A Bio Medical Waste Identification and Classification Algorithm Using Mltrp and Rvm
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Abstract
Background: We aimed to extract the histogram features for text analysis and, to classify the types of Bio Medical Waste (BMW) for garbage disposal and management.
Methods: The given BMW was preprocessed by using the median filtering technique that efficiently reduced the noise in the image. After that, the histogram features of the filtered image were extracted with the help of proposed Modified Local Tetra Pattern (MLTrP) technique. Finally, the Relevance Vector Machine (RVM) was used to classify the BMW into human body parts, plastics, cotton and liquids.
Results: The BMW image was collected from the garbage image dataset for analysis. The performance of the proposed BMW identification and classification system was evaluated in terms of sensitivity, specificity, classification rate and accuracy with the help of MATLAB. When compared to the existing techniques, the proposed techniques provided the better results.
Conclusion: This work proposes a new texture analysis and classification technique for BMW management and disposal. It can be used in many real time applications such as hospital and healthcare management systems for proper BMW disposal.
Chauhan A, Singh A (2016). A hybrid multi-criteria decision making method approach for selecting a sustainable location of healthcare waste disposal facility. J Clean Prod, 139: 1001-1010.
Lakshmi, Refonaa J, Vivek J (2015). Tracking of bio medical waste using global positioning system. 2015. International Conference on Circuit, Power and Computing Technologies (ICCPCT).
Kumar R, Gupta AK, Aggarwal AK, Kumar A (2014). A descriptive study on evaluation of bio-medical waste management in a tertiary care public hospital of North India. J Environ Health Sci Eng, 12:69.
Moreira AMM and Günther WMR (2013). Assessment of medical waste management at a primary health-care center in São Paulo, Brazil. Waste Manag, 33(1):162-167.
Kalaivani K, Anjalipriya V, Sivakumar R, Srimeena R (2015). An efficient Bio-key Management scheme for telemedicine applications. in Technological Innovation in ICT for Agriculture and Rural Development (TIAR), 2015 IEEE.
Naik B, Nayak J, Behera HS, Abraham A (2016). A self adaptive harmony search based functional link higher order ANN for non-linear data classification. Neurocomputing, 179: 69-87.
Farid DM, Zhang L, Rahman CM, Hossain MA, Strachan R (2014). Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks. Expert Syst Appl, 41(4):1937-1946.
Wang D, Zhang X, Fan M, Ye X (2016). Hierarchical mixing linear support vector machines for nonlinear classification. Pattern Recogn, 59: 255-267.
Korytkowski M, Rutkowski L, Scherer R (2016). Fast image classification by boosting fuzzy classifiers. Inform Sci, 327: 175-182.
Rosen GL, Reichenberger ER, Rosenfeld AM (2011). NBC: the Naive Bayes Classification tool webserver for taxonomic classification of metagenomic reads. Bioinformatics, 27(1): 127-129.
Faragallah OS, Ibrahem HM (2016). Adaptive switching weighted median filter framework for suppressing salt-and-pepper noise. AEU-Int J Electron C, 70(8): 1034-1040.
Lu CT (2014). Noise reduction using three-step gain factor and iterative-directional-median filter. Appl Acoust, 76: 249-261.
Meher SK (2014). Recursive and noise-exclusive fuzzy switching median filter for impulse noise reduction. Eng Appl Artif Intell, 30:145-154.
Murala S, Maheshwari R, Balasubramanian R (2012). Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans Image Process, 21(5): 2874-2886.
Shrivastava N, Tyagi V (2014). An effective scheme for image texture classification based on binary local structure pattern. Visual Comput, 30(11): 1223-1232.
Murala S, Wu (2014). Local mesh patterns versus local binary patterns: biomedical image indexing and retrieval. IEEE J Biomed Health Inform, 18(3): 929-938.
Yuan, F (2014). Rotation and scale invariant local binary pattern based on high order directional derivatives for texture classification. Digit Signal Process, 26: 142-152.
Yuan F, Shi J, Xia X, Fang Y, Fang Z, Mei T (2016). High-order local ternary patterns with locality preserving projection for smoke detection and image classification. Inform Sci, 372: 225-240.
Huanga M, Mu Z, Zenga H, Huangb S (2015). Local image region description using orthogonal symmetric local ternary pattern. Pattern Recognit Lett, 54: 56-62.
Wang K, Jia H (2009). Image Classification Using No-balance Binary Tree Relevance Vector Machine. in ASIA '09. International Asia Symposium on Intelligent Interaction and Affective Computing.
Raju J, Durai CAD (2013). A survey on texture classification techniques. 2013 International Conference on Information Communication and Embedded Systems (ICICES).
Pal M, Foody GM (2012). Evaluation of SVM, RVM and SMLR for accurate image classification with limited ground data. IEEE J Sel Topics Appl Earth Observ, 5(5): 1344-1355.
Braun, AC, Weidner U, Hinz S (2012). Classification in high-dimensional feature spaces—Assessment using SVM, IVM and RVM with focus on simulated EnMAP data. IEEE J Sel Topics Appl Earth Observ , 5(2): 436-443.
Liu L, Long Y, Fieguth PW, Lao S, Zhao G (2014). BRINT: binary rotation invariant and noise tolerant texture classification. IEEE Trans Image Process, 23(7): 3071-3084.
| Files | ||
| Issue | Vol 45 No 10 (2016) | |
| Section | Original Article(s) | |
| Keywords | ||
| Bio medical waste Median filter Sensitivity Specificity | ||
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