In vivo observation and tracking of cell division in the Arabidopsis thaliana root meristem, by time-lapse confocal microscopy, is central to biology research. This paper discusses an automatic cell segmentation method, which selects the best cell candidates from a starting watershed segmentation. The selection of individual cells is obtained using a Support Vector Machine (SVM) classifier, bas...

Image Segmentation has a vital role in the field of Medical Science especially in the area of cancer research for detecting tumors. Proper diagnosis and effective treatment is only possible if we have an efficient segmenting technique. In this paper we present a sound segmentation technique named as marker controlled watershed technique which is based on repeated morphological image dilation an...

We have implemented an algorithm for detection and segmentation of protein spots in 2-D gel electrophoresis images using symmetry derivative features computed using low level image processing operations. The implementation was compared with a previously published Watershed segmentation and a commercial software. Our algorithm was found to yield segmentation results that were either better than ...

This paper presents a new segmentation method of complex nucleus configurations. The specificity of this work is to introduce prior information about the usual shape of cells nuclei, in order to optimize the selection of markers from which the flooding will start, during the watershed-based segmentation.

Hyperspectral imaging, which records a detailed spectrum of light for each pixel, provides an invaluable source of information regarding the physical nature of the different materials, leading to the potential of a more accurate classification. However, high dimensionality of hyperspectral data, usually coupled with limited reference data available, limits the performances of supervised classif...

We introduce a noise-tolerant segmentation algorithm efficient on 3D multiscale granular materials. The approach uses a graphbased version of the stochastic watershed and relies on the morphological granulometry of the image to achieve a content-driven unsupervised segmentation. We present results on both a virtual material and a real X-ray microtomographic image of solid propellant.

A hybrid split and merge segmentation method for color images is presented in this work. It combines edge and region information to merge adjacent regions produced in the initial watershed-based segmentation stage. A novel technique is introduced to simplify the Region Adjacency Graph (RAG) structure and speed-up the merging process along with a merging termination criterion for automatic segme...

This paper proposes to incorporate boundary curvature ratio, region homogeneity and boundary smoothness into a single new merging criterion to improve the oversegmentation of marker-controlled watershed segmentation algorithm. The result is a more refined segmentation result with smooth boundaries and regular shapes. To pursue a final segmentation result with higher inter-variance and lower int...

Brain tumour detection is one of the challenging tasks in medical image processing. The present study discusses in detail the segmentation process by means of histogram clustering, Global thresholding, Watershed segmentation and edge based segmentation. Six MRI images from radiologists were collected and the experiments were conducted for statistical analysis also. A comparative study is made a...