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What are the unique features of medical image analysis software?

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    Posted in Healthcare Software

    Last Updated | April 4, 2022

    Overview: Unique Features Of Medical Image Analysis Software

    Medical images make up 90% of all healthcare data today.

    These images include lab slides, computed tomography (CT) scans, magnetic resonance imaging (MRI), and X-rays, to name a few.

    Healthcare professionals use medical images for different purposes: health diagnosis, intraoperative navigation, surgical planning, postoperative monitoring, and biomedical research. They present a trove of valuable patient data.

    Therefore, in the US alone, patients spend over $100 billion on diagnostic imaging procedures expecting appropriate diagnoses of their health conditions through medical image technologies.

    That alone is a compelling reason for healthcare practices to transform and acquire a whole new set of analysis tools to generate knowledge from the wealth of medical image data.

    In addition to that, practices need to ensure the analysis tools they use are powerful enough to give accurate results and provide patients the value they are looking for.

    So, what technology can underpin the accuracy of medical image analysis? And how do medical professionals provide value to patients?

    The answer is: by leveraging specialized medical image analysis software.

    With medical image analysis software, as a practitioner, you can achieve accurate analytic results and provide patients with better diagnoses.

    A specialized medical imaging software company can even help you develop medical image analysis software that can perform analysis functions unique to your practice workflows.

    This means, with medical image analysis software, you can not only diagnose patients better but also expedite the workflow of your practice.

    With this software, you also have the advantage to add all the essential features you need for better analysis.

    You can get a better understanding of what features you can add once you know what a medical image analysis software is and what built-in capabilities it already has.

    What Is Medical Image Analysis Software?

    Medical image analysis software is any software that can analyze data from medical images. This analysis can support the diagnosis of patients or the same patient at different intervals to assess the progress of disease and evaluate prognosis.

    With an increasing number of medical images that need examination, the demand for analysis has skyrocketed. For professionals, viewing and analyzing severe diseases from mere medical images requires tremendous skill- plus, it is quite a time-consuming and tedious task.

    Therefore, healthcare providers use computer-aided image analysis to generate in-depth insight into patient conditions.

    Image analysis software can analyze images such as SPECT scans, CT scans, MRIs, and X-rays- at a better rate with greater accuracy.

    They include a range of capabilities and built-in tools to help clinicians understand patient disease patterns and unveil hidden diseases years before they show symptoms.

    What is Medical Imaging Software Used For?

    With medical imaging playing a crucial part in detecting medical anomalies, the imaging software is built to expedite the image analysis process.

    Medical imaging software leverages artificial intelligence (AI) to understand complex quantitative data and use them in new ways. The software is capable of sifting through hundreds of medical images at the same time and handling large clinical workflows.

    Underpinned by AI and machine learning algorithms, the software has the ability to read and evaluate different types of scans and X-rays. This allows medical professionals to track, analyze, manipulate, archive, and manage medical images.

    Many other imaging software programs also support the diagnosis of full-body CT scans, significantly reducing turnaround time.

    Besides scans and X-rays, medical image analysis software is also used for the evaluation of lung MRIs, liver MRIs, and mammograms to identify pathological lesions.

    With more and more features added to them, the analytic ability of imaging software advances and can aid in more precise diagnosis.

    Top Unique Features Of Medical Image Analysis Software

    The wide array of analysis software available today vary in complexity and core functionalities. You can get as many features and functionalities in your image analysis software as there.

    However, there are some essential features that make image analysis software efficient to serve their basic purpose. So, when you look around for computer-aided medical imaging software, take into consideration the following top features:

    1. Image Quality Improvement

    Clinical image analysis must provide optimum, high-quality images that allow practitioners to discern complex details precisely. Precision in images comes with image enhancement which allows accurate analysis of medical images. Image enhancement addresses the blur, noise, artifacts, contrast, and distortion to increase the visibility of images. This is crucial for precise diagnosis and reducing risk to a patient’s well-being.

    1. Image Segmentation

    Segmentation is the process of breaking down medical images into separate parts such as tissues, organs, bones, or blood vessels to reduce the complexity of images. This process prepares images for further processing and analysis. It might also detect pathologies in the areas of interest, such as nodules, tumors, and other abnormalities.

    1. Image Registration

    Image registration involves pulling together data from multiple images to enable geometric alignment. Image registration overlays two or more images taken at different angles from the same or various equipment.

    This feature helps combine images in two ways:

    Image Fusion

    Image data coming from different sources are combined through image fusion. For example, when clinicians want to learn the correlation between a patient’s anatomy and metabolism, they can fuse PET images with CT scans.

    4D fusion

    4D fusion aligns images taken at different intervals in time. The application of this technique helps monitor long-term health changes or changes during the same sessions (such as identifying respiratory patterns).

    1. Quantification

    When it comes to building a medical imaging software with the best features, considering quantification is a must.

    Quantification implies associating different image parts with their diagnostic information. This information consists of their form, texture, size, morphology, and changes over a certain period of time.

    This capability of image analysis software auto-generates diagnostic information about different organs to help clinicians identify changes in biological structures.

    1. 3D Reconstruction and 2D Visualization

    Once images are processed, 3D reconstruction comes into play. This ability of software helps combine multiple 2D images that depict the same area from different angles into one single image.

    This enables clinicians to view a single part of an image in 3D and get a better anatomical orientation to interpret abnormalities efficiently.

    In contrast to 3D reconstruction, 2D visualization divides 3D or 4D reconstructions into 2D components or breaks 2D images down to smaller parts for greater detail.

    In other words, it displays original data sets into 3D or 4D reconstructions.

    That’s all!

    These are the five must-have features of medical image analysis software.

    They are fundamental, and you can find them in most medical imaging software. Still, the best software will provide these features with optimum quality to accelerate image analysis and processing tasks.

    With these functionalities in mind, you know what capabilities and tools you need to have in medical image analysis software for your practice and are familiar with the basics of the software, and possibly ready to implement the system.

    If so, before you think of implementing the software, you must take into account the challenges faced during the implementation of medical image analysis and processing software

    Challenges In Implementation Of Medical Image Analysis And Processing Software

    Implementing new software in your current workflow might not be as easy as you might think. There are a lot of intricacies that you need to be aware of. Therefore, you cannot fully leverage the software if you overlook these challenges.

    A few of the most common implementation challenges you may also need to cope with are:

    1. Security and Compliance

    One of the most persistent challenges for any healthcare software is HIPAA compliance software development. Since medical image analysis software deal with patient-identifiable health data which falls under HIPAA law, compliance with HIPAA is crucial.

    Non-compliance leads to penalties of millions of dollars when the security of patient health data is compromised. So, you cannot take a chance here.

    To comply with HIPAA standards, make sure your tech stack includes technologies that respect all standards. Plus, they do not reveal the identity of patients when their health data goes outside your premise.

    Additionally, achieving compliance also involves HIPAA compliance audit costs you need to pay at one or another point of the implementation, which is certainly less than what you incur due to non-compliance.

    1. AI Model Training

    AI presents immense potential for better analysis and diagnosis. However, the major limitation of AI algorithmic models is that their ability is limited to what you train them to do.

    Therefore, you must train the software on high-standard data sets using only applicable algorithms and models.

    So, make sure the data you feed your AI models is accurate, consistent, and complete. Otherwise, your algorithmic models will generate inaccurate results.

    1. Training Data

    It is never possible to develop a medical image analysis software that makes clinical decision-making easier without using large volumes of data.

    Extracting information from complex image data is challenging, whether clinicians do it or software.

    However, training software based on deep learning models can reduce the effort of sifting through large amounts of image data and provide quality results at an increased pace.

    So, before the development begins, you need to know exactly where and how you are going to collect data for training models, how you will prepare them, and if your systems have enough space for secure storage.

    1. Multimodality Issues

    Realizing the potential of image fusion, healthcare practitioners increasingly demand multimodality image processing platforms that can align images of various modalities. Multimodality systems are combined systems such as PET/MR, MR/CT, and PET/CT.

    The combined systems like PET/CT and PET/MR are already there. However, the development of other systems such as MR/CT is still in the consideration process.

    Thus, it might become a long tough grind for your development team to add this capability to your image analysis software.

    Despite these challenges, the implementation of image analysis software is of great significance. As you implement image analysis software in your healthcare practice, you can:

    • Reduce clinical errors and radiation exposure
    • Facilitate early diagnosis and avoid unnecessary procedures
    • Increase recovery rates
    • Speed up image examination workflow
    • Decrease hospital stays
    • Save time and resources as well as improve quality of care


    Medical image analysis is a critical aspect of patient diagnoses. Though technologies have provided immense support for better diagnosis, they also present harm even if there is a slight inaccuracy of software to generate accurate outcomes.

    However, leveraging a novel medical image analysis software cuts down this concern. Such analysis software, built with unique features, can go beyond the capabilities of conventional software and promise accuracy for large-scale image analysis.

    This way, you, as a healthcare practitioner, can leverage large amounts of medical image data to ensure promising results and appropriate disease characterization.

    To achieve those results, all you need to do is take a holistic approach toward adding the right features to your image analysis software to make sure your patient’s health is diagnosed as accurately as possible.


    What is medical image management?

    Medical image management is a collective term used for describing the management of all types of images in a clinical setting using computer software.

    Medical software development services for image management work across the healthcare organization and perform core functions such as:

    • Collecting and collaborating medical images from different equipment
    • Storing and retrieving medical images
    • Streamlining image management to make it an unburdening task
    • Automating image management to save valuable assets (time and resources)

    How medical images are stored in software?

    Medical images are stored in the files as groups of bites. One bite is a combination of 8 bits, representing the smallest quantity of memory stored in computers.

    Most medical images are stored in software in the form of DICOM (Digital Imaging and Communications in Medicine) format.

    What is diagnostic medical imaging?

    Medical imaging provides a visual representation of how some tissues or organs function in the body and clues about a medical condition.

    Medical diagnostic systems for images rely on invisible waves such as sound waves, magnetic fields, and electromagnetic radiation to image the interior of a patient’s body for clinical diagnosis.

    What are some data integration solutions for healthcare?

    In healthcare, data integration solutions refer to technical tools and processes designed to combine massive data sets to make meaningful use of them.

    Two widely used data integration solutions are HL7 data integration and FHIR. Though there is a difference in the nature of HL7 vs FHIR, fundamentally, both define standards for sharing data among disparate software systems hailing from different healthcare organizations.

    What are the telemedicine startup costs?

    Before learning about the costs of setting up a telemedicine business, let’s first go over what telemedicine is.

    Telemedicine can be defined as developing solutions that can help provide health-related care using telecommunication technologies. A telemedicine business is usually based on an application or software that supports remote delivery of clinical services.

    When it comes to costs, what telemedicine software vendors charge is similar to what other healthcare app development services cost.

    The average cost of telemedicine software for healthcare providers starts at $15,000 and goes up to $150,000. These costs can rise as providers add more complexity or features to the apps.

    What are the top three EHR system integration solutions?

    Taking into account the entire EHR market, Epic, Cerner, and Allscripts are the top three EHR systems. They offer the following integration solutions:

    While Allscripts holds fair value in the market, Cerner and Epic are closely competing as the two best EHR integration solutions.

    These integration solutions (along with medical device integration) are different and Cerner vs Epic pros and cons make it clear which one is a good fit for a particular healthcare facility. Both EHRs have their own specialization and features to provide for the integration needs of different medical practices.

    In addition to these integrations, there is another widely employed set of standards known as HL7 standards. This integration solution uses HL7 messages to exchange clinical data with other providers in the healthcare industry in less amount of time.

    (To better understand HL7 messages for integration, check out what are some HL7 messages examples and how they can integrate multiple EHR systems.)

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