Penggabungan Fitur Tekstur yang Invariant terhadap Iluminasi dan Fitur Bentuk untuk Deteksi Acute Lymphoblastic Leukemia


  • Rizal A Saputra Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Institut Teknologi Sepuluh Nopember Surabaya
  • Chastine Fatichah Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Institut Teknologi Sepuluh Nopember Surabaya
  • Nanik Suciati Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Institut Teknologi Sepuluh Nopember Surabaya



Abstract. Detection with microscopic blood image can help early detection of Accute Lymphoblastic Leukemia (ALL). Therefore, image acquisition process under lighting variation cause varying illumination image, so it’s needed to find texture feature extraction method that is invariant towards illumination. Shape feature also needed in this study because can represent characteristics of microscopic blood image.This study proposes combination of texture feature that is illumination invariant and shape feature for ALL detection. Texture feature will be extracted using Complete Robust Local Binary Pattern (CRLBP) method and will be tested on microscopic blood image dataset named ALL_IDB1. Testing will be conducted by using various combination of different texture feature and shape feature. Combination of shape feature and CRLBP is perform better than others. In indvidual cell test, highest result using SVM Linear with accuracy 90.89%, sensitivity 94.24% and specificity 64.82%. Classification using ALL image reach accuracy 88.00 %, sensitivity 82.35% and specificity 100%.

Keywords: Acute Lymphoblastic Leukemia detection, Complete Robust Local Bianry Pattern, Local Binary Pattern, shape feature, texture feature.


Abstrak. Deteksi dengan citra mikroskopik sel darah dapat membantu untuk deteksi dini Accute Lymphoblastic Leukemia (ALL). Namun, proses akuisisi citra mikroskopik dengan variasi pencahayaan yang berbeda menyebabkan iluminasi citra menjadi beragam sehingga dibutuhkan metode yang dapat mengekstraksi fitur tekstur yang invariant terhadap iluminasi. Fitur bentuk juga dibutuhkan dalam penelitian ini karena dapat merepresentasikan perbedaan pada citra mikroskopik sel darah. Penelitian ini mengusulkan penggabungan fitur tekstur yang invariant terhadap iluminasi dan fitur bentuk untuk deteksi dini ALL. Fitur tekstur akan diekstraksi dengan menggunakan metode Complete Robust Local Binary Pattern (CRLBP) dan diuji coba pada dataset ALL_IDB1. Uji coba dilakukan dengan variasi penggabungan fitur bentuk dan fitur tekstur. Penggabungan fitur bentuk dan CRLBP merupakan kombinasi fitur dengan performansi paling baik. Pada pengujian sel tunggal memberikan hasil tertinggi pada klasifikasi SVM Linear dengan akurasi 90,89%, sensitifitas 94,24% dan sepesifisitas 64,82%. Pada klasifikasi citra ALL akurasi mencapai 88,00%, dengan sensitifitas 82,35% dan spesifisitas 100%.

Kata Kunci: Complete Robust Local Binary Pattern, deteksi Acute Lymphoblastic Leukemia, Local Binary Pattern, fitur bentuk, fitur tekstur


. Bassan, R., & Hoelzer, D. 2011. Modern Therapy of Acute Lymphoblastic Leukemia. Journal of clinical oncology, 29(5), 532-543.

. Bassan, R., Spinelli, O., Oldani, E., Intermesoli, T., Tosi, M., Peruta, B. & Rambaldi, A. 2009. Improved Risk Classification for Risk-Specific Therapy Based on The Molecular Study of Minimal Residual Disease (Mrd) in Adult Acute Lymphoblastic Leukemia (ALL). BLOOD, 113(18).

. Fatichah, C., Tangel, M. L., Widyanto, M. R., Dong, F., & Hirota, K. 2012. Parameter Optimization of Local Fuzzy Patterns Based on Fuzzy Contrast Measure for White Blood Cell Texture Feature Extraction. JACIII, 16(3), 412-419.

. Guo, Z., & Zhang, D. 2010. A Completed Modeling of Local Binary Pattern Operator for Texture Classification. Image Processing, IEEE Transactions on,19(6), 1657-1663.

. Hanbury, A., Kandaswamy, U., & Adjeroh, D. A. 2005. Illumination-invariant Morphological Texture Classification. In Mathematical Morphology: 40 Years On (pp. 377-386). Springer Netherlands.

. Labati, R. D., Piuri, V., & Scotti, F. 2011. All-IDB: The Acute Lymphoblastic Leukemia Image Database for Image Processing. In Image processing (ICIP), 2011 18th IEEE international conference on (pp. 2045-2048). IEEE

. Madhloom, H. T., Kareem, S. A., & Ariffin, H. 2012. A Robust Feature Extraction and Selection Method for the Recognition of Lymphocytes versus Acute Lymphoblastic Leukemia. In Advanced Computer Science Applications and Technologies (ACSAT), 2012 International Conference on (pp. 330-335). IEEE.

. Mäenpää, T., & Pietikäinen, M. 2004. Classification with Color and Texture: Jointly or Separately?. Pattern recognition, 37(8), 1629-1640.

. Mohapatra, S., & Patra, D. 2010. Automated Leukemia Detection using Hausdorff Dimension in Blood Microscopic Images. In Emerging Trends in Robotics and Communication Technologies (INTERACT), 2010 International Conference on (pp. 64-68). IEEE.

. Mohapatra, S., Patra, D., & Satpathy, S. 2014. An Ensemble Classifier System for Early Diagnosis of Acute Lymphoblastic Leukemia in Blood Microscopic Images. Neural Computing and Applications, 24(7-8), 1887-1904.

. Mohapatra, S., Samanta, S. S., Patra, D., & Satpathi, S. 2011. Fuzzy Based Blood Image Segmentation for Automated Leukemia Detection. In Devices and Communications (ICDeCom), 2011 International Conference on (pp. 1-5). IEEE.

. Nanni, L., Lumini, A., & Brahnam, S. 2010. Local Binary Patterns Variants as Texture Descriptors for Medical Image Analysis. Artificial intelligence in medicine, 49(2), 117-125

. Nixon, M., & Aguado, A. S. 2012. Feature Extraction and Image Processing for Computer Vision.

. Ojala, T., Pietikäinen, M., & Harwood, D. 1996. A Comparative Study of Texture Measures with Classification Based on Featured Distributions. Pattern recognition, 29(1), 51-59.

. Ojala, T., Pietikainen, M., & Maenpaa, T. 2002. Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7), 971-987.

. Paolini, S., Gazzola, A., Sabattini, E., Bacci, F., Pileri, S., & Piccaluga, P. P. 2011. Pathobiology of Acute Lymphoblastic Leukemia. In Seminars in diagnostic pathology (Vol. 28, No. 2, pp. 124-134). WB Saunders.

. Putzu, L., Caocci, G., & Di Ruberto, C. 2014. Leucocyte Classification for Leukaemia Detection using Image Processing Techniques. Artificial intelligence in medicine, 62(3), 179-191.

. Raje, C., & Rangole, J.2014. Detection of Leukemia in Microscopic Images Using Image Processing. In Communications and Signal Processing (ICCSP), 2014 International Conference on (pp. 255-259). IEEE.

. Scotti, F. 2005. Automatic Morphological Analysis for Acute Leukemia Identification in Peripheral Blood Microscope Images. In Computational Intelligence for Measurement Systems and Applications, 2005. CIMSA. 2005 IEEE International Conference on (pp. 96-101). IEEE.

. Scotti, F., Labati, R.D., Piuri, V. 2005. Acute Lymphoblastic Leukemia Image Database for Image Processing. Department of Information Technology-Università degli Studi di Milano, (Online), (

. Singhal, V., & Singh, P. 2014. Local Binary Pattern for Automatic Detection of Acute Lymphoblastic Leukemia. In Communications (NCC), 2014 Twentieth National Conference on (pp. 1-5). IEEE.

. Zhao, Y., Jia, W., Hu, R. X., & Min, H. 2013. Completed Robust Local Binary Pattern for Texture Classification. Neurocomputing, 106, 68-76.