VIEW CLASSIFICATION OF MEDICAL X-RAY IMAGES USING PNN CLASSIFIER, DECISION TREE ALGORITHM AND SVM CLASSIFIER

Sumathi Ganesan, T.S.Subashini

Abstract: In this era of electronic advancements in the field of medical image processing, the quantum of medical X-ray images so produced exorbitantly can be effectively addressed by means of automated indexing, comparing, analysing and annotating that will really be pivotal to the radiologists in interpreting and diagnosing the diseases. In order to envisage such an objective, it has been humbly endeavoured in this paper by proposing an efficient methodology that takes care of the view classification of the X-ray images for the automated annotation from their vast database, with which the decision making for the physicians and radiologists becomes simpler despite an immeasurable and ever-growing trends of the X-ray images. In this paper, X-ray images of six different classes namely chest, head, foot, palm, spine and neck have been collected. The framework proposed in this paper involves the following: The images are pre-processed using M3 filter and segmentation by Expectation Maximization (EM) algorithm, followed by feature extraction through Discrete Wavelet Transform. The orientation of X-ray images has been performed in this work by comparing among the Probabilistic Neural Network (PNN), Decision Tree algorithm and Support Vector Machine (SVM), while the PNN yields an accuracy of 75%, the Decision Tree with 92.77% and the SVM of 93.33%.

Keywords: M3 filter, Expectation Maximaization, Discrete Wavelet Transformation, Probabilistic Neural Network, Decision Tree Algorithm and Support Vector Machine

DOI: https://doi.org/10.15623/ijret.2015.0411049