{"id":3582,"date":"2025-11-19T10:00:01","date_gmt":"2025-11-19T15:00:01","guid":{"rendered":"https:\/\/www.mymiller.name\/wordpress\/?p=3582"},"modified":"2025-11-19T10:00:01","modified_gmt":"2025-11-19T15:00:01","slug":"leveraging-dicom-in-spring-boot-applications","status":"publish","type":"post","link":"https:\/\/www.mymiller.name\/wordpress\/spring\/leveraging-dicom-in-spring-boot-applications\/","title":{"rendered":"Leveraging DICOM in Spring Boot Applications"},"content":{"rendered":"\n<div class=\"wp-block-jetpack-markdown\"><p><strong>Leveraging DICOM in Spring Boot Applications<\/strong><\/p>\n<p>DICOM, the cornerstone of medical imaging, is a specialized format for storing and transmitting medical images and related information. Integrating it into your Spring Boot project opens up a world of possibilities for medical applications. Let\u2019s explore how to achieve this.<\/p>\n<p><strong>Understanding DICOM<\/strong><\/p>\n<p>DICOM is more than just an image format. It encapsulates patient details, study information, and image metadata, making it ideal for healthcare scenarios.<\/p>\n<p><strong>DICOM Image Sources<\/strong><\/p>\n<p>A wide array of medical imaging modalities generate DICOM images:<\/p>\n<ul>\n<li><strong>Computed Tomography (CT):<\/strong> Produces detailed cross-sectional images of the body using X-rays.<\/li>\n<li><strong>Magnetic Resonance Imaging (MRI):<\/strong> Creates images of organs and tissues using strong magnetic fields and radio waves.<\/li>\n<li><strong>Positron Emission Tomography (PET):<\/strong> Captures metabolic activity in the body using radioactive tracers.<\/li>\n<li><strong>X-rays:<\/strong> Generates images of bones and internal organs using electromagnetic radiation.<\/li>\n<li><strong>Ultrasound:<\/strong> Utilizes sound waves to create images of soft tissues and organs.<\/li>\n<li><strong>Mammography:<\/strong> Specifically designed for breast imaging.<\/li>\n<\/ul>\n<p><strong>Key Libraries<\/strong><\/p>\n<ul>\n<li><strong>dcm4che:<\/strong> A powerful toolkit for DICOM parsing, manipulation, and storage.<\/li>\n<li><strong>PixelMed:<\/strong> Provides utilities for DICOM image processing and analysis.<\/li>\n<li><strong>ND4J:<\/strong> Provides numerical computing capabilities for DL4J.<\/li>\n<li><strong>DataVec:<\/strong> DL4J\u2019s library for ETL (Extract, Transform, Load) and data preprocessing.<\/li>\n<\/ul>\n<p><strong>Integration Steps<\/strong><\/p>\n<ol>\n<li><strong>Dependency Addition:<\/strong> Include the necessary libraries in your <code>pom.xml<\/code>:<\/li>\n<\/ol>\n<pre><code class=\"language-xml\">&lt;dependency&gt;\n    &lt;groupId&gt;org.dcm4che&lt;\/groupId&gt;\n    &lt;artifactId&gt;dcm4che-core&lt;\/artifactId&gt;\n    &lt;version&gt;5.25.0&lt;\/version&gt;\n&lt;\/dependency&gt;\n&lt;dependency&gt;\n    &lt;groupId&gt;com.pixelmed&lt;\/groupId&gt;\n    &lt;artifactId&gt;dicom&lt;\/artifactId&gt;\n    &lt;version&gt;2.2.7&lt;\/version&gt;\n&lt;\/dependency&gt;\n&lt;dependency&gt;\n    &lt;groupId&gt;org.nd4j&lt;\/groupId&gt;\n    &lt;artifactId&gt;nd4j-native-platform&lt;\/artifactId&gt;\n    &lt;version&gt;1.0.0-M2&lt;\/version&gt;\n&lt;\/dependency&gt;\n&lt;dependency&gt;\n    &lt;groupId&gt;org.datavec&lt;\/groupId&gt;\n    &lt;artifactId&gt;datavec-api&lt;\/artifactId&gt;\n    &lt;version&gt;1.0.0-M2&lt;\/version&gt;\n&lt;\/dependency&gt;\n<\/code><\/pre>\n<ol start=\"2\">\n<li><strong>DICOM Parsing:<\/strong> Utilize <code>dcm4che<\/code> to read DICOM files:<\/li>\n<\/ol>\n<pre><code class=\"language-java\">DicomObject dcmObj = new DicomInputStream(new File(&quot;path\/to\/dicomfile.dcm&quot;)).readDicomObject();\n<\/code><\/pre>\n<ol start=\"3\">\n<li><strong>Data Extraction:<\/strong> Access patient data, study details, or image pixels:<\/li>\n<\/ol>\n<pre><code class=\"language-java\">String patientName = dcmObj.getString(Tag.PatientName);\n<\/code><\/pre>\n<ol start=\"4\">\n<li><strong>Image Processing:<\/strong> Employ <code>PixelMed<\/code> or other libraries for image manipulation tasks.<\/li>\n<\/ol>\n<p><strong>Image Manipulation with PixelMed<\/strong><\/p>\n<p>PixelMed offers a rich set of features for working with DICOM images:<\/p>\n<ul>\n<li><strong>Image Loading:<\/strong> Read DICOM images into Java <code>BufferedImage<\/code> objects.<\/li>\n<li><strong>Pixel Access:<\/strong> Manipulate individual pixel values.<\/li>\n<li><strong>Image Transformations:<\/strong> Resize, rotate, or apply filters to images.<\/li>\n<li><strong>Image Conversion:<\/strong> Convert DICOM images to other formats (JPEG, PNG, etc.).<\/li>\n<\/ul>\n<p><strong>Example: DICOM Image Resizing<\/strong><\/p>\n<pre><code class=\"language-java\">SourceImage sImg = new SourceImage(new File(&quot;path\/to\/dicomfile.dcm&quot;));\nAttributeList list = new AttributeList();\nlist.put(TagFromName.Columns, new IntegerStringAttribute(512)); \/\/ New width\nlist.put(TagFromName.Rows, new IntegerStringAttribute(512));    \/\/ New height\nTransformedImage tImg = new TransformedImage(sImg, list);\nBufferedImage img = tImg.getBufferedImage();\n<\/code><\/pre>\n<p><strong>Spring Boot Integration<\/strong><\/p>\n<ul>\n<li><strong>REST API:<\/strong> Create endpoints to upload, retrieve, and process DICOM files.<\/li>\n<li><strong>Storage:<\/strong> Consider database integration (PostgreSQL with DICOM extensions) or specialized PACS servers for DICOM storage.<\/li>\n<\/ul>\n<p><strong>Important Considerations<\/strong><\/p>\n<ul>\n<li><strong>DICOM Compliance:<\/strong> Ensure your implementation adheres to DICOM standards for interoperability.<\/li>\n<li><strong>Data Privacy:<\/strong> Handle sensitive patient information with utmost care, following HIPAA or relevant regulations.<\/li>\n<\/ul>\n<p><strong>Example: DICOM Metadata Extraction<\/strong><\/p>\n<pre><code class=\"language-java\">@RestController\npublic class DicomController {\n\n    @PostMapping(&quot;\/dicom\/metadata&quot;)\n    public DicomMetadata extractMetadata(@RequestParam(&quot;file&quot;) MultipartFile file) throws IOException {\n        \/\/ ... (Use dcm4che to parse the DICOM file and extract metadata)\n    }\n}\n<\/code><\/pre>\n<p><strong>Preparing DICOM for DL4J Analysis<\/strong><\/p>\n<ol>\n<li><strong>Image Extraction:<\/strong> Use <code>dcm4che<\/code> or <code>PixelMed<\/code> to extract image pixel data from the DICOM file.<\/li>\n<li><strong>Normalization:<\/strong> Normalize pixel values to a standard range (e.g., 0-1) for optimal model performance.<\/li>\n<li><strong>Image Transformation:<\/strong> Resize or crop images to a consistent size required by your DL4J model.<\/li>\n<li><strong>DataVec Transformation:<\/strong> Utilize <code>DataVec<\/code> to convert image data into <code>INDArray<\/code> format for DL4J input.<\/li>\n<\/ol>\n<p><strong>Example: DICOM to INDArray Conversion<\/strong><\/p>\n<pre><code class=\"language-java\">BufferedImage image = ImageIO.read(new File(&quot;path\/to\/dicomfile.dcm&quot;));\nINDArray imageArray = new NativeImageLoader(height, width, channels).asMatrix(image);\n<\/code><\/pre>\n<p>By following these steps, you can seamlessly integrate DICOM image processing and analysis into your Spring Boot applications, leveraging the power of DL4J for machine learning tasks in the medical domain.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":3583,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[318],"tags":[69,319],"series":[],"class_list":["post-3582","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-spring","tag-java-2","tag-spring"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/www.mymiller.name\/wordpress\/wp-content\/uploads\/2024\/06\/x-ray-6841384_1280.jpg?fit=1280%2C853&ssl=1","jetpack-related-posts":[{"id":3951,"url":"https:\/\/www.mymiller.name\/wordpress\/java\/scaling-streams-mastering-virtual-threads-in-spring-boot-4-and-java-25\/","url_meta":{"origin":3582,"position":0},"title":"Scaling Streams: Mastering Virtual Threads in Spring Boot 4 and Java 25","author":"Jeffery Miller","date":"December 22, 2025","format":false,"excerpt":"As a software architect, I\u2019ve seen the industry shift from heavy platform threads to reactive streams, and finally to the \"best of both worlds\": Virtual Threads. 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