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PTH-006 Computer-Aided Lesion Measurement in Capsule Endoscopy Images
  1. A Koulaouzidis1,
  2. D Chatzis2,
  3. P Chrysanthopoulos2,
  4. DK Iakovidis2
  1. 1Endoscopy Unit, The Royal Infirmary of Edinburgh, Edinburgh, UK
  2. 2Department of Computer Science & Biomedical Informatics, University of Thessaly, Lamia, Greece


Introduction A novel image segmentation algorithm is applied & assessed for lesion size measurements in capsule endoscopy (CE). It is based solely on colour features of CE lesions.

Methods The lesion images are available online from KID database (Dataset 1).1 Size reference standards were made by manual annotation. The proposed algorithm uses the CIE-Lab colour representation instead of the standard RGB used in CE.1 The components of this space represent lightness (L), the quantity of red (a > 0) or the quantity of green (-a >0), the quantity of yellow (b > 0) or the quantity of blue (-b >0) of a pixel. Initially, the user specifies a point on the lesion of interest (with a single click). Simple Linear Iterative Clustering (SLIC) is applied to group the pixels of similar colour into contiguous regions, called superpixels.2 Subsequently the superpixels are clustered using the k-means approach into three clusters using information from component a. Thereafter, each superpixel neighbouring to the superpixel that contains the user-specified point is represented by a colour vector (a, b). The proposed algorithm estimates a) its Euclidean distance d1 from the respective vector of the selected superpixel, b) its Euclidean distance d2 from the mean of the respective vectors obtained from all superpixels that do not belong to the cluster of the selected superpixel. Then, the neigerpixel is considered to belong to the abnormal region of interest if d1

Results Seven types of GI lesions were used for evaluation. The measurement accuracy was assessed by comparing the area of the lesion (as identified by the method) with the reference standard area of each lesion. Average accuracies obtained for the measurement of angioectasias, aphthae, chylous cysts, lymphangiectasias, polypoid lesions, stenoses, and ulcers are 98.6%, 92.8%, 94.3%, 99.1%, 80.0%, 82.9%, and 94.8%, respectively. Comparatively, using the well-known colour space proposed by Ohta (I1I2I3) for image segmentation instead of CIE-Lab2 the results are lower; 97.1%, 92.1%, 92.6%, 97.8%, 75.2%, 78.8%, and 91%, respectively.

Conclusion A novel algorithm was proposed & evaluated for accurate computer-aided size measurement of lesions in CE. The overall accuracy on a public dataset was ~92%. This algorithm can be incorporated as a novel measurement tool in contemporary CE reading software.

References 1 Iakovidis DK, Koulaouzidis A. Software for enhanced video capsule endoscopy: challenges for essential progress. Nat Rev Gastroenterol Hepatol. 2015;12:172–8.

2 Achanta R, et al. Slic superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2012;34:2274–8.

Disclosure of Interest None Declared

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