Ekstrasi Fitur Citra MRI Otak Menggunakan Data Wavelet Transform (DWT) untuk Klasifikasi Penyakit Tumor Otak

Lastri Widya Astuti

Abstract


ABSTRACT

The brain is formed from two types of cells: glia and neurons. Glia functions to support and protect neurons, while neurons carry information in the form of electrical pulses known as potential action. The brain regulates and coordinates most of the body's movements, behavior, and homeostasis functions such as heart rate, blood pressure, body fluid balance and body temperature. A brain tumor is a mass of abnormally growing brain cells. Most brain tumors can spread through brain tissue, but rarely spread to other areas of the body. But in the case of benign brain tumors, as they grow they can destroy and suppress other normal brain tissue, which can result in paralysis. Several methods are used to detect disorders of the brain nerve tissue, including: Magnetic Resonance Imaging (MRI). This research is intended to build a classification system for brain image data using Magnetic Resonance Imaging (MRI) with the category, normal, Glioma, metastatic bronchogenic carcinoma or Alzheimer's using Magnetic Resonance Imaging (MRI) so that it can assist in decision making for medical experts. While the method used in this research is Discrete Wavelet Transformation (DWT) for the feature extraction process so that the unique characteristics of an object can be recognized, as well as the classification process using the adaptive neighborhood neural network method. This research is able to integrate the two methods with the acquisition of significant accuracy.

Keywords : feature extraction, classification, MRI, Brain

ABSTRAK

Otak terbentuk dari dua jenis sel: glia dan neuron. Glia berfungsi untuk menunjang dan melindungi neuron, sedangkan neuron membawa informasi dalam bentuk pulsa listrik yang di kenal sebagai potensi aksi. Otak mengatur dan mengkordinir sebagian besar,gerakan, perilaku dan fungsi tubuh homeostasis seperti detak jantung, tekanan darah, keseimbangan cairan tubuh dan suhu tubuh. Tumor otak adalah sekumpulan massa sel-sel otak yang tumbuh abnormal. Sebagian besar tumor otak dapat menyebar melalui jaringan otak, tetapi jarang sekali menyebar ke area lain dari tubuh. Namun pada kasus tumor otak yang jinak, saat mereka tumbuh dapat menghancurkan dan menekan jaringan otak normal lainnya, yang dapat berakibat pada kelumpuhan. Beberapa metode dipergunakan untuk mendeteksi gangguan pada jaringan syaraf otak, diantaranya: Magnetic Resonance Imaging (MRI). Penelitian ini dimaksudkan untuk membangun sistem klasifikasi untuk data citra otak menggunakan Magnetic Resonance Imaging (MRI) dengan kategori, normal, Glioma, metastatic bronchogenic carcinoma atau Alzheimer menggunakan Magnetic Resonance Imaging (MRI) sehingga dapat membantu  dalam pengambilan keputusan bagi tenaga ahli dibidang kedokteran. Sedangkan metode yang digunakan dalam penelitian adalah Discrete Wavelet Transformation (DWT) untuk proses ekstrasi fitur (feature extraction) agar karakteristik unik dari suatu objek dapat dikenali, serta proses klasifikasi menggunakan metode adaptive neighborhood neural network. Penelitian ini mampu mengintegrasikan kedua metoda dengan perolehan hasil akurasi yang signifikan.

Kata kunci : ekstrasi fitur, klasifikasi, MRI, Otak

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References


DD, Lee, H.S, Seung. 2001, “Learning the part of objects by non negative matrixs factorization”, Nature 401(6755), hal 788-791

DD, Lee, H.S, Seung. 2000, “Algorithms for non-negative matrix factorization”, Advance in Neural Information Processing System, Vol 13, hal 556-562

El-Dahshan, E.-S. A., T. Hosny, and A.-B. M. Salem. 2010, “Hybrid intelligent techniques for MRI brain images classification," DigitalSignal Processing, Vol. 20, No. 2, hal 433-441.

Ekayuda, I. 2005, Radiologi Diagnostik, edisi kedua, Fakultas Kedokteran Universitas Indonesia, Jakarta.

F, Gorunescu. 2007, “Data mining techniques in computer-aided diagnosis: Non-invasive cancer detection”, PWASET 25, hal 427–430.

Gonzalez R. C, Richard E.Woods. 2002, Digital Image Processing, second edition, Paerson education, Boston.

Gonzalez R. C, Richard E.Woods, Steven L.Eddins. 2004, Digital Image Processing using Matlab, Paerson education, Boston.

Hynynen, K. 2010, “MRI-guided focused ultrasound treatments", Ultrasonics, Vol. 50, No. 2, hal 221-229.

H.P. Mauridhi, K. Agus 2006, Supervised Neural Network, edisi pertama, Graha ilmu, Yogyakarta.

K. Karibasappa, S. Patnaik. (2004), “Face recognition by ANN using wavelet transform coefficients”, IE (India) J. Computer Eng. 85, hal 17–23.

Kemal Polat, Bayram Akdemir, Salih Güne. 2008, “Computer aided diagnosis of ECG data on the least square support vector machine”, Digital Signal Process. 18, hal 25–32.

L.M. Fletcher-Heath, L.O. Hall, D.B. Goldgof, F.R. Murtagh. 2001, “Automatic segmentation of non-enhancing brain tumors in magnetic resonance images”, Artif. Intell. Med. 21, hal 43–63.

Mohsin, S. A., N. M. Sheikh, and U. Saeed. 2008, “MRI induced heating of deep brain stimulation leads: Effect of the air-tissue interface," Progress In Electromagnetics Research, Vol. 83, hal 81-91.

M. Maitra, A. Chatterje. 2007, “Hybrid multiresolusion slanted transform and fuzzy c means clustering approach for normal-pathological brain MR image segregation”, Med. Eng. Phys.

M. W. Berry, M. Browne, A. N. Langville, P. V. Pauca, R. J. Plemmons. 2007, “Algorithms and application for approximate nonnegative matrix factorization”,Computational Statistic and Data Analysis 52 (1), hal 155-173

Mousa. R, Munib. Q, Mousa. A. 2005, “Breast Cancer Diagnosis System Based on Wavelet Analysis and Fuzzy Neural”, Jordan, hal 713–723

R. Payam, Tang. L 2008, “Cross Validation”, Arizona State University, File path://ppdys1108/womat3/production/PRODEN/000000005/0000008302/0000000016/0000875816.3D

Sengur.A. 2007, “An expert system based on principal component analysis, artificial immune system and fuzzy k-NN for diagnosis of valvular heart diseases”, Comp. Biol. Med

S. Chaplot, L.M. Patnaik, N.R. Jagannathan. 2006, “Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network”, Biomed. Signal Process, Control 1, hal 86–92.

S. Haykin. 1999, Neural Networks: A Comprehensive Foundation, Prentice Hall.

T. Kathirvalavakumar, T., Subavathi, S.J. 2009, “Neighborhood based modified backpropagation algorithm using adaptive learning parameters for training feedforward neural networks”,Journal of Neurocomputing, Elsevier

Y. Zhang, Z. Dong, L. Wu, S. Wang. 2011, “ A hybrid method for MRI brain image classification”, Experts System with Application, 38, hal 10049-10053

Zhu, Z., Guo, Y.F., Zhu, X., Xue, X. 2010, “Normalized dimensionality reduction using nonnegative matrix factorization”, Journal of Neurocomputing, Elsevier.

Harvard Medical School, Web, data available at http://med.harvard.edu/AANLIB




DOI: http://dx.doi.org/10.36982/jig.v10i2.854

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