Active Contour Models for Manifold Valued Image Segmentation

Image segmentation is the process of partitioning a image into different regions or groups based on some characteristics like color, texture, motion or shape etc. Active contours is a popular variational method for object segmentation in images, in which the user initializes a contour which evolves in order to optimize an objective function designed such that the desired object boundary is the optimal solution. Recently, imaging modalities that produce Manifold valued images have come up, for example, DT-MRI images, vector fields. The traditional active contour model does not work on such images. In this paper, we generalize the active contour model to work on Manifold valued images. As expected, our algorithm detects regions with similar Manifold values in the image. Our algorithm also produces expected results on usual gray-scale images, since these are nothing but trivial examples of Manifold valued images. As another application of our general active contour model, we perform texture segmentation on gray-scale images by first creating an appropriate Manifold valued image. We demonstrate segmentation results for manifold valued images and texture images

Flexible shape extraction for micro/nano scale structured surfaces.

Surface feature is the one of the most important factors affecting the functionality and reliability of micro scale patterned surfaces. For micro scale patterned surface characterisation, it’s important to extract the surface feature effectively and accurately. The active contours, known as “snakes”, have been successfully used to segment, match and track the objects of interest. The active contours have been applied to facial boundary detection, medical image processing, motion correction, etc. In this paper, surface feature extraction techniques based on active contours have been investigated. Parametric active contour models and geometric active contour models have been presented. Also, a group of examples has been selected here to demonstrate the feasibility and applicability of the surface pattern extraction techniques based on active contours. At last, experimental results will be given and discussed

The Potential of Active Contour Models in Extracting Road Edges from Mobile Laser Scanning Data

Active contour models present a robust segmentation approach, which makes efficient use of specific information about objects in the input data rather than processing all of the data. They have been widely-used in many applications, including image segmentation, object boundary localisation, motion tracking, shape modelling, stereo matching and object reconstruction. In this paper, we investigate the potential of active contour models in extracting road edges from Mobile Laser Scanning (MLS) data. The categorisation of active contours based on their mathematical representation and implementation is discussed in detail. We discuss an integrated version in which active contour models are combined to overcome their limitations. We review various active contour-based methodologies, which have been developed to extract road features from LiDAR and digital imaging datasets. We present a case study in which an integrated version of active contour models is applied to extract road edges from MLS dataset. An accurate extraction of left and right edges from the tested road section validates the use of active contour models. The present study provides valuable insight into the potential of active contours for extracting roads from 3D LiDAR point cloud data

Microscopy image segmentation by active contour models

In this thesis a semi-automated cell analysis system is described through image processing. To achieve this, an image processing algorithm was studied in order to segment cells in a semi-automatic way. The main goal of this analysis is to increase the performance of cell image segmentation process, without affecting the results in a significant way. Even though, a totally manual system has the ability of producing the best results, it has the disadvantage of taking too long and being repetitive, when a large number of images need to be processed. An active contour algorithm was tested in a sequence of images taken by a microscope. This algorithm, more commonly known as snakes, allowed the user to define an initial region in which the cell was incorporated. Then, the algorithm would run several times, making the initial region contours to converge to the cell boundaries. With the final contour, it was possible to extract region properties and produce statistical data. This data allowed to say that this algorithm produces similar results to a purely manual system but at a faster rate. On the other hand, it is slower than a purely automatic way but it allows the user to adjust the contour, making it more versatile and tolerant to image variations

Active Contour Models

Active contour models have been widely applied to image segmentation and analysis. It has been successfully used in contour detection for object recognition, computer vision, computer graphics, and biomedical image processing such as X-ray, MRI and Ultrasound images. The energy-minimizing active contour models or snakes were developed by Kass, Witkin and Terzopoulos in 1987. Snakes are curves defined in the image domain that can move under the influence of internal forces within the curve itself and external forces derived from the image data. Snakes perform well on certain types of images (such as well-defined, convex shapes). There have been several improvements proposed to the original snake or active contour model. These improvements include balloon snakes, adaptive snakes, and GVF snakes. In this project, I reviewed and implemented their algorithms as well as the original snake model. GCBAC (Graph Cut Based Active Contour) is one of alternative solutions to the object extraction problem. Although the GCBAC belongs to family of active contour models, it differs fundamentally from original active contours. In this project, I also review and implement the GCBAC algorithm as well

Mitral valve contour extraction using active contour models

To perform mitral valve contour extraction a software application is presented to support the surgeon in the implant size decision. The system is based on the application, to mitral valve surgery images, of active contour models. First, current repair surgery to mitral valve disease is discussed. Active contour models are presented and using different implementation approaches a comparison was done. The algorithms proposed by Kass, Amini, Cohen, Eviatar and Shah (Greedy algorithm) were implemented in test environment. The implementation to be used in the software application, is the one due to Kass with a few modifications related to Cohen’s approach. During surgery, the system needs to be calibrated and the active contour initialised. These processes are supported by a colour segmentation technique, tested with real images, using fuzzy sets. Real open-heart surgery images have been used to test the system developed