The identification of mammary gland regions is a necessary processing step during the anatomical structure recognition of human body and can be expected to provide the useful information for breast tumor diagnosis. This paper proposes a fully-automated scheme for segmenting the mammary gland regions in non-contrast torso CT images. This scheme calculates the probability for each voxel belonging to the mammary gland or other regions (for example pectoralis major muscles) in CT images and decides the mammary gland regions automatically. The probability is estimated from the location of the mammary gland and pectoralis major muscles in CT images. The location (named as a probabilistic atlas) is investigated from the pre-segmentation results in a number of different CT scans and the CT number distribution is approximated using a Gaussian function. We applied this scheme to 66 patient cases (female, age: 40-80) and evaluated the accuracy by using the coincidence rate between the segmented result and gold standard that is generated manually by a radiologist for each CT case. The mean value of the coincidence rate was 0.82 with the standard deviation of 0.09 for 66 CT cases.
[1]
Xiangrong Zhou,et al.
Automatic segmentation and recognition of anatomical lung structures from high-resolution chest CT images
,
2006,
Comput. Medical Imaging Graph..
[2]
Adam W. M. Mitchell,et al.
Gray's Anatomy for Students
,
2004
.
[3]
Fred L. Bookstein,et al.
Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
,
1989,
IEEE Trans. Pattern Anal. Mach. Intell..
[4]
R. Yokoyama,et al.
Preliminary Study for Automated Recognition of Anatomical Structure from Torso CT images
,
2005,
2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.