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Classification of Magnetic Resonance Brain Images Using Feature Extraction and Adaptive Neuro-Fuzzy Inference

Abdullah-Al-Zubaer Imran, Tomasz G. Smolinski, David D. PokrajacDepartment of Computer and Information Sciences, Delaware State University, Dover DE 19901

INTRODUCTION

KEYWORDS

WORKING PROCEDURE

INPUT TRAINING DATASET

PERFORMANCE EVALUATION

CONCLUSION

REFERENCES

ACKNOWLEDGEMENTS

The classification of malignant and benign brain tumors has been shown here. In the feature extraction purpose, several other methods can be applied in order to acquire better features of brain tumors from MRI images. Though the ANFIS has been used as the classifier here, segmentation of medical images can be performed by the ANFIS itself. Thus, in medical imaging ANFIS network could be an useful tool for assisting neurologists to better diagnose. The performance of classifier network decreases depending on the limited input training data set and also with so many nodes associated with it. Hence, large input data could improve the performance of the classifier.

This project was supported by a grant from the National Institute of General Medical Sciences (P20 GM103446) from the National Institutes of Health. Also, the work was partially supported in part by the US Department of Defense Breast Cancer Research Program (HBCU Partnership Training Award #BC083639), the US National Science Foundation (CREST grant #HRD-1242067), and the US Department of Defense/Department of Army (Award #W911NF-11-2-0046).

RESULTS

Diagnosing brain through magnetic resonance image (MRI) is gaining popularity rapidly. To assist in diagnosis, an automated classification of brain MRI is developed using feature extraction and the hybrid combination of artificial neural network (ANN) and fuzzy inference system (FIS). Without any kind of prior expert knowledge, the adaptive neuro-fuzzy inference system (ANFIS) can efficiently classify malignant and benign tumors of MRI brain images .

MRI brain image, Feature extraction, Skull stripping, Tumor segmentation, ANFIS, Automatic classification

The ANFIS is normally represented by a six-layer feed forward neural network.

Fig. 2 shows the ANFIS architecture that corresponds to the first order Takagi Sugeno fuzzy model.

The ANFIS network is trained using the input data set as the same way back propagation learning network learns.

MRI brain images are introduced to the system as input data set.

The feature extraction stage having sub-stages as shown in Fig. 1 produce the required feature data for the next stage.

The extracted features data are fed to the ANFIS network and the MRI brain tumors are classified.

Fig.2: The hybrid neuro-fuzzy inference network

Fig. 3: The physical meaning: c determines the center of the corresponding

membership function; a is the half width; and b (together with a) controls the slopes at the crossover points.

Fig. 4: Working Equations in each layer of ANFIS

In the forward pass of ANFIS, a vector k containing the consequent parameters is tuned by least square estimation.

In the backward pass, the antecedent parameters a, b, c are adjusted based on the error calculated for training patterns.

Fig. 5: Sample MRI brain images input data set

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[4] Noorhayati Mohamed Noor#1, Noor Elaiza Abdul Khalid#2, Rohaida Hassan#3, Shafaf Ibrahim#4,Ihsan Mohd Yassin*5. Adaptive Neuro-Fuzzy Inference System for Brain Abnormality Segmentation; 2010 IEEE Control and System Graduate Research Colloquium.

[5] Selvaraj.D, Dhanasekaran.R. Mri Brain Tumour Detection By Histogram And Segmentation By Modified Gvf Model; IJECET Volume 4, Issue 1, January- February (2013), pp. 55-68

[6] Dr Mohammad. V. Malakooti, Seyed Ali Mousavi, and Dr Navid Hashemi Taba. MRI Brain Image Segmentation Using Combined Fuzzy Logic and Neural Networks for Tumor Detection; Journal of Academic and Applied Studies Vol. 3(5) May 2013, pp. 1-15.

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