An approach to automatic detection of body parts and their size estimation from computed tomography image

Computer-aided diagnosis (CAD) systems usually require information about regions of interest in images, like: lungs (for nodule detection), colon (for identifying polyps), etc. Many times, it is computationally intensive to process large data sets as in CT to find these areas of interest. A fast and accurate recognition of the different regions of interest in the human body from images is therefore necessary. In this paper we propose a fast and efficient algorithm that can detect the organs of interest in a CT volume and estimate their sizes. Instead of analyzing the whole 3D volume; which is computationally expensive, a binary search technique is adapted to search in a few slices. The slices selected in the search process is segmented and different regions are labeled. Decision over whether the image belongs to a lung or colon or both is based on the count of lung/colon pixels in the slice. Once the detection is done we look for the start and end slice of the body part to have an estimate of their sizes. The algorithm involves certain search decisions based on some predefined threshold values which are empirically chosen from a training data set. The effectiveness of our technique is confirmed by applying it on an independent test data set. Detection accuracy of 100% is obtained on a test set. This algorithm can be integrated in a CAD system for running the right application, or can be used in pre-sets for visualization purposes and other post-processing like image registration etc.