Main Article Content
Tuberculosis (TB) is a disease caused by a bacterium called Mycobacterium tuberculosis. Basically there are two types of TB, latent and pulmonary TB. It symptoms include persistent cough for more than two weeks, cough with blood, night sweat, tiredness, fever, chest pain, lymph node enlargement. It treatment depends on severity level such as mild, moderate, severe and very severe and this can be obtained from TB diagnosis such as chest x-ray image, nucleic acid amplification test and culture and sensitivity. The main aim of this work is to determine severity level of TB using image processing techniques. Chest x-ray (CXR) images of TB patients were obtained from Google image. Firstly, the CXR images were enhanced from Red Green Blue (RGB) to gray image (GI) form. Secondly, GI were decomposed, convolved, compressed and filtered these processing is called image degradation. Thirdly, These GI were restored and binarized. Threshold value ≥ 53 was obtained to classify the severity level of the infected image Furthermore, average error was calculated and system performance obtained. This work revealed that image 1, 2, 6 and 11 has a total pixel value 11067, 6735, 9256 and 3894 respectively ≥ 53 which indicates that the % of the infected areas as 99.62%, 99.52%, 96.70% and 82.78% respectively which shows the severity level are very severe. While the image 3, 4, 5, 8 and 9 has a percentage of infected areas 73.13%, 72.90%, 76.80%, and 76.84% respectively which indicate the severity level as severe. Image 7 has a percentage of infected area 51.21 which shows the severity level as moderate. This research work revealed that image processing is a suitable technique for determine the severity level of TB.
[Accessed 3rd March, 2017]
Mahmoud RS, Shahaboddin S, Shahram G, Teh Y, Aghabozorgi S, Laiha M, Valentina E, et al. RAIRS2 A new expert system for diagnosing Tuberculosis with real world tournament selection mechanism inside artificial immune recognition system Int. Fed. for Med. and Bio. Engr. 2015;15:1323-6
Navneet W, Sharad KT, Rahul M. Design and identification of tuberculosis using fuzzy based decision support system Adv. in Comp. Sci. and Inf. Tech. 2015;2:57-62.
Kamble PA, Anagire VV, Chamtagoudar NS. CXR tuberculosis detection using MATLAB Im. Proc. Int. Res. J. of Engr. and Tech. 2016;3:2342-4.
Pradhan, et al. A genetic programming approach for detection of diabetes. International Journal of Computational Engineering Research. 2012;2(6):91-94.
Chandrika V, Parvathi CS, Bhaskar P. Diagnosis of tuberculosis using MATLAB Based Artificial Neural Network IJIPA. 2012;3(1):37-42.
Ekong VE, Uyinomen O, Uwadiae Enobakhare E, Abasiubong Festus, Onibere, Emmanuel A. A fuzzy inference system for predicting depression risk levels. African Journal of Mathematics and Computer Science Research. 2013;6(10): 197-204.
Prakashgoud P, Samina M. Fuzzy logic based health care system using wireless body area network. International Journal of Computer Applications. 2013;80(13):46-51.
Smita SS, Sushil SM, Ali S. Generic medical fuzzy expert system for diagnosis of cardiac diseases. International Journal of Computer Application. 2013;66(13):35-44.
Chandrika V, Parvathi CS, Bhaskar P. Multi-level image enhancement for pulmonary tuberculosis analysis. Inter-national Journal of Science and Applied Information Technology. 2012;1(4):102-106.
Patil SA. Texture analysis of TB X-ray images using image processing techni-ques. Journal of Biomedical and Bioengineering. 2012;3(1):53-56.
Mokhled SAT. Lung cancer detection using image processing techniques Leonardo electronic. Journal of Practices and Technologies. 2012;147-158.
Kusworo A, Rahmad G, Aris S, Sofjan KF, Adi P, Ari B. Tuberculosis (TB) identification in the ziehl-neelsen sputum sample in NTSC channel and support vector machine (SVM) classification. International Journal of Innovative Research in Science, Engineering and Technology. 2013;2(9):5030-5035.
Kamble PA, Anagire VV, Chamtagoudar NS. CXR tuberculosis detection using MATLAB image processing. International Research Journal of Engineering and Technology. 2016;3(6):2342-2344.
Rohan KG, Savita AL, Santosh GV. Identification of brain tumor using image processing technique: Overviews of methods SSRG. International Journal of Computer Science and Engineering. 2016; 3:89-93.
Cicero FFCF, Pamela C, Clahildek MX, Luciana BMF, Marly, Guimarães FC. Automatic identification of Tuberculosis mycobacterium. Research on Biomedical Engineering. 2015;31(1):33-43.
Panicker RO, Soman B, Saini G, Rajan J. A review of automatic methods based on image processing techniques for tuberculosis detection from microscopic sputum smear images. Journal of Medical System. 2016;40(1).
Chetarn CP, Ganorkar SR. Tuberculosis screening using digital image processing techniques. International Research Journal of Engineering and Technology. 2016;3(1): 623-627.
Nesma S, Meryem S, Mohamed AC. Interpretable classifier of diabetes disease. International Journal of Computer Theory and Engineering. 2012;4(3):438-442.
Pradhan MA, Bamnote GR, Vinit T, Kiran J, Vijay C, Vijay D. A genetic programming approach for detection of diabetes. International Journal of Computational Engineering Research. 2012;2(6):91-94.
Ajay D, Corina P, Gerrit C, Tarlochan S, Fleming YML, Sean K. Automated detection of tuberculosis on sputum smeared slides using stepwise classification proceeding SPIE medical imaging conference (8315-123). Newport Beach, CA; 2012.
Anant B, Kapil KS. An approach to medical image classification using neuro fuzzy logic and ANFIS classifier. International Journal of Computer Trends and Technology. 2013;4(3):236-240.
Hoang C, Thuan N, Trung L. Neuro-fuzzy approach to heart rate variability analysis. International Journal of Bioscience, Biochemistry and Bioinformatics. 2013; 3(5):456-459.
Jerome MG, Ibrahim Goni, Timothy UM. Adaptive neuro-fuzzy system for determining the severity level of osteo-myelitis and control. Archives of Applied Science Research. 2017;9(2):9-15.
Jerome MG, Ibrahim Goni, Mohammed Isa. Neuro-fuzzy approach for diagnosing and control of tuberculosis. International Journal of Computational Science, Information Technology and Control Engineering. 2018;5(1).
Annie OE, Jonathan CO. An adaptive neuro-fuzzy inference system for diagnosis of ebola haemorrhagic fever. The Pacific Journal of Science and Technology. 2015; 16(1):251-261.
Ibrahim Goni, Christopher UN, Manga I, Nataala A, Sunday JC. Intelligent system for diagnosing tuberculosis using adaptive neuro-fuzzy. Asian Journal of Computer Science. 2018;2(1):1-9.
Ibrahim G, Jerome MG, Timothy UM. Designing a neuro-fuzzy model for predicting the existence of mycobacterium tuberculosis. 36th Annual Conference of the Nigerian Mathematical Society (NMS) 9th – 12th May (2017) held at University of Agriculture Makurdi, Benue State; 2017.
Vally D, Sarma CHV. Diagnosis chest diseases using neural network and genetic hybrid algorithm. International Journal of Engineering Research and Applications. 2015;5(1):20-26.
Rupali Z, Jyoti A. Pre-prediction of tuberculosis disease using soft computing technique. International Journal of Advanced Research in Computer and Communication Engineering. 2016;5(6): 56-61.