Abstract: Crawling movement as a motive mode seen in nature
of some animals such as snakes possesses a specific syntactic and
dynamic analysis. Serpentine robot designed by inspiration from
nature and snake-s crawling motion, is regarded as a crawling robot.
In this paper, a serpentine robot with spiral motion model will be
analyzed. The purpose of this analysis is to calculate the vertical and
tangential forces along snake-s body and to determine the parameters
affecting on these forces. Two types of serpentine robots have been
designed in order to examine the achieved relations explained below.
Abstract: This paper presents a technique for diagnosis of the abdominal aorta aneurysm in magnetic resonance imaging (MRI) images. First, our technique is designed to segment the aorta image in MRI images. This is a required step to determine the volume of aorta image which is the important step for diagnosis of the abdominal aorta aneurysm. Our proposed technique can detect the volume of aorta in MRI images using a new external energy for snakes model. The new external energy for snakes model is calculated from Law-s texture. The new external energy can increase the capture range of snakes model efficiently more than the old external energy of snakes models. Second, our technique is designed to diagnose the abdominal aorta aneurysm by Bayesian classifier which is classification models based on statistical theory. The feature for data classification of abdominal aorta aneurysm was derived from the contour of aorta images which was a result from segmenting of our snakes model, i.e., area, perimeter and compactness. We also compare the proposed technique with the traditional snakes model. In our experiment results, 30 images are trained, 20 images are tested and compared with expert opinion. The experimental results show that our technique is able to provide more accurate results than 95%.
Abstract: A mammography image is composed of low contrast area where the breast tissues and the breast abnormalities such as microcalcification can hardly be differentiated by the medical practitioner. This paper presents the application of active contour models (Snakes) for the segmentation of microcalcification in mammography images. Comparison on the microcalcifiation areas segmented by the Balloon Snake, Gradient Vector Flow (GVF) Snake, and Distance Snake is done against the true value of the microcalcification area. The true area value is the average microcalcification area in the original mammography image traced by the expert radiologists. From fifty images tested, the result obtained shows that the accuracy of the Balloon Snake, GVF Snake, and Distance Snake in segmenting boundaries of microcalcification are 96.01%, 95.74%, and 95.70% accuracy respectively. This implies that the Balloon Snake is a better segmentation method to locate the exact boundary of a microcalcification region.
Abstract: Deformable active contours are widely used in
computer vision and image processing applications for image
segmentation, especially in biomedical image analysis. The active
contour or “snake" deforms towards a target object by controlling the
internal, image and constraint forces. However, if the contour
initialized with a lesser number of control points, there is a high
probability of surpassing the sharp corners of the object during
deformation of the contour. In this paper, a new technique is
proposed to construct the initial contour by incorporating prior
knowledge of significant corners of the object detected using the
Harris operator. This new reconstructed contour begins to deform, by
attracting the snake towards the targeted object, without missing the
corners. Experimental results with several synthetic images show the
ability of the new technique to deal with sharp corners with a high
accuracy than traditional methods.