The watershed transformation is a popular image segmentation algorithm for grey scale images. Sequential watershed algorithms perform a highly data dependent ooding process over the global image. Parallel algorithms which distribute the image over the available processors and simulate the ooding process have a limited, data dependent speedup. This is due to global data dependencies over the sub...

The classical morphological segmentation paradigm is based on the watershed transform, constructed by flooding the gradient image seen as a topographic surface. For flooding a topographic surface, a topographic distance is defined from which a minimum distance algorithm is derived for the watershed. In a continuous formulation, this is modeled via the eikonal PDE, which can be solved using curv...

A new method based on directional wavelet transform and the watershed method is proposed for the segmentation of image. In this paper, more attention is paid for the efficient extraction and representation of feature associated with pixel and their classification. In order to achieve the better representation of image contents anisotropic wavelet transform is utilized. Segmented image is obtain...

In this paper we present a new postal envelope segmentation method based on 2-D histogram clustering and watershed transform. Segmentation task consists in detecting the modes associated with homogeneous regions in envelope images such as handwritten address block, postmarks, stamps and background. The homogeneous modes in 2-D histogram are segmented through the morphological watershed transfor...

Biomedical image is often complex. An applied image analysis system should deal with the images which are of quite low quality and are challenging to segment. This paper presents a framework for color cell image segmentation by learning and classification online. It is a robust two-stage scheme using kernel method and watershed transform. In first stage, a twoclass SVM is employed to discrimina...

Colour information is usually not enough to segment natural complex scenes. Texture contains relevant information that segmentation approaches should consider. Martin et al. (2004) proposed a particularly interesting colour-texture gradient. This gradient is not suitable for Watershed based approaches because it contains gaps. In this paper we propose a method based on the distance function to ...

Microarrays are novel and dominant techniques that are being made use in the analysis of the expression level of DNA, with pharmacology, medical diagnosis, environmental engineering, and biological sciences being its current applications. Studies on microarray have shown that image processing techniques can considerably influence the precision of microarray data. A crucial issue identified in g...

We propose a set of tools for region-based processing of volumetric data. Here, the basic entity is a spectrally homogeneous region instead of the traditional voxel. This provides for, on the one hand, higher robustness and, on the other hand, speeds up processing owing to many times smaller amount of elements to work with. Homogeneous regions are in our approach detected by the well-known segm...

A novel deformable model for image segmentation and shape recovery is presented. The model is inspired by fluid dynamics and is based on a flooding simulation similar to the watershed paradigm. Unlike most watershed methods, our model has a continuous formulation, being described by two partial differential equations. In this model, different fluids, added by placing density (dye) sources manua...