@inproceedings{b5f0ba42d13a49e4ba8dc04a8b2cf46c,
title = "Analysis of brain MRI for tumor detection & segmentation",
abstract = "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.",
keywords = "2D adaptive filter, Image fusion, Image segmentation, MRI imaging",
author = "Imran Ahmed and Qazi Nida-Ur-rehman and Ghulam Masood and Muhammad Nawaz",
year = "2016",
language = "English",
series = "Lecture Notes in Engineering and Computer Science",
publisher = "Newswood Limited",
pages = "456--461",
editor = "Len Gelman and Hukins, {David W.L.} and Ao, {S. I.} and Ao, {S. I.} and Len Gelman and Ao, {S. I.} and Korsunsky, {Alexander M.} and Andrew Hunter",
booktitle = "WCE 2016 - World Congress on Engineering 2016",
note = "World Congress on Engineering 2016, WCE 2016 ; Conference date: 29-06-2016 Through 01-07-2016",
}