Abstract
Purpose With the development of digital X-ray imaging and processing methods, the categorization and analysis of massive digital radiographic images need to be automatically finished. What is crucial in this processing is the automatic retrieval and recognition of radiographic position. To address these concerns, we developed an automatic method to identify a patient's position and body region using only frequency curve classification and gray matching. Methods Our new method is combined with frequency analysis and gray image matching. The radiographic position was determined from frequency similarity and amplitude classification. The body region recognition was performed by image matching in the whole-body phantom image with prior knowledge of templates. The whole-body phantom image was stitched by radiological images of different parts. Results The proposed method can automatically retrieve and recognize the radiographic position and body region using frequency and intensity information. It replaces 2D image retrieval with 1D frequency curve classification, with higher speed and accuracy up to 93.78%. Conclusion The proposed method is able to outperform the digital X-ray image's position recognition with a limited time cost and a simple algorithm. The frequency information of radiography can make image classification quicker and more accurate.
Highlights
Digital X-ray imaging technique has generated massive amounts of clinical image data in radiology departments every day
The results were verified by the clinical physicians of the Radiology Department
The input images were processed by dot matrix matching, correlation matching, and histogram retrieval algorithms
Summary
Digital X-ray imaging technique has generated massive amounts of clinical image data in radiology departments every day. These data need to be classified, retrieved, and analyzed in Picture Archiving and Communication Systems (PACS) or Radiology Information Systems (RIS). The urgent requirements to process these massive image data demand an automated and computationally efficient approach [1, 2] Among these approaches, image classification, radiographic position identification, and artificial intelligence analysis are the most widely used ones. Traditional medical image retrieval is semimanual, which obtains clinical information from manually retrieved image annotations and databases The disadvantage of this approach involves human errors and operator variations, which is labor intensive and results in lower accuracy [1]
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