1 |
Huang, Q., Sun, Y., Huang, L., and Zhang, P.: The liver CT image sequence segmentation based on region growing. In:5th International Conference on Advanced Engineering Materials and Technology, AEMT, (2015)
|
2 |
Caponetti, L., Castellano, G., and Corsini.: MR Brain Image Segmentation: A Framework to Compare Different Clustering Techniques. In: MDPI, Information ,8, 138, (2018)
DOI
|
3 |
Bora, D., Gupta, A.: A Novel Approach Towards Clustering Based Image Segmentation. In: International Journal of emerging Science and Engineering (IJESE) ISSN: 2319-6378, Volume-2 Issue-11, September (2014)
|
4 |
Kesavaraja, D., Balasubramanian, R., Rajesh, R., and Sasireka, D.: Advance Cluster Based Image Segmentation. In: ICTACT Journal on Image and Video Processing, Volume: 02, Issue: 02, November (2011)
|
5 |
Bora, D., and Gupta, A.: A Novel Approach Towards Clustering Based Image Segmentation. In: International Journal of Emerging Science and Engineering (IJESE), ISSN: 2319-6378, Volume-2 Issue- 11, September (2014)
|
6 |
Geng L., Li, S., Xiao, Z., and Zhang, F.: Multi-Channel Feature Pyramid Networks for Prostate Segmentation, Based on Transrectal Ultrasound Imaging. In: MDPI, Appl. Sci. 2020, 10, 3834, (2020)
|
7 |
Saini, K., Dewal, M., and Rohit, M.: Ultrasound Imaging and Image Segmentation in the area of Ultrasound: A Review. In: International Journal International Journal of Advanced Science and Technology Advanced Science and Technology Advanced Science and Technology Vol. 24 Vol. 24, November, (2010)
|
8 |
Ricardo Alonso Castillejos-Molina., MD., Fernando Bernardo Gabilondo-Navarro., MD.: Prostate cancer. In: Instituto Nacional de Ciencias M'edicas y Nutrici'on Salvador Zubir'an. Ciudad de M'exico, M'exico, vol. 58, no. 2, March-April (2016)
|
9 |
Ghosh, S., Olivera, A., Mart, R., Xavier L., Joan C., Vilanovac, Freixeneta, J., Mitraa, J., Sidibeb, D., Meriaudeaub, F.: A Survey of Prostate Segmentation Methodologies in Ultrasound, Magnetic Resonance and Computed Tomography Images. In: Preprint submitted to Computer Methods and Programs in Biomedicine, Elsevier April 11, (2012)
|
10 |
Wang, Y., Dou, H., Xiaowei H., Zhu, L., Yang, X., Xu,M., Jing Q., Heng, P., Wang, T., Ni, D.: Deep Attentive feature for prostate segmentation in 3 D Transrectal Ultrasound. In: IEEE Transaction on Medical Imaging, arXiv 1907.01743v1 [eess.IV], Jul 3, (2019)
|
11 |
Dhanachandra, N., and Chanu, Y.: Image Segmentation Method using K-means Clustering Algorithm for Colour image. In: Advanced Research in Electrical and Electronic Engineering, p-ISSN: 2349-5804; e-ISSN: 2349-5812 Volume 2, Issue 11 July- September pp. 68-72, (2015)
|
12 |
Erwin, Saparudin, Nevriyanto, A., Purnamasari, D.: Performance Analysis of Comparison between Region Growing, Adaptive Threshold and Watershed Methods for Image Segmentation. In: Proceedings of the International Multiconference of Engineers and Computer Scientists 2018 Vol I, IMECS 2018, March 14-16, 2018, Hong Kong (2018)
|
13 |
Bala, A., Sharma, A.: Color Image segmentation using K-means Clustering and Morphological Edge Detection Algorithm. In: International Journal of Latest Trends in Engineering and technology (IJLTET)
|
14 |
Hamada, M., Kanat, Y., Abiche, A.: Multi- Spectral Image Segmentation Based on the K-means Clustering. In: International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-9 Issue-2, December (2019)
|
15 |
Sedelaar, J., JJMCH de la Rosette, Beerlage, H., Wijkstra, H., Debruyne, F., and Aarnink R.: Transrectal ultrasound imaging of the prostate: review and perspectives of recent developments. In: Prostate Cancer and Prostatic Diseases (1999) 2, 241-252 (1999)
DOI
|
16 |
Borges, V., Cristina, F., de, Oliveira., Silva, G., Fellow, Armando A., Hamann B.: Region Growing for Segmenting Green Microalgae Images. In: Journal of Latex Class files, Vol. 13, No. 9, September (2014)
|
17 |
Zhu, S., and Yuille.: Region Competition: Unifying Snakes, Region Growing and Bayes/MDL for Multiband Image Segmentation. In: IEEE Transactions on Multi Pattern Analysis and Machine Intelligence, Vol. 18, No.9 September (1996)
|
18 |
Chena, Z., Qib, Z., Menga, F., Cuic, L., Shi, Y.: Image Segmentation via Improving Clustering Algorithms with Density and Distance. In: ScienceDirect (ELSEVIER), Information Technology and Quantitative Management (ITQM 2015), Procedia Computer Science 55 1015 - 1022, (2015)
DOI
|
19 |
Grinias, I., Mavrikakis, Y., and Tziritas.: Region Growing Color Image Segmentation Applied to Face Detection. In: Department of Computer Science, University of Crete
|
20 |
Rahmani, Md., Pal, N., and Arora, K.: Clustering of Image Data Using K-Means and Fuzzy K-Means. In: (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 5, No. 7, (2014)
|
21 |
Viji, A., and Jayaraj, L.: Modified Texture, Intensity and Orientation Constraint Based Region Growing Segmentation of 2D MR Brain Tumor Images. In: The International Arab Journal of Information Technology, Vol. 13, No. 6A, (2016)
|
22 |
Senthil, Kumar, K., Venkatalakshmi, K., and Karthikeyan, K.: Lung Cancer Detection Using Image Segmentation by means of Various Evolutionary Algorithms. In: Hindawi Computational and Mathematical Methods in Medicine Volume 2019, Article ID 4909846 (2019)
|