In this paper, we propose a novel level set method based on selective homogeneous and inhomogeneous object segmentation
driven by an image region, edge and saliency functions. Initially, we adapt an intensity edge term based on the zero crossing
feature detector (ZCD), which is used to highlight significant edges of an image. Secondly, a saliency function is formulated
to detect salient regions from an image. We have also included a globally tuned region based SPF (signed pressure force)
term to move contour away and capture homogeneous regions. ZCD, saliency and global SPF are jointly incorporated with
some scaled value for the level set evolution to develop an effective image segmentation model. Moreover, proposed method is
capable to perform selective object segmentation, which enables us to choose any single or multiple objects inside an image.
Saliency function and ZCD detector are considered feature enhancement tools, which are used to get important features of an
image, so this method has a solid capacity to segment nature images (homogeneous or inhomogeneous) precisely. Finally, the
adaption of the Gaussian kernel removes the need of any penalization term for level set reinitialization. Experimental results
will exhibit the efficiency of the proposed method.