Medical image segmentation: Automated design of border detection criteria from examples

Abstract

This paper provides examples of several medical image analysis applications for which single-purpose border detection approaches were developed in the past. However, the utility of these and other existing automated and semiautomated medical image analysis systems is limited by their narrow, frequently singlepurpose orientation. After a general approach to graph-based optimal border detection is overviewed, a new method for design of image segmentation systems is reported, in which the criterion of optimality is automatically determined by learning from border tracing examples. Border features employed in the designed method are selected from a predefined global set using radial-basis neural networks. The method was validated in intracardiac, intravascular, and ovarian ultrasound images. The achieved performance was comparable to that of our previously reported single-purpose border detection methods. Our approach facilitates development of general multi-purpose image segmentation systems that can be trained for different types of image segmentation applications. © 1999 SPIE and IS&T. [S1017-9909(99)

Application of Cognitive Information Systems in Medical Image Semantic Analysis

“…The choice of the right type of grammar depends on the type of data it will be used to describe. The choice of the right grammar (string, tree, or graph) implies the use of the right path of data processing and interpretation in the analysis process [7].…”

“…During data preprocessing, the cognitive system also conducts the process of segmentation and identification of the primary components, and further determines the relationships that exist between the aforementioned stages [7]. The classification stage consists of recognizing whether the representation of input data belongs to the class of data generated by a formal language, which is defined by a specific grammar.…”

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Multigradient field-active contour model for multilayer boundary detection of ultrasound rectal wall image

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Region based segmentation technique and algorithms for 3D image

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