Pathological Brain Detection (PBD)

Magnetic resonance (MR) images are widely applied to help doctors and technicians for disease diagnosis because this imaging technique provides clearer soft tissue details without causing damages to the patient’s tissues.[1] However, it is tedious and time-consuming for manual analysis, since the amount of data it associates is too large.

Hence, it is of necessity and urgency to develop automatic classification systems of MR brain images, which could perform with high accuracy rapidly in clinical medicine. This task is called "pathological brain detection (PBD)".

Cost Matrix

The cost of predicting pathological to healthy is terrible. Nevertheless, the cost of misclassification of healthy to pathological is low. So this is a cost-sensitive problem.

State-of-the-art

2006

Chaplot et al. was the first to use DWT coefficients to detect pathological brains.[2]

Maitra and Chatterjee employed the Slantlet transform, which is an improved version of DWT. Their feature vector of each image is created by considering the magnitudes of Slantlet transform outputs corresponding to six spatial positions chosen according to a specific logic.[3]

2008

Georgiadis et al. designed a software system for discriminating between metastatic and primary brain tumors on MRI.[4]

2010

In 2010, Wang and Wu presented a forward neural network (FNN) based method to classify a given MR brain image as normal or abnormal. The parameters of FNN were optimized via adaptive chaotic particle swarm optimization (ACPSO). Results over 160 images showed that the classification accuracy was 98.75%.[5]

2011

In 2011, Wu and Wang proposed using DWT for feature extraction, PCA for feature reduction, and FNN with scaled chaotic artificial bee colony (SCABC) as classifier.[6]

2013

In 2013, Saritha et al. were the first to apply wavelet entropy (WE) to detect pathological brains. Saritha also suggested to use spider-web plots.[7]

Later, Zhang et al. in 2015 proved removing spider-web plots did not influence the performance.[8]

Back to 2013, Genetic pattern search method was applied to identify abnormal brain from normal controls. Its classification accuracy was reported as 95.188%.[9]

Das et al. proposed to use Ripplet transform.[10] Zhang et al. proposed to use particle swarm optimization (PSO).[11] Kalbkhani et al. suggested to use GARCH model.[12]

2014

In 2014, El-Dahshan et al. suggested to use pulse coupled neural network.The PCNN was used for segmentation in their study.[13]

Wang et al. presented a diagnosis method to distinguish Alzheimer’s Disease (AD) and mild cognitive impairment (MCI) from normal controls.[14]

Padma et al. used combined wavelet statistical texture features, to segment and classify AD benign and malignant tumor slices.[15]

2015

In 2015, Zhou et al. suggested to apply naive Bayes classifier to detect pathological brains.[16]

Zhang et al. suggested to use discrete wavelet packet transform.[17]

Wahid and Khan suggested to use filters for the removal of noises, and extracted color moments as mean features.[18]

Wang et al. suggested to use stationary wavelet transform (SWT) to replace DWT, and then they proposed a hybridization of PSO and ABC (HPA) algorithm to train the classifier.[19]

Dong et al. suggested to use stationary wavelet transform (SWT) and to use generalized eigenvalue proximal SVM for classification.[20]

References

  1. ↑ Goh, Susan (2014). "Mitochondrial dysfunction as a neurobiological subtype of autism spectrum disorder: Evidence from brain imaging". JAMA Psychiatry 71 (6r): 665–671.
  2. ↑ Chaplot, Sandeep (2006). "Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network". Biomedical Signal Processing and Control 1 (1): 86–92. doi:10.1016/j.bspc.2006.05.002.
  3. ↑ Maitra, Madhubanti (2006). "A Slantlet transform based intelligent system for magnetic resonance brain image classification". Biomedical Signal Processing and Control 1 (4): 299–306. doi:10.1016/j.bspc.2006.12.001.
  4. ↑ Georgiadis, Pantelis (2008). "Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features". Computer Methods and Programs in Biomedicine 89 (1): 24–32. doi:10.1016/j.cmpb.2007.10.007.
  5. ↑ Wang, S.; Wu, W. (2010). "A Novel Method for Magnetic Resonance Brain Image Classification based on Adaptive Chaotic PSO". Progress in Electromagnetics Research 109: 325–343.
  6. ↑ Zhang, Yudong; Wu, L. (2011). "Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm". Progress in Electromagnetics Research 2011: 65–79.
  7. ↑ Saritha, M.; Joseph, K. Paul (2013). "Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network". Pattern Recognition Letters 34 (16): 2151–2156. doi:10.1016/j.patrec.2013.08.017.
  8. ↑ Zhang, Yudong; Dong, Zhengchao; Ji, Genlin (2015). "Effect of spider-web-plot in MR brain image classification". Pattern Recognition Letters 62: 14–16. doi:10.1016/j.patrec.2015.04.016.
  9. ↑ Zhang, Y.; Wang, S.; Ji, G.; Dong, Z. (2013). "Genetic Pattern Search and its Application to Brain Image Classification". Mathematical Problems in Engineering 2013: 1–8. doi:10.1155/2013/580876.
  10. ↑ Das, S.; Chowdhury, M.; Kundu, M.K. (2013). "Brain MR Image Classification Using Multiscale Geometric Analysis of Ripplet". Progress In Electromagnetics Research 137: 1–17. doi:10.2528/pier13010105.
  11. ↑ Zhang, Y.; Wang, S. (2013). "An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine". The Scientific World Journal 2013: 9. doi:10.1155/2013/130134.
  12. ↑ Kalbkhani, H., M.G. Shayesteh, and B. Zali-Vargahan, Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series. Biomedical Signal Processing and Control, 2013. 8(6): p. 909-919.
  13. ↑ El-Dahshan, E.S.A. (2014). "Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm". Expert Systems with Applications 41 (11): 5526–5545. doi:10.1016/j.eswa.2014.01.021.
  14. ↑ Wang, S. (2014). "Classification of Alzheimer Disease Based on Structural Magnetic Resonance Imaging by Kernel Support Vector Machine Decision Tree". Progress In Electromagnetics Research 144: 171–184.
  15. ↑ Padma, A.; Sukanesh, R. (2014). "Segmentation and Classification of Brain CT Images Using Combined Wavelet Statistical Texture Features". Arabian Journal for Science and Engineering 39 (2): 767–776. doi:10.1007/s13369-013-0649-3.
  16. ↑ Zhou, Xing-Xing (2015). "Detection of Pathological Brain in MRI Scanning Based on Wavelet-Entropy and Naive Bayes Classifier". Bioinformatics and Biomedical Engineering: 201–209. doi:10.1007/978-3-319-16483-0_20.
  17. ↑ Zhang, Y.; Dong, Z. (2015). "Preclinical Diagnosis of Magnetic Resonance (MR) Brain Images via Discrete Wavelet Packet Transform with Tsallis Entropy and Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM)". Entropy 17 (4): 1795–1813. doi:10.3390/e17041795.
  18. ↑ Nazir, M.; Wahid, F.; Khan, S.A. (2015). "A simple and intelligent approach for brain MRI classification". Journal of Intelligent & Fuzzy Systems 28 (3): 1127–1135. doi:10.3233/IFS-141396.
  19. ↑ Wang, S. (2015). "Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection". International Journal of Imaging Systems and Technology 25 (2): 153–164. doi:10.1002/ima.22132.
  20. ↑ Dong, Z.; Liu, A. (2015). "Magnetic Resonance Brain Image Classification via Stationary Wavelet Transform and Generalized Eigenvalue Proximal Support Vector Machine". Journal of Medical Imaging and Health Informatics 5 (7): 1395–1403.
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