Title: Efficient tumour detection from brain MR image with morphological processing and classification using unified algorithm

Authors: G. Sethuram Rao; D. Vydeki

Addresses: Velammal Institute of Technology, Chennai, Tamil Nadu 600066, India ' VIT Chennai, Tamil Nadu 600127, India

Abstract: Brain diseases caused due to malignant are the biggest concern among all the age groups. Studies show that almost 80% of death cases are reported due to presence of malignant tumour. Hence diagnosing brain tumour at an early stage would increase the survival rate. Magnetic resonance imaging (MRI) plays a major role in diagnosing tumours in human brain. However, it is considered to be a time consuming and tedious process which could lead to deviation in the opinion of radiologists. This has led to the development of computer-based automatic extraction of tumour cells from the images obtained by MRI. This paper proposes an efficient tumour detection mechanism from MR images using morphological processing and unified algorithm. A neural network that uses bounding boxes and associated class probabilities detects the packets of tumour that exist in a full MR image. Simulated results of the proposed technique on the BRATS 2016 dataset show that a detection accuracy of 95.97% is achieved, while reducing the likelihood of false positives. This approach is compared with other detection methods such as DPM and R-CNN and the analysis proves that our method proposed outclasses the other detection methods.

Keywords: terms-magnetic resonance image; brain tumour; thresholding; histogram; segmentation; CLAHE; unified detection; malignant; benign.

DOI: 10.1504/IJMEI.2021.118762

International Journal of Medical Engineering and Informatics, 2021 Vol.13 No.6, pp.461 - 473

Received: 21 Dec 2019
Accepted: 25 Jan 2020
Published online: 05 Nov 2021 *

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