Simple Threshold Segmentation of Medical CT Data in DICOM Format

In this article, the author discusses a simple threshold segmentation method for medical CT data represented in DICOM format. This method proves highly valuable in medical image processing as it enables differentiation between various tissue types, thereby assisting physicians in better diagnosing patients' health conditions. The implementation typically involves loading DICOM files using libraries like pydicom, followed by applying a predetermined threshold value where pixels above or below this value are classified into different tissue categories. For instance, this segmentation technique can be employed to identify tumors, blood vessels, or other abnormal regions, providing physicians with clearer insights into patient conditions. The author also examines the method's advantages and limitations, along with potential future improvements, offering valuable references for research in medical image processing. Key implementation considerations include handling DICOM metadata, selecting optimal threshold values through histogram analysis, and managing potential noise reduction requirements in clinical applications.