An Image Registration Method for Head CTA and MRA Images Using Mutual Information on Volumes of Interest

Image registration is an important and a fundamental task in computer vision and image processing field. For example, to make a surgical plan for head operation, the surgeons should gain more detailed information from CT angiography (CTA) and MR angiography (MRA) images. And the abnormalities can be easily detected from the fusion image which is obtained from two different modalities. One of the multiple modal image registration methods is matching the CTA and MRA, by which the image of head vascular could be enhanced. In general, the procedure for fusion is completed manually. It is time-consuming and subjective. Particularly the anatomical knowledge is required as well. Therefore, the development of automatic registration methods is expected in medical fields. In this paper, we propose a method for high accurate registration, which concentrates the structure of head vascular. We use 2-D projection images and restrict volume of interests to improve the processing affection. In experiments, we performed our proposed method for registration on five sets of CTA and MRA images and a better result from our previous method is obtained.SCIS&ISIS 2014 : Joint 7th International Conference on Soft Computing and Intelligent Systems and 15th International Symposium on Advanced Intelligent, December 3-6, 2014, Kitakyushu, Japa

A graph-based mathematical morphology reader

This survey paper aims at providing a "literary" anthology of mathematical morphology on graphs. It describes in the English language many ideas stemming from a large number of different papers, hence providing a unified view of an active and diverse field of research

Acceleration of stereo-matching on multi-core CPU and GPU

This paper presents an accelerated version of a dense stereo-correspondence algorithm for two different parallelism enabled architectures, multi-core CPU and GPU. The algorithm is part of the vision system developed for a binocular robot-head in the context of the CloPeMa 1 research project. This research project focuses on the conception of a new clothes folding robot with real-time and high resolution requirements for the vision system. The performance analysis shows that the parallelised stereo-matching algorithm has been significantly accelerated, maintaining 12x and 176x speed-up respectively for multi-core CPU and GPU, compared with non-SIMD singlethread CPU. To analyse the origin of the speed-up and gain deeper understanding about the choice of the optimal hardware, the algorithm was broken into key sub-tasks and the performance was tested for four different hardware architectures

Rock falls impacting railway tracks. Detection analysis through an artificial intelligence camera prototype

During the last few years, several approaches have been proposed to improve early warning systems for managing geological risk due to landslides, where important infrastructures (such as railways, highways, pipelines, and aqueducts) are exposed elements. In this regard, an Artificial intelligence Camera Prototype (AiCP) for real-time monitoring has been integrated in a multisensor monitoring system devoted to rock fall detection. An abandoned limestone quarry was chosen at Acuto (central Italy) as test-site for verifying the reliability of the integratedmonitoring system. A portion of jointed rockmass, with dimensions suitable for optical monitoring, was instrumented by extensometers. One meter of railway track was used as a target for fallen blocks and a weather station was installed nearby. Main goals of the test were (i) evaluating the reliability of the AiCP and (ii) detecting rock blocks that reach the railway track by the AiCP. At this aim, several experiments were carried out by throwing rock blocks over the railway track. During these experiments, the AiCP detected the blocks and automatically transmitted an alarm signal