Michael Liedlgruber

Computer-Aided Decision Support Systems for Endoscopy in the Gastrointestinal Tract: A Review

Today, medical endoscopy is a widely used procedure to inspect the inner cavities of the human body. The advent of endoscopic imaging techniques-allowing the acquisition of images or videos-created the possibility for the development of the whole new branch of computer-aided decision support systems. Such systems aim at helping physicians to identify possibly malignant abnormalities more accurately. At the beginning of this paper, we give a brief introduction to the history of endoscopy, followed by introducing the main types of endoscopes which emerged so far (flexible endoscope, wireless capsule endoscope, and confocal laser endomicroscope). We then give a brief introduction to computer-aided decision support systems specifically targeted at endoscopy in the gastrointestinal tract. Then we present general facts and figures concerning computer-aided decision support systems and summarize work specifically targeted at computer-aided decision support in the gastrointestinal tract. This summary is followed by a discussion of some common issues concerning the approaches reviewed and suggestions of possible ways to resolve them.

Color treatment in endoscopic image classification using multi-scale local color vector patterns

Graphical abstract In this work we propose a novel multi-scale operator which is based on the full color information within an image. In order to evaluate the method, we extract features from endoscopic images using this operator and classify the images according to the respective class of polyps. Highlights ► Compared to other LBP-based operators LCVP uses all color information available, yet yielding a more compact descriptor for an image. ► LCVP is up to 7.5 times faster compared to other LBP-based methods evaluated. ► In terms of a classification of polyps the accuracy of LCVP differs insignificantly only from previously developed methods.

Automated Marsh-like classification of celiac disease in children using local texture operators

Automated classification of duodenal texture patches with histological ground truth in case of pediatric celiac disease is proposed. The classical focus of classification in this context is a two-class problem: mucosa affected by celiac disease and unaffected duodenal tissue. We extend this focus and apply classification according to a modified Marsh scheme into four classes. In addition to other techniques used previously for classification of endoscopic imagery, we apply local binary pattern (LBP) operators and propose two new operator types, one of which adapts to the different properties of wavelet transform subbands. The achieved results are promising in that operators based on LBP turn out to achieve better results compared to many other texture classification techniques as used in earlier work. Specifically, the proposed wavelet-based LBP scheme achieved the best overall accuracy of all feature extraction techniques considered in the two-class case and was among the best in the four-class scheme. Results also show that a classification into four classes is feasible in principle however when compared to the two-class case we note that there is still room for improvement due to various reasons discussed.

Impact of duodenal image capturing techniques and duodenal regions on the performance of automated diagnosis of celiac disease

Various techniques have been developed for an automated classification of endoscopic images. Besides the classical methods for endoscopic image capturing, new methods like the modified immersion technique have been devised and are in use. The impact of specific image capturing techniques for feature extraction and classification in automated diagnosis is unclear. This work applies several well tested methods for feature extraction and classification on images captured with the conventional and the modified immersion technique. We compare the classification rates and the impact on feature extraction of each specific capturing technique. We also compare the classification rates of different duodenal regions. Finally we advise an optimal combination of image capturing technique, duodenal region and feature extraction methods for automated celiac disease diagnosis.

Experimental study on the impact of endoscope distortion correction on computer-assisted celiac disease diagnosis

The impact of applying barrel distortion correction to endoscopic imagery in the context of automated celiac disease diagnosis is experimentally investigated. For a large set of feature extraction techniques, it is found that contrasting to intuition, no improvement but even significant result degradation of classification accuracy can be observed. For techniques relying on geometrical properties of the image material (“shape”), moderate improvements of classification accuracy can be achieved. Reasons for this somewhat unexpected results are discussed and ways how to exploit potential distortion correction benefits are sketched.

Automated classification of duodenal imagery in celiac disease using evolved Fourier feature vectors

Feature extraction techniques based on selection of highly discriminant Fourier filters have been developed for an automated classification of magnifying endoscope images with respect to pit patterns of colon lesions. These are applied to duodenal imagery for diagnosis of celiac disease. Features are extracted from the Fourier domain by selecting the most discriminant features using an evolutionary algorithm. Subsequent classification is performed with various standard algorithms (KNN, SVM, Bayes classifier) and combination of several Fourier filters and classifiers which is called multiclassifier. The obtained results are promising, due to a high specificity for the detection of mucosal damage typical of untreated celiac disease.

Endoscopic image processing - an overview

After a brief introduction to the history of endoscopy we describe the different techniques which exist to perform endoscopic procedures (traditional endoscopy, wireless capsule endoscopy, virtual endoscopy, and confocal endomicroscopy). Then we review different medical applications and decision support systems targeted at endoscopy and summarize work in this field.

Combining Gaussian Markov random fields with the discrete-wavelet transform for endoscopic image classification

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 using the k-NN classifier and the Bayes classifier.

Pit pattern classification using extended Local Binary Patterns

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 the nearest-neighbors (1-NN) classifier in conjunction with the Bhattacharyya distance metric. The experimental results show that the extended LBP operator delivers superior results and an automated classification of endoscopic images based on the pit pattern classification scheme is feasible.

Delaunay triangulation-based pit density estimation for the classification of polyps in high-magnification chromo-colonoscopy

Highlights ► Exploiting the visual nature of pit patterns on the colonic mucosa. ► Roughly four times faster compared to a previously developed approach. ► Significantly higher classification rates compared to our previous work. ► More robust against overfitting when compared to other methods.