بخش بندي خودكار كبد در تصاوير سي تي مبتني بر لبه ياب Kirsch، انتقال ميانگين و خوشه بندي K-means

Automatic Liver Segmentation in CT images based on Kirsch edge detector, Mean Shift, and K-means Clustering

ﯾﮑﯽ از ﻣﺴﺎﺋﻞ ﻣﻄﺮح در ﻋﻠـﻢ ﭘﺰﺷـﮑﯽ ﮐـﻪ ﺗﻮﺟـﻪ ﺑﺴـﯿﺎري از ﻣﺤﻘﻘـﺎن را ﺑـﻪﺧـﻮد ﺟﻠـﺐ ﮐـﺮده، ﺟﺪاﺳـﺎزي ﮐﺒـﺪ از ﺗﺼـﺎوﯾﺮ ﺗﻮﻣـﻮﮔﺮاﻓﯽ ﮐﺎﻣﭙﯿﻮﺗﺮي اﺳﺖ. زﯾﺮا اوﻟﯿﻦ ﻣﺮﺣﻠﻪ در ﻓﺮآﯾﻨﺪ ﺗﺸﺨﯿﺺ ﺑﯿﻤﺎريﻫﺎ و ﺗﻮﻣﻮرﻫﺎي ﮐﺒﺪي، داﺷﺘﻦ ﺗﺼﻮﯾﺮي ﻣﻨﺎﺳﺐ از ﮐﺒـﺪ ﺑﺨـﺶﺑﻨـﺪي-ﺷﺪه در اﯾﻦ ﺗﺼﺎوﯾﺮ ﻣﯽﺑﺎﺷﺪ. ﻫﺪف از اﯾﻦ ﻣﻘﺎﻟﻪ اراﺋﻪ اﻟﮕﻮرﯾﺘﻤﯽ ﺧﻮدﮐﺎر ﺑﺮاي ﺑﺨﺶﺑﻨﺪي ﮐﺒﺪ در ﺗﺼـﺎوﯾﺮ ﺳـﯽﺗـﯽ اﺳـﺖ. ﺗﺤﻘﯿﻘـﺎت ﮔﺬﺷﺘﻪ ﻧﺸﺎن دادهاﺳﺖ اﺳﺘﻔﺎده از وﯾﮋﮔﯽ ﺑﺎﻓﺖ ﻧﺘﺎﯾﺞ ﻣﻄﻠﻮب ﺗﺮي را در اﯾﻦ ﺑﺨﺶﺑﻨﺪي ﺣﺎﺻﻞ ﻣﯽ ﮐﻨﺪ. اﻟﮕﻮرﯾﺘﻢ ﭘﯿﺸﻨﻬﺎدي اﯾﻦ ﻣﻘﺎﻟـﻪ ﻣﺒﺘﻨﯽ ﺑﺮ ﺗﺤﻠﯿﻞ ﺑﺎﻓﺖ ﺑﺎاﺳﺘﻔﺎده از ﻟﺒﻪﯾﺎب Kirsch، اﻧﺘﻘﺎل ﻣﯿﺎﻧﮕﯿﻦ، و ﺧﻮﺷﻪﺑﻨﺪي K-means ﺑﻪﺑﺨﺶﺑﻨـﺪي ﮐﺒـﺪ ﻣـﯽﭘـﺮدازد. ﻧﺘـﺎﯾﺞ ﭘﯿﺎدهﺳﺎزي اﻟﮕﻮرﯾﺘﻢ ﭘﯿﺸﻨﻬﺎدي ﺑﺮ روي 400 ﺗﺼﻮﯾﺮ ﺳﯽﺗﯽ ﺑﯿﻤﺎرﺳﺘﺎن ﻣﯿﻼد ﺗﻬﺮان ﺣﺎوي ﮐﺒﺪ و ارﮔﺎنﻫـﺎي ﺟـﺎﻧﺒﯽ، ﻣﯿـﺎﻧﮕﯿﻦ ﻣﻌﯿـﺎر Dice را 96 ٪ ﻧﺸﺎن داده اﺳﺖ. ﻫﻤﭽﻨﯿﻦ در ﺑﺮرﺳﯽ ﻋﻤﻠﮑﺮد اﻟﮕﻮرﯾﺘﻢ ﭘﯿﺸﻨﻬﺎدي ﺑـﺮ روي ﭘﺎﯾﮕـﺎه دادهي Sliver07، ﻣﯿـﺎﻧﮕﯿﻦ ﻣﻌﯿـﺎر Dice ﺑﺮاﺑﺮ ﺑﺎ 86/96 ٪ ﺑﻪدﺳﺖ آﻣﺪه اﺳﺖ. ﻟﺬا اﻟﮕﻮرﯾﺘﻢ ﭘﯿﺸﻨﻬﺎدي ﻣﯽ ﺗﻮاﻧﺪ ﺑﻪﻋﻨﻮان اوﻟـﯿﻦ ﻣﺮﺣﻠـﻪ در ﻓﺮآﯾﻨـﺪ ﺗﺸـﺨﯿﺺ ﺗﻮﻣﻮرﻫـﺎ و ﺑﯿﻤﺎري ﻫﺎي ﮐﺒﺪي ﻣﻮرداﺳﺘﻔﺎده ﻗﺮار ﮔﯿﺮد.

One of the issues in medical science, which has attracted the attention of many researchers, isliver segmentation from computer tomography images. Because the first step in the process of diagnosis of liver illnesses and its tumors is, having an appropriate image of the segmented liver in these images. The purpose of this paper is to provide an automated algorithm for liver segmentation in the CT images. Previous research has shown that the use of texture feature results in more favorable results in liver segmentation. The proposed algorithm of this paper is based on texture analysis to liver segmentation using the Kirsch edge detector, Mean shift, and k-means clustering. Results of the implementation of the proposed algorithm on 400 images of Milad hospital in Tehran containing liver and its lateral organs, showed the average of Dice criterion of 96%. Also, in the performance of the proposed algorithm on the sliver07 database, the average of Dice criterion is equal to 96.86%. Therefore, the proposed algorithm can be used as the first step in the process of diagnosis of liverillnesses and its tumors.