Computational and Mathematical Methods in Medicine / 2013 / Article / Fig 1

Research Article

Semiautomatic Segmentation of Ventilated Airspaces in Healthy and Asthmatic Subjects Using Hyperpolarized MRI

Figure 1

Detailed schematic of semiautomatic segmentation algorithm. The example shown here is from a healthy subject. The first step is a statistical noise subtraction to generate an initial binary mask of the input image (a). Thereafter, the resultant lung mask (b) is refined through a four-class FCM clustering which partitions the entire image into four categories: negligible ventilation, low ventilation, intermediate ventilation, and high ventilation (c). Pixels that fall within the negligible ventilation class are subsequently discarded to form a corrected mask (d). Through a semiautomatic trachea removal involving a seeded region-growing algorithm, an area filter for connectivity, and a series of morphological operations, a final binary image representative of ventilated airspaces is obtained (e).
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