ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Analysis of Morphological Operations on Image Segmentation Techniques


Affiliations
1 Department of Computer Science Engineering, GD Goenka University, India
     

   Subscribe/Renew Journal


Image segmentation is a process of partitioning an image into different subregions based on edge detection, area based or clustering based methods. Segmentation of brain MRI images is a challenging task. This paper provides a thorough analysis of different segmentation techniques with morphological operators for brain tumor detection. After segmenting the image, morphological operators are used to eliminate and add some pixels from tumor boundaries and to improve the performance of segmentation algorithm. Manual segmentation is used to construct the gold standard for comparing the segmented image. Comparison is performed using performance parameters such as dice, Jaccard coefficient, selectivity, recall and precision. The experimental results show that precision can be improved up to 85% in clustering-based segmentation and full selectivity can be achieved by combining segmentation techniques with morphological operation of erosion. The other performance parameters have also improved by applying erosion than dilation.

Keywords

Segmentation, Dice Coefficient, Threshold Segmentation, Jaccard Coefficient.
Subscription Login to verify subscription
User
Notifications
Font Size

  • S. Bauer, T. Fejes, J. Slotboom, R. Wiest, L.P. Nolte and M. Reyes, “Segmentation of Brain Tumor Images based on Integrated Hierarchical Classification and Regularization”, Proceedings of International Conference on Multimodal Brain Tumor Segementation, pp. 33-39, 2012.

  • A. Christe, K. Malathy and A. Kandaswamy. “Improved Hybrid Segmentation of Brain MRI Tissue and Tumor using Statistical Features”, ICTACT J Image Video Processing, Vol. 1, No. 1, pp. 34-49, 2010.

  • J. Vijay and J. Subhashini, “An Efficient Brain Tumor Detection Methodology using K-means Clustering Algorithm”, Proceedings of International Conference on Communication and Signal Processing, pp. 653-657, 2013.

  • B.K. Bala, “Enhanced Palm Vein Recognition Algorithm with Equalizer Technique”, International Journal of Engineering and Advanced Technology, Vol. 8, No. 5, pp. 888-890, 2019.

  • R.P. Joseph, C. Senthil Singh and M. Manikandan, “Brain Tumor MRI Image Segmentation and Detection in Image Processing”, International Journal of Research in Engineering and Technology, Vol. 3, No. 11, pp. 1-5, 2014.

  • D. Comaniciu and Peter Meer, “Robust Analysis of Feature Spaces: Color Image Segmentation”, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 750-755, 1997.

  • M.C. Cooper, “The Tractability of Segmentation and Scene Analysis”, International Journal of Computer Vision, Vol. 30, No. 1, pp. 27-42, 1998.

  • Mark Schmidt, Ilya Levner, Russell Greiner, Albert Murtha and Aalo Bistritz, “Segmenting Brain Tumors using Alignment-Based Features”, Proceedings of 4th International Conference on Machine Learning and Applications, pp. 1-6, 2005.

  • Pedro F. Felzenszwalb and P. Daniel, “Efficient Graph-Based Image Segmentation”, International Journal of Computer Vision, Vol. 59, No. 2, pp. 167-181, 2004.

  • Dinesh D. Patil and Sonal G. Deore, “Medical Image Segmentation: A Review”, International Journal of Computer Science and Mobile Computing, Vol. 2, No. 1, pp. 22-27, 2013.

  • L.T. Mariappan, “Analysis on Cardiovascular Disease Classification using Machine Learning Framework”, Solid State Technology, Vol. 63, No. 6, pp. 10374-10383, 2020.

  • Dinesh D. Patil and Sonal G. Deore, “Medical Image Segmentation: A Review”, International Journal of Computer Science and Mobile Computing, Vol. 2, No. 1, pp. 22-27, 2013.

  • Diya Chudasama, Tanvi Patel, Shubham Joshi and Ghanshyam I. Prajapati, “Image Segmentation using Morphological Operations”, International Journal of Computer Applications, Vol.117, No. 18, pp. 1-16, 2015.

  • R.R. Shamir, Yuval Duchin, Jinyoung Kim, Guillermo Sapiro and Noam Harel, “Continuous Dice Coefficient: A Method for Evaluating Probabilistic Segmentations”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2019.

  • Hapsari Peni Agustin, “Brain Tumor Image Segmentation in MRI Image”, Proceedings of IOP Conference Series: Materials Science and Engineering, Vol. 336, No. 1, pp. 1-14, 2018.

  • Chong Zhang, Xuanjing Shen, Hang Cheng and Qingji Qian, “Brain Tumor Segmentation based on Hybrid Clustering and Morphological Operations”, International Journal of Biomedical Imaging, Vol. 2019, pp. 1-15, 2019.


Abstract Views: 222

PDF Views: 1




  • Analysis of Morphological Operations on Image Segmentation Techniques

Abstract Views: 222  |  PDF Views: 1

Authors

Akanksha Kulshreshtha
Department of Computer Science Engineering, GD Goenka University, India
Arpita Nagpal
Department of Computer Science Engineering, GD Goenka University, India

Abstract


Image segmentation is a process of partitioning an image into different subregions based on edge detection, area based or clustering based methods. Segmentation of brain MRI images is a challenging task. This paper provides a thorough analysis of different segmentation techniques with morphological operators for brain tumor detection. After segmenting the image, morphological operators are used to eliminate and add some pixels from tumor boundaries and to improve the performance of segmentation algorithm. Manual segmentation is used to construct the gold standard for comparing the segmented image. Comparison is performed using performance parameters such as dice, Jaccard coefficient, selectivity, recall and precision. The experimental results show that precision can be improved up to 85% in clustering-based segmentation and full selectivity can be achieved by combining segmentation techniques with morphological operation of erosion. The other performance parameters have also improved by applying erosion than dilation.

Keywords


Segmentation, Dice Coefficient, Threshold Segmentation, Jaccard Coefficient.

References