Analysis of brain MRI for tumor detection & segmentation

Imran Ahmed, Qazi Nida-Ur-rehman, Ghulam Masood, Muhammad Nawaz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


This research work introduces a simple yet effective method for brain tumor detection using proposed dataset of 1500 images. There are different types of brain tumor; among the existing we have considered four different types i.e. CNS Lymphoma, Glioblastoma, Meningioma, and Metastases. The four major steps in the proposed method are pre-processing, segmentation, post-processing and image fusion. In the pre-processing, 2D-Adptive filter is applied to enhance the quality of the image. Otsu's segmentation is used to extract tumor region from normal tissues. The segmented region contains skull boundaries in the form of noise; hence morphological operations i.e. erosion and dilation have been applied to remove the extra noise caused by segmentation. Overlay based image fusion is applied to get a clear visual of segmented tumor region. We achieved a detection rate of 93 percent with 7 percent error rate using this dataset. Furthermore, we classify the tumor into benign and malignant based on the size of tumor.

Original languageEnglish
Title of host publicationWCE 2016 - World Congress on Engineering 2016
EditorsLen Gelman, David W.L. Hukins, S. I. Ao, S. I. Ao, Len Gelman, S. I. Ao, Alexander M. Korsunsky, Andrew Hunter
PublisherNewswood Limited
Number of pages6
ISBN (Electronic)9789881925305
Publication statusPublished - 2016
Externally publishedYes
EventWorld Congress on Engineering 2016, WCE 2016 - London, United Kingdom
Duration: 29 Jun 20161 Jul 2016

Publication series

NameLecture Notes in Engineering and Computer Science
ISSN (Print)2078-0958


ConferenceWorld Congress on Engineering 2016, WCE 2016
Country/TerritoryUnited Kingdom


  • 2D adaptive filter
  • Image fusion
  • Image segmentation
  • MRI imaging


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