Partitioning intensity inhomogeneity colour images via Saliency-based active contour

Abstract

Partitioning or segmenting intensity inhomogeneity colour images is a challenging problem in computer vision and image shape analysis. Given an input image, the active contour model (ACM) which is formulated in variational framework is regularly used to partition objects in the image. A selective type of variational ACM approach is better than a global approach for segmenting specific target objects, which is useful for applications such as tumor segmentation or tissue classification in medical imaging. However, the existing selective ACMs yield unsatisfactory outcomes when performing the segmentation for colour (vector-valued) with intensity variations. Therefore, our new approach incorporates both local image fitting and saliency maps into a new variational selective ACM to tackle the problem. The euler-lagrange (EL) equations were presented to solve the proposed model. Thirty combinations of synthetic and medical images were tested. The visual observation and quantitative results show that the proposed model outshines the other existing models by average, with the accuracy of 2.23% more than the compared model and the Dice and Jaccard coefficients which were around 12.78% and 19.53% higher, respectively, than the compared model.

Generalized Active Contour Model in an Anisotropic Variable Exponent Sobolev Space

“…In fact, it provided an absolutely new way of thinking for image segmentation, which was called the focus and hot spot of research in [12]. In recent years, many excellent algorithms based on active contour models have been proposed in the field of the image segmentation (we refer the readers to the recent works [10,20,21,33,34,37,41] and the bibliography therein).…”