IEEE Trans Image Process - A new energy framework with distribution descriptors for image segmentation.

Tópicos

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{ motion(1329) object(1292) video(1091) }
{ framework(1458) process(801) describ(734) }
{ high(1669) rate(1365) level(1280) }
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Resumo

Segmentation of the target object(s) from images that have multiple complicated regions, mixture intensity distributions or are corrupted by noise poses a challenge for the level set models. In addition, the conventional piecewise smooth level set models normally require prior knowledge about the number of image segments. To address these problems, we propose a novel segmentation energy function with two distribution descriptors to model the background and the target. The single background descriptor models the heterogeneous background with multiple regions. Then, the target descriptor takes into account the intensity distribution and incorporates local spatial constraint. Our descriptors, which have more complete distribution information, construct the unique energy function to differentiate the target from the background and are more tolerant of image noise. We compare our approach to three other level set models: 1) the Chan-Vese; 2) the multiphase level set; and 3) the geodesic level set. This comparison using 260 synthetic images with varying levels and types of image noise and medical images with more complicated backgrounds showed that our method outperforms these models for accuracy and immunity to noise. On an additional set of 300 synthetic images, our model is also less sensitive to the contour initialization as well as to different types and levels of noise.

Resumo Limpo

segment target object imag multipl complic region mixtur intens distribut corrupt nois pose challeng level set model addit convent piecewis smooth level set model normal requir prior knowledg number imag segment address problem propos novel segment energi function two distribut descriptor model background target singl background descriptor model heterogen background multipl region target descriptor take account intens distribut incorpor local spatial constraint descriptor complet distribut inform construct uniqu energi function differenti target background toler imag nois compar approach three level set model chanves multiphas level set geodes level set comparison use synthet imag vari level type imag nois medic imag complic background show method outperform model accuraci immun nois addit set synthet imag model also less sensit contour initi well differ type level nois

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