Automatic lung segmentation by using histogram based k-means algorithm
Esra Dinçer, N. Jeremi Duru · 2016
In this study, it was developed an histogram based k-means algorithm by considering the region features in order to segment whole lung region automatically. After the segmentation the lung lobes are extracted from the surrounding tissue. This method could be used as a first step of various computer aided analysis of lungs. In the study, 34 cases which were scanned by the three different tomography systems, the processed. The method provides 96% segmentation success rate without performing normalization process. The histogram values have been used to calculate the distance between the points instead of using the cartesian system of the traditional k-means method. Thus, similar tissues which are not connected and far from each other, were included in the same set. The histogram based k-means algorithm was compared with Fuzzy C-means (FCM) and optimal thresholding method, found more efficient for the iteration number and segmentation accuracy.