PENGEMBANGAN SISTEM KLASIFIKASI MAMMOGRAM BERBANTUKAN KOMPUTER DENGAN MENGGUNAKAN ANALISIS TEKSTUR DAN KNN

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1 1 PENGEMBANGAN SISTEM KLASIFIKASI MAMMOGRAM BERBANTUKAN KOMPUTER DENGAN MENGGUNAKAN ANALISIS TEKSTUR DAN KNN Tri Martin Sanjaya, AsrizalPratama Putra ABSTRAK Pengembangansistemklasifikasi mammogram yang bertujuanmengklasifikasikankelasdaripenyakit yang terdapatdari mammogram denganmenggunakanmetodepra-proses NSCT (Non Subsampled ContourletTransform), metodeanalisistekstur LTEM (Law s Texture Energy Measures), danklasifikasi KNN (K Nearest Neighbour) denganderajat k = 5. Kata Kunci : Computer Vision, KecerdasanBuatan, IntelegensiaSemu, Pengembangan, Klasifikasi, Mammogram, AnalisisTekstur, LTEM, Law Texture Energy Measure, KNN, K Nearest Neighbour, NSCT, Non Subsampled Contourlet Transform. PENDAHULUAN

2 2 Denganmunculnyaberbagaimacamanalisisteksturuntukklasifikasi mammogram, munculpertanyaan manakahmetode yang paling tepatuntukmelakukanklasifikasimammogram?.dikarenakanbanyaknyametode-metode yang digunakandalamklasifikasi mammogram makaselalumunculperdebatantersebut. Klasifikasi mammogram terdiridarikumpulanberbagaimetodedalamimage Processing diantaranyaadalahmetodepra-proses, analisistekstur,danklasifikasidimanamasingmasingmetodememilikiperananpentinguntukkeakuratanhasildaripeng-klasifikasian mammogram. Tujuanutamadariklasifikasi mammogram tentusajauntukmenghasilkansuatu program analisistekstur yang diikutidenganklasifikasipada mammogram yang akurat.tentusajasemakinbaiksistemklasifikasimakasemakinakurathasil yang didapatpadasistemklasifikasi mammogram tersebut.sistemklasifikasi mammogram yang baikterdiridarikumpulan-kumpulanmetode yang tepatdenganfitur set yang tepatjuga yang akanmenghasilkan data akurat yang di ekstrak yang akanmenjadiacuandalamklasifikasi. NSCT (NON SUBSAMPLED CONTOURLET TRANSFORM) NSCT adalahmetode yang digunakandalam proses pra-proses. Kegunaanpra-proses adalahmelakukan proses-proses perbaikanpadagambardanmembuang noise-noise padagambar yang bertujuanmemastikangambartersebutberisi data akuratdanmemenuhikriteriauntukdigunakanpada proses berikutnya. NSCT merupakanpustakadari MATLAB yang dapat di download pada website MATLAB. Namapustaka NSCT nyayaitu NSCT Toolbox yang dibuatolehjose

3 3 Mejia.Berikutadalahcontohdari mammogram yang telahmelewati proses pra-proses menggunakanmetodee NSCT: Gambar1 : (Kiri) sebelumdilakukanpra-proses, (Kanan) setelahdilakukanpra-proses menggunakan NSCT. Dapatdilihatperbedaan cukupsignifikandimanakalsifikasibahkandapatdilihatdenganmatatelanjang yang titikpadakananataspojok). LTEM ( LAW S TEXTURE ENERGY MEASURES) Setelahmelewatitahappra-proses, (titik- makaakandilanjutkandengantahapekstraksifitur. Kegunaandariekstraksifituradalahmengambilfitur-fiturunik yang terdapatgambarmeliputi: garis, sisi, bubung, sudut, gumpal, dantitik yang kemudianakanmenjadiacuandalamklasifikasi mammogram.

4 4 Padatahapekstraksifiturini, penulismenggunakanmetode LTEM (Law s Texture Energy Measures). LTEM yang digunakanadalah LTEM dengan 5 matriksyang meliputi: L5 = [ ] E5 = [ ] S5 = [ ] W5 = [ ] R5 = [ ] Huruf-huruf yang ada pada setiap matriks dimensi memiliki arti sebagai berikut: L = Level (average grey level) E = Edge (extract edge features) S = Spot (extract spots) W = Wave (extract wave features) R = Ripple (extract ripple features) Fitur-fitur yang digunakandalamekstraksifiturmenggunakan LTEM antara lain:skewness S5L5TR, Mean R5L5TR, Mean L5L5TR, STD S5L5TR, dan STD W5L5TR.Penggunaandari 5 fitur set diatasberdasarkandarijurnal Texture Analysis of Tissue Surrounding Microcalcifications on Mammograms for Breast Cancer Diagnosis yang ditulis oleh A. Karahaliou, Msc dan S Skiadopoulos, Phd dari universitas Patras Yunani. Pemilihaniniberdasarkankarenanilaidariakurasi rata-rata dari 5 set fiturtersebutmemilikinilaiterbaikdibandingkandengan yang lain.

5 5 Tabel 1 Tabel fitur Textural Feature Category Best Feature Set Sensitivity (%) Specifity (%) Overall Accuracy (%) FOS Mean 92, Skewness GLCM Mean of Difference Entropy 85,2 78,3 82 Range of Local Homogeneity Range of Different Variance GLRLM Mean SRE 72,2 54,4 64 Mean LRE LTEM Skewness from S5L5TR 90, Mean from R5L5TR Mean from L5L5TR STD from S5L5TR STD from W5L5TR KNN (K NEAREST NEIGHBOUR) KNN merupakan klasifikasi dan penarikan kesimpulan dari data pelatihan yang telah dilakukan. KNN menggunakan nilai K yaitu nilai yang menghitung kumpulan-kumpulan data terdekat. Nilai K besar akan mengurangi efek noise pada klasifikasi tapi membuat batasan pada data menjadi kabur. Biasanya nilai K yang digunakan berupa ganjil tergantung juga dengan kebutuhan pembuat dan jumlah kelas yang digunakanpadaklasifikasi.

6 6 Prinsiputamadariteori KNN yaitudenganterlebihdahulumenghitungjarakeuclidian distance antaratitik-titiksejumlahnilai K yang dekatdengannilaipelatihan. Dari sejumlahnilai K, makaakandiketahuinilaieuclidian Distance yang terkecildankemudianakandirataratakanuntukmengklasifikasikankelasdarinilaipelatihan. ANALISIS DAN PERANCANGAN SISTEM Langkah-langkah yang akandilakukanantara lain: Gambar 2: Flow chart perancangansistem. Testing path merupakan path untukmengujisistem yang telahdibuatsedangkan Training path merupakan proses untukmelakukanpelatihan. Training Path meliputi: Penggunaterlebihdahulumemasukkan mammogram padasistem yang kemudiandiikutidenganpra-proses menggunakan NSCT laludiikutdenganekstraksifiturmenggunakan LTEM dengan 5 fitur yang akandiekstraks yang kemudianakandilakukan proses penyimpananfitur set dari mammogram tersebutdalam data training.

7 7 Testing Path akanmeliputi:penggunaterlebihdahulumemasukkan mammogram padasistem yang kemudiandiikutidenganpra-proses menggunakan NSCT laludiikutdenganekstraksifiturmenggunakan LTEM dengan 5 fitur yang akandiekstraks yang kemudianakandilakukan proses perbandingandengannilai-nilai yang terdapatpada data training yang kemudianakandiklasifikasidengan KNN yang akanmenghasilkanhasildariklasifikasi. IMPLEMENTASI SISTEM Implementasidarisistemmenggunakan program MATLAB dengan interface awaldanmetodepra-proses menggunakansource code dan GUI yang telahditulisoleh Jose Mejia yang kemudiandimodifikasimenjadi :

8 8 Gambar3 :Implementasisistem EVALUASI SISTEM ut: Sisteminimenggunakansampelpelatihandansampelpengujiandenganketentuansebagaiberik Tabel2 :Rinciansampel. Training Malignant 40 Benign 40 Normal 40 Testing Malignant 10 Benign 10 Normal 10 Total Malignant 50 Benign 50 Normal 50 Masing-masingkelasmenggunakan 40 mammogram pelatihandanmasingmasingkelasmenggunakan 10 mammogram pengujiandengan total 50 mammogram yang digunakanpadatiapkelas. Dari hasilperhitunganakurasi, presisi, sensitivitas, danspesifitasakandiperolehangkasebagaiberikut: Akurasi = 90% Presisi = 90% Sensitivitas = 94,7%

9 9 Spesifitas = 81.8% Dan dengannilaiakurasi rata-rata sebesar 88.25%. SIMPULAN Dalamanalisisteksturdanklasifikasi mammogram, metodepra-proses, metodeanalisistekstur, danmetodeklasifikasimempunyaiperanpentingdalammenentukankeakuratanhasil.selainitu, pemilihanfitur yang di-ekstrakdalamanalisisteksturjugamemilikipengaruh yang cukupsignifikandalamhasilklasifikasi. SARAN Metodeanalisistesktur yang digunakan (LTEM) masihdapatdieksplorlebihjauhdantidakterbataspadapenggunaan 5 fitur yang digunakan.masihbanyaksekalimetode-metode lain dalam topic bahasancomputer Vision yang dapatdigunakanuntukmenghasilkanhasil yang lebihbaik. DAFTAR PUSTAKA Alpkocak, Adil. (2002). Ontology-Based Annotations and Retrieval Systems for Digital Mammography Images. (1-34). Cunha, A.L., (2005). The Nonsubsampled Contourlet Transform : Theory, Design, and Applications. IEEE Transactions on Image Processing. 15(10). (1-30) Kalergi, M, Lucier, B.J, Berman, C.G, et al. (2006). High-Performance Wavelet Compression for Mammography. Radiology, 238,

10 10 Karahaliou, A. (2007). Texture Analysis of Tissue Surrounding Microcalcifications on Mammograms for Breast Cancer Diagnosis. The British Journal of Radiology, 80, Kusumadewi, Sri. (2003). Artificial Intelligence. (Cetakan pertama). ISBN Yogyakarta:Penerbit Graha Ilmu. Lemaitre, G. & Rodojevic, M. (2006). Texture Segmentation : Co-occurence matrix and Laws s texture masks methods. (1-34). Selesnick, I.W. (2007). Wavelet Transforms A Quick Study. Physics Today Magazine. (1-11) Srinivasan, G.N., Shobha G. (2008). Statistical Texture Analysis. Proceeedings of World Academy of Science, Engineering and Technology. (36), ( ). Tomasi, Carlo. (2007). Computer Vision. Supplementary Lecture Notes. CPS ( ). Verma, Brijesh & Zakosh, John. (2001). A Computer-Aided Diagnosis System for Digital Mammograms Based on Fuzzy-Neural and Feature Extraction Techniques. IEEE Transactions on Information Technology in Biomedicine, 5(1), (46-54). Wu, Ying. (2004). An Introduction to Computer Vision. EECS 432- Advanced Computer Vision Notes. 1. (1-7). Alpkocak, Adil. (2002). Ontology-Based Annotations and Retrieval Systems for Digital Mammography Images. (1-34). Cunha, A.L., (2005). The Nonsubsampled Contourlet Transform : Theory, Design, and Applications. IEEE Transactions on Image Processing. 15(10). (1-30)

11 11 Kalergi, M, Lucier, B.J, Berman, C.G, et al. (2006). High-Performance Wavelet Compression for Mammography. Radiology, 238, Karahaliou, A. (2007). Texture Analysis of Tissue Surrounding Microcalcifications on Mammograms for Breast Cancer Diagnosis. The British Journal of Radiology, 80, Kusumadewi, Sri. (2003). Artificial Intelligence. (Cetakan pertama). ISBN Yogyakarta:Penerbit Graha Ilmu. Lemaitre, G. & Rodojevic, M. (2006). Texture Segmentation : Co-occurence matrix and Laws s texture masks methods. (1-34). Selesnick, I.W. (2007). Wavelet Transforms A Quick Study. Physics Today Magazine. (1-11) Srinivasan, G.N., Shobha G. (2008). Statistical Texture Analysis. Proceeedings of World Academy of Science, Engineering and Technology. (36), ( ). Tomasi, Carlo. (2007). Computer Vision. Supplementary Lecture Notes. CPS ( ). Verma, Brijesh & Zakosh, John. (2001). A Computer-Aided Diagnosis System for Digital Mammograms Based on Fuzzy-Neural and Feature Extraction Techniques. IEEE Transactions on Information Technology in Biomedicine, 5(1), (46-54). Wu, Ying. (2004). An Introduction to Computer Vision. EECS 432- Advanced Computer Vision Notes. 1. (1-7). Alpkocak, Adil. (2002). Ontology-Based Annotations and Retrieval Systems for Digital Mammography Images. (1-34).

12 12 Cunha, A.L., (2005). The Nonsubsampled Contourlet Transform : Theory, Design, and Applications. IEEE Transactions on Image Processing. 15(10). (1-30) Kalergi, M, Lucier, B.J, Berman, C.G, et al. (2006). High-Performance Wavelet Compression for Mammography. Radiology, 238, Karahaliou, A. (2007). Texture Analysis of Tissue Surrounding Microcalcifications on Mammograms for Breast Cancer Diagnosis. The British Journal of Radiology, 80, Kusumadewi, Sri. (2003). Artificial Intelligence. (Cetakan pertama). ISBN Yogyakarta:Penerbit Graha Ilmu. Lemaitre, G. & Rodojevic, M. (2006). Texture Segmentation : Co-occurence matrix and Laws s texture masks methods. (1-34). Selesnick, I.W. (2007). Wavelet Transforms A Quick Study. Physics Today Magazine. (1-11) Srinivasan, G.N., Shobha G. (2008). Statistical Texture Analysis. Proceeedings of World Academy of Science, Engineering and Technology. (36), ( ). Tomasi, Carlo. (2007). Computer Vision. Supplementary Lecture Notes. CPS ( ). Verma, Brijesh & Zakosh, John. (2001). A Computer-Aided Diagnosis System for Digital Mammograms Based on Fuzzy-Neural and Feature Extraction Techniques. IEEE Transactions on Information Technology in Biomedicine, 5(1), (46-54). Wu, Ying. (2004). An Introduction to Computer Vision. EECS 432- Advanced Computer Vision Notes. 1. (1-7).

13 13 Alpkocak, Adil. (2002). Ontology-Based Annotations and Retrieval Systems for Digital Mammography Images. (1-34). Cunha, A.L., (2005). The Nonsubsampled Contourlet Transform : Theory, Design, and Applications. IEEE Transactions on Image Processing. 15(10). (1-30) Kalergi, M, Lucier, B.J, Berman, C.G, et al. (2006). High-Performance Wavelet Compression for Mammography. Radiology, 238, Karahaliou, A. (2007). Texture Analysis of Tissue Surrounding Microcalcifications on Mammograms for Breast Cancer Diagnosis. The British Journal of Radiology, 80, Kusumadewi, Sri. (2003). Artificial Intelligence. (Cetakan pertama). ISBN Yogyakarta:Penerbit Graha Ilmu. Lemaitre, G. & Rodojevic, M. (2006). Texture Segmentation : Co-occurence matrix and Laws s texture masks methods. (1-34). Selesnick, I.W. (2007). Wavelet Transforms A Quick Study. Physics Today Magazine. (1-11) Srinivasan, G.N., Shobha G. (2008). Statistical Texture Analysis. Proceeedings of World Academy of Science, Engineering and Technology. (36), ( ). Tomasi, Carlo. (2007). Computer Vision. Supplementary Lecture Notes. CPS ( ). Verma, Brijesh & Zakosh, John. (2001). A Computer-Aided Diagnosis System for Digital Mammograms Based on Fuzzy-Neural and Feature Extraction Techniques. IEEE Transactions on Information Technology in Biomedicine, 5(1), (46-54).

14 14 Wu, Ying. (2004). An Introduction to Computer Vision. EECS 432- Advanced Computer Vision Notes. 1. (1-7).

15 1 DEVELOPMENT OF COMPUTER AIDED MAMMOGRAM CLASSIFICATION SYSTEM USING TEXTURE ANALYSIS AND KNN Tri Martin Sanjaya, AsrizalPratama Putra ABSTRACT The purpose of mammogram classification system development is to classify class from disease that can be found in mammogram using NSCT (Non Subsampled Contourlet Transforms) preprocessing method, LTEM (Law s Texture Energy Measure) texture analysis method, and KNN (K Nearest Neighbour) Classifier using k = 5. Keyword :Computer Vision, Artificial Intelligence, Intelligent System, Development, Classification, Mammogram, Texture Analysis, LTEM, Law Texture Energy Measure, KNN, K Nearest Neighbour, NSCT, Non Subsampled Contourlet Transform.

16 2 INTRODUCTION With appearance of various texture analysis to mammogram classification, there will be question arise which method is the best way to classify mammogram?. Because there are so many methods that can be used in mammogram classification so there s always emerge debate. Mammogram classification consists of collection of various methods in Image Processing which is preprocessing method, texture analysis method, and classification method which every method have important role to decide the accuracy of the mammogram classification result. The main purpose of mammogram classification is obviously to produce a texture analysis program followed by accurate mammogram classification. Of course the better the classification system, the more accurate the result produced by that mammogram classification system. A good mammogram classification system is consists of collection of right methods with right feature sets too that will produce accurate data that can be extracted to be a reference in classification. NSCT (NON SUBSAMPLED CONTOURLET TRANSFORM) NSCT is a method that can be used during preprocessing process. The purpose of preprocessing is to do enhancement process to image and dispose noise that can be found in image aim to ensure image contain accurate data and fulfill the criteria so that image can be use at later process.

17 3 NCST is MATLAB s library that can be downloaded from MATLAB website. NSCT s library named NSCT Toolbox created by Jose Mejia. This is the example of mammogram which already preprocessed using NSCT method: Fig1 : (Left) Beforee preprocess, (Right) After preprocess using NSCT Can be seen the difference is quiet significant where the calsification can be seen even by naked eye (spots in right upper corner). LTEM ( LAW S TEXTURE ENERGY MEASURES) After throughh the preprocess step, so it will be continue to feature extraction step. The purpose of feature extraction is to get unique features in imageinvolve :line, side, ridge, corner, lump, andpoint and then will be reference in mammogramm classification. In this featuree extraction step, we use LTEM (Law s Texture Energy Measures) method. The LTEM method we use is LTEM method using 5 matrixinvolving:

18 4 L5 = [ ] E5 = [ ] S5 = [ ] W5 = [ ] R5 = [ ] Letters that exist in every dimension matrix have the following meanings: L = Level (average grey level) E = Edge (extract edge features) S = Spot (extract spots) W = Wave (extract wave features) R = Ripple (extract ripple features) Features that can be used in LTEM feature extraction is Skewness S5L5TR, Mean R5L5TR, Mean L5L5TR, STD S5L5TR, dan STD W5L5TR..the utilization of that 5 feature sets above is based upon an journal titled : Texture Analysis of Tissue Surrounding Microcalcifications on Mammograms for Breast Cancer Diagnosis written by A. Karahaliou, Msc dan S Skiadopoulos, Phd from University of Patras, Greece. This choice is based upon the overall accuracy and LTEM is the best among them,

19 5 Table 1Feature Table Textural Feature Category Best Feature Set Sensitivity (%) Specifity (%) Overall Accuracy (%) FOS Mean 92, Skewness GLCM Mean of Difference Entropy 85,2 78,3 82 Range of Local Homogeneity Range of Different Variance GLRLM Mean SRE 72,2 54,4 64 Mean LRE LTEM Skewness from S5L5TR 90, Mean from R5L5TR Mean from L5L5TR STD from S5L5TR STD from W5L5TR KNN (K NEAREST NEIGHBOUR) KNN is classifier and inferences from training data that already done. KNN using K value which is value that used to calculate the collection of nearest data.big K value will decrease the noise effect in classification but make the data boundary become fuzzy. Usually, the K value is odd but depending of the needs of the creator and number of classes that will be used on classification. The main principle of KNN theory is to firstly count the Euclidian Distance between dots equal to K value which is nearest with the training value. From that amount of K value, it will

20 6 be known the smallest Euclidian Distance which will be averaged to determine the class of traning value. SYSTEM ANALYSIS AND DESIGN Steps that will be done is : Fig 2: System Design Flow Chart. Testing path is a path to test the system that has been made while training path is a path to do the training. Traning Path covers: first, user input mammogram into system followed by preprocessing using NSCT then feature extraction using LTEM using 5 features which will be extracted and then the extracted feature set from mammogram will be stored in data training. Testing Path covers: first, user input mammogram into system followed by preprocessing using NSCT then feature extraction using LTEM using 5 features which will be extracted and

21 7 then the extracted feature set from mammogram will be compared with another value in data traning and classified using KNN to determine the result of classification. SYSTEM IMPLEMENTATION The implementation of this system is using MATLAB program with interface and preprocessing method using source code and GUI that is already written by Jose Mejia and then will be modified into: Fig3 : System Implementation

22 8 SYSTEM EVALUATION This system is using training sample and testing sample with the following details: Table2 :Detail of samples Training Malignant 40 Benign 40 Normal 40 Testing Malignant 10 Benign 10 Normal 10 Total Malignant 50 Benign 50 Normal 50 Each classes is using 40 training mammograms and each classes using 10 testing mammograms with total is 50 mammograms will be used for each class. From the result calculation of accuracy, precision, sensitivity, and specifity will be obtained numbers as follow: Accuracy = 90% Precision = 90% Sensitivity = 94,7% Specifity = 81.8% And with the overall accuracy is 88.25%.

23 9 CONCLUSION In texture analysis and mammogram classification, preprocess method, texture analysis method, and classification method have an important roles to determine the accuracy of the result. Moreover, the choice of feature which will be extracted in texture analysis also have a significant influence to the classification result. SUGGESTION Texture analysis method that used (LTEM) still can be explored further and is not limited to the utilization by that 5 features. Theres still many methods in Computer Vision topics that can be used to produce a better result. REFERENCES Alpkocak, Adil. (2002). Ontology-Based Annotations and Retrieval Systems for Digital Mammography Images. (1-34). Cunha, A.L., (2005). The Nonsubsampled Contourlet Transform : Theory, Design, and Applications. IEEE Transactions on Image Processing. 15(10). (1-30) Kalergi, M, Lucier, B.J, Berman, C.G, et al. (2006). High-Performance Wavelet Compression for Mammography. Radiology, 238, Karahaliou, A. (2007). Texture Analysis of Tissue Surrounding Microcalcifications on Mammograms for Breast Cancer Diagnosis. The British Journal of Radiology, 80, Kusumadewi, Sri. (2003). Artificial Intelligence. (Cetakan pertama). ISBN Yogyakarta:Penerbit Graha Ilmu.

24 10 Lemaitre, G. & Rodojevic, M. (2006). Texture Segmentation : Co-occurence matrix and Laws s texture masks methods. (1-34). Selesnick, I.W. (2007). Wavelet Transforms A Quick Study. Physics Today Magazine. (1-11) Srinivasan, G.N., Shobha G. (2008). Statistical Texture Analysis. Proceeedings of World Academy of Science, Engineering and Technology. (36), ( ). Tomasi, Carlo. (2007). Computer Vision. Supplementary Lecture Notes. CPS ( ). Verma, Brijesh & Zakosh, John. (2001). A Computer-Aided Diagnosis System for Digital Mammograms Based on Fuzzy-Neural and Feature Extraction Techniques. IEEE Transactions on Information Technology in Biomedicine, 5(1), (46-54). Wu, Ying. (2004). An Introduction to Computer Vision. EECS 432- Advanced Computer Vision Notes. 1. (1-7). Alpkocak, Adil. (2002). Ontology-Based Annotations and Retrieval Systems for Digital Mammography Images. (1-34). Cunha, A.L., (2005). The Nonsubsampled Contourlet Transform : Theory, Design, and Applications. IEEE Transactions on Image Processing. 15(10). (1-30) Kalergi, M, Lucier, B.J, Berman, C.G, et al. (2006). High-Performance Wavelet Compression for Mammography. Radiology, 238, Karahaliou, A. (2007). Texture Analysis of Tissue Surrounding Microcalcifications on Mammograms for Breast Cancer Diagnosis. The British Journal of Radiology, 80,

25 11 Kusumadewi, Sri. (2003). Artificial Intelligence. (Cetakan pertama). ISBN Yogyakarta:Penerbit Graha Ilmu. Lemaitre, G. & Rodojevic, M. (2006). Texture Segmentation : Co-occurence matrix and Laws s texture masks methods. (1-34). Selesnick, I.W. (2007). Wavelet Transforms A Quick Study. Physics Today Magazine. (1-11) Srinivasan, G.N., Shobha G. (2008). Statistical Texture Analysis. Proceeedings of World Academy of Science, Engineering and Technology. (36), ( ). Tomasi, Carlo. (2007). Computer Vision. Supplementary Lecture Notes. CPS ( ). Verma, Brijesh & Zakosh, John. (2001). A Computer-Aided Diagnosis System for Digital Mammograms Based on Fuzzy-Neural and Feature Extraction Techniques. IEEE Transactions on Information Technology in Biomedicine, 5(1), (46-54). Wu, Ying. (2004). An Introduction to Computer Vision. EECS 432- Advanced Computer Vision Notes. 1. (1-7). Alpkocak, Adil. (2002). Ontology-Based Annotations and Retrieval Systems for Digital Mammography Images. (1-34). Cunha, A.L., (2005). The Nonsubsampled Contourlet Transform : Theory, Design, and Applications. IEEE Transactions on Image Processing. 15(10). (1-30) Kalergi, M, Lucier, B.J, Berman, C.G, et al. (2006). High-Performance Wavelet Compression for Mammography. Radiology, 238,

26 12 Karahaliou, A. (2007). Texture Analysis of Tissue Surrounding Microcalcifications on Mammograms for Breast Cancer Diagnosis. The British Journal of Radiology, 80, Kusumadewi, Sri. (2003). Artificial Intelligence. (Cetakan pertama). ISBN Yogyakarta:Penerbit Graha Ilmu. Lemaitre, G. & Rodojevic, M. (2006). Texture Segmentation : Co-occurence matrix and Laws s texture masks methods. (1-34). Selesnick, I.W. (2007). Wavelet Transforms A Quick Study. Physics Today Magazine. (1-11) Srinivasan, G.N., Shobha G. (2008). Statistical Texture Analysis. Proceeedings of World Academy of Science, Engineering and Technology. (36), ( ). Tomasi, Carlo. (2007). Computer Vision. Supplementary Lecture Notes. CPS ( ). Verma, Brijesh & Zakosh, John. (2001). A Computer-Aided Diagnosis System for Digital Mammograms Based on Fuzzy-Neural and Feature Extraction Techniques. IEEE Transactions on Information Technology in Biomedicine, 5(1), (46-54). Wu, Ying. (2004). An Introduction to Computer Vision. EECS 432- Advanced Computer Vision Notes. 1. (1-7). Alpkocak, Adil. (2002). Ontology-Based Annotations and Retrieval Systems for Digital Mammography Images. (1-34). Cunha, A.L., (2005). The Nonsubsampled Contourlet Transform : Theory, Design, and Applications. IEEE Transactions on Image Processing. 15(10). (1-30)

27 13 Kalergi, M, Lucier, B.J, Berman, C.G, et al. (2006). High-Performance Wavelet Compression for Mammography. Radiology, 238, Karahaliou, A. (2007). Texture Analysis of Tissue Surrounding Microcalcifications on Mammograms for Breast Cancer Diagnosis. The British Journal of Radiology, 80, Kusumadewi, Sri. (2003). Artificial Intelligence. (Cetakan pertama). ISBN Yogyakarta:Penerbit Graha Ilmu. Lemaitre, G. & Rodojevic, M. (2006). Texture Segmentation : Co-occurence matrix and Laws s texture masks methods. (1-34). Selesnick, I.W. (2007). Wavelet Transforms A Quick Study. Physics Today Magazine. (1-11) Srinivasan, G.N., Shobha G. (2008). Statistical Texture Analysis. Proceeedings of World Academy of Science, Engineering and Technology. (36), ( ). Tomasi, Carlo. (2007). Computer Vision. Supplementary Lecture Notes. CPS ( ). Verma, Brijesh & Zakosh, John. (2001). A Computer-Aided Diagnosis System for Digital Mammograms Based on Fuzzy-Neural and Feature Extraction Techniques. IEEE Transactions on Information Technology in Biomedicine, 5(1), (46-54). Wu, Ying. (2004). An Introduction to Computer Vision. EECS 432- Advanced Computer Vision Notes. 1. (1-7).