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We present a system for an automated colon cancer detection based on the pit pattern classification. In contrast to previous work we exploit the visual nature of the underlying classification scheme by extracting features based on detected edges. To focus on the most discriminative subset of features we use a greedy forward feature subset selection. The classification is then carried out using the...
In this work we present a method for automated classification of endoscopic images according to the pit pattern classification scheme. Images taken during colonoscopy are transformed using a modified version of the local binary patterns operator (LBP). Then, two-dimensional histograms based on the LBP data from different color channels are created. Finally, the classification is carried out by employing...
In this work we present a method for automated classification of endoscopic images according to the pit pattern classification scheme. Images taken during colonoscopy are transformed to the wavelet domain using the pyramidal discrete wavelet transform. Then, Gaussian Markov random fields are used to extract features from the resulting wavelet coefficients. Finally, these features are used for a classification...
Colonic polyps appear as elliptical protrusions on the inner wall of the colon. Previous algorithms assumed the shape of a polyp to be a spherical cap, so these algorithms are not flexible when the polyps are various cap shapes. This paper proposes an explicit parametric model for the polyps. The model captures the overall shape of the polyp and is used to derive the probability distribution of features...
Computed tomography colonography (CTC) is a rapidly evolving noninvasive medical investigation that is viewed by radiologists as a potential screening technique for the detection of colorectal polyps. Due to the technical advances in CT system design, the volume of data required to be processed by radiologists has increased significantly, and as a consequence the manual analysis of this information...
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