@article{(Open Science Index):https://publications.waset.org/pdf/10011999,
	  title     = {A Comparative Study of Medical Image Segmentation Methods for Tumor Detection},
	  author    = {Mayssa Bensalah and  Atef Boujelben and  Mouna Baklouti and  Mohamed Abid},
	  country	= {},
	  institution	= {},
	  abstract     = {Image segmentation has a fundamental role in analysis and interpretation for many applications. The automated segmentation of organs and tissues throughout the body using computed imaging has been rapidly increasing. Indeed, it represents one of the most important parts of clinical diagnostic tools. In this paper, we discuss a thorough literature review of recent methods of tumour segmentation from medical images which are briefly explained with the recent contribution of various researchers. This study was followed by comparing these methods in order to define new directions to develop and improve the performance of the segmentation of the tumour area from medical images.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {15},
	  number    = {4},
	  year      = {2021},
	  pages     = {285 - 290},
	  ee        = {https://publications.waset.org/pdf/10011999},
	  url   	= {https://publications.waset.org/vol/172},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 172, 2021},
	}