Automatic symmetry-integrated brain injury detection in MRI sequences

2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 06/2009; DOI: 10.1109/CVPRW.2009.5204052


This paper presents a fully automated symmetry-integrated brain injury detection method for magnetic resonance imaging (MRI) sequences. One of the limitations of current injury detection methods often involves a large amount of training data or a prior model that is only applicable to a limited domain of brain slices, with low computational efficiency and robustness. Our proposed approach can detect injuries from a wide variety of brain images since it makes use of symmetry as a dominant feature, and does not rely on any prior models and training phases. The approach consists of the following steps: (a) symmetry integrated segmentation of brain slices based on symmetry affinity matrix, (b) computation of kurtosis and skewness of symmetry affinity matrix to find potential asymmetric regions, (c) clustering of the pixels in symmetry affinity matrix using a 3D relaxation algorithm, (d) fusion of the results of (b) and (c) to obtain refined asymmetric regions, (e) Gaussian mixture model for unsupervised classification of potential asymmetric regions as the set of regions corresponding to brain injuries. Experimental results are carried out to demonstrate the efficacy of the approach.

Download full-text


Available from: Bir Bhanu, Aug 22, 2014
  • Source
    • "An image is said to have bilateral symmetry when it is unchanged following a reflection about its symmetry axis. Even though a number of work has exploited symmetry of the image for certain recognition tasks [9] [10], the incorporation of symmetry into generalized image segmentation is still immature [11]. A recent work [11] based on region-growing technique integrates bilateral symmetry into the image segmentation algorithm. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a new modified fuzzy c-means (FCM) clustering algorithm that exploits bilateral symmetry information in image data. With the assumption of pixels that are located symmetrically tend to have similar intensity values; we compute the degree of symmetry for each pixel with respect to a global symmetry axis of the image. This information is integrated into the objective function of the standard FCM algorithm. Experimental results show the effectiveness of the approach. The method was further improved using neighbourhood information, and was compared with conventional fuzzy c-means algorithms.
    Full-text · Conference Paper · Jul 2013
  • Source
    • "These two problems caused [24] to consider lesion within NABM. The symmetry approach [24] is best suited for stroke data where the lesions are more uniform and have higher contrast. The pure model-based approach [22] also had difficulties due to the low number of lesion voxels and the injury not occurring within the same locations consistently . "
    [Show abstract] [Hide abstract]
    ABSTRACT: Mild traumatic brain injury (mTBI) is difficult to detect as the current tools are qualitative, which can lead to poor diagnosis and treatment. The low contrast appearance of mTBI abnormalities on magnetic resonance (MR) images makes quantification problematic for image processing and analysis techniques. To overcome these difficulties, an algorithm is proposed that takes advantage of subject information and texture information from MR images. A contextual model is developed to simulate the progression of the disease using multiple inputs, such as the time post-injury and the location of injury. Textural features are used along with feature selection for a single MR modality. Results from a probabilistic support vector machine using textural features are fused with the contextual model to obtain a robust estimation of abnormal tissue. A novel rat temporal dataset demonstrates the ability of our approach to outperform other state of the art approaches.
    Full-text · Conference Paper · Sep 2012
  • Source
    • "Since symmetry is a high level geometric feature compared to other lower level features like color and texture, there is an extensive literature concerning application of symmetry into higher level tasks. Many approaches have been developed for the segmentation and abnormality detection in brain in magnetic resonance images [16] [17] [18] [19] [33]. There is also extensive work on face detection [20] [21] [22] [23], human tracking and identification [24] [25] [60] [61], and image pattern detection [58] [59]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a new symmetry integrated region-based image segmentation method. The method is developed to obtain improved image segmentation by exploiting image symmetry. It is realized by constructing a symmetry token that can be flexibly embedded into segmentation cues. Interesting points are initially extracted from an image by the SIFT operator and they are further refined for detecting the global bilateral symmetry. A symmetry affinity matrix is then computed using the symmetry axis and it is used explicitly as a constraint in a region growing algorithm in order to refine the symmetry of the segmented regions. A multi-objective genetic search finds the segmentation result with the highest performance for both segmentation and symmetry, which is close to the global optimum. The method has been investigated experimentally in challenging natural images and images containing man-made objects. It is shown that the proposed method outperforms current segmentation methods both with and without exploiting symmetry. A thorough experimental analysis indicates that symmetry plays an important role as a segmentation cue, in conjunction with other attributes like color and texture.
    Full-text · Article · Dec 2011 · IEEE Transactions on Software Engineering
Show more