BGrowth: an efficient approach for the segmentation of vertebral compression fractures in magnetic resonance imaging

Segmentation of medical images is a critical issue: several process of analysis and classification rely on this segmentation. With the growing number of people presenting back pain and problems related to it, the automatic or semi-automatic segmentation of fractured vertebral bodies became a challenging task. In general, those fractures present several regions with non-homogeneous intensities and the dark regions are quite similar to the structures nearby. Aimed at overriding this challenge, in this paper we present a semi-automatic segmentation method, called Balanced Growth (BGrowth). The experimental results on a dataset with 102 crushed and 89 normal vertebrae show that our approach significantly outperforms well-known methods from the literature. We have achieved an accuracy up to 95% while keeping acceptable processing time performance, that is equivalent to the state-of-the-artmethods. Moreover, BGrowth presents the best results even with a rough (sloppy) manual annotation (seed points).Comment: This is a pre-print of an article published in Symposium on Applied Computing. The final authenticated version is available online at https://doi.org/10.1145/3297280.329972

Robust and accurate eye contour extraction

This paper describes a novel algorithm for exact eye contour detection in frontal face image. The exact eye shape is a useful piece of input information for applications like facial expression recognition, feature-based face recognition and face modelling. In contrast to well-known eye-segmentation methods, we do not rely on deformable models or image luminance gradient (edge) map. The eye windows (rough eye regions) are assumed to be known. The detection algorithm works in several steps. First, iris center and radius is estimated, then, exact upper eyelid contour is detected by searching for luminance valley points. Finally, lower eyelid is estimated from the eye corners coordinates and iris. The proposed technique has been tested on images of about fifty individuals taken under different lighting conditions with different cameras. It proved to be sufficiently robust and accurate for wide variety of images

"GrowCut" -- Interactive Multi-Label N-D Image Segmentation By Cellular Automata

In this paper we describe a novel algorithm for interactive multilabel segmentation of N-dimensional images. Given a small number of user-labelled pixels, the rest of the image is segmented automatically by a Cellular Automaton. The process is iterative, as the automaton labels the image, user can observe the segmentation evolution and guide the algorithm with human input where the segmentation is difficult to compute. In the areas, where the segmentation is reliably computed automatically no additional user effort is required. Results of segmenting generic photos and medical images are presented. Our experiments show that modest user effort is required for segmentation of moderately hard images

Abstract A Survey on Pixel-Based Skin Color Detection Techniques

Skin color has proven to be a useful and robust cue for face detection, localization and tracking. Image content filtering, content-aware video compression and image color balancing applications can also benefit from automatic detection of skin in images. Numerous techniques for skin color modelling and recognition have been proposed during several past years. A few papers comparing different approaches have been published [Zarit et al. 1999], [Terrillon et al. 2000], [Brand and Mason 2000]. However, a comprehensive survey on the topic is still missing. We try to fill this vacuum by reviewing most widely used methods and techniques and collecting their numerical evaluation results