Automatic segmentation of sub-acute ischemic stroke lesion by using DTCWT and DBN with parameter fine tuning

  • Sunil Babu MelingiEmail author
  • V. Vijayalakshmi
Research Paper


In image processing the ischemic stroke lesion segmentation is a major procedure used to extricate suspicious regions from the given MRI brain image. For classification and segmentation of MRI in this paper, we proposed a three-step framework. To remove noise the initial step utilizes a de-noising technique based on dual tree complex wavelet transform (DTCWT) test without affective the essential image features and content. In the second step, an un-supervised deep belief network (DBN) is intended for learning the unlabelled features. Here, the noise in MRI can cause a significant corruption of data that impedes the execution of DBNs. The DTCWT in the initial step enhances execution of DBNs. Additionally, we manage the issue of DBNs parameters fine-tuning by means of a quick meta-heuristic approach named salp swarm algorithm. Based on the simulation behaviour of salps this new meta-heuristic algorithm is planned to solve optimisation issues. It is validated against different benchmark test functions and afterward contrasted with well known state-of-the-art optimisation algorithms like genetic algorithm, particle swarm optimisation, bat algorithm, artificial bee colony algorithm and cuckoo search algorithm for performance efficiency.


Dual tree complex wavelet transforms deep belief network Salp swarm algorithm Parameter fine-tuning Stroke lesion Segmentation Classification 



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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringPondicherry Engineering CollegePillaichavadiIndia

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