Towards cascaded V-Net for automatic accurate kidney segmentation from abdominal CT images

Author(s):  
Xiongbiao Luo ◽  
Wankang Zeng ◽  
Wenkang Fan ◽  
Song Zheng ◽  
Jianhui Chen ◽  
...  
2014 ◽  
Vol 14 (05) ◽  
pp. 1450073 ◽  
Author(s):  
AICHA BELGHERBI ◽  
ISMAHEN HADJIDJ ◽  
ABDELHAFID BESSAID

The phase of segmentation is an important step in the processing and interpretation of medical images. In this paper, we focus on the segmentation of kidneys from the abdomen computed tomography (CT) images. The importance of our study comes from the fact that the segmentation of kidneys from CT images is usually a difficult task. This difficulty is the gray's level which is similar to the spine level. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to remove the spine by applying morphological filters. This first step makes the extraction of interest regions easier. This step is fulfilled by using various transformations such as the geodesic reconstruction. In the second step, we apply the watershed algorithm controlled by marker for kidney segmentation. The validation of the developed algorithm is done using several images. Obtained results show the good performances of our proposed algorithm.


2021 ◽  
Author(s):  
Xianru Zhang ◽  
Yujie Nie ◽  
Xu Qiao ◽  
Kai Li ◽  
Wei Chen ◽  
...  

2018 ◽  
Vol 7 (2.6) ◽  
pp. 306
Author(s):  
Aravinda H.L ◽  
M.V Sudhamani

The major reasons for liver carcinoma are cirrhosis and hepatitis.  In order to  identify carcinoma in the liver abdominal CT images are used. From abdominal CT images, segmentation of liver portion using adaptive region growing, tumor segmentation from extracted liver using Simple Linear Iterative Clustering is already implemented. In this paper, classification of tumors as benign or malignant is accomplished using Rough-set classifier based on texture feature extracted using Average Correction Higher Order Local Autocorrelation Coefficients and Legendre moments. Classification accuracy achieved in proposed scheme is 90%. The results obtained are promising and have been compared with existing methods.


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