Classification-Driven Watershed Segmentation

Abstract

This paper presents a novel approach for creation of topographical function and object markers used within watershed segmentation. Typically, marker-driven watershed segmentation extracts seeds indicating the presence of objects or background at specific image locations. The marker locations are then set to be regional minima within the topological surface (typically, the gradient of the original input image), and the watershed algorithm is applied. In contrast, our approach uses two classifiers, one trained to produce markers, the other trained to produce object boundaries. As a result of using machine-learned pixel classification, the proposed algorithm is directly applicable to both single channel and multichannel image data. Additionally, rather than flooding the gradient image, we use the inverted probability map produced by the second aforementioned classifier as input to the watershed algorithm. Experimental results demonstrate the superior performance of the classification-driven watershed segmentation algorithm for the tasks of 1) image-based granulometry and 2) remote sensing.

Generalized <i>α</i>-Entropy Based Medical Image Segmentation

“…Then it has applied across physics, information theory, mathematics and other branches of science and engineering [9]. When given a system whose exact description is not precisely known, the entropy is defined as the expected amount of information needed to exactly specify the state of the system, given what we know about the system.…”

“…Especially, algorithms for the delineation of anatomical structures and other regions of interest are a key component in assisting and automating specific radiological tasks. These algorithms, named image segmentation algorithms, play a fundamental role in many medical imaging applications such as the quantification of tissue volumes [3,4], diagnosis [5], localization of pathology [6,7], study of anatomical structure [8,9], treatment planning [10], partial volume correction of functional imaging data [11], and computer integrated surgery [12][13][14]. Techniques for carrying out segmentations vary broadly depending on some factors such as specific application, imaging modality, etc.…”

Segmentation Methods for Synthetic Aperture Radar

“…By setting marker locations as the only local minima within the watershed image, the number of regions can be automatically controlled. Also, particular approach to finding and utilizing markers can be found in [18], [19]and [20], where researchers used a naive Bayes classifier to identify (i.e., classify) pixel groups as internal markers. Of course,these particular approach have good performance in controlling over-segmentation, but they are complex, low speed.…”

Graph-based tools for microscopic cellular image segmentation

“…In image processing a common approach is to use the image local extrema (minima or maxima) in a fixed search window. Other schemes, based on machine learning approaches have also been proposed to extract the initial seeds [12,27,28].…”