The human lungs are divided into five distinct anatomic compartments called lobes. The physical boundaries between the lung lobes are called the lobar fissures. Detection of the lobar fissures in an image data set can be used to help identify the major components of the pulmonary anatomy, guide image registration with a standard lung atlas, drive additional image segmentation processing to find airways and vessels, and to provide an anatomic framework within which image-based measurements can be reported. Little work has been done to develop methods for detecting the lobar fissures. We have developed a semi-automatic method to identify the left and right oblique fissures in 3-D X-ray CT data sets. Our method is based on using fuzzy sets to describe the anatomic and image-based characteristics of likely fissure pixels, and we then use a graph search to select the most probable fissure location on 2-D slices of the data set. The user initializes the search once by defining starting pixels, initial direction and ending pixels on one slice. Once the fissure has identified on a singe slice, it can be used to guide automatic fissure detection on neighboring slices. Thus, the entire 3-D surface defined by a fissure can be identified with a little intervention. The method has been tested by processing two CT data sets from a normal subject. We present results comparing our method against results obtained by manual analysis. The average RMS error between the manual analysis and our approach is approximately 1.9 pixels (corresponding to about 1.3 mm), while the fissures themselves typically appear 3 to 6 pixels wide on a CT slice.
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