-467, (2005)

[6]

Chen C.W., Luo J., Parker K.J., Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications, IEEE Trans Image Process, 7, 12, pp. 1673-1683, (1998)

[7]

Li X., Bhide S., Kabuka M., Labeling of MR brain images using Boolean neural network, IEEE Trans Med Imaging, 15, pp. 628-638, (1996)

[8]

Havaei M., Jodoin P.M., Larochelle H., Efficient interactive brain tumor segmentation as within-brain KNN classification, IEEE International Conference on Pattern Recognition, pp. 556-561, (2014)

[9]

Ruan S., Lebonvallet S., Merabet A., Constans J., Tumor segmentation from a multispectral MRI images by using support vector machine classification, IEEE International Symposium Biomedical Imaging: From Nano to Micro, pp. 1236-1239, (2007)

[10]

Lafferty J., Pereira F., McCallum A., Conditional random fields: probabilistic models for segmenting and labeling sequence data, Proceedings of the eighteenth international conference on machine learning (ICML), pp. 282-289, (2001)

← 1 2 3 →