Interaction in the Segmentation of Medical Images: A Survey

Image segmentation is often described as partitioning an image into a finite number of semantically non-overlapping regions. In medical applications, it is a fundamental process in most systems that support medical diagnosis, surgical planning and treatments. Generally, this process is done manually by clinicians, which may be time-consuming and tedious. To alleviate the problem, a number of interactive segmentation methods have been proposed in the literature. These techniques take advantage of automatic segmentation and allow users to intervene the segmentation process by incorporating prior-knowledge, validating results and correcting errors, thus potentially lead to accurate segmentation results. In this paper, we present an overview on interactive segmentation techniques for medical images.

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An Overview on Interactive Medical Image Segmentation

High-resolution imaging modalities, such as magnetic resonance imaging (MRI) and computer tomography (CT), combined with advances in computer technology, have prompted a renewed interest and have led to significant progress in the volumetric reconstruction of medical images. Assessment of such techniques for various clinical applications and medical educational interpretations are currently under investigation by many medical and scientific groups. The purpose of the paper is to highlight various clinical applications that show potential for the utilization of volumetric rendering of medical images. Such applications include (1) diagnostics, (2) preoperative planning, (3) intraoperative navigation, (4) surgical robotics, (5) postoperative validation, (6) training, and (7) telesurgery. First, however, the authors try to identify the sources of the patients' imaging data, outlining several popular segmentation and volumetric rendering techniques. At the conclusion, they discuss the necessary requirements to deploy a practical three-dimensional (3D) software for clinicians. The paper is intended for those who would like to obtain and general understanding of the clinical 3-D rendering process and its applications. No effort is made at detailing the various volumetric rendering, segmentation, or user interface design techniques, nor is any effort made to review the commercially available software. Although some sample publications are listed to illustrate the various mythologies, no attempt is made at providing a complete coverage of the literature.

Clinical applications of three-dimensional rendering of medical data sets

We propose a new algorithm to find the corpus callosum automatically from midsagittal brain MR (magnetic resonance) images using the statistical characteristics and shape information of the corpus callosum. We first extract regions satisfying the statistical characteristics (gray level distributions) of the corpus callosum that have relatively high intensity values. Then we try to find a region matching the shape information of the corpus callosum. In order to match the shape information, we propose a new directed window region growing algorithm instead of using conventional contour matching. An innovative feature of the algorithm is that we adaptively relax the statistical requirement until we find a region matching the shape information. After the initial segmentation, a directed border path pruning algorithm is proposed in order to remove some undesired artifacts, especially on the top of the corpus callosum. The proposed algorithm was applied to over 120 images and provided promising results.

/pdf/automated-segmentation-of-the-corpus-callosum-in-midsagittal-334ggxahve.pdf

Automated segmentation of the corpus callosum in midsagittal brain magnetic resonance images

The detection of masses and tumors in a mammogram is a difficult problem that could benefit from the use of multiple approaches. We propose an abstract concept of information fusion based on a finite automaton and fuzzy sets to integrate and evaluate results of multiple image segmentation procedures. We give examples on how the method can be applied to the problem of mammographic image segmentation, combining results of region growing and closed-contour detection techniques. We also propose a measure of fuzzyness to assess the agreement between a segmented region and a reference contour. Application of the fusion technique to breast tumor detection in mammograms indicates that the fusion results agree with the reference contours provided by a radiologist to a higher extent than the results of the individual methods.

Fuzzy fusion operators to combine results of complementary medical image segmentation techniques

Active contours, known as snakes, have been widely used for medical images. In this paper a new algorithm for 3D segmentation is proposed based on the snake technique, which can include both gradient and textural features as image forces and a new external force to improve the sensitivity of a normal snake against image noise. After the contour has been well defined on one slice of a volume data by a 2D segmentation method, the contours of the subsequent slices can be obtained through our modified snake method. Experiments on medical volume images have proved the validity of the algorithm.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Interactive surface segmentation for medical images

In this paper a new algorithm for direct 3D segmentation is presented. The algorithm combines region growing, edge detection and a new edge preserving smoothing method. The combined method helps to avoid characteristic segmentation errors which occur when using region growing or edge detection separately. Boundary modification is also introduced to overcome noise in the boundary location and ensure more realistic region boundaries. The algorithm has been validated through application to medical image visualization.

Direct segmentation in 3D and its application to medical images

Segmentation is a fundamental step in computer assisted visualization and analysis of medical data. In this paper we present a segmentation system for the processing of medical images from various imaging modalities. The software operates on standard UNIX workstations and utilizes X Window System and OSF/Motif standards for the user interface and the graphics functions. The segmentation methods implemented include edge detection, region growing, texture based segmentation, and methods which combine several different approaches. Particular emphasis has been placed on the development of interactive tools and methods to complement the automatic techniques. We have applied the system to the segmentation of clinical MRI data.

Segmentation System for Medical Images

Methods are described for an automated recognition of height contours from graphic maps. The methods are applied to a large-scale conversion of contour data into digital format. Two data structures are used during the recognition process. The actual contour data is fully described by a line structure obtained by vectorizing the thinned medial axis of the original scanned contours. A second line structure is obtained by applying a similar procedure for the background of the image. This process yields a vectorized medial axis of the background consisting of so-called dual lines. By definition, each dual line represents a neighborhood relation between two contour line segments. The recognition of the contours consists of two phases. Closed height contours are first constructed from broken contour segments followed by attaching absolute height labels to the recognized contours. The formation of closed contours relies on a knowledge-based algorithm which utilizes both attributes of actual contour line segments and their inter-relations described by the dual lines. The dual lines are also valuable when attaching height labels on the contours.

Automatic digitization of contour lines for digital map production

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Interactive surface segmentation for medical images

Direct segmentation in 3D and its application to medical images

Segmentation System for Medical Images

Automatic digitization of contour lines for digital map production