Academic journal article Iranian Journal of Public Health

A Bio Medical Waste Identification and Classification Algorithm Using Mltrp and Rvm

Academic journal article Iranian Journal of Public Health

A Bio Medical Waste Identification and Classification Algorithm Using Mltrp and Rvm

Article excerpt

(ProQuest: ... denotes formulae omitted.)


Bio Medical Waste (BMW) originates from human, animal health care, medical teaching facilitates, medical research, biological laboratory waste and other facilities. A part of that waste stream is infectious or potentially infectious and it must be appropriately managed to defend the sanitation and healthcare personnel. Normally, the BMWs (1-3) are regulated and managed according to various standards and protocols in different countries. In health care facilities, the wastes are generated during improper management, which causes a direct health impact in the community, the environment and the health care workers. BMW is a dangerous health hazard to the public, hospital, health care units, flora and fauna of the area. BMW must be stored in a secure environment at all times, whenever possible, BMW should not be mixed with chemical, radioactive or other laboratory trash. Containers for BMW must be appropriate for its contents, there are different kinds of containers, and bags are available for the containment and disposal of BMW (4). The Government of India specifies that BMW is a part of hospital hygiene and maintenance activities. The World Health Organization (WHO) has categorized the BMW into eight categories, includes,

* General Waste

* Infectious or dangerous waste

* Radioactive

* Chemical

* Pathological

* Pressurized containers

* Pharmaceuticals

Preprocessing is an essential step in image processing applications, which eliminates the irrelevant noises in the given input image. Median filters are widely used in many images processing application, because it provides excellent noise reduction capabilities for noise removal. In this paper, an improved median filtering technique is applied to remove the noises in the given BMW image. After denoising, the texture features of the preprocessed BMW image is extracted based on the histogram value by using the MLTrP.

In this research, the type of BMW is identified and classified with the help of MLTrP and the RVM classifier. Image processing algorithms apply local and global operations on an input image for some particular reasons, such as, noise elimination, edge detection and contrast stretching. The Local Binary Pattern (LBP), Local Derivative Pattern (LDP) and Local Tetra Pattern (LTP) are the existing feature extraction approaches. These techniques are mainly used to extract the information based on the distribution of edges, coded using only two directions such as, positive and negative directions. The performance of these methods can be enhanced by differentiating the edges in more than two directions. In order to overcome this limitation, the MLTrP extraction method is used in this work. The MLTrP is a three direction code that illustrates the spatial structure of the local texture by using the direction of the central gray pixel. BMW should be classified according to their source, type and risk factors (5). In this paper, MLTrP for BMW classification is proposed based on a diagonal, horizontal and vertical direction. The text on is an essential concept in texture analysis, applied to develop effectual models in texture recognition. Moreover, the extraction of pattern is used to classify each pixel using tetra direction and the extraction of magnitude pattern is collected using the magnitude of derivatives. There are different classification techniques are available for an efficient image classification. Some of the existing classification techniques compared in this paper is, Artificial Neural Network (ANN) (6), decision tree (7), Support Vector Machine (SVM) (8) and fuzzy measure (9). ANN is a non-parametric universal classifier and it efficiently handles the noise input. The disadvantages of this technique are, its computational rate is high, it is semantically poor, it has the over fitting problem and it consumes more time for training. Decision tree is a nonparametric training data that provides hierarchical associations between input variables to forecast class membership. …

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