Publications

Filter by type:

Medical images offer visual representations of human bodies’ complex internal structures. One of the most common process applied to those images is segmentation. It consists in dividing an image into a set of regions of interest. Human anatomical complexity and medical image acquisition methods make the segmentation of medical images very complex. Several solutions (algorithms and devices) have thus been proposed to automatize this process. However, most existing solutions were developed for one type of images and/or require several inputs of the user. In this demo, we propose a generic multi-agent framework for medical image segmentation. This framework is based on a set of autonomous and interactive agents that use a modified region growing algorithm and cooperate to segment the images. Experiments were performed on brain MRI simulated images and the obtained results are promising.
CEUR, vol 2056, 2018

Medical image segmentation is a difficult task, essentially due to the inherent complexity of human body structures and the acquisition methods of this kind of images. Manual segmentation of medical images requires advance radiological expertize and is also very time-consuming. Several methods have been developed to automatize medical image segmentation, including multi-agent approaches. In this paper, we propose a new multi-agent approach based on a set of autonomous and interactive agents that integrates an enhanced region growing algorithm. It does not require any prior knowledge. This approach was implemented and experiments were performed on brain MRI simulated images and the obtained results are promising.
In LNCS, vol 10621, 2017

In this paper we introduce a new multi-agent based approach with which a 2D image could be segmented into it’s connected homogeneous regions. it consists in an adaptive approach in the sense that it does not need neither thresholds nor calibration. Moreover, the approach is robust and is stable against the presence of noise in the image. It can be included as it is in classical image processing systems, while being a new approach to enhance as a perspective toward a self-adaptive artificial vision system. Experiment results on synthetic and real images have shown that the approach is well appropriate to image segmentation without any kind of parameter learning.
AMINA 2012, 2012