TY - GEN
T1 - Bladder cancer segmentation on multispectral images
AU - van der Putten, Joost
AU - van der Sommen, Fons
AU - Zinger, Svitlana
AU - de Bruin, Daniel M.
AU - Kamphuis, Guido
AU - de With, Peter H. N.
PY - 2018
Y1 - 2018
N2 - Nonmuscle Invasive Bladder Cancer (NMIBC) has high incidence, and close follow-up with cystoscopy is necessary due to its high recurrence rate after initial treatment, estimated to be as high as 75%. Because of the high recurrence rate, it is vital that the detection of bladder cancer is improved. Computer automated detection algorithms have shown to be exceptionally effective in achieving this goal. This paper presents the first automated segmentation algorithm for bladder cancer in endoscopic images. The second purpose of this study is to determine which modality is best suited for computer-aided segmentation of bladder cancer. Gabor and color features are extracted from 20 patients in four different modalities with a block-based strategy. Three different classifiers are used to classify the blocks and post-processing is applied to obtain a segmented region. The best classification results were obtained by using a support vector machine and the Spectrum B modality. Additionally, color features were found to be effective for obtaining segmentations comparable to experts.
AB - Nonmuscle Invasive Bladder Cancer (NMIBC) has high incidence, and close follow-up with cystoscopy is necessary due to its high recurrence rate after initial treatment, estimated to be as high as 75%. Because of the high recurrence rate, it is vital that the detection of bladder cancer is improved. Computer automated detection algorithms have shown to be exceptionally effective in achieving this goal. This paper presents the first automated segmentation algorithm for bladder cancer in endoscopic images. The second purpose of this study is to determine which modality is best suited for computer-aided segmentation of bladder cancer. Gabor and color features are extracted from 20 patients in four different modalities with a block-based strategy. Three different classifiers are used to classify the blocks and post-processing is applied to obtain a segmented region. The best classification results were obtained by using a support vector machine and the Spectrum B modality. Additionally, color features were found to be effective for obtaining segmentations comparable to experts.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85056621694&origin=inward
U2 - https://doi.org/10.1145/3243394.3243702
DO - https://doi.org/10.1145/3243394.3243702
M3 - Conference contribution
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 12th International Conference on Distributed Smart Cameras, ICDSC 2018
PB - Association for Computing Machinery
T2 - 12th International Conference on Distributed Smart Cameras, ICDSC 2018
Y2 - 3 September 2018 through 4 September 2018
ER -