Reflections of LED light cause unwanted noise effects called specular reflection (SR) on colonoscopic images. The aim of this study was to seek answers to the following two questions. (a) How are the texture features used in automatic detection of polyps affected by the interpolation on specular reflections? (b) If they are affected does it really affect the classification performance? In order to answer these questions, we used 610 colonoscopy images, and divided each image into tiles whose sizes were 32‐by‐32 pixels. From these tiles, we selected the ones without any specular reflection. We added different shape and size specular reflections cropped from real images onto the reflection‐free tiles. We then used the nearest neighbors, bilinear and bicubic interpolation techniques on the tiles on which SRs were added. On these tiles we extracted 116 texture features using 3 second‐order approaches, and 4 first‐order statistics. First, we used paired sample t test. Second, we performed automatic classification of polyps and background using random forest and k nearest neighbors (k‐NN) approaches using the texture features for different combinations of specular reflections added on the tiles from the polyp or background. The results showed that depending on the size of specular reflection, interpolation can cause a significant difference between the texture features that were coming from reflection‐free tiles and the same tiles on which interpolation was performed. In addition, we note that bicubic interpolation may be preferred to eliminate specular reflection when texture features are used for background and polyp discrimination.

Effect of interpolation on specular reflections in texture‐based automatic colonic polyp detection

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 relevant to polyp detection. The probability distribution represents the glocal properties of the candidates (where glocal properties capture both the global information and local information of the object). A unit sphere, referred to as a brilliant sphere, is used to represent the glocal information of the polyp. The observation sequence is obtained for the polyp candidates from the brilliant sphere information, and the observation sequence is then assessed by explicit models for classification.

/pdf/classification-of-colonic-polyps-using-hidden-markov-models-3glf7q42h4.pdf

Classification of colonic polyps using Hidden Markov Models

This paper proposes an explicit parametric model for colonic polyps. The model captures the overall shape of the polyp and is then used to derive the probability distribution of features relevant for polyp detection. The probability distribution represents the glocal properties of the polyp candidates, where the glocal properties capture both global and local information of an object. The probability distribution is implemented on the unit sphere, which is divided into 26 partitions, and each partition captures the local properties of a polyp candidate. From the partitions on the sphere, an observation sequence also defines global properties of the polyp candidate and the observation sequence is assessed by explicit models for classification. When it represents glocal parameters of a polyp candidate, we call the unit sphere a brilliant sphere. The parametric models are estimated from 20 geometric models typifying the various cap shapes of colonic polyps.

False Positive Reduction in Colonic Polyp Detection Using Glocal Information

Colonic polyps appear like elliptical protrusions on the inner wall of the colon. The many proposed algorithms assumed the shape of a polyp as a spherical cap, so the algorithms are not flexible when the polyps are irregular shapes. In this paper, we propose a mapping using regional unit sphere (MuRUS) method to overcome the problem caused by unexpected polyp shapes. The MuRUS has shape invariant and size invariant properties. Our method was applied to colon CT images from 37 patients each having a prone and supine scan. There are 45 colonscopically confirmed polyps. The results obtained by our algorithm were compared with those gold standards. 100% of polyps >= 10mm in diameter were detected, 90% of polyps >= 6mm in diameter were detected and 70% of polyps < 6mm in diameter were detected at 7.0 FPs per patient.

Autonomic Colonic Polyp Detection by the Mapping using Regional Unit Sphere

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Autonomic Colonic Polyp Detection by the Mapping using Regional Unit Sphere