• ROSHMY DAVIS
• ROSMI THOMAS
• MAGY K.B

Liver cancers are one of the most popular cancers occurring now a day. The larger parts of liver carcinomas are because of liquor related cirrhosis and hepatitis. Early detection and diagnosis of liver tumor is important for the prevention of liver tumor. Since the tumor tissues and normal liver tissues have almost same intensity, it is a very difficult and challenging task to detect the liver tumor. For the accurate detection, efficient segmentation and classification methods are essential. Several techniques have been developed for the segmentation of liver tumor. Manual detection of liver tumor requires human interaction and is time consuming. As a result, automation of the tumor detection, segmentation and classification process is essential. This paper reviews different automatic segmentation techniques for the liver tumor detection.

1.Region Growing Method 2. Thresholding Method 3. Clustering Method 4. Graph Cut Method 5. Level Based Segmentation

"SURVEY ON AN EFFICIENT AUTOMATIC LIVER TUMOR DETECTION FROM CT IMAGES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.3, Issue 10, page no.177-179, October-2016, Available :http://www.jetir.org/papers/JETIR1610026.pdf

"SURVEY ON AN EFFICIENT AUTOMATIC LIVER TUMOR DETECTION FROM CT IMAGES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.3, Issue 10, page no. pp177-179, October-2016, Available at : http://www.jetir.org/papers/JETIR1610026.pdf