研究目的
To present a method for segmentation and selections of Region of Interest (ROI) in 3D medical images to facilitate diagnosis and treatment by overcoming the drawbacks of manual segmentation methods such as being time-consuming, inefficient, and interoperable.
研究成果
The proposed computational method for segmenting and selecting ROI in 3D medical images offers a solution to the drawbacks of manual segmentation methods. It allows for automatic segmentation of suspected or required areas, facilitating easier evaluation by experts or radiologists.
研究不足
The method relies on the availability of real data from medical institutes or hospitals for practical application. The demonstration uses a sourced image, which may not fully represent the complexity of real medical images.
1：Experimental Design and Method Selection:
The proposed algorithm involves reading a 3D medical image, displaying it, selecting the ROI, creating a binary mask from the ROI, and applying various masking processes to highlight or exclude regions.
2：Sample Selection and Data Sources:
A three-dimensional medical image of a human head from Google source is used for demonstration.
3：List of Experimental Equipment and Materials:
MATLAB image processing tool is used for all comparisons, specifically using the freehand tool for selecting the ROI.
4：Experimental Procedures and Operational Workflow:
The process includes converting the original image into a binary image, applying masking to highlight or exclude regions, and displaying the selected ROI.
5：Data Analysis Methods:
The method allows for the calculation of attributes and properties of the image data for analysis.
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