19
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Brain Tumor Detection from MR Images Employing Fuzzy Graph Cut Technique

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background:

          This research aims at the accurate selection of the seed points from the brain MRI image for the detection of the tumor region. Since, the conventional way of manual seed selection leads to inappropriate tumor extraction therefore, fuzzy clustering technique is employed for the accurate seed selection for performing the segmentation through graph cut method.

          Methods:

          In the proposed method Fuzzy Kernel Seed Selection technique is used to define the complete brain MRI image into different groups of similar intensity. Among these groups the most accurate kernels are selected empirically that show highest resemblance with the tumor. The concept of fuzziness helps making the selection even at the boundary regions.

          Results:

          The proposed Fuzzy kernel selection technique is applied on the BraTS dataset. Among the four modalities, the proposed technique is applied on Flair images. This dataset consists of Low Grade Glioma (LGG) and High Grade Glioma (HGG) tumor images. The experiment is conducted on more than 40 images and validated by evaluating the following performance metrics: 1. Disc Similarity Coefficient (DSC), 2. Jaccard Index (JI) and 3. Positive Predictive Value (PPV). The mean DSC and PPV values obtained for LGG images are 0.89 and 0.87 respectively; and for HGG images it is 0.92 and 0.90 respectively.

          Conclusion:

          On comparing the proposed Fuzzy kernel selection graph cut technique approach with the existing techniques it is observed that the former provides an automatic accurate tumor detection. It is highly efficient and can provide a better performance for HGG and LGG tumor segmentation in clinical application.

          Related collections

          Most cited references5

          • Record: found
          • Abstract: not found
          • Article: not found

          A possibilistic fuzzy c-means clustering algorithm

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A robust fuzzy local information C-Means clustering algorithm.

              This paper presents a variation of fuzzy c-means (FCM) algorithm that provides image clustering. The proposed algorithm incorporates the local spatial information and gray level information in a novel fuzzy way. The new algorithm is called fuzzy local information C-Means (FLICM). FLICM can overcome the disadvantages of the known fuzzy c-means algorithms and at the same time enhances the clustering performance. The major characteristic of FLICM is the use of a fuzzy local (both spatial and gray level) similarity measure, aiming to guarantee noise insensitiveness and image detail preservation. Furthermore, the proposed algorithm is fully free of the empirically adjusted parameters (a, ¿(g), ¿(s), etc.) incorporated into all other fuzzy c-means algorithms proposed in the literature. Experiments performed on synthetic and real-world images show that FLICM algorithm is effective and efficient, providing robustness to noisy images.
                Bookmark

                Author and article information

                Journal
                Recent Advances in Computer Science and Communications
                RACSC
                Bentham Science Publishers Ltd.
                26662558
                August 12 2020
                August 12 2020
                : 13
                : 3
                : 362-369
                Affiliations
                [1 ]Department of Electronics and Communication Engineering Jaypee University of Information Technology, Waknaghat, Solan, India
                Article
                10.2174/2213275912666181207152633
                517e58d7-ae11-4988-afff-40c5965d974f
                © 2020
                History

                Comments

                Comment on this article