{"id":962903,"date":"2024-12-27T04:12:39","date_gmt":"2024-12-26T20:12:39","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/962903.html"},"modified":"2024-12-27T04:12:41","modified_gmt":"2024-12-26T20:12:41","slug":"python%e5%a6%82%e4%bd%95%e8%af%bb%e5%8f%96dcm%e8%bd%ae%e5%bb%93","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/962903.html","title":{"rendered":"python\u5982\u4f55\u8bfb\u53d6dcm\u8f6e\u5ed3"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/24180502\/1ab9fa54-eaa5-4644-9d2b-ccfb94e94fab.webp\" alt=\"python\u5982\u4f55\u8bfb\u53d6dcm\u8f6e\u5ed3\" \/><\/p>\n<p><p> <strong>Python\u8bfb\u53d6DCM\u8f6e\u5ed3\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u5e93\u5982pydicom\u3001numpy\u548cmatplotlib\u6765\u5b9e\u73b0\uff0c\u4f7f\u7528pydicom\u8bfb\u53d6DICOM\u6587\u4ef6\u3001\u89e3\u6790\u6587\u4ef6\u4e2d\u7684\u5143\u6570\u636e\u548c\u50cf\u7d20\u6570\u636e\uff0c\u5e76\u5229\u7528numpy\u548cmatplotlib\u8fdb\u884c\u5904\u7406\u548c\u53ef\u89c6\u5316\u3002\u901a\u8fc7\u8fd9\u4e9b\u5de5\u5177\uff0c\u4f60\u53ef\u4ee5\u63d0\u53d6\u51fa\u611f\u5174\u8da3\u7684\u8f6e\u5ed3\u6570\u636e\uff0c\u5e76\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u5206\u6790\u548c\u5904\u7406\u3002<\/strong><\/p>\n<\/p>\n<p><p>\u5728\u8be6\u7ec6\u8ba8\u8bba\u5176\u4e2d\u4e00\u4e2a\u70b9\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u4e86\u89e3DICOM\u6587\u4ef6\u7684\u7ed3\u6784\u548c\u6570\u636e\u5b58\u50a8\u65b9\u5f0f\u3002DICOM\uff08Digital Imaging and Communications in Medicine\uff09\u662f\u4e00\u79cd\u7528\u4e8e\u5b58\u50a8\u548c\u4f20\u8f93\u533b\u5b66\u56fe\u50cf\u7684\u6807\u51c6\u683c\u5f0f\u3002\u5b83\u5305\u542b\u4e86\u4e30\u5bcc\u7684\u5143\u6570\u636e\u548c\u50cf\u7d20\u6570\u636e\u3002\u5bf9\u4e8e\u8bfb\u53d6DICOM\u6587\u4ef6\u4e2d\u7684\u8f6e\u5ed3\uff0c\u9996\u5148\u9700\u8981\u89e3\u6790\u5176\u4e2d\u7684\u5143\u6570\u636e\uff0c\u627e\u5230\u76f8\u5173\u7684\u56fe\u50cf\u6570\u636e\u548c\u8f6e\u5ed3\u4fe1\u606f\u3002\u53ef\u4ee5\u4f7f\u7528pydicom\u5e93\u6765\u8bfb\u53d6DICOM\u6587\u4ef6\u548c\u89e3\u6790\u5176\u5185\u5bb9\u3002\u901a\u8fc7\u67e5\u770bDICOM\u6587\u4ef6\u4e2d\u7684\u5143\u6570\u636e\uff0c\u53ef\u4ee5\u786e\u5b9a\u8f6e\u5ed3\u6570\u636e\u7684\u5b58\u50a8\u4f4d\u7f6e\uff0c\u7136\u540e\u63d0\u53d6\u548c\u5904\u7406\u8fd9\u4e9b\u6570\u636e\u3002<\/p>\n<\/p>\n<p><p>\u4e0b\u9762\u5c06\u5bf9\u5982\u4f55\u4f7f\u7528Python\u8bfb\u53d6\u548c\u5904\u7406DICOM\u6587\u4ef6\u4e2d\u7684\u8f6e\u5ed3\u8fdb\u884c\u8be6\u7ec6\u4ecb\u7ecd\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001\u5b89\u88c5\u548c\u8bbe\u7f6e\u73af\u5883<\/p>\n<\/p>\n<p><p>\u5728\u5f00\u59cb\u5904\u7406DICOM\u6587\u4ef6\u4e4b\u524d\uff0c\u9700\u8981\u786e\u4fdd\u5b89\u88c5\u4e86\u4e00\u4e9b\u5fc5\u8981\u7684Python\u5e93\u3002\u5e38\u7528\u7684\u5e93\u5305\u62ecpydicom\u3001numpy\u548cmatplotlib\u3002\u53ef\u4ee5\u4f7f\u7528pip\u547d\u4ee4\u6765\u5b89\u88c5\u8fd9\u4e9b\u5e93\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-bash\">pip install pydicom numpy matplotlib<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u786e\u4fdd\u8fd9\u4e9b\u5e93\u5df2\u5b89\u88c5\u540e\uff0c\u4fbf\u53ef\u4ee5\u5f00\u59cb\u5904\u7406DICOM\u6587\u4ef6\u3002<\/p>\n<\/p>\n<p><p>\u4e8c\u3001\u4f7f\u7528pydicom\u8bfb\u53d6DICOM\u6587\u4ef6<\/p>\n<\/p>\n<p><p>pydicom\u662f\u4e00\u4e2a\u7528\u4e8e\u5904\u7406DICOM\u6587\u4ef6\u7684Python\u5e93\u3002\u5b83\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u8bfb\u53d6DICOM\u6587\u4ef6\u5e76\u89e3\u6790\u5176\u4e2d\u7684\u5143\u6570\u636e\u548c\u50cf\u7d20\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u7528\u4e8e\u8bfb\u53d6DICOM\u6587\u4ef6\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pydicom<\/p>\n<h2><strong>\u8bfb\u53d6DICOM\u6587\u4ef6<\/strong><\/h2>\n<p>dcm_file_path = &#39;path\/to\/your\/dicom\/file.dcm&#39;<\/p>\n<p>dcm_data = pydicom.dcmread(dcm_file_path)<\/p>\n<h2><strong>\u6253\u5370DICOM\u6587\u4ef6\u7684\u5143\u6570\u636e<\/strong><\/h2>\n<p>print(dcm_data)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u901a\u8fc7\u4ee5\u4e0a\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u8bfb\u53d6DICOM\u6587\u4ef6\u5e76\u6253\u5370\u5176\u5143\u6570\u636e\u3002\u5143\u6570\u636e\u4e2d\u5305\u542b\u4e86\u56fe\u50cf\u7684\u5404\u79cd\u5c5e\u6027\uff0c\u5982\u60a3\u8005\u4fe1\u606f\u3001\u56fe\u50cf\u5c3a\u5bf8\u3001\u50cf\u7d20\u95f4\u8ddd\u7b49\u3002<\/p>\n<\/p>\n<p><p>\u4e09\u3001\u63d0\u53d6\u548c\u5904\u7406\u50cf\u7d20\u6570\u636e<\/p>\n<\/p>\n<p><p>DICOM\u6587\u4ef6\u4e2d\u7684\u56fe\u50cf\u6570\u636e\u901a\u5e38\u4ee5\u50cf\u7d20\u6570\u636e\u7684\u5f62\u5f0f\u5b58\u50a8\u3002\u53ef\u4ee5\u4f7f\u7528pydicom\u63d0\u53d6\u8fd9\u4e9b\u50cf\u7d20\u6570\u636e\uff0c\u5e76\u4f7f\u7528numpy\u8fdb\u884c\u5904\u7406\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff0c\u7528\u4e8e\u63d0\u53d6\u548c\u663e\u793a\u56fe\u50cf\u6570\u636e\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<h2><strong>\u63d0\u53d6\u50cf\u7d20\u6570\u636e<\/strong><\/h2>\n<p>pixel_array = dcm_data.pixel_array<\/p>\n<h2><strong>\u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n<p>plt.imshow(pixel_array, cmap=&#39;gray&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u901a\u8fc7\u4ee5\u4e0a\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u63d0\u53d6DICOM\u6587\u4ef6\u4e2d\u7684\u50cf\u7d20\u6570\u636e\uff0c\u5e76\u4f7f\u7528matplotlib\u663e\u793a\u56fe\u50cf\u3002<\/p>\n<\/p>\n<p><p>\u56db\u3001\u89e3\u6790\u8f6e\u5ed3\u6570\u636e<\/p>\n<\/p>\n<p><p>\u5728\u533b\u5b66\u56fe\u50cf\u5904\u7406\u4e2d\uff0c\u8f6e\u5ed3\u6570\u636e\u901a\u5e38\u7528\u4e8e\u8868\u793a\u611f\u5174\u8da3\u533a\u57df\uff08ROI\uff09\u3002DICOM\u6587\u4ef6\u53ef\u80fd\u5305\u542b\u8f6e\u5ed3\u4fe1\u606f\uff0c\u53ef\u4ee5\u901a\u8fc7\u89e3\u6790\u76f8\u5173\u7684\u5143\u6570\u636e\u6765\u83b7\u53d6\u8fd9\u4e9b\u6570\u636e\u3002\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u8f6e\u5ed3\u6570\u636e\u53ef\u80fd\u5b58\u50a8\u5728DICOM\u6587\u4ef6\u7684\u7279\u5b9a\u6807\u7b7e\u4e2d\uff0c\u5982RT\u7ed3\u6784\u96c6\uff08RT Structure Set\uff09\u6587\u4ef6\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u89e3\u6790\u8f6e\u5ed3\u6570\u636e\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u68c0\u67e5DICOM\u6587\u4ef6\u662f\u5426\u5305\u542bRT\u7ed3\u6784\u96c6\u4fe1\u606f<\/p>\n<p>if &#39;RTSTRUCT&#39; in dcm_data.Modality:<\/p>\n<p>    # \u89e3\u6790RT\u7ed3\u6784\u96c6\u4fe1\u606f<\/p>\n<p>    structures = dcm_data.StructureSetROISequence<\/p>\n<p>    for structure in structures:<\/p>\n<p>        print(&quot;ROI Name:&quot;, structure.ROIName)<\/p>\n<p>        # \u8fdb\u4e00\u6b65\u89e3\u6790\u548c\u5904\u7406\u8f6e\u5ed3\u6570\u636e<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u901a\u8fc7\u4ee5\u4e0a\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u89e3\u6790DICOM\u6587\u4ef6\u4e2d\u7684RT\u7ed3\u6784\u96c6\u4fe1\u606f\uff0c\u5e76\u63d0\u53d6\u6bcf\u4e2aROI\u7684\u540d\u79f0\u548c\u76f8\u5173\u4fe1\u606f\u3002\u53ef\u4ee5\u6839\u636e\u9700\u8981\u8fdb\u4e00\u6b65\u5904\u7406\u8fd9\u4e9b\u8f6e\u5ed3\u6570\u636e\u3002<\/p>\n<\/p>\n<p><p>\u4e94\u3001\u5904\u7406\u548c\u5206\u6790\u8f6e\u5ed3\u6570\u636e<\/p>\n<\/p>\n<p><p>\u63d0\u53d6\u5230\u8f6e\u5ed3\u6570\u636e\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528\u5404\u79cd\u56fe\u50cf\u5904\u7406\u548c\u5206\u6790\u6280\u672f\u5bf9\u5176\u8fdb\u884c\u5904\u7406\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528\u56fe\u50cf\u5206\u5272\u7b97\u6cd5\u63d0\u53d6\u611f\u5174\u8da3\u533a\u57df\uff0c\u6216\u8005\u4f7f\u7528\u5f62\u6001\u5b66\u64cd\u4f5c\u5bf9\u8f6e\u5ed3\u8fdb\u884c\u5904\u7406\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u7528\u4e8e\u5904\u7406\u548c\u5206\u6790\u8f6e\u5ed3\u6570\u636e\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<h2><strong>\u793a\u4f8b\uff1a\u5bf9\u8f6e\u5ed3\u6570\u636e\u8fdb\u884c\u7b80\u5355\u5904\u7406<\/strong><\/h2>\n<p>def process_contour_data(contour_data):<\/p>\n<p>    # \u6267\u884c\u4e00\u4e9b\u5904\u7406\u64cd\u4f5c\uff0c\u4f8b\u5982\u5e73\u6ed1\u6216\u6ee4\u6ce2<\/p>\n<p>    processed_data = np.array(contour_data)  # \u793a\u4f8b\u5904\u7406\u64cd\u4f5c<\/p>\n<p>    return processed_data<\/p>\n<h2><strong>\u5904\u7406\u63d0\u53d6\u5230\u7684\u8f6e\u5ed3\u6570\u636e<\/strong><\/h2>\n<p>for structure in structures:<\/p>\n<p>    contour_data = structure.ContourSequence<\/p>\n<p>    processed_data = process_contour_data(contour_data)<\/p>\n<p>    # \u8fdb\u4e00\u6b65\u5206\u6790\u548c\u5904\u7406\u8f6e\u5ed3\u6570\u636e<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u901a\u8fc7\u4ee5\u4e0a\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u5bf9\u63d0\u53d6\u5230\u7684\u8f6e\u5ed3\u6570\u636e\u8fdb\u884c\u7b80\u5355\u7684\u5904\u7406\u548c\u5206\u6790\u3002\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u5e94\u7528\u4e0d\u540c\u7684\u56fe\u50cf\u5904\u7406\u6280\u672f\u6765\u5206\u6790\u548c\u5904\u7406\u8f6e\u5ed3\u6570\u636e\u3002<\/p>\n<\/p>\n<p><p>\u603b\u7ed3<\/p>\n<\/p>\n<p><p>\u901a\u8fc7\u4f7f\u7528Python\u4e2d\u7684pydicom\u5e93\uff0c\u6211\u4eec\u53ef\u4ee5\u65b9\u4fbf\u5730\u8bfb\u53d6\u548c\u89e3\u6790DICOM\u6587\u4ef6\u4e2d\u7684\u6570\u636e\uff0c\u5305\u62ec\u56fe\u50cf\u6570\u636e\u548c\u8f6e\u5ed3\u4fe1\u606f\u3002\u901a\u8fc7\u7ed3\u5408numpy\u548cmatplotlib\u7b49\u5de5\u5177\uff0c\u53ef\u4ee5\u5bf9\u63d0\u53d6\u5230\u7684\u6570\u636e\u8fdb\u884c\u5904\u7406\u548c\u53ef\u89c6\u5316\u3002\u5bf9\u4e8e\u533b\u5b66\u56fe\u50cf\u5904\u7406\u548c\u5206\u6790\uff0c\u4e86\u89e3\u5982\u4f55\u89e3\u6790\u548c\u5904\u7406DICOM\u6587\u4ef6\u4e2d\u7684\u8f6e\u5ed3\u6570\u636e\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002\u5e0c\u671b\u901a\u8fc7\u8fd9\u7bc7\u6587\u7ae0\uff0c\u4f60\u80fd\u591f\u66f4\u597d\u5730\u7406\u89e3\u5982\u4f55\u4f7f\u7528Python\u5904\u7406DICOM\u6587\u4ef6\u4e2d\u7684\u8f6e\u5ed3\u6570\u636e\uff0c\u5e76\u5e94\u7528\u4e8e\u5b9e\u9645\u7684\u533b\u5b66\u56fe\u50cf\u5904\u7406\u9879\u76ee\u4e2d\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u4f7f\u7528Python\u8bfb\u53d6DICOM\u6587\u4ef6\u4e2d\u7684\u8f6e\u5ed3\u4fe1\u606f\uff1f<\/strong><br \/>\u8981\u8bfb\u53d6DICOM\u6587\u4ef6\u4e2d\u7684\u8f6e\u5ed3\u4fe1\u606f\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u7684pydicom\u5e93\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u5b89\u88c5pydicom\u5e93\uff0c\u7136\u540e\u4f7f\u7528<code>pydicom.dcmread()<\/code>\u51fd\u6570\u8bfb\u53d6DICOM\u6587\u4ef6\u3002\u8bfb\u53d6\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u904d\u5386\u6587\u4ef6\u4e2d\u7684\u6570\u636e\u5143\u7d20\u6765\u67e5\u627e\u8f6e\u5ed3\u4fe1\u606f\uff0c\u901a\u5e38\u8fd9\u4e9b\u4fe1\u606f\u5b58\u50a8\u5728\u7279\u5b9a\u7684\u6807\u7b7e\u4e2d\uff0c\u6bd4\u5982<code>(3006, 0039)<\/code>\u8868\u793a\u8f6e\u5ed3\u5e8f\u5217\u3002\u786e\u4fdd\u60a8\u4e86\u89e3DICOM\u6570\u636e\u7ed3\u6784\uff0c\u4ee5\u4fbf\u6b63\u786e\u63d0\u53d6\u6240\u9700\u7684\u4fe1\u606f\u3002<\/p>\n<p><strong>\u5728\u8bfb\u53d6DICOM\u8f6e\u5ed3\u65f6\uff0c\u5982\u4f55\u5904\u7406\u591a\u79cd\u8f6e\u5ed3\u683c\u5f0f\uff1f<\/strong><br \/>DICOM\u6587\u4ef6\u4e2d\u53ef\u80fd\u5305\u542b\u591a\u79cd\u8f6e\u5ed3\u683c\u5f0f\uff0c\u6bd4\u5982RT\u7ed3\u6784\u62a5\u544a\u6216\u5176\u4ed6\u7c7b\u578b\u7684\u8f6e\u5ed3\u6570\u636e\u3002\u4e3a\u6b64\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528pydicom\u4e0e\u5176\u4ed6\u5e93\uff08\u5982SimpleITK\u6216numpy\uff09\u7ed3\u5408\uff0c\u6765\u5904\u7406\u4e0d\u540c\u7684\u8f6e\u5ed3\u683c\u5f0f\u3002\u9996\u5148\uff0c\u68c0\u67e5\u6587\u4ef6\u7684\u5143\u6570\u636e\uff0c\u786e\u5b9a\u5305\u542b\u7684\u8f6e\u5ed3\u7c7b\u578b\u3002\u63a5\u7740\uff0c\u6839\u636e\u8f6e\u5ed3\u683c\u5f0f\uff0c\u9009\u62e9\u9002\u5f53\u7684\u89e3\u6790\u65b9\u6cd5\uff0c\u4ee5\u63d0\u53d6\u5e76\u5904\u7406\u8f6e\u5ed3\u6570\u636e\u3002<\/p>\n<p><strong>\u5982\u4f55\u5728Python\u4e2d\u53ef\u89c6\u5316DICOM\u8f6e\u5ed3\u6570\u636e\uff1f<\/strong><br \/>\u53ef\u89c6\u5316DICOM\u8f6e\u5ed3\u6570\u636e\u53ef\u4ee5\u4f7f\u7528matplotlib\u548cSimpleITK\u7b49\u5e93\u3002\u8bfb\u53d6\u8f6e\u5ed3\u6570\u636e\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528matplotlib\u7684<code>imshow()<\/code>\u51fd\u6570\u663e\u793a\u76f8\u5173\u5207\u7247\uff0c\u540c\u65f6\u7ed8\u5236\u8f6e\u5ed3\u7ebf\u3002\u901a\u8fc7\u8c03\u6574\u56fe\u5f62\u53c2\u6570\u548c\u8272\u5f69\u6620\u5c04\uff0c\u53ef\u4ee5\u66f4\u6e05\u6670\u5730\u5c55\u793a\u8f6e\u5ed3\u4e0e\u56fe\u50cf\u7684\u5173\u7cfb\u3002\u8fd9\u79cd\u53ef\u89c6\u5316\u65b9\u5f0f\u80fd\u591f\u5e2e\u52a9\u60a8\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u7684\u7ed3\u6784\u548c\u7279\u5f81\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u8bfb\u53d6DCM\u8f6e\u5ed3\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u5e93\u5982pydicom\u3001numpy\u548cmatplotlib\u6765\u5b9e\u73b0\uff0c\u4f7f\u7528pydi 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