Comput Math Methods Med - Extraction of lesion-partitioned features and retrieval of contrast-enhanced liver images.

Tópicos

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Resumo

The most critical step in grayscale medical image retrieval systems is feature extraction. Understanding the interrelatedness between the characteristics of lesion images and corresponding imaging features is crucial for image training, as well as for features extraction. A feature-extraction algorithm is developed based on different imaging properties of lesions and on the discrepancy in density between the lesions and their surrounding normal liver tissues in triple-phase contrast-enhanced computed tomographic (CT) scans. The algorithm includes mainly two processes: (1) distance transformation, which is used to divide the lesion into distinct regions and represents the spatial structure distribution and (2) representation using bag of visual words (BoW) based on regions. The evaluation of this system based on the proposed feature extraction algorithm shows excellent retrieval results for three types of liver lesions visible on triple-phase scans CT images. The results of the proposed feature extraction algorithm show that although single-phase scans achieve the average precision of 81.9%, 80.8%, and 70.2%, dual- and triple-phase scans achieve 86.3% and 88.0%.

Resumo Limpo

critic step grayscal medic imag retriev system featur extract understand interrelated characterist lesion imag correspond imag featur crucial imag train well featur extract featureextract algorithm develop base differ imag properti lesion discrep densiti lesion surround normal liver tissu triplephas contrastenhanc comput tomograph ct scan algorithm includ main two process distanc transform use divid lesion distinct region repres spatial structur distribut represent use bag visual word bow base region evalu system base propos featur extract algorithm show excel retriev result three type liver lesion visibl triplephas scan ct imag result propos featur extract algorithm show although singlephas scan achiev averag precis dual triplephas scan achiev

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