A joint intensity and edge magnitude-based multilevel thresholding algorithm for the automatic segmentation of pathological MR brain images
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Multilevel thresholding is one of the most popular image segmentation techniques due to its simplicity and accuracy. Most of the thresholding approaches use either the histogram of an image or information from the grey-level co-occurrence matrix (GLCM) to compute the threshold. The medical images like MRI usually have vague boundaries and poor contrast. So, segmenting these images using solely histogram or texture attributes of GLCM proves to be insufficient. This paper proposes a novel multilevel thresholding approach for automatic segmentation of tumour lesions from magnetic resonance images. The proposed technique exploits both intensity and edge magnitude information present in image histogram and GLCM to compute the multiple thresholds. Subsequently, using both attributes, a hybrid fitness function has been formulated which can capture the variations in intensity and the edge magnitude present in different tumour groups effectively. Mutation-based particle swarm optimization (MPSO) technique has been used to optimize the fitness function so as to mitigate the problem of high computational complexity existing in the exhaustive search methods. Moreover, MPSO has better exploration capabilities as compared to conventional particle swarm optimization. The performance of the devised technique has been evaluated and compared with two other intensity- and texture-based approaches using three different measures: Jaccard, Dice and misclassification error. To compute these quantitative metrics, experiments were conducted on a series of images, including low-grade glioma tumour volumes taken from brain tumour image segmentation benchmark 2012 and 2015 data sets and real clinical tumour images. Experimental results show that the proposed approach outperforms the other competing algorithms by achieving an average value equal to 0.752, 0.854, 0.0052; 0.648, 0.762, 0.0177; 0.710, 0.813, 0.0148 and 0.886, 0.937, 0.0037 for four different data sets.
KeywordsAutomatic segmentation Multilevel thresholding MPSO Intensity Edge magnitude
The authors gratefully acknowledge the efforts by Dr. Sandeep Singh Pawar (Advance Diagnostic Centre, Ludhiana, Punjab) for providing the clinical data and the interpretations.
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Compliance with ethical standards
Conflict of interest
This research has been approved by the Research Advisory Committee of the Institute. In addition, all of the procedures performed during the image acquisition process comply with the ethical standards of the diagnostic centre from which the image data have been taken.
- 11.Cordier N, Menze B, Delingette H, Ayache N (2013) Patch-based segmentation of brain tissues. In: Menze B, Reyes M, Jakab A, Gerstner E, Kirby J, Kalpathy-Cramer J, Farahani K (eds) MICCAI chall. Multimodal brain tumor segmentation. IEEE, Nagoya, pp 6–17Google Scholar
- 12.Doyle S, Vasseur F, Dojat M, Forbes F (2013) Fully automatic brain tumor segmentation from multiple MR sequences using hidden Markov fields and variational EM. In: Menze B, Reyes M, Jakab A, Gerstner E, Kirby J, Kalpathy-Cramer J, Farahani K (eds) MICCAI chall. Multimodal brain tumor segmentation. IEEE, Nagoya, pp 18–22Google Scholar
- 13.Festa J, Pereira S, Mariz JA et al (2013) Automatic brain tumor segmentation of multi-sequence MR images using random decision forests. In: Menze B, Reyes M, Jakab A, Gerstner E, Kirby J, Kalpathy-Cramer J, Farahani K (eds) MICCAI chall. Multimodal brain tumor segmentation. IEEE, Nagoya, pp 23–26Google Scholar
- 14.Meier R, Bauer S, Slotboom J et al (2013) A hybrid model for multimodal brain tumor segmentation. In: Menze B, Reyes M, Jakab A, Gerstner E, Kirby J, Kalpathy-Cramer J, Farahani K (eds) MICCAI chall. Multimodal brain tumor segmentation. IEEE, Nagoya, pp 31–37Google Scholar
- 15.Reza S, Iftekharuddin KM (2013) Multi-class abnormal brain tissue segmentation using texture features. In: Menze B, Reyes M, Jakab A, Gerstner E, Kirby J, Kalpathy-Cramer J, Farahani K (eds) MICCAI chall. Multimodal brain tumor segmentation. IEEE, Nagoya, pp 38–42Google Scholar
- 16.Zhao L, Sarikaya D, Corso JJ (2013) Automatic brain tumor segmentation with MRF on supervoxels. In: Menze B, Reyes M, Jakab A, Gerstner E, Kirby J, Kalpathy-Cramer J, Farahani K (eds) MICCAI chall. Multimodal brain tumor segmentation. IEEE, Nagoya, pp 51–57Google Scholar
- 17.Geremia E, Menze BH, Ayache N (2012) Spatial decision forests for glioma segmentation in multi-channel MR images. In: MICCAI chall. Multimodal brain tumor segmentation. pp 14–18Google Scholar
- 20.Chaddad A (2015) Automated feature extraction in brain tumor by magnetic resonance imaging using gaussian mixture models. J Biomed Imaging 2015:8Google Scholar
- 29.Andrews PS (2006) An investigation into mutation operators for particle swarm optimization. In: 2006 IEEE international conference evolutionary computation. IEEE, Vancouver, pp 1044–1051Google Scholar
- 30.Kennedy J, Eberhart R (1995) Particle swarm optimization. In: International conference on neural networks (ICNN’95). IEEE, Perth, pp 1942–1948Google Scholar
- 32.Shi Y, Eberhart RC (1999) Emperical study of particle swarm optimization. In: IEEE congress on evolutionary computation. IEEE, Washington, DC, pp 101–106Google Scholar
- 41.Acharya UR, Raghavendra U, Fujita H et al (2016) Automated characterization of fatty liver disease and cirrhosis using curvelet transform and entropy features extracted from ultrasound images. Comput Biol, MedGoogle Scholar