Abstract
The ever increasing number of medical images in hospitals urges on the need for generic content based image retrieval systems. These systems are in an area of great importance to the healthcare providers. The first and foremost function in such system is feature extraction. In this paper, different feature extraction techniques have been utilized to represent medical blood cell images. They are categorized into two groups; low-level image representation such as color and shape analysis and local patch-based image representation such as Bag of Words (BoW). These features have been exploited for retrieving similar images. We have also used a generative model such as Probabilistic Latent Semantic Analysis (PLSA) on extracted BoW for retrieval task. Lastly, the retrieval results obtained from all the above features are integrated with one another to increase the retrieval performance. Experimental results using four different classes of 600 blood cell images showed 92.25% of retrieval accuracy.
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Zare, M.R., Seng, W.C. (2014). Blood Cell Image Retrieval System Using Color, Shape and Bag of Words. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham. https://doi.org/10.1007/978-3-319-12643-2_27
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DOI: https://doi.org/10.1007/978-3-319-12643-2_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12642-5
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