Change detection of pulmonary embolism using isomeric cluster and computer vision

Mekala Srinivasa Rao, Sagenela Vijaya Kumar, Rambabu Pemula, Anil Kumar Prathipati

Abstract


Visual change detection functions in X-ray analytics and computer vision attempt to divide X-ray images toward front and backside areas. There are various difficulties in change detection such as weather changes and shadows; real-time processing; intermittent object motion; lighting variation; and diverse object forms. Traditionally, this issue has been addressed via backdrop modeling methods and the creation of custom features. We present a new feature descriptor called pulmonary embolism detection using isomeric cluster (PEDIC), uses the concept of isomerism. The isomeric and cluster isomerism characteristics of the PEDIC are distinguish it from other graphs. At isomeric thetical orientations, the cluster pattern corresponds to consecutive differences in pixel intensity between the two images. Also, the clusters are oppositely orientated, and both clusters conform to a specified isomeric feature. The local area's lines and corner point information are identified and recorded using the PEDIC in several different directions. We introduced multiresolution PEDIC, which incorporates the multiresolution Gaussian filter to achieve increased resilience in the system. We expanded our research to include rotation-invariant characteristics. We also proposed inter-PEDIC and intra-PEDIC to identify motion changes in X-ray sequences, which allowed them to extract spatiotemporal characteristics.


Keywords


computer vision; deep clustering; isomeric cluster; machine learning; pulmonary embolism;

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DOI: http://doi.org/10.11591/ijai.v11.i2.pp787-798

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