Computer-aided colorectal tumor classification in NBI endoscopy using local features

• Published in: Medical image analysis Vol. 17; no. 1; pp. 78 - 100
• Main Authors: Tamaki, Toru, Yoshimuta, Junki, Kawakami, Misato, Raytchev, Bisser, Kaneda, Kazufumi, Yoshida, Shigeto, Takemura, Yoshito, Onji, Keiichi, Miyaki, Rie, Tanaka, Shinji
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2013
• Subjects: Bag-of-visual-words; Colonoscopy; Colonoscopy - methods; Colorectal cancer; Colorectal Neoplasms - classification; Colorectal Neoplasms - diagnosis; Diagnosis, Computer-Assisted; Humans; Narrow Band Imaging; NBI; Pit-pattern; Bag-of-visual-words; Pit-pattern; NBI; Colonoscopy; Colorectal cancer
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2012.08.003

[Display omitted] ► We report performances of a computer-aided classification of NBI images. ► Images of colorectal tumors are classified into three types A, B, and C3. ► Bag-of-features of densely sampled SIFT and SVM classifiers are used. ► Extensive experiments with varying the parameters have been conducted. ► A recognition rate of 96% on a real dataset of 908 NBI images is achieved. An early detection of colorectal cancer through colorectal endoscopy is important and widely used in hospitals as a standard medical procedure. During colonoscopy, the lesions of colorectal tumors on the colon surface are visually inspected by a Narrow Band Imaging (NBI) zoom-videoendoscope. By using the visual appearance of colorectal tumors in endoscopic images, histological diagnosis is presumed based on classification schemes for NBI magnification findings. In this paper, we report on the performance of a recognition system for classifying NBI images of colorectal tumors into three types (A, B, and C3) based on the NBI magnification findings. To deal with the problem of computer-aided classification of NBI images, we explore a local feature-based recognition method, bag-of-visual-words (BoW), and provide extensive experiments on a variety of technical aspects. The proposed prototype system, used in the experiments, consists of a bag-of-visual-words representation of local features followed by Support Vector Machine (SVM) classifiers. A number of local features are extracted by using sampling schemes such as Difference-of-Gaussians and grid sampling. In addition, in this paper we propose a new combination of local features and sampling schemes. Extensive experiments with varying the parameters for each component are carried out, for the performance of the system is usually affected by those parameters, e.g. the sampling strategy for the local features, the representation of the local feature histograms, the kernel types of the SVM classifiers, the number of classes to be considered, etc. The recognition results are compared in terms of recognition rates, precision/recall, and F-measure for different numbers of visual words. The proposed system achieves a recognition rate of 96% for 10-fold cross validation on a real dataset of 908 NBI images collected during actual colonoscopy, and 93% for a separate test dataset.