Comparing watershed and FCM segmentation in detecting reticular pattern for interstitial lung disease

Lung is an important organ in human respiratory system. However a group of lung diseases known as interstitial lung diseases (ILD) may affect the tissue and space around the air sacs of the lung that prohibit the transferring of enough oxygen into bloodstream. Presently, ILD patients are diagnosed manually by the medical practitioner based on the clinical findings and High-Resolution Computed Tomography (HRCT) thorax images. The process of diagnosing using HRCT images is time-consuming and the outcomes are subjective in nature. One of the indicators of the ILD is the existence of reticular pattern on the HRCT Thorax images. The severity of ILD basically depends on the coarseness of this reticular pattern. The research focuses on the segmentation of the reticular pattern on the infected region based on the grades given by the ILD scoring index; grade 0 - absent, grade 1 - fine intralobular fibrosis predominating, grade 2 - microcystic pattern with airspace less than 3mm in diameter, and grade 3 - larger cysts 3-6mm in diameter. This paper discussed the two segmentation techniques, watershed segmentation algorithm and Fuzzy C-Means (FCM). The study shows that both methods able to segment the reticular pattern for grade 2 and grade 3 of the disease. FCM yielded better result compared to the watershed in term of having higher accuracy of cyst detection and less over-segmented region.