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Automatic segmentation of liver structure in CT images
Author(s) -
Bae Kyongtae T.,
Giger Maryellen L.,
Chen ChinTu,
Kahn Charles E.
Publication year - 1993
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.597064
Subject(s) - artificial intelligence , image processing , computer vision , thresholding , segmentation , computer science , image segmentation , pixel , digital image processing , mathematical morphology , pattern recognition (psychology) , image (mathematics)
The segmentation and three‐dimensional representation of the liver from a computed tomography (CT) scan is an important step in many medical applications, such as in the surgical planning for a living‐donor liver transplant and in the automatic detection and documentation of pathological states. A method is being developed to automatically extract liver structure from abdominal CT scans using a priori information about liver morphology and digital image‐processing techniques. Segmentation is performed sequentially image‐by‐image (slice‐by‐slice), starting with a reference image in which the liver occupies almost the entire right half of the abdomen cross section. Image processing techniques include gray‐level thresholding, Gaussian smoothing, and eight‐point connectivity tracking. For each case, the shape, size, and pixel density distribution of the liver are recorded for each CT image and used in the processing of other CT images. Extracted boundaries of the liver are smoothed using mathematical morphology techniques and B‐splines. Computer‐determined boundaries were compared with those drawn by a radiologist. The boundary descriptions from the two methods were in agreement, and the calculated areas were within 10%.