Multi-Object Segmentation for Abdominal CT Images Based on Gestalt Cognitive Framework

doi:10.13190/j.jbupt.2016.05.011

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2016, Vol. 39 ›› Issue (5): 51-55.doi: 10.13190/j.jbupt.2016.05.011

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Multi-Object Segmentation for Abdominal CT Images Based on Gestalt Cognitive Framework

FENG Jun, LIU Fei-hong, LI Pan, BU Qi-rong, WANG Hong-yu

School of Information and Technology Northwest University, Xi'an 710127, China

Received:2016-01-06 Online:2016-10-28 Published:2016-12-02

Abstract

Abstract: In order to acquire better organ segmentation from abdominal computed tomography (CT) images, a multi-object segmentation algorithm based on cognitive framework is proposed. Inspired by the proximity and similarity idea in gestalt, super pixel concept of CT image processing has been produced. Specifically, by establishing the directed adjacency relationship of super-pixel, the spatial relationships of abdominal organs are modeled as prior knowledge to improve the classification accuracy. Experiments in public datasets illustrate that the proposed algorithm achieves better performance in either speed or accuracy than that of the state-of-art methods.

Key words: Gestalt, abdominal computed tomography, segmentation, visual patch, classification, spatial relationship

CLC Number:

TP399

FENG Jun, LIU Fei-hong, LI Pan, BU Qi-rong, WANG Hong-yu. Multi-Object Segmentation for Abdominal CT Images Based on Gestalt Cognitive Framework[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2016, 39(5): 51-55.

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Multi-Object Segmentation for Abdominal CT Images Based on Gestalt Cognitive Framework

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