SEGMENTATION OF ANTI-NUCLEAR ANTIBODY IMAGES BASED ON WATERSHEDS AND FAST REGION MERGING

In this paper, segmentation based on watersheds and fast region merging is proposed to be the main segmentation technique in this project. Segmentation of Anti-Nuclear Antibody (ANA) images is the data or input of this project by concentrating in four different patterns of ANA images. Watershed segmentation algorithms are combining edge and region based techniques. Furthermore, watershed segmentation algorithm has several drawbacks which are the image to be over segmented and separation of homogenous region. Therefore, fast region merging techniques are proposed and implement to overcome the problem. In addition, this technique is used to merge homogenous region based on several criteria which is intensity and some other homogenous parts. Moreover, in this paper, basic Gaussian filter is been implement to enhance the watershed segmentation by reducing some of the unwanted noise inside the images and highlighting the edge of cells. Gaussian filter is the fundamental technique in segmentation process of an image. This paper describes the watershed and fast region merging segmentation techniques to be implement in ANA images and the result will be the behaviour of each ANA images based on the selected pattern to be use in this paper

On Segmentation Evaluation Metrics and Region Counts

Abstract Five image segmentation algorithms are evaluated: mean shift, normalised cuts, efficient graph-based segmentation, hierarchical watershed, and waterfall. The evaluation is done using three evaluation metrics: probabilistic Rand index, global consistency error, and boundary precision-recall. We examine region-based metrics as a function of the number of regions produced by an algorithm. This allows new insights into algorithms and evaluation metrics to be gained

A graph-based mathematical morphology reader

This survey paper aims at providing a "literary" anthology of mathematical morphology on graphs. It describes in the English language many ideas stemming from a large number of different papers, hence providing a unified view of an active and diverse field of research

Automatic Image Segmentation by Dynamic Region Merging

This paper addresses the automatic image segmentation problem in a region merging style. With an initially over-segmented image, in which the many regions (or super-pixels) with homogeneous color are detected, image segmentation is performed by iteratively merging the regions according to a statistical test. There are two essential issues in a region merging algorithm: order of merging and the stopping criterion. In the proposed algorithm, these two issues are solved by a novel predicate, which is defined by the sequential probability ratio test (SPRT) and the maximum likelihood criterion. Starting from an over-segmented image, neighboring regions are progressively merged if there is an evidence for merging according to this predicate. We show that the merging order follows the principle of dynamic programming. This formulates image segmentation as an inference problem, where the final segmentation is established based on the observed image. We also prove that the produced segmentation satisfies certain global properties. In addition, a faster algorithm is developed to accelerate the region merging process, which maintains a nearest neighbor graph in each iteration. Experiments on real natural images are conducted to demonstrate the performance of the proposed dynamic region merging algorithm.Comment: 28 pages. This paper is under review in IEEE TI