Elsevier

Cognitive Systems Research

Volume 59, January 2020, Pages 273-286
Cognitive Systems Research

A unified patch based method for brain tumor detection using features fusion

https://doi.org/10.1016/j.cogsys.2019.10.001Get rights and content

Abstract

The manuscript authenticates the effectiveness of fusing texture and geometrical (GEO) features in magnetic resonance imaging (MRI) for tumor classification. The presented technique is evaluated on two MRI including T2 and FLAIR. The tumor region is enhanced using fast non-local mean (FNLM) method with 4 × 4 patch size. Otsu algorithm is used for tumor segmentation. Moreover, multiple features are extracted for example local binary pattern (LBP), histogram of oriented gradients (HOG) and GEO (area, circularity, filled area, and perimeter) across each segmented image. These acquired features are merged into a single dimensional vector for prediction. In the end, the fused vector is used with multiple classifiers which proved that features fusion provides good results as compared with individual features.

Introduction

Brain tumor is the consequence of the production of abnormal cells. It is a substantial reason in increasing mortality rate among adults and children (Amin et al., 2017, Rajinikanth et al., 2017). Based on tumor malignancy and growth rate, brain tumor is divided into two major categories named as primary and secondary. Main tumor initiates from surrounding or inside the brain while secondary level tumor is caused when abnormal cells grow from the primary tumor in other body parts. Based on the origin and behavior of cells, brain tumor is classified by world health organization (WHO) into benign (least dangerous) and malignant (more dangerous). Glioma is a known kind of brain tumor which instigates from the glial cells and infiltrates around the brain tissues (Louis et al., 2007).

A statistical report (http://www.cbtrus.org/factsheet/factsheet.html, 2018) illustrates that overall occurrence frequency of benign, malignant and central nervous system (CNS) tumor is 23.03 cases per 100,000 in 392,982 total incident tumor (7.12 cases per 100,000 malignant tumor in 121,277 total incident tumor as well as 15.91 cases per 100,000 benign tumor in 271,705 tumor incident). The occurrence rate of tumor is higher in females (25.31 cases per 100,000 in 227,834 tumor incident) as compared with males (20.59 cases per 100,000 in 165,148 total incident tumor).

MRI is a painless and non-invasive test that creates images with detailed information about brain and brain stem. MRI images are produced by using radio waves and magnetic field. This scan is also called cranial or brain MRI (Nida, Sharif, Khan, Yasmin, & Fernandes, 2016). MRI is effective in highlighting the brain abnormalities as compared with other modalities because it detects the small structure in brain (shown in Fig. 1). It performs scanning without moving the patient physically (Bullmore and Sporns, 2009, DeAngelis, 2001).

The manual diagnosis process is a hectic and time-taking procedure which degrades the ability of radiologists. Multi-grade detection of brain tumor is a necessary job for better investigation of MRI (Acharya et al., 2019) but still several challenges existwhich reduce the accuracy of tumor segmentation and classification (Rajinikanth, Fernandes, Bhushan, & Sunder, 2018). The major challneges which are faced by researchers in the area of computer vision (CV) are noise in MRI scans, irragular tumors and selection of relevent and irrelevant features (Havaei et al., 2017, Mughal et al., 2018, Rewari, 2019, Sehgal et al., 2016, Swathi and Balasubramanian, 2016). The major contribution of this work is as below:

  • I.

    Fast Non-Local Mean (FNLM) method is used as a pre-processing step in which 4 × 4 patch and 2 × 2 window size is selected to aid accurate segmentation.

  • II.

    Otsu algorithm is used for segmentation and applied morphological operations to refine tumor region.

  • III.

    GEO, LBP and HOG features are extracted and fused in terms of mean and variance to a single feature vector for good classification results. The detailed experiments are performed to compare the results of fused vector as well as individual vector with seven classifiers.

This manuscript is divided into the sections such as existing work in II, proposed approach in III; results in IV and conclusion in V are presented.

Section snippets

Related work

The most relevant work published regarding brain tumor detection using MRI (Amin et al., 2018, Masood et al., 2015, Raza et al., 2012) is discussed briefly. The wavelet (Zhang, Fadili, & Starck, 2008) and combination of adaptive, wiener, median and weighted median filters (Jaya et al., 2009, Krajsek and Mester, 2006) is utilized for the reduction of noise (Greenspan, Ruf, & Goldberger, 2006). The modified curvature diffusion equation (Yang & Huang, 2006), parametric bias field correction, N4ITK

Motivation

We have used the implementation presented by (Amin et al., 2017) for early brain tumor detection in our research. Our research work is greatly inspired by the approach presented for early brain tumor detection in (Amin et al., 2017) where segmentation of brain tumor is performed using Gaussian filter and morphological operations and investigated for shape, texture, and intensity features with variants of SVM (Cristianini and Shawe-Taylor, 2000) classifiers. The authors achieved promising

Proposed methodology

The four major steps (enhancement, segmentation, feature acquisition and prediction) are exploited for the recognition of brain tumor such as FNLM for enhancement, Otsu (Otsu, 1979) algorithm for segmentation, features fusion and classifiers for classification as depicted in Fig. 2.

Material and experimentation

Performance is evaluated on three publicly available datasets, BRATS 2013, BRATS 2015 (Menze et al., 2015) and Harvard (Summers, 2003). BRATS 2013 consists of 30 subjects of glioma, twenty high grade (HG) and ten low grade (LG) while BRATS 2015 consists of 220 subjects of HG, 54 LG and 110 both HG and LG glioma. The Harvard consists of T2 weighted MRI with 39LG and 46 HG glioma images. For validation of this work, two tests are performed, 1st is pixel based while 2nd is based classification on

Conclusion and discussion

Segmenting brain tumor manually is a tedious work. Accurate and efficient detection of early stage brain tumor is the major theme of our work so that the patient could be treated in time before any complication. This method performs well because of the combination of FNLM for enhancement, Ostu for segmentation, features fusion and multiple classifiers.

The proposed technique achieved 0.98, 1.00 and 1.00 ACC on BRATS 2013, 2015 and Harvard datasets respectively. The comparison of results with

Declaration of Competing Interest

The authors declared that there is no conflict of interest.

References (78)

  • H. Li et al.

    A novel end-to-end brain tumor segmentation method using improved fully convolutional networks

    Computers in Biology and Medicine

    (2019)
  • V. Rajinikanth et al.

    Entropy based segmentation of tumor from brain MR images–a study with teaching learning based optimization

    Pattern Recognition Letters

    (2017)
  • X. Zhao et al.

    A deep learning model integrating FCNNs and CRFs for brain tumor segmentation

    Medical Image Analysis

    (2018)
  • U.R. Acharya et al.

    Automated detection of Alzheimer’s disease using brain MRI images–A study with various feature extraction techniques

    Journal of Medical Systems

    (2019)
  • M. Agn et al.

    Brain tumor segmentation using a generative model with an RBM prior on tumor shape

  • I. Ahmed et al.

    Analysis of Brain MRI for Tumor Detection & Segmentation

    Proceedings of the world congress on engineering

    (2016)
  • P. Aljabar et al.

    Classifier selection strategies for label fusion using large atlas databases

  • J. Amin et al.

    A distinctive approach in brain tumor detection and classification using MRI

    Pattern Recognition Letters

    (2017)
  • J. Amin et al.

    Big data analysis for brain tumor detection: Deep convolutional neural networks

    Future Generation Computer Systems

    (2018)
  • R. Anderson

    The credit scoring toolkit: Theory and practice for retail credit risk management and decision automation

    (2007)
  • E.D. Angelini et al.

    Glioma dynamics and computational models: A review of segmentation, registration, and in silico growth algorithms and their clinical applications

    Current Medical Imaging Reviews

    (2007)
  • S. Bakas et al.

    Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features

    Scientific Data

    (2017)
  • S. Bauer et al.

    A survey of MRI-based medical image analysis for brain tumor studies

    Physics in Medicine & Biology

    (2013)
  • L. Breiman

    Bagging predictors

    Machine Learning

    (1996)
  • L. Breiman et al.

    Classification and regression trees

    (1984)
  • E. Bullmore et al.

    Complex brain networks: Graph theoretical analysis of structural and functional systems

    Nature Reviews Neuroscience

    (2009)
  • X. Chen et al.

    Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders

    Medical Imaging for Deep Learning

    (2018)
  • N. Cristianini et al.

    An introduction to support vector machines and other kernel-based learning methods

    (2000)
  • Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human...
  • L.M. DeAngelis

    Brain tumors

    New England Journal of Medicine

    (2001)
  • H. Dong et al.

    Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks

  • R.A. Fisher

    The use of multiple measurements in taxonomic problems

    Annals of Eugenics

    (1936)
  • M. Goetz et al.

    Extremely randomized trees based brain tumor segmentation

    Proceeding of BRATS Challenge-MICCA

    (2014)
  • R.C. Gonzalez et al.

    Digital image processing, in

    (2002)
  • H. Greenspan et al.

    Constrained Gaussian mixture model framework for automatic segmentation of MR brain images

    IEEE Transactions on Medical Imaging

    (2006)
  • A.E. Hassanien et al.

    Advances in soft computing and machine learning in image processing

    (2017)
  • M. Huang et al.

    Brain tumor segmentation based on local independent projection-based classification

    IEEE Transactions on Biomedical Engineering

    (2014)
  • S. Iqbal et al.

    Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN)

    Microscopy Research and Technique

    (2018)
  • A. Islam et al.

    Multifractal texture estimation for detection and segmentation of brain tumors

    IEEE Transactions on Biomedical Engineering

    (2013)
  • Cited by (0)

    View full text