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Research on the Content-Based Classification of Medical Image

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Medical images have increased tremendously in numbers and categories these years as the devices generating them become more and more advanced. In this paper, four classifiers for automatically identifying medical images of different body parts are explored and implemented. Classic and recognized image descriptors such as wavelet transform and SIFT are utilized and combined with SVM and proposed modified KNN to verify the validity of the traditional classification methods when applied to medical images. In the process, a novel representation of wavelet feature is advanced in combination with a proposed tuned KNN. This wavelet feature is also applied with SVM. SIFT and its variety, dense SIFT are both employed to extract image features and they are formatted by the spatial pyramid model into a concatenated histogram. All these methods are compared with one another for accuracy and efficiency. Moreover, a convolutional network (CNN) is constructed to classify medical images. We show that in regards to the various types and huge numbers of medical images, traditional methods and deep learning approach such as CNN can both achieve high accuracy results. The methods illustrated in this paper can all be reasonably applied to medical image application with variance in speed and accuracy.
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Keywords: CLASSIFIER; CNN; KNN; MEDICAL IMAGES; SIFT; SVM; WAVELET TRANSFORM

Document Type: Research Article

Publication date: February 1, 2017

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  • Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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