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Recent Advances in Computer Science and Communications

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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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General Research Article

Brain Tumor Detection via Asymmetry Quantification Across Mid Sagittal Plane

Author(s): Shoaib A. Banday* and Mohammad K. Pandit

Volume 15, Issue 2, 2022

Published on: 31 August, 2020

Page: [266 - 273] Pages: 8

DOI: 10.2174/2666255813999200831104047

Price: $65

Abstract

Introduction: Brain tumor is among the major causes of morbidity and mortality rates worldwide. According to the National Brain Tumor Foundation (NBTS), the death rate has nearly increased by as much as 300% over the last couple of decades. Tumors can be categorized as benign (non-cancerous) and malignant (cancerous). The type of the brain tumor significantly depends on various factors like the site of its occurrence, its shape, the age of the subject, etc. On the other hand, Computer-Aided Detection (CAD) has been improving significantly in recent times. The concept, design and implementation of these systems ascend from fairly simple ones to computationally intense ones. For efficient and effective diagnosis and treatment plans in brain tumor studies, it is imperative that an abnormality is detected at an early stage as it provides a little more time for medical professionals to respond. The early detection of diseases has predominantly been possible because of medical imaging techniques developed from the past many decades like CT, MRI, PET, SPECT, FMRI, etc. The detection of brain tumors, however, has always been a challenging task because of the complex structure of the brain, diverse tumor sizes and locations in the brain.

Methods: This paper proposes an algorithm that can detect the brain tumors in the presence of the Radio-Frequency (RF) inhomogeneity. The algorithm utilizes the Mid Sagittal Plane as a landmark point across which the asymmetry between the two brain hemispheres is estimated using various intensity and texture-based parameters.

Results: The results show the efficacy of the proposed method for the detection of the brain tumors with an acceptable detection rate.

Conclusion: In this paper, we have calculated three textural features from the two hemispheres of the brain viz: Contrast (CON), Entropy (ENT) and Homogeneity (HOM) and three parameters viz: Root Mean Square Error (RMSE), Correlation Co-efficient (CC), and Integral of Absolute Difference (IAD) from the intensity distribution profiles of the two brain hemispheres to predict any presence of the pathology. First, a Mid-Sagittal Plane (MSP) is obtained on the Magnetic Resonance Images that virtually divides the brain into two bilaterally symmetric hemispheres. The block-wise texture asymmetry is estimated for these hemispheres using the above 6 parameters.

Keywords: Detection accuracy, Mid Sagittal Plane (MSP), Magnetic Resonance Imaging (MRI), Gray Level Co-Occurrence Matrix (GLCM), texture, True Positive (TP), True Negative (TN), False Positive (FP).

Graphical Abstract

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