Automatic Detection and Quantification of Calcium Objects from Clinical Images for Risk Level Assessment of Coronary Disease

Part of the Studies in Computational Intelligence book series (SCI, volume 730)


Medical diagnosis is often challenging, owing to the diversity of medical information sources. Significant advancements in healthcare technologies, potentially improving the benefits of diagnosis, may also result in data overload while the obtained information is being processed. From the beginning of time, humans have been susceptible to a surplus of diseases. Of the innumerable life-threatening diseases around, heart disease has garnered a great deal of consideration from medical researchers. Coronary Heart Disease is indubitably the commonest manifestation of Cardiovascular Disease (CVD), representing some 50% of the whole range of cardiovascular events. Medical imaging plays a key role in modern-day health care. Automatic detection and quantification of lesions from clinical images is quite an active research area where the challenge to obtain high accuracy rates is an ongoing process. This chapter presents an approach for mining the disease patterns from Cardiac CT (Computed Tomography) to assess the risk level of an individual with suspected coronary disease.


Image segmentation Active contour model Coronary disease diagnosis Calcium object detection Risk level categorization 


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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Information TechnologyEaswari Engineering CollegeChennaiIndia
  2. 2.Department of Computer Science and EngineeringAnna UniversityChennaiIndia

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