A Volumetric Shape Registration Based on Locally Affine-Invariant Constraint

A novel mobile system has been developed for use by radiologists in managing Digital Imaging and Communications in Medicine (DICOM) image data. The system consists of a mobile DICOM server (MDS) and personal digital assistants (PDAs), including a Linux PDA with a video graphics array (VGA) display (307,200 pixels, 3.7 inches). The MDS weighs 410 g, has a 60-GB hard disk drive and a built-in wireless local area network (LAN) access point, and supports a DICOM server (Central Test Node). The Linux-based MDS can be accessed with personal computers (PCs) and PDAs by means of a wireless or wired LAN, and client-server communications can be established at any time. DICOM images can be displayed by using any PDA or PC by means of a Web browser. Simultaneous access to the MDS is possible for multiple authenticated users. With most PDAs, image compression is necessary for complete display of DICOM images; however, the VGA screen can display a 512 x 512-pixel DICOM image almost in its entirety. This wireless system allows efficient management of heavy loads of lossless DICOM image data and will be useful for collaborative work by radiologists in education, conferences, and research.

The standard file format of digital imaging and communications in medicine (DICOM) is typically used for medical imaging. These are not directly viewable by the built-in image viewer of most Operating Systems (OSs) and require additional software support for visualization. At the same time, DICOM images contain sensitive additional information related to the patient’s condition, which is confidential. That is why working with DICOM images imposes additional challenges. An innovative framework has been proposed in this paper that effortlessly converts the DICOM into an Encrypted Joint Photographic Experts Group (EJPEG) format. Viewers with the decryption key can view this image without the support of additional software. This effective framework ensures medical image confidentiality, improves viewability, and optimizes disk storage. The proposed framework takes less than 0.5 seconds on average to encrypt and decrypt the image. As a result, the users experience no conversion downtime. This innovative and effective framework betters the user’s experience with DICOM images while ensuring the confidentiality of the medical images. It is a potential solution to the rising concern of confidentiality in feature engineering for machine learning.

Although PowerPoint has become a ubiquitous presentation tool in medical imaging, it does not support the Digital Imaging and Communications in Medicine (DICOM) standard. Users must go through a laborious conversion process that includes guessing the appropriate brightness and contrast to convert 16-bit DICOM images into 8-bit formats. A PowerPoint add-in was developed that incorporates features of a DICOM viewer into a presentation. Users can interactively manipulate large series of 16-bit images in stack mode with scroll, crop, zoom, and window width and level functions, as well as sort through images by location or series. Multiple DICOM image series can be placed on a single slide, and one can interactively scroll through stacks of images during a presentation to demonstrate imaging findings. The problem created by the varying contrast and brightness of different projector systems is overcome by interactively adjusting the image window level during presentations. Bone and lung window views can be shown without having to create separate images. Combining DICOM images into stacks as part of a PowerPoint presentation can result in a more effective and higher-quality presentation of medical images.

Nowadays, Digital Imaging and Communications in Medicine (DICOM), an international standard used worldwide to access and handle various medical images through Picture Archiving and Communication System (PACS). This helps eliminate completely the use of costly and harmful traditional films. Besides, the existing infrastructures of the Internet have allowed us to develop the telemedicine services. However, the usage of foreign PACS is not suitable to diagnosis and medical consultation over the distance in Vietnam. This paper researches to implement PACS, online medical video consultation system and security DICOM Web Viewer serving diagnosis in Vietnam. First, we mention our methods of implementation. Then we discuss the results of completing image compression technology, connection technology, load balance technology for manufacturing PACS and online medical video consultation system. Next, the result of designing DICOM Web Viewer is introduced and updated. In order to solving security issue over Internet, modern security techniques are integrated into software system. Specially, by exploiting different techniques of data encoding, cryptography and watermarking, we propose an efficient solution to enhance information security in DICOM images over the Internet. Finally, our completed system has also been implemented and evaluated in hospitals.

3D printing has a wide range of applications and has brought significant change to many medical fields. However, ensuring quality assurance (QA) is essential for patient safety and requires a QA program that encompasses the entire production process. This process begins with imaging and continues on with segmentation, which is the conversion of Digital Imaging and Communications in Medicine (DICOM) data into virtual 3D-models. Since segmentation is highly influenced by manual intervention the influence of the users background on segmentation accuracy should be thoroughly investigated. Seventeen computed tomography (CT) scans of the pelvis with physiological bony structures were identified, anonymized, exported as DICOM data sets, and pelvic bones were segmented by four observers with different backgrounds. Landmarks were measured on DICOM images and in the segmentations. Intraclass correlation coefficients (ICCs) were calculated to assess inter-observer agreement, and the trueness of the segmentation results was analyzed by comparing the DICOM landmark measurements with the measurements of the segmentation results. The correlation between segmentation trueness and segmentation time was analyzed. The lower limits of the 95% confidence intervals of the ICCs for the seven landmarks analyzed ranged from 0.511 to 0.986. The distance between the iliac crests showed the highest agreement between observers, while the distance between the ischial tuberosities showed the lowest. The distance between the upper edge of the symphysis and the promontory showed the lowest deviation between DICOM measurements and segmentation measurements (mean deviations < 1mm), while the intertuberous distance showed the highest deviation (mean deviations 14.5-18.2mm). Investigators with diverse backgrounds in segmentation and varying experience with slice images achieved pelvic bone segmentations with landmark measurements of mostly high agreement in a setup with high realism. In contrast, high variability was observed in the segmentation of the coccyx. In general, interobserver agreement was high, but due to measurement inaccuracies, landmark-based approaches cannot conclusively show that segmentation accuracy is within a clinically tolerable range of 2mm for the pelvis. If the segmentation is performed by a very inexperienced user, the result should be reviewed critically by the clinician in charge.

Digital Imaging and Communications in Medicine (DICOM) is a standard for handling, storing, printing, and transmitting information in medical images. The DICOM file contains the image data and a number of attributes such as identified patient data (name, age, insurance ID card,…), and non-identified patient data (doctor’s interpretation, image type,…). Medical images serve not only for examination, but can also be used for research and education purposes. For research they are used to prevent illegal use of information; before authorizing researchers to use these images, the medical staff deletes all the data which would reveal the patient identity to prevent patient privacy. This manipulation is called anonymization. In this paper, we propose a reversible anonymization of DICOM images. Identifying patient data with image digest, computed by the well-known SHA-256 hash function, are encrypted using the proposed probabilistic public key crypto-system. After compressing the Least Significant Bit (LSB) bitplan of the image using Hofmann coding algorithm, the encrypted data is inserted into a liberated zone of the LSB bitplan of the image. The proposed method allows researchers to use anonymous DICOM images and keep to authorized staff -if necessary- the possibility to return to the original image with all related patient data.

CAD (Computer Aided Diagnosis) system that has 3-D viewer function to display the DICOM (Digital Imaging and Communication in Medicine) image has been developed in recent years. Now, the function of CAD system for artificial implant to which development is advanced includes the removal of artifact in the input DICOM image, region extraction of bone, major vessel, and nerve and mounting of function to presume place where implant is stricken. However, it is still required a software with high performance because of taking the processing time to do a series of operation before simulating by the strike of the implant on the operation on a clinical site. In this paper, we propose the function to extract the area of bone separately for teeth and mandibles automatically as addition to the system that can do a series of operation before simulating by the strike of the implant. In addition, we proposed also the function to extract each tooth separately. The main aim of this paper is to develop the integrative system for the dental screening that can be operated in a personal computer.

To describe a system for measurement of the pretraumatic femoral antetorsion angle post-bilateral femoral shaft fracture with the use of new imaging software which allows segmentation and three dimensional (3D) reconstruction of DICOM (digital imaging and communications in medicine) images. This case involved a 20-year-old patient with bilateral femoral shaft fractures. Following initial clinical examination, CT scans of both femurs were performed. Subsequently, the DICOM datasets were uploaded to the new software tool. Following segmentation and 3D reconstruction, pretraumatic femoral antetorsion angles were determined. Femoral antetorsion was described and assessed in two ways by referring to the intersection of the posterior condylar plane and (1) a line drawn between the center of the femoral head and femoral neck, (2) a line drawn between the centers of the femoral head and greater trochanter. Using these definitions, values for femoral antetorsion were found to be, respectively, 20 degrees at the right fracture site and 19 degrees on the left site, and 33 degrees bilaterally. The investigators describe in this current technical report the use of new imaging software which enables the calculation of femoral AV following reduction of virtual fracture fragments which are created from standard DICOM images. We believe that this 3D reconstruction method of measuring the antetorsion angle can be integrated into a regular treatment algorithm and may potentially optimize clinical outcomes.

The security and integrity of Digital Imaging and Communications in Medicine (DICOM) images are critical in the healthcare industry, where unauthorized access and tampering can have severe consequences. This paper presents a comprehensive AI-driven approach to enhancing DICOM image security through triple watermarking techniques. The proposed method integrates robust watermarking schemes at three distinct levels: spatial, frequency, and hybrid domains, to ensure multi-layered protection against a variety of attacks. Utilizing advanced AI algorithms, the approach optimizes the embedding process to maintain the balance between imperceptibility and robustness. Experimental results demonstrate the effectiveness of the proposed technique in safeguarding DICOM images, showing significant improvements in security metrics compared to traditional methods. This research provides a novel solution to the ongoing challenge of DICOM image protection, leveraging AI to enhance the reliability and security of medical imaging data.

Abstract— The paper deals with evaluation of medical images by objective quality measures. Two main approaches to assess image quality are objective testing and subjective testing. Objective measures correlate well with the perceived image quality for the proposed compression algorithm. This paper presents an effective algorithm to compress and to reconstruct DICOM (Digital Imaging and COmmunications in Medicine) images. DICOM is a standard for handling, storing, printing and transmitting information in medical imaging. These medical images are volumetric consisting of a series of sequences of slices through a given part of the body. DICOM series of images are decomposed using Cohen-Daubechies-Feauveau (CDF) biorthoganal wavelet. The wavelet coefficients are encoded using Set Partitioning In Hierarchical Trees (SPIHT). Consistent quality images are generated by this method at a lower bit rate compared to JPEG and Fractal compression algorithms. The image quality is evaluated by various objective quality measures.

Accurate classification and early detection of lung cancer are crucial for effective treatment and improved patient outcomes. Although recent advances in medical imaging have improved diagnostic workflows, many traditional approaches remain limited in their ability to efficiently analyze large volumes of imaging data. Accordingly, this study proposes a deep learning framework for lung cancer classification using Digital Imaging and Communications in Medicine (DICOM) images. The proposed approach integrates an Intelligent Squirrel Search (ISS)–tuned VGG16 network (ISS-VGG16) to improve classification performance. A set of lung cancer DICOM images was obtained from an open-source dataset. Preprocessing steps, including image resizing and contrast enhancement, were applied to standardize the inputs for model training. ISS was used to optimize key hyperparameters of the VGG16 model, thereby improving classification performance. The model was implemented in Python. Experimental results indicate that the proposed ISS-VGG16 approach outperforms baseline methods, achieving a recall of 0.92, precision of 0.96, F1-score of 0.95, and overall accuracy of 0.97 on the evaluated lung cancer image dataset. These results demonstrate reliable classification performance across the defined lung cancer classes. Overall, the proposed framework provides an effective and dependable approach for lung cancer image classification.

In this manuscript, we proposed a watermarking algorithm based on phase-only computer-generated holography (CGH) in the fractional Hartley domain for digital imaging and communications in medicine (DICOM) images. The proposed algorithm improves the security of the CGH-based algorithm. The cascaded use of fractional Hartley transform and attenuation factor β increases the keyspace of the proposed watermarking algorithm. The robustness and effectiveness of the proposed watermarking algorithm is validated using simulations on DICOM images. The effectiveness of the proposed watermarking algorithm is assessed using statistical tools in terms of mean-squared error, information entropy, correlation coefficient, histogram, and mesh plots. The robustness is evaluated by testing the proposed algorithm’s performance under real-time threats, including contamination and data loss attacks. Furthermore, the security of the proposed algorithm is also tested for existing cryptographic attacks, such as chosen-plaintext attacks, and known-plaintext attacks. The simulation results indicate that the proposed watermarking algorithm is robust and effective.

Problem statement: One of the most common degradations in medical images is their poor contrast quality and noise. The DICOM image consists of speckle (multiplicative noise). While the image is enhanced, the multiplicative noise present in the image is also enhanced. Approach: This study describes the hybrid method to improve the image quality of Digital Imaging and Communications in Medicine (DICOM) images. The idea of image enhancement technique is to improve the quality of an image for early diagnosis . Then followed by a noise reduction using speckle reduction anisotropic filter. This suggests the use of contrast enhancement methods as an attempt to modify the intensity distribution of the image and to reduce the multiplicative noise. Results: In this research study, a new approach for DICOM image is done by applying contrast stretching and anisotropic diffusion where denoising of multiplica tive noise is carried out and the level of contrast is improved. The quality of the image is enhanced and noise free for DICOM image analysis. The effectiveness of hybrid method is proved by quantit ative approach. Conclusion and Recommendation: The performance of the proposed study is compared with the existing traditional algorithm and real time medical diagnosis image.

Online public repositories for sharing research data allow investigators to validate existing research or perform secondary research without the expense of collecting new data. Patient data made publicly available through such repositories may constitute a breach of personally identifiable information if not properly de-identified. Imaging data are especially at risk because some intricacies of the Digital Imaging and Communications in Medicine (DICOM) format are not widely understood by researchers. If imaging data still containing protected health information (PHI) were released through a public repository, a number of different parties could be held liable, including the original researcher who collected and submitted the data, the original researcher's institution, and the organization managing the repository. To minimize these risks through proper de-identification of image data, one must understand what PHI exists and where that PHI resides, and one must have the tools to remove PHI without compromising the scientific integrity of the data. DICOM public elements are defined by the DICOM Standard. Modality vendors use private elements to encode acquisition parameters that are not yet defined by the DICOM Standard, or the vendor may not have updated an existing software product after DICOM defined new public elements. Because private elements are not standardized, a common de-identification practice is to delete all private elements, removing scientifically useful data as well as PHI. Researchers and publishers of imaging data can use the tools and process described in this article to de-identify DICOM images according to current best practices.

This study aimed to investigate the vertical magnification ratios of various digital panoramic radiography devices and assess their consistency. It also examined the relationship between magnification ratios and Digital Imaging and Communications in Medicine (DICOM) header information to determine the potential use of key metadata in inferring or standardizing magnification corrections. A ball phantom with metal spheres and an implant phantom were used to assess vertical magnification ratios. Eleven panoramic radiography systems were tested, and three DICOM tags-pixel spacing, imager pixel spacing, and aspect ratio-were analyzed. Three specialists measured phantom lengths in randomized DICOM images using PACS software, repeating measurements after 4 weeks. Magnification ratios were calculated by averaging 24 measurements per sample and comparing them to actual lengths. Intra- and interobserver reliability were high (ICC > 0.99). Most devices showed magnification ratios of 0.95-1.08, whereas those of devices 3 and 9 ranged from 1.21 to 1.30. Although some magnification ratios, which were used for system-based calibration, could be indirectly inferred by dividing the imager pixel spacing value by the pixel spacing value when both values were precisely recorded, no DICOM headers explicitly provided magnification ratios. This study demonstrated that many digital panoramic radiography devices apply software-based corrections to achieve a vertical magnification ratio close to 1.0; however, inconsistencies remain across different systems. Measurement accuracy and clinical reliability can be enhanced by standardization of magnification correction methods and improved documentation in product specifications and DICOM headers, which is clinically relevant for increasing the accuracy of vertical measurements in procedures such as dental implant planning and thereby enhancing treatment predictability and patient safety.