Computational Dynamic Features Extraction from Anonymized Medical Images
Keywords:DICOM, Medical Image, CBIR, Datasets, Texture
Images depict clearer meaning than written words and this is reason they are used in a variety of human endeavors, including but not limited to medicine. Medical image datasets are used in medical environment to diagnose and confirm medical disorders for which physical examination may not be sufficient. However, the medical profession's ethics of patient confidentiality policy creates barrier to availability of medical datasets for research; thus, this research work was able to solve the above stated barrier through anonymization of sensitive identity information. Furthermore, the Content Based Image Retrieval (CBIR) using texture as the content was developed to overcome the challenge of information overloading associated with data retrieval systems.
Images acquired from various imaging modalities and placed into Digital Imaging and Communications in Medicine (DICOM) formats were obtained from several hospitals in Nigeria. The database of these images was created and consequently anonymized, then a new anonymized database was created. On anonymized images, feature extraction was done using textures as content and the content was considered for the implementation of retrieval system.
The anonymized images were tested in DICOM view to see if all files were successfully anonymized; the result obtained was 100%. A texture retrieval test was performed, and the number of precisely returned search images using the Similarity Distance Measure formulae resulted in a significant reduction in image overload. Thus, this research work solved the problem of non-availability of datasets for researchers in medical imaging field by providing datasets that can be used without violating law of patient confidentiality by the interested parties. It also solves the problem of hackers obtaining useful information about patients’ datasets. The CBIR using texture as content also enhances and solves the problem of information overloading.
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